a sample of research design

Research Design 101

Everything You Need To Get Started (With Examples)

By: Derek Jansen (MBA) | Reviewers: Eunice Rautenbach (DTech) & Kerryn Warren (PhD) | April 2023

Navigating the world of research can be daunting, especially if you’re a first-time researcher. One concept you’re bound to run into fairly early in your research journey is that of “ research design ”. Here, we’ll guide you through the basics using practical examples , so that you can approach your research with confidence.

Overview: Research Design 101

What is research design.

• Research design types for quantitative studies
• Video explainer : quantitative research design
• Research design types for qualitative studies
• Video explainer : qualitative research design
• How to choose a research design
• Key takeaways

Research design refers to the overall plan, structure or strategy that guides a research project , from its conception to the final data analysis. A good research design serves as the blueprint for how you, as the researcher, will collect and analyse data while ensuring consistency, reliability and validity throughout your study.

Understanding different types of research designs is essential as helps ensure that your approach is suitable  given your research aims, objectives and questions , as well as the resources you have available to you. Without a clear big-picture view of how you’ll design your research, you run the risk of potentially making misaligned choices in terms of your methodology – especially your sampling , data collection and data analysis decisions.

The problem with defining research design…

One of the reasons students struggle with a clear definition of research design is because the term is used very loosely across the internet, and even within academia.

Some sources claim that the three research design types are qualitative, quantitative and mixed methods , which isn’t quite accurate (these just refer to the type of data that you’ll collect and analyse). Other sources state that research design refers to the sum of all your design choices, suggesting it’s more like a research methodology . Others run off on other less common tangents. No wonder there’s confusion!

In this article, we’ll clear up the confusion. We’ll explain the most common research design types for both qualitative and quantitative research projects, whether that is for a full dissertation or thesis, or a smaller research paper or article.

Research Design: Quantitative Studies

Quantitative research involves collecting and analysing data in a numerical form. Broadly speaking, there are four types of quantitative research designs: descriptive , correlational , experimental , and quasi-experimental . 

Descriptive Research Design

As the name suggests, descriptive research design focuses on describing existing conditions, behaviours, or characteristics by systematically gathering information without manipulating any variables. In other words, there is no intervention on the researcher’s part – only data collection.

For example, if you’re studying smartphone addiction among adolescents in your community, you could deploy a survey to a sample of teens asking them to rate their agreement with certain statements that relate to smartphone addiction. The collected data would then provide insight regarding how widespread the issue may be – in other words, it would describe the situation.

The key defining attribute of this type of research design is that it purely describes the situation . In other words, descriptive research design does not explore potential relationships between different variables or the causes that may underlie those relationships. Therefore, descriptive research is useful for generating insight into a research problem by describing its characteristics . By doing so, it can provide valuable insights and is often used as a precursor to other research design types.

Correlational Research Design

Correlational design is a popular choice for researchers aiming to identify and measure the relationship between two or more variables without manipulating them . In other words, this type of research design is useful when you want to know whether a change in one thing tends to be accompanied by a change in another thing.

For example, if you wanted to explore the relationship between exercise frequency and overall health, you could use a correlational design to help you achieve this. In this case, you might gather data on participants’ exercise habits, as well as records of their health indicators like blood pressure, heart rate, or body mass index. Thereafter, you’d use a statistical test to assess whether there’s a relationship between the two variables (exercise frequency and health).

As you can see, correlational research design is useful when you want to explore potential relationships between variables that cannot be manipulated or controlled for ethical, practical, or logistical reasons. It is particularly helpful in terms of developing predictions , and given that it doesn’t involve the manipulation of variables, it can be implemented at a large scale more easily than experimental designs (which will look at next).

That said, it’s important to keep in mind that correlational research design has limitations – most notably that it cannot be used to establish causality . In other words, correlation does not equal causation . To establish causality, you’ll need to move into the realm of experimental design, coming up next…

Need a helping hand?

Experimental Research Design

Experimental research design is used to determine if there is a causal relationship between two or more variables . With this type of research design, you, as the researcher, manipulate one variable (the independent variable) while controlling others (dependent variables). Doing so allows you to observe the effect of the former on the latter and draw conclusions about potential causality.

For example, if you wanted to measure if/how different types of fertiliser affect plant growth, you could set up several groups of plants, with each group receiving a different type of fertiliser, as well as one with no fertiliser at all. You could then measure how much each plant group grew (on average) over time and compare the results from the different groups to see which fertiliser was most effective.

Overall, experimental research design provides researchers with a powerful way to identify and measure causal relationships (and the direction of causality) between variables. However, developing a rigorous experimental design can be challenging as it’s not always easy to control all the variables in a study. This often results in smaller sample sizes , which can reduce the statistical power and generalisability of the results.

Moreover, experimental research design requires random assignment . This means that the researcher needs to assign participants to different groups or conditions in a way that each participant has an equal chance of being assigned to any group (note that this is not the same as random sampling ). Doing so helps reduce the potential for bias and confounding variables . This need for random assignment can lead to ethics-related issues . For example, withholding a potentially beneficial medical treatment from a control group may be considered unethical in certain situations.

Quasi-Experimental Research Design

Quasi-experimental research design is used when the research aims involve identifying causal relations , but one cannot (or doesn’t want to) randomly assign participants to different groups (for practical or ethical reasons). Instead, with a quasi-experimental research design, the researcher relies on existing groups or pre-existing conditions to form groups for comparison.

For example, if you were studying the effects of a new teaching method on student achievement in a particular school district, you may be unable to randomly assign students to either group and instead have to choose classes or schools that already use different teaching methods. This way, you still achieve separate groups, without having to assign participants to specific groups yourself.

Naturally, quasi-experimental research designs have limitations when compared to experimental designs. Given that participant assignment is not random, it’s more difficult to confidently establish causality between variables, and, as a researcher, you have less control over other variables that may impact findings.

All that said, quasi-experimental designs can still be valuable in research contexts where random assignment is not possible and can often be undertaken on a much larger scale than experimental research, thus increasing the statistical power of the results. What’s important is that you, as the researcher, understand the limitations of the design and conduct your quasi-experiment as rigorously as possible, paying careful attention to any potential confounding variables .

Research Design: Qualitative Studies

There are many different research design types when it comes to qualitative studies, but here we’ll narrow our focus to explore the “Big 4”. Specifically, we’ll look at phenomenological design, grounded theory design, ethnographic design, and case study design.

Phenomenological Research Design

Phenomenological design involves exploring the meaning of lived experiences and how they are perceived by individuals. This type of research design seeks to understand people’s perspectives , emotions, and behaviours in specific situations. Here, the aim for researchers is to uncover the essence of human experience without making any assumptions or imposing preconceived ideas on their subjects.

For example, you could adopt a phenomenological design to study why cancer survivors have such varied perceptions of their lives after overcoming their disease. This could be achieved by interviewing survivors and then analysing the data using a qualitative analysis method such as thematic analysis to identify commonalities and differences.

Phenomenological research design typically involves in-depth interviews or open-ended questionnaires to collect rich, detailed data about participants’ subjective experiences. This richness is one of the key strengths of phenomenological research design but, naturally, it also has limitations. These include potential biases in data collection and interpretation and the lack of generalisability of findings to broader populations.

Grounded Theory Research Design

Grounded theory (also referred to as “GT”) aims to develop theories by continuously and iteratively analysing and comparing data collected from a relatively large number of participants in a study. It takes an inductive (bottom-up) approach, with a focus on letting the data “speak for itself”, without being influenced by preexisting theories or the researcher’s preconceptions.

As an example, let’s assume your research aims involved understanding how people cope with chronic pain from a specific medical condition, with a view to developing a theory around this. In this case, grounded theory design would allow you to explore this concept thoroughly without preconceptions about what coping mechanisms might exist. You may find that some patients prefer cognitive-behavioural therapy (CBT) while others prefer to rely on herbal remedies. Based on multiple, iterative rounds of analysis, you could then develop a theory in this regard, derived directly from the data (as opposed to other preexisting theories and models).

Grounded theory typically involves collecting data through interviews or observations and then analysing it to identify patterns and themes that emerge from the data. These emerging ideas are then validated by collecting more data until a saturation point is reached (i.e., no new information can be squeezed from the data). From that base, a theory can then be developed .

As you can see, grounded theory is ideally suited to studies where the research aims involve theory generation , especially in under-researched areas. Keep in mind though that this type of research design can be quite time-intensive , given the need for multiple rounds of data collection and analysis.

Ethnographic Research Design

Ethnographic design involves observing and studying a culture-sharing group of people in their natural setting to gain insight into their behaviours, beliefs, and values. The focus here is on observing participants in their natural environment (as opposed to a controlled environment). This typically involves the researcher spending an extended period of time with the participants in their environment, carefully observing and taking field notes .

All of this is not to say that ethnographic research design relies purely on observation. On the contrary, this design typically also involves in-depth interviews to explore participants’ views, beliefs, etc. However, unobtrusive observation is a core component of the ethnographic approach.

As an example, an ethnographer may study how different communities celebrate traditional festivals or how individuals from different generations interact with technology differently. This may involve a lengthy period of observation, combined with in-depth interviews to further explore specific areas of interest that emerge as a result of the observations that the researcher has made.

As you can probably imagine, ethnographic research design has the ability to provide rich, contextually embedded insights into the socio-cultural dynamics of human behaviour within a natural, uncontrived setting. Naturally, however, it does come with its own set of challenges, including researcher bias (since the researcher can become quite immersed in the group), participant confidentiality and, predictably, ethical complexities . All of these need to be carefully managed if you choose to adopt this type of research design.

