limitations of research in research methodology

How to Write Limitations of the Study (with examples)

This blog emphasizes the importance of recognizing and effectively writing about limitations in research. It discusses the types of limitations, their significance, and provides guidelines for writing about them, highlighting their role in advancing scholarly research.

Updated on August 24, 2023

No matter how well thought out, every research endeavor encounters challenges. There is simply no way to predict all possible variances throughout the process.

These uncharted boundaries and abrupt constraints are known as limitations in research . Identifying and acknowledging limitations is crucial for conducting rigorous studies. Limitations provide context and shed light on gaps in the prevailing inquiry and literature.

This article explores the importance of recognizing limitations and discusses how to write them effectively. By interpreting limitations in research and considering prevalent examples, we aim to reframe the perception from shameful mistakes to respectable revelations.

What are limitations in research?

In the clearest terms, research limitations are the practical or theoretical shortcomings of a study that are often outside of the researcher’s control . While these weaknesses limit the generalizability of a study’s conclusions, they also present a foundation for future research.

Sometimes limitations arise from tangible circumstances like time and funding constraints, or equipment and participant availability. Other times the rationale is more obscure and buried within the research design. Common types of limitations and their ramifications include:

• Theoretical: limits the scope, depth, or applicability of a study.
• Methodological: limits the quality, quantity, or diversity of the data.
• Empirical: limits the representativeness, validity, or reliability of the data.
• Analytical: limits the accuracy, completeness, or significance of the findings.
• Ethical: limits the access, consent, or confidentiality of the data.

Regardless of how, when, or why they arise, limitations are a natural part of the research process and should never be ignored . Like all other aspects, they are vital in their own purpose.

Why is identifying limitations important?

Whether to seek acceptance or avoid struggle, humans often instinctively hide flaws and mistakes. Merging this thought process into research by attempting to hide limitations, however, is a bad idea. It has the potential to negate the validity of outcomes and damage the reputation of scholars.

By identifying and addressing limitations throughout a project, researchers strengthen their arguments and curtail the chance of peer censure based on overlooked mistakes. Pointing out these flaws shows an understanding of variable limits and a scrupulous research process.

Showing awareness of and taking responsibility for a project’s boundaries and challenges validates the integrity and transparency of a researcher. It further demonstrates the researchers understand the applicable literature and have thoroughly evaluated their chosen research methods.

Presenting limitations also benefits the readers by providing context for research findings. It guides them to interpret the project’s conclusions only within the scope of very specific conditions. By allowing for an appropriate generalization of the findings that is accurately confined by research boundaries and is not too broad, limitations boost a study’s credibility .

Limitations are true assets to the research process. They highlight opportunities for future research. When researchers identify the limitations of their particular approach to a study question, they enable precise transferability and improve chances for reproducibility. 

Simply stating a project’s limitations is not adequate for spurring further research, though. To spark the interest of other researchers, these acknowledgements must come with thorough explanations regarding how the limitations affected the current study and how they can potentially be overcome with amended methods.

How to write limitations

Typically, the information about a study’s limitations is situated either at the beginning of the discussion section to provide context for readers or at the conclusion of the discussion section to acknowledge the need for further research. However, it varies depending upon the target journal or publication guidelines. 

Don’t hide your limitations

It is also important to not bury a limitation in the body of the paper unless it has a unique connection to a topic in that section. If so, it needs to be reiterated with the other limitations or at the conclusion of the discussion section. Wherever it is included in the manuscript, ensure that the limitations section is prominently positioned and clearly introduced.

While maintaining transparency by disclosing limitations means taking a comprehensive approach, it is not necessary to discuss everything that could have potentially gone wrong during the research study. If there is no commitment to investigation in the introduction, it is unnecessary to consider the issue a limitation to the research. Wholly consider the term ‘limitations’ and ask, “Did it significantly change or limit the possible outcomes?” Then, qualify the occurrence as either a limitation to include in the current manuscript or as an idea to note for other projects. 

Writing limitations

Once the limitations are concretely identified and it is decided where they will be included in the paper, researchers are ready for the writing task. Including only what is pertinent, keeping explanations detailed but concise, and employing the following guidelines is key for crafting valuable limitations:

1) Identify and describe the limitations : Clearly introduce the limitation by classifying its form and specifying its origin. For example:

• An unintentional bias encountered during data collection
• An intentional use of unplanned post-hoc data analysis

2) Explain the implications : Describe how the limitation potentially influences the study’s findings and how the validity and generalizability are subsequently impacted. Provide examples and evidence to support claims of the limitations’ effects without making excuses or exaggerating their impact. Overall, be transparent and objective in presenting the limitations, without undermining the significance of the research. 

3) Provide alternative approaches for future studies : Offer specific suggestions for potential improvements or avenues for further investigation. Demonstrate a proactive approach by encouraging future research that addresses the identified gaps and, therefore, expands the knowledge base.

Whether presenting limitations as an individual section within the manuscript or as a subtopic in the discussion area, authors should use clear headings and straightforward language to facilitate readability. There is no need to complicate limitations with jargon, computations, or complex datasets.

Examples of common limitations

Limitations are generally grouped into two categories , methodology and research process .

Methodology limitations

Methodology may include limitations due to:

• Sample size
• Lack of available or reliable data
• Lack of prior research studies on the topic
• Measure used to collect the data
• Self-reported data

The researcher is addressing how the large sample size requires a reassessment of the measures used to collect and analyze the data.

Research process limitations

Limitations during the research process may arise from:

• Access to information
• Longitudinal effects
• Cultural and other biases
• Language fluency
• Time constraints

The author is pointing out that the model’s estimates are based on potentially biased observational studies.

Final thoughts

Successfully proving theories and touting great achievements are only two very narrow goals of scholarly research. The true passion and greatest efforts of researchers comes more in the form of confronting assumptions and exploring the obscure.

In many ways, recognizing and sharing the limitations of a research study both allows for and encourages this type of discovery that continuously pushes research forward. By using limitations to provide a transparent account of the project's boundaries and to contextualize the findings, researchers pave the way for even more robust and impactful research in the future.

Charla Viera, MS

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21 Research Limitations Examples

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Research limitations refer to the potential weaknesses inherent in a study. All studies have limitations of some sort, meaning declaring limitations doesn’t necessarily need to be a bad thing, so long as your declaration of limitations is well thought-out and explained.

Rarely is a study perfect. Researchers have to make trade-offs when developing their studies, which are often based upon practical considerations such as time and monetary constraints, weighing the breadth of participants against the depth of insight, and choosing one methodology or another.

In research, studies can have limitations such as limited scope, researcher subjectivity, and lack of available research tools.

Acknowledging the limitations of your study should be seen as a strength. It demonstrates your willingness for transparency, humility, and submission to the scientific method and can bolster the integrity of the study. It can also inform future research direction.

Typically, scholars will explore the limitations of their study in either their methodology section, their conclusion section, or both.

Research Limitations Examples

Qualitative and quantitative research offer different perspectives and methods in exploring phenomena, each with its own strengths and limitations. So, I’ve split the limitations examples sections into qualitative and quantitative below.

Qualitative Research Limitations

Qualitative research seeks to understand phenomena in-depth and in context. It focuses on the ‘why’ and ‘how’ questions.

It’s often used to explore new or complex issues, and it provides rich, detailed insights into participants’ experiences, behaviors, and attitudes. However, these strengths also create certain limitations, as explained below.

1. Subjectivity

Qualitative research often requires the researcher to interpret subjective data. One researcher may examine a text and identify different themes or concepts as more dominant than others.

Close qualitative readings of texts are necessarily subjective – and while this may be a limitation, qualitative researchers argue this is the best way to deeply understand everything in context.

Suggested Solution and Response: To minimize subjectivity bias, you could consider cross-checking your own readings of themes and data against other scholars’ readings and interpretations. This may involve giving the raw data to a supervisor or colleague and asking them to code the data separately, then coming together to compare and contrast results.

2. Researcher Bias

The concept of researcher bias is related to, but slightly different from, subjectivity.

Researcher bias refers to the perspectives and opinions you bring with you when doing your research.

For example, a researcher who is explicitly of a certain philosophical or political persuasion may bring that persuasion to bear when interpreting data.

In many scholarly traditions, we will attempt to minimize researcher bias through the utilization of clear procedures that are set out in advance or through the use of statistical analysis tools.

However, in other traditions, such as in postmodern feminist research , declaration of bias is expected, and acknowledgment of bias is seen as a positive because, in those traditions, it is believed that bias cannot be eliminated from research, so instead, it is a matter of integrity to present it upfront.

Suggested Solution and Response: Acknowledge the potential for researcher bias and, depending on your theoretical framework , accept this, or identify procedures you have taken to seek a closer approximation to objectivity in your coding and analysis.

3. Generalizability

If you’re struggling to find a limitation to discuss in your own qualitative research study, then this one is for you: all qualitative research, of all persuasions and perspectives, cannot be generalized.

This is a core feature that sets qualitative data and quantitative data apart.

The point of qualitative data is to select case studies and similarly small corpora and dig deep through in-depth analysis and thick description of data.

Often, this will also mean that you have a non-randomized sample size.

While this is a positive – you’re going to get some really deep, contextualized, interesting insights – it also means that the findings may not be generalizable to a larger population that may not be representative of the small group of people in your study.

Suggested Solution and Response: Suggest future studies that take a quantitative approach to the question.

4. The Hawthorne Effect

The Hawthorne effect refers to the phenomenon where research participants change their ‘observed behavior’ when they’re aware that they are being observed.

This effect was first identified by Elton Mayo who conducted studies of the effects of various factors ton workers’ productivity. He noticed that no matter what he did – turning up the lights, turning down the lights, etc. – there was an increase in worker outputs compared to prior to the study taking place.

Mayo realized that the mere act of observing the workers made them work harder – his observation was what was changing behavior.

So, if you’re looking for a potential limitation to name for your observational research study , highlight the possible impact of the Hawthorne effect (and how you could reduce your footprint or visibility in order to decrease its likelihood).

Suggested Solution and Response: Highlight ways you have attempted to reduce your footprint while in the field, and guarantee anonymity to your research participants.

5. Replicability

Quantitative research has a great benefit in that the studies are replicable – a researcher can get a similar sample size, duplicate the variables, and re-test a study. But you can’t do that in qualitative research.

Qualitative research relies heavily on context – a specific case study or specific variables that make a certain instance worthy of analysis. As a result, it’s often difficult to re-enter the same setting with the same variables and repeat the study.

Furthermore, the individual researcher’s interpretation is more influential in qualitative research, meaning even if a new researcher enters an environment and makes observations, their observations may be different because subjectivity comes into play much more. This doesn’t make the research bad necessarily (great insights can be made in qualitative research), but it certainly does demonstrate a weakness of qualitative research.

6. Limited Scope

“Limited scope” is perhaps one of the most common limitations listed by researchers – and while this is often a catch-all way of saying, “well, I’m not studying that in this study”, it’s also a valid point.

No study can explore everything related to a topic. At some point, we have to make decisions about what’s included in the study and what is excluded from the study.

So, you could say that a limitation of your study is that it doesn’t look at an extra variable or concept that’s certainly worthy of study but will have to be explored in your next project because this project has a clearly and narrowly defined goal.

Suggested Solution and Response: Be clear about what’s in and out of the study when writing your research question.

7. Time Constraints

This is also a catch-all claim you can make about your research project: that you would have included more people in the study, looked at more variables, and so on. But you’ve got to submit this thing by the end of next semester! You’ve got time constraints.

And time constraints are a recognized reality in all research.

But this means you’ll need to explain how time has limited your decisions. As with “limited scope”, this may mean that you had to study a smaller group of subjects, limit the amount of time you spent in the field, and so forth.

Suggested Solution and Response: Suggest future studies that will build on your current work, possibly as a PhD project.

8. Resource Intensiveness

Qualitative research can be expensive due to the cost of transcription, the involvement of trained researchers, and potential travel for interviews or observations.

So, resource intensiveness is similar to the time constraints concept. If you don’t have the funds, you have to make decisions about which tools to use, which statistical software to employ, and how many research assistants you can dedicate to the study.

Suggested Solution and Response: Suggest future studies that will gain more funding on the back of this ‘ exploratory study ‘.

9. Coding Difficulties

Data analysis in qualitative research often involves coding, which can be subjective and complex, especially when dealing with ambiguous or contradicting data.

After naming this as a limitation in your research, it’s important to explain how you’ve attempted to address this. Some ways to ‘limit the limitation’ include:

• Triangulation: Have 2 other researchers code the data as well and cross-check your results with theirs to identify outliers that may need to be re-examined, debated with the other researchers, or removed altogether.
• Procedure: Use a clear coding procedure to demonstrate reliability in your coding process. I personally use the thematic network analysis method outlined in this academic article by Attride-Stirling (2001).

Suggested Solution and Response: Triangulate your coding findings with colleagues, and follow a thematic network analysis procedure.

10. Risk of Non-Responsiveness

There is always a risk in research that research participants will be unwilling or uncomfortable sharing their genuine thoughts and feelings in the study.

This is particularly true when you’re conducting research on sensitive topics, politicized topics, or topics where the participant is expressing vulnerability .

This is similar to the Hawthorne effect (aka participant bias), where participants change their behaviors in your presence; but it goes a step further, where participants actively hide their true thoughts and feelings from you.

Suggested Solution and Response: One way to manage this is to try to include a wider group of people with the expectation that there will be non-responsiveness from some participants.

11. Risk of Attrition

Attrition refers to the process of losing research participants throughout the study.

This occurs most commonly in longitudinal studies , where a researcher must return to conduct their analysis over spaced periods of time, often over a period of years.

Things happen to people over time – they move overseas, their life experiences change, they get sick, change their minds, and even die. The more time that passes, the greater the risk of attrition.

Suggested Solution and Response: One way to manage this is to try to include a wider group of people with the expectation that there will be attrition over time.

12. Difficulty in Maintaining Confidentiality and Anonymity

Given the detailed nature of qualitative data , ensuring participant anonymity can be challenging.

If you have a sensitive topic in a specific case study, even anonymizing research participants sometimes isn’t enough. People might be able to induce who you’re talking about.

Sometimes, this will mean you have to exclude some interesting data that you collected from your final report. Confidentiality and anonymity come before your findings in research ethics – and this is a necessary limiting factor.

Suggested Solution and Response: Highlight the efforts you have taken to anonymize data, and accept that confidentiality and accountability place extremely important constraints on academic research.

13. Difficulty in Finding Research Participants

A study that looks at a very specific phenomenon or even a specific set of cases within a phenomenon means that the pool of potential research participants can be very low.

Compile on top of this the fact that many people you approach may choose not to participate, and you could end up with a very small corpus of subjects to explore. This may limit your ability to make complete findings, even in a quantitative sense.

You may need to therefore limit your research question and objectives to something more realistic.

Suggested Solution and Response: Highlight that this is going to limit the study’s generalizability significantly.

14. Ethical Limitations

Ethical limitations refer to the things you cannot do based on ethical concerns identified either by yourself or your institution’s ethics review board.

This might include threats to the physical or psychological well-being of your research subjects, the potential of releasing data that could harm a person’s reputation, and so on.

Furthermore, even if your study follows all expected standards of ethics, you still, as an ethical researcher, need to allow a research participant to pull out at any point in time, after which you cannot use their data, which demonstrates an overlap between ethical constraints and participant attrition.

Suggested Solution and Response: Highlight that these ethical limitations are inevitable but important to sustain the integrity of the research.

For more on Qualitative Research, Explore my Qualitative Research Guide

Quantitative Research Limitations

Quantitative research focuses on quantifiable data and statistical, mathematical, or computational techniques. It’s often used to test hypotheses, assess relationships and causality, and generalize findings across larger populations.

Quantitative research is widely respected for its ability to provide reliable, measurable, and generalizable data (if done well!). Its structured methodology has strengths over qualitative research, such as the fact it allows for replication of the study, which underpins the validity of the research.

However, this approach is not without it limitations, explained below.

1. Over-Simplification

Quantitative research is powerful because it allows you to measure and analyze data in a systematic and standardized way. However, one of its limitations is that it can sometimes simplify complex phenomena or situations.

In other words, it might miss the subtleties or nuances of the research subject.

For example, if you’re studying why people choose a particular diet, a quantitative study might identify factors like age, income, or health status. But it might miss other aspects, such as cultural influences or personal beliefs, that can also significantly impact dietary choices.

When writing about this limitation, you can say that your quantitative approach, while providing precise measurements and comparisons, may not capture the full complexity of your subjects of study.

Suggested Solution and Response: Suggest a follow-up case study using the same research participants in order to gain additional context and depth.

2. Lack of Context

Another potential issue with quantitative research is that it often focuses on numbers and statistics at the expense of context or qualitative information.

Let’s say you’re studying the effect of classroom size on student performance. You might find that students in smaller classes generally perform better. However, this doesn’t take into account other variables, like teaching style , student motivation, or family support.

When describing this limitation, you might say, “Although our research provides important insights into the relationship between class size and student performance, it does not incorporate the impact of other potentially influential variables. Future research could benefit from a mixed-methods approach that combines quantitative analysis with qualitative insights.”

3. Applicability to Real-World Settings

Oftentimes, experimental research takes place in controlled environments to limit the influence of outside factors.

This control is great for isolation and understanding the specific phenomenon but can limit the applicability or “external validity” of the research to real-world settings.

For example, if you conduct a lab experiment to see how sleep deprivation impacts cognitive performance, the sterile, controlled lab environment might not reflect real-world conditions where people are dealing with multiple stressors.

Therefore, when explaining the limitations of your quantitative study in your methodology section, you could state:

“While our findings provide valuable information about [topic], the controlled conditions of the experiment may not accurately represent real-world scenarios where extraneous variables will exist. As such, the direct applicability of our results to broader contexts may be limited.”

Suggested Solution and Response: Suggest future studies that will engage in real-world observational research, such as ethnographic research.

4. Limited Flexibility

Once a quantitative study is underway, it can be challenging to make changes to it. This is because, unlike in grounded research, you’re putting in place your study in advance, and you can’t make changes part-way through.

Your study design, data collection methods, and analysis techniques need to be decided upon before you start collecting data.

For example, if you are conducting a survey on the impact of social media on teenage mental health, and halfway through, you realize that you should have included a question about their screen time, it’s generally too late to add it.

When discussing this limitation, you could write something like, “The structured nature of our quantitative approach allows for consistent data collection and analysis but also limits our flexibility to adapt and modify the research process in response to emerging insights and ideas.”

Suggested Solution and Response: Suggest future studies that will use mixed-methods or qualitative research methods to gain additional depth of insight.

5. Risk of Survey Error

Surveys are a common tool in quantitative research, but they carry risks of error.

There can be measurement errors (if a question is misunderstood), coverage errors (if some groups aren’t adequately represented), non-response errors (if certain people don’t respond), and sampling errors (if your sample isn’t representative of the population).

For instance, if you’re surveying college students about their study habits , but only daytime students respond because you conduct the survey during the day, your results will be skewed.

In discussing this limitation, you might say, “Despite our best efforts to develop a comprehensive survey, there remains a risk of survey error, including measurement, coverage, non-response, and sampling errors. These could potentially impact the reliability and generalizability of our findings.”

Suggested Solution and Response: Suggest future studies that will use other survey tools to compare and contrast results.

6. Limited Ability to Probe Answers

With quantitative research, you typically can’t ask follow-up questions or delve deeper into participants’ responses like you could in a qualitative interview.

For instance, imagine you are surveying 500 students about study habits in a questionnaire. A respondent might indicate that they study for two hours each night. You might want to follow up by asking them to elaborate on what those study sessions involve or how effective they feel their habits are.

However, quantitative research generally disallows this in the way a qualitative semi-structured interview could.

When discussing this limitation, you might write, “Given the structured nature of our survey, our ability to probe deeper into individual responses is limited. This means we may not fully understand the context or reasoning behind the responses, potentially limiting the depth of our findings.”

Suggested Solution and Response: Suggest future studies that engage in mixed-method or qualitative methodologies to address the issue from another angle.

7. Reliance on Instruments for Data Collection

In quantitative research, the collection of data heavily relies on instruments like questionnaires, surveys, or machines.

The limitation here is that the data you get is only as good as the instrument you’re using. If the instrument isn’t designed or calibrated well, your data can be flawed.

For instance, if you’re using a questionnaire to study customer satisfaction and the questions are vague, confusing, or biased, the responses may not accurately reflect the customers’ true feelings.

When discussing this limitation, you could say, “Our study depends on the use of questionnaires for data collection. Although we have put significant effort into designing and testing the instrument, it’s possible that inaccuracies or misunderstandings could potentially affect the validity of the data collected.”

Suggested Solution and Response: Suggest future studies that will use different instruments but examine the same variables to triangulate results.

8. Time and Resource Constraints (Specific to Quantitative Research)

Quantitative research can be time-consuming and resource-intensive, especially when dealing with large samples.

It often involves systematic sampling, rigorous design, and sometimes complex statistical analysis.

If resources and time are limited, it can restrict the scale of your research, the techniques you can employ, or the extent of your data analysis.

For example, you may want to conduct a nationwide survey on public opinion about a certain policy. However, due to limited resources, you might only be able to survey people in one city.

When writing about this limitation, you could say, “Given the scope of our research and the resources available, we are limited to conducting our survey within one city, which may not fully represent the nationwide public opinion. Hence, the generalizability of the results may be limited.”

Suggested Solution and Response: Suggest future studies that will have more funding or longer timeframes.

How to Discuss Your Research Limitations

1. in your research proposal and methodology section.

In the research proposal, which will become the methodology section of your dissertation, I would recommend taking the four following steps, in order:

• Be Explicit about your Scope – If you limit the scope of your study in your research question, aims, and objectives, then you can set yourself up well later in the methodology to say that certain questions are “outside the scope of the study.” For example, you may identify the fact that the study doesn’t address a certain variable, but you can follow up by stating that the research question is specifically focused on the variable that you are examining, so this limitation would need to be looked at in future studies.
• Acknowledge the Limitation – Acknowledging the limitations of your study demonstrates reflexivity and humility and can make your research more reliable and valid. It also pre-empts questions the people grading your paper may have, so instead of them down-grading you for your limitations; they will congratulate you on explaining the limitations and how you have addressed them!
• Explain your Decisions – You may have chosen your approach (despite its limitations) for a very specific reason. This might be because your approach remains, on balance, the best one to answer your research question. Or, it might be because of time and monetary constraints that are outside of your control.
• Highlight the Strengths of your Approach – Conclude your limitations section by strongly demonstrating that, despite limitations, you’ve worked hard to minimize the effects of the limitations and that you have chosen your specific approach and methodology because it’s also got some terrific strengths. Name the strengths.

Overall, you’ll want to acknowledge your own limitations but also explain that the limitations don’t detract from the value of your study as it stands.

2. In the Conclusion Section or Chapter

In the conclusion of your study, it is generally expected that you return to a discussion of the study’s limitations. Here, I recommend the following steps:

• Acknowledge issues faced – After completing your study, you will be increasingly aware of issues you may have faced that, if you re-did the study, you may have addressed earlier in order to avoid those issues. Acknowledge these issues as limitations, and frame them as recommendations for subsequent studies.
• Suggest further research – Scholarly research aims to fill gaps in the current literature and knowledge. Having established your expertise through your study, suggest lines of inquiry for future researchers. You could state that your study had certain limitations, and “future studies” can address those limitations.
• Suggest a mixed methods approach – Qualitative and quantitative research each have pros and cons. So, note those ‘cons’ of your approach, then say the next study should approach the topic using the opposite methodology or could approach it using a mixed-methods approach that could achieve the benefits of quantitative studies with the nuanced insights of associated qualitative insights as part of an in-study case-study.

Overall, be clear about both your limitations and how those limitations can inform future studies.

In sum, each type of research method has its own strengths and limitations. Qualitative research excels in exploring depth, context, and complexity, while quantitative research excels in examining breadth, generalizability, and quantifiable measures. Despite their individual limitations, each method contributes unique and valuable insights, and researchers often use them together to provide a more comprehensive understanding of the phenomenon being studied.

Attride-Stirling, J. (2001). Thematic networks: an analytic tool for qualitative research. Qualitative research , 1 (3), 385-405. ( Source )

Atkinson, P., Delamont, S., Cernat, A., Sakshaug, J., & Williams, R. A. (2021).  SAGE research methods foundations . London: Sage Publications.

Clark, T., Foster, L., Bryman, A., & Sloan, L. (2021).  Bryman’s social research methods . Oxford: Oxford University Press.

Köhler, T., Smith, A., & Bhakoo, V. (2022). Templates in qualitative research methods: Origins, limitations, and new directions.  Organizational Research Methods ,  25 (2), 183-210. ( Source )

Lenger, A. (2019). The rejection of qualitative research methods in economics.  Journal of Economic Issues ,  53 (4), 946-965. ( Source )

Taherdoost, H. (2022). What are different research approaches? Comprehensive review of qualitative, quantitative, and mixed method research, their applications, types, and limitations.  Journal of Management Science & Engineering Research ,  5 (1), 53-63. ( Source )

Walliman, N. (2021).  Research methods: The basics . New York: Routledge.

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The limitations of the study are those characteristics of design or methodology that impacted or influenced the interpretation of the findings from your research. Study limitations are the constraints placed on the ability to generalize from the results, to further describe applications to practice, and/or related to the utility of findings that are the result of the ways in which you initially chose to design the study or the method used to establish internal and external validity or the result of unanticipated challenges that emerged during the study.

Price, James H. and Judy Murnan. “Research Limitations and the Necessity of Reporting Them.” American Journal of Health Education 35 (2004): 66-67; Theofanidis, Dimitrios and Antigoni Fountouki. "Limitations and Delimitations in the Research Process." Perioperative Nursing 7 (September-December 2018): 155-163. .

Importance of...

Always acknowledge a study's limitations. It is far better that you identify and acknowledge your study’s limitations than to have them pointed out by your professor and have your grade lowered because you appeared to have ignored them or didn't realize they existed.

Keep in mind that acknowledgment of a study's limitations is an opportunity to make suggestions for further research. If you do connect your study's limitations to suggestions for further research, be sure to explain the ways in which these unanswered questions may become more focused because of your study.

Acknowledgment of a study's limitations also provides you with opportunities to demonstrate that you have thought critically about the research problem, understood the relevant literature published about it, and correctly assessed the methods chosen for studying the problem. A key objective of the research process is not only discovering new knowledge but also to confront assumptions and explore what we don't know.

Claiming limitations is a subjective process because you must evaluate the impact of those limitations . Don't just list key weaknesses and the magnitude of a study's limitations. To do so diminishes the validity of your research because it leaves the reader wondering whether, or in what ways, limitation(s) in your study may have impacted the results and conclusions. Limitations require a critical, overall appraisal and interpretation of their impact. You should answer the question: do these problems with errors, methods, validity, etc. eventually matter and, if so, to what extent?

Price, James H. and Judy Murnan. “Research Limitations and the Necessity of Reporting Them.” American Journal of Health Education 35 (2004): 66-67; Structure: How to Structure the Research Limitations Section of Your Dissertation. Dissertations and Theses: An Online Textbook. Laerd.com.

Descriptions of Possible Limitations

All studies have limitations . However, it is important that you restrict your discussion to limitations related to the research problem under investigation. For example, if a meta-analysis of existing literature is not a stated purpose of your research, it should not be discussed as a limitation. Do not apologize for not addressing issues that you did not promise to investigate in the introduction of your paper.

Here are examples of limitations related to methodology and the research process you may need to describe and discuss how they possibly impacted your results. Note that descriptions of limitations should be stated in the past tense because they were discovered after you completed your research.

Possible Methodological Limitations

• Sample size -- the number of the units of analysis you use in your study is dictated by the type of research problem you are investigating. Note that, if your sample size is too small, it will be difficult to find significant relationships from the data, as statistical tests normally require a larger sample size to ensure a representative distribution of the population and to be considered representative of groups of people to whom results will be generalized or transferred. Note that sample size is generally less relevant in qualitative research if explained in the context of the research problem.
• Lack of available and/or reliable data -- a lack of data or of reliable data will likely require you to limit the scope of your analysis, the size of your sample, or it can be a significant obstacle in finding a trend and a meaningful relationship. You need to not only describe these limitations but provide cogent reasons why you believe data is missing or is unreliable. However, don’t just throw up your hands in frustration; use this as an opportunity to describe a need for future research based on designing a different method for gathering data.
• Lack of prior research studies on the topic -- citing prior research studies forms the basis of your literature review and helps lay a foundation for understanding the research problem you are investigating. Depending on the currency or scope of your research topic, there may be little, if any, prior research on your topic. Before assuming this to be true, though, consult with a librarian! In cases when a librarian has confirmed that there is little or no prior research, you may be required to develop an entirely new research typology [for example, using an exploratory rather than an explanatory research design ]. Note again that discovering a limitation can serve as an important opportunity to identify new gaps in the literature and to describe the need for further research.
• Measure used to collect the data -- sometimes it is the case that, after completing your interpretation of the findings, you discover that the way in which you gathered data inhibited your ability to conduct a thorough analysis of the results. For example, you regret not including a specific question in a survey that, in retrospect, could have helped address a particular issue that emerged later in the study. Acknowledge the deficiency by stating a need for future researchers to revise the specific method for gathering data.
• Self-reported data -- whether you are relying on pre-existing data or you are conducting a qualitative research study and gathering the data yourself, self-reported data is limited by the fact that it rarely can be independently verified. In other words, you have to the accuracy of what people say, whether in interviews, focus groups, or on questionnaires, at face value. However, self-reported data can contain several potential sources of bias that you should be alert to and note as limitations. These biases become apparent if they are incongruent with data from other sources. These are: (1) selective memory [remembering or not remembering experiences or events that occurred at some point in the past]; (2) telescoping [recalling events that occurred at one time as if they occurred at another time]; (3) attribution [the act of attributing positive events and outcomes to one's own agency, but attributing negative events and outcomes to external forces]; and, (4) exaggeration [the act of representing outcomes or embellishing events as more significant than is actually suggested from other data].

Possible Limitations of the Researcher

• Access -- if your study depends on having access to people, organizations, data, or documents and, for whatever reason, access is denied or limited in some way, the reasons for this needs to be described. Also, include an explanation why being denied or limited access did not prevent you from following through on your study.
• Longitudinal effects -- unlike your professor, who can literally devote years [even a lifetime] to studying a single topic, the time available to investigate a research problem and to measure change or stability over time is constrained by the due date of your assignment. Be sure to choose a research problem that does not require an excessive amount of time to complete the literature review, apply the methodology, and gather and interpret the results. If you're unsure whether you can complete your research within the confines of the assignment's due date, talk to your professor.
• Cultural and other type of bias -- we all have biases, whether we are conscience of them or not. Bias is when a person, place, event, or thing is viewed or shown in a consistently inaccurate way. Bias is usually negative, though one can have a positive bias as well, especially if that bias reflects your reliance on research that only support your hypothesis. When proof-reading your paper, be especially critical in reviewing how you have stated a problem, selected the data to be studied, what may have been omitted, the manner in which you have ordered events, people, or places, how you have chosen to represent a person, place, or thing, to name a phenomenon, or to use possible words with a positive or negative connotation. NOTE :   If you detect bias in prior research, it must be acknowledged and you should explain what measures were taken to avoid perpetuating that bias. For example, if a previous study only used boys to examine how music education supports effective math skills, describe how your research expands the study to include girls.
• Fluency in a language -- if your research focuses , for example, on measuring the perceived value of after-school tutoring among Mexican-American ESL [English as a Second Language] students and you are not fluent in Spanish, you are limited in being able to read and interpret Spanish language research studies on the topic or to speak with these students in their primary language. This deficiency should be acknowledged.

Aguinis, Hermam and Jeffrey R. Edwards. “Methodological Wishes for the Next Decade and How to Make Wishes Come True.” Journal of Management Studies 51 (January 2014): 143-174; Brutus, Stéphane et al. "Self-Reported Limitations and Future Directions in Scholarly Reports: Analysis and Recommendations." Journal of Management 39 (January 2013): 48-75; Senunyeme, Emmanuel K. Business Research Methods. Powerpoint Presentation. Regent University of Science and Technology; ter Riet, Gerben et al. “All That Glitters Isn't Gold: A Survey on Acknowledgment of Limitations in Biomedical Studies.” PLOS One 8 (November 2013): 1-6.

Structure and Writing Style

Information about the limitations of your study are generally placed either at the beginning of the discussion section of your paper so the reader knows and understands the limitations before reading the rest of your analysis of the findings, or, the limitations are outlined at the conclusion of the discussion section as an acknowledgement of the need for further study. Statements about a study's limitations should not be buried in the body [middle] of the discussion section unless a limitation is specific to something covered in that part of the paper. If this is the case, though, the limitation should be reiterated at the conclusion of the section.

If you determine that your study is seriously flawed due to important limitations , such as, an inability to acquire critical data, consider reframing it as an exploratory study intended to lay the groundwork for a more complete research study in the future. Be sure, though, to specifically explain the ways that these flaws can be successfully overcome in a new study.

But, do not use this as an excuse for not developing a thorough research paper! Review the tab in this guide for developing a research topic . If serious limitations exist, it generally indicates a likelihood that your research problem is too narrowly defined or that the issue or event under study is too recent and, thus, very little research has been written about it. If serious limitations do emerge, consult with your professor about possible ways to overcome them or how to revise your study.

When discussing the limitations of your research, be sure to:

• Describe each limitation in detailed but concise terms;
• Explain why each limitation exists;
• Provide the reasons why each limitation could not be overcome using the method(s) chosen to acquire or gather the data [cite to other studies that had similar problems when possible];
• Assess the impact of each limitation in relation to the overall findings and conclusions of your study; and,
• If appropriate, describe how these limitations could point to the need for further research.

Remember that the method you chose may be the source of a significant limitation that has emerged during your interpretation of the results [for example, you didn't interview a group of people that you later wish you had]. If this is the case, don't panic. Acknowledge it, and explain how applying a different or more robust methodology might address the research problem more effectively in a future study. A underlying goal of scholarly research is not only to show what works, but to demonstrate what doesn't work or what needs further clarification.

Aguinis, Hermam and Jeffrey R. Edwards. “Methodological Wishes for the Next Decade and How to Make Wishes Come True.” Journal of Management Studies 51 (January 2014): 143-174; Brutus, Stéphane et al. "Self-Reported Limitations and Future Directions in Scholarly Reports: Analysis and Recommendations." Journal of Management 39 (January 2013): 48-75; Ioannidis, John P.A. "Limitations are not Properly Acknowledged in the Scientific Literature." Journal of Clinical Epidemiology 60 (2007): 324-329; Pasek, Josh. Writing the Empirical Social Science Research Paper: A Guide for the Perplexed. January 24, 2012. Academia.edu; Structure: How to Structure the Research Limitations Section of Your Dissertation. Dissertations and Theses: An Online Textbook. Laerd.com; What Is an Academic Paper? Institute for Writing Rhetoric. Dartmouth College; Writing the Experimental Report: Methods, Results, and Discussion. The Writing Lab and The OWL. Purdue University.

Writing Tip

Don't Inflate the Importance of Your Findings!

After all the hard work and long hours devoted to writing your research paper, it is easy to get carried away with attributing unwarranted importance to what you’ve done. We all want our academic work to be viewed as excellent and worthy of a good grade, but it is important that you understand and openly acknowledge the limitations of your study. Inflating the importance of your study's findings could be perceived by your readers as an attempt hide its flaws or encourage a biased interpretation of the results. A small measure of humility goes a long way!

Another Writing Tip

Negative Results are Not a Limitation!

Negative evidence refers to findings that unexpectedly challenge rather than support your hypothesis. If you didn't get the results you anticipated, it may mean your hypothesis was incorrect and needs to be reformulated. Or, perhaps you have stumbled onto something unexpected that warrants further study. Moreover, the absence of an effect may be very telling in many situations, particularly in experimental research designs. In any case, your results may very well be of importance to others even though they did not support your hypothesis. Do not fall into the trap of thinking that results contrary to what you expected is a limitation to your study. If you carried out the research well, they are simply your results and only require additional interpretation.

Lewis, George H. and Jonathan F. Lewis. “The Dog in the Night-Time: Negative Evidence in Social Research.” The British Journal of Sociology 31 (December 1980): 544-558.

Yet Another Writing Tip

Sample Size Limitations in Qualitative Research

Sample sizes are typically smaller in qualitative research because, as the study goes on, acquiring more data does not necessarily lead to more information. This is because one occurrence of a piece of data, or a code, is all that is necessary to ensure that it becomes part of the analysis framework. However, it remains true that sample sizes that are too small cannot adequately support claims of having achieved valid conclusions and sample sizes that are too large do not permit the deep, naturalistic, and inductive analysis that defines qualitative inquiry. Determining adequate sample size in qualitative research is ultimately a matter of judgment and experience in evaluating the quality of the information collected against the uses to which it will be applied and the particular research method and purposeful sampling strategy employed. If the sample size is found to be a limitation, it may reflect your judgment about the methodological technique chosen [e.g., single life history study versus focus group interviews] rather than the number of respondents used.

Boddy, Clive Roland. "Sample Size for Qualitative Research." Qualitative Market Research: An International Journal 19 (2016): 426-432; Huberman, A. Michael and Matthew B. Miles. "Data Management and Analysis Methods." In Handbook of Qualitative Research . Norman K. Denzin and Yvonna S. Lincoln, eds. (Thousand Oaks, CA: Sage, 1994), pp. 428-444; Blaikie, Norman. "Confounding Issues Related to Determining Sample Size in Qualitative Research." International Journal of Social Research Methodology 21 (2018): 635-641; Oppong, Steward Harrison. "The Problem of Sampling in qualitative Research." Asian Journal of Management Sciences and Education 2 (2013): 202-210.

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Research Limitations 101 📖

A Plain-Language Explainer (With Practical Examples)

By: Derek Jansen (MBA) | Expert Reviewer: Dr. Eunice Rautenbach | May 2024

Research limitations are one of those things that students tend to avoid digging into, and understandably so. No one likes to critique their own study and point out weaknesses. Nevertheless, being able to understand the limitations of your study – and, just as importantly, the implications thereof – a is a critically important skill.

In this post, we’ll unpack some of the most common research limitations you’re likely to encounter, so that you can approach your project with confidence.

Overview: Research Limitations 101

• What are research limitations ?
• Access – based limitations
• Temporal & financial limitations
• Sample & sampling limitations
• Design limitations
• Researcher limitations
• Key takeaways

What (exactly) are “research limitations”?

At the simplest level, research limitations (also referred to as “the limitations of the study”) are the constraints and challenges that will invariably influence your ability to conduct your study and draw reliable conclusions .

Research limitations are inevitable. Absolutely no study is perfect and limitations are an inherent part of any research design. These limitations can stem from a variety of sources , including access to data, methodological choices, and the more mundane constraints of budget and time. So, there’s no use trying to escape them – what matters is that you can recognise them.

Acknowledging and understanding these limitations is crucial, not just for the integrity of your research, but also for your development as a scholar. That probably sounds a bit rich, but realistically, having a strong understanding of the limitations of any given study helps you handle the inevitable obstacles professionally and transparently, which in turn builds trust with your audience and academic peers.

Simply put, recognising and discussing the limitations of your study demonstrates that you know what you’re doing , and that you’ve considered the results of your project within the context of these limitations. In other words, discussing the limitations is a sign of credibility and strength – not weakness. Contrary to the common misconception, highlighting your limitations (or rather, your study’s limitations) will earn you (rather than cost you) marks.

So, with that foundation laid, let’s have a look at some of the most common research limitations you’re likely to encounter – and how to go about managing them as effectively as possible.

Need a helping hand?

Limitation #1: Access To Information

One of the first hurdles you might encounter is limited access to necessary information. For example, you may have trouble getting access to specific literature or niche data sets. This situation can manifest due to several reasons, including paywalls, copyright and licensing issues or language barriers.

To minimise situations like these, it’s useful to try to leverage your university’s resource pool to the greatest extent possible. In practical terms, this means engaging with your university’s librarian and/or potentially utilising interlibrary loans to get access to restricted resources. If this sounds foreign to you, have a chat with your librarian 🙃

In emerging fields or highly specific study areas, you might find that there’s very little existing research (i.e., literature) on your topic. This scenario, while challenging, also offers a unique opportunity to contribute significantly to your field , as it indicates that there’s a significant research gap .

All of that said, be sure to conduct an exhaustive search using a variety of keywords and Boolean operators before assuming that there’s a lack of literature. Also, remember to snowball your literature base . In other words, scan the reference lists of the handful of papers that are directly relevant and then scan those references for more sources. You can also consider using tools like Litmaps and Connected Papers (see video below).

Limitation #2: Time & Money

Almost every researcher will face time and budget constraints at some point. Naturally, these limitations can affect the depth and breadth of your research – but they don’t need to be a death sentence.

Effective planning is crucial to managing both the temporal and financial aspects of your study. In practical terms, utilising tools like Gantt charts can help you visualise and plan your research timeline realistically, thereby reducing the risk of any nasty surprises. Always take a conservative stance when it comes to timelines, especially if you’re new to academic research. As a rule of thumb, things will generally take twice as long as you expect – so, prepare for the worst-case scenario.

If budget is a concern, you might want to consider exploring small research grants or adjusting the scope of your study so that it fits within a realistic budget. Trimming back might sound unattractive, but keep in mind that a smaller, well-planned study can often be more impactful than a larger, poorly planned project.

If you find yourself in a position where you’ve already run out of cash, don’t panic. There’s usually a pivot opportunity hidden somewhere within your project. Engage with your research advisor or faculty to explore potential solutions – don’t make any major changes without first consulting your institution.

Limitation #3: Sample Size & Composition

As we’ve discussed before , the size and representativeness of your sample are crucial , especially in quantitative research where the robustness of your conclusions often depends on these factors. All too often though, students run into issues achieving a sufficient sample size and composition.

To ensure adequacy in terms of your sample size, it’s important to plan for potential dropouts by oversampling from the outset . In other words, if you aim for a final sample size of 100 participants, aim to recruit 120-140 to account for unexpected challenges. If you still find yourself short on participants, consider whether you could complement your dataset with secondary data or data from an adjacent sample – for example, participants from another city or country. That said, be sure to engage with your research advisor before making any changes to your approach.

A related issue that you may run into is sample composition. In other words, you may have trouble securing a random sample that’s representative of your population of interest. In cases like this, you might again want to look at ways to complement your dataset with other sources, but if that’s not possible, it’s not the end of the world. As with all limitations, you’ll just need to recognise this limitation in your final write-up and be sure to interpret your results accordingly. In other words, don’t claim generalisability of your results if your sample isn’t random.

Limitation #4: Methodological Limitations

As we alluded earlier, every methodological choice comes with its own set of limitations . For example, you can’t claim causality if you’re using a descriptive or correlational research design. Similarly, as we saw in the previous example, you can’t claim generalisability if you’re using a non-random sampling approach.

Making good methodological choices is all about understanding (and accepting) the inherent trade-offs . In the vast majority of cases, you won’t be able to adopt the “perfect” methodology – and that’s okay. What’s important is that you select a methodology that aligns with your research aims and research questions , as well as the practical constraints at play (e.g., time, money, equipment access, etc.). Just as importantly, you must recognise and articulate the limitations of your chosen methods, and justify why they were the most suitable, given your specific context.

Limitation #5: Researcher (In)experience 

A discussion about research limitations would not be complete without mentioning the researcher (that’s you!). Whether we like to admit it or not, researcher inexperience and personal biases can subtly (and sometimes not so subtly) influence the interpretation and presentation of data within a study. This is especially true when it comes to dissertations and theses , as these are most commonly undertaken by first-time (or relatively fresh) researchers.

When it comes to dealing with this specific limitation, it’s important to remember the adage “ We don’t know what we don’t know ”. In other words, recognise and embrace your (relative) ignorance and subjectivity – and interpret your study’s results within that context . Simply put, don’t be overly confident in drawing conclusions from your study – especially when they contradict existing literature.

Cultivating a culture of reflexivity within your research practices can help reduce subjectivity and keep you a bit more “rooted” in the data. In practical terms, this simply means making an effort to become aware of how your perspectives and experiences may have shaped the research process and outcomes.

As with any new endeavour in life, it’s useful to garner as many outsider perspectives as possible. Of course, your university-assigned research advisor will play a large role in this respect, but it’s also a good idea to seek out feedback and critique from other academics. To this end, you might consider approaching other faculty at your institution, joining an online group, or even working with a private coach .

Key Takeaways

Understanding and effectively navigating research limitations is key to conducting credible and reliable academic work. By acknowledging and addressing these limitations upfront, you not only enhance the integrity of your research, but also demonstrate your academic maturity and professionalism.

Whether you’re working on a dissertation, thesis or any other type of formal academic research, remember the five most common research limitations and interpret your data while keeping them in mind.

• Access to Information (literature and data)
• Time and money
• Sample size and composition
• Research design and methodology
• Researcher (in)experience and bias

If you need a hand identifying and mitigating the limitations within your study, check out our 1:1 private coaching service .

Psst… there’s more!

This post is an extract from our bestselling short course, Methodology Bootcamp . If you want to work smart, you don't want to miss this .

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How to present limitations in research

Last updated

30 January 2024

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Limitations don’t invalidate or diminish your results, but it’s best to acknowledge them. This will enable you to address any questions your study failed to answer because of them.

In this guide, learn how to recognize, present, and overcome limitations in research.

• What is a research limitation?

Research limitations are weaknesses in your research design or execution that may have impacted outcomes and conclusions. Uncovering limitations doesn’t necessarily indicate poor research design—it just means you encountered challenges you couldn’t have anticipated that limited your research efforts.

Does basic research have limitations?

Basic research aims to provide more information about your research topic . It requires the same standard research methodology and data collection efforts as any other research type, and it can also have limitations.

• Common research limitations

Researchers encounter common limitations when embarking on a study. Limitations can occur in relation to the methods you apply or the research process you design. They could also be connected to you as the researcher.

Methodology limitations

Not having access to data or reliable information can impact the methods used to facilitate your research. A lack of data or reliability may limit the parameters of your study area and the extent of your exploration.

Your sample size may also be affected because you won’t have any direction on how big or small it should be and who or what you should include. Having too few participants won’t adequately represent the population or groups of people needed to draw meaningful conclusions.

Research process limitations

The study’s design can impose constraints on the process. For example, as you’re conducting the research, issues may arise that don’t conform to the data collection methodology you developed. You may not realize until well into the process that you should have incorporated more specific questions or comprehensive experiments to generate the data you need to have confidence in your results.

Constraints on resources can also have an impact. Being limited on participants or participation incentives may limit your sample sizes. Insufficient tools, equipment, and materials to conduct a thorough study may also be a factor.

Common researcher limitations

Here are some of the common researcher limitations you may encounter:

Time: some research areas require multi-year longitudinal approaches, but you might not be able to dedicate that much time. Imagine you want to measure how much memory a person loses as they age. This may involve conducting multiple tests on a sample of participants over 20–30 years, which may be impossible.

Bias: researchers can consciously or unconsciously apply bias to their research. Biases can contribute to relying on research sources and methodologies that will only support your beliefs about the research you’re embarking on. You might also omit relevant issues or participants from the scope of your study because of your biases.

Limited access to data : you may need to pay to access specific databases or journals that would be helpful to your research process. You might also need to gain information from certain people or organizations but have limited access to them. These cases require readjusting your process and explaining why your findings are still reliable.

• Why is it important to identify limitations?

Identifying limitations adds credibility to research and provides a deeper understanding of how you arrived at your conclusions.

Constraints may have prevented you from collecting specific data or information you hoped would prove or disprove your hypothesis or provide a more comprehensive understanding of your research topic.

However, identifying the limitations contributing to your conclusions can inspire further research efforts that help gather more substantial information and data.

• Where to put limitations in a research paper

A research paper is broken up into different sections that appear in the following order:

Introduction

Methodology

The discussion portion of your paper explores your findings and puts them in the context of the overall research. Either place research limitations at the beginning of the discussion section before the analysis of your findings or at the end of the section to indicate that further research needs to be pursued.

What not to include in the limitations section

Evidence that doesn’t support your hypothesis is not a limitation, so you shouldn’t include it in the limitation section. Don’t just list limitations and their degree of severity without further explanation.

• How to present limitations

You’ll want to present the limitations of your study in a way that doesn’t diminish the validity of your research and leave the reader wondering if your results and conclusions have been compromised.

Include only the limitations that directly relate to and impact how you addressed your research questions. Following a specific format enables the reader to develop an understanding of the weaknesses within the context of your findings without doubting the quality and integrity of your research.

Identify the limitations specific to your study

You don’t have to identify every possible limitation that might have occurred during your research process. Only identify those that may have influenced the quality of your findings and your ability to answer your research question.

Explain study limitations in detail

This explanation should be the most significant portion of your limitation section.

Link each limitation with an interpretation and appraisal of their impact on the study. You’ll have to evaluate and explain whether the error, method, or validity issues influenced the study’s outcome and how.

Propose a direction for future studies and present alternatives

In this section, suggest how researchers can avoid the pitfalls you experienced during your research process.

If an issue with methodology was a limitation, propose alternate methods that may help with a smoother and more conclusive research project . Discuss the pros and cons of your alternate recommendation.

Describe steps taken to minimize each limitation

You probably took steps to try to address or mitigate limitations when you noticed them throughout the course of your research project. Describe these steps in the limitation section.

• Limitation example

“Approaches like stem cell transplantation and vaccination in AD [Alzheimer’s disease] work on a cellular or molecular level in the laboratory. However, translation into clinical settings will remain a challenge for the next decade.”

The authors are saying that even though these methods showed promise in helping people with memory loss when conducted in the lab (in other words, using animal studies), more studies are needed. These may be controlled clinical trials, for example. 

However, the short life span of stem cells outside the lab and the vaccination’s severe inflammatory side effects are limitations. Researchers won’t be able to conduct clinical trials until these issues are overcome.

• How to overcome limitations in research

You’ve already started on the road to overcoming limitations in research by acknowledging that they exist. However, you need to ensure readers don’t mistake weaknesses for errors within your research design.

To do this, you’ll need to justify and explain your rationale for the methods, research design, and analysis tools you chose and how you noticed they may have presented limitations.

Your readers need to know that even when limitations presented themselves, you followed best practices and the ethical standards of your field. You didn’t violate any rules and regulations during your research process.

You’ll also want to reinforce the validity of your conclusions and results with multiple sources, methods, and perspectives. This prevents readers from assuming your findings were derived from a single or biased source.

• Learning and improving starts with limitations in research

Dealing with limitations with transparency and integrity helps identify areas for future improvements and developments. It’s a learning process, providing valuable insights into how you can improve methodologies, expand sample sizes, or explore alternate approaches to further support the validity of your findings.

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What are the limitations in research and how to write them?

Learn about the potential limitations in research and how to appropriately address them in order to deliver honest and ethical research.

It is fairly uncommon for researchers to stumble into the term research limitations when working on their research paper. Limitations in research can arise owing to constraints on design, methods, materials, and so on, and these aspects, unfortunately, may have an influence on your subject’s findings.

In this Mind The Graph’s article, we’ll discuss some recommendations for writing limitations in research , provide examples of various common types of limitations, and suggest how to properly present this information.

What are the limitations in research?

The limitations in research are the constraints in design, methods or even researchers’ limitations that affect and influence the interpretation of your research’s ultimate findings. These are limitations on the generalization and usability of findings that emerge from the design of the research and/or the method employed to ensure validity both internally and externally. 

Researchers are usually cautious to acknowledge the limitations of their research in their publications for fear of undermining the research’s scientific validity. No research is faultless or covers every possible angle. As a result, addressing the constraints of your research exhibits honesty and integrity .

Why should include limitations of research in my paper?

Though limitations tackle potential flaws in research, commenting on them at the conclusion of your paper, by demonstrating that you are aware of these limitations and explaining how they impact the conclusions that may be taken from the research, improves your research by disclosing any issues before other researchers or reviewers do . 

Additionally, emphasizing research constraints implies that you have thoroughly investigated the ramifications of research shortcomings and have a thorough understanding of your research problem. 

Limits exist in any research; being honest about them and explaining them would impress researchers and reviewers more than disregarding them. 

Remember that acknowledging a research’s shortcomings offers a chance to provide ideas for future research, but be careful to describe how your study may help to concentrate on these outstanding problems .

Possible limitations examples

Here are some limitations connected to methodology and the research procedure that you may need to explain and discuss in connection to your findings.

Methodological limitations

Sample size.

The number of units of analysis used in your study is determined by the sort of research issue being investigated. It is important to note that if your sample is too small, finding significant connections in the data will be challenging, as statistical tests typically require a larger sample size to ensure a fair representation and this can be limiting. 

Lack of available or reliable data

A lack of data or trustworthy data will almost certainly necessitate limiting the scope of your research or the size of your sample, or it can be a substantial impediment to identifying a pattern and a relevant connection.

Lack of prior research on the subject

Citing previous research papers forms the basis of your literature review and aids in comprehending the research subject you are researching. Yet there may be little if any, past research on your issue.

The measure used to collect data

After finishing your analysis of the findings, you realize that the method you used to collect data limited your capacity to undertake a comprehensive evaluation of the findings. Recognize the flaw by mentioning that future researchers should change the specific approach for data collection.

Issues with research samples and selection

Sampling inaccuracies arise when a probability sampling method is employed to choose a sample, but that sample does not accurately represent the overall population or the relevant group. As a result, your study suffers from “sampling bias” or “selection bias.”

Limitations of the research

When your research requires polling certain persons or a specific group, you may have encountered the issue of limited access to these interviewees. Because of the limited access, you may need to reorganize or rearrange your research. In this scenario, explain why access is restricted and ensure that your findings are still trustworthy and valid despite the constraint.

Time constraints

Practical difficulties may limit the amount of time available to explore a research issue and monitor changes as they occur. If time restrictions have any detrimental influence on your research, recognize this impact by expressing the necessity for a future investigation.

Due to their cultural origins or opinions on observed events, researchers may carry biased opinions, which can influence the credibility of a research. Furthermore, researchers may exhibit biases toward data and conclusions that only support their hypotheses or arguments.

The structure of the limitations section 

The limitations of your research are usually stated at the beginning of the discussion section of your paper so that the reader is aware of and comprehends the limitations prior to actually reading the rest of your findings, or they are stated at the end of the discussion section as an acknowledgment of the need for further research.

The ideal way is to divide your limitations section into three steps: 

1. Identify the research constraints; 

2. Describe in great detail how they affect your research; 

3. Mention the opportunity for future investigations and give possibilities. 

By following this method while addressing the constraints of your research, you will be able to effectively highlight your research’s shortcomings without jeopardizing the quality and integrity of your research.

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How to Present the Limitations of a Study in Research?

The limitations of the study convey to the reader how and under which conditions your study results will be evaluated. Scientific research involves investigating research topics, both known and unknown, which inherently includes an element of risk. The risk could arise due to human errors, barriers to data gathering, limited availability of resources, and researcher bias. Researchers are encouraged to discuss the limitations of their research to enhance the process of research, as well as to allow readers to gain an understanding of the study’s framework and value.

Limitations of the research are the constraints placed on the ability to generalize from the results and to further describe applications to practice. It is related to the utility value of the findings based on how you initially chose to design the study, the method used to establish internal and external validity, or the result of unanticipated challenges that emerged during the study. Knowing about these limitations and their impact can explain how the limitations of your study can affect the conclusions and thoughts drawn from your research. 1

Table of Contents

What are the limitations of a study

Researchers are probably cautious to acknowledge what the limitations of the research can be for fear of undermining the validity of the research findings. No research can be faultless or cover all possible conditions. These limitations of your research appear probably due to constraints on methodology or research design and influence the interpretation of your research’s ultimate findings. 2 These are limitations on the generalization and usability of findings that emerge from the design of the research and/or the method employed to ensure validity internally and externally. But such limitations of the study can impact the whole study or research paper. However, most researchers prefer not to discuss the different types of limitations in research for fear of decreasing the value of their paper amongst the reviewers or readers.

Importance of limitations of a study

Writing the limitations of the research papers is often assumed to require lots of effort. However, identifying the limitations of the study can help structure the research better. Therefore, do not underestimate the importance of research study limitations. 3

• Opportunity to make suggestions for further research. Suggestions for future research and avenues for further exploration can be developed based on the limitations of the study.
• Opportunity to demonstrate critical thinking. A key objective of the research process is to discover new knowledge while questioning existing assumptions and exploring what is new in the particular field. Describing the limitation of the research shows that you have critically thought about the research problem, reviewed relevant literature, and correctly assessed the methods chosen for studying the problem.
• Demonstrate Subjective learning process. Writing limitations of the research helps to critically evaluate the impact of the said limitations, assess the strength of the research, and consider alternative explanations or interpretations. Subjective evaluation contributes to a more complex and comprehensive knowledge of the issue under study.

Why should I include limitations of research in my paper

All studies have limitations to some extent. Including limitations of the study in your paper demonstrates the researchers’ comprehensive and holistic understanding of the research process and topic. The major advantages are the following:

• Understand the study conditions and challenges encountered . It establishes a complete and potentially logical depiction of the research. The boundaries of the study can be established, and realistic expectations for the findings can be set. They can also help to clarify what the study is not intended to address.
• Improve the quality and validity of the research findings. Mentioning limitations of the research creates opportunities for the original author and other researchers to undertake future studies to improve the research outcomes.
• Transparency and accountability. Including limitations of the research helps maintain mutual integrity and promote further progress in similar studies.
• Identify potential bias sources.  Identifying the limitations of the study can help researchers identify potential sources of bias in their research design, data collection, or analysis. This can help to improve the validity and reliability of the findings.

Where do I need to add the limitations of the study in my paper

The limitations of your research can be stated at the beginning of the discussion section, which allows the reader to comprehend the limitations of the study prior to reading the rest of your findings or at the end of the discussion section as an acknowledgment of the need for further research.

Types of limitations in research

There are different types of limitations in research that researchers may encounter. These are listed below:

• Research Design Limitations : Restrictions on your research or available procedures may affect the research outputs. If the research goals and objectives are too broad, explain how they should be narrowed down to enhance the focus of your study. If there was a selection bias in your sample, explain how this may affect the generalizability of your findings. This can help readers understand the limitations of the study in terms of their impact on the overall validity of your research.
• Impact Limitations : Your study might be limited by a strong regional-, national-, or species-based impact or population- or experimental-specific impact. These inherent limitations on impact affect the extendibility and generalizability of the findings.
• Data or statistical limitations : Data or statistical limitations in research are extremely common in experimental (such as medicine, physics, and chemistry) or field-based (such as ecology and qualitative clinical research) studies. Sometimes, it is either extremely difficult to acquire sufficient data or gain access to the data. These limitations of the research might also be the result of your study’s design and might result in an incomplete conclusion to your research.

Limitations of study examples

All possible limitations of the study cannot be included in the discussion section of the research paper or dissertation. It will vary greatly depending on the type and nature of the study. These include types of research limitations that are related to methodology and the research process and that of the researcher as well that you need to describe and discuss how they possibly impacted your results.

Common methodological limitations of the study

Limitations of research due to methodological problems are addressed by identifying the potential problem and suggesting ways in which this should have been addressed. Some potential methodological limitations of the study are as follows. 1

• Sample size: The sample size 4 is dictated by the type of research problem investigated. If the sample size is too small, finding a significant relationship from the data will be difficult, as statistical tests require a large sample size to ensure a representative population distribution and generalize the study findings.
• Lack of available/reliable data: A lack of available/reliable data will limit the scope of your analysis and the size of your sample or present obstacles in finding a trend or meaningful relationship. So, when writing about the limitations of the study, give convincing reasons why you feel data is absent or untrustworthy and highlight the necessity for a future study focused on developing a new data-gathering strategy.
• Lack of prior research studies: Citing prior research studies is required to help understand the research problem being investigated. If there is little or no prior research, an exploratory rather than an explanatory research design will be required. Also, discovering the limitations of the study presents an opportunity to identify gaps in the literature and describe the need for additional study.
• Measure used to collect the data: Sometimes, the data gathered will be insufficient to conduct a thorough analysis of the results. A limitation of the study example, for instance, is identifying in retrospect that a specific question could have helped address a particular issue that emerged during data analysis. You can acknowledge the limitation of the research by stating the need to revise the specific method for gathering data in the future.
• Self-reported data: Self-reported data cannot be independently verified and can contain several potential bias sources, such as selective memory, attribution, and exaggeration. These biases become apparent if they are incongruent with data from other sources.

General limitations of researchers

Limitations related to the researcher can also influence the study outcomes. These should be addressed, and related remedies should be proposed.

• Limited access to data : If your study requires access to people, organizations, data, or documents whose access is denied or limited, the reasons need to be described. An additional explanation stating why this limitation of research did not prevent you from following through on your study is also needed.
• Time constraints : Researchers might also face challenges in meeting research deadlines due to a lack of timely participant availability or funds, among others. The impacts of time constraints must be acknowledged by mentioning the need for a future study addressing this research problem.
• Conflicts due to biased views and personal issues : Differences in culture or personal views can contribute to researcher bias, as they focus only on the results and data that support their main arguments. To avoid this, pay attention to the problem statement and data gathering.

Steps for structuring the limitations section

Limitations are an inherent part of any research study. Issues may vary, ranging from sampling and literature review to methodology and bias. However, there is a structure for identifying these elements, discussing them, and offering insight or alternatives on how the limitations of the study can be mitigated. This enhances the process of the research and helps readers gain a comprehensive understanding of a study’s conditions.

• Identify the research constraints : Identify those limitations having the greatest impact on the quality of the research findings and your ability to effectively answer your research questions and/or hypotheses. These include sample size, selection bias, measurement error, or other issues affecting the validity and reliability of your research.
• Describe their impact on your research : Reflect on the nature of the identified limitations and justify the choices made during the research to identify the impact of the study’s limitations on the research outcomes. Explanations can be offered if needed, but without being defensive or exaggerating them. Provide context for the limitations of your research to understand them in a broader context. Any specific limitations due to real-world considerations need to be pointed out critically rather than justifying them as done by some other author group or groups.
• Mention the opportunity for future investigations : Suggest ways to overcome the limitations of the present study through future research. This can help readers understand how the research fits into the broader context and offer a roadmap for future studies.

Frequently Asked Questions

• Should I mention all the limitations of my study in the research report?

Restrict limitations to what is pertinent to the research question under investigation. The specific limitations you include will depend on the nature of the study, the research question investigated, and the data collected.

• Can the limitations of a study affect its credibility?

Stating the limitations of the research is considered favorable by editors and peer reviewers. Connecting your study’s limitations with future possible research can help increase the focus of unanswered questions in this area. In addition, admitting limitations openly and validating that they do not affect the main findings of the study increases the credibility of your study. However, if you determine that your study is seriously flawed, explain ways to successfully overcome such flaws in a future study. For example, if your study fails to acquire critical data, consider reframing the research question as an exploratory study to lay the groundwork for more complete research in the future.

• How can I mitigate the limitations of my study?

Strategies to minimize limitations of the research should focus on convincing reviewers and readers that the limitations do not affect the conclusions of the study by showing that the methods are appropriate and that the logic is sound. Here are some steps to follow to achieve this:

• Use data that are valid.
• Use methods that are appropriate and sound logic to draw inferences.
• Use adequate statistical methods for drawing inferences from the data that studies with similar limitations have been published before.

Admit limitations openly and, at the same time, show how they do not affect the main conclusions of the study.

• Can the limitations of a study impact its publication chances?

Limitations in your research can arise owing to restrictions in methodology or research design. Although this could impact your chances of publishing your research paper, it is critical to explain your study’s limitations to your intended audience. For example, it can explain how your study constraints may impact the results and views generated from your investigation. It also shows that you have researched the flaws of your study and have a thorough understanding of the subject.

• How can limitations in research be used for future studies?

The limitations of a study give you an opportunity to offer suggestions for further research. Your study’s limitations, including problems experienced during the study and the additional study perspectives developed, are a great opportunity to take on a new challenge and help advance knowledge in a particular field.

References:

• Brutus, S., Aguinis, H., & Wassmer, U. (2013). Self-reported limitations and future directions in scholarly reports: Analysis and recommendations.  Journal of Management ,  39 (1), 48-75.
• Ioannidis, J. P. (2007). Limitations are not properly acknowledged in the scientific literature.  Journal of Clinical Epidemiology ,  60 (4), 324-329.
• Price, J. H., & Murnan, J. (2004). Research limitations and the necessity of reporting them.  American Journal of Health Education ,  35 (2), 66.
• Boddy, C. R. (2016). Sample size for qualitative research.  Qualitative Market Research: An International Journal ,  19 (4), 426-432.

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Organizing Academic Research Papers: Limitations of the Study

• Purpose of Guide
• Design Flaws to Avoid
• Glossary of Research Terms
• Narrowing a Topic Idea
• Broadening a Topic Idea
• Extending the Timeliness of a Topic Idea
• Academic Writing Style
• Choosing a Title
• Making an Outline
• Paragraph Development
• Executive Summary
• Background Information
• The Research Problem/Question
• Theoretical Framework
• Citation Tracking
• Content Alert Services
• Evaluating Sources
• Primary Sources
• Secondary Sources
• Tertiary Sources
• What Is Scholarly vs. Popular?
• Qualitative Methods
• Quantitative Methods
• Using Non-Textual Elements
• Limitations of the Study
• Common Grammar Mistakes
• Avoiding Plagiarism
• Footnotes or Endnotes?
• Further Readings
• Annotated Bibliography
• Dealing with Nervousness
• Using Visual Aids
• Grading Someone Else's Paper
• How to Manage Group Projects
• Multiple Book Review Essay
• Reviewing Collected Essays
• About Informed Consent
• Writing Field Notes
• Writing a Policy Memo
• Writing a Research Proposal
• Acknowledgements

The limitations of the study are those characteristics of design or methodology that impacted or influenced the application or interpretation of the results of your study. They are the constraints on generalizability and utility of findings that are the result of the ways in which you chose to design the study and/or the method used to establish internal and external validity.

Importance of...

Always acknowledge a study's limitations. It is far better for you to identify and acknowledge your study’s limitations than to have them pointed out by your professor and be graded down because you appear to have ignored them.

Keep in mind that acknowledgement of a study's limitations is an opportunity to make suggestions for further research. If you do connect your study's limitations to suggestions for further research, be sure to explain the ways in which these unanswered questions may become more focused because of your study.

Acknowledgement of a study's limitations also provides you with an opportunity to demonstrate to your professor that you have thought critically about the research problem, understood the relevant literature published about it, and correctly assessed the methods chosen for studying the problem. A key objective of the research process is not only discovering new knowledge but also to confront assumptions and explore what we don't know.

Claiming limitiations is a subjective process because you must evaluate the impact of those limitations . Don't just list key weaknesses and the magnitude of a study's limitations. To do so diminishes the validity of your research because it leaves the reader wondering whether, or in what ways, limitation(s) in your study may have impacted the findings and conclusions. Limitations require a critical, overall appraisal and interpretation of their impact. You should answer the question: do these problems with errors, methods, validity, etc. eventually matter and, if so, to what extent?

Structure: How to Structure the Research Limitations Section of Your Dissertation . Dissertations and Theses: An Online Textbook. Laerd.com.

Descriptions of Possible Limitations

All studies have limitations . However, it is important that you restrict your discussion to limitations related to the research problem under investigation. For example, if a meta-analysis of existing literature is not a stated purpose of your research, it should not be discussed as a limitation. Do not apologize for not addressing issues that you did not promise to investigate in your paper.

Here are examples of limitations you may need to describe and to discuss how they possibly impacted your findings. Descriptions of limitations should be stated in the past tense.

Possible Methodological Limitations

• Sample size -- the number of the units of analysis you use in your study is dictated by the type of research problem you are investigating. Note that, if your sample size is too small, it will be difficult to find significant relationships from the data, as statistical tests normally require a larger sample size to ensure a representative distribution of the population and to be considered representative of groups of people to whom results will be generalized or transferred.
• Lack of available and/or reliable data -- a lack of data or of reliable data will likely require you to limit the scope of your analysis, the size of your sample, or it can be a significant obstacle in finding a trend and a meaningful relationship. You need to not only describe these limitations but to offer reasons why you believe data is missing or is unreliable. However, don’t just throw up your hands in frustration; use this as an opportunity to describe the need for future research.
• Lack of prior research studies on the topic -- citing prior research studies forms the basis of your literature review and helps lay a foundation for understanding the research problem you are investigating. Depending on the currency or scope of your research topic, there may be little, if any, prior research on your topic. Before assuming this to be true, consult with a librarian! In cases when a librarian has confirmed that there is a lack of prior research, you may be required to develop an entirely new research typology [for example, using an exploratory rather than an explanatory research design]. Note that this limitation can serve as an important opportunity to describe the need for further research.
• Measure used to collect the data -- sometimes it is the case that, after completing your interpretation of the findings, you discover that the way in which you gathered data inhibited your ability to conduct a thorough analysis of the results. For example, you regret not including a specific question in a survey that, in retrospect, could have helped address a particular issue that emerged later in the study. Acknowledge the deficiency by stating a need in future research to revise the specific method for gathering data.
• Self-reported data -- whether you are relying on pre-existing self-reported data or you are conducting a qualitative research study and gathering the data yourself, self-reported data is limited by the fact that it rarely can be independently verified. In other words, you have to take what people say, whether in interviews, focus groups, or on questionnaires, at face value. However, self-reported data contain several potential sources of bias that should be noted as limitations: (1) selective memory (remembering or not remembering experiences or events that occurred at some point in the past); (2) telescoping [recalling events that occurred at one time as if they occurred at another time]; (3) attribution [the act of attributing positive events and outcomes to one's own agency but attributing negative events and outcomes to external forces]; and, (4) exaggeration [the act of representing outcomes or embellishing events as more significant than is actually suggested from other data].

Possible Limitations of the Researcher

• Access -- if your study depends on having access to people, organizations, or documents and, for whatever reason, access is denied or otherwise limited, the reasons for this need to be described.
• Longitudinal effects -- unlike your professor, who can literally devote years [even a lifetime] to studying a single research problem, the time available to investigate a research problem and to measure change or stability within a sample is constrained by the due date of your assignment. Be sure to choose a topic that does not require an excessive amount of time to complete the literature review, apply the methodology, and gather and interpret the results. If you're unsure, talk to your professor.
• Cultural and other type of bias -- we all have biases, whether we are conscience of them or not. Bias is when a person, place, or thing is viewed or shown in a consistently inaccurate way. It is usually negative, though one can have a positive bias as well. When proof-reading your paper, be especially critical in reviewing how you have stated a problem, selected the data to be studied, what may have been omitted, the manner in which you have ordered events, people, or places and how you have chosen to represent a person, place, or thing, to name a phenomenon, or to use possible words with a positive or negative connotation. Note that if you detect bias in prior research, it must be acknowledged and you should explain what measures were taken to avoid perpetuating bias.
• Fluency in a language -- if your research focuses on measuring the perceived value of after-school tutoring among Mexican-American ESL [English as a Second Language] students, for example, and you are not fluent in Spanish, you are limited in being able to read and interpret Spanish language research studies on the topic. This deficiency should be acknowledged.

Brutus, Stéphane et al. Self-Reported Limitations and Future Directions in Scholarly Reports: Analysis and Recommendations. Journal of Management 39 (January 2013): 48-75; Senunyeme, Emmanuel K. Business Research Methods . Powerpoint Presentation. Regent University of Science and Technology.

Structure and Writing Style

Information about the limitations of your study are generally placed either at the beginning of the discussion section of your paper so the reader knows and understands the limitations before reading the rest of your analysis of the findings, or, the limitations are outlined at the conclusion of the discussion section as an acknowledgement of the need for further study. Statements about a study's limitations should not be buried in the body [middle] of the discussion section unless a limitation is specific to something covered in that part of the paper. If this is the case, though, the limitation should be reiterated at the conclusion of the section.

If you determine that your study is seriously flawed due to important limitations , such as, an inability to acquire critical data, consider reframing it as a pilot study intended to lay the groundwork for a more complete research study in the future. Be sure, though, to specifically explain the ways that these flaws can be successfully overcome in later studies.

But, do not use this as an excuse for not developing a thorough research paper! Review the tab in this guide for developing a research topic . If serious limitations exist, it generally indicates a likelihood that your research problem is too narrowly defined or that the issue or event under study  is too recent and, thus, very little research has been written about it. If serious limitations do emerge, consult with your professor about possible ways to overcome them or how to reframe your study.

When discussing the limitations of your research, be sure to:

• Describe each limitation in detailed but concise terms;
• Explain why each limitation exists;
• Provide the reasons why each limitation could not be overcome using the method(s) chosen to gather the data [cite to other studies that had similar problems when possible];
• Assess the impact of each limitation in relation to  the overall findings and conclusions of your study; and,
• If appropriate, describe how these limitations could point to the need for further research.

Remember that the method you chose may be the source of a significant limitation that has emerged during your interpretation of the results [for example, you didn't ask a particular question in a survey that you later wish you had]. If this is the case, don't panic. Acknowledge it, and explain how applying a different or more robust methodology might address the research problem more effectively in any future study. A underlying goal of scholarly research is not only to prove what works, but to demonstrate what doesn't work or what needs further clarification.

Brutus, Stéphane et al. Self-Reported Limitations and Future Directions in Scholarly Reports: Analysis and Recommendations. Journal of Management 39 (January 2013): 48-75; Ioannidis, John P.A. Limitations are not Properly Acknowledged in the Scientific Literature. Journal of Clinical Epidemiology 60 (2007): 324-329; Pasek, Josh. Writing the Empirical Social Science Research Paper: A Guide for the Perplexed . January 24, 2012. Academia.edu; Structure: How to Structure the Research Limitations Section of Your Dissertation . Dissertations and Theses: An Online Textbook. Laerd.com; What Is an Academic Paper? Institute for Writing Rhetoric. Dartmouth College; Writing the Experimental Report: Methods, Results, and Discussion. The Writing Lab and The OWL. Purdue University.

Writing Tip

Don't Inflate the Importance of Your Findings! After all the hard work and long hours devoted to writing your research paper, it is easy to get carried away with attributing unwarranted importance to what you’ve done. We all want our academic work to be viewed as excellent and worthy of a good grade, but it is important that you understand and openly acknowledge the limitiations of your study. Inflating of the importance of your study's findings in an attempt hide its flaws is a big turn off to your readers. A measure of humility goes a long way!

Another Writing Tip

Negative Results are Not a Limitation!

Negative evidence refers to findings that unexpectedly challenge rather than support your hypothesis. If you didn't get the results you anticipated, it may mean your hypothesis was incorrect and needs to be reformulated, or, perhaps you have stumbled onto something unexpected that warrants further study. Moreover, the absence of an effect may be very telling in many situations, particularly in experimental research designs. In any case, your results may be of importance to others even though they did not support your hypothesis. Do not fall into the trap of thinking that results contrary to what you expected is a limitation to your study. If you carried out the research well, they are simply your results and only require additional interpretation.

Yet Another Writing Tip

A Note about Sample Size Limitations in Qualitative Research

Sample sizes are typically smaller in qualitative research because, as the study goes on, acquiring more data does not necessarily lead to more information. This is because one occurrence of a piece of data, or a code, is all that is necessary to ensure that it becomes part of the analysis framework. However, it remains true that sample sizes that are too small cannot adequately support claims of having achieved valid conclusions and sample sizes that are too large do not permit the deep, naturalistic, and inductive analysis that defines qualitative inquiry. Determining adequate sample size in qualitative research is ultimately a matter of judgment and experience in evaluating the quality of the information collected against the uses to which it will be applied and the particular research method and purposeful sampling strategy employed. If the sample size is found to be a limitation, it may reflect your judgement about the methodological technique chosen [e.g., single life history study versus focus group interviews] rather than the number of respondents used.