Case Study Design

With case study research design, you, as the researcher, investigate a single individual (or a single group of individuals) to gain an in-depth understanding of their experiences, behaviours or outcomes. Unlike other research designs that are aimed at larger sample sizes, case studies offer a deep dive into the specific circumstances surrounding a person, group of people, event or phenomenon, generally within a bounded setting or context .

As an example, a case study design could be used to explore the factors influencing the success of a specific small business. This would involve diving deeply into the organisation to explore and understand what makes it tick – from marketing to HR to finance. In terms of data collection, this could include interviews with staff and management, review of policy documents and financial statements, surveying customers, etc.

While the above example is focused squarely on one organisation, it’s worth noting that case study research designs can have different variation s, including single-case, multiple-case and longitudinal designs. As you can see in the example, a single-case design involves intensely examining a single entity to understand its unique characteristics and complexities. Conversely, in a multiple-case design , multiple cases are compared and contrasted to identify patterns and commonalities. Lastly, in a longitudinal case design , a single case or multiple cases are studied over an extended period of time to understand how factors develop over time.

As you can see, a case study research design is particularly useful where a deep and contextualised understanding of a specific phenomenon or issue is desired. However, this strength is also its weakness. In other words, you can’t generalise the findings from a case study to the broader population. So, keep this in mind if you’re considering going the case study route.

How To Choose A Research Design

Having worked through all of these potential research designs, you’d be forgiven for feeling a little overwhelmed and wondering, “ But how do I decide which research design to use? ”. While we could write an entire post covering that alone, here are a few factors to consider that will help you choose a suitable research design for your study.

Data type: The first determining factor is naturally the type of data you plan to be collecting – i.e., qualitative or quantitative. This may sound obvious, but we have to be clear about this – don’t try to use a quantitative research design on qualitative data (or vice versa)!

Research aim(s) and question(s): As with all methodological decisions, your research aim and research questions will heavily influence your research design. For example, if your research aims involve developing a theory from qualitative data, grounded theory would be a strong option. Similarly, if your research aims involve identifying and measuring relationships between variables, one of the experimental designs would likely be a better option.

Time: It’s essential that you consider any time constraints you have, as this will impact the type of research design you can choose. For example, if you’ve only got a month to complete your project, a lengthy design such as ethnography wouldn’t be a good fit.

Resources: Take into account the resources realistically available to you, as these need to factor into your research design choice. For example, if you require highly specialised lab equipment to execute an experimental design, you need to be sure that you’ll have access to that before you make a decision.

Keep in mind that when it comes to research, it’s important to manage your risks and play as conservatively as possible. If your entire project relies on you achieving a huge sample, having access to niche equipment or holding interviews with very difficult-to-reach participants, you’re creating risks that could kill your project. So, be sure to think through your choices carefully and make sure that you have backup plans for any existential risks. Remember that a relatively simple methodology executed well generally will typically earn better marks than a highly-complex methodology executed poorly.

Recap: Key Takeaways

We’ve covered a lot of ground here. Let’s recap by looking at the key takeaways:

• Research design refers to the overall plan, structure or strategy that guides a research project, from its conception to the final analysis of data.
• Research designs for quantitative studies include descriptive , correlational , experimental and quasi-experimenta l designs.
• Research designs for qualitative studies include phenomenological , grounded theory , ethnographic and case study designs.
• When choosing a research design, you need to consider a variety of factors, including the type of data you’ll be working with, your research aims and questions, your time and the resources available to you.

If you need a helping hand with your research design (or any other aspect of your research), check out our private coaching services .

Psst... there’s more!

This post was based on one of our popular Research Bootcamps . If you're working on a research project, you'll definitely want to check this out ...

13 Comments

Is there any blog article explaining more on Case study research design? Is there a Case study write-up template? Thank you.

Thanks this was quite valuable to clarify such an important concept.

Thanks for this simplified explanations. it is quite very helpful.

This was really helpful. thanks

Thank you for your explanation. I think case study research design and the use of secondary data in researches needs to be talked about more in your videos and articles because there a lot of case studies research design tailored projects out there.

Please is there any template for a case study research design whose data type is a secondary data on your repository?

This post is very clear, comprehensive and has been very helpful to me. It has cleared the confusion I had in regard to research design and methodology.

This post is helpful, easy to understand, and deconstructs what a research design is. Thanks

This post is really helpful.

how to cite this page

Thank you very much for the post. It is wonderful and has cleared many worries in my mind regarding research designs. I really appreciate .

how can I put this blog as my reference(APA style) in bibliography part?

This post has been very useful to me. Confusing areas have been cleared

This is very helpful and very useful!

Submit a Comment Cancel reply

Your email address will not be published. Required fields are marked *

Save my name, email, and website in this browser for the next time I comment.

• Print Friendly

• Privacy Policy

Home » Research Design – Types, Methods and Examples

Research Design – Types, Methods and Examples

Table of Contents

Research Design

Definition:

Research design refers to the overall strategy or plan for conducting a research study. It outlines the methods and procedures that will be used to collect and analyze data, as well as the goals and objectives of the study. Research design is important because it guides the entire research process and ensures that the study is conducted in a systematic and rigorous manner.

Types of Research Design

Types of Research Design are as follows:

Descriptive Research Design

This type of research design is used to describe a phenomenon or situation. It involves collecting data through surveys, questionnaires, interviews, and observations. The aim of descriptive research is to provide an accurate and detailed portrayal of a particular group, event, or situation. It can be useful in identifying patterns, trends, and relationships in the data.

Correlational Research Design

Correlational research design is used to determine if there is a relationship between two or more variables. This type of research design involves collecting data from participants and analyzing the relationship between the variables using statistical methods. The aim of correlational research is to identify the strength and direction of the relationship between the variables.

Experimental Research Design

Experimental research design is used to investigate cause-and-effect relationships between variables. This type of research design involves manipulating one variable and measuring the effect on another variable. It usually involves randomly assigning participants to groups and manipulating an independent variable to determine its effect on a dependent variable. The aim of experimental research is to establish causality.

Quasi-experimental Research Design

Quasi-experimental research design is similar to experimental research design, but it lacks one or more of the features of a true experiment. For example, there may not be random assignment to groups or a control group. This type of research design is used when it is not feasible or ethical to conduct a true experiment.

Case Study Research Design

Case study research design is used to investigate a single case or a small number of cases in depth. It involves collecting data through various methods, such as interviews, observations, and document analysis. The aim of case study research is to provide an in-depth understanding of a particular case or situation.

Longitudinal Research Design

Longitudinal research design is used to study changes in a particular phenomenon over time. It involves collecting data at multiple time points and analyzing the changes that occur. The aim of longitudinal research is to provide insights into the development, growth, or decline of a particular phenomenon over time.

Structure of Research Design

The format of a research design typically includes the following sections:

• Introduction : This section provides an overview of the research problem, the research questions, and the importance of the study. It also includes a brief literature review that summarizes previous research on the topic and identifies gaps in the existing knowledge.
• Research Questions or Hypotheses: This section identifies the specific research questions or hypotheses that the study will address. These questions should be clear, specific, and testable.
• Research Methods : This section describes the methods that will be used to collect and analyze data. It includes details about the study design, the sampling strategy, the data collection instruments, and the data analysis techniques.
• Data Collection: This section describes how the data will be collected, including the sample size, data collection procedures, and any ethical considerations.
• Data Analysis: This section describes how the data will be analyzed, including the statistical techniques that will be used to test the research questions or hypotheses.
• Results : This section presents the findings of the study, including descriptive statistics and statistical tests.
• Discussion and Conclusion : This section summarizes the key findings of the study, interprets the results, and discusses the implications of the findings. It also includes recommendations for future research.
• References : This section lists the sources cited in the research design.

Example of Research Design

An Example of Research Design could be:

Research question: Does the use of social media affect the academic performance of high school students?

Research design:

• Research approach : The research approach will be quantitative as it involves collecting numerical data to test the hypothesis.
• Research design : The research design will be a quasi-experimental design, with a pretest-posttest control group design.
• Sample : The sample will be 200 high school students from two schools, with 100 students in the experimental group and 100 students in the control group.
• Data collection : The data will be collected through surveys administered to the students at the beginning and end of the academic year. The surveys will include questions about their social media usage and academic performance.
• Data analysis : The data collected will be analyzed using statistical software. The mean scores of the experimental and control groups will be compared to determine whether there is a significant difference in academic performance between the two groups.
• Limitations : The limitations of the study will be acknowledged, including the fact that social media usage can vary greatly among individuals, and the study only focuses on two schools, which may not be representative of the entire population.
• Ethical considerations: Ethical considerations will be taken into account, such as obtaining informed consent from the participants and ensuring their anonymity and confidentiality.

How to Write Research Design

Writing a research design involves planning and outlining the methodology and approach that will be used to answer a research question or hypothesis. Here are some steps to help you write a research design:

• Define the research question or hypothesis : Before beginning your research design, you should clearly define your research question or hypothesis. This will guide your research design and help you select appropriate methods.
• Select a research design: There are many different research designs to choose from, including experimental, survey, case study, and qualitative designs. Choose a design that best fits your research question and objectives.
• Develop a sampling plan : If your research involves collecting data from a sample, you will need to develop a sampling plan. This should outline how you will select participants and how many participants you will include.
• Define variables: Clearly define the variables you will be measuring or manipulating in your study. This will help ensure that your results are meaningful and relevant to your research question.
• Choose data collection methods : Decide on the data collection methods you will use to gather information. This may include surveys, interviews, observations, experiments, or secondary data sources.
• Create a data analysis plan: Develop a plan for analyzing your data, including the statistical or qualitative techniques you will use.
• Consider ethical concerns : Finally, be sure to consider any ethical concerns related to your research, such as participant confidentiality or potential harm.