Huberman, A. Michael and Matthew B. Miles. Data Management and Analysis Methods. In Handbook of Qualitative Research. Norman K. Denzin and Yvonna S. Lincoln, eds. (Thousand Oaks, CA: Sage, 1994), pp. 428-444.

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Writing Limitations of Research Study — 4 Reasons Why It Is Important!

It is not unusual for researchers to come across the term limitations of research during their academic paper writing. More often this is interpreted as something terrible. However, when it comes to research study, limitations can help structure the research study better. Therefore, do not underestimate significance of limitations of research study.

Allow us to take you through the context of how to evaluate the limits of your research and conclude an impactful relevance to your results.

Table of Contents

What Are the Limitations of a Research Study?

Every research has its limit and these limitations arise due to restrictions in methodology or research design.  This could impact your entire research or the research paper you wish to publish. Unfortunately, most researchers choose not to discuss their limitations of research fearing it will affect the value of their article in the eyes of readers.

However, it is very important to discuss your study limitations and show it to your target audience (other researchers, journal editors, peer reviewers etc.). It is very important that you provide an explanation of how your research limitations may affect the conclusions and opinions drawn from your research. Moreover, when as an author you state the limitations of research, it shows that you have investigated all the weaknesses of your study and have a deep understanding of the subject. Being honest could impress your readers and mark your study as a sincere effort in research.

Why and Where Should You Include the Research Limitations?

The main goal of your research is to address your research objectives. Conduct experiments, get results and explain those results, and finally justify your research question . It is best to mention the limitations of research in the discussion paragraph of your research article.

At the very beginning of this paragraph, immediately after highlighting the strengths of the research methodology, you should write down your limitations. You can discuss specific points from your research limitations as suggestions for further research in the conclusion of your thesis.

1. Common Limitations of the Researchers

Limitations that are related to the researcher must be mentioned. This will help you gain transparency with your readers. Furthermore, you could provide suggestions on decreasing these limitations in you and your future studies.

2. Limited Access to Information

Your work may involve some institutions and individuals in research, and sometimes you may have problems accessing these institutions. Therefore, you need to redesign and rewrite your work. You must explain your readers the reason for limited access.

3. Limited Time

All researchers are bound by their deadlines when it comes to completing their studies. Sometimes, time constraints can affect your research negatively. However, the best practice is to acknowledge it and mention a requirement for future study to solve the research problem in a better way.

4. Conflict over Biased Views and Personal Issues

Biased views can affect the research. In fact, researchers end up choosing only those results and data that support their main argument, keeping aside the other loose ends of the research.

Types of Limitations of Research

Before beginning your research study, know that there are certain limitations to what you are testing or possible research results. There are different types that researchers may encounter, and they all have unique characteristics, such as:

1. Research Design Limitations

Certain restrictions on your research or available procedures may affect your final results or research outputs. You may have formulated research goals and objectives too broadly. However, this can help you understand how you can narrow down the formulation of research goals and objectives, thereby increasing the focus of your study.

2. Impact Limitations

Even if your research has excellent statistics and a strong design, it can suffer from the influence of the following factors:

• Presence of increasing findings as researched
• Being population specific
• A strong regional focus.

3. Data or statistical limitations

In some cases, it is impossible to collect sufficient data for research or very difficult to get access to the data. This could lead to incomplete conclusion to your study. Moreover, this insufficiency in data could be the outcome of your study design. The unclear, shabby research outline could produce more problems in interpreting your findings.

How to Correctly Structure Your Research Limitations?

There are strict guidelines for narrowing down research questions, wherein you could justify and explain potential weaknesses of your academic paper. You could go through these basic steps to get a well-structured clarity of research limitations:

• Declare that you wish to identify your limitations of research and explain their importance,
• Provide the necessary depth, explain their nature, and justify your study choices.
• Write how you are suggesting that it is possible to overcome them in the future.

In this section, your readers will see that you are aware of the potential weaknesses in your business, understand them and offer effective solutions, and it will positively strengthen your article as you clarify all limitations of research to your target audience.

Know that you cannot be perfect and there is no individual without flaws. You could use the limitations of research as a great opportunity to take on a new challenge and improve the future of research. In a typical academic paper, research limitations may relate to:

1. Formulating your goals and objectives

If you formulate goals and objectives too broadly, your work will have some shortcomings. In this case, specify effective methods or ways to narrow down the formula of goals and aim to increase your level of study focus.

2. Application of your data collection methods in research

If you do not have experience in primary data collection, there is a risk that there will be flaws in the implementation of your methods. It is necessary to accept this, and learn and educate yourself to understand data collection methods.

3. Sample sizes

This depends on the nature of problem you choose. Sample size is of a greater importance in quantitative studies as opposed to qualitative ones. If your sample size is too small, statistical tests cannot identify significant relationships or connections within a given data set.

You could point out that other researchers should base the same study on a larger sample size to get more accurate results.

4. The absence of previous studies in the field you have chosen

Writing a literature review is an important step in any scientific study because it helps researchers determine the scope of current work in the chosen field. It is a major foundation for any researcher who must use them to achieve a set of specific goals or objectives.

However, if you are focused on the most current and evolving research problem or a very narrow research problem, there may be very little prior research on your topic. For example, if you chose to explore the role of Bitcoin as the currency of the future, you may not find tons of scientific papers addressing the research problem as Bitcoins are only a new phenomenon.

It is important that you learn to identify research limitations examples at each step. Whatever field you choose, feel free to add the shortcoming of your work. This is mainly because you do not have many years of experience writing scientific papers or completing complex work. Therefore, the depth and scope of your discussions may be compromised at different levels compared to academics with a lot of expertise. Include specific points from limitations of research. Use them as suggestions for the future.

Have you ever faced a challenge of writing the limitations of research study in your paper? How did you overcome it? What ways did you follow? Were they beneficial? Let us know in the comments below!

Frequently Asked Questions

Setting limitations in our study helps to clarify the outcomes drawn from our research and enhance understanding of the subject. Moreover, it shows that the author has investigated all the weaknesses in the study.

Scope is the range and limitations of a research project which are set to define the boundaries of a project. Limitations are the impacts on the overall study due to the constraints on the research design.

Limitation in research is an impact of a constraint on the research design in the overall study. They are the flaws or weaknesses in the study, which may influence the outcome of the research.

1. Limitations in research can be written as follows: Formulate your goals and objectives 2. Analyze the chosen data collection method and the sample sizes 3. Identify your limitations of research and explain their importance 4. Provide the necessary depth, explain their nature, and justify your study choices 5. Write how you are suggesting that it is possible to overcome them in the future

Excellent article ,,,it has helped me big

This is very helpful information. It has given me an insight on how to go about my study limitations.

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the topic is well covered

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CPS Online Graduate Studies Research Paper (UNH Manchester Library): Limitations of the Study

• Overview of the Research Process for Capstone Projects
• Types of Research Design
• Selecting a Research Problem
• The Title of Your Research Paper
• Before You Begin Writing
• 7 Parts of the Research Paper
• Background Information
• Quanitative and Qualitative Methods
• Qualitative Methods
• Quanitative Methods
• Resources to Help You With the Literature Review
• Non-Textual Elements

Limitations of the Study

• Format of Capstone Research Projects at GSC
• Editing and Proofreading Your Paper
• Acknowledgements
• UNH Scholar's Repository

The limitations of the study are those characteristics of design or methodology that impacted or influenced the interpretation of the findings from your research. They are the constraints on generalizability, applications to practice, and/or utility of findings that are the result of the ways in which you initially chose to design the study and/or the method used to establish internal and external validity.

Price, James H. and Judy Murnan. “Research Limitations and the Necessity of Reporting Them.” American Journal of Health Education 35 (2004): 66-67.

Always acknowledge a study's limitations. It is far better that you identify and acknowledge your study’s limitations than to have them pointed out by your professor and be graded down because you appear to have ignored them.

Keep in mind that acknowledgement of a study's limitations is an opportunity to make suggestions for further research. If you do connect your study's limitations to suggestions for further research, be sure to explain the ways in which these unanswered questions may become more focused because of your study.

Acknowledgement of a study's limitations also provides you with an opportunity to demonstrate that you have thought critically about the research problem, understood the relevant literature published about it, and correctly assessed the methods chosen for studying the problem. A key objective of the research process is not only discovering new knowledge but to also confront assumptions and explore what we don't know.

Claiming limitations is a subjective process because you must evaluate the impact of those limitations . Don't just list key weaknesses and the magnitude of a study's limitations. To do so diminishes the validity of your research because it leaves the reader wondering whether, or in what ways, limitation(s) in your study may have impacted the results and conclusions. Limitations require a critical, overall appraisal and interpretation of their impact. You should answer the question: do these problems with errors, methods, validity, etc. eventually matter and, if so, to what extent?

Price, James H. and Judy Murnan. “Research Limitations and the Necessity of Reporting Them.” American Journal of Health Education 35 (2004): 66-67; Structure: How to Structure the Research Limitations Section of Your Dissertation . Dissertations and Theses: An Online Textbook. Laerd.com.

Descriptions of Possible Limitations

All studies have limitations . However, it is important that you restrict your discussion to limitations related to the research problem under investigation. For example, if a meta-analysis of existing literature is not a stated purpose of your research, it should not be discussed as a limitation. Do not apologize for not addressing issues that you did not promise to investigate in the introduction of your paper.

Here are examples of limitations related to methodology and the research process you may need to describe and to discuss how they possibly impacted your results. Descriptions of limitations should be stated in the past tense because they were discovered after you completed your research.

Possible Methodological Limitations

• Sample size -- the number of the units of analysis you use in your study is dictated by the type of research problem you are investigating. Note that, if your sample size is too small, it will be difficult to find significant relationships from the data, as statistical tests normally require a larger sample size to ensure a representative distribution of the population and to be considered representative of groups of people to whom results will be generalized or transferred. Note that sample size is less relevant in qualitative research.
• Lack of available and/or reliable data -- a lack of data or of reliable data will likely require you to limit the scope of your analysis, the size of your sample, or it can be a significant obstacle in finding a trend and a meaningful relationship. You need to not only describe these limitations but to offer reasons why you believe data is missing or is unreliable. However, don’t just throw up your hands in frustration; use this as an opportunity to describe the need for future research.
• Lack of prior research studies on the topic -- citing prior research studies forms the basis of your literature review and helps lay a foundation for understanding the research problem you are investigating. Depending on the currency or scope of your research topic, there may be little, if any, prior research on your topic. Before assuming this to be true, though, consult with a librarian. In cases when a librarian has confirmed that there is no prior research, you may be required to develop an entirely new research typology [for example, using an exploratory rather than an explanatory research design]. Note again that discovering a limitation can serve as an important opportunity to identify new gaps in the literature and to describe the need for further research.
• Measure used to collect the data -- sometimes it is the case that, after completing your interpretation of the findings, you discover that the way in which you gathered data inhibited your ability to conduct a thorough analysis of the results. For example, you regret not including a specific question in a survey that, in retrospect, could have helped address a particular issue that emerged later in the study. Acknowledge the deficiency by stating a need for future researchers to revise the specific method for gathering data.
• Self-reported data -- whether you are relying on pre-existing data or you are conducting a qualitative research study and gathering the data yourself, self-reported data is limited by the fact that it rarely can be independently verified. In other words, you have to take what people say, whether in interviews, focus groups, or on questionnaires, at face value. However, self-reported data can contain several potential sources of bias that you should be alert to and note as limitations. These biases become apparent if they are incongruent with data from other sources. These are: (1) selective memory [remembering or not remembering experiences or events that occurred at some point in the past]; (2) telescoping [recalling events that occurred at one time as if they occurred at another time]; (3) attribution [the act of attributing positive events and outcomes to one's own agency but attributing negative events and outcomes to external forces]; and, (4) exaggeration [the act of representing outcomes or embellishing events as more significant than is actually suggested from other data].

Possible Limitations of the Researcher

• Access -- if your study depends on having access to people, organizations, or documents and, for whatever reason, access is denied or limited in some way, the reasons for this need to be described.
• Longitudinal effects -- unlike your professor, who can literally devote years [even a lifetime] to studying a single topic, the time available to investigate a research problem and to measure change or stability over time is pretty much constrained by the due date of your assignment. Be sure to choose a research problem that does not require an excessive amount of time to complete the literature review, apply the methodology, and gather and interpret the results. If you're unsure whether you can complete your research within the confines of the assignment's due date, talk to your professor.
• Cultural and other type of bias -- we all have biases, whether we are conscience of them or not. Bias is when a person, place, or thing is viewed or shown in a consistently inaccurate way. Bias is usually negative, though one can have a positive bias as well, especially if that bias reflects your reliance on research that only support for your hypothesis. When proof-reading your paper, be especially critical in reviewing how you have stated a problem, selected the data to be studied, what may have been omitted, the manner in which you have ordered events, people, or places, how you have chosen to represent a person, place, or thing, to name a phenomenon, or to use possible words with a positive or negative connotation.

NOTE:   If you detect bias in prior research, it must be acknowledged and you should explain what measures were taken to avoid perpetuating that bias.

• Fluency in a language -- if your research focuses on measuring the perceived value of after-school tutoring among Mexican-American ESL [English as a Second Language] students, for example, and you are not fluent in Spanish, you are limited in being able to read and interpret Spanish language research studies on the topic. This deficiency should be acknowledged.

Aguinis, Hermam and Jeffrey R. Edwards. “Methodological Wishes for the Next Decade and How to Make Wishes Come True.” Journal of Management Studies 51 (January 2014): 143-174; Brutus, Stéphane et al. "Self-Reported Limitations and Future Directions in Scholarly Reports: Analysis and Recommendations." Journal of Management 39 (January 2013): 48-75; Senunyeme, Emmanuel K. Business Research Methods . Powerpoint Presentation. Regent University of Science and Technology; ter Riet, Gerben et al. “All That Glitters Isn't Gold: A Survey on Acknowledgment of Limitations in Biomedical Studies.” PLOS One 8 (November 2013): 1-6.

Structure and Writing Style

Information about the limitations of your study are generally placed either at the beginning of the discussion section of your paper so the reader knows and understands the limitations before reading the rest of your analysis of the findings, or, the limitations are outlined at the conclusion of the discussion section as an acknowledgement of the need for further study. Statements about a study's limitations should not be buried in the body [middle] of the discussion section unless a limitation is specific to something covered in that part of the paper. If this is the case, though, the limitation should be reiterated at the conclusion of the section. If you determine that your study is seriously flawed due to important limitations, such as, an inability to acquire critical data, consider reframing it as an exploratory study intended to lay the groundwork for a more complete research study in the future. Be sure, though, to specifically explain the ways that these flaws can be successfully overcome in a new study. But, do not use this as an excuse for not developing a thorough research paper! Review the tab in this guide for developing a research topic. If serious limitations exist, it generally indicates a likelihood that your research problem is too narrowly defined or that the issue or event under study is too recent and, thus, very little research has been written about it. If serious limitations do emerge, consult with your professor about possible ways to overcome them or how to revise your study. When discussing the limitations of your research, be sure to: Describe each limitation in detailed but concise terms; Explain why each limitation exists; Provide the reasons why each limitation could not be overcome using the method(s) chosen to acquire or gather the data [cite to other studies that had similar problems when possible]; Assess the impact of each limitation in relation to the overall findings and conclusions of your study; and, If appropriate, describe how these limitations could point to the need for further research. Remember that the method you chose may be the source of a significant limitation that has emerged during your interpretation of the results [for example, you didn't interview a group of people that you later wish you had]. If this is the case, don't panic. Acknowledge it, and explain how applying a different or more robust methodology might address the research problem more effectively in a future study. A underlying goal of scholarly research is not only to show what works, but to demonstrate what doesn't work or what needs further clarification. Aguinis, Hermam and Jeffrey R. Edwards. “Methodological Wishes for the Next Decade and How to Make Wishes Come True.” Journal of Management Studies 51 (January 2014): 143-174; Brutus, Stéphane et al. "Self-Reported Limitations and Future Directions in Scholarly Reports: Analysis and Recommendations." Journal of Management 39 (January 2013): 48-75; Ioannidis, John P.A. "Limitations are not Properly Acknowledged in the Scientific Literature." Journal of Clinical Epidemiology 60 (2007): 324-329; Pasek, Josh. Writing the Empirical Social Science Research Paper: A Guide for the Perplexed. January 24, 2012. Academia.edu; Structure: How to Structure the Research Limitations Section of Your Dissertation. Dissertations and Theses: An Online Textbook. Laerd.com; What Is an Academic Paper? Institute for Writing Rhetoric. Dartmouth College; Writing the Experimental Report: Methods, Results, and Discussion. The Writing Lab and The OWL. Purdue University.

Information about the limitations of your study are generally placed either at the beginning of the discussion section of your paper so the reader knows and understands the limitations before reading the rest of your analysis of the findings, or, the limitations are outlined at the conclusion of the discussion section as an acknowledgement of the need for further study. Statements about a study's limitations should not be buried in the body [middle] of the discussion section unless a limitation is specific to something covered in that part of the paper. If this is the case, though, the limitation should be reiterated at the conclusion of the section.

If you determine that your study is seriously flawed due to important limitations , such as, an inability to acquire critical data, consider reframing it as an exploratory study intended to lay the groundwork for a more complete research study in the future. Be sure, though, to specifically explain the ways that these flaws can be successfully overcome in a new study.

But, do not use this as an excuse for not developing a thorough research paper! Review the tab in this guide for developing a research topic . If serious limitations exist, it generally indicates a likelihood that your research problem is too narrowly defined or that the issue or event under study is too recent and, thus, very little research has been written about it. If serious limitations do emerge, consult with your professor about possible ways to overcome them or how to revise your study.

When discussing the limitations of your research, be sure to:

• Describe each limitation in detailed but concise terms;
• Explain why each limitation exists;
• Provide the reasons why each limitation could not be overcome using the method(s) chosen to acquire or gather the data [cite to other studies that had similar problems when possible];
• Assess the impact of each limitation in relation to the overall findings and conclusions of your study; and,
• If appropriate, describe how these limitations could point to the need for further research.

Remember that the method you chose may be the source of a significant limitation that has emerged during your interpretation of the results [for example, you didn't interview a group of people that you later wish you had]. If this is the case, don't panic. Acknowledge it, and explain how applying a different or more robust methodology might address the research problem more effectively in a future study. A underlying goal of scholarly research is not only to show what works, but to demonstrate what doesn't work or what needs further clarification.

Aguinis, Hermam and Jeffrey R. Edwards. “Methodological Wishes for the Next Decade and How to Make Wishes Come True.” Journal of Management Studies 51 (January 2014): 143-174; Brutus, Stéphane et al. "Self-Reported Limitations and Future Directions in Scholarly Reports: Analysis and Recommendations." Journal of Management 39 (January 2013): 48-75; Ioannidis, John P.A. "Limitations are not Properly Acknowledged in the Scientific Literature." Journal of Clinical Epidemiology 60 (2007): 324-329; Pasek, Josh. Writing the Empirical Social Science Research Paper: A Guide for the Perplexed . January 24, 2012. Academia.edu; Structure: How to Structure the Research Limitations Section of Your Dissertation . Dissertations and Theses: An Online Textbook. Laerd.com; What Is an Academic Paper? Institute for Writing Rhetoric. Dartmouth College; Writing the Experimental Report: Methods, Results, and Discussion . The Writing Lab and The OWL. Purdue University.

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Limitations of the Study – How to Write & Examples

What are the limitations of a study?

The limitations of a study are the elements of methodology or study design that impact the interpretation of your research results. The limitations essentially detail any flaws or shortcomings in your study. Study limitations can exist due to constraints on research design, methodology, materials, etc., and these factors may impact the findings of your study. However, researchers are often reluctant to discuss the limitations of their study in their papers, feeling that bringing up limitations may undermine its research value in the eyes of readers and reviewers.

In spite of the impact it might have (and perhaps because of it) you should clearly acknowledge any limitations in your research paper in order to show readers—whether journal editors, other researchers, or the general public—that you are aware of these limitations and to explain how they affect the conclusions that can be drawn from the research.

In this article, we provide some guidelines for writing about research limitations, show examples of some frequently seen study limitations, and recommend techniques for presenting this information. And after you have finished drafting and have received manuscript editing for your work, you still might want to follow this up with academic editing before submitting your work to your target journal.

Why do I need to include limitations of research in my paper?

Although limitations address the potential weaknesses of a study, writing about them toward the end of your paper actually strengthens your study by identifying any problems before other researchers or reviewers find them.

Furthermore, pointing out study limitations shows that you’ve considered the impact of research weakness thoroughly and have an in-depth understanding of your research topic. Since all studies face limitations, being honest and detailing these limitations will impress researchers and reviewers more than ignoring them.

Where should I put the limitations of the study in my paper?

Some limitations might be evident to researchers before the start of the study, while others might become clear while you are conducting the research. Whether these limitations are anticipated or not, and whether they are due to research design or to methodology, they should be clearly identified and discussed in the discussion section —the final section of your paper. Most journals now require you to include a discussion of potential limitations of your work, and many journals now ask you to place this “limitations section” at the very end of your article. 

Some journals ask you to also discuss the strengths of your work in this section, and some allow you to freely choose where to include that information in your discussion section—make sure to always check the author instructions of your target journal before you finalize a manuscript and submit it for peer review .

Limitations of the Study Examples

There are several reasons why limitations of research might exist. The two main categories of limitations are those that result from the methodology and those that result from issues with the researcher(s).

Common Methodological Limitations of Studies

Limitations of research due to methodological problems can be addressed by clearly and directly identifying the potential problem and suggesting ways in which this could have been addressed—and SHOULD be addressed in future studies. The following are some major potential methodological issues that can impact the conclusions researchers can draw from the research.

Issues with research samples and selection

Sampling errors occur when a probability sampling method is used to select a sample, but that sample does not reflect the general population or appropriate population concerned. This results in limitations of your study known as “sample bias” or “selection bias.”

For example, if you conducted a survey to obtain your research results, your samples (participants) were asked to respond to the survey questions. However, you might have had limited ability to gain access to the appropriate type or geographic scope of participants. In this case, the people who responded to your survey questions may not truly be a random sample.

Insufficient sample size for statistical measurements

When conducting a study, it is important to have a sufficient sample size in order to draw valid conclusions. The larger the sample, the more precise your results will be. If your sample size is too small, it will be difficult to identify significant relationships in the data.

Normally, statistical tests require a larger sample size to ensure that the sample is considered representative of a population and that the statistical result can be generalized to a larger population. It is a good idea to understand how to choose an appropriate sample size before you conduct your research by using scientific calculation tools—in fact, many journals now require such estimation to be included in every manuscript that is sent out for review.

Lack of previous research studies on the topic

Citing and referencing prior research studies constitutes the basis of the literature review for your thesis or study, and these prior studies provide the theoretical foundations for the research question you are investigating. However, depending on the scope of your research topic, prior research studies that are relevant to your thesis might be limited.

When there is very little or no prior research on a specific topic, you may need to develop an entirely new research typology. In this case, discovering a limitation can be considered an important opportunity to identify literature gaps and to present the need for further development in the area of study.

Methods/instruments/techniques used to collect the data

After you complete your analysis of the research findings (in the discussion section), you might realize that the manner in which you have collected the data or the ways in which you have measured variables has limited your ability to conduct a thorough analysis of the results.

For example, you might realize that you should have addressed your survey questions from another viable perspective, or that you were not able to include an important question in the survey. In these cases, you should acknowledge the deficiency or deficiencies by stating a need for future researchers to revise their specific methods for collecting data that includes these missing elements.

Common Limitations of the Researcher(s)

Study limitations that arise from situations relating to the researcher or researchers (whether the direct fault of the individuals or not) should also be addressed and dealt with, and remedies to decrease these limitations—both hypothetically in your study, and practically in future studies—should be proposed.

Limited access to data

If your research involved surveying certain people or organizations, you might have faced the problem of having limited access to these respondents. Due to this limited access, you might need to redesign or restructure your research in a different way. In this case, explain the reasons for limited access and be sure that your finding is still reliable and valid despite this limitation.

Time constraints

Just as students have deadlines to turn in their class papers, academic researchers might also have to meet deadlines for submitting a manuscript to a journal or face other time constraints related to their research (e.g., participants are only available during a certain period; funding runs out; collaborators move to a new institution). The time available to study a research problem and to measure change over time might be constrained by such practical issues. If time constraints negatively impacted your study in any way, acknowledge this impact by mentioning a need for a future study (e.g., a longitudinal study) to answer this research problem.

Conflicts arising from cultural bias and other personal issues

Researchers might hold biased views due to their cultural backgrounds or perspectives of certain phenomena, and this can affect a study’s legitimacy. Also, it is possible that researchers will have biases toward data and results that only support their hypotheses or arguments. In order to avoid these problems, the author(s) of a study should examine whether the way the research problem was stated and the data-gathering process was carried out appropriately.

Steps for Organizing Your Study Limitations Section

When you discuss the limitations of your study, don’t simply list and describe your limitations—explain how these limitations have influenced your research findings. There might be multiple limitations in your study, but you only need to point out and explain those that directly relate to and impact how you address your research questions.

We suggest that you divide your limitations section into three steps: (1) identify the study limitations; (2) explain how they impact your study in detail; and (3) propose a direction for future studies and present alternatives. By following this sequence when discussing your study’s limitations, you will be able to clearly demonstrate your study’s weakness without undermining the quality and integrity of your research.

Step 1. Identify the limitation(s) of the study

• This part should comprise around 10%-20% of your discussion of study limitations.

The first step is to identify the particular limitation(s) that affected your study. There are many possible limitations of research that can affect your study, but you don’t need to write a long review of all possible study limitations. A 200-500 word critique is an appropriate length for a research limitations section. In the beginning of this section, identify what limitations your study has faced and how important these limitations are.

You only need to identify limitations that had the greatest potential impact on: (1) the quality of your findings, and (2) your ability to answer your research question.

Step 2. Explain these study limitations in detail

• This part should comprise around 60-70% of your discussion of limitations.

After identifying your research limitations, it’s time to explain the nature of the limitations and how they potentially impacted your study. For example, when you conduct quantitative research, a lack of probability sampling is an important issue that you should mention. On the other hand, when you conduct qualitative research, the inability to generalize the research findings could be an issue that deserves mention.

Explain the role these limitations played on the results and implications of the research and justify the choice you made in using this “limiting” methodology or other action in your research. Also, make sure that these limitations didn’t undermine the quality of your dissertation .

Step 3. Propose a direction for future studies and present alternatives (optional)

• This part should comprise around 10-20% of your discussion of limitations.

After acknowledging the limitations of the research, you need to discuss some possible ways to overcome these limitations in future studies. One way to do this is to present alternative methodologies and ways to avoid issues with, or “fill in the gaps of” the limitations of this study you have presented.  Discuss both the pros and cons of these alternatives and clearly explain why researchers should choose these approaches.

Make sure you are current on approaches used by prior studies and the impacts they have had on their findings. Cite review articles or scientific bodies that have recommended these approaches and why. This might be evidence in support of the approach you chose, or it might be the reason you consider your choices to be included as limitations. This process can act as a justification for your approach and a defense of your decision to take it while acknowledging the feasibility of other approaches.

P hrases and Tips for Introducing Your Study Limitations in the Discussion Section

The following phrases are frequently used to introduce the limitations of the study:

• “There may be some possible limitations in this study.”
• “The findings of this study have to be seen in light of some limitations.”
•  “The first is the…The second limitation concerns the…”
•  “The empirical results reported herein should be considered in the light of some limitations.”
• “This research, however, is subject to several limitations.”
• “The primary limitation to the generalization of these results is…”
• “Nonetheless, these results must be interpreted with caution and a number of limitations should be borne in mind.”
• “As with the majority of studies, the design of the current study is subject to limitations.”
• “There are two major limitations in this study that could be addressed in future research. First, the study focused on …. Second ….”

For more articles on research writing and the journal submissions and publication process, visit Wordvice’s Academic Resources page.

And be sure to receive professional English editing and proofreading services , including paper editing services , for your journal manuscript before submitting it to journal editors.

Wordvice Resources

Proofreading & Editing Guide

Writing the Results Section for a Research Paper

How to Write a Literature Review

Research Writing Tips: How to Draft a Powerful Discussion Section

How to Captivate Journal Readers with a Strong Introduction

Tips That Will Make Your Abstract a Success!

APA In-Text Citation Guide for Research Writing

Additional Resources

• Diving Deeper into Limitations and Delimitations (PhD student)
• Organizing Your Social Sciences Research Paper: Limitations of the Study (USC Library)
• Research Limitations (Research Methodology)
• How to Present Limitations and Alternatives (UMASS)

Article References

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Research Methodology – Types, Examples and writing Guide

Table of Contents

Research Methodology

Definition:

Research Methodology refers to the systematic and scientific approach used to conduct research, investigate problems, and gather data and information for a specific purpose. It involves the techniques and procedures used to identify, collect , analyze , and interpret data to answer research questions or solve research problems . Moreover, They are philosophical and theoretical frameworks that guide the research process.

Structure of Research Methodology

Research methodology formats can vary depending on the specific requirements of the research project, but the following is a basic example of a structure for a research methodology section:

I. Introduction

• Provide an overview of the research problem and the need for a research methodology section
• Outline the main research questions and objectives

II. Research Design

• Explain the research design chosen and why it is appropriate for the research question(s) and objectives
• Discuss any alternative research designs considered and why they were not chosen
• Describe the research setting and participants (if applicable)

III. Data Collection Methods

• Describe the methods used to collect data (e.g., surveys, interviews, observations)
• Explain how the data collection methods were chosen and why they are appropriate for the research question(s) and objectives
• Detail any procedures or instruments used for data collection

IV. Data Analysis Methods

• Describe the methods used to analyze the data (e.g., statistical analysis, content analysis )
• Explain how the data analysis methods were chosen and why they are appropriate for the research question(s) and objectives
• Detail any procedures or software used for data analysis

V. Ethical Considerations

• Discuss any ethical issues that may arise from the research and how they were addressed
• Explain how informed consent was obtained (if applicable)
• Detail any measures taken to ensure confidentiality and anonymity

VI. Limitations

• Identify any potential limitations of the research methodology and how they may impact the results and conclusions

VII. Conclusion

• Summarize the key aspects of the research methodology section
• Explain how the research methodology addresses the research question(s) and objectives

Research Methodology Types

Types of Research Methodology are as follows:

Quantitative Research Methodology

This is a research methodology that involves the collection and analysis of numerical data using statistical methods. This type of research is often used to study cause-and-effect relationships and to make predictions.

Qualitative Research Methodology

This is a research methodology that involves the collection and analysis of non-numerical data such as words, images, and observations. This type of research is often used to explore complex phenomena, to gain an in-depth understanding of a particular topic, and to generate hypotheses.

Mixed-Methods Research Methodology

This is a research methodology that combines elements of both quantitative and qualitative research. This approach can be particularly useful for studies that aim to explore complex phenomena and to provide a more comprehensive understanding of a particular topic.

Case Study Research Methodology

This is a research methodology that involves in-depth examination of a single case or a small number of cases. Case studies are often used in psychology, sociology, and anthropology to gain a detailed understanding of a particular individual or group.

Action Research Methodology

This is a research methodology that involves a collaborative process between researchers and practitioners to identify and solve real-world problems. Action research is often used in education, healthcare, and social work.

Experimental Research Methodology

This is a research methodology that involves the manipulation of one or more independent variables to observe their effects on a dependent variable. Experimental research is often used to study cause-and-effect relationships and to make predictions.

Survey Research Methodology

This is a research methodology that involves the collection of data from a sample of individuals using questionnaires or interviews. Survey research is often used to study attitudes, opinions, and behaviors.

Grounded Theory Research Methodology

This is a research methodology that involves the development of theories based on the data collected during the research process. Grounded theory is often used in sociology and anthropology to generate theories about social phenomena.

Research Methodology Example

An Example of Research Methodology could be the following:

Research Methodology for Investigating the Effectiveness of Cognitive Behavioral Therapy in Reducing Symptoms of Depression in Adults

Introduction:

The aim of this research is to investigate the effectiveness of cognitive-behavioral therapy (CBT) in reducing symptoms of depression in adults. To achieve this objective, a randomized controlled trial (RCT) will be conducted using a mixed-methods approach.

Research Design:

The study will follow a pre-test and post-test design with two groups: an experimental group receiving CBT and a control group receiving no intervention. The study will also include a qualitative component, in which semi-structured interviews will be conducted with a subset of participants to explore their experiences of receiving CBT.

Participants:

Participants will be recruited from community mental health clinics in the local area. The sample will consist of 100 adults aged 18-65 years old who meet the diagnostic criteria for major depressive disorder. Participants will be randomly assigned to either the experimental group or the control group.

Intervention :

The experimental group will receive 12 weekly sessions of CBT, each lasting 60 minutes. The intervention will be delivered by licensed mental health professionals who have been trained in CBT. The control group will receive no intervention during the study period.

Data Collection:

Quantitative data will be collected through the use of standardized measures such as the Beck Depression Inventory-II (BDI-II) and the Generalized Anxiety Disorder-7 (GAD-7). Data will be collected at baseline, immediately after the intervention, and at a 3-month follow-up. Qualitative data will be collected through semi-structured interviews with a subset of participants from the experimental group. The interviews will be conducted at the end of the intervention period, and will explore participants’ experiences of receiving CBT.

Data Analysis:

Quantitative data will be analyzed using descriptive statistics, t-tests, and mixed-model analyses of variance (ANOVA) to assess the effectiveness of the intervention. Qualitative data will be analyzed using thematic analysis to identify common themes and patterns in participants’ experiences of receiving CBT.

Ethical Considerations:

This study will comply with ethical guidelines for research involving human subjects. Participants will provide informed consent before participating in the study, and their privacy and confidentiality will be protected throughout the study. Any adverse events or reactions will be reported and managed appropriately.

Data Management:

All data collected will be kept confidential and stored securely using password-protected databases. Identifying information will be removed from qualitative data transcripts to ensure participants’ anonymity.

Limitations:

One potential limitation of this study is that it only focuses on one type of psychotherapy, CBT, and may not generalize to other types of therapy or interventions. Another limitation is that the study will only include participants from community mental health clinics, which may not be representative of the general population.

Conclusion:

This research aims to investigate the effectiveness of CBT in reducing symptoms of depression in adults. By using a randomized controlled trial and a mixed-methods approach, the study will provide valuable insights into the mechanisms underlying the relationship between CBT and depression. The results of this study will have important implications for the development of effective treatments for depression in clinical settings.

How to Write Research Methodology

Writing a research methodology involves explaining the methods and techniques you used to conduct research, collect data, and analyze results. It’s an essential section of any research paper or thesis, as it helps readers understand the validity and reliability of your findings. Here are the steps to write a research methodology:

• Start by explaining your research question: Begin the methodology section by restating your research question and explaining why it’s important. This helps readers understand the purpose of your research and the rationale behind your methods.
• Describe your research design: Explain the overall approach you used to conduct research. This could be a qualitative or quantitative research design, experimental or non-experimental, case study or survey, etc. Discuss the advantages and limitations of the chosen design.
• Discuss your sample: Describe the participants or subjects you included in your study. Include details such as their demographics, sampling method, sample size, and any exclusion criteria used.
• Describe your data collection methods : Explain how you collected data from your participants. This could include surveys, interviews, observations, questionnaires, or experiments. Include details on how you obtained informed consent, how you administered the tools, and how you minimized the risk of bias.
• Explain your data analysis techniques: Describe the methods you used to analyze the data you collected. This could include statistical analysis, content analysis, thematic analysis, or discourse analysis. Explain how you dealt with missing data, outliers, and any other issues that arose during the analysis.
• Discuss the validity and reliability of your research : Explain how you ensured the validity and reliability of your study. This could include measures such as triangulation, member checking, peer review, or inter-coder reliability.
• Acknowledge any limitations of your research: Discuss any limitations of your study, including any potential threats to validity or generalizability. This helps readers understand the scope of your findings and how they might apply to other contexts.
• Provide a summary: End the methodology section by summarizing the methods and techniques you used to conduct your research. This provides a clear overview of your research methodology and helps readers understand the process you followed to arrive at your findings.

When to Write Research Methodology

Research methodology is typically written after the research proposal has been approved and before the actual research is conducted. It should be written prior to data collection and analysis, as it provides a clear roadmap for the research project.

The research methodology is an important section of any research paper or thesis, as it describes the methods and procedures that will be used to conduct the research. It should include details about the research design, data collection methods, data analysis techniques, and any ethical considerations.

The methodology should be written in a clear and concise manner, and it should be based on established research practices and standards. It is important to provide enough detail so that the reader can understand how the research was conducted and evaluate the validity of the results.

Applications of Research Methodology

Here are some of the applications of research methodology:

• To identify the research problem: Research methodology is used to identify the research problem, which is the first step in conducting any research.
• To design the research: Research methodology helps in designing the research by selecting the appropriate research method, research design, and sampling technique.
• To collect data: Research methodology provides a systematic approach to collect data from primary and secondary sources.
• To analyze data: Research methodology helps in analyzing the collected data using various statistical and non-statistical techniques.
• To test hypotheses: Research methodology provides a framework for testing hypotheses and drawing conclusions based on the analysis of data.
• To generalize findings: Research methodology helps in generalizing the findings of the research to the target population.
• To develop theories : Research methodology is used to develop new theories and modify existing theories based on the findings of the research.
• To evaluate programs and policies : Research methodology is used to evaluate the effectiveness of programs and policies by collecting data and analyzing it.
• To improve decision-making: Research methodology helps in making informed decisions by providing reliable and valid data.