When to Write Research Design

Research design should be written before conducting any research study. It is an important planning phase that outlines the research methodology, data collection methods, and data analysis techniques that will be used to investigate a research question or problem. The research design helps to ensure that the research is conducted in a systematic and logical manner, and that the data collected is relevant and reliable.

Ideally, the research design should be developed as early as possible in the research process, before any data is collected. This allows the researcher to carefully consider the research question, identify the most appropriate research methodology, and plan the data collection and analysis procedures in advance. By doing so, the research can be conducted in a more efficient and effective manner, and the results are more likely to be valid and reliable.

Purpose of Research Design

The purpose of research design is to plan and structure a research study in a way that enables the researcher to achieve the desired research goals with accuracy, validity, and reliability. Research design is the blueprint or the framework for conducting a study that outlines the methods, procedures, techniques, and tools for data collection and analysis.

Some of the key purposes of research design include:

• Providing a clear and concise plan of action for the research study.
• Ensuring that the research is conducted ethically and with rigor.
• Maximizing the accuracy and reliability of the research findings.
• Minimizing the possibility of errors, biases, or confounding variables.
• Ensuring that the research is feasible, practical, and cost-effective.
• Determining the appropriate research methodology to answer the research question(s).
• Identifying the sample size, sampling method, and data collection techniques.
• Determining the data analysis method and statistical tests to be used.
• Facilitating the replication of the study by other researchers.
• Enhancing the validity and generalizability of the research findings.

Applications of Research Design

There are numerous applications of research design in various fields, some of which are:

• Social sciences: In fields such as psychology, sociology, and anthropology, research design is used to investigate human behavior and social phenomena. Researchers use various research designs, such as experimental, quasi-experimental, and correlational designs, to study different aspects of social behavior.
• Education : Research design is essential in the field of education to investigate the effectiveness of different teaching methods and learning strategies. Researchers use various designs such as experimental, quasi-experimental, and case study designs to understand how students learn and how to improve teaching practices.
• Health sciences : In the health sciences, research design is used to investigate the causes, prevention, and treatment of diseases. Researchers use various designs, such as randomized controlled trials, cohort studies, and case-control studies, to study different aspects of health and healthcare.
• Business : Research design is used in the field of business to investigate consumer behavior, marketing strategies, and the impact of different business practices. Researchers use various designs, such as survey research, experimental research, and case studies, to study different aspects of the business world.
• Engineering : In the field of engineering, research design is used to investigate the development and implementation of new technologies. Researchers use various designs, such as experimental research and case studies, to study the effectiveness of new technologies and to identify areas for improvement.

Advantages of Research Design

Here are some advantages of research design:

• Systematic and organized approach : A well-designed research plan ensures that the research is conducted in a systematic and organized manner, which makes it easier to manage and analyze the data.
• Clear objectives: The research design helps to clarify the objectives of the study, which makes it easier to identify the variables that need to be measured, and the methods that need to be used to collect and analyze data.
• Minimizes bias: A well-designed research plan minimizes the chances of bias, by ensuring that the data is collected and analyzed objectively, and that the results are not influenced by the researcher’s personal biases or preferences.
• Efficient use of resources: A well-designed research plan helps to ensure that the resources (time, money, and personnel) are used efficiently and effectively, by focusing on the most important variables and methods.
• Replicability: A well-designed research plan makes it easier for other researchers to replicate the study, which enhances the credibility and reliability of the findings.
• Validity: A well-designed research plan helps to ensure that the findings are valid, by ensuring that the methods used to collect and analyze data are appropriate for the research question.
• Generalizability : A well-designed research plan helps to ensure that the findings can be generalized to other populations, settings, or situations, which increases the external validity of the study.

Research Design Vs Research Methodology

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

You may also like

APA Research Paper Format – Example, Sample and...

Research Approach – Types Methods and Examples

Background of The Study – Examples and Writing...

Research Paper Title Page – Example and Making...

Limitations in Research – Types, Examples and...

Research Problem – Examples, Types and Guide

Leave a comment x.

Save my name, email, and website in this browser for the next time I comment.

• How it works

How to Write a Research Design – Guide with Examples

Published by Alaxendra Bets at August 14th, 2021 , Revised On June 24, 2024

A research design is a structure that combines different components of research. It involves the use of different data collection and data analysis techniques logically to answer the  research questions .

It would be best to make some decisions about addressing the research questions adequately before starting the research process, which is achieved with the help of the research design.

Below are the key aspects of the decision-making process:

• Data type required for research
• Research resources
• Participants required for research
• Hypothesis based upon research question(s)
• Data analysis  methodologies
• Variables (Independent, dependent, and confounding)
• The location and timescale for conducting the data
• The time period required for research

The research design provides the strategy of investigation for your project. Furthermore, it defines the parameters and criteria to compile the data to evaluate results and conclude.

Your project’s validity depends on the data collection and  interpretation techniques.  A strong research design reflects a strong  dissertation , scientific paper, or research proposal .

Step 1: Establish Priorities for Research Design

Before conducting any research study, you must address an important question: “how to create a research design.”

The research design depends on the researcher’s priorities and choices because every research has different priorities. For a complex research study involving multiple methods, you may choose to have more than one research design.

Multimethodology or multimethod research includes using more than one data collection method or research in a research study or set of related studies.

If one research design is weak in one area, then another research design can cover that weakness. For instance, a  dissertation analyzing different situations or cases will have more than one research design.

For example:

• Experimental research involves experimental investigation and laboratory experience, but it does not accurately investigate the real world.
• Quantitative research is good for the  statistical part of the project, but it may not provide an in-depth understanding of the  topic .
• Also, correlational research will not provide experimental results because it is a technique that assesses the statistical relationship between two variables.

While scientific considerations are a fundamental aspect of the research design, It is equally important that the researcher think practically before deciding on its structure. Here are some questions that you should think of;

• Do you have enough time to gather data and complete the write-up?
• Will you be able to collect the necessary data by interviewing a specific person or visiting a specific location?
• Do you have in-depth knowledge about the  different statistical analysis and data collection techniques to address the research questions  or test the  hypothesis ?

If you think that the chosen research design cannot answer the research questions properly, you can refine your research questions to gain better insight.

Step 2: Data Type you Need for Research

Decide on the type of data you need for your research. The type of data you need to collect depends on your research questions or research hypothesis. Two types of research data can be used to answer the research questions:

Primary Data Vs. Secondary Data

Qualitative Vs. Quantitative Data

Also, see; Research methods, design, and analysis .

Need help with a thesis chapter?

• Hire an expert from ResearchProspect today!
• Statistical analysis, research methodology, discussion of the results or conclusion – our experts can help you no matter how complex the requirements are.

Step 3: Data Collection Techniques

Once you have selected the type of research to answer your research question, you need to decide where and how to collect the data.

It is time to determine your research method to address the  research problem . Research methods involve procedures, techniques, materials, and tools used for the study.

For instance, a dissertation research design includes the different resources and data collection techniques and helps establish your  dissertation’s structure .

The following table shows the characteristics of the most popularly employed research methods.

Research Methods

Step 4: Procedure of Data Analysis

Use of the  correct data and statistical analysis technique is necessary for the validity of your research. Therefore, you need to be certain about the data type that would best address the research problem. Choosing an appropriate analysis method is the final step for the research design. It can be split into two main categories;

Quantitative Data Analysis

The quantitative data analysis technique involves analyzing the numerical data with the help of different applications such as; SPSS, STATA, Excel, origin lab, etc.

This data analysis strategy tests different variables such as spectrum, frequencies, averages, and more. The research question and the hypothesis must be established to identify the variables for testing.

Qualitative Data Analysis

Qualitative data analysis of figures, themes, and words allows for flexibility and the researcher’s subjective opinions. This means that the researcher’s primary focus will be interpreting patterns, tendencies, and accounts and understanding the implications and social framework.

You should be clear about your research objectives before starting to analyze the data. For example, you should ask yourself whether you need to explain respondents’ experiences and insights or do you also need to evaluate their responses with reference to a certain social framework.

Step 5: Write your Research Proposal

The research design is an important component of a research proposal because it plans the project’s execution. You can share it with the supervisor, who would evaluate the feasibility and capacity of the results  and  conclusion .

Read our guidelines to write a research proposal  if you have already formulated your research design. The research proposal is written in the future tense because you are writing your proposal before conducting research.

The  research methodology  or research design, on the other hand, is generally written in the past tense.

How to Write a Research Design – Conclusion

A research design is the plan, structure, strategy of investigation conceived to answer the research question and test the hypothesis. The dissertation research design can be classified based on the type of data and the type of analysis.

Above mentioned five steps are the answer to how to write a research design. So, follow these steps to  formulate the perfect research design for your dissertation .

ResearchProspect writers have years of experience creating research designs that align with the dissertation’s aim and objectives. If you are struggling with your dissertation methodology chapter, you might want to look at our dissertation part-writing service.

Our dissertation writers can also help you with the full dissertation paper . No matter how urgent or complex your need may be, ResearchProspect can help. We also offer PhD level research paper writing services.

Frequently Asked Questions

What is research design.

Research design is a systematic plan that guides the research process, outlining the methodology and procedures for collecting and analysing data. It determines the structure of the study, ensuring the research question is answered effectively, reliably, and validly. It serves as the blueprint for the entire research project.

How to write a research design?

To write a research design, define your research question, identify the research method (qualitative, quantitative, or mixed), choose data collection techniques (e.g., surveys, interviews), determine the sample size and sampling method, outline data analysis procedures, and highlight potential limitations and ethical considerations for the study.

How to write the design section of a research paper?

In the design section of a research paper, describe the research methodology chosen and justify its selection. Outline the data collection methods, participants or samples, instruments used, and procedures followed. Detail any experimental controls, if applicable. Ensure clarity and precision to enable replication of the study by other researchers.

How to write a research design in methodology?

To write a research design in methodology, clearly outline the research strategy (e.g., experimental, survey, case study). Describe the sampling technique, participants, and data collection methods. Detail the procedures for data collection and analysis. Justify choices by linking them to research objectives, addressing reliability and validity.

You May Also Like

Let’s briefly examine the concept of research paradigms, their pillars, purposes, types, examples, and how they can be combined.

Make sure that your selected topic is intriguing, manageable, and relevant. Here are some guidelines to help understand how to find a good dissertation topic.

How to write a hypothesis for dissertation,? A hypothesis is a statement that can be tested with the help of experimental or theoretical research.

USEFUL LINKS

LEARNING RESOURCES

COMPANY DETAILS

• How It Works

Educational resources and simple solutions for your research journey

What is Research Design? Understand Types of Research Design, with Examples

Have you been wondering “ what is research design ?” or “what are some research design examples ?” Are you unsure about the research design elements or which of the different types of research design best suit your study? Don’t worry! In this article, we’ve got you covered!   

Table of Contents

What is research design?  

Have you been wondering “ what is research design ?” or “what are some research design examples ?” Don’t worry! In this article, we’ve got you covered!  

A research design is the plan or framework used to conduct a research study. It involves outlining the overall approach and methods that will be used to collect and analyze data in order to answer research questions or test hypotheses. A well-designed research study should have a clear and well-defined research question, a detailed plan for collecting data, and a method for analyzing and interpreting the results. A well-thought-out research design addresses all these features.  

Research design elements  

Research design elements include the following:  

• Clear purpose: The research question or hypothesis must be clearly defined and focused.  
• Sampling: This includes decisions about sample size, sampling method, and criteria for inclusion or exclusion. The approach varies for different research design types .  
• Data collection: This research design element involves the process of gathering data or information from the study participants or sources. It includes decisions about what data to collect, how to collect it, and the tools or instruments that will be used.  
• Data analysis: All research design types require analysis and interpretation of the data collected. This research design element includes decisions about the statistical tests or methods that will be used to analyze the data, as well as any potential confounding variables or biases that may need to be addressed.  
• Type of research methodology: This includes decisions about the overall approach for the study.  
• Time frame: An important research design element is the time frame, which includes decisions about the duration of the study, the timeline for data collection and analysis, and follow-up periods.  
• Ethical considerations: The research design must include decisions about ethical considerations such as informed consent, confidentiality, and participant protection.  
• Resources: A good research design takes into account decisions about the budget, staffing, and other resources needed to carry out the study.  

The elements of research design should be carefully planned and executed to ensure the validity and reliability of the study findings. Let’s go deeper into the concepts of research design .    

Characteristics of research design  

Some basic characteristics of research design are common to different research design types . These characteristics of research design are as follows:  

• Neutrality : Right from the study assumptions to setting up the study, a neutral stance must be maintained, free of pre-conceived notions. The researcher’s expectations or beliefs should not color the findings or interpretation of the findings. Accordingly, a good research design should address potential sources of bias and confounding factors to be able to yield unbiased and neutral results.   
•   Reliability : Reliability is one of the characteristics of research design that refers to consistency in measurement over repeated measures and fewer random errors. A reliable research design must allow for results to be consistent, with few errors due to chance.   
•   Validity : Validity refers to the minimization of nonrandom (systematic) errors. A good research design must employ measurement tools that ensure validity of the results.  
•   Generalizability: The outcome of the research design should be applicable to a larger population and not just a small sample . A generalized method means the study can be conducted on any part of a population with similar accuracy.   
•   Flexibility: A research design should allow for changes to be made to the research plan as needed, based on the data collected and the outcomes of the study  

A well-planned research design is critical for conducting a scientifically rigorous study that will generate neutral, reliable, valid, and generalizable results. At the same time, it should allow some level of flexibility.  

Different types of research design  

A research design is essential to systematically investigate, understand, and interpret phenomena of interest. Let’s look at different types of research design and research design examples .  

Broadly, research design types can be divided into qualitative and quantitative research.  

Qualitative research is subjective and exploratory. It determines relationships between collected data and observations. It is usually carried out through interviews with open-ended questions, observations that are described in words, etc.  

Quantitative research is objective and employs statistical approaches. It establishes the cause-and-effect relationship among variables using different statistical and computational methods. This type of research is usually done using surveys and experiments.  

Qualitative research vs. Quantitative research  

Qualitative research design types and qualitative research design examples  

The following will familiarize you with the research design categories in qualitative research:  

• Grounded theory: This design is used to investigate research questions that have not previously been studied in depth. Also referred to as exploratory design , it creates sequential guidelines, offers strategies for inquiry, and makes data collection and analysis more efficient in qualitative research.   

Example: A researcher wants to study how people adopt a certain app. The researcher collects data through interviews and then analyzes the data to look for patterns. These patterns are used to develop a theory about how people adopt that app.  

•   Thematic analysis: This design is used to compare the data collected in past research to find similar themes in qualitative research.  

Example: A researcher examines an interview transcript to identify common themes, say, topics or patterns emerging repeatedly.  

• Discourse analysis : This research design deals with language or social contexts used in data gathering in qualitative research.   

Example: Identifying ideological frameworks and viewpoints of writers of a series of policies.  

Quantitative research design types and quantitative research design examples  

Note the following research design categories in quantitative research:  

• Descriptive research design : This quantitative research design is applied where the aim is to identify characteristics, frequencies, trends, and categories. It may not often begin with a hypothesis. The basis of this research type is a description of an identified variable. This research design type describes the “what,” “when,” “where,” or “how” of phenomena (but not the “why”).   

Example: A study on the different income levels of people who use nutritional supplements regularly.  

• Correlational research design : Correlation reflects the strength and/or direction of the relationship among variables. The direction of a correlation can be positive or negative. Correlational research design helps researchers establish a relationship between two variables without the researcher controlling any of them.  

Example : An example of correlational research design could be studying the correlation between time spent watching crime shows and aggressive behavior in teenagers.  

•   Diagnostic research design : In diagnostic design, the researcher aims to understand the underlying cause of a specific topic or phenomenon (usually an area of improvement) and find the most effective solution. In simpler terms, a researcher seeks an accurate “diagnosis” of a problem and identifies a solution.  

Example : A researcher analyzing customer feedback and reviews to identify areas where an app can be improved.    

• Explanatory research design : In explanatory research design , a researcher uses their ideas and thoughts on a topic to explore their theories in more depth. This design is used to explore a phenomenon when limited information is available. It can help increase current understanding of unexplored aspects of a subject. It is thus a kind of “starting point” for future research.  

Example : Formulating hypotheses to guide future studies on delaying school start times for better mental health in teenagers.  

•   Causal research design : This can be considered a type of explanatory research. Causal research design seeks to define a cause and effect in its data. The researcher does not use a randomly chosen control group but naturally or pre-existing groupings. Importantly, the researcher does not manipulate the independent variable.   

Example : Comparing school dropout levels and possible bullying events.  

•   Experimental research design : This research design is used to study causal relationships . One or more independent variables are manipulated, and their effect on one or more dependent variables is measured.  

Example: Determining the efficacy of a new vaccine plan for influenza.  

Benefits of research design  

 T here are numerous benefits of research design . These are as follows:  

• Clear direction: Among the benefits of research design , the main one is providing direction to the research and guiding the choice of clear objectives, which help the researcher to focus on the specific research questions or hypotheses they want to investigate.  
• Control: Through a proper research design , researchers can control variables, identify potential confounding factors, and use randomization to minimize bias and increase the reliability of their findings.
• Replication: Research designs provide the opportunity for replication. This helps to confirm the findings of a study and ensures that the results are not due to chance or other factors. Thus, a well-chosen research design also eliminates bias and errors.  
• Validity: A research design ensures the validity of the research, i.e., whether the results truly reflect the phenomenon being investigated.  
• Reliability: Benefits of research design also include reducing inaccuracies and ensuring the reliability of the research (i.e., consistency of the research results over time, across different samples, and under different conditions).  
• Efficiency: A strong research design helps increase the efficiency of the research process. Researchers can use a variety of designs to investigate their research questions, choose the most appropriate research design for their study, and use statistical analysis to make the most of their data. By effectively describing the data necessary for an adequate test of the hypotheses and explaining how such data will be obtained, research design saves a researcher’s time.   

Overall, an appropriately chosen and executed research design helps researchers to conduct high-quality research, draw meaningful conclusions, and contribute to the advancement of knowledge in their field.

Frequently Asked Questions (FAQ) on Research Design

Q: What are th e main types of research design?

Broadly speaking there are two basic types of research design –

qualitative and quantitative research. Qualitative research is subjective and exploratory; it determines relationships between collected data and observations. It is usually carried out through interviews with open-ended questions, observations that are described in words, etc. Quantitative research , on the other hand, is more objective and employs statistical approaches. It establishes the cause-and-effect relationship among variables using different statistical and computational methods. This type of research design is usually done using surveys and experiments.

Q: How do I choose the appropriate research design for my study?