Purpose of Research Methodology

Research methodology serves several important purposes, including:

• To guide the research process: Research methodology provides a systematic framework for conducting research. It helps researchers to plan their research, define their research questions, and select appropriate methods and techniques for collecting and analyzing data.
• To ensure research quality: Research methodology helps researchers to ensure that their research is rigorous, reliable, and valid. It provides guidelines for minimizing bias and error in data collection and analysis, and for ensuring that research findings are accurate and trustworthy.
• To replicate research: Research methodology provides a clear and detailed account of the research process, making it possible for other researchers to replicate the study and verify its findings.
• To advance knowledge: Research methodology enables researchers to generate new knowledge and to contribute to the body of knowledge in their field. It provides a means for testing hypotheses, exploring new ideas, and discovering new insights.
• To inform decision-making: Research methodology provides evidence-based information that can inform policy and decision-making in a variety of fields, including medicine, public health, education, and business.

Advantages of Research Methodology

Research methodology has several advantages that make it a valuable tool for conducting research in various fields. Here are some of the key advantages of research methodology:

• Systematic and structured approach : Research methodology provides a systematic and structured approach to conducting research, which ensures that the research is conducted in a rigorous and comprehensive manner.
• Objectivity : Research methodology aims to ensure objectivity in the research process, which means that the research findings are based on evidence and not influenced by personal bias or subjective opinions.
• Replicability : Research methodology ensures that research can be replicated by other researchers, which is essential for validating research findings and ensuring their accuracy.
• Reliability : Research methodology aims to ensure that the research findings are reliable, which means that they are consistent and can be depended upon.
• Validity : Research methodology ensures that the research findings are valid, which means that they accurately reflect the research question or hypothesis being tested.
• Efficiency : Research methodology provides a structured and efficient way of conducting research, which helps to save time and resources.
• Flexibility : Research methodology allows researchers to choose the most appropriate research methods and techniques based on the research question, data availability, and other relevant factors.
• Scope for innovation: Research methodology provides scope for innovation and creativity in designing research studies and developing new research techniques.

Research Methodology Vs Research Methods

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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A tutorial on methodological studies: the what, when, how and why

Lawrence mbuagbaw.

1 Department of Health Research Methods, Evidence and Impact, McMaster University, Hamilton, ON Canada

2 Biostatistics Unit/FSORC, 50 Charlton Avenue East, St Joseph’s Healthcare—Hamilton, 3rd Floor Martha Wing, Room H321, Hamilton, Ontario L8N 4A6 Canada

3 Centre for the Development of Best Practices in Health, Yaoundé, Cameroon

Daeria O. Lawson

Livia puljak.

4 Center for Evidence-Based Medicine and Health Care, Catholic University of Croatia, Ilica 242, 10000 Zagreb, Croatia

David B. Allison

5 Department of Epidemiology and Biostatistics, School of Public Health – Bloomington, Indiana University, Bloomington, IN 47405 USA

Lehana Thabane

6 Departments of Paediatrics and Anaesthesia, McMaster University, Hamilton, ON Canada

7 Centre for Evaluation of Medicine, St. Joseph’s Healthcare-Hamilton, Hamilton, ON Canada

8 Population Health Research Institute, Hamilton Health Sciences, Hamilton, ON Canada

Associated Data

Data sharing is not applicable to this article as no new data were created or analyzed in this study.

Methodological studies – studies that evaluate the design, analysis or reporting of other research-related reports – play an important role in health research. They help to highlight issues in the conduct of research with the aim of improving health research methodology, and ultimately reducing research waste.

We provide an overview of some of the key aspects of methodological studies such as what they are, and when, how and why they are done. We adopt a “frequently asked questions” format to facilitate reading this paper and provide multiple examples to help guide researchers interested in conducting methodological studies. Some of the topics addressed include: is it necessary to publish a study protocol? How to select relevant research reports and databases for a methodological study? What approaches to data extraction and statistical analysis should be considered when conducting a methodological study? What are potential threats to validity and is there a way to appraise the quality of methodological studies?

Appropriate reflection and application of basic principles of epidemiology and biostatistics are required in the design and analysis of methodological studies. This paper provides an introduction for further discussion about the conduct of methodological studies.

The field of meta-research (or research-on-research) has proliferated in recent years in response to issues with research quality and conduct [ 1 – 3 ]. As the name suggests, this field targets issues with research design, conduct, analysis and reporting. Various types of research reports are often examined as the unit of analysis in these studies (e.g. abstracts, full manuscripts, trial registry entries). Like many other novel fields of research, meta-research has seen a proliferation of use before the development of reporting guidance. For example, this was the case with randomized trials for which risk of bias tools and reporting guidelines were only developed much later – after many trials had been published and noted to have limitations [ 4 , 5 ]; and for systematic reviews as well [ 6 – 8 ]. However, in the absence of formal guidance, studies that report on research differ substantially in how they are named, conducted and reported [ 9 , 10 ]. This creates challenges in identifying, summarizing and comparing them. In this tutorial paper, we will use the term methodological study to refer to any study that reports on the design, conduct, analysis or reporting of primary or secondary research-related reports (such as trial registry entries and conference abstracts).

In the past 10 years, there has been an increase in the use of terms related to methodological studies (based on records retrieved with a keyword search [in the title and abstract] for “methodological review” and “meta-epidemiological study” in PubMed up to December 2019), suggesting that these studies may be appearing more frequently in the literature. See Fig.  1 .

Trends in the number studies that mention “methodological review” or “meta-

epidemiological study” in PubMed.

The methods used in many methodological studies have been borrowed from systematic and scoping reviews. This practice has influenced the direction of the field, with many methodological studies including searches of electronic databases, screening of records, duplicate data extraction and assessments of risk of bias in the included studies. However, the research questions posed in methodological studies do not always require the approaches listed above, and guidance is needed on when and how to apply these methods to a methodological study. Even though methodological studies can be conducted on qualitative or mixed methods research, this paper focuses on and draws examples exclusively from quantitative research.

The objectives of this paper are to provide some insights on how to conduct methodological studies so that there is greater consistency between the research questions posed, and the design, analysis and reporting of findings. We provide multiple examples to illustrate concepts and a proposed framework for categorizing methodological studies in quantitative research.

What is a methodological study?

Any study that describes or analyzes methods (design, conduct, analysis or reporting) in published (or unpublished) literature is a methodological study. Consequently, the scope of methodological studies is quite extensive and includes, but is not limited to, topics as diverse as: research question formulation [ 11 ]; adherence to reporting guidelines [ 12 – 14 ] and consistency in reporting [ 15 ]; approaches to study analysis [ 16 ]; investigating the credibility of analyses [ 17 ]; and studies that synthesize these methodological studies [ 18 ]. While the nomenclature of methodological studies is not uniform, the intents and purposes of these studies remain fairly consistent – to describe or analyze methods in primary or secondary studies. As such, methodological studies may also be classified as a subtype of observational studies.

Parallel to this are experimental studies that compare different methods. Even though they play an important role in informing optimal research methods, experimental methodological studies are beyond the scope of this paper. Examples of such studies include the randomized trials by Buscemi et al., comparing single data extraction to double data extraction [ 19 ], and Carrasco-Labra et al., comparing approaches to presenting findings in Grading of Recommendations, Assessment, Development and Evaluations (GRADE) summary of findings tables [ 20 ]. In these studies, the unit of analysis is the person or groups of individuals applying the methods. We also direct readers to the Studies Within a Trial (SWAT) and Studies Within a Review (SWAR) programme operated through the Hub for Trials Methodology Research, for further reading as a potential useful resource for these types of experimental studies [ 21 ]. Lastly, this paper is not meant to inform the conduct of research using computational simulation and mathematical modeling for which some guidance already exists [ 22 ], or studies on the development of methods using consensus-based approaches.

When should we conduct a methodological study?

Methodological studies occupy a unique niche in health research that allows them to inform methodological advances. Methodological studies should also be conducted as pre-cursors to reporting guideline development, as they provide an opportunity to understand current practices, and help to identify the need for guidance and gaps in methodological or reporting quality. For example, the development of the popular Preferred Reporting Items of Systematic reviews and Meta-Analyses (PRISMA) guidelines were preceded by methodological studies identifying poor reporting practices [ 23 , 24 ]. In these instances, after the reporting guidelines are published, methodological studies can also be used to monitor uptake of the guidelines.

These studies can also be conducted to inform the state of the art for design, analysis and reporting practices across different types of health research fields, with the aim of improving research practices, and preventing or reducing research waste. For example, Samaan et al. conducted a scoping review of adherence to different reporting guidelines in health care literature [ 18 ]. Methodological studies can also be used to determine the factors associated with reporting practices. For example, Abbade et al. investigated journal characteristics associated with the use of the Participants, Intervention, Comparison, Outcome, Timeframe (PICOT) format in framing research questions in trials of venous ulcer disease [ 11 ].

How often are methodological studies conducted?

There is no clear answer to this question. Based on a search of PubMed, the use of related terms (“methodological review” and “meta-epidemiological study”) – and therefore, the number of methodological studies – is on the rise. However, many other terms are used to describe methodological studies. There are also many studies that explore design, conduct, analysis or reporting of research reports, but that do not use any specific terms to describe or label their study design in terms of “methodology”. This diversity in nomenclature makes a census of methodological studies elusive. Appropriate terminology and key words for methodological studies are needed to facilitate improved accessibility for end-users.

Why do we conduct methodological studies?

Methodological studies provide information on the design, conduct, analysis or reporting of primary and secondary research and can be used to appraise quality, quantity, completeness, accuracy and consistency of health research. These issues can be explored in specific fields, journals, databases, geographical regions and time periods. For example, Areia et al. explored the quality of reporting of endoscopic diagnostic studies in gastroenterology [ 25 ]; Knol et al. investigated the reporting of p -values in baseline tables in randomized trial published in high impact journals [ 26 ]; Chen et al. describe adherence to the Consolidated Standards of Reporting Trials (CONSORT) statement in Chinese Journals [ 27 ]; and Hopewell et al. describe the effect of editors’ implementation of CONSORT guidelines on reporting of abstracts over time [ 28 ]. Methodological studies provide useful information to researchers, clinicians, editors, publishers and users of health literature. As a result, these studies have been at the cornerstone of important methodological developments in the past two decades and have informed the development of many health research guidelines including the highly cited CONSORT statement [ 5 ].

Where can we find methodological studies?

Methodological studies can be found in most common biomedical bibliographic databases (e.g. Embase, MEDLINE, PubMed, Web of Science). However, the biggest caveat is that methodological studies are hard to identify in the literature due to the wide variety of names used and the lack of comprehensive databases dedicated to them. A handful can be found in the Cochrane Library as “Cochrane Methodology Reviews”, but these studies only cover methodological issues related to systematic reviews. Previous attempts to catalogue all empirical studies of methods used in reviews were abandoned 10 years ago [ 29 ]. In other databases, a variety of search terms may be applied with different levels of sensitivity and specificity.

Some frequently asked questions about methodological studies

In this section, we have outlined responses to questions that might help inform the conduct of methodological studies.

Q: How should I select research reports for my methodological study?

A: Selection of research reports for a methodological study depends on the research question and eligibility criteria. Once a clear research question is set and the nature of literature one desires to review is known, one can then begin the selection process. Selection may begin with a broad search, especially if the eligibility criteria are not apparent. For example, a methodological study of Cochrane Reviews of HIV would not require a complex search as all eligible studies can easily be retrieved from the Cochrane Library after checking a few boxes [ 30 ]. On the other hand, a methodological study of subgroup analyses in trials of gastrointestinal oncology would require a search to find such trials, and further screening to identify trials that conducted a subgroup analysis [ 31 ].

The strategies used for identifying participants in observational studies can apply here. One may use a systematic search to identify all eligible studies. If the number of eligible studies is unmanageable, a random sample of articles can be expected to provide comparable results if it is sufficiently large [ 32 ]. For example, Wilson et al. used a random sample of trials from the Cochrane Stroke Group’s Trial Register to investigate completeness of reporting [ 33 ]. It is possible that a simple random sample would lead to underrepresentation of units (i.e. research reports) that are smaller in number. This is relevant if the investigators wish to compare multiple groups but have too few units in one group. In this case a stratified sample would help to create equal groups. For example, in a methodological study comparing Cochrane and non-Cochrane reviews, Kahale et al. drew random samples from both groups [ 34 ]. Alternatively, systematic or purposeful sampling strategies can be used and we encourage researchers to justify their selected approaches based on the study objective.

Q: How many databases should I search?

A: The number of databases one should search would depend on the approach to sampling, which can include targeting the entire “population” of interest or a sample of that population. If you are interested in including the entire target population for your research question, or drawing a random or systematic sample from it, then a comprehensive and exhaustive search for relevant articles is required. In this case, we recommend using systematic approaches for searching electronic databases (i.e. at least 2 databases with a replicable and time stamped search strategy). The results of your search will constitute a sampling frame from which eligible studies can be drawn.

Alternatively, if your approach to sampling is purposeful, then we recommend targeting the database(s) or data sources (e.g. journals, registries) that include the information you need. For example, if you are conducting a methodological study of high impact journals in plastic surgery and they are all indexed in PubMed, you likely do not need to search any other databases. You may also have a comprehensive list of all journals of interest and can approach your search using the journal names in your database search (or by accessing the journal archives directly from the journal’s website). Even though one could also search journals’ web pages directly, using a database such as PubMed has multiple advantages, such as the use of filters, so the search can be narrowed down to a certain period, or study types of interest. Furthermore, individual journals’ web sites may have different search functionalities, which do not necessarily yield a consistent output.

Q: Should I publish a protocol for my methodological study?

A: A protocol is a description of intended research methods. Currently, only protocols for clinical trials require registration [ 35 ]. Protocols for systematic reviews are encouraged but no formal recommendation exists. The scientific community welcomes the publication of protocols because they help protect against selective outcome reporting, the use of post hoc methodologies to embellish results, and to help avoid duplication of efforts [ 36 ]. While the latter two risks exist in methodological research, the negative consequences may be substantially less than for clinical outcomes. In a sample of 31 methodological studies, 7 (22.6%) referenced a published protocol [ 9 ]. In the Cochrane Library, there are 15 protocols for methodological reviews (21 July 2020). This suggests that publishing protocols for methodological studies is not uncommon.

Authors can consider publishing their study protocol in a scholarly journal as a manuscript. Advantages of such publication include obtaining peer-review feedback about the planned study, and easy retrieval by searching databases such as PubMed. The disadvantages in trying to publish protocols includes delays associated with manuscript handling and peer review, as well as costs, as few journals publish study protocols, and those journals mostly charge article-processing fees [ 37 ]. Authors who would like to make their protocol publicly available without publishing it in scholarly journals, could deposit their study protocols in publicly available repositories, such as the Open Science Framework ( https://osf.io/ ).

Q: How to appraise the quality of a methodological study?

A: To date, there is no published tool for appraising the risk of bias in a methodological study, but in principle, a methodological study could be considered as a type of observational study. Therefore, during conduct or appraisal, care should be taken to avoid the biases common in observational studies [ 38 ]. These biases include selection bias, comparability of groups, and ascertainment of exposure or outcome. In other words, to generate a representative sample, a comprehensive reproducible search may be necessary to build a sampling frame. Additionally, random sampling may be necessary to ensure that all the included research reports have the same probability of being selected, and the screening and selection processes should be transparent and reproducible. To ensure that the groups compared are similar in all characteristics, matching, random sampling or stratified sampling can be used. Statistical adjustments for between-group differences can also be applied at the analysis stage. Finally, duplicate data extraction can reduce errors in assessment of exposures or outcomes.

Q: Should I justify a sample size?

A: In all instances where one is not using the target population (i.e. the group to which inferences from the research report are directed) [ 39 ], a sample size justification is good practice. The sample size justification may take the form of a description of what is expected to be achieved with the number of articles selected, or a formal sample size estimation that outlines the number of articles required to answer the research question with a certain precision and power. Sample size justifications in methodological studies are reasonable in the following instances:

• Comparing two groups
• Determining a proportion, mean or another quantifier
• Determining factors associated with an outcome using regression-based analyses

For example, El Dib et al. computed a sample size requirement for a methodological study of diagnostic strategies in randomized trials, based on a confidence interval approach [ 40 ].

Q: What should I call my study?

A: Other terms which have been used to describe/label methodological studies include “ methodological review ”, “methodological survey” , “meta-epidemiological study” , “systematic review” , “systematic survey”, “meta-research”, “research-on-research” and many others. We recommend that the study nomenclature be clear, unambiguous, informative and allow for appropriate indexing. Methodological study nomenclature that should be avoided includes “ systematic review” – as this will likely be confused with a systematic review of a clinical question. “ Systematic survey” may also lead to confusion about whether the survey was systematic (i.e. using a preplanned methodology) or a survey using “ systematic” sampling (i.e. a sampling approach using specific intervals to determine who is selected) [ 32 ]. Any of the above meanings of the words “ systematic” may be true for methodological studies and could be potentially misleading. “ Meta-epidemiological study” is ideal for indexing, but not very informative as it describes an entire field. The term “ review ” may point towards an appraisal or “review” of the design, conduct, analysis or reporting (or methodological components) of the targeted research reports, yet it has also been used to describe narrative reviews [ 41 , 42 ]. The term “ survey ” is also in line with the approaches used in many methodological studies [ 9 ], and would be indicative of the sampling procedures of this study design. However, in the absence of guidelines on nomenclature, the term “ methodological study ” is broad enough to capture most of the scenarios of such studies.

Q: Should I account for clustering in my methodological study?

A: Data from methodological studies are often clustered. For example, articles coming from a specific source may have different reporting standards (e.g. the Cochrane Library). Articles within the same journal may be similar due to editorial practices and policies, reporting requirements and endorsement of guidelines. There is emerging evidence that these are real concerns that should be accounted for in analyses [ 43 ]. Some cluster variables are described in the section: “ What variables are relevant to methodological studies?”

A variety of modelling approaches can be used to account for correlated data, including the use of marginal, fixed or mixed effects regression models with appropriate computation of standard errors [ 44 ]. For example, Kosa et al. used generalized estimation equations to account for correlation of articles within journals [ 15 ]. Not accounting for clustering could lead to incorrect p -values, unduly narrow confidence intervals, and biased estimates [ 45 ].

Q: Should I extract data in duplicate?

A: Yes. Duplicate data extraction takes more time but results in less errors [ 19 ]. Data extraction errors in turn affect the effect estimate [ 46 ], and therefore should be mitigated. Duplicate data extraction should be considered in the absence of other approaches to minimize extraction errors. However, much like systematic reviews, this area will likely see rapid new advances with machine learning and natural language processing technologies to support researchers with screening and data extraction [ 47 , 48 ]. However, experience plays an important role in the quality of extracted data and inexperienced extractors should be paired with experienced extractors [ 46 , 49 ].

Q: Should I assess the risk of bias of research reports included in my methodological study?

A : Risk of bias is most useful in determining the certainty that can be placed in the effect measure from a study. In methodological studies, risk of bias may not serve the purpose of determining the trustworthiness of results, as effect measures are often not the primary goal of methodological studies. Determining risk of bias in methodological studies is likely a practice borrowed from systematic review methodology, but whose intrinsic value is not obvious in methodological studies. When it is part of the research question, investigators often focus on one aspect of risk of bias. For example, Speich investigated how blinding was reported in surgical trials [ 50 ], and Abraha et al., investigated the application of intention-to-treat analyses in systematic reviews and trials [ 51 ].

Q: What variables are relevant to methodological studies?

A: There is empirical evidence that certain variables may inform the findings in a methodological study. We outline some of these and provide a brief overview below:

• Country: Countries and regions differ in their research cultures, and the resources available to conduct research. Therefore, it is reasonable to believe that there may be differences in methodological features across countries. Methodological studies have reported loco-regional differences in reporting quality [ 52 , 53 ]. This may also be related to challenges non-English speakers face in publishing papers in English.
• Authors’ expertise: The inclusion of authors with expertise in research methodology, biostatistics, and scientific writing is likely to influence the end-product. Oltean et al. found that among randomized trials in orthopaedic surgery, the use of analyses that accounted for clustering was more likely when specialists (e.g. statistician, epidemiologist or clinical trials methodologist) were included on the study team [ 54 ]. Fleming et al. found that including methodologists in the review team was associated with appropriate use of reporting guidelines [ 55 ].
• Source of funding and conflicts of interest: Some studies have found that funded studies report better [ 56 , 57 ], while others do not [ 53 , 58 ]. The presence of funding would indicate the availability of resources deployed to ensure optimal design, conduct, analysis and reporting. However, the source of funding may introduce conflicts of interest and warrant assessment. For example, Kaiser et al. investigated the effect of industry funding on obesity or nutrition randomized trials and found that reporting quality was similar [ 59 ]. Thomas et al. looked at reporting quality of long-term weight loss trials and found that industry funded studies were better [ 60 ]. Kan et al. examined the association between industry funding and “positive trials” (trials reporting a significant intervention effect) and found that industry funding was highly predictive of a positive trial [ 61 ]. This finding is similar to that of a recent Cochrane Methodology Review by Hansen et al. [ 62 ]
• Journal characteristics: Certain journals’ characteristics may influence the study design, analysis or reporting. Characteristics such as journal endorsement of guidelines [ 63 , 64 ], and Journal Impact Factor (JIF) have been shown to be associated with reporting [ 63 , 65 – 67 ].
• Study size (sample size/number of sites): Some studies have shown that reporting is better in larger studies [ 53 , 56 , 58 ].
• Year of publication: It is reasonable to assume that design, conduct, analysis and reporting of research will change over time. Many studies have demonstrated improvements in reporting over time or after the publication of reporting guidelines [ 68 , 69 ].
• Type of intervention: In a methodological study of reporting quality of weight loss intervention studies, Thabane et al. found that trials of pharmacologic interventions were reported better than trials of non-pharmacologic interventions [ 70 ].
• Interactions between variables: Complex interactions between the previously listed variables are possible. High income countries with more resources may be more likely to conduct larger studies and incorporate a variety of experts. Authors in certain countries may prefer certain journals, and journal endorsement of guidelines and editorial policies may change over time.

Q: Should I focus only on high impact journals?

A: Investigators may choose to investigate only high impact journals because they are more likely to influence practice and policy, or because they assume that methodological standards would be higher. However, the JIF may severely limit the scope of articles included and may skew the sample towards articles with positive findings. The generalizability and applicability of findings from a handful of journals must be examined carefully, especially since the JIF varies over time. Even among journals that are all “high impact”, variations exist in methodological standards.

Q: Can I conduct a methodological study of qualitative research?

A: Yes. Even though a lot of methodological research has been conducted in the quantitative research field, methodological studies of qualitative studies are feasible. Certain databases that catalogue qualitative research including the Cumulative Index to Nursing & Allied Health Literature (CINAHL) have defined subject headings that are specific to methodological research (e.g. “research methodology”). Alternatively, one could also conduct a qualitative methodological review; that is, use qualitative approaches to synthesize methodological issues in qualitative studies.

Q: What reporting guidelines should I use for my methodological study?

A: There is no guideline that covers the entire scope of methodological studies. One adaptation of the PRISMA guidelines has been published, which works well for studies that aim to use the entire target population of research reports [ 71 ]. However, it is not widely used (40 citations in 2 years as of 09 December 2019), and methodological studies that are designed as cross-sectional or before-after studies require a more fit-for purpose guideline. A more encompassing reporting guideline for a broad range of methodological studies is currently under development [ 72 ]. However, in the absence of formal guidance, the requirements for scientific reporting should be respected, and authors of methodological studies should focus on transparency and reproducibility.

Q: What are the potential threats to validity and how can I avoid them?

A: Methodological studies may be compromised by a lack of internal or external validity. The main threats to internal validity in methodological studies are selection and confounding bias. Investigators must ensure that the methods used to select articles does not make them differ systematically from the set of articles to which they would like to make inferences. For example, attempting to make extrapolations to all journals after analyzing high-impact journals would be misleading.

Many factors (confounders) may distort the association between the exposure and outcome if the included research reports differ with respect to these factors [ 73 ]. For example, when examining the association between source of funding and completeness of reporting, it may be necessary to account for journals that endorse the guidelines. Confounding bias can be addressed by restriction, matching and statistical adjustment [ 73 ]. Restriction appears to be the method of choice for many investigators who choose to include only high impact journals or articles in a specific field. For example, Knol et al. examined the reporting of p -values in baseline tables of high impact journals [ 26 ]. Matching is also sometimes used. In the methodological study of non-randomized interventional studies of elective ventral hernia repair, Parker et al. matched prospective studies with retrospective studies and compared reporting standards [ 74 ]. Some other methodological studies use statistical adjustments. For example, Zhang et al. used regression techniques to determine the factors associated with missing participant data in trials [ 16 ].

With regard to external validity, researchers interested in conducting methodological studies must consider how generalizable or applicable their findings are. This should tie in closely with the research question and should be explicit. For example. Findings from methodological studies on trials published in high impact cardiology journals cannot be assumed to be applicable to trials in other fields. However, investigators must ensure that their sample truly represents the target sample either by a) conducting a comprehensive and exhaustive search, or b) using an appropriate and justified, randomly selected sample of research reports.

Even applicability to high impact journals may vary based on the investigators’ definition, and over time. For example, for high impact journals in the field of general medicine, Bouwmeester et al. included the Annals of Internal Medicine (AIM), BMJ, the Journal of the American Medical Association (JAMA), Lancet, the New England Journal of Medicine (NEJM), and PLoS Medicine ( n  = 6) [ 75 ]. In contrast, the high impact journals selected in the methodological study by Schiller et al. were BMJ, JAMA, Lancet, and NEJM ( n  = 4) [ 76 ]. Another methodological study by Kosa et al. included AIM, BMJ, JAMA, Lancet and NEJM ( n  = 5). In the methodological study by Thabut et al., journals with a JIF greater than 5 were considered to be high impact. Riado Minguez et al. used first quartile journals in the Journal Citation Reports (JCR) for a specific year to determine “high impact” [ 77 ]. Ultimately, the definition of high impact will be based on the number of journals the investigators are willing to include, the year of impact and the JIF cut-off [ 78 ]. We acknowledge that the term “generalizability” may apply differently for methodological studies, especially when in many instances it is possible to include the entire target population in the sample studied.

Finally, methodological studies are not exempt from information bias which may stem from discrepancies in the included research reports [ 79 ], errors in data extraction, or inappropriate interpretation of the information extracted. Likewise, publication bias may also be a concern in methodological studies, but such concepts have not yet been explored.

A proposed framework

In order to inform discussions about methodological studies, the development of guidance for what should be reported, we have outlined some key features of methodological studies that can be used to classify them. For each of the categories outlined below, we provide an example. In our experience, the choice of approach to completing a methodological study can be informed by asking the following four questions:

• What is the aim?

A methodological study may be focused on exploring sources of bias in primary or secondary studies (meta-bias), or how bias is analyzed. We have taken care to distinguish bias (i.e. systematic deviations from the truth irrespective of the source) from reporting quality or completeness (i.e. not adhering to a specific reporting guideline or norm). An example of where this distinction would be important is in the case of a randomized trial with no blinding. This study (depending on the nature of the intervention) would be at risk of performance bias. However, if the authors report that their study was not blinded, they would have reported adequately. In fact, some methodological studies attempt to capture both “quality of conduct” and “quality of reporting”, such as Richie et al., who reported on the risk of bias in randomized trials of pharmacy practice interventions [ 80 ]. Babic et al. investigated how risk of bias was used to inform sensitivity analyses in Cochrane reviews [ 81 ]. Further, biases related to choice of outcomes can also be explored. For example, Tan et al investigated differences in treatment effect size based on the outcome reported [ 82 ].

Methodological studies may report quality of reporting against a reporting checklist (i.e. adherence to guidelines) or against expected norms. For example, Croituro et al. report on the quality of reporting in systematic reviews published in dermatology journals based on their adherence to the PRISMA statement [ 83 ], and Khan et al. described the quality of reporting of harms in randomized controlled trials published in high impact cardiovascular journals based on the CONSORT extension for harms [ 84 ]. Other methodological studies investigate reporting of certain features of interest that may not be part of formally published checklists or guidelines. For example, Mbuagbaw et al. described how often the implications for research are elaborated using the Evidence, Participants, Intervention, Comparison, Outcome, Timeframe (EPICOT) format [ 30 ].

Sometimes investigators may be interested in how consistent reports of the same research are, as it is expected that there should be consistency between: conference abstracts and published manuscripts; manuscript abstracts and manuscript main text; and trial registration and published manuscript. For example, Rosmarakis et al. investigated consistency between conference abstracts and full text manuscripts [ 85 ].

In addition to identifying issues with reporting in primary and secondary studies, authors of methodological studies may be interested in determining the factors that are associated with certain reporting practices. Many methodological studies incorporate this, albeit as a secondary outcome. For example, Farrokhyar et al. investigated the factors associated with reporting quality in randomized trials of coronary artery bypass grafting surgery [ 53 ].

Methodological studies may also be used to describe methods or compare methods, and the factors associated with methods. Muller et al. described the methods used for systematic reviews and meta-analyses of observational studies [ 86 ].

Some methodological studies synthesize results from other methodological studies. For example, Li et al. conducted a scoping review of methodological reviews that investigated consistency between full text and abstracts in primary biomedical research [ 87 ].

Some methodological studies may investigate the use of names and terms in health research. For example, Martinic et al. investigated the definitions of systematic reviews used in overviews of systematic reviews (OSRs), meta-epidemiological studies and epidemiology textbooks [ 88 ].

In addition to the previously mentioned experimental methodological studies, there may exist other types of methodological studies not captured here.

• 2. What is the design?

Most methodological studies are purely descriptive and report their findings as counts (percent) and means (standard deviation) or medians (interquartile range). For example, Mbuagbaw et al. described the reporting of research recommendations in Cochrane HIV systematic reviews [ 30 ]. Gohari et al. described the quality of reporting of randomized trials in diabetes in Iran [ 12 ].

Some methodological studies are analytical wherein “analytical studies identify and quantify associations, test hypotheses, identify causes and determine whether an association exists between variables, such as between an exposure and a disease.” [ 89 ] In the case of methodological studies all these investigations are possible. For example, Kosa et al. investigated the association between agreement in primary outcome from trial registry to published manuscript and study covariates. They found that larger and more recent studies were more likely to have agreement [ 15 ]. Tricco et al. compared the conclusion statements from Cochrane and non-Cochrane systematic reviews with a meta-analysis of the primary outcome and found that non-Cochrane reviews were more likely to report positive findings. These results are a test of the null hypothesis that the proportions of Cochrane and non-Cochrane reviews that report positive results are equal [ 90 ].

• 3. What is the sampling strategy?

Methodological reviews with narrow research questions may be able to include the entire target population. For example, in the methodological study of Cochrane HIV systematic reviews, Mbuagbaw et al. included all of the available studies ( n  = 103) [ 30 ].

Many methodological studies use random samples of the target population [ 33 , 91 , 92 ]. Alternatively, purposeful sampling may be used, limiting the sample to a subset of research-related reports published within a certain time period, or in journals with a certain ranking or on a topic. Systematic sampling can also be used when random sampling may be challenging to implement.

• 4. What is the unit of analysis?

Many methodological studies use a research report (e.g. full manuscript of study, abstract portion of the study) as the unit of analysis, and inferences can be made at the study-level. However, both published and unpublished research-related reports can be studied. These may include articles, conference abstracts, registry entries etc.

Some methodological studies report on items which may occur more than once per article. For example, Paquette et al. report on subgroup analyses in Cochrane reviews of atrial fibrillation in which 17 systematic reviews planned 56 subgroup analyses [ 93 ].

This framework is outlined in Fig.  2 .

A proposed framework for methodological studies

Conclusions

Methodological studies have examined different aspects of reporting such as quality, completeness, consistency and adherence to reporting guidelines. As such, many of the methodological study examples cited in this tutorial are related to reporting. However, as an evolving field, the scope of research questions that can be addressed by methodological studies is expected to increase.

In this paper we have outlined the scope and purpose of methodological studies, along with examples of instances in which various approaches have been used. In the absence of formal guidance on the design, conduct, analysis and reporting of methodological studies, we have provided some advice to help make methodological studies consistent. This advice is grounded in good contemporary scientific practice. Generally, the research question should tie in with the sampling approach and planned analysis. We have also highlighted the variables that may inform findings from methodological studies. Lastly, we have provided suggestions for ways in which authors can categorize their methodological studies to inform their design and analysis.

Acknowledgements

Abbreviations.

Authors’ contributions

LM conceived the idea and drafted the outline and paper. DOL and LT commented on the idea and draft outline. LM, LP and DOL performed literature searches and data extraction. All authors (LM, DOL, LT, LP, DBA) reviewed several draft versions of the manuscript and approved the final manuscript.

This work did not receive any dedicated funding.

Availability of data and materials

Ethics approval and consent to participate.

Not applicable.

Consent for publication

Competing interests.

DOL, DBA, LM, LP and LT are involved in the development of a reporting guideline for methodological studies.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

What is Research Methodology? Definition, Types, and Examples

Research methodology 1,2 is a structured and scientific approach used to collect, analyze, and interpret quantitative or qualitative data to answer research questions or test hypotheses. A research methodology is like a plan for carrying out research and helps keep researchers on track by limiting the scope of the research. Several aspects must be considered before selecting an appropriate research methodology, such as research limitations and ethical concerns that may affect your research.

The research methodology section in a scientific paper describes the different methodological choices made, such as the data collection and analysis methods, and why these choices were selected. The reasons should explain why the methods chosen are the most appropriate to answer the research question. A good research methodology also helps ensure the reliability and validity of the research findings. There are three types of research methodology—quantitative, qualitative, and mixed-method, which can be chosen based on the research objectives.

What is research methodology ?

A research methodology describes the techniques and procedures used to identify and analyze information regarding a specific research topic. It is a process by which researchers design their study so that they can achieve their objectives using the selected research instruments. It includes all the important aspects of research, including research design, data collection methods, data analysis methods, and the overall framework within which the research is conducted. While these points can help you understand what is research methodology, you also need to know why it is important to pick the right methodology.

Having a good research methodology in place has the following advantages: 3

• Helps other researchers who may want to replicate your research; the explanations will be of benefit to them.
• You can easily answer any questions about your research if they arise at a later stage.
• A research methodology provides a framework and guidelines for researchers to clearly define research questions, hypotheses, and objectives.
• It helps researchers identify the most appropriate research design, sampling technique, and data collection and analysis methods.
• A sound research methodology helps researchers ensure that their findings are valid and reliable and free from biases and errors.
• It also helps ensure that ethical guidelines are followed while conducting research.
• A good research methodology helps researchers in planning their research efficiently, by ensuring optimum usage of their time and resources.

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Types of research methodology.

There are three types of research methodology based on the type of research and the data required. 1

• Quantitative research methodology focuses on measuring and testing numerical data. This approach is good for reaching a large number of people in a short amount of time. This type of research helps in testing the causal relationships between variables, making predictions, and generalizing results to wider populations.
• Qualitative research methodology examines the opinions, behaviors, and experiences of people. It collects and analyzes words and textual data. This research methodology requires fewer participants but is still more time consuming because the time spent per participant is quite large. This method is used in exploratory research where the research problem being investigated is not clearly defined.
• Mixed-method research methodology uses the characteristics of both quantitative and qualitative research methodologies in the same study. This method allows researchers to validate their findings, verify if the results observed using both methods are complementary, and explain any unexpected results obtained from one method by using the other method.

What are the types of sampling designs in research methodology?

Sampling 4 is an important part of a research methodology and involves selecting a representative sample of the population to conduct the study, making statistical inferences about them, and estimating the characteristics of the whole population based on these inferences. There are two types of sampling designs in research methodology—probability and nonprobability.

• Probability sampling

In this type of sampling design, a sample is chosen from a larger population using some form of random selection, that is, every member of the population has an equal chance of being selected. The different types of probability sampling are:

• Systematic —sample members are chosen at regular intervals. It requires selecting a starting point for the sample and sample size determination that can be repeated at regular intervals. This type of sampling method has a predefined range; hence, it is the least time consuming.
• Stratified —researchers divide the population into smaller groups that don’t overlap but represent the entire population. While sampling, these groups can be organized, and then a sample can be drawn from each group separately.
• Cluster —the population is divided into clusters based on demographic parameters like age, sex, location, etc.
• Convenience —selects participants who are most easily accessible to researchers due to geographical proximity, availability at a particular time, etc.
• Purposive —participants are selected at the researcher’s discretion. Researchers consider the purpose of the study and the understanding of the target audience.
• Snowball —already selected participants use their social networks to refer the researcher to other potential participants.
• Quota —while designing the study, the researchers decide how many people with which characteristics to include as participants. The characteristics help in choosing people most likely to provide insights into the subject.

What are data collection methods?

During research, data are collected using various methods depending on the research methodology being followed and the research methods being undertaken. Both qualitative and quantitative research have different data collection methods, as listed below.

Qualitative research 5

• One-on-one interviews: Helps the interviewers understand a respondent’s subjective opinion and experience pertaining to a specific topic or event
• Document study/literature review/record keeping: Researchers’ review of already existing written materials such as archives, annual reports, research articles, guidelines, policy documents, etc.
• Focus groups: Constructive discussions that usually include a small sample of about 6-10 people and a moderator, to understand the participants’ opinion on a given topic.
• Qualitative observation : Researchers collect data using their five senses (sight, smell, touch, taste, and hearing).

Quantitative research 6

• Sampling: The most common type is probability sampling.
• Interviews: Commonly telephonic or done in-person.
• Observations: Structured observations are most commonly used in quantitative research. In this method, researchers make observations about specific behaviors of individuals in a structured setting.
• Document review: Reviewing existing research or documents to collect evidence for supporting the research.
• Surveys and questionnaires. Surveys can be administered both online and offline depending on the requirement and sample size.

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What are data analysis methods.

The data collected using the various methods for qualitative and quantitative research need to be analyzed to generate meaningful conclusions. These data analysis methods 7 also differ between quantitative and qualitative research.

Quantitative research involves a deductive method for data analysis where hypotheses are developed at the beginning of the research and precise measurement is required. The methods include statistical analysis applications to analyze numerical data and are grouped into two categories—descriptive and inferential.

Descriptive analysis is used to describe the basic features of different types of data to present it in a way that ensures the patterns become meaningful. The different types of descriptive analysis methods are:

• Measures of frequency (count, percent, frequency)
• Measures of central tendency (mean, median, mode)
• Measures of dispersion or variation (range, variance, standard deviation)
• Measure of position (percentile ranks, quartile ranks)

Inferential analysis is used to make predictions about a larger population based on the analysis of the data collected from a smaller population. This analysis is used to study the relationships between different variables. Some commonly used inferential data analysis methods are:

• Correlation: To understand the relationship between two or more variables.
• Cross-tabulation: Analyze the relationship between multiple variables.
• Regression analysis: Study the impact of independent variables on the dependent variable.
• Frequency tables: To understand the frequency of data.
• Analysis of variance: To test the degree to which two or more variables differ in an experiment.

Qualitative research involves an inductive method for data analysis where hypotheses are developed after data collection. The methods include:

• Content analysis: For analyzing documented information from text and images by determining the presence of certain words or concepts in texts.
• Narrative analysis: For analyzing content obtained from sources such as interviews, field observations, and surveys. The stories and opinions shared by people are used to answer research questions.
• Discourse analysis: For analyzing interactions with people considering the social context, that is, the lifestyle and environment, under which the interaction occurs.
• Grounded theory: Involves hypothesis creation by data collection and analysis to explain why a phenomenon occurred.
• Thematic analysis: To identify important themes or patterns in data and use these to address an issue.

How to choose a research methodology?

Here are some important factors to consider when choosing a research methodology: 8

• Research objectives, aims, and questions —these would help structure the research design.
• Review existing literature to identify any gaps in knowledge.
• Check the statistical requirements —if data-driven or statistical results are needed then quantitative research is the best. If the research questions can be answered based on people’s opinions and perceptions, then qualitative research is most suitable.
• Sample size —sample size can often determine the feasibility of a research methodology. For a large sample, less effort- and time-intensive methods are appropriate.
• Constraints —constraints of time, geography, and resources can help define the appropriate methodology.

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How to write a research methodology .

A research methodology should include the following components: 3,9

• Research design —should be selected based on the research question and the data required. Common research designs include experimental, quasi-experimental, correlational, descriptive, and exploratory.
• Research method —this can be quantitative, qualitative, or mixed-method.
• Reason for selecting a specific methodology —explain why this methodology is the most suitable to answer your research problem.
• Research instruments —explain the research instruments you plan to use, mainly referring to the data collection methods such as interviews, surveys, etc. Here as well, a reason should be mentioned for selecting the particular instrument.
• Sampling —this involves selecting a representative subset of the population being studied.
• Data collection —involves gathering data using several data collection methods, such as surveys, interviews, etc.
• Data analysis —describe the data analysis methods you will use once you’ve collected the data.
• Research limitations —mention any limitations you foresee while conducting your research.
• Validity and reliability —validity helps identify the accuracy and truthfulness of the findings; reliability refers to the consistency and stability of the results over time and across different conditions.
• Ethical considerations —research should be conducted ethically. The considerations include obtaining consent from participants, maintaining confidentiality, and addressing conflicts of interest.

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Frequently Asked Questions

Q1. What are the key components of research methodology?

A1. A good research methodology has the following key components:

• Research design
• Data collection procedures
• Data analysis methods
• Ethical considerations

Q2. Why is ethical consideration important in research methodology?

A2. Ethical consideration is important in research methodology to ensure the readers of the reliability and validity of the study. Researchers must clearly mention the ethical norms and standards followed during the conduct of the research and also mention if the research has been cleared by any institutional board. The following 10 points are the important principles related to ethical considerations: 10

• Participants should not be subjected to harm.
• Respect for the dignity of participants should be prioritized.
• Full consent should be obtained from participants before the study.
• Participants’ privacy should be ensured.
• Confidentiality of the research data should be ensured.
• Anonymity of individuals and organizations participating in the research should be maintained.
• The aims and objectives of the research should not be exaggerated.
• Affiliations, sources of funding, and any possible conflicts of interest should be declared.
• Communication in relation to the research should be honest and transparent.
• Misleading information and biased representation of primary data findings should be avoided.

Q3. What is the difference between methodology and method?

A3. Research methodology is different from a research method, although both terms are often confused. Research methods are the tools used to gather data, while the research methodology provides a framework for how research is planned, conducted, and analyzed. The latter guides researchers in making decisions about the most appropriate methods for their research. Research methods refer to the specific techniques, procedures, and tools used by researchers to collect, analyze, and interpret data, for instance surveys, questionnaires, interviews, etc.

Research methodology is, thus, an integral part of a research study. It helps ensure that you stay on track to meet your research objectives and answer your research questions using the most appropriate data collection and analysis tools based on your research design.

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• Research methodologies. Pfeiffer Library website. Accessed August 15, 2023. https://library.tiffin.edu/researchmethodologies/whatareresearchmethodologies
• Types of research methodology. Eduvoice website. Accessed August 16, 2023. https://eduvoice.in/types-research-methodology/
• The basics of research methodology: A key to quality research. Voxco. Accessed August 16, 2023. https://www.voxco.com/blog/what-is-research-methodology/
• Sampling methods: Types with examples. QuestionPro website. Accessed August 16, 2023. https://www.questionpro.com/blog/types-of-sampling-for-social-research/
• What is qualitative research? Methods, types, approaches, examples. Researcher.Life blog. Accessed August 15, 2023. https://researcher.life/blog/article/what-is-qualitative-research-methods-types-examples/
• What is quantitative research? Definition, methods, types, and examples. Researcher.Life blog. Accessed August 15, 2023. https://researcher.life/blog/article/what-is-quantitative-research-types-and-examples/
• Data analysis in research: Types & methods. QuestionPro website. Accessed August 16, 2023. https://www.questionpro.com/blog/data-analysis-in-research/#Data_analysis_in_qualitative_research
• Factors to consider while choosing the right research methodology. PhD Monster website. Accessed August 17, 2023. https://www.phdmonster.com/factors-to-consider-while-choosing-the-right-research-methodology/
• What is research methodology? Research and writing guides. Accessed August 14, 2023. https://paperpile.com/g/what-is-research-methodology/
• Ethical considerations. Business research methodology website. Accessed August 17, 2023. https://research-methodology.net/research-methodology/ethical-considerations/

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• What Is a Research Methodology? | Steps & Tips

What Is a Research Methodology? | Steps & Tips

Published on August 25, 2022 by Shona McCombes and Tegan George. Revised on November 20, 2023.

Your research methodology discusses and explains the data collection and analysis methods you used in your research. A key part of your thesis, dissertation , or research paper , the methodology chapter explains what you did and how you did it, allowing readers to evaluate the reliability and validity of your research and your dissertation topic .

It should include:

• The type of research you conducted
• How you collected and analyzed your data
• Any tools or materials you used in the research
• How you mitigated or avoided research biases
• Why you chose these methods
• Your methodology section should generally be written in the past tense .
• Academic style guides in your field may provide detailed guidelines on what to include for different types of studies.
• Your citation style might provide guidelines for your methodology section (e.g., an APA Style methods section ).

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Table of contents

How to write a research methodology, why is a methods section important, step 1: explain your methodological approach, step 2: describe your data collection methods, step 3: describe your analysis method, step 4: evaluate and justify the methodological choices you made, tips for writing a strong methodology chapter, other interesting articles, frequently asked questions about methodology.

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Your methods section is your opportunity to share how you conducted your research and why you chose the methods you chose. It’s also the place to show that your research was rigorously conducted and can be replicated .

It gives your research legitimacy and situates it within your field, and also gives your readers a place to refer to if they have any questions or critiques in other sections.

You can start by introducing your overall approach to your research. You have two options here.

Option 1: Start with your “what”

What research problem or question did you investigate?

• Aim to describe the characteristics of something?
• Explore an under-researched topic?
• Establish a causal relationship?

And what type of data did you need to achieve this aim?

• Quantitative data , qualitative data , or a mix of both?
• Primary data collected yourself, or secondary data collected by someone else?
• Experimental data gathered by controlling and manipulating variables, or descriptive data gathered via observations?

Option 2: Start with your “why”

Depending on your discipline, you can also start with a discussion of the rationale and assumptions underpinning your methodology. In other words, why did you choose these methods for your study?

• Why is this the best way to answer your research question?
• Is this a standard methodology in your field, or does it require justification?
• Were there any ethical considerations involved in your choices?
• What are the criteria for validity and reliability in this type of research ? How did you prevent bias from affecting your data?

Once you have introduced your reader to your methodological approach, you should share full details about your data collection methods .

Quantitative methods

In order to be considered generalizable, you should describe quantitative research methods in enough detail for another researcher to replicate your study.

Here, explain how you operationalized your concepts and measured your variables. Discuss your sampling method or inclusion and exclusion criteria , as well as any tools, procedures, and materials you used to gather your data.

Surveys Describe where, when, and how the survey was conducted.

• How did you design the questionnaire?
• What form did your questions take (e.g., multiple choice, Likert scale )?
• Were your surveys conducted in-person or virtually?
• What sampling method did you use to select participants?
• What was your sample size and response rate?

Experiments Share full details of the tools, techniques, and procedures you used to conduct your experiment.

• How did you design the experiment ?
• How did you recruit participants?
• How did you manipulate and measure the variables ?
• What tools did you use?

Existing data Explain how you gathered and selected the material (such as datasets or archival data) that you used in your analysis.

• Where did you source the material?
• How was the data originally produced?
• What criteria did you use to select material (e.g., date range)?

The survey consisted of 5 multiple-choice questions and 10 questions measured on a 7-point Likert scale.

The goal was to collect survey responses from 350 customers visiting the fitness apparel company’s brick-and-mortar location in Boston on July 4–8, 2022, between 11:00 and 15:00.

Here, a customer was defined as a person who had purchased a product from the company on the day they took the survey. Participants were given 5 minutes to fill in the survey anonymously. In total, 408 customers responded, but not all surveys were fully completed. Due to this, 371 survey results were included in the analysis.

• Information bias
• Omitted variable bias
• Regression to the mean
• Survivorship bias
• Undercoverage bias
• Sampling bias

Qualitative methods

In qualitative research , methods are often more flexible and subjective. For this reason, it’s crucial to robustly explain the methodology choices you made.

Be sure to discuss the criteria you used to select your data, the context in which your research was conducted, and the role you played in collecting your data (e.g., were you an active participant, or a passive observer?)

Interviews or focus groups Describe where, when, and how the interviews were conducted.

• How did you find and select participants?
• How many participants took part?
• What form did the interviews take ( structured , semi-structured , or unstructured )?
• How long were the interviews?
• How were they recorded?

Participant observation Describe where, when, and how you conducted the observation or ethnography .

• What group or community did you observe? How long did you spend there?
• How did you gain access to this group? What role did you play in the community?
• How long did you spend conducting the research? Where was it located?
• How did you record your data (e.g., audiovisual recordings, note-taking)?

Existing data Explain how you selected case study materials for your analysis.

• What type of materials did you analyze?
• How did you select them?

In order to gain better insight into possibilities for future improvement of the fitness store’s product range, semi-structured interviews were conducted with 8 returning customers.

Here, a returning customer was defined as someone who usually bought products at least twice a week from the store.

Surveys were used to select participants. Interviews were conducted in a small office next to the cash register and lasted approximately 20 minutes each. Answers were recorded by note-taking, and seven interviews were also filmed with consent. One interviewee preferred not to be filmed.

• The Hawthorne effect
• Observer bias
• The placebo effect
• Response bias and Nonresponse bias
• The Pygmalion effect
• Recall bias
• Social desirability bias
• Self-selection bias

Mixed methods

Mixed methods research combines quantitative and qualitative approaches. If a standalone quantitative or qualitative study is insufficient to answer your research question, mixed methods may be a good fit for you.

Mixed methods are less common than standalone analyses, largely because they require a great deal of effort to pull off successfully. If you choose to pursue mixed methods, it’s especially important to robustly justify your methods.

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Next, you should indicate how you processed and analyzed your data. Avoid going into too much detail: you should not start introducing or discussing any of your results at this stage.

In quantitative research , your analysis will be based on numbers. In your methods section, you can include:

• How you prepared the data before analyzing it (e.g., checking for missing data , removing outliers , transforming variables)
• Which software you used (e.g., SPSS, Stata or R)
• Which statistical tests you used (e.g., two-tailed t test , simple linear regression )

In qualitative research, your analysis will be based on language, images, and observations (often involving some form of textual analysis ).

Specific methods might include:

• Content analysis : Categorizing and discussing the meaning of words, phrases and sentences
• Thematic analysis : Coding and closely examining the data to identify broad themes and patterns
• Discourse analysis : Studying communication and meaning in relation to their social context

Mixed methods combine the above two research methods, integrating both qualitative and quantitative approaches into one coherent analytical process.

Above all, your methodology section should clearly make the case for why you chose the methods you did. This is especially true if you did not take the most standard approach to your topic. In this case, discuss why other methods were not suitable for your objectives, and show how this approach contributes new knowledge or understanding.

In any case, it should be overwhelmingly clear to your reader that you set yourself up for success in terms of your methodology’s design. Show how your methods should lead to results that are valid and reliable, while leaving the analysis of the meaning, importance, and relevance of your results for your discussion section .

• Quantitative: Lab-based experiments cannot always accurately simulate real-life situations and behaviors, but they are effective for testing causal relationships between variables .
• Qualitative: Unstructured interviews usually produce results that cannot be generalized beyond the sample group , but they provide a more in-depth understanding of participants’ perceptions, motivations, and emotions.
• Mixed methods: Despite issues systematically comparing differing types of data, a solely quantitative study would not sufficiently incorporate the lived experience of each participant, while a solely qualitative study would be insufficiently generalizable.

Remember that your aim is not just to describe your methods, but to show how and why you applied them. Again, it’s critical to demonstrate that your research was rigorously conducted and can be replicated.

1. Focus on your objectives and research questions

The methodology section should clearly show why your methods suit your objectives and convince the reader that you chose the best possible approach to answering your problem statement and research questions .

2. Cite relevant sources

Your methodology can be strengthened by referencing existing research in your field. This can help you to:

• Show that you followed established practice for your type of research
• Discuss how you decided on your approach by evaluating existing research
• Present a novel methodological approach to address a gap in the literature

3. Write for your audience

Consider how much information you need to give, and avoid getting too lengthy. If you are using methods that are standard for your discipline, you probably don’t need to give a lot of background or justification.

Regardless, your methodology should be a clear, well-structured text that makes an argument for your approach, not just a list of technical details and procedures.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Normal distribution
• Measures of central tendency
• Chi square tests
• Confidence interval
• Quartiles & Quantiles

Methodology

• Cluster sampling
• Stratified sampling
• Thematic analysis
• Cohort study
• Peer review
• Ethnography

Research bias

• Implicit bias
• Cognitive bias
• Conformity bias
• Hawthorne effect
• Availability heuristic
• Attrition bias

Methodology refers to the overarching strategy and rationale of your research project . It involves studying the methods used in your field and the theories or principles behind them, in order to develop an approach that matches your objectives.

Methods are the specific tools and procedures you use to collect and analyze data (for example, experiments, surveys , and statistical tests ).

In shorter scientific papers, where the aim is to report the findings of a specific study, you might simply describe what you did in a methods section .

In a longer or more complex research project, such as a thesis or dissertation , you will probably include a methodology section , where you explain your approach to answering the research questions and cite relevant sources to support your choice of methods.

In a scientific paper, the methodology always comes after the introduction and before the results , discussion and conclusion . The same basic structure also applies to a thesis, dissertation , or research proposal .

Depending on the length and type of document, you might also include a literature review or theoretical framework before the methodology.

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.

Reliability and validity are both about how well a method measures something:

• Reliability refers to the  consistency of a measure (whether the results can be reproduced under the same conditions).
• Validity   refers to the  accuracy of a measure (whether the results really do represent what they are supposed to measure).

If you are doing experimental research, you also have to consider the internal and external validity of your experiment.

A sample is a subset of individuals from a larger population . Sampling means selecting the group that you will actually collect data from in your research. For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

In statistics, sampling allows you to test a hypothesis about the characteristics of a population.

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• Published: 31 August 2024

Knowledge mapping and evolution of research on older adults’ technology acceptance: a bibliometric study from 2013 to 2023

• Xianru Shang   ORCID: orcid.org/0009-0000-8906-3216 1 ,
• Zijian Liu 1 ,
• Chen Gong 1 ,
• Zhigang Hu 1 ,
• Yuexuan Wu 1 &
• Chengliang Wang   ORCID: orcid.org/0000-0003-2208-3508 2  

Humanities and Social Sciences Communications volume  11 , Article number:  1115 ( 2024 ) Cite this article

Metrics details

• Science, technology and society

The rapid expansion of information technology and the intensification of population aging are two prominent features of contemporary societal development. Investigating older adults’ acceptance and use of technology is key to facilitating their integration into an information-driven society. Given this context, the technology acceptance of older adults has emerged as a prioritized research topic, attracting widespread attention in the academic community. However, existing research remains fragmented and lacks a systematic framework. To address this gap, we employed bibliometric methods, utilizing the Web of Science Core Collection to conduct a comprehensive review of literature on older adults’ technology acceptance from 2013 to 2023. Utilizing VOSviewer and CiteSpace for data assessment and visualization, we created knowledge mappings of research on older adults’ technology acceptance. Our study employed multidimensional methods such as co-occurrence analysis, clustering, and burst analysis to: (1) reveal research dynamics, key journals, and domains in this field; (2) identify leading countries, their collaborative networks, and core research institutions and authors; (3) recognize the foundational knowledge system centered on theoretical model deepening, emerging technology applications, and research methods and evaluation, uncovering seminal literature and observing a shift from early theoretical and influential factor analyses to empirical studies focusing on individual factors and emerging technologies; (4) moreover, current research hotspots are primarily in the areas of factors influencing technology adoption, human-robot interaction experiences, mobile health management, and aging-in-place technology, highlighting the evolutionary context and quality distribution of research themes. Finally, we recommend that future research should deeply explore improvements in theoretical models, long-term usage, and user experience evaluation. Overall, this study presents a clear framework of existing research in the field of older adults’ technology acceptance, providing an important reference for future theoretical exploration and innovative applications.

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Introduction.

In contemporary society, the rapid development of information technology has been intricately intertwined with the intensifying trend of population aging. According to the latest United Nations forecast, by 2050, the global population aged 65 and above is expected to reach 1.6 billion, representing about 16% of the total global population (UN 2023 ). Given the significant challenges of global aging, there is increasing evidence that emerging technologies have significant potential to maintain health and independence for older adults in their home and healthcare environments (Barnard et al. 2013 ; Soar 2010 ; Vancea and Solé-Casals 2016 ). This includes, but is not limited to, enhancing residential safety with smart home technologies (Touqeer et al. 2021 ; Wang et al. 2022 ), improving living independence through wearable technologies (Perez et al. 2023 ), and increasing medical accessibility via telehealth services (Kruse et al. 2020 ). Technological innovations are redefining the lifestyles of older adults, encouraging a shift from passive to active participation (González et al. 2012 ; Mostaghel 2016 ). Nevertheless, the effective application and dissemination of technology still depends on user acceptance and usage intentions (Naseri et al. 2023 ; Wang et al. 2023a ; Xia et al. 2024 ; Yu et al. 2023 ). Particularly, older adults face numerous challenges in accepting and using new technologies. These challenges include not only physical and cognitive limitations but also a lack of technological experience, along with the influences of social and economic factors (Valk et al. 2018 ; Wilson et al. 2021 ).

User acceptance of technology is a significant focus within information systems (IS) research (Dai et al. 2024 ), with several models developed to explain and predict user behavior towards technology usage, including the Technology Acceptance Model (TAM) (Davis 1989 ), TAM2, TAM3, and the Unified Theory of Acceptance and Use of Technology (UTAUT) (Venkatesh et al. 2003 ). Older adults, as a group with unique needs, exhibit different behavioral patterns during technology acceptance than other user groups, and these uniquenesses include changes in cognitive abilities, as well as motivations, attitudes, and perceptions of the use of new technologies (Chen and Chan 2011 ). The continual expansion of technology introduces considerable challenges for older adults, rendering the understanding of their technology acceptance a research priority. Thus, conducting in-depth research into older adults’ acceptance of technology is critically important for enhancing their integration into the information society and improving their quality of life through technological advancements.

Reviewing relevant literature to identify research gaps helps further solidify the theoretical foundation of the research topic. However, many existing literature reviews primarily focus on the factors influencing older adults’ acceptance or intentions to use technology. For instance, Ma et al. ( 2021 ) conducted a comprehensive analysis of the determinants of older adults’ behavioral intentions to use technology; Liu et al. ( 2022 ) categorized key variables in studies of older adults’ technology acceptance, noting a shift in focus towards social and emotional factors; Yap et al. ( 2022 ) identified seven categories of antecedents affecting older adults’ use of technology from an analysis of 26 articles, including technological, psychological, social, personal, cost, behavioral, and environmental factors; Schroeder et al. ( 2023 ) extracted 119 influencing factors from 59 articles and further categorized these into six themes covering demographics, health status, and emotional awareness. Additionally, some studies focus on the application of specific technologies, such as Ferguson et al. ( 2021 ), who explored barriers and facilitators to older adults using wearable devices for heart monitoring, and He et al. ( 2022 ) and Baer et al. ( 2022 ), who each conducted in-depth investigations into the acceptance of social assistive robots and mobile nutrition and fitness apps, respectively. In summary, current literature reviews on older adults’ technology acceptance exhibit certain limitations. Due to the interdisciplinary nature and complex knowledge structure of this field, traditional literature reviews often rely on qualitative analysis, based on literature analysis and periodic summaries, which lack sufficient objectivity and comprehensiveness. Additionally, systematic research is relatively limited, lacking a macroscopic description of the research trajectory from a holistic perspective. Over the past decade, research on older adults’ technology acceptance has experienced rapid growth, with a significant increase in literature, necessitating the adoption of new methods to review and examine the developmental trends in this field (Chen 2006 ; Van Eck and Waltman 2010 ). Bibliometric analysis, as an effective quantitative research method, analyzes published literature through visualization, offering a viable approach to extracting patterns and insights from a large volume of papers, and has been widely applied in numerous scientific research fields (Achuthan et al. 2023 ; Liu and Duffy 2023 ). Therefore, this study will employ bibliometric methods to systematically analyze research articles related to older adults’ technology acceptance published in the Web of Science Core Collection from 2013 to 2023, aiming to understand the core issues and evolutionary trends in the field, and to provide valuable references for future related research. Specifically, this study aims to explore and answer the following questions:

RQ1: What are the research dynamics in the field of older adults’ technology acceptance over the past decade? What are the main academic journals and fields that publish studies related to older adults’ technology acceptance?

RQ2: How is the productivity in older adults’ technology acceptance research distributed among countries, institutions, and authors?

RQ3: What are the knowledge base and seminal literature in older adults’ technology acceptance research? How has the research theme progressed?

RQ4: What are the current hot topics and their evolutionary trajectories in older adults’ technology acceptance research? How is the quality of research distributed?

Methodology and materials

Research method.

In recent years, bibliometrics has become one of the crucial methods for analyzing literature reviews and is widely used in disciplinary and industrial intelligence analysis (Jing et al. 2023 ; Lin and Yu 2024a ; Wang et al. 2024a ; Xu et al. 2021 ). Bibliometric software facilitates the visualization analysis of extensive literature data, intuitively displaying the network relationships and evolutionary processes between knowledge units, and revealing the underlying knowledge structure and potential information (Chen et al. 2024 ; López-Robles et al. 2018 ; Wang et al. 2024c ). This method provides new insights into the current status and trends of specific research areas, along with quantitative evidence, thereby enhancing the objectivity and scientific validity of the research conclusions (Chen et al. 2023 ; Geng et al. 2024 ). VOSviewer and CiteSpace are two widely used bibliometric software tools in academia (Pan et al. 2018 ), recognized for their robust functionalities based on the JAVA platform. Although each has its unique features, combining these two software tools effectively constructs mapping relationships between literature knowledge units and clearly displays the macrostructure of the knowledge domains. Particularly, VOSviewer, with its excellent graphical representation capabilities, serves as an ideal tool for handling large datasets and precisely identifying the focal points and hotspots of research topics. Therefore, this study utilizes VOSviewer (version 1.6.19) and CiteSpace (version 6.1.R6), combined with in-depth literature analysis, to comprehensively examine and interpret the research theme of older adults’ technology acceptance through an integrated application of quantitative and qualitative methods.

Data source

Web of Science is a comprehensively recognized database in academia, featuring literature that has undergone rigorous peer review and editorial scrutiny (Lin and Yu 2024b ; Mongeon and Paul-Hus 2016 ; Pranckutė 2021 ). This study utilizes the Web of Science Core Collection as its data source, specifically including three major citation indices: Science Citation Index Expanded (SCIE), Social Sciences Citation Index (SSCI), and Arts & Humanities Citation Index (A&HCI). These indices encompass high-quality research literature in the fields of science, social sciences, and arts and humanities, ensuring the comprehensiveness and reliability of the data. We combined “older adults” with “technology acceptance” through thematic search, with the specific search strategy being: TS = (elder OR elderly OR aging OR ageing OR senile OR senior OR old people OR “older adult*”) AND TS = (“technology acceptance” OR “user acceptance” OR “consumer acceptance”). The time span of literature search is from 2013 to 2023, with the types limited to “Article” and “Review” and the language to “English”. Additionally, the search was completed by October 27, 2023, to avoid data discrepancies caused by database updates. The initial search yielded 764 journal articles. Given that searches often retrieve articles that are superficially relevant but actually non-compliant, manual screening post-search was essential to ensure the relevance of the literature (Chen et al. 2024 ). Through manual screening, articles significantly deviating from the research theme were eliminated and rigorously reviewed. Ultimately, this study obtained 500 valid sample articles from the Web of Science Core Collection. The complete PRISMA screening process is illustrated in Fig. 1 .

Presentation of the data culling process in detail.

Data standardization

Raw data exported from databases often contain multiple expressions of the same terminology (Nguyen and Hallinger 2020 ). To ensure the accuracy and consistency of data, it is necessary to standardize the raw data (Strotmann and Zhao 2012 ). This study follows the data standardization process proposed by Taskin and Al ( 2019 ), mainly executing the following operations:

(1) Standardization of author and institution names is conducted to address different name expressions for the same author. For instance, “Chan, Alan Hoi Shou” and “Chan, Alan H. S.” are considered the same author, and distinct authors with the same name are differentiated by adding identifiers. Diverse forms of institutional names are unified to address variations caused by name changes or abbreviations, such as standardizing “FRANKFURT UNIV APPL SCI” and “Frankfurt University of Applied Sciences,” as well as “Chinese University of Hong Kong” and “University of Hong Kong” to consistent names.

(2) Different expressions of journal names are unified. For example, “International Journal of Human-Computer Interaction” and “Int J Hum Comput Interact” are standardized to a single name. This ensures consistency in journal names and prevents misclassification of literature due to differing journal names. Additionally, it involves checking if the journals have undergone name changes in the past decade to prevent any impact on the analysis due to such changes.

(3) Keywords data are cleansed by removing words that do not directly pertain to specific research content (e.g., people, review), merging synonyms (e.g., “UX” and “User Experience,” “aging-in-place” and “aging in place”), and standardizing plural forms of keywords (e.g., “assistive technologies” and “assistive technology,” “social robots” and “social robot”). This reduces redundant information in knowledge mapping.

Bibliometric results and analysis

Distribution power (rq1), literature descriptive statistical analysis.

Table 1 presents a detailed descriptive statistical overview of the literature in the field of older adults’ technology acceptance. After deduplication using the CiteSpace software, this study confirmed a valid sample size of 500 articles. Authored by 1839 researchers, the documents encompass 792 research institutions across 54 countries and are published in 217 different academic journals. As of the search cutoff date, these articles have accumulated 13,829 citations, with an annual average of 1156 citations, and an average of 27.66 citations per article. The h-index, a composite metric of quantity and quality of scientific output (Kamrani et al. 2021 ), reached 60 in this study.

Trends in publications and disciplinary distribution

The number of publications and citations are significant indicators of the research field’s development, reflecting its continuity, attention, and impact (Ale Ebrahim et al. 2014 ). The ranking of annual publications and citations in the field of older adults’ technology acceptance studies is presented chronologically in Fig. 2A . The figure shows a clear upward trend in the amount of literature in this field. Between 2013 and 2017, the number of publications increased slowly and decreased in 2018. However, in 2019, the number of publications increased rapidly to 52 and reached a peak of 108 in 2022, which is 6.75 times higher than in 2013. In 2022, the frequency of document citations reached its highest point with 3466 citations, reflecting the widespread recognition and citation of research in this field. Moreover, the curve of the annual number of publications fits a quadratic function, with a goodness-of-fit R 2 of 0.9661, indicating that the number of future publications is expected to increase even more rapidly.

A Trends in trends in annual publications and citations (2013–2023). B Overlay analysis of the distribution of discipline fields.

Figure 2B shows that research on older adults’ technology acceptance involves the integration of multidisciplinary knowledge. According to Web of Science Categories, these 500 articles are distributed across 85 different disciplines. We have tabulated the top ten disciplines by publication volume (Table 2 ), which include Medical Informatics (75 articles, 15.00%), Health Care Sciences & Services (71 articles, 14.20%), Gerontology (61 articles, 12.20%), Public Environmental & Occupational Health (57 articles, 11.40%), and Geriatrics & Gerontology (52 articles, 10.40%), among others. The high output in these disciplines reflects the concentrated global academic interest in this comprehensive research topic. Additionally, interdisciplinary research approaches provide diverse perspectives and a solid theoretical foundation for studies on older adults’ technology acceptance, also paving the way for new research directions.

Knowledge flow analysis

A dual-map overlay is a CiteSpace map superimposed on top of a base map, which shows the interrelationships between journals in different domains, representing the publication and citation activities in each domain (Chen and Leydesdorff 2014 ). The overlay map reveals the link between the citing domain (on the left side) and the cited domain (on the right side), reflecting the knowledge flow of the discipline at the journal level (Leydesdorff and Rafols 2012 ). We utilize the in-built Z-score algorithm of the software to cluster the graph, as shown in Fig. 3 .

The left side shows the citing journal, and the right side shows the cited journal.

Figure 3 shows the distribution of citing journals clusters for older adults’ technology acceptance on the left side, while the right side refers to the main cited journals clusters. Two knowledge flow citation trajectories were obtained; they are presented by the color of the cited regions, and the thickness of these trajectories is proportional to the Z-score scaled frequency of citations (Chen et al. 2014 ). Within the cited regions, the most popular fields with the most records covered are “HEALTH, NURSING, MEDICINE” and “PSYCHOLOGY, EDUCATION, SOCIAL”, and the elliptical aspect ratio of these two fields stands out. Fields have prominent elliptical aspect ratios, highlighting their significant influence on older adults’ technology acceptance research. Additionally, the major citation trajectories originate in these two areas and progress to the frontier research area of “PSYCHOLOGY, EDUCATION, HEALTH”. It is worth noting that the citation trajectory from “PSYCHOLOGY, EDUCATION, SOCIAL” has a significant Z-value (z = 6.81), emphasizing the significance and impact of this development path. In the future, “MATHEMATICS, SYSTEMS, MATHEMATICAL”, “MOLECULAR, BIOLOGY, IMMUNOLOGY”, and “NEUROLOGY, SPORTS, OPHTHALMOLOGY” may become emerging fields. The fields of “MEDICINE, MEDICAL, CLINICAL” may be emerging areas of cutting-edge research.

Main research journals analysis

Table 3 provides statistics for the top ten journals by publication volume in the field of older adults’ technology acceptance. Together, these journals have published 137 articles, accounting for 27.40% of the total publications, indicating that there is no highly concentrated core group of journals in this field, with publications being relatively dispersed. Notably, Computers in Human Behavior , Journal of Medical Internet Research , and International Journal of Human-Computer Interaction each lead with 15 publications. In terms of citation metrics, International Journal of Medical Informatics and Computers in Human Behavior stand out significantly, with the former accumulating a total of 1,904 citations, averaging 211.56 citations per article, and the latter totaling 1,449 citations, with an average of 96.60 citations per article. These figures emphasize the academic authority and widespread impact of these journals within the research field.

Research power (RQ2)

Countries and collaborations analysis.

The analysis revealed the global research pattern for country distribution and collaboration (Chen et al. 2019 ). Figure 4A shows the network of national collaborations on older adults’ technology acceptance research. The size of the bubbles represents the amount of publications in each country, while the thickness of the connecting lines expresses the closeness of the collaboration among countries. Generally, this research subject has received extensive international attention, with China and the USA publishing far more than any other countries. China has established notable research collaborations with the USA, UK and Malaysia in this field, while other countries have collaborations, but the closeness is relatively low and scattered. Figure 4B shows the annual publication volume dynamics of the top ten countries in terms of total publications. Since 2017, China has consistently increased its annual publications, while the USA has remained relatively stable. In 2019, the volume of publications in each country increased significantly, this was largely due to the global outbreak of the COVID-19 pandemic, which has led to increased reliance on information technology among the elderly for medical consultations, online socialization, and health management (Sinha et al. 2021 ). This phenomenon has led to research advances in technology acceptance among older adults in various countries. Table 4 shows that the top ten countries account for 93.20% of the total cumulative number of publications, with each country having published more than 20 papers. Among these ten countries, all of them except China are developed countries, indicating that the research field of older adults’ technology acceptance has received general attention from developed countries. Currently, China and the USA were the leading countries in terms of publications with 111 and 104 respectively, accounting for 22.20% and 20.80%. The UK, Germany, Italy, and the Netherlands also made significant contributions. The USA and China ranked first and second in terms of the number of citations, while the Netherlands had the highest average citations, indicating the high impact and quality of its research. The UK has shown outstanding performance in international cooperation, while the USA highlights its significant academic influence in this field with the highest h-index value.

A National collaboration network. B Annual volume of publications in the top 10 countries.

Institutions and authors analysis

Analyzing the number of publications and citations can reveal an institution’s or author’s research strength and influence in a particular research area (Kwiek 2021 ). Tables 5 and 6 show the statistics of the institutions and authors whose publication counts are in the top ten, respectively. As shown in Table 5 , higher education institutions hold the main position in this research field. Among the top ten institutions, City University of Hong Kong and The University of Hong Kong from China lead with 14 and 9 publications, respectively. City University of Hong Kong has the highest h-index, highlighting its significant influence in the field. It is worth noting that Tilburg University in the Netherlands is not among the top five in terms of publications, but the high average citation count (130.14) of its literature demonstrates the high quality of its research.

After analyzing the authors’ output using Price’s Law (Redner 1998 ), the highest number of publications among the authors counted ( n  = 10) defines a publication threshold of 3 for core authors in this research area. As a result of quantitative screening, a total of 63 core authors were identified. Table 6 shows that Chen from Zhejiang University, China, Ziefle from RWTH Aachen University, Germany, and Rogers from Macquarie University, Australia, were the top three authors in terms of the number of publications, with 10, 9, and 8 articles, respectively. In terms of average citation rate, Peek and Wouters, both scholars from the Netherlands, have significantly higher rates than other scholars, with 183.2 and 152.67 respectively. This suggests that their research is of high quality and widely recognized. Additionally, Chen and Rogers have high h-indices in this field.

Knowledge base and theme progress (RQ3)

Research knowledge base.

Co-citation relationships occur when two documents are cited together (Zhang and Zhu 2022 ). Co-citation mapping uses references as nodes to represent the knowledge base of a subject area (Min et al. 2021). Figure 5A illustrates co-occurrence mapping in older adults’ technology acceptance research, where larger nodes signify higher co-citation frequencies. Co-citation cluster analysis can be used to explore knowledge structure and research boundaries (Hota et al. 2020 ; Shiau et al. 2023 ). The co-citation clustering mapping of older adults’ technology acceptance research literature (Fig. 5B ) shows that the Q value of the clustering result is 0.8129 (>0.3), and the average value of the weight S is 0.9391 (>0.7), indicating that the clusters are uniformly distributed with a significant and credible structure. This further proves that the boundaries of the research field are clear and there is significant differentiation in the field. The figure features 18 cluster labels, each associated with thematic color blocks corresponding to different time slices. Highlighted emerging research themes include #2 Smart Home Technology, #7 Social Live, and #10 Customer Service. Furthermore, the clustering labels extracted are primarily classified into three categories: theoretical model deepening, emerging technology applications, research methods and evaluation, as detailed in Table 7 .

A Co-citation analysis of references. B Clustering network analysis of references.