Choosing the appropriate research design for your study requires careful consideration of various factors. Start by clarifying your research objectives and the type of data you need to collect. Determine whether your study is exploratory, descriptive, or experimental in nature. Consider the availability of resources, time constraints, and the feasibility of implementing the different research designs. Review existing literature to identify similar studies and their research designs, which can serve as a guide. Ultimately, the chosen research design should align with your research questions, provide the necessary data to answer them, and be feasible given your own specific requirements/constraints.

Q: Can research design be modified during the course of a study?

Yes, research design can be modified during the course of a study based on emerging insights, practical constraints, or unforeseen circumstances. Research is an iterative process and, as new data is collected and analyzed, it may become necessary to adjust or refine the research design. However, any modifications should be made judiciously and with careful consideration of their impact on the study’s integrity and validity. It is advisable to document any changes made to the research design, along with a clear rationale for the modifications, in order to maintain transparency and allow for proper interpretation of the results.

Q: How can I ensure the validity and reliability of my research design?

Validity refers to the accuracy and meaningfulness of your study’s findings, while reliability relates to the consistency and stability of the measurements or observations. To enhance validity, carefully define your research variables, use established measurement scales or protocols, and collect data through appropriate methods. Consider conducting a pilot study to identify and address any potential issues before full implementation. To enhance reliability, use standardized procedures, conduct inter-rater or test-retest reliability checks, and employ appropriate statistical techniques for data analysis. It is also essential to document and report your methodology clearly, allowing for replication and scrutiny by other researchers.

Editage All Access is a subscription-based platform that unifies the best AI tools and services designed to speed up, simplify, and streamline every step of a researcher’s journey. The Editage All Access Pack is a one-of-a-kind subscription that unlocks full access to an AI writing assistant, literature recommender, journal finder, scientific illustration tool, and exclusive discounts on professional publication services from Editage.  

Based on 22+ years of experience in academia, Editage All Access empowers researchers to put their best research forward and move closer to success. Explore our top AI Tools pack, AI Tools + Publication Services pack, or Build Your Own Plan. Find everything a researcher needs to succeed, all in one place –  Get All Access now starting at just $14 a month !    

Related Posts

Back to School – Lock-in All Access Pack for a Year at the Best Price

Journal Turnaround Time: Researcher.Life and Scholarly Intelligence Join Hands to Empower Researchers with Publication Time Insights 

• USC Libraries
• Research Guides

Organizing Your Social Sciences Research Paper

• Types of Research Designs
• Purpose of Guide
• Design Flaws to Avoid
• Independent and Dependent Variables
• Glossary of Research Terms
• Reading Research Effectively
• Narrowing a Topic Idea
• Broadening a Topic Idea
• Extending the Timeliness of a Topic Idea
• Academic Writing Style
• Applying Critical Thinking
• Choosing a Title
• Making an Outline
• Paragraph Development
• Research Process Video Series
• Executive Summary
• The C.A.R.S. Model
• Background Information
• The Research Problem/Question
• Theoretical Framework
• Citation Tracking
• Content Alert Services
• Evaluating Sources
• Primary Sources
• Secondary Sources
• Tiertiary Sources
• Scholarly vs. Popular Publications
• Qualitative Methods
• Quantitative Methods
• Insiderness
• Using Non-Textual Elements
• Limitations of the Study
• Common Grammar Mistakes
• Writing Concisely
• Avoiding Plagiarism
• Footnotes or Endnotes?
• Further Readings
• Generative AI and Writing
• USC Libraries Tutorials and Other Guides
• Bibliography

Introduction

Before beginning your paper, you need to decide how you plan to design the study .

The research design refers to the overall strategy and analytical approach that you have chosen in order to integrate, in a coherent and logical way, the different components of the study, thus ensuring that the research problem will be thoroughly investigated. It constitutes the blueprint for the collection, measurement, and interpretation of information and data. Note that the research problem determines the type of design you choose, not the other way around!

De Vaus, D. A. Research Design in Social Research . London: SAGE, 2001; Trochim, William M.K. Research Methods Knowledge Base. 2006.

General Structure and Writing Style

The function of a research design is to ensure that the evidence obtained enables you to effectively address the research problem logically and as unambiguously as possible . In social sciences research, obtaining information relevant to the research problem generally entails specifying the type of evidence needed to test the underlying assumptions of a theory, to evaluate a program, or to accurately describe and assess meaning related to an observable phenomenon.

With this in mind, a common mistake made by researchers is that they begin their investigations before they have thought critically about what information is required to address the research problem. Without attending to these design issues beforehand, the overall research problem will not be adequately addressed and any conclusions drawn will run the risk of being weak and unconvincing. As a consequence, the overall validity of the study will be undermined.

The length and complexity of describing the research design in your paper can vary considerably, but any well-developed description will achieve the following :

• Identify the research problem clearly and justify its selection, particularly in relation to any valid alternative designs that could have been used,
• Review and synthesize previously published literature associated with the research problem,
• Clearly and explicitly specify hypotheses [i.e., research questions] central to the problem,
• Effectively describe the information and/or data which will be necessary for an adequate testing of the hypotheses and explain how such information and/or data will be obtained, and
• Describe the methods of analysis to be applied to the data in determining whether or not the hypotheses are true or false.

The research design is usually incorporated into the introduction of your paper . You can obtain an overall sense of what to do by reviewing studies that have utilized the same research design [e.g., using a case study approach]. This can help you develop an outline to follow for your own paper.

NOTE: Use the SAGE Research Methods Online and Cases and the SAGE Research Methods Videos databases to search for scholarly resources on how to apply specific research designs and methods . The Research Methods Online database contains links to more than 175,000 pages of SAGE publisher's book, journal, and reference content on quantitative, qualitative, and mixed research methodologies. Also included is a collection of case studies of social research projects that can be used to help you better understand abstract or complex methodological concepts. The Research Methods Videos database contains hours of tutorials, interviews, video case studies, and mini-documentaries covering the entire research process.

Creswell, John W. and J. David Creswell. Research Design: Qualitative, Quantitative, and Mixed Methods Approaches . 5th edition. Thousand Oaks, CA: Sage, 2018; De Vaus, D. A. Research Design in Social Research . London: SAGE, 2001; Gorard, Stephen. Research Design: Creating Robust Approaches for the Social Sciences . Thousand Oaks, CA: Sage, 2013; Leedy, Paul D. and Jeanne Ellis Ormrod. Practical Research: Planning and Design . Tenth edition. Boston, MA: Pearson, 2013; Vogt, W. Paul, Dianna C. Gardner, and Lynne M. Haeffele. When to Use What Research Design . New York: Guilford, 2012.

Action Research Design

Definition and Purpose

The essentials of action research design follow a characteristic cycle whereby initially an exploratory stance is adopted, where an understanding of a problem is developed and plans are made for some form of interventionary strategy. Then the intervention is carried out [the "action" in action research] during which time, pertinent observations are collected in various forms. The new interventional strategies are carried out, and this cyclic process repeats, continuing until a sufficient understanding of [or a valid implementation solution for] the problem is achieved. The protocol is iterative or cyclical in nature and is intended to foster deeper understanding of a given situation, starting with conceptualizing and particularizing the problem and moving through several interventions and evaluations.

What do these studies tell you ?

• This is a collaborative and adaptive research design that lends itself to use in work or community situations.
• Design focuses on pragmatic and solution-driven research outcomes rather than testing theories.
• When practitioners use action research, it has the potential to increase the amount they learn consciously from their experience; the action research cycle can be regarded as a learning cycle.
• Action research studies often have direct and obvious relevance to improving practice and advocating for change.
• There are no hidden controls or preemption of direction by the researcher.

What these studies don't tell you ?

• It is harder to do than conducting conventional research because the researcher takes on responsibilities of advocating for change as well as for researching the topic.
• Action research is much harder to write up because it is less likely that you can use a standard format to report your findings effectively [i.e., data is often in the form of stories or observation].
• Personal over-involvement of the researcher may bias research results.
• The cyclic nature of action research to achieve its twin outcomes of action [e.g. change] and research [e.g. understanding] is time-consuming and complex to conduct.
• Advocating for change usually requires buy-in from study participants.

Coghlan, David and Mary Brydon-Miller. The Sage Encyclopedia of Action Research . Thousand Oaks, CA:  Sage, 2014; Efron, Sara Efrat and Ruth Ravid. Action Research in Education: A Practical Guide . New York: Guilford, 2013; Gall, Meredith. Educational Research: An Introduction . Chapter 18, Action Research. 8th ed. Boston, MA: Pearson/Allyn and Bacon, 2007; Gorard, Stephen. Research Design: Creating Robust Approaches for the Social Sciences . Thousand Oaks, CA: Sage, 2013; Kemmis, Stephen and Robin McTaggart. “Participatory Action Research.” In Handbook of Qualitative Research . Norman Denzin and Yvonna S. Lincoln, eds. 2nd ed. (Thousand Oaks, CA: SAGE, 2000), pp. 567-605; McNiff, Jean. Writing and Doing Action Research . London: Sage, 2014; Reason, Peter and Hilary Bradbury. Handbook of Action Research: Participative Inquiry and Practice . Thousand Oaks, CA: SAGE, 2001.

Case Study Design

A case study is an in-depth study of a particular research problem rather than a sweeping statistical survey or comprehensive comparative inquiry. It is often used to narrow down a very broad field of research into one or a few easily researchable examples. The case study research design is also useful for testing whether a specific theory and model actually applies to phenomena in the real world. It is a useful design when not much is known about an issue or phenomenon.