Seminal literature analysis

The top ten nodes in terms of co-citation frequency were selected for further analysis. Table 8 displays the corresponding node information. Studies were categorized into four main groups based on content analysis. (1) Research focusing on specific technology usage by older adults includes studies by Peek et al. ( 2014 ), Ma et al. ( 2016 ), Hoque and Sorwar ( 2017 ), and Li et al. ( 2019 ), who investigated the factors influencing the use of e-technology, smartphones, mHealth, and smart wearables, respectively. (2) Concerning the development of theoretical models of technology acceptance, Chen and Chan ( 2014 ) introduced the Senior Technology Acceptance Model (STAM), and Macedo ( 2017 ) analyzed the predictive power of UTAUT2 in explaining older adults’ intentional behaviors and information technology usage. (3) In exploring older adults’ information technology adoption and behavior, Lee and Coughlin ( 2015 ) emphasized that the adoption of technology by older adults is a multifactorial process that includes performance, price, value, usability, affordability, accessibility, technical support, social support, emotion, independence, experience, and confidence. Yusif et al. ( 2016 ) conducted a literature review examining the key barriers affecting older adults’ adoption of assistive technology, including factors such as privacy, trust, functionality/added value, cost, and stigma. (4) From the perspective of research into older adults’ technology acceptance, Mitzner et al. ( 2019 ) assessed the long-term usage of computer systems designed for the elderly, whereas Guner and Acarturk ( 2020 ) compared information technology usage and acceptance between older and younger adults. The breadth and prevalence of this literature make it a vital reference for researchers in the field, also providing new perspectives and inspiration for future research directions.

Research thematic progress

Burst citation is a node of literature that guides the sudden change in dosage, which usually represents a prominent development or major change in a particular field, with innovative and forward-looking qualities. By analyzing the emergent literature, it is often easy to understand the dynamics of the subject area, mapping the emerging thematic change (Chen et al. 2022 ). Figure 6 shows the burst citation mapping in the field of older adults’ technology acceptance research, with burst citations represented by red nodes (Fig. 6A ). For the ten papers with the highest burst intensity (Fig. 6B ), this study will conduct further analysis in conjunction with literature review.

A Burst detection of co-citation. B The top 10 references with the strongest citation bursts.

As shown in Fig. 6 , Mitzner et al. ( 2010 ) broke the stereotype that older adults are fearful of technology, found that they actually have positive attitudes toward technology, and emphasized the centrality of ease of use and usefulness in the process of technology acceptance. This finding provides an important foundation for subsequent research. During the same period, Wagner et al. ( 2010 ) conducted theory-deepening and applied research on technology acceptance among older adults. The research focused on older adults’ interactions with computers from the perspective of Social Cognitive Theory (SCT). This expanded the understanding of technology acceptance, particularly regarding the relationship between behavior, environment, and other SCT elements. In addition, Pan and Jordan-Marsh ( 2010 ) extended the TAM to examine the interactions among predictors of perceived usefulness, perceived ease of use, subjective norm, and convenience conditions when older adults use the Internet, taking into account the moderating roles of gender and age. Heerink et al. ( 2010 ) adapted and extended the UTAUT, constructed a technology acceptance model specifically designed for older users’ acceptance of assistive social agents, and validated it using controlled experiments and longitudinal data, explaining intention to use by combining functional assessment and social interaction variables.

Then the research theme shifted to an in-depth analysis of the factors influencing technology acceptance among older adults. Two papers with high burst strengths emerged during this period: Peek et al. ( 2014 ) (Strength = 12.04), Chen and Chan ( 2014 ) (Strength = 9.81). Through a systematic literature review and empirical study, Peek STM and Chen K, among others, identified multidimensional factors that influence older adults’ technology acceptance. Peek et al. ( 2014 ) analyzed literature on the acceptance of in-home care technology among older adults and identified six factors that influence their acceptance: concerns about technology, expected benefits, technology needs, technology alternatives, social influences, and older adult characteristics, with a focus on differences between pre- and post-implementation factors. Chen and Chan ( 2014 ) constructed the STAM by administering a questionnaire to 1012 older adults and adding eight important factors, including technology anxiety, self-efficacy, cognitive ability, and physical function, based on the TAM. This enriches the theoretical foundation of the field. In addition, Braun ( 2013 ) highlighted the role of perceived usefulness, trust in social networks, and frequency of Internet use in older adults’ use of social networks, while ease of use and social pressure were not significant influences. These findings contribute to the study of older adults’ technology acceptance within specific technology application domains.

Recent research has focused on empirical studies of personal factors and emerging technologies. Ma et al. ( 2016 ) identified key personal factors affecting smartphone acceptance among older adults through structured questionnaires and face-to-face interviews with 120 participants. The study found that cost, self-satisfaction, and convenience were important factors influencing perceived usefulness and ease of use. This study offers empirical evidence to comprehend the main factors that drive smartphone acceptance among Chinese older adults. Additionally, Yusif et al. ( 2016 ) presented an overview of the obstacles that hinder older adults’ acceptance of assistive technologies, focusing on privacy, trust, and functionality.

In summary, research on older adults’ technology acceptance has shifted from early theoretical deepening and analysis of influencing factors to empirical studies in the areas of personal factors and emerging technologies, which have greatly enriched the theoretical basis of older adults’ technology acceptance and provided practical guidance for the design of emerging technology products.

Research hotspots, evolutionary trends, and quality distribution (RQ4)

Core keywords analysis.

Keywords concise the main idea and core of the literature, and are a refined summary of the research content (Huang et al. 2021 ). In CiteSpace, nodes with a centrality value greater than 0.1 are considered to be critical nodes. Analyzing keywords with high frequency and centrality helps to visualize the hot topics in the research field (Park et al. 2018 ). The merged keywords were imported into CiteSpace, and the top 10 keywords were counted and sorted by frequency and centrality respectively, as shown in Table 9 . The results show that the keyword “TAM” has the highest frequency (92), followed by “UTAUT” (24), which reflects that the in-depth study of the existing technology acceptance model and its theoretical expansion occupy a central position in research related to older adults’ technology acceptance. Furthermore, the terms ‘assistive technology’ and ‘virtual reality’ are both high-frequency and high-centrality terms (frequency = 17, centrality = 0.10), indicating that the research on assistive technology and virtual reality for older adults is the focus of current academic attention.

Research hotspots analysis

Using VOSviewer for keyword co-occurrence analysis organizes keywords into groups or clusters based on their intrinsic connections and frequencies, clearly highlighting the research field’s hot topics. The connectivity among keywords reveals correlations between different topics. To ensure accuracy, the analysis only considered the authors’ keywords. Subsequently, the keywords were filtered by setting the keyword frequency to 5 to obtain the keyword clustering map of the research on older adults’ technology acceptance research keyword clustering mapping (Fig. 7 ), combined with the keyword co-occurrence clustering network (Fig. 7A ) and the corresponding density situation (Fig. 7B ) to make a detailed analysis of the following four groups of clustered themes.

A Co-occurrence clustering network. B Keyword density.

Cluster #1—Research on the factors influencing technology adoption among older adults is a prominent topic, covering age, gender, self-efficacy, attitude, and and intention to use (Berkowsky et al. 2017 ; Wang et al. 2017 ). It also examined older adults’ attitudes towards and acceptance of digital health technologies (Ahmad and Mozelius, 2022 ). Moreover, the COVID-19 pandemic, significantly impacting older adults’ technology attitudes and usage, has underscored the study’s importance and urgency. Therefore, it is crucial to conduct in-depth studies on how older adults accept, adopt, and effectively use new technologies, to address their needs and help them overcome the digital divide within digital inclusion. This will improve their quality of life and healthcare experiences.

Cluster #2—Research focuses on how older adults interact with assistive technologies, especially assistive robots and health monitoring devices, emphasizing trust, usability, and user experience as crucial factors (Halim et al. 2022 ). Moreover, health monitoring technologies effectively track and manage health issues common in older adults, like dementia and mild cognitive impairment (Lussier et al. 2018 ; Piau et al. 2019 ). Interactive exercise games and virtual reality have been deployed to encourage more physical and cognitive engagement among older adults (Campo-Prieto et al. 2021 ). Personalized and innovative technology significantly enhances older adults’ participation, improving their health and well-being.

Cluster #3—Optimizing health management for older adults using mobile technology. With the development of mobile health (mHealth) and health information technology, mobile applications, smartphones, and smart wearable devices have become effective tools to help older users better manage chronic conditions, conduct real-time health monitoring, and even receive telehealth services (Dupuis and Tsotsos 2018 ; Olmedo-Aguirre et al. 2022 ; Kim et al. 2014 ). Additionally, these technologies can mitigate the problem of healthcare resource inequality, especially in developing countries. Older adults’ acceptance and use of these technologies are significantly influenced by their behavioral intentions, motivational factors, and self-management skills. These internal motivational factors, along with external factors, jointly affect older adults’ performance in health management and quality of life.

Cluster #4—Research on technology-assisted home care for older adults is gaining popularity. Environmentally assisted living enhances older adults’ independence and comfort at home, offering essential support and security. This has a crucial impact on promoting healthy aging (Friesen et al. 2016 ; Wahlroos et al. 2023 ). The smart home is a core application in this field, providing a range of solutions that facilitate independent living for the elderly in a highly integrated and user-friendly manner. This fulfills different dimensions of living and health needs (Majumder et al. 2017 ). Moreover, eHealth offers accurate and personalized health management and healthcare services for older adults (Delmastro et al. 2018 ), ensuring their needs are met at home. Research in this field often employs qualitative methods and structural equation modeling to fully understand older adults’ needs and experiences at home and analyze factors influencing technology adoption.

Evolutionary trends analysis

To gain a deeper understanding of the evolutionary trends in research hotspots within the field of older adults’ technology acceptance, we conducted a statistical analysis of the average appearance times of keywords, using CiteSpace to generate the time-zone evolution mapping (Fig. 8 ) and burst keywords. The time-zone mapping visually displays the evolution of keywords over time, intuitively reflecting the frequency and initial appearance of keywords in research, commonly used to identify trends in research topics (Jing et al. 2024a ; Kumar et al. 2021 ). Table 10 lists the top 15 keywords by burst strength, with the red sections indicating high-frequency citations and their burst strength in specific years. These burst keywords reveal the focus and trends of research themes over different periods (Kleinberg 2002 ). Combining insights from the time-zone mapping and burst keywords provides more objective and accurate research insights (Wang et al. 2023b ).

Reflecting the frequency and time of first appearance of keywords in the study.

An integrated analysis of Fig. 8 and Table 10 shows that early research on older adults’ technology acceptance primarily focused on factors such as perceived usefulness, ease of use, and attitudes towards information technology, including their use of computers and the internet (Pan and Jordan-Marsh 2010 ), as well as differences in technology use between older adults and other age groups (Guner and Acarturk 2020 ). Subsequently, the research focus expanded to improving the quality of life for older adults, exploring how technology can optimize health management and enhance the possibility of independent living, emphasizing the significant role of technology in improving the quality of life for the elderly. With ongoing technological advancements, recent research has shifted towards areas such as “virtual reality,” “telehealth,” and “human-robot interaction,” with a focus on the user experience of older adults (Halim et al. 2022 ). The appearance of keywords such as “physical activity” and “exercise” highlights the value of technology in promoting physical activity and health among older adults. This phase of research tends to make cutting-edge technology genuinely serve the practical needs of older adults, achieving its widespread application in daily life. Additionally, research has focused on expanding and quantifying theoretical models of older adults’ technology acceptance, involving keywords such as “perceived risk”, “validation” and “UTAUT”.

In summary, from 2013 to 2023, the field of older adults’ technology acceptance has evolved from initial explorations of influencing factors, to comprehensive enhancements in quality of life and health management, and further to the application and deepening of theoretical models and cutting-edge technologies. This research not only reflects the diversity and complexity of the field but also demonstrates a comprehensive and in-depth understanding of older adults’ interactions with technology across various life scenarios and needs.

Research quality distribution

To reveal the distribution of research quality in the field of older adults’ technology acceptance, a strategic diagram analysis is employed to calculate and illustrate the internal development and interrelationships among various research themes (Xie et al. 2020 ). The strategic diagram uses Centrality as the X-axis and Density as the Y-axis to divide into four quadrants, where the X-axis represents the strength of the connection between thematic clusters and other themes, with higher values indicating a central position in the research field; the Y-axis indicates the level of development within the thematic clusters, with higher values denoting a more mature and widely recognized field (Li and Zhou 2020 ).

Through cluster analysis and manual verification, this study categorized 61 core keywords (Frequency ≥5) into 11 thematic clusters. Subsequently, based on the keywords covered by each thematic cluster, the research themes and their directions for each cluster were summarized (Table 11 ), and the centrality and density coordinates for each cluster were precisely calculated (Table 12 ). Finally, a strategic diagram of the older adults’ technology acceptance research field was constructed (Fig. 9 ). Based on the distribution of thematic clusters across the quadrants in the strategic diagram, the structure and developmental trends of the field were interpreted.

Classification and visualization of theme clusters based on density and centrality.

As illustrated in Fig. 9 , (1) the theme clusters of #3 Usage Experience and #4 Assisted Living Technology are in the first quadrant, characterized by high centrality and density. Their internal cohesion and close links with other themes indicate their mature development, systematic research content or directions have been formed, and they have a significant influence on other themes. These themes play a central role in the field of older adults’ technology acceptance and have promising prospects. (2) The theme clusters of #6 Smart Devices, #9 Theoretical Models, and #10 Mobile Health Applications are in the second quadrant, with higher density but lower centrality. These themes have strong internal connections but weaker external links, indicating that these three themes have received widespread attention from researchers and have been the subject of related research, but more as self-contained systems and exhibit independence. Therefore, future research should further explore in-depth cooperation and cross-application with other themes. (3) The theme clusters of #7 Human-Robot Interaction, #8 Characteristics of the Elderly, and #11 Research Methods are in the third quadrant, with lower centrality and density. These themes are loosely connected internally and have weak links with others, indicating their developmental immaturity. Compared to other topics, they belong to the lower attention edge and niche themes, and there is a need for further investigation. (4) The theme clusters of #1 Digital Healthcare Technology, #2 Psychological Factors, and #5 Socio-Cultural Factors are located in the fourth quadrant, with high centrality but low density. Although closely associated with other research themes, the internal cohesion within these clusters is relatively weak. This suggests that while these themes are closely linked to other research areas, their own development remains underdeveloped, indicating a core immaturity. Nevertheless, these themes are crucial within the research domain of elderly technology acceptance and possess significant potential for future exploration.

Discussion on distribution power (RQ1)

Over the past decade, academic interest and influence in the area of older adults’ technology acceptance have significantly increased. This trend is evidenced by a quantitative analysis of publication and citation volumes, particularly noticeable in 2019 and 2022, where there was a substantial rise in both metrics. The rise is closely linked to the widespread adoption of emerging technologies such as smart homes, wearable devices, and telemedicine among older adults. While these technologies have enhanced their quality of life, they also pose numerous challenges, sparking extensive research into their acceptance, usage behaviors, and influencing factors among the older adults (Pirzada et al. 2022 ; Garcia Reyes et al. 2023 ). Furthermore, the COVID-19 pandemic led to a surge in technology demand among older adults, especially in areas like medical consultation, online socialization, and health management, further highlighting the importance and challenges of technology. Health risks and social isolation have compelled older adults to rely on technology for daily activities, accelerating its adoption and application within this demographic. This phenomenon has made technology acceptance a critical issue, driving societal and academic focus on the study of technology acceptance among older adults.

The flow of knowledge at the level of high-output disciplines and journals, along with the primary publishing outlets, indicates the highly interdisciplinary nature of research into older adults’ technology acceptance. This reflects the complexity and breadth of issues related to older adults’ technology acceptance, necessitating the integration of multidisciplinary knowledge and approaches. Currently, research is primarily focused on medical health and human-computer interaction, demonstrating academic interest in improving health and quality of life for older adults and addressing the urgent needs related to their interactions with technology. In the field of medical health, research aims to provide advanced and innovative healthcare technologies and services to meet the challenges of an aging population while improving the quality of life for older adults (Abdi et al. 2020 ; Wilson et al. 2021 ). In the field of human-computer interaction, research is focused on developing smarter and more user-friendly interaction models to meet the needs of older adults in the digital age, enabling them to actively participate in social activities and enjoy a higher quality of life (Sayago, 2019 ). These studies are crucial for addressing the challenges faced by aging societies, providing increased support and opportunities for the health, welfare, and social participation of older adults.

Discussion on research power (RQ2)

This study analyzes leading countries and collaboration networks, core institutions and authors, revealing the global research landscape and distribution of research strength in the field of older adults’ technology acceptance, and presents quantitative data on global research trends. From the analysis of country distribution and collaborations, China and the USA hold dominant positions in this field, with developed countries like the UK, Germany, Italy, and the Netherlands also excelling in international cooperation and research influence. The significant investment in technological research and the focus on the technological needs of older adults by many developed countries reflect their rapidly aging societies, policy support, and resource allocation.

China is the only developing country that has become a major contributor in this field, indicating its growing research capabilities and high priority given to aging societies and technological innovation. Additionally, China has close collaborations with countries such as USA, the UK, and Malaysia, driven not only by technological research needs but also by shared challenges and complementarities in aging issues among these nations. For instance, the UK has extensive experience in social welfare and aging research, providing valuable theoretical guidance and practical experience. International collaborations, aimed at addressing the challenges of aging, integrate the strengths of various countries, advancing in-depth and widespread development in the research of technology acceptance among older adults.

At the institutional and author level, City University of Hong Kong leads in publication volume, with research teams led by Chan and Chen demonstrating significant academic activity and contributions. Their research primarily focuses on older adults’ acceptance and usage behaviors of various technologies, including smartphones, smart wearables, and social robots (Chen et al. 2015 ; Li et al. 2019 ; Ma et al. 2016 ). These studies, targeting specific needs and product characteristics of older adults, have developed new models of technology acceptance based on existing frameworks, enhancing the integration of these technologies into their daily lives and laying a foundation for further advancements in the field. Although Tilburg University has a smaller publication output, it holds significant influence in the field of older adults’ technology acceptance. Particularly, the high citation rate of Peek’s studies highlights their excellence in research. Peek extensively explored older adults’ acceptance and usage of home care technologies, revealing the complexity and dynamics of their technology use behaviors. His research spans from identifying systemic influencing factors (Peek et al. 2014 ; Peek et al. 2016 ), emphasizing familial impacts (Luijkx et al. 2015 ), to constructing comprehensive models (Peek et al. 2017 ), and examining the dynamics of long-term usage (Peek et al. 2019 ), fully reflecting the evolving technology landscape and the changing needs of older adults. Additionally, the ongoing contributions of researchers like Ziefle, Rogers, and Wouters in the field of older adults’ technology acceptance demonstrate their research influence and leadership. These researchers have significantly enriched the knowledge base in this area with their diverse perspectives. For instance, Ziefle has uncovered the complex attitudes of older adults towards technology usage, especially the trade-offs between privacy and security, and how different types of activities affect their privacy needs (Maidhof et al. 2023 ; Mujirishvili et al. 2023 ; Schomakers and Ziefle 2023 ; Wilkowska et al. 2022 ), reflecting a deep exploration and ongoing innovation in the field of older adults’ technology acceptance.

Discussion on knowledge base and thematic progress (RQ3)

Through co-citation analysis and systematic review of seminal literature, this study reveals the knowledge foundation and thematic progress in the field of older adults’ technology acceptance. Co-citation networks and cluster analyses illustrate the structural themes of the research, delineating the differentiation and boundaries within this field. Additionally, burst detection analysis offers a valuable perspective for understanding the thematic evolution in the field of technology acceptance among older adults. The development and innovation of theoretical models are foundational to this research. Researchers enhance the explanatory power of constructed models by deepening and expanding existing technology acceptance theories to address theoretical limitations. For instance, Heerink et al. ( 2010 ) modified and expanded the UTAUT model by integrating functional assessment and social interaction variables to create the almere model. This model significantly enhances the ability to explain the intentions of older users in utilizing assistive social agents and improves the explanation of actual usage behaviors. Additionally, Chen and Chan ( 2014 ) extended the TAM to include age-related health and capability features of older adults, creating the STAM, which substantially improves predictions of older adults’ technology usage behaviors. Personal attributes, health and capability features, and facilitating conditions have a direct impact on technology acceptance. These factors more effectively predict older adults’ technology usage behaviors than traditional attitudinal factors.

With the advancement of technology and the application of emerging technologies, new research topics have emerged, increasingly focusing on older adults’ acceptance and use of these technologies. Prior to this, the study by Mitzner et al. ( 2010 ) challenged the stereotype of older adults’ conservative attitudes towards technology, highlighting the central roles of usability and usefulness in the technology acceptance process. This discovery laid an important foundation for subsequent research. Research fields such as “smart home technology,” “social life,” and “customer service” are emerging, indicating a shift in focus towards the practical and social applications of technology in older adults’ lives. Research not only focuses on the technology itself but also on how these technologies integrate into older adults’ daily lives and how they can improve the quality of life through technology. For instance, studies such as those by Ma et al. ( 2016 ), Hoque and Sorwar ( 2017 ), and Li et al. ( 2019 ) have explored factors influencing older adults’ use of smartphones, mHealth, and smart wearable devices.

Furthermore, the diversification of research methodologies and innovation in evaluation techniques, such as the use of mixed methods, structural equation modeling (SEM), and neural network (NN) approaches, have enhanced the rigor and reliability of the findings, enabling more precise identification of the factors and mechanisms influencing technology acceptance. Talukder et al. ( 2020 ) employed an effective multimethodological strategy by integrating SEM and NN to leverage the complementary strengths of both approaches, thus overcoming their individual limitations and more accurately analyzing and predicting older adults’ acceptance of wearable health technologies (WHT). SEM is utilized to assess the determinants’ impact on the adoption of WHT, while neural network models validate SEM outcomes and predict the significance of key determinants. This combined approach not only boosts the models’ reliability and explanatory power but also provides a nuanced understanding of the motivations and barriers behind older adults’ acceptance of WHT, offering deep research insights.

Overall, co-citation analysis of the literature in the field of older adults’ technology acceptance has uncovered deeper theoretical modeling and empirical studies on emerging technologies, while emphasizing the importance of research methodological and evaluation innovations in understanding complex social science issues. These findings are crucial for guiding the design and marketing strategies of future technology products, especially in the rapidly growing market of older adults.

Discussion on research hotspots and evolutionary trends (RQ4)

By analyzing core keywords, we can gain deep insights into the hot topics, evolutionary trends, and quality distribution of research in the field of older adults’ technology acceptance. The frequent occurrence of the keywords “TAM” and “UTAUT” indicates that the applicability and theoretical extension of existing technology acceptance models among older adults remain a focal point in academia. This phenomenon underscores the enduring influence of the studies by Davis ( 1989 ) and Venkatesh et al. ( 2003 ), whose models provide a robust theoretical framework for explaining and predicting older adults’ acceptance and usage of emerging technologies. With the widespread application of artificial intelligence (AI) and big data technologies, these theoretical models have incorporated new variables such as perceived risk, trust, and privacy issues (Amin et al. 2024 ; Chen et al. 2024 ; Jing et al. 2024b ; Seibert et al. 2021 ; Wang et al. 2024b ), advancing the theoretical depth and empirical research in this field.

Keyword co-occurrence cluster analysis has revealed multiple research hotspots in the field, including factors influencing technology adoption, interactive experiences between older adults and assistive technologies, the application of mobile health technology in health management, and technology-assisted home care. These studies primarily focus on enhancing the quality of life and health management of older adults through emerging technologies, particularly in the areas of ambient assisted living, smart health monitoring, and intelligent medical care. In these domains, the role of AI technology is increasingly significant (Qian et al. 2021 ; Ho 2020 ). With the evolution of next-generation information technologies, AI is increasingly integrated into elder care systems, offering intelligent, efficient, and personalized service solutions by analyzing the lifestyles and health conditions of older adults. This integration aims to enhance older adults’ quality of life in aspects such as health monitoring and alerts, rehabilitation assistance, daily health management, and emotional support (Lee et al. 2023 ). A survey indicates that 83% of older adults prefer AI-driven solutions when selecting smart products, demonstrating the increasing acceptance of AI in elder care (Zhao and Li 2024 ). Integrating AI into elder care presents both opportunities and challenges, particularly in terms of user acceptance, trust, and long-term usage effects, which warrant further exploration (Mhlanga 2023 ). These studies will help better understand the profound impact of AI technology on the lifestyles of older adults and provide critical references for optimizing AI-driven elder care services.

The Time-zone evolution mapping and burst keyword analysis further reveal the evolutionary trends of research hotspots. Early studies focused on basic technology acceptance models and user perceptions, later expanding to include quality of life and health management. In recent years, research has increasingly focused on cutting-edge technologies such as virtual reality, telehealth, and human-robot interaction, with a concurrent emphasis on the user experience of older adults. This evolutionary process demonstrates a deepening shift from theoretical models to practical applications, underscoring the significant role of technology in enhancing the quality of life for older adults. Furthermore, the strategic coordinate mapping analysis clearly demonstrates the development and mutual influence of different research themes. High centrality and density in the themes of Usage Experience and Assisted Living Technology indicate their mature research status and significant impact on other themes. The themes of Smart Devices, Theoretical Models, and Mobile Health Applications demonstrate self-contained research trends. The themes of Human-Robot Interaction, Characteristics of the Elderly, and Research Methods are not yet mature, but they hold potential for development. Themes of Digital Healthcare Technology, Psychological Factors, and Socio-Cultural Factors are closely related to other themes, displaying core immaturity but significant potential.

In summary, the research hotspots in the field of older adults’ technology acceptance are diverse and dynamic, demonstrating the academic community’s profound understanding of how older adults interact with technology across various life contexts and needs. Under the influence of AI and big data, research should continue to focus on the application of emerging technologies among older adults, exploring in depth how they adapt to and effectively use these technologies. This not only enhances the quality of life and healthcare experiences for older adults but also drives ongoing innovation and development in this field.

Research agenda

Based on the above research findings, to further understand and promote technology acceptance and usage among older adults, we recommend future studies focus on refining theoretical models, exploring long-term usage, and assessing user experience in the following detailed aspects:

Refinement and validation of specific technology acceptance models for older adults: Future research should focus on developing and validating technology acceptance models based on individual characteristics, particularly considering variations in technology acceptance among older adults across different educational levels and cultural backgrounds. This includes factors such as age, gender, educational background, and cultural differences. Additionally, research should examine how well specific technologies, such as wearable devices and mobile health applications, meet the needs of older adults. Building on existing theoretical models, this research should integrate insights from multiple disciplines such as psychology, sociology, design, and engineering through interdisciplinary collaboration to create more accurate and comprehensive models, which should then be validated in relevant contexts.

Deepening the exploration of the relationship between long-term technology use and quality of life among older adults: The acceptance and use of technology by users is a complex and dynamic process (Seuwou et al. 2016 ). Existing research predominantly focuses on older adults’ initial acceptance or short-term use of new technologies; however, the impact of long-term use on their quality of life and health is more significant. Future research should focus on the evolution of older adults’ experiences and needs during long-term technology usage, and the enduring effects of technology on their social interactions, mental health, and life satisfaction. Through longitudinal studies and qualitative analysis, this research reveals the specific needs and challenges of older adults in long-term technology use, providing a basis for developing technologies and strategies that better meet their requirements. This understanding aids in comprehensively assessing the impact of technology on older adults’ quality of life and guiding the optimization and improvement of technological products.

Evaluating the Importance of User Experience in Research on Older Adults’ Technology Acceptance: Understanding the mechanisms of information technology acceptance and use is central to human-computer interaction research. Although technology acceptance models and user experience models differ in objectives, they share many potential intersections. Technology acceptance research focuses on structured prediction and assessment, while user experience research concentrates on interpreting design impacts and new frameworks. Integrating user experience to assess older adults’ acceptance of technology products and systems is crucial (Codfrey et al. 2022 ; Wang et al. 2019 ), particularly for older users, where specific product designs should emphasize practicality and usability (Fisk et al. 2020 ). Researchers need to explore innovative age-appropriate design methods to enhance older adults’ usage experience. This includes studying older users’ actual usage preferences and behaviors, optimizing user interfaces, and interaction designs. Integrating feedback from older adults to tailor products to their needs can further promote their acceptance and continued use of technology products.

Conclusions

This study conducted a systematic review of the literature on older adults’ technology acceptance over the past decade through bibliometric analysis, focusing on the distribution power, research power, knowledge base and theme progress, research hotspots, evolutionary trends, and quality distribution. Using a combination of quantitative and qualitative methods, this study has reached the following conclusions:

Technology acceptance among older adults has become a hot topic in the international academic community, involving the integration of knowledge across multiple disciplines, including Medical Informatics, Health Care Sciences Services, and Ergonomics. In terms of journals, “PSYCHOLOGY, EDUCATION, HEALTH” represents a leading field, with key publications including Computers in Human Behavior , Journal of Medical Internet Research , and International Journal of Human-Computer Interaction . These journals possess significant academic authority and extensive influence in the field.

Research on technology acceptance among older adults is particularly active in developed countries, with China and USA publishing significantly more than other nations. The Netherlands leads in high average citation rates, indicating the depth and impact of its research. Meanwhile, the UK stands out in terms of international collaboration. At the institutional level, City University of Hong Kong and The University of Hong Kong in China are in leading positions. Tilburg University in the Netherlands demonstrates exceptional research quality through its high average citation count. At the author level, Chen from China has the highest number of publications, while Peek from the Netherlands has the highest average citation count.

Co-citation analysis of references indicates that the knowledge base in this field is divided into three main categories: theoretical model deepening, emerging technology applications, and research methods and evaluation. Seminal literature focuses on four areas: specific technology use by older adults, expansion of theoretical models of technology acceptance, information technology adoption behavior, and research perspectives. Research themes have evolved from initial theoretical deepening and analysis of influencing factors to empirical studies on individual factors and emerging technologies.

Keyword analysis indicates that TAM and UTAUT are the most frequently occurring terms, while “assistive technology” and “virtual reality” are focal points with high frequency and centrality. Keyword clustering analysis reveals that research hotspots are concentrated on the influencing factors of technology adoption, human-robot interaction experiences, mobile health management, and technology for aging in place. Time-zone evolution mapping and burst keyword analysis have revealed the research evolution from preliminary exploration of influencing factors, to enhancements in quality of life and health management, and onto advanced technology applications and deepening of theoretical models. Furthermore, analysis of research quality distribution indicates that Usage Experience and Assisted Living Technology have become core topics, while Smart Devices, Theoretical Models, and Mobile Health Applications point towards future research directions.

Through this study, we have systematically reviewed the dynamics, core issues, and evolutionary trends in the field of older adults’ technology acceptance, constructing a comprehensive Knowledge Mapping of the domain and presenting a clear framework of existing research. This not only lays the foundation for subsequent theoretical discussions and innovative applications in the field but also provides an important reference for relevant scholars.

Limitations

To our knowledge, this is the first bibliometric analysis concerning technology acceptance among older adults, and we adhered strictly to bibliometric standards throughout our research. However, this study relies on the Web of Science Core Collection, and while its authority and breadth are widely recognized, this choice may have missed relevant literature published in other significant databases such as PubMed, Scopus, and Google Scholar, potentially overlooking some critical academic contributions. Moreover, given that our analysis was confined to literature in English, it may not reflect studies published in other languages, somewhat limiting the global representativeness of our data sample.

It is noteworthy that with the rapid development of AI technology, its increasingly widespread application in elderly care services is significantly transforming traditional care models. AI is profoundly altering the lifestyles of the elderly, from health monitoring and smart diagnostics to intelligent home systems and personalized care, significantly enhancing their quality of life and health care standards. The potential for AI technology within the elderly population is immense, and research in this area is rapidly expanding. However, due to the restrictive nature of the search terms used in this study, it did not fully cover research in this critical area, particularly in addressing key issues such as trust, privacy, and ethics.

Consequently, future research should not only expand data sources, incorporating multilingual and multidatabase literature, but also particularly focus on exploring older adults’ acceptance of AI technology and its applications, in order to construct a more comprehensive academic landscape of older adults’ technology acceptance, thereby enriching and extending the knowledge system and academic trends in this field.

Data availability

The datasets analyzed during the current study are available in the Dataverse repository: https://doi.org/10.7910/DVN/6K0GJH .

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This research was supported by the Social Science Foundation of Shaanxi Province in China (Grant No. 2023J014).

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Xianru Shang, Zijian Liu, Chen Gong, Zhigang Hu & Yuexuan Wu

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Shang, X., Liu, Z., Gong, C. et al. Knowledge mapping and evolution of research on older adults’ technology acceptance: a bibliometric study from 2013 to 2023. Humanit Soc Sci Commun 11 , 1115 (2024). https://doi.org/10.1057/s41599-024-03658-2

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• Published: 02 September 2024

Benefits, barriers and recommendations for youth engagement in health research: combining evidence-based and youth perspectives

• Katherine Bailey 1 , 2   na1 ,
• Brooke Allemang 3   na1 ,
• Ashley Vandermorris 4 , 5 ,
• Sarah Munce 6 , 7 , 8 ,
• Kristin Cleverley 1 , 9 , 10 ,
• Cassandra Chisholm 11 ,
• Eva Cohen 12 ,
• Cedar Davidson 13 ,
• Asil El Galad 14 ,
• Dahlia Leibovich 15 ,
• Trinity Lowthian 16 ,
• Jeanna Pillainayagam 17 ,
• Harshini Ramesh 18 ,
• Anna Samson 19 ,
• Vjura Senthilnathan 6 , 7 ,
• Paul Siska 18 ,
• Madison Snider 18 &
• Alene Toulany 2 , 4 , 5  

Research Involvement and Engagement volume  10 , Article number:  92 ( 2024 ) Cite this article

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Youth engagement refers to the collaboration between researchers and youth to produce research. Youth engagement in health research has been shown to inform effective interventions aimed at improving health outcomes. However, limited evidence has identified promising practices to meaningfully engage youth. This synthesis aims to describe youth engagement approaches, frameworks, and barriers, as well as provide both evidence-based and youth-generated recommendations for meaningful engagement.

This review occurred in two stages: 1) a narrative review of existing literature on youth engagement and 2) a Youth Advisory Council (YAC) to review and supplement findings with their perspectives, experiences, and recommendations. The terms ‘youth engagement’ and ‘health research’ were searched in Google Scholar, PubMed, Web of Science, Scopus, and PsycINFO. Articles and non-peer reviewed research works related to youth engagement in health research were included, reviewed, and summarized. The YAC met with research team members and in separate youth-only forums to complement the narrative review with their perspectives. Types of youth engagement include participation as research participants, advisors, partners, and co-investigators. Barriers to youth engagement were organized into youth- (e.g., time commitments), researcher- (e.g., attitudes towards youth engagement), organizational- (e.g., inadequate infrastructure to support youth engagement), and system-level (e.g., systemic discrimination and exclusion from research). To enhance youth engagement, recommendations focus on preparing and supporting youth by offering flexible communication approaches, mentorship opportunities, diverse and inclusive recruitment, and ensuring youth understand the commitment and benefits involved.

Conclusions

To harness the potential of youth engagement, researchers need to establish an inclusive and enabling environment that fosters collaboration, trust, and valuable contributions from youth. Future research endeavors should prioritize investigating the dynamics of power-sharing between researchers and youth, assessing the impact of youth engagement on young participants, and youth-specific evaluation frameworks.

Plain English summary

Engaging and partnering with youth in research related to healthcare is important, but often not done well. As researchers, we recognize that youth perspectives are needed to make sure we are asking the right questions, using appropriate research methods, and interpreting the results correctly. We searched the literature to identify challenges researchers have faced engaging youth in health research, as well as strategies to partner with youth in a meaningful way. We worked closely with 11 youth from across Canada with experience in healthcare, who formed a Youth Advisory Council. The youth advisors reviewed the literature we found and discussed how it fit with their own experiences and perspectives through group meetings with the research team. Youth advisors divided into four groups to co-author parts of this paper, including identifying the importance, benefits, and challenges of engaging in research and providing reflections on their positive and negative previous experiences as youth advisors. This paper provides an overview of recommendations for researchers to engage with youth in a meaningful way, including how they communicate and meet with youth, recognize their contributions, and implement feedback to improve the experiences of youth partners.

Peer Review reports

Introduction

Patient engagement in health research is essential to improving the relevance, processes, and impact of their findings [ 1 , 2 , 3 ]. Defined as the collaboration between researchers and those with lived experience in planning and conducting research, interpreting findings, and informing knowledge translation activities [ 1 ], patient engagement in research has been shown to produce and disseminate findings that are more applicable and comprehensible for patients, their families, and the greater community [ 3 , 4 , 5 , 6 , 7 ]. Youth engagement refers specifically to the involvement of youth populations in the research process, with youth often being defined as young people between the ages of 15 to 24 years old [ 8 , 9 , 10 , 11 ]. Youth, particularly those with chronic physical health (e.g., cystic fibrosis, congenital heart disease, diabetes), mental health (e.g., anxiety, depression), and neurodevelopmental conditions (e.g., cerebral palsy), face unique challenges in engaging with the healthcare system compared to adult populations. These include navigating healthcare transitions, developing relationships with multiple care providers, learning to advocate for themselves, and assuming greater responsibility for their healthcare as they grow and mature [ 12 , 13 ]. Existing research has shown that engaging youth in research leads to more effective and impactful interventions, policies, and healthcare services aimed at supporting health outcomes of young people, informed by the priorities and experiences of youth themselves [ 14 , 15 , 16 , 17 , 18 , 19 ]. Several nationally representative child health organizations and leaders have identified youth engagement as a priority area in youth health, highlighting the urgent imperative to include their voices in health research and public policy decisions [ 20 ]. Despite the evidence suggesting that youth are eager and capable of being engaged, there is limited evidence on the unique considerations needed to meaningfully involve youth in health research given their distinct developmental stage [ 8 , 10 , 19 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 ]. These considerations include an emphasis on peer connections, mentorship, flexibility given competing priorities, and the use of technology to allow for broad participation [ 30 , 31 ]. In collaboration with a Youth Advisory Council (YAC), this review aims to:

Outline key types of youth engagement identified in the literature (Aim 1);

Review existing youth engagement frameworks identified in the literature (Aim 2);

Explore barriers to youth engagement identified in the literature and from YAC member perspectives (Aim 3);

Summarize recommendations for engaging youth in research identified in the literature and from YAC member perspectives (Aim 4).

The YAC identified a secondary aim, which was to:

Describe the benefits and impact of youth engagement from YAC member perspectives (Aim 5).

This project was comprised of two phases. First, the research team conducted a narrative review of the literature. Next, a project-specific YAC was established to review the literature findings and integrate the essential insights and perspectives of youth into the project. The methods pertaining to each phase are elaborated upon below. Our Research Ethics Board did not require a formal review of this project as it did not involve research participants.

Phase 1: Narrative Review

A narrative review was conducted to explore existing research on engaging youth in health research. Narrative review methodology is often employed to broadly describe the current state of the literature and provide insights for future research [ 32 ]. This review method was chosen to establish a broad understanding of the youth engagement literature and provide recommendations for researchers seeking to gain an overview of strategies for meaningful engagement. Narrative reviews also provide flexibility in terms of methodology (often based on the subjectivity of the research team) [ 33 ] and are less formal than other types of knowledge syntheses (e.g., systematic reviews) [ 34 , 35 ]. This review methodology allowed the research team to prioritize and integrate the perspectives of youth into the synthesis of information. Aims 1 to 4 were addressed in Phase 1. Aim 5 was not initially identified as an objective by the research team, and was therefore not included in the review of the literature. Upon establishment of the YAC, youth advisors deemed personal reflections on the benefits and impact of youth engagement from their perspectives critical to the manuscript.

Inclusion and Exclusion Criteria

Articles included in this narrative review met the following primary inclusion criteria: 1) published in English language, 2) published prior to April 2023, 3) focused on youth engagement in health research, and 4) described key types of youth engagement strategies (Aim 1), youth engagement frameworks (Aim 2), barriers to youth engagement (Aim 3), or recommendations for youth engagement (Aim 4). For the purposes of this review, ‘youth’ was defined as individuals between the ages of 15 to 24 years old, which is consistent with the definition provided by the United Nations [ 11 ], and ‘youth engagement’ was defined as the involvement of young people within this age range in research processes. This population was chosen for the focus of this review as the needs of youth are often distinct from children and adults due to their unique developmental stage (e.g., navigating healthcare transitions, increasing autonomy, etc.) [ 12 , 13 ]. Articles from any geographic location were included. Grey literature, websites, and non-peer reviewed research works (e.g., conference abstracts, theses) were also included using the same criteria as above.

Search Strategy and Synthesis

The search terms ‘youth engagement’ and ‘health research’ were searched in Google Scholar, PubMed, Web of Science, Scopus, and PsycInfo. Articles were hand-searched by members of the research team and selected according to the inclusion criteria above. Reference lists of relevant articles were also scanned. While other knowledge syntheses (e.g., systematic or scoping reviews) review all works identified by the literature search, narrative reviews do not aim to be inclusive of all literature available on a given topic [ 36 ]. As such, our review of the literature was concluded once we felt that sufficiency was achieved, which was characterized by reviewing works that yielded recurrent concepts. Additionally, the literature was reviewed iteratively following feedback from youth advisors who critically reviewed the narrative review manuscript. Some aspects of the manuscript were deemed critical to expand upon by youth advisors, and literature was reviewed again accordingly.

Relevant peer-reviewed and non-peer reviewed literature was organized and summarized descriptively according to study aims 1 to 4. Barriers to youth engagement were organized into individual-, organizational-, and systems-level. Recommendations for youth engagement were organized into common overarching themes.

Phase 2: Collaboration with Youth Advisory Council

The research team identified the criticality of collaborating with youth themselves in the review, formatting, and presentation of findings from the narrative review. As the review was being conducted and written, the research team began recruiting a group of youth advisors to contribute their perspectives, experiences, and recommendations for the manuscript. The development and procedural aspects of the YAC as they relate to the review are described below and in Fig.  1 . The operation of the YAC was guided by the McCain Model of Youth Engagement [ 31 ] and the Canadian Institutes of Health Research’s (CIHR) Patient Engagement Framework [ 1 ]. These frameworks, which prioritize reciprocity, respect, mutual learning, flexibility, and mentorship, supported the use of youth-driven and adaptable engagement strategies throughout the project [ 1 , 31 ]. Specifically, the research team employed engagement practices including co-building of a terms of reference document, inviting YAC members to co-chair meetings to foster mutual learning, and offering YAC members a menu of options for contribution, that aligned with the principles outlined in these models [ 1 , 31 ]. Aims 3 (i.e., identifying barriers to youth engagement) and 4 (i.e., summarizing recommendations for youth engagement) were expanded upon by the YAC in Phase 2. As described above, Aim 5 (i.e., benefits and impact of engagement on youth themselves) was deemed crucial by members of the YAC and was exclusively addressed in Phase 2 of this project. It should be noted that while the YAC specifically contributed reflections to Aims 3–5, each member critically reviewed the manuscript and offered feedback as co-authors.

Recruitment of Youth Advisory Council Members

Recruitment for the YAC began in June 2023 through distribution of a recruitment poster via professional contacts (e.g., researchers conducting youth-engaged research, youth advisory council facilitators), social media pages, and email lists (e.g., patient-oriented research listservs, youth advisory council lists). Eligible youth advisors were Canadian youth between the ages of 15–24 years with an expressed interest in youth engagement in health research. Youth applicants completed a Google Form to describe their motivations to become involved and past experience, if applicable. To ensure a diverse range of perspectives, we considered age, sex/gender, race and ethnicity, geographic location, and a range of previous experiences with research (from limited to extensive) in our recruitment process. The research team received interest from 55 individuals, of which 17 were invited to complete a 30-min virtual interview co-led by a researcher and a youth research partner. Eleven youth were selected to join the YAC, and all accepted the team’s invitation to participate. The youth invited to compose the YAC predominantly had previous experience with health care, including as a patient, advocate, youth advisor, research participant, or research assistant. Having and/or disclosing a diagnosis of a chronic health condition was not a criterion for participation in the YAC. A collective discussion was held with youth advisors and it was determined that members preferred not to share their demographic information, though there was representation of members with varying ages, ethnicities, years of experience with engagement, and from different provinces. The research team consisted of female-identified researchers, clinicians, and trainees across interdisciplinary professional backgrounds (e.g., medicine, nursing, social work) with experience engaging youth in research and/or clinical care. As many team members do not have previous youth lived experiences in research and/or clinical care, we were committed to closely collaborating and amplifying youth voices in our research, recognizing that our work, interpretations, and applications to the broader community were limited by our non-experiential understanding of youth engagement in research. The composition of the research team and YAC allowed for critical reflection on the roles of positionality, intersectionality, power, and privilege within youth engagement. The team engaged in reflexive discussions about the importance of prioritizing equity and addressing discrimination in engagement, especially for youth with marginalized identities.

Scheduling and Meetings

In July 2023, a Doodle Poll link was sent out to all youth advisors to find three meeting times that could accommodate the majority of the youth advisors and research team. Subsequently, Microsoft Teams invites were sent via email, and meetings were recorded and transcribed for notetaking purposes.

Prior to each meeting, a meeting agenda and documents were sent for review. Meetings lasted between 1.5 and 2 h and were recorded for those who could not attend. Both the recording and the minutes were collated following each meeting and made available to all youth advisors. Prior to the first meeting, a draft terms of reference document (ToR) was distributed to all youth advisors for review. The ToR contained the purpose and expectations of youth contributing to the project. A preliminary draft of the narrative review was provided to each youth advisor for their consideration both in advance of and during the meetings. Throughout the meetings, a range of communication methods, including Jamboards, chat messaging, and online verbal discussions, were employed to enable youth to exchange ideas and actively facilitate discussions.

During the initial meeting, youth advisors were provided with guidelines aimed at creating a secure environment using a digital interactive whiteboard on Google Jamboard. To maintain confidentiality and facilitate continuous improvement, the youth advisors proposed and subsequently implemented an anonymous feedback form, accessible for youth to complete at their discretion. Subsequently, the youth advisors engaged in a collaborative ideation session to conceptualize their contributions to the synthesis. It was decided that a Slack channel would serve as the primary platform for communication among the youth advisors.

In the second meeting, the council deliberated on the ToR initially formulated by the research team, with the ToR subsequently revised to incorporate the feedback and insights provided by the youth advisors. Additions to the ToR from YAC members included greater options for compensation, strategies for addressing microaggressions, more clarity regarding YAC tasks, roles, and responsibilities, and rationale for selecting 11 advisors for the group. Following this, the group engaged in a comprehensive discussion centered on their reflections concerning the draft of the narrative review. This dialogue highlighted the identified gaps and obstacles associated with involving youth in research from YAC members’ perspectives, proposed recommendations for future research endeavors, and stressed the importance of integrating youth voices into the research process.

In the third meeting, the focus shifted towards the establishment of more focused working groups. These smaller working groups were structured to address specific aspects, including 1) the rationale behind the research (the “why”), 2) reflections on past experiences with youth engagement, 3) methodologies for engaging youth in the context of this review, and 4) formulating recommendations for future research endeavors. Youth advisors were invited to complete a form to rank their areas of interest in these four areas. Based on their ranked responses, working groups were formed and considered the alignment between youth advisor’s preferred method of contribution (e.g., developing visuals, writing a personal reflection, contributing to a table) and the specific topic of the working group.

During the fourth meeting, which was co-chaired by a research team member and a youth advisor (TL) who volunteered for this role, youth advisors and members of the research team reviewed written materials from each working group, discussed each section of the paper, and reached consensus on how the sections would be presented within the article. It was determined that youth advisor work would be combined with the existing narrative review and showcased using textboxes, figures, and tables.

Independent Working Groups

All youth advisors worked in four designated working groups over a 3-week period. Youth advisors communicated via Slack channels, email or personal messaging, with the research team available for support and guidance, as needed. Guidelines for authorship, methods of contributing to each section of the paper (e.g., brainstorming, making point form notes, developing figures), and suggestions on length/format were discussed at YAC meetings. Youth advisors were also provided with a series of resources on a collaborative drive to support their contributions to the review, including a youth-friendly guide to academic writing and examples of reports/journal articles co-authored by youth. All groups worked independently and provided finalized drafts to the research team prior to the fourth meeting.

Compensation

All youth advisors were compensated $25 per hour at the end of their involvement. All youth advisors tracked their hours with a maximum of 20 h. Youth advisors were able to track meetings, self-directed work, and all time dedicated to the project outside of meetings.

Methodology used to engage the Youth Advisory Council in the co-development of this article. Figure developed by the Youth Advisory Council

A total of 65 articles were included, of which 56 were peer-reviewed and 9 were non-peer reviewed. Of the peer-reviewed articles, 14 were qualitative studies, 12 case studies, 7 mixed-methods, 6 commentaries, 2 curriculum development studies, and 2 randomized controlled trials. Additionally, 13 syntheses were included ( n  = 7 unstructured literature reviews, n  = 3 scoping reviews, n  = 2 systematic reviews, n  = 1 scoping review protocol). Of the non-peer reviewed studies, 4 were websites and 5 were reports. A table is available in Appendix A displaying included article citations, categorization of peer-reviewed versus non-peer reviewed works, and study methods used.

In this section of the article, results pertaining to each of the five aims are presented. Aims 1 to 4 were addressed in Phase 1 of this project to outline types, frameworks, and barriers to youth engagement and summarize the literature’s recommendations on how to meaningfully engage youth. Aims 3 and 4 were addressed in collaboration with youth advisors in Phase 2 to highlight the benefits and barriers of youth engagement and recommendations from the perspectives of the youth advisors on meaningful youth engagement. Aim 5 was identified as a priority for youth advisors and their reflections are provided on the benefits and impact of engagement on youth themselves.

Aim 1: Key Types of Youth Engagement

There are several approaches to youth engagement in health research, which are based on the aim(s) of a given project, resources available, and preferences of youth themselves (shown in Table  1 ) [ 37 ]. Youth may be involved as research participants , such as completing a survey or participating in a focus group [ 24 , 31 , 38 , 39 , 40 ]. Youth may also take on advisory or consultation roles , where they provide input on the research scope, recruitment strategies, and methods, as well as reviews analyses, results, and/or manuscripts, from which the researcher may decide if or how to implement their suggestions (e.g., advisory councils) [ 24 , 38 , 39 , 40 , 41 ]. Youth may assume co-production roles , which actively involves youth in the development of research objectives and design, funding proposals, study informational materials, recruitment of participants, data collection instruments, co-facilitating focus groups/interviews, analysis of data, presentations, manuscripts, and knowledge translation activities [ 10 , 24 , 41 ]. This may also be referred to as partnership , which involves active collaboration of youth with researchers to support and/or lead aspects of the project (e.g., collaborate on research methodology, lead certain research activities) [ 24 , 31 , 38 , 39 , 40 ]. Finally, youth-led research refers to projects that are entirely led by youth, with or without the support of an adult researcher [ 24 , 31 , 38 , 39 , 40 ].

A recent systematic review identified youth engagement practices in mental health-specific research, highlighting the most common youth engagement types were advisory roles, where youth were often involved in providing feedback on the research topic, analysis of qualitative data, and dissemination of findings, with less emphasis placed on co-production methods [ 10 ]. Authors identified one study which utilized a youth-led participatory action research approach in the mental health research setting, which is a power-equalizing methodology involving collaborative decision-making and viewing youth as experts based on their own lived experience [ 44 , 46 , 47 , 48 ].

Aim 2: Frameworks for Youth Engagement

A significant body of literature has proposed various frameworks for supporting patient engagement in research, with research teams more recently developing frameworks specific to youth engagement [ 49 ]. For example, the Youth Engagement in Research Framework , designed by youth and researchers at the University of Manitoba, identified seven strategies to create a culturally-inclusive research environment for youth to meaningfully contribute to the research process [ 50 ]. Strategies included 1) understanding motivations of youth to engage in research, 2) sharing intentions to implement research findings, 3) supporting diverse youth identities in engagement, 4) actively addressing the barriers to youth engagement, 5) reinforcing that engaging in research is a choice, 6) developing trusting relationships through listening and acknowledging contributions, and 7) respecting different forms of knowledge creation, acquisition, and dissemination [ 51 ].

Youth engagement has also been achieved through health research communities of practice , a framework aimed at promoting a space for youth to develop identity, build capacity for youth to develop research, communication, and advocacy skills, lead projects, and develop relationships with the research team [ 52 , 53 , 54 ]. A Canadian research team developed IN•GAUGE®, a health research community of practice which aims to promote collaboration between youth, families, researchers, and policy makers and support the development of strategies to improve child and family health [ 51 , 52 ]. This program uses Youth and Family Advisory Councils, a group of youth and family members who contribute to the direction of the project and provide input on research methods based on their own lived experiences [ 51 ]. This community of practice has built a robust network of youth and family researchers, which helps alleviate some challenges associated with finding youth to support a project.

Researchers at the Centre for Addiction and Mental Health (CAMH) in Toronto, Ontario, Canada have developed the McCain Model for Youth Engagement, which is specific to mental health populations [ 55 ]. This model is based on flexibility (i.e., the youth and research team work together to co-design deliverables/timelines and develop skills that are relevant to the youth’s goals), mentorship (i.e., in the development of research skills, incorporating youth strengths into research design), authentic decision-making (i.e., avoiding ‘tokenism’, carefully considering and implementing youth feedback), and reciprocal learning (i.e., both youth and researchers are ‘teachers’ and ‘learners’). Based on the implementation of the McCain Model, researchers propose that youth engagement should be established when research projects are in the early planning stages, reflect on organizational-level barriers to youth engagement and plan policies and practices around them, and train researchers on the value of engaging youth [ 55 ].

A recent commentary made key recommendations for youth engagement in the context of the COVID-19 pandemic [ 30 ]. First, authors propose adapting youth engagement strategies to facilitate rapid decision-making, such as utilizing connections with pre-existing youth advisory councils, providing additional compensation, and offering opportunities for online participation. Additionally, they suggest leveraging virtual platforms for youth engagement methods, while ensuring that youth with disabilities or chronic health conditions are offered appropriate accommodations. Finally, subsidies or shared tablets or computers may be offered to youth researchers to ensure virtual platforms are accessible and reduce technological barriers [ 30 ].

Aim 3: Barriers to Engaging Youth in Research

A series of barriers for engaging youth in health research have been identified in the literature through a narrative review. These barriers are grouped into individual, organizational, and systemic factors and are presented below. In Table  2 , a summary of these barriers, as outlined in the published literature is presented. Youth advisors were invited to review this list and provide their own expansions, reactions, and additions based on their knowledge and experiences. A key limitation in the exploration of barriers related to youth engagement is that much of the existing literature does not specify what level of youth enagagement was being employed.

Individual-Level Barriers: Youth-Specific

Many youth may be discouraged from engaging in research due to their own negative lived experiences with the healthcare system. For example, youth may be distrustful of adult clinicians and researchers, particularly those who may have had traumatic medical experiences (e.g., lengthy hospital/intensive care unit admissions, surgeries, invasive treatments), complex and chronic healthcare conditions, or marginalized identities [ 56 ]. While understanding these perspectives and experiences is crucial to improve health service structures and delivery, they may not be captured without carefully considering and applying appropriate youth engagement methods. Similarly, those with negative previous experiences with youth engagement may feel tokenized or patronized, particularly if they did not feel authentically valued or listened to by the research team [ 57 , 59 ].

Youth characteristics may also result in exclusion from youth engagement and/or exacerbate existing barriers to partnering, particularly the presence of physical disabilities, visual/hearing impairments, intellectual disabilities, neurological conditions, mental health conditions, and/or socioeconomic factors [ 69 , 70 , 78 ]. Youth with disabilities may experience mobility impairments preventing them from easily attending research team meetings, may require additional time and supports to complete research tasks, or utilize assistive devices (e.g., communication tools) [ 69 , 70 , 78 ]. Low literacy levels and/or language barriers may also make engagement inaccessible without appropriate accommodations [ 78 ].

Furthermore, youth priorities may impact willingness to engage in research. Specifically, youth may not feel valued without formal recognition for their contributions, such as financial compensation, volunteer hours, authorship on manuscripts, or opportunities to present research at academic meetings [ 59 ]. They may also not want youth engagement opportunities to infringe on their leisure or personal time, or may be hesitant to engage in projects with long time commitments [ 61 ]. A study highlighting experiences with engaging youth with Bipolar Disorder as peer researchers identified that attrition was also affected by illness relapse, as well as difficulties balancing the responsibilities of the research project with post-secondary education and employment commitments [ 44 ].

Individual-Level Barriers: Adult Researcher-Specific

Research team members may also hold specific beliefs or attitudes towards youth engagement. For example, some researchers may feel anxious about losing control over the research process, may not see youth as experts themselves, or hold biases about the value of youth perspectives [ 24 ]. Researchers may also perceive youth engagement as an added layer of complexity, fear that engagement may impact the scientific rigor of the research design, or be concerned that youth engagement may negatively impact the research quality [ 24 , 26 , 27 , 79 , 80 , 81 ]. Further, some studies have highlighted that researchers do not feel equipped with the skills or knowledge to engage and communicate with youth, or to design studies using youth engagement principles [ 24 , 62 ]. Finally, researchers may experience challenges navigating differing priorities between youth partners and members of the research team. For example, researchers may prioritize more traditional markers of research success, including peer-reviewed manuscripts and grant proposals which often require rapid turnaround times, and be concerned that youth engagement may add to the timeline of a project [ 24 , 62 ].

Organizational-Level Barriers

As youth engagement has emerged as a best practice recently, many academic institutions do not yet have the infrastructure or resources to support engagement opportunities [ 24 ]. While examples of capacity-building programs for youth co-researchers exist in the participatory action research literature [ 82 ], there is a need for further development of training resources to support youth who are engaging in health research [ 83 ]. Formal education on youth engagement is often not included in research training programs, despite many granting agencies recently making changes to require and/or promote patient engagement considerations in funding applications [ 1 , 62 ]. Further, many organizations have not adopted policies to outline best practices for youth engagement, and academic workplace culture also may not yet value youth engagement, resulting in limited willingness to adapt research practices [ 24 , 62 ]. These factors may exacerbate existing difficulties with securing sufficient time and resources to support relationship-building between youth partners and adult members of the research team, which is a commonly cited challenge with youth engagement [ 26 , 27 , 84 , 85 ].

System-Level Barriers

Youth with complex health conditions, such as those with developmental disabilities, often experience stigma and exclusion from clinical research [ 69 , 70 , 71 , 72 ]. Specifically, research teams may inaccurately perceive youth with chronic medical conditions as ‘vulnerable’ or ‘fragile’, thus deeming them unable or incapable to contribute meaningfully or complete study-related tasks [ 24 , 70 , 72 , 73 , 86 , 87 ]. Youth with marginalized identities, including Black, Indigenous, and 2SLGBTQIA+ youth, often experience discrimination within the healthcare system, with several studies suggesting mistrust of research institutions, researchers, and healthcare systems stemming from community experiences of mistreatment in research as the most significant barrier to participating in clinical research [ 65 , 66 , 67 , 68 ]. Furthermore, youth from racial and ethnic minorities often receive less information and attention from healthcare providers compared to white youth, potentially limiting awareness of the opportunities and/or value in contributing to health services research [ 68 , 88 ]. Notably, limited literature has considered the impact of other social and structural determinants of health on youth engagement, including income, housing, and geographic location.

Youth may also be apprehensive to share their perspectives, critiques, or suggestions for improvement with adult researchers due to inherent power imbalances [ 74 , 75 , 76 , 77 ]. Given the differences in power between adults and youth, as well as between patients and clinicians/researchers, youth engagement may involve researchers dominating the conversation, thus preventing equal contribution and collaboration. Ultimately, these dynamics have the potential to produce harmful cultures or practices for youth entering research environments, especially among youth from marginalized groups. These barriers and possible outcomes resulting from these power imbalances are elaborated on in Table  2 .

Finally, researchers themselves may face barriers as many major funding agencies have yet to prioritize or incorporate youth engagement in their strategy, resulting in limited funding opportunities to support this type of engagement work or a lack of dedicated time and resources for researchers to build relationships with youth [ 73 ]. Of note, the CIHR has developed a Strategy for Patient-Oriented Research, and requires grant proposals in certain funding streams to utilize patient engagement methods [ 1 ]. However, this is not yet universally implemented across funding agencies and does not guide engagement with youth specifically. Additionally, funding agencies often have strict eligibility and assessment criteria, including level of education and evidence of prior research and scholarly outputs, which may inherently exclude youth researchers from participating in funding applications. Finally, granting agencies have funding deadlines which may not accommodate the flexibility needed to build meaningful relationships with youth partners.

Further, while some academic journals have incorporated mandatory reporting on stakeholder and patient involvement in the research design, this is not a standard of practice, and many of these journals are engagement-focused [ 55 , 62 , 89 ]. Finally, there is a lack of consensus around how to report on engagement practice and outcomes of engagement across studies, which contributes to inconsistencies in what constitutes meaningful and effective engagement. While tools are emerging to enhance transparency in reporting engagement, including the Guidance for Reporting Involvement of Patients and the Public (GRIPP), no tools exist for youth engagement specifically [ 90 , 91 ]. Barriers to engaging youth in health research from both the literature and the perspectives of the youth advisors involved in this project are summarized in Table  2 .

Aim 4: Facilitators and Recommendations for Youth Engagement

Many studies have highlighted recommendations to improve the implementation of youth engagement across research contexts. Canada’s Youth Policy was created in 2020 to develop a greater understanding of the experiences and perspectives of youth living in Canada [ 92 ]. As part of this, funding opportunities through Canada’s major funding body for health research (CIHR) have begun to focus on providing meaningful opportunities to empower youth in research such as the Healthy Youth Initiative [ 93 ]. Our study findings are in line with these newly implemented policies as they lay the foundation for researchers on how to meaningfully engage youth in health research. In the following section, current strategies, strengths, and facilitators in the health sector that can support youth engagement are outlined, along with areas for improvement. As in Table  2 , these recommendations were reviewed and expanded upon by the YAC in Table  3 .

Engaging Youth from Structurally Marginalized Populations

Engagement of youth with intersecting marginalized identities, such as Black, Indigenous, or 2SLGBTQIA+ youth, and youth with disabilities, language/communication barriers, immigrants and refugees, experiencing homelessness, or living in foster care, may involve several unique considerations [ 31 ]. Research teams should engage both youth and researchers from communities with lived experience to provide insights and support engagement strategies [ 31 ]. It is also important to recognize that engaging youth from Indigenous communities may involve a unique approach. Practices adopted by Indigenous-led organizations may exist that focus on youth empowerment that are specific to their communities. For example, the ‘Indigenous Youth Voices Report ’ produced by The Yellowhead Institute at Toronto Metropolitan University in collaboration with the First Nations Child and Family Caring Society outlined requirements for engaging and conducting research with and by Indigenous youth, which included themes such as ensuring research is accessible, uplifting Indigenous youth to co-create research, relationship-building and reciprocity, and using holistic approaches to ensure Two-Spirit, 2SLGBTQ+ youth, and Elders are meaningfully included in research approaches [ 107 ]. Further, a recent study showed evidence supporting the use of web-conferencing technology to engage Aboriginal and Torres Strait Islander in Australia through co-facilitation of an Online Yarning Circle, an Indigenous methodology that involves sharing, listening, interpreting, and understanding information in an informal setting [ 108 , 109 ].

Additionally, teams should partner with researchers who have experience working with youth from these populations. Women’s College Hospital in Toronto, Ontario, Canada has recently developed an innovative and inclusive patient engagement model, called Equity-Mobilizing Partnerships in Community (EMPaCT) , designed to highlight the priorities and needs of diverse communities informed by the perspectives of individuals with lived experience [ 110 , 111 ]. Research teams can consult this service to identify approaches to advance equity and social justice within their projects [ 110 , 111 ]. Researchers may also consider using the ‘Valuing All Voices Framework’ , which is a trauma-informed, intersectional framework that guides researchers on how to embed a social justice and health equity lens into patient engagement, with the goal of enhancing inclusivity within health research [ 112 ]. This framework is based on four core concepts, including trust (e.g., focusing on resilience/strength rather than challenges, allowing time to build relationships), self-awareness (e.g., practicing honesty, creating safe spaces), empathy (e.g., allowing the space to share stories), and relationship building (e.g., share experiences, promote ongoing communication, show awareness and sensitivity towards cultural differences) [ 112 ].

All research team members engaged in this work should be offered training on best practices for communicating and engaging with specific populations [ 31 ]. Appropriate accommodations, such as communication tools, accessibility aids, and financial support for involvement, should be offered consistently to optimize engagement of youth with diverse experiences and perspectives [ 78 ]. While not specific to youth engagement, the National Health Service in the United Kingdom has a guidance document which outlines considerations to increase diversity in research participation, including a focus on building trust, conducting research in places familiar to participants, developing accessible recruitment materials, and incorporating peer-led activities [ 113 ]. Finally, researchers should adhere to existing ethical standards for specific marginalized communities, such as the CIHR guidelines for conducting research involving Indigenous people [ 114 ].

Evaluation of Youth Engagement

Robust evaluation of youth engagement strategies is a core component of youth involvement in research and should be used to enhance implementation of principles in research, provide feedback, and ensure researchers are held accountable in upholding best practices [ 104 , 115 ]. While there are no empirically-tested tools for the evaluation of youth engagement in research, qualitative, quantitative, and mixed methods may be used, including the Youth Engagement Guidebook developed through the CAMH [ 31 ], the Public and Patient Engagement Evaluation Tool (PPEET) [ 116 ], and the Patient Engagement in Research Scale (PEIRS) [ 117 ]. These instruments are co-designed by patients and are used to evaluate the quality of engagement strategies from the perspective of patient partners themselves [ 117 ]. It should be noted, however, that empirically-tested tools for measuring youth-adult partnerships more broadly do exist [ 118 , 119 , 120 ] and could likely contribute useful information to the measurement of youth engagement in research, specifically. It is also recommended to evaluate the impact of youth engagement from the researchers’ perspectives, which may include reflecting on how valuable the team considered youth partners to be, the extent of youth involvement, and the impact of youth engagement on project outcomes [ 31 ]. Alberta Health Services has developed a resource tool kit containing survey instruments to assist research teams with routine evaluation of their collaboration skills [ 121 ]. Research teams should carefully evaluate and iteratively modify their engagement strategies to ensure youth are meaningfully involved.

Capacity Development

Several independent training programs exist to educate researchers, community stakeholders, patients, youth, and caregivers on engaging patients in health research, including the Patient and Community Engagement in Research (PaCER) program [ 122 ], McMaster University Family Engagement in Research (FER) course [ 123 ], Patient-Oriented Research Curriculum in Child Health (PORCCH) [ 124 ], and Partners in Research (PiR) [ 125 ]. Further, a recent study was conducted to develop simulations in collaboration with interdisciplinary stakeholders to train researchers on how to engage youth in childhood disability research [ 126 ]. These simulation videos focused on aspects of the research process where challenges may arise based on previous experiences of youth and family advisors [ 126 ].

Aim 5: Youth Advisor Reflections on the Impact of Youth Engagement

While describing the evidence-based benefits of youth engagement in research within the literature was beyond the initial scope of the narrative review, youth advisors deemed it critical to present their experiences regarding their motivations for becoming involved in research and the impact of research opportunities on youth. Two youth advisors reflected on the benefits of youth engagement in research from their own experiences and collectively developed the content displayed in Table 4 in a small working group. The same two advisors considered their prior involvement in research and outlined the impact of engagement on their lives in Table 5 . They were invited to share any aspects of their experiences they felt were important to communicate with a broad audience, and selected the format and method of organization of their reflections. These reflections offer unique and valuable insights into the importance of creating opportunities for meaningful and conscientious youth engagement in research using youths’ own language.

Conclusions, Limitations & Future Directions

This narrative review provides an overview of the current literature in youth engagement in health research in combination with the perspectives of youth advisors themselves. The research team and YAC collectively identified key types and frameworks for youth engagement, synthesized several barriers and recommendations for implementing youth engagement, and provided critical reflections on the impact and benefits of youth engagement in the youth voice. While many evidence-based frameworks exist to incorporate and evaluate patient engagement in research, gaps remain in the identification of the best practices for youth engagement specifically [ 49 ]. Much of the available youth engagement literature has focused on involving youth in mental health research, with limited evidence regarding best practices to engage youth with chronic physical health and neurodevelopmental conditions [ 10 , 21 , 24 ]. Further, a paucity of evidence has highlighted the barriers and best practices to engaging youth with low income, those experiencing homelessness, and rural/remote communities in health research.

Limitations

This article employed narrative review methodology to provide an overview of existing research in youth engagement in research. A more structured and systematic review and critical appraisal of included literature by multiple independent reviewers was not within the scope of this paper, which may have excluded relevant literature. The information presented in this article may serve as a foundation for a systematic review of the literature on this topic, which our research team endeavours to complete in the future. Additionally, the search was limited to articles published in English, which may have excluded relevant literature, including potential barriers or recommendations specific to non-English speaking youth. Future research should consider a fulsome exploration of youth engagement strategies, barriers, and recommendations published in languages other than English. Demographic information of youth advisors was not collected or presented as part of this article due to YAC member preference. In addition, a previous diagnosis of a chronic health condition and/or lived experience as a patient was not a criterion for inclusion in the YAC. Rather, youth advisors had a diverse set of experiences with health care (e.g., as patients, advocates, previous youth advisors, research assistants, and/or research participants). Furthermore, youth members were self-selected by the research team, and not recruited from established youth organizations with elected representatives. As such, we are unable to determine whether the youth composing the YAC are representative of the target population. Future studies could examine how demographic characteristics and/or prior experiences with engagement influence youths’ perceptions of barriers, enablers, and recommendations for youth engagement.

Future Directions

To address many of the barriers identified in this review, further work is needed at the organizational- and systems-levels to build policies and programs that support youth engagement in research. As such, youth advisors developed a call to action for researchers and their hopes for the future of youth engagement in research, available in Table 6 . Finally, robust studies are needed to develop and validate youth engagement evaluation tools [ 31 ].

Availability of data and materials

No datasets were generated or analysed during the current study.

Abbreviations

Youth Advisory Council

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Acknowledgements

The authors would like to acknowledge the Edwin S.H. Leong Centre for Healthy Children, The Hospital for Sick Children for supporting this work through the Leong Centre Studentship Award.

This work is supported by the Leong Centre Studentship Award received by Katherine Bailey and Dr. Alene Toulany. The other authors received no additional funding.

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Katherine Bailey and Brooke Allemang contributed equally as co-primary authors.

Authors and Affiliations

Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada

Katherine Bailey & Kristin Cleverley

Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada

Katherine Bailey & Alene Toulany

Child Health Evaluative Sciences, SickKids Research Institute, Toronto, ON, Canada

Brooke Allemang

Department of Pediatrics, Faculty of Medicine, University of Toronto, Toronto, ON, Canada

Ashley Vandermorris & Alene Toulany

Division of Adolescent Medicine, The Hospital for Sick Children, 555 University Ave, Toronto, ON, M5G 1X8, Canada

KITE, Toronto Rehabilitation Institute, University Health Network, Toronto, ON, Canada

Sarah Munce & Vjura Senthilnathan

Rehabilitation Sciences Institute, University of Toronto, Toronto, ON, Canada

Department of Occupational Science and Occupational Therapy, University of Toronto, Toronto, ON, Canada

Sarah Munce

Lawrence S. Bloomberg School of Nursing, University of Toronto, Toronto, ON, Canada

Kristin Cleverley

Margaret and Wallace McCain Centre for Child, Youth & Family Mental Health, Centre for Addiction and Mental Health, Toronto, ON, Canada

Faculty of Medicine and Dentistry, University of Alberta, Edmonton, AB, Canada

Cassandra Chisholm

Department of Psychology and Neuroscience, Dalhousie University, Halifax, NS, Canada

Neurosciences and Mental Health, SickKids Research Institute, Toronto, ON, Canada

Cedar Davidson

Michael De Groote School of Medicine, McMaster University, Hamilton, ON, Canada

Asil El Galad

McGill University, Montreal, QC, Canada

Dahlia Leibovich

Department of Health Sciences, University of Ottawa, Ottawa, ON, Canada

Trinity Lowthian

McMaster University, Hamilton, ON, Canada

Jeanna Pillainayagam

Collaborator, Toronto, ON, Canada

Harshini Ramesh, Paul Siska & Madison Snider

Patient Partner, Canadian Arthritis Patient Alliance, Toronto, ON, Canada

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KB synthesized the literature, drafted the initial manuscript, and approved the final manuscript as submitted. BA provided youth engagement expertise, facilitated youth advisor meetings, revised the manuscript, and approved the final manuscript as submitted. CC, EC, CD, AEG, DL, TL, JP, HR, AS, PS, MS contributed their perspectives and expertise as part of the Youth Advisory Council, drafted components of the manuscript, revised the manuscript, and approved the final manuscript as submitted. BA, AV, SM, KC, VS, and AT conceptualized the design and methods of this study, revised the manuscript, and approved the final manuscript as submitted.

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Bailey, K., Allemang, B., Vandermorris, A. et al. Benefits, barriers and recommendations for youth engagement in health research: combining evidence-based and youth perspectives. Res Involv Engagem 10 , 92 (2024). https://doi.org/10.1186/s40900-024-00607-w

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Defining mental health literacy: a systematic literature review and educational inspiration

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Research Article

How position in the network determines the fate of lexical innovations on Twitter

Roles Conceptualization, Data curation, Methodology, Visualization, Writing – original draft

* E-mail: [email protected] (LT); [email protected] (J-PC)

Affiliation ICAR laboratory (UMR 5191), École Normale Supérieure de Lyon, France

Roles Conceptualization, Funding acquisition, Supervision, Validation, Writing – review & editing

Affiliations ICAR laboratory (UMR 5191), École Normale Supérieure de Lyon, France, LIDILEM laboratory (EA 609), Université Grenoble Alpes, France

Roles Conceptualization, Data curation, Funding acquisition, Methodology, Project administration, Supervision, Validation, Writing – review & editing

Affiliations ICAR laboratory (UMR 5191), École Normale Supérieure de Lyon, France, IXXI, Complex Systems Institute, Lyon, France

• Louise Tarrade, 
• Jean-Pierre Chevrot, 
• Jean-Philippe Magué

• Published: September 3, 2024
• https://doi.org/10.1371/journal.pcsy.0000005
• Peer Review
• Reader Comments

This study analyzes the diffusion of lexical innovations on Twitter to understand how the social network position of adopters impacts their success. Looking at both successful and failed neologisms, we categorize them into "changes" which become established and "buzzes" which decline over time. Using a corpus of 650 million French tweets, we reconstruct user networks and characterize adopters of innovations during different diffusion phases based on prestige, centrality, clustering, and external ties. In the early innovation phase, change and buzz adopters have similar peripheral profiles. During propagation, changes spread to prestigious, central individuals while buzzes do not, which predicts their eventual success or failure. By the establishment phase, changes reach highly central users with closer external ties. The results align with sociolinguistic theories about weak ties for innovation and strong ties for establishment. Additionally, logistic regression models based on early adopter profiles can predict the fate of innovations. This work sheds light on the diffusion dynamics of online lexical innovations and the crucial role of user network factors.

Author summary

In everyday language, words are constantly being created, and these words either persist or disappear. Although this phenomenon has been the subject of much linguistic research, the factors which influence the fate of a new word remain largely unknown, partly because of the difficulty of recording spontaneous language use over time. Examining the varieties of language used on social media allows us to overcome these limitations. We collected over 650 million tweets written in French, covering several years of ordinary interactions between 2.5 million users. We also collected the network of social links between these users. We identified nearly 400 words that appeared in the corpus between 2012 and 2014, and tracked their diffusion over 5 years within the network of users. Some of these words lead to changes, while others generate only ephemeral buzz. By looking at the position in the network of users who adopt these innovations, we show that words adopted by users who are more central in their community and easily in contact with other communities become established in the language, and vice versa. Thus, the position in the network of speakers who adopt these words is enough to predict their fate.