• Approach excels at bringing us to an understanding of a complex issue through detailed contextual analysis of a limited number of events or conditions and their relationships.
• A researcher using a case study design can apply a variety of methodologies and rely on a variety of sources to investigate a research problem.
• Design can extend experience or add strength to what is already known through previous research.
• Social scientists, in particular, make wide use of this research design to examine contemporary real-life situations and provide the basis for the application of concepts and theories and the extension of methodologies.
• The design can provide detailed descriptions of specific and rare cases.
• A single or small number of cases offers little basis for establishing reliability or to generalize the findings to a wider population of people, places, or things.
• Intense exposure to the study of a case may bias a researcher's interpretation of the findings.
• Design does not facilitate assessment of cause and effect relationships.
• Vital information may be missing, making the case hard to interpret.
• The case may not be representative or typical of the larger problem being investigated.
• If the criteria for selecting a case is because it represents a very unusual or unique phenomenon or problem for study, then your interpretation of the findings can only apply to that particular case.

Case Studies. Writing@CSU. Colorado State University; Anastas, Jeane W. Research Design for Social Work and the Human Services . Chapter 4, Flexible Methods: Case Study Design. 2nd ed. New York: Columbia University Press, 1999; Gerring, John. “What Is a Case Study and What Is It Good for?” American Political Science Review 98 (May 2004): 341-354; Greenhalgh, Trisha, editor. Case Study Evaluation: Past, Present and Future Challenges . Bingley, UK: Emerald Group Publishing, 2015; Mills, Albert J. , Gabrielle Durepos, and Eiden Wiebe, editors. Encyclopedia of Case Study Research . Thousand Oaks, CA: SAGE Publications, 2010; Stake, Robert E. The Art of Case Study Research . Thousand Oaks, CA: SAGE, 1995; Yin, Robert K. Case Study Research: Design and Theory . Applied Social Research Methods Series, no. 5. 3rd ed. Thousand Oaks, CA: SAGE, 2003.

Causal Design

Causality studies may be thought of as understanding a phenomenon in terms of conditional statements in the form, “If X, then Y.” This type of research is used to measure what impact a specific change will have on existing norms and assumptions. Most social scientists seek causal explanations that reflect tests of hypotheses. Causal effect (nomothetic perspective) occurs when variation in one phenomenon, an independent variable, leads to or results, on average, in variation in another phenomenon, the dependent variable.

Conditions necessary for determining causality:

• Empirical association -- a valid conclusion is based on finding an association between the independent variable and the dependent variable.
• Appropriate time order -- to conclude that causation was involved, one must see that cases were exposed to variation in the independent variable before variation in the dependent variable.
• Nonspuriousness -- a relationship between two variables that is not due to variation in a third variable.
• Causality research designs assist researchers in understanding why the world works the way it does through the process of proving a causal link between variables and by the process of eliminating other possibilities.
• Replication is possible.
• There is greater confidence the study has internal validity due to the systematic subject selection and equity of groups being compared.
• Not all relationships are causal! The possibility always exists that, by sheer coincidence, two unrelated events appear to be related [e.g., Punxatawney Phil could accurately predict the duration of Winter for five consecutive years but, the fact remains, he's just a big, furry rodent].
• Conclusions about causal relationships are difficult to determine due to a variety of extraneous and confounding variables that exist in a social environment. This means causality can only be inferred, never proven.
• If two variables are correlated, the cause must come before the effect. However, even though two variables might be causally related, it can sometimes be difficult to determine which variable comes first and, therefore, to establish which variable is the actual cause and which is the  actual effect.

Beach, Derek and Rasmus Brun Pedersen. Causal Case Study Methods: Foundations and Guidelines for Comparing, Matching, and Tracing . Ann Arbor, MI: University of Michigan Press, 2016; Bachman, Ronet. The Practice of Research in Criminology and Criminal Justice . Chapter 5, Causation and Research Designs. 3rd ed. Thousand Oaks, CA: Pine Forge Press, 2007; Brewer, Ernest W. and Jennifer Kubn. “Causal-Comparative Design.” In Encyclopedia of Research Design . Neil J. Salkind, editor. (Thousand Oaks, CA: Sage, 2010), pp. 125-132; Causal Research Design: Experimentation. Anonymous SlideShare Presentation; Gall, Meredith. Educational Research: An Introduction . Chapter 11, Nonexperimental Research: Correlational Designs. 8th ed. Boston, MA: Pearson/Allyn and Bacon, 2007; Trochim, William M.K. Research Methods Knowledge Base. 2006.

Cohort Design

Often used in the medical sciences, but also found in the applied social sciences, a cohort study generally refers to a study conducted over a period of time involving members of a population which the subject or representative member comes from, and who are united by some commonality or similarity. Using a quantitative framework, a cohort study makes note of statistical occurrence within a specialized subgroup, united by same or similar characteristics that are relevant to the research problem being investigated, rather than studying statistical occurrence within the general population. Using a qualitative framework, cohort studies generally gather data using methods of observation. Cohorts can be either "open" or "closed."

• Open Cohort Studies [dynamic populations, such as the population of Los Angeles] involve a population that is defined just by the state of being a part of the study in question (and being monitored for the outcome). Date of entry and exit from the study is individually defined, therefore, the size of the study population is not constant. In open cohort studies, researchers can only calculate rate based data, such as, incidence rates and variants thereof.
• Closed Cohort Studies [static populations, such as patients entered into a clinical trial] involve participants who enter into the study at one defining point in time and where it is presumed that no new participants can enter the cohort. Given this, the number of study participants remains constant (or can only decrease).
• The use of cohorts is often mandatory because a randomized control study may be unethical. For example, you cannot deliberately expose people to asbestos, you can only study its effects on those who have already been exposed. Research that measures risk factors often relies upon cohort designs.
• Because cohort studies measure potential causes before the outcome has occurred, they can demonstrate that these “causes” preceded the outcome, thereby avoiding the debate as to which is the cause and which is the effect.
• Cohort analysis is highly flexible and can provide insight into effects over time and related to a variety of different types of changes [e.g., social, cultural, political, economic, etc.].
• Either original data or secondary data can be used in this design.
• In cases where a comparative analysis of two cohorts is made [e.g., studying the effects of one group exposed to asbestos and one that has not], a researcher cannot control for all other factors that might differ between the two groups. These factors are known as confounding variables.
• Cohort studies can end up taking a long time to complete if the researcher must wait for the conditions of interest to develop within the group. This also increases the chance that key variables change during the course of the study, potentially impacting the validity of the findings.
• Due to the lack of randominization in the cohort design, its external validity is lower than that of study designs where the researcher randomly assigns participants.

Healy P, Devane D. “Methodological Considerations in Cohort Study Designs.” Nurse Researcher 18 (2011): 32-36; Glenn, Norval D, editor. Cohort Analysis . 2nd edition. Thousand Oaks, CA: Sage, 2005; Levin, Kate Ann. Study Design IV: Cohort Studies. Evidence-Based Dentistry 7 (2003): 51–52; Payne, Geoff. “Cohort Study.” In The SAGE Dictionary of Social Research Methods . Victor Jupp, editor. (Thousand Oaks, CA: Sage, 2006), pp. 31-33; Study Design 101. Himmelfarb Health Sciences Library. George Washington University, November 2011; Cohort Study. Wikipedia.

Cross-Sectional Design

Cross-sectional research designs have three distinctive features: no time dimension; a reliance on existing differences rather than change following intervention; and, groups are selected based on existing differences rather than random allocation. The cross-sectional design can only measure differences between or from among a variety of people, subjects, or phenomena rather than a process of change. As such, researchers using this design can only employ a relatively passive approach to making causal inferences based on findings.

• Cross-sectional studies provide a clear 'snapshot' of the outcome and the characteristics associated with it, at a specific point in time.
• Unlike an experimental design, where there is an active intervention by the researcher to produce and measure change or to create differences, cross-sectional designs focus on studying and drawing inferences from existing differences between people, subjects, or phenomena.
• Entails collecting data at and concerning one point in time. While longitudinal studies involve taking multiple measures over an extended period of time, cross-sectional research is focused on finding relationships between variables at one moment in time.
• Groups identified for study are purposely selected based upon existing differences in the sample rather than seeking random sampling.
• Cross-section studies are capable of using data from a large number of subjects and, unlike observational studies, is not geographically bound.
• Can estimate prevalence of an outcome of interest because the sample is usually taken from the whole population.
• Because cross-sectional designs generally use survey techniques to gather data, they are relatively inexpensive and take up little time to conduct.
• Finding people, subjects, or phenomena to study that are very similar except in one specific variable can be difficult.
• Results are static and time bound and, therefore, give no indication of a sequence of events or reveal historical or temporal contexts.
• Studies cannot be utilized to establish cause and effect relationships.
• This design only provides a snapshot of analysis so there is always the possibility that a study could have differing results if another time-frame had been chosen.
• There is no follow up to the findings.

Bethlehem, Jelke. "7: Cross-sectional Research." In Research Methodology in the Social, Behavioural and Life Sciences . Herman J Adèr and Gideon J Mellenbergh, editors. (London, England: Sage, 1999), pp. 110-43; Bourque, Linda B. “Cross-Sectional Design.” In  The SAGE Encyclopedia of Social Science Research Methods . Michael S. Lewis-Beck, Alan Bryman, and Tim Futing Liao. (Thousand Oaks, CA: 2004), pp. 230-231; Hall, John. “Cross-Sectional Survey Design.” In Encyclopedia of Survey Research Methods . Paul J. Lavrakas, ed. (Thousand Oaks, CA: Sage, 2008), pp. 173-174; Helen Barratt, Maria Kirwan. Cross-Sectional Studies: Design Application, Strengths and Weaknesses of Cross-Sectional Studies. Healthknowledge, 2009. Cross-Sectional Study. Wikipedia.

Descriptive Design

Descriptive research designs help provide answers to the questions of who, what, when, where, and how associated with a particular research problem; a descriptive study cannot conclusively ascertain answers to why. Descriptive research is used to obtain information concerning the current status of the phenomena and to describe "what exists" with respect to variables or conditions in a situation.

• The subject is being observed in a completely natural and unchanged natural environment. True experiments, whilst giving analyzable data, often adversely influence the normal behavior of the subject [a.k.a., the Heisenberg effect whereby measurements of certain systems cannot be made without affecting the systems].
• Descriptive research is often used as a pre-cursor to more quantitative research designs with the general overview giving some valuable pointers as to what variables are worth testing quantitatively.
• If the limitations are understood, they can be a useful tool in developing a more focused study.
• Descriptive studies can yield rich data that lead to important recommendations in practice.
• Appoach collects a large amount of data for detailed analysis.
• The results from a descriptive research cannot be used to discover a definitive answer or to disprove a hypothesis.
• Because descriptive designs often utilize observational methods [as opposed to quantitative methods], the results cannot be replicated.
• The descriptive function of research is heavily dependent on instrumentation for measurement and observation.

Anastas, Jeane W. Research Design for Social Work and the Human Services . Chapter 5, Flexible Methods: Descriptive Research. 2nd ed. New York: Columbia University Press, 1999; Given, Lisa M. "Descriptive Research." In Encyclopedia of Measurement and Statistics . Neil J. Salkind and Kristin Rasmussen, editors. (Thousand Oaks, CA: Sage, 2007), pp. 251-254; McNabb, Connie. Descriptive Research Methodologies. Powerpoint Presentation; Shuttleworth, Martyn. Descriptive Research Design, September 26, 2008; Erickson, G. Scott. "Descriptive Research Design." In New Methods of Market Research and Analysis . (Northampton, MA: Edward Elgar Publishing, 2017), pp. 51-77; Sahin, Sagufta, and Jayanta Mete. "A Brief Study on Descriptive Research: Its Nature and Application in Social Science." International Journal of Research and Analysis in Humanities 1 (2021): 11; K. Swatzell and P. Jennings. “Descriptive Research: The Nuts and Bolts.” Journal of the American Academy of Physician Assistants 20 (2007), pp. 55-56; Kane, E. Doing Your Own Research: Basic Descriptive Research in the Social Sciences and Humanities . London: Marion Boyars, 1985.

Experimental Design

A blueprint of the procedure that enables the researcher to maintain control over all factors that may affect the result of an experiment. In doing this, the researcher attempts to determine or predict what may occur. Experimental research is often used where there is time priority in a causal relationship (cause precedes effect), there is consistency in a causal relationship (a cause will always lead to the same effect), and the magnitude of the correlation is great. The classic experimental design specifies an experimental group and a control group. The independent variable is administered to the experimental group and not to the control group, and both groups are measured on the same dependent variable. Subsequent experimental designs have used more groups and more measurements over longer periods. True experiments must have control, randomization, and manipulation.

• Experimental research allows the researcher to control the situation. In so doing, it allows researchers to answer the question, “What causes something to occur?”
• Permits the researcher to identify cause and effect relationships between variables and to distinguish placebo effects from treatment effects.
• Experimental research designs support the ability to limit alternative explanations and to infer direct causal relationships in the study.
• Approach provides the highest level of evidence for single studies.
• The design is artificial, and results may not generalize well to the real world.
• The artificial settings of experiments may alter the behaviors or responses of participants.
• Experimental designs can be costly if special equipment or facilities are needed.
• Some research problems cannot be studied using an experiment because of ethical or technical reasons.
• Difficult to apply ethnographic and other qualitative methods to experimentally designed studies.

Anastas, Jeane W. Research Design for Social Work and the Human Services . Chapter 7, Flexible Methods: Experimental Research. 2nd ed. New York: Columbia University Press, 1999; Chapter 2: Research Design, Experimental Designs. School of Psychology, University of New England, 2000; Chow, Siu L. "Experimental Design." In Encyclopedia of Research Design . Neil J. Salkind, editor. (Thousand Oaks, CA: Sage, 2010), pp. 448-453; "Experimental Design." In Social Research Methods . Nicholas Walliman, editor. (London, England: Sage, 2006), pp, 101-110; Experimental Research. Research Methods by Dummies. Department of Psychology. California State University, Fresno, 2006; Kirk, Roger E. Experimental Design: Procedures for the Behavioral Sciences . 4th edition. Thousand Oaks, CA: Sage, 2013; Trochim, William M.K. Experimental Design. Research Methods Knowledge Base. 2006; Rasool, Shafqat. Experimental Research. Slideshare presentation.

Exploratory Design

An exploratory design is conducted about a research problem when there are few or no earlier studies to refer to or rely upon to predict an outcome . The focus is on gaining insights and familiarity for later investigation or undertaken when research problems are in a preliminary stage of investigation. Exploratory designs are often used to establish an understanding of how best to proceed in studying an issue or what methodology would effectively apply to gathering information about the issue.

The goals of exploratory research are intended to produce the following possible insights:

• Familiarity with basic details, settings, and concerns.
• Well grounded picture of the situation being developed.
• Generation of new ideas and assumptions.
• Development of tentative theories or hypotheses.
• Determination about whether a study is feasible in the future.
• Issues get refined for more systematic investigation and formulation of new research questions.
• Direction for future research and techniques get developed.
• Design is a useful approach for gaining background information on a particular topic.
• Exploratory research is flexible and can address research questions of all types (what, why, how).
• Provides an opportunity to define new terms and clarify existing concepts.
• Exploratory research is often used to generate formal hypotheses and develop more precise research problems.
• In the policy arena or applied to practice, exploratory studies help establish research priorities and where resources should be allocated.
• Exploratory research generally utilizes small sample sizes and, thus, findings are typically not generalizable to the population at large.
• The exploratory nature of the research inhibits an ability to make definitive conclusions about the findings. They provide insight but not definitive conclusions.
• The research process underpinning exploratory studies is flexible but often unstructured, leading to only tentative results that have limited value to decision-makers.
• Design lacks rigorous standards applied to methods of data gathering and analysis because one of the areas for exploration could be to determine what method or methodologies could best fit the research problem.

Cuthill, Michael. “Exploratory Research: Citizen Participation, Local Government, and Sustainable Development in Australia.” Sustainable Development 10 (2002): 79-89; Streb, Christoph K. "Exploratory Case Study." In Encyclopedia of Case Study Research . Albert J. Mills, Gabrielle Durepos and Eiden Wiebe, editors. (Thousand Oaks, CA: Sage, 2010), pp. 372-374; Taylor, P. J., G. Catalano, and D.R.F. Walker. “Exploratory Analysis of the World City Network.” Urban Studies 39 (December 2002): 2377-2394; Exploratory Research. Wikipedia.

Field Research Design

Sometimes referred to as ethnography or participant observation, designs around field research encompass a variety of interpretative procedures [e.g., observation and interviews] rooted in qualitative approaches to studying people individually or in groups while inhabiting their natural environment as opposed to using survey instruments or other forms of impersonal methods of data gathering. Information acquired from observational research takes the form of “ field notes ” that involves documenting what the researcher actually sees and hears while in the field. Findings do not consist of conclusive statements derived from numbers and statistics because field research involves analysis of words and observations of behavior. Conclusions, therefore, are developed from an interpretation of findings that reveal overriding themes, concepts, and ideas. More information can be found HERE .

• Field research is often necessary to fill gaps in understanding the research problem applied to local conditions or to specific groups of people that cannot be ascertained from existing data.
• The research helps contextualize already known information about a research problem, thereby facilitating ways to assess the origins, scope, and scale of a problem and to gage the causes, consequences, and means to resolve an issue based on deliberate interaction with people in their natural inhabited spaces.
• Enables the researcher to corroborate or confirm data by gathering additional information that supports or refutes findings reported in prior studies of the topic.
• Because the researcher in embedded in the field, they are better able to make observations or ask questions that reflect the specific cultural context of the setting being investigated.
• Observing the local reality offers the opportunity to gain new perspectives or obtain unique data that challenges existing theoretical propositions or long-standing assumptions found in the literature.

What these studies don't tell you

• A field research study requires extensive time and resources to carry out the multiple steps involved with preparing for the gathering of information, including for example, examining background information about the study site, obtaining permission to access the study site, and building trust and rapport with subjects.
• Requires a commitment to staying engaged in the field to ensure that you can adequately document events and behaviors as they unfold.
• The unpredictable nature of fieldwork means that researchers can never fully control the process of data gathering. They must maintain a flexible approach to studying the setting because events and circumstances can change quickly or unexpectedly.
• Findings can be difficult to interpret and verify without access to documents and other source materials that help to enhance the credibility of information obtained from the field  [i.e., the act of triangulating the data].
• Linking the research problem to the selection of study participants inhabiting their natural environment is critical. However, this specificity limits the ability to generalize findings to different situations or in other contexts or to infer courses of action applied to other settings or groups of people.
• The reporting of findings must take into account how the researcher themselves may have inadvertently affected respondents and their behaviors.

Historical Design

The purpose of a historical research design is to collect, verify, and synthesize evidence from the past to establish facts that defend or refute a hypothesis. It uses secondary sources and a variety of primary documentary evidence, such as, diaries, official records, reports, archives, and non-textual information [maps, pictures, audio and visual recordings]. The limitation is that the sources must be both authentic and valid.

• The historical research design is unobtrusive; the act of research does not affect the results of the study.
• The historical approach is well suited for trend analysis.
• Historical records can add important contextual background required to more fully understand and interpret a research problem.
• There is often no possibility of researcher-subject interaction that could affect the findings.
• Historical sources can be used over and over to study different research problems or to replicate a previous study.
• The ability to fulfill the aims of your research are directly related to the amount and quality of documentation available to understand the research problem.
• Since historical research relies on data from the past, there is no way to manipulate it to control for contemporary contexts.
• Interpreting historical sources can be very time consuming.
• The sources of historical materials must be archived consistently to ensure access. This may especially challenging for digital or online-only sources.
• Original authors bring their own perspectives and biases to the interpretation of past events and these biases are more difficult to ascertain in historical resources.
• Due to the lack of control over external variables, historical research is very weak with regard to the demands of internal validity.
• It is rare that the entirety of historical documentation needed to fully address a research problem is available for interpretation, therefore, gaps need to be acknowledged.

Howell, Martha C. and Walter Prevenier. From Reliable Sources: An Introduction to Historical Methods . Ithaca, NY: Cornell University Press, 2001; Lundy, Karen Saucier. "Historical Research." In The Sage Encyclopedia of Qualitative Research Methods . Lisa M. Given, editor. (Thousand Oaks, CA: Sage, 2008), pp. 396-400; Marius, Richard. and Melvin E. Page. A Short Guide to Writing about History . 9th edition. Boston, MA: Pearson, 2015; Savitt, Ronald. “Historical Research in Marketing.” Journal of Marketing 44 (Autumn, 1980): 52-58;  Gall, Meredith. Educational Research: An Introduction . Chapter 16, Historical Research. 8th ed. Boston, MA: Pearson/Allyn and Bacon, 2007.

Longitudinal Design

A longitudinal study follows the same sample over time and makes repeated observations. For example, with longitudinal surveys, the same group of people is interviewed at regular intervals, enabling researchers to track changes over time and to relate them to variables that might explain why the changes occur. Longitudinal research designs describe patterns of change and help establish the direction and magnitude of causal relationships. Measurements are taken on each variable over two or more distinct time periods. This allows the researcher to measure change in variables over time. It is a type of observational study sometimes referred to as a panel study.

• Longitudinal data facilitate the analysis of the duration of a particular phenomenon.
• Enables survey researchers to get close to the kinds of causal explanations usually attainable only with experiments.
• The design permits the measurement of differences or change in a variable from one period to another [i.e., the description of patterns of change over time].
• Longitudinal studies facilitate the prediction of future outcomes based upon earlier factors.
• The data collection method may change over time.
• Maintaining the integrity of the original sample can be difficult over an extended period of time.
• It can be difficult to show more than one variable at a time.
• This design often needs qualitative research data to explain fluctuations in the results.
• A longitudinal research design assumes present trends will continue unchanged.
• It can take a long period of time to gather results.
• There is a need to have a large sample size and accurate sampling to reach representativness.

Anastas, Jeane W. Research Design for Social Work and the Human Services . Chapter 6, Flexible Methods: Relational and Longitudinal Research. 2nd ed. New York: Columbia University Press, 1999; Forgues, Bernard, and Isabelle Vandangeon-Derumez. "Longitudinal Analyses." In Doing Management Research . Raymond-Alain Thiétart and Samantha Wauchope, editors. (London, England: Sage, 2001), pp. 332-351; Kalaian, Sema A. and Rafa M. Kasim. "Longitudinal Studies." In Encyclopedia of Survey Research Methods . Paul J. Lavrakas, ed. (Thousand Oaks, CA: Sage, 2008), pp. 440-441; Menard, Scott, editor. Longitudinal Research . Thousand Oaks, CA: Sage, 2002; Ployhart, Robert E. and Robert J. Vandenberg. "Longitudinal Research: The Theory, Design, and Analysis of Change.” Journal of Management 36 (January 2010): 94-120; Longitudinal Study. Wikipedia.

Meta-Analysis Design

Meta-analysis is an analytical methodology designed to systematically evaluate and summarize the results from a number of individual studies, thereby, increasing the overall sample size and the ability of the researcher to study effects of interest. The purpose is to not simply summarize existing knowledge, but to develop a new understanding of a research problem using synoptic reasoning. The main objectives of meta-analysis include analyzing differences in the results among studies and increasing the precision by which effects are estimated. A well-designed meta-analysis depends upon strict adherence to the criteria used for selecting studies and the availability of information in each study to properly analyze their findings. Lack of information can severely limit the type of analyzes and conclusions that can be reached. In addition, the more dissimilarity there is in the results among individual studies [heterogeneity], the more difficult it is to justify interpretations that govern a valid synopsis of results. A meta-analysis needs to fulfill the following requirements to ensure the validity of your findings:

• Clearly defined description of objectives, including precise definitions of the variables and outcomes that are being evaluated;
• A well-reasoned and well-documented justification for identification and selection of the studies;
• Assessment and explicit acknowledgment of any researcher bias in the identification and selection of those studies;
• Description and evaluation of the degree of heterogeneity among the sample size of studies reviewed; and,
• Justification of the techniques used to evaluate the studies.
• Can be an effective strategy for determining gaps in the literature.
• Provides a means of reviewing research published about a particular topic over an extended period of time and from a variety of sources.
• Is useful in clarifying what policy or programmatic actions can be justified on the basis of analyzing research results from multiple studies.
• Provides a method for overcoming small sample sizes in individual studies that previously may have had little relationship to each other.
• Can be used to generate new hypotheses or highlight research problems for future studies.
• Small violations in defining the criteria used for content analysis can lead to difficult to interpret and/or meaningless findings.
• A large sample size can yield reliable, but not necessarily valid, results.
• A lack of uniformity regarding, for example, the type of literature reviewed, how methods are applied, and how findings are measured within the sample of studies you are analyzing, can make the process of synthesis difficult to perform.
• Depending on the sample size, the process of reviewing and synthesizing multiple studies can be very time consuming.

Beck, Lewis W. "The Synoptic Method." The Journal of Philosophy 36 (1939): 337-345; Cooper, Harris, Larry V. Hedges, and Jeffrey C. Valentine, eds. The Handbook of Research Synthesis and Meta-Analysis . 2nd edition. New York: Russell Sage Foundation, 2009; Guzzo, Richard A., Susan E. Jackson and Raymond A. Katzell. “Meta-Analysis Analysis.” In Research in Organizational Behavior , Volume 9. (Greenwich, CT: JAI Press, 1987), pp 407-442; Lipsey, Mark W. and David B. Wilson. Practical Meta-Analysis . Thousand Oaks, CA: Sage Publications, 2001; Study Design 101. Meta-Analysis. The Himmelfarb Health Sciences Library, George Washington University; Timulak, Ladislav. “Qualitative Meta-Analysis.” In The SAGE Handbook of Qualitative Data Analysis . Uwe Flick, editor. (Los Angeles, CA: Sage, 2013), pp. 481-495; Walker, Esteban, Adrian V. Hernandez, and Micheal W. Kattan. "Meta-Analysis: It's Strengths and Limitations." Cleveland Clinic Journal of Medicine 75 (June 2008): 431-439.

Mixed-Method Design

• Narrative and non-textual information can add meaning to numeric data, while numeric data can add precision to narrative and non-textual information.
• Can utilize existing data while at the same time generating and testing a grounded theory approach to describe and explain the phenomenon under study.
• A broader, more complex research problem can be investigated because the researcher is not constrained by using only one method.
• The strengths of one method can be used to overcome the inherent weaknesses of another method.
• Can provide stronger, more robust evidence to support a conclusion or set of recommendations.
• May generate new knowledge new insights or uncover hidden insights, patterns, or relationships that a single methodological approach might not reveal.
• Produces more complete knowledge and understanding of the research problem that can be used to increase the generalizability of findings applied to theory or practice.
• A researcher must be proficient in understanding how to apply multiple methods to investigating a research problem as well as be proficient in optimizing how to design a study that coherently melds them together.
• Can increase the likelihood of conflicting results or ambiguous findings that inhibit drawing a valid conclusion or setting forth a recommended course of action [e.g., sample interview responses do not support existing statistical data].
• Because the research design can be very complex, reporting the findings requires a well-organized narrative, clear writing style, and precise word choice.
• Design invites collaboration among experts. However, merging different investigative approaches and writing styles requires more attention to the overall research process than studies conducted using only one methodological paradigm.
• Concurrent merging of quantitative and qualitative research requires greater attention to having adequate sample sizes, using comparable samples, and applying a consistent unit of analysis. For sequential designs where one phase of qualitative research builds on the quantitative phase or vice versa, decisions about what results from the first phase to use in the next phase, the choice of samples and estimating reasonable sample sizes for both phases, and the interpretation of results from both phases can be difficult.
• Due to multiple forms of data being collected and analyzed, this design requires extensive time and resources to carry out the multiple steps involved in data gathering and interpretation.

Burch, Patricia and Carolyn J. Heinrich. Mixed Methods for Policy Research and Program Evaluation . Thousand Oaks, CA: Sage, 2016; Creswell, John w. et al. Best Practices for Mixed Methods Research in the Health Sciences . Bethesda, MD: Office of Behavioral and Social Sciences Research, National Institutes of Health, 2010Creswell, John W. Research Design: Qualitative, Quantitative, and Mixed Methods Approaches . 4th edition. Thousand Oaks, CA: Sage Publications, 2014; Domínguez, Silvia, editor. Mixed Methods Social Networks Research . Cambridge, UK: Cambridge University Press, 2014; Hesse-Biber, Sharlene Nagy. Mixed Methods