Citation: Tarrade L, Chevrot J-P, Magué J-P (2024) How position in the network determines the fate of lexical innovations on Twitter. PLOS Complex Syst 1(1): e0000005. https://doi.org/10.1371/journal.pcsy.0000005

Editor: Jennifer Badham, Durham University, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND

Received: January 21, 2024; Accepted: July 9, 2024; Published: September 3, 2024

Copyright: © 2024 Tarrade et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: Data is available on the following repository Ortolang, that is a French government supported infrastructure for the language data. url: www.ortolang.fr/market/corpora/sosweet .

Funding: J.-P. M., J.-P. C. and L.T. are grateful to the ASLAN project (ANR-10-LABX-0081, https://aslan.universite-lyon.fr/ ) of the Université de Lyon for its financial support within the French program "Investments for the Future" operated by the National Research Agency (ANR). The data collection has been supported by the SoSweet ANR project (ANR-15-CE38-0011-03, https://anr.fr/ ) attributed to J.-P. M. and J.-P. C. The authors are also grateful to University of Grenoble Alpes and Ecole Normale Supérieure de Lyon for the support for publication. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Previous work

Since language evolves within a social context, its usage diversifies according to the heterogeneity and changes in society, and sociolinguistic variation is omnipresent. Different variants of the same form are constantly in competition at all levels of the linguistic structure. Every human being is able to vary his or her way of speaking or to opt for a particular variant depending on whom he or she is addressing, for what purpose and in what context, with a varying degree of consciousness. Variation is the phenomenon observed in synchrony, change is its outcome from a diachronic point of view: "all change (with the exception of certain lexical innovations) results from a situation of variation—but not all variation leads to change [our translation]" (p. 23) [ 1 ].

As theorised by Weinreich et al. [ 2 ], variationist sociolinguistics is mainly concerned with explaining the mechanisms of linguistic change and establishing the influence of linguistic, cognitive, cultural and social factors on change. While external pressure and the influence of ones social groups (e.g. class, race, gender) have been shown to be explanatory factors for variation, social ties between individuals are also an important parameter to take into account when looking at the dynamics of the circulation of change. Thus, in his survey in Philadelphia Labov [ 3 , 4 ] establishes a significant correlation, particularly for women, between the use of advanced forms of the sound changes in progress and the structure of the individual’s network. Thus, the people leading the change are people with a certain local prestige, having both a high density of interaction in their local block, but also a large proportion of their friends living outside it. For their part, Milroy & Milroy [ 5 , 6 ] were particularly interested in the influence of network structures on the circulation of sociolinguistic variants. Significant results concerning the relation between linguistic change and network emerge from their study of Belfast. First, they confirm and complete Granovetter’s contribution [ 7 ] on the importance of weak ties in the transmission of innovations by defining innovators as people with weak ties, peripheral to communities. The denser a network, and therefore the stronger its ties, the more conservative it is regarding the vernacular local norms and the more resistant it will be to change. In contrast, speakers with weaker and more peripheral ties will be less close to these norms and more exposed to external variants. The different variants thus pass from one linguistic community to another through peripheral individuals acting as bridges between the groups. However, according to Milroy & Milroy [ 5 ], the adoption of a variant by individuals who are both central and well-established in the community is essential for its establishment within the community. In addition, before central members adopt it, the variant must be transmitted through a large number of ties as it is less socially risky to accept an innovation that is already widely spread at the margins of the community.

While these studies have considerably highlighted the process of change circulation, they have also revealed a few limitations such as the limited number of speakers considered or the lack of continuous, homogeneous longitudinal data implying a synchronic approach to linguistic change—a process which, by nature, extends over time. Furthermore, sociolinguists historically favoured field surveys—inspired by the sociological approaches—often focusing on phonetic variables.

The diachronic study of linguistic change has thus long been left to the domain of historical linguistics which, by definition, is concerned with long-term changes, often spanning several centuries, and generally of a morphosyntactic nature. Moreover, the corpora on which it relies are written corpora often reflecting a language much more standardised than oral language. Emerging with the digital age, computational sociolinguistics [ 8 ], applied to social media, allows us to study less standardised varieties of language, which are highly propitious to variation and innovation, both synchronically and diachronically. The focus on media has increased the amount of attention paid to the lexicon, and work on lexical variation and diffusion has flourished [ 9 – 18 ]. Observations of lexical changes are indeed more tractable on a shorter time scale, "the lexicon [being] the component where change is the quickest (new words are constantly being created), and grammar the most stable, change taking place over a long period of time [our translation]" [ 19 ]. Furthermore, one can assume that the acceleration and multiplicity of exchanges on social media induce a phenomenon pointed out by Lorenz-Spreen et al. [ 20 ], namely that the ever-faster dissemination and consumption of information leads to a decrease in the collective attention span given to it. Consequently, the ever-increasing mass of content can lead to an acceleration of the diffusion process of linguistic innovations, whose fate would also be sealed more quickly.

Computational sociolinguistics has leveraged on social interaction data to address the relationship between the diffusion of linguistic innovations and the network structure connecting individuals. Particular attention has been paid to the importance of weak ties in the introduction of innovation and strong ties in their establishment within the language community. For instance, the innovative nature of information transmitted via weak ties and the greater influence of strong ties has been confirmed by a large-scale study on the transmission of information on Facebook, involving 250 million users [ 21 ]. At the linguistic level, studies on a short time scale on Twitter and Reddit have shown that the innovators, the people who introduce new linguistic forms, are individuals who have many weak ties and who are more central to the network [ 14 ]. This is in line with both Milroy’s definition of innovators [ 5 ] and Labov’s definition of linguistic change leaders [ 3 , 4 ] in terms of their centrality. On the other hand, it has been shown that people with strong ties have more influence than others [ 12 , 14 ].

The belonging of individuals to an area of high density in their local network generally results in the maintenance of vernacular forms [ 22 ] and, in the same way, the more isolated a community is from others, the more its members converge linguistically [ 23 ]. On the other hand, it is likely, as Milroy & Milroy [ 5 ] suggest, that the adoption of an innovation by individuals strongly embedded in local groups facilitates the spread of the innovation through these more cohesive subgroups and the establishment of this innovation in the linguistic community more generally. Multi-agent simulations effectively showed that while the absence of solitary and very peripheral members in a network leads to a lack of innovation, the absence of people defined as leaders (highly connected agents) prevents variants from stabilizing as norms [ 24 ].

Other studies have examined the relationship between some structural properties of the network and the circulation of innovations. At the egocentric network level for instance, individuals with smaller networks are more linguistically malleable [ 25 ] and are therefore more likely to adopt a linguistic innovation. At the level of the network as a whole, the study of the diffusion of neologisms has showed that a larger network as well as dense connections within and between communities increase the number of new words as well as their chances of survival, in contrast to communities fragmented into many local clusters [ 26 ]. The diffusion of a neologism is also more likely to succeed if it is not limited to a few subgroups of speakers but rather spreads across different speaker communities [ 17 ].

As we have seen, variationist sociolinguistics has highlighted the fact that the position occupied by speakers in their community can play an important role in the diffusion of linguistic change. In brief, two main theories have emerged about individuals driving change in their local networks: one defining them as people with weak ties, peripheral to their community [ 5 ] and the other as people central to their community, but with many ties outside it [ 4 ]. As the starting point of linguistic change is complicated to identify, it is likely that these two descriptions simply refer to two different phases in the diffusion of linguistic change. Computational studies on this issue have relied mainly on social media corpora to examine the link between networks and the diffusion of change on a larger scale. In addition to the impact of certain structural properties of the network on the diffusion of linguistic innovations, they have mainly confirmed the role of the margin and weak ties in the introduction of innovations, as well as the influence of strong ties on their stabilization. They have also shown the conservative attitudes towards vernacular norms of more closely-knit groups. Fig 1 schematises a hypothetical toy social network formed by 18 individuals, each belonging to one of the three communities represented by the colours green, blue, and yellow. Speakers with a very closed network–such as those belonging to the triads 0-7-14 and 1-12-17, or the tetrad 2-9-10-11 –should therefore tend to be less innovative than others and intervene at a later stage of propagation. Conversely, individuals whose networks are smaller or who are located on the periphery of communities–such as nodes 6, 16 or 13—are more linguistically malleable, less conservative, and therefore more likely to take up innovations and, by extension, to facilitate their circulation. The role played by the centrality of innovators remains slightly unclear at this stage. The research carried out to date, which has focused almost exclusively on English, highlights the importance of links between individuals in the process of diffusion of linguistic innovations and sheds light on certain aspects in its own way, without however offering a complete overview of this phenomenon. Moreover, with a few exceptions, they have generally concentrated on successful innovations, leaving aside unsuccessful innovations.

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https://doi.org/10.1371/journal.pcsy.0000005.g001

Based on a corpus of tweets in French and a short diachronic observation of the diffusion of successful and unsuccessful lexical innovations from their appearance to their stabilization or decline, we will examine a) how the structural properties of their adopters within the social network evolve over time, and b) whether the position of the speakers who adopt them at the successive phases of their diffusion can predict the fate of the lexical innovations. Our contribution is to provide a global overview of the circulation of lexical innovations within a social network. Moreover, we work with data in French, a language that is rarely studied in this type of study, where English is over-represented.

Materials and methods

For this work, we rely on a corpus of around 650 million tweets in French coming from about 2.5 million users, and spanning the period from 2007 to early 2019, the largest part of which is contained between March 2012 and January 2019. An initial collection of 170 million tweets produced between 2014 and 2017 was collected using the data providers Gnip and Datasift and constitutes the user base of this corpus [ 27 ]. The selection criteria for the tweets were that they should be written in French and come from the GMT and GMT+1 time zones. In a second phase the corpus was completed—directly via the Twitter API (using the Tweepy library)—by retrieving iteratively the latest tweets of the users having produced this initial corpus, excluding retweets. The corpus was filtered according to language and client used in order to keep only tweets in French and to eliminate as much as possible tweets from bots. For the language, we simply relied on the language of the tweet as automatically identified by twitter. For the bots, we relied on the Twitter clients. Since bots produce very stereotyped tweets, we have kept the clients exhibiting sufficient tweet lengths variability. The list of retained clients and the selection criteria are available at [ 28 ]. The corpus of tweets is available on the Ortolang platform [ 29 ].

Lexical innovations

As explained in [ 30 ], we first selected all the words (i.e. any sequence of alphanumeric characters that can contain an apostrophe or a hyphen) that appeared in the corpus for the first time between March 2012 and February 2014. For each of these words, we then reconstructed their usage trajectory over 5 years from their first appearance, by recovering their usage rate—i.e. the number of people who used this form out of the number of people who tweeted during the month.

For each of the trajectories obtained, we used a curve-fitting method using the LMFIT library for Python to fit them as closely as possible to two functions: the logistic function and the lognormal function. These functions correspond respectively to the ideal theoretical S-shaped trajectory of successful innovations [ 31 – 34 ] and the skewed bell-shaped trajectory of innovations whose use, after a growth phase, declines rather than stabilizes. We then used the adjustment output parameters to retain the words whose trajectory of use over 5 years most closely obeyed one or other of these laws.

A manual filtering stage was then necessary to remove the named entities from the almost 500 words retained. In the end, we have two types of lexical innovation:

• The changes correspond to lexical innovations whose monthly trajectory of use follows a (logistic) S-shaped curve. It is possible to identify three distinct phases in the diffusion of this type of innovation: an initial phase—the innovation phase—during which the usage rate of the word remains at a very low level for a few months, followed by a more or less long propagation phase during which its usage rate takes off exponentially, to finally stabilise in the fixation phase. We identified 141 changes.
• The buzzes correspond to lexical innovations whose use trajectory per month follows a Gaussian curve. The first two phases of diffusion of innovations categorised as buzz are identical to those observed for changes. However, the last phase shows a significant decline in the rate of use of the word, until it returns to a very low rate; this is what we call the decline phase. The number of buzzes is 251.

To automatically delimit the three diffusion phases described above—innovation, propagation, then fixation for changes or decline for buzzes—we used the third derivative of the fitted distribution. More precisely, we looked for its maximums to identify the moments in the trajectory where the acceleration varies the most, delimiting the beginning and end of the propagation phase.

Fig 2 shows two changes ("rainté" and "malaisante") and two buzzes ("sweg" and "masculiste") identified with this method, their trajectory of use over 5 years, the adjustment to the reference function, and the three phases of diffusion.

The usage rate per month of two changes (left) and two buzzes (right) represented by a rolling average with a three-month window (blue), as well as the result of the curve fitting (green). The three diffusion phases are represented by the grey shading in the background [ 30 ].

https://doi.org/10.1371/journal.pcsy.0000005.g002

The scripts used to detecting and categorizing the lexical innovations and the resulting data are available at [ 35 ].

Control words

In order to characterize the dynamics of lexical innovations in the network of users, we designed a third group of control words whose use is stable. The period and duration taken into account for the control words was matched with the lexical innovations, 5 years from February 2013 to January 2018.

We retrieve all the words of this period with at least 100 occurrences, as well as their number of users per month. Stable words are defined by a five-year usage rate whose standard deviation is below a certain threshold. In order to make this threshold comparable from one word to another, the monthly uses were normalized over the 5 years period. After manual observation of a large sample of words, this threshold was set at 0.007. In parallel, we check that each form has at least as many non-zero values as the linguistic innovation that has the least, in order to avoid words with to long periods with a zero use rate.

We obtain almost 40,000 words from which we randomly select 200 words whose number of users is matched to that of lexical innovations. Fig 3 shows a random sample of 20 forms belonging to each of the categories, change, buzz and control word.

https://doi.org/10.1371/journal.pcsy.0000005.g003

User network

For each user of the corpus, we have retrieved the list of his followees, i.e. the people he follows. From this information, we reconstructed the static network restricted to the other users of our corpus. We did not rely on mentions to reconstruct the network of users in the corpus because this would have led to the exclusion of the vast majority of users who do not use mentioning. The resulting network counts 2.5 million users and 300 million ties.

From this network, we can then characterize each user according to the following network variables: local clustering coefficient, PageRank score, betweenness centrality and proximity to the outside of the community. The computations of the different network variables—except for the proximity to the outside of the community—were performed using the Python library NetworKit [ 36 ].

Clustering coefficient.

The local clustering coefficient is the proportion of existing edges between the neighbours of a node among all possible edges. It is a measure whose values are between 0 and 1, and which therefore reflects the degree of openness of a user’s network. A clustering coefficient of 0 means that the neighbours of user u have no ties with each other, while a clustering coefficient of 1 would mean that all its neighbours also have ties to each other. Thus, the higher a user’s clustering coefficient, the closer his or her egocentric network is from a clique, i.e. a cohesive subgroup.

People belonging to dense sub-groups of the network with strong ties uniting their members will generally show more linguistic conservatism and be more resistant to change, and their adoption of an innovative variant is crucial to their maintenance within the community [ 6 ]. To demonstrate the relationship between maintaining vernacular norms and belonging to such a group, [ 22 ] have measured the strength of integration of nodes into their local group. Other studies have instead mobilised the notion of strength of ties—measured either by remaining as close as possible to its initial definition [ 7 , 21 ], or by inferring it from the interconnection of nodes [ 12 , 14 ] -, generally to highlight the stronger influence of strong ties. Nevertheless, the strength of ties, network density and overlap of egocentric networks are very closely interconnected concepts. In a network, dense sub-groups with strong links between their members generally go hand in hand with overlapping egocentric networks [ 5 ]. The local clustering coefficient therefore seemed to us to be an easier measure to implement on a large network such as ours, and one that indicates, to a certain measure, of whether an individual belongs to a closely linked sub-group.

In this way, user with a very closed network is similar to an individual with strong ties, evolving within a more closed sub-group, and therefore less exposed to innovations coming from outside.

PageRank score.

The PageRank score of a user u is a measure of the prestige of an individual. This measure depends both on the number of incoming ties of u , but also on whether these incoming ties themselves have a high PageRank score. That is to say, a user followed by many people, who are themselves followed by a large number of people, will a priori have a higher PageRank score than a user followed by a larger number of people, but who are themselves followed by very few people.

Applied to our network of Twitter users, we consider this measure to reflect a user’s overall popularity level. This measure of popularity can to some extent be transposed, on a much larger scale, to the notion of prestige as used by Labov in his description of the leaders of linguistic change in Philadelphia [ 3 , 4 ]. In addition, the higher a user’s PageRank score, the more likely it is that the content they produce will be exposed to a greater number of people.

Centrality measure.

The measure of centrality for a user here corresponds to their centrality within the community to which they belong. The more central an individual is to his community, the more he acts as a "bridge" between its members. To calculate this score, it was therefore first necessary to detect the communities within our user network. To do this, we used the parallel implementation of the Louvain method [ 37 ] proposed by NetworKit, which allows us to identify the most densely connected groups in the network. As this method is non-overlapping, it implies that a user can only belong to one community. This shows that the great majority of the network’s users belong to large communities, most of which have hundreds of thousands of individuals.

Betweenness centrality defines the centrality of a node as the number of times it is on the shortest path between two other nodes in the network. As the complexity of its computation increases strongly with the size of the network, we use approximate centrality measures for communities with more than 10,000 nodes, and exact centrality for the remaining, smaller communities. We use for this the parallel implementation of the KADABRA algorithm [ 38 , 39 ] provided by NetworKit. For each community, we calculate the centrality measures of its users by considering the network as an undirected graph. Since the centrality scores obtained in this way depends on the size of the community, they are not comparable from one community to another. For this reason, for each community, the set of centrality values obtained for each of its users has been standardised so that the median of this set is equal to 0 and the interquartile range (IQR = Q3—Q1) to 1. The scaled centrality measures can then be compared between users from different communities. It should be noted that we observe a slight correlation between the centrality measures thus obtained and the PageRank scores (Spearman correlation: 0.59).

While [ 14 ] have explored several measures of centrality to define the importance of a node in their social network, we will focus exclusively on betweenness centrality. In addition to the fact that the size of our network—more than 300 million ties—does not reasonably allow us to calculate all possible network measures, we believe that this measure is the one that comes closest to centrality in Labov’s sense. For Labov, the notion of centrality refers to important people in their local community, who are often mentioned by the other inhabitants of the block, and who are strongly involved in local life [ 4 ]. These people therefore act as a bridge within their local community, which is what betweenness centrality allows us to measure at the scale of the communities in our network.

Proximity to the community outside.

We designed the last network variable, that indicates how fast a user is able to get in touch with a different community than his own. More precisely, from each node in the network, 10,000 random walks are performed, and for each of them we keep the number of steps that it was necessary to take before arriving in another community. The average of these 10,000 values thus obtained constitutes the final score attributed to the user for this variable.

The smaller the average number of steps of a user, the more directly he is in contact with another community. However, if he is located close to another community, this does not mean that he is more isolated in his own. The same user can have a central position within his community, but still have quick connections with people outside the community. We also observe a Spearman correlation of only -0.14 between these two variables.

This measure of proximity to the community outside is intended to reflect in part the profile of innovators described by Milroy [ 5 ], who are likely to bring innovations to their community through more direct ties with other communities.

Each of the users in the corpus is therefore characterized according to this set of four network variables giving information about the degree of openness of their egocentric network, their relative prestige, their centrality within their community, and their proximity to the outside of the community.

Comparison of the distributions of the different network variables at the three diffusion phases and prediction

Characterisation of words..

Contrary to what was previously initiated in [ 30 ], we do not aggregate all the users who have used a word of a given category (e.g. buzz) at a given phase of diffusion, but each word is characterized independently. We take the view that although the set of words making up a category of lexical innovation (buzz or change) follows a global dynamic, each word nevertheless has its own dynamics. Users of innovations such as morphological derivations may not be exactly the same as users of phonetic spellings or lengthenings. Analyzing the distributions for each variable at the word level rather than aggregating users by innovation type allows us to avoid overlooking the different dynamics that may exist within the same category of innovations.

Finally, each of the words in each phase is represented by a four-dimensional vector corresponding to the clustering coefficient, the PageRank score, the centrality, and the average number of steps to exit the community.

For control words, the same procedure is used but without distinguishing the different phases of diffusion.

Univariate tests.

One of our goals is to characterize the actors of change. This is addressed by comparing phase by phase the distribution of the network variables of the three groups: changes, buzzes and control words. To check the significance of our observations, we use non-parametric tests, given the non-normality of the distributions. More precisely, we use the Kruskal-Wallis test which tests the null hypothesis that the population median of all groups is equal, and then as a post-hoc test the Dunn’s test which allows us to compare each pair of distributions. We applied the Bonferroni adjustment to the Dunn’s test to correct the significance level. In both cases, we set the significance threshold to p<0.05.

Predicting the fate of lexical innovations.

We then tried to predict the fate of lexical innovations before their trajectory stabilizes or declines, i.e. as early as the innovation or propagation phase. To do this, we train a logistic regression model using the scikit-learn library on all the lexical innovations in our dataset—i.e. the 141 changes and 251 buzzes. This involves training a model for binary classification: the variable to be predicted is the type of lexical innovation: buzz vs change. The explanatory variables are the median values of the set of adopters of each word for each network variable. A first prediction is made with the data characterizing each word in the innovation phase, and a second with the data from the propagation phase.

To ensure that the model results are not biased by the greater number of buzzes than changes, the dataset is reduced to balanced classes by randomly selecting as many buzzes as there are changes. The data is also standardized before training the model, so that all medians are 0 and the IQR is 1. It is then split into training and test data representing 75% and 25% of the data respectively—this represents a training set of 211 items for a test set of 71 items. Given the small number of inputs and the fact that only 60% of the buzzes is considered, we train 10,000 models in this way varying the buzzes. Thus, in the training phase, the changes will always be the same, but the buzzes will vary systematically.

We then evaluate the quality of the prediction on the data in the innovation phase, and then in the propagation phase, by retrieving for each of the 10,000 models the following evaluation metrics: the area under the ROC curve (now AUC), the precision, and the confusion matrices. An AUC score lies between 0 and 1. If it is 0.5, it means that the model predicts as well as the hazard. The precision, also between 0 and 1, corresponds to the average rate of correct predictions. Finally, the confusion matrices give the distribution of true and false positives and true and false negatives. More precisely, we will carry out a Fisher test on each of the matrices obtained to ensure that this distribution is not due to hazard.

The scripts used to calculate the network variables, characterize the words, create the group of control words, and perform the univariate tests and predictions are available at [ 28 ].

We will first ask whether and how the network characteristics of the individuals who adopt lexical innovations differ from those of the users of the control words composing our control group at the different phases of diffusion. At the same time, we will extend this questioning to the level of lexical innovations and ask which network characteristics are the most discriminating between changes and buzzes, always considering the timing of their diffusion. Secondly, we will try to find out whether it is possible to predict the fate of lexical innovations simply based on the four network characteristics of their adopters, described in the previous section.

Comparison of distributions

The figure below ( Fig 4 ) shows the different distributions of median values used to characterize each word by type, by network variables and by phase of diffusion; each point thus represents a word, and each distribution a word category. Lexical innovations are shown in blue and green, representing changes and buzzes respectively, and control words in yellow. The distribution of the latter does not vary from one phase to another, since we cannot distinguish between different phases.

Distributions of median values characterizing each word by type (in blue the changes, in green the buzzes, and in yellow the control words), by network variable (rows) and by diffusion phase (columns).

https://doi.org/10.1371/journal.pcsy.0000005.g004

The results of the univariate tests performed on each set and each pair of distributions are presented in Fig 5 , which should therefore be systematically compared with the distributions commented in Fig 4 . Non-significant results are indicated by a hatched background. A yellow background indicates that the values in distribution A (top) are globally higher than the values of distribution B (bottom); a green background indicates the opposite. For example, the p-value obtained with Dunn’s test for the centrality of adopters in the fixation phase is 0.0275 and is therefore significant since it is lower than the significance threshold set at 0.05. The green background means that lower centrality values are more often observed for users of control words than for users who adopted a change in the fixation phase.

https://doi.org/10.1371/journal.pcsy.0000005.g005

A first element that can be noted is that the correlation observed between the PageRank scores and user centrality measures emerges particularly well when we look at the graph of distributions, as their dynamics are similar for each category over the diffusion phases. While these variables may seem redundant from this point of view, a Spearman correlation of 0.62 for all phases considered ( Fig 6 ) indicates a positive but moderate correlation. Indeed, it is quite possible to have high prestige but low centrality, as is the case for node 16 in the Fig 1 , given that the centrality of a user is calculated in relation to the community to which he belongs. In the same way, a user can be not very central to his community while being very isolated from other communities, like nodes 1, 3 or 17 in Fig 1 , or conversely be not very central but almost immediately in contact with other communities, like nodes 5 or 13 for example. Finally, a user can also be central to his or her community while, on average, being in contact with other communities relatively quickly—or not (node 10)—and have a relatively open (node 8) or closed (node 9) egocentric network.

Spearman correlations between the different variables—all phases and all types of words considered.

https://doi.org/10.1371/journal.pcsy.0000005.g006

In the innovation phase, we do not observe significant differences between the distributions of the clustering coefficient. In the propagation and fixation phase, however, a distinction is observed, lexical innovations having lower clustering coefficient than control words. No evolution is observed between these two phases. Thus, the first adopters of lexical innovations do not differ in the degree of openness of their own network.

If the PageRank scores of lexical innovations are significantly lower than those of control words during the first two phases of diffusion, this difference decreases during the fixation phase regarding changes. Users who adopt lexical innovations during the first two phases of diffusion are less prestigious than normal, particularly regarding the buzzes, whose values remain in the same range from one phase to the next, whereas those of the changes gradually approach those of the control words until they reach their level in the fixation phase. It should be noted that although the buzz adopters have significantly lower PageRank scores than the other two categories in the fixation phase, they are nevertheless higher than those observed in the two previous phases.

While lexical innovations have significantly lower centrality measures than control words in the innovation phase, in the propagation phase the changes stand out from the buzzes by reaching users as central as those of the control words—no significant difference being observed between these two distributions -, while the distribution of buzz adopters remains significantly lower. While the latter, like the PageRank scores, rises in the fixation phase, it remains slightly lower than the other two. The distribution of centrality measures for change adopters is even higher than that of control words. Thus, from the propagation phase onwards, changes, unlike buzz, are adopted by more central users, which would a priori facilitate their diffusion within the community.

In the innovation phase, the distributions of the average number of steps of the lexical innovations are lower than those of the control words, while not being distinguished from each other. The lexical innovations are therefore initially adopted by users who can generally reach outside their community more quickly, which facilitates their subsequent dissemination. Indeed, when we look at the distribution of these values in the propagation phase, the distribution of changes has not really changed, whereas the distribution of buzzes increases significantly, until it is positioned at a higher level than that of the control words. While the position of the distributions remains almost identical in the fixation phase, that of the changes is concentrated around lower values.

What emerges from these observations is that the first adopters of both successful changes and unsuccessful buzzes have similar network profiles. These innovators tend to be less prestigious and more peripheral compared to average users. This effect is even more pronounced for buzzes. Innovators can also reach outside their communities more easily. This likely helps facilitate the future diffusion of these new terms. This similarity fades in the propagation phase, where changes succeed in reaching much more prestigious and central users than buzzes, while maintaining a rapid proximity to the outside of the community, which should facilitate their circulation within the community but also outside it. Buzzes, on the other hand, continue to spread, but do not manage to reach more central or prestigious individuals, on the contrary. Moreover, as they are adopted at this phase by users who are less directly connected to different communities, the circulation between them will probably be obstructed later. The fixation phase confirms the dynamics of the changes, which are therefore adopted by people who are as prestigious as the users of control words, slightly more central, but also with an even more direct proximity to the outside of the community than in the propagation phase. While the distribution of prestige and centrality values of adopters during this phase tends to realign with those of changes and control words during their decline phase, buzzes continue to be adopted by less central and less prestigious people, and with a more laborious contact with the outside of their community. Finally, if the distribution of clustering coefficients is discriminating between lexical innovations and control words, the fact of being adopted by users with a more open network is characteristic of innovations in the last two phases of diffusion.

Prediction of the fate of lexical innovations.

We can now wonder whether these differences we observe between the distributions of median values of adopters of lexical innovations are sufficiently discriminating to allow us to predict, in the innovation or propagation phases, whether a lexical innovation will maintain in the linguistic community, and become a change or, on the contrary, whether its use will eventually decline, thus becoming a buzz.

Fig 7 shows the results obtained for the precision of the 10,000 prediction models trained by logistic regression, first on the values attributed to changes and buzzes in the innovation phase, in green, and then on those in the propagation phase, in blue. Fig 8 allows us to visualize the results of the AUC scores in the same way. Prediction made from the innovation phase are imprecise, with an average precision of 0.56 and an average AUC score of 0.61. If in general the models do slightly better than chance, these scores show that it is not possible to predict the fate of lexical innovations in the innovation phase.

Precision obtained by the logistic regression models trained on the 10,000 datasets in the innovation phase (green) and in the propagation phase (blue).

https://doi.org/10.1371/journal.pcsy.0000005.g007

AUC scores obtained by the logistic regression models trained on the 10,000 data sets in the innovation phase (green) and in the propagation phase (blue).

https://doi.org/10.1371/journal.pcsy.0000005.g008

If we look at these scores more closely with the confusion matrices resulting from these models trained on the innovation phase data, we can see that, in general, buzzes are slightly easier to predict than changes at this stage, with an average of 61% of buzzes correctly predicted (true positives) versus 52% of changes (true negatives). However, Fisher’s exact tests on these matrices provide p-values greater than 0.05 in almost 80% of cases, which means that when we observe imbalances in the distribution of true/false positives and negatives, these are mostly non-significant. On average, these p-values are around 0.34. However, for the confusion matrices resulting from the models trained using the propagation phase data, this average p-value of the Fisher exact tests is now 8.3e-05 and only 0.03% of the observed ratios between percentages of true/false positives and negatives are non-significant. At this stage, buzzes still seem to be slightly easier to predict than changes, with an average of 83% of buzzes correctly predicted compared to about 79% for changes.

This significant improvement in prediction quality when using the propagation phase data is confirmed by an improvement of precision from 0.56 to 0.81, and an average AUC score of 0.86, which confirms that the classification of lexical innovations as buzz or change at this stage leaves little to chance.

In summary, it would appear that despite the significant differences in the positioning of the buzz and change distributions observed in the innovation phase for PageRank score and centrality measures, it is not possible at this stage to predict what a lexical innovation will become in the future based only on the network characteristics of its first adopters. However, when we rely on the network characteristics of the adopters of innovations in the propagation phase, it becomes quite possible to predict their fate. It is the position in the community and the more or less direct link with the outside of the adopters of an innovation at this stage that seem to seal their fate and favor (or not) their future stabilization.

In this study, we examined the network characteristics of the users of our corpus and, in particular, whether it was possible to identify a ’typical profile’ of adopters of lexical innovations at each of their diffusion phases. We also wondered whether this profile is different according to the type of lexical innovation, i.e. whether adopters of changes differ from adopters of buzzes. In this way, we seek to highlight the process of diffusion of lexical innovations and the factors in terms of network structure that contribute to their success or failure in a linguistic community, and to determine whether these large-scale results are consistent with or different from those obtained by the field surveys conducted in traditional variationist sociolinguistics, notably by Lesley and James Milroy as well as by William Labov.

First, we established that the initial adopters of lexical innovations are users with relatively similar network characteristics, regardless of whether these innovations later succeed or fail. Contrary to what we might expect, these individuals do not have a more open or closed personal network than average. They have the possibility to be in contact with other communities more quickly, without being central in their own community, nor prestigious within the global network. As such, these observations are largely transposable to those made by Milroy & Milroy [ 5 ] who define innovators as being more peripheral and having ties in several communities. Although Milroy & Milroy [ 5 ] referred to local communities (different parts of Belfast), it is possible to transpose these observations to users during the innovation period. First, their position in the community is less central and therefore a priori more peripheral; and second, they maintain ties with at least two communities—on a much larger scale—that of social media, comprising several million users and that are defined not spatially but in relation to areas with a higher density of ties than in the rest of the network. Moreover, ties are inherently different from those maintained by the inhabitants of a city, for example.

While we did not characterize users in terms of strong- or weak-tied users, the local clustering coefficient as a measure of the degree of openness of a user’s network captures to some extent a similar reality. In the propagation phase, clustering coefficients of change adopters are lower than average, which is consistent with the findings of previous work. However, we do not observe evidence that the changes were subsequently adopted by people who could be described as strongly connected, or at least belonging to a more closed subgroup, which would increase the likelihood that an innovation would spread in the community [ 14 ]. On the contrary, the degree of openness of adopters of changes seems to be higher as the diffusion of these changes progresses. However, nothing suggest that they were not taken up by a few individuals belonging to more closed subgroups, but not in sufficient numbers for this to be reflected in our results. Further studies on the strength of the ties between the users of our network and their degree of embeddedness would be desirable in order to be able to study in more detail the impact of this variable on the establishment of changes in the linguistic community.

While a profile of early adopters emerges in the first diffusion phase for lexical innovations, it is not yet possible at this stage to know whether they will become buzzes or changes, as our low prediction results in the innovation phase indicate. However, in the propagation phase, i.e. when the rate of adoption of buzzes and changes increases exponentially, we can identify a characteristic profile for users who adopt changes or buzzes. The success or failure of an innovation seems to depend on the combination of several factors. On the one hand, changes are adopted by individuals with a PageRank score that is always lower than normal, but much higher than those of buzzes. We can suggest that the adoption of lexical innovations by individuals with very low visibility implies that buzzes have a much lower frequency of exposure than changes at this stage. Repeated exposure to a term can in some cases have a significant effect on its adoption [ 40 ]. In addition, it appears that the first phase of diffusion of changes has an average duration of 18.5 months compared with 6.5 months for buzzes, i.e. almost three times longer. Changes therefore generally remain in circulation longer before entering their growth phase, which also increases the chances of being exposed to them. Thus, the higher exposure of future changes, being longer in circulation and adopted by people whose tweets are more likely to be made visible to a larger number of users, surely increases the likelihood of some changes being adopted in the future.

Next, the changes are characterized by adopters who are relatively central to their community, or at least as central as those in our control group, and located closer to other communities, whereas the opposite pattern emerges from the future buzzes. Indeed, the latter are characterized by adopters who are still very peripheral in their community and have much more distant ties to other communities. The fact that changes are adopted at this phase by users who are central to their community, acting as a bridge within it and thus facilitating their diffusion, but who also have a more direct proximity to individuals from other communities is directly in line with the observations made by Labov [ 4 ] in his Philadelphia survey when he describes the leaders of change. The adoption of innovations during the propagation phase by prestigious and central individuals, having direct ties outside the community, predicts their success. Meanwhile, innovations that do not spread to prestigious, central users tend to fail. Our prediction results confirm that the profiles of early adopters influence the ultimate fate of new terms.

In the fixation phase, where the fate of lexical innovations is already sealed, the prestige of their adopters reaches that of our control group, when their centrality even exceeds it. Conversely, the average number of steps required to reach the outside of the community is even lower than in the previous phases. The observations of high measures of centrality within the community and immediate proximity to the outside of the community may be reminiscent of the conditions for adoption of an innovation described by Milroy & Milroy [ 5 ], i.e. for a variant to become established within a community, it is necessary that it has been adopted by people central to it, who themselves will only risk adopting the variant if it is already widely used at the margins of the community. That said, it should be noted that Milroy & Milroy [ 5 ] were describing adoption within a local community, whereas in our case we do not know whether the adoption of the innovation takes place within a single community or within the overall linguistic community of our corpus. It would also be interesting, when looking at the conditions for the success or failure of an innovation, to determine whether the fact that an innovation has succeeded in reaching several communities is a determining factor in the success of its diffusion, as Würschinger [ 17 ] finds for example. It is partly for this reason that it would be welcome in a future work to further develop the one started on communities, both by finely characterizing them, but also by observing the circulation of innovations within and between them.

One point to which we must turn our attention, and which has not been studied in this work, is the role played by the category of lexical innovation. The lexical innovations we have detected cover several categories and do not seem to be homogeneously distributed between buzzes and changes. While we find borrowings, morphological derivations, lengthenings, truncations, phonetic spellings, etc. in both types of innovation, it is immediately apparent that a greater number of lengthenings are observed in buzzes, for example, while more neologisms designating new realities or practices are present in changes. Words that have a greater communicative utility, that fill a semantic gap or that can also be used in spoken language are more likely to be maintained over time [ 41 ], as well as words used in a wider range of linguistic contexts [ 42 ]. The nature of the word itself therefore has a certain impact on its chances of survival and would be an interesting factor to consider in future research. Finally, it has been shown that other factors, notably demographic and geographical [ 11 ], play an important role in the diffusion of innovations. In future research, it would be interesting to consider all of these factors, both intra- and extra-linguistic, in order to refine and complete the results presented here on the impact of the position of speakers in the network on the diffusion of innovations.

To conclude, our study found similar general diffusion patterns for lexical innovations as previous sociolinguistic studies [ 4 , 5 ]. Those studies focused on phonetic innovations, localized communities, and surveys of hundreds. In contrast, our research examined lexical innovations at scale across millions of social media users. It should be noted, however, that it is easier for a speaker to act at the lexical level than at the phonological or morphosyntactic level, for example. This is because, once acquired, speakers generally do not change the way they pronounce, just as they are less likely to change their grammar. On the contrary, lexical variables are easier to manipulate, and are also more conscious and therefore more likely to be linked to identity issues. However, although they are less malleable, the other types of variables are not hermetic to change—even if this generally involves a longer time span. Thus, the question remains open as to whether the underlying mechanisms are the same and whether the influence of the network factors highlighted here can be generalized to non-lexical variables.

Acknowledgments

We gratefully acknowledge the support of the Centre Blaise Pascal’s IT test platform at ENS de Lyon (Lyon, France) for the computing facilities. The platform operates the SIDUS solution [ 43 ] developed by Emmanuel Quemener.

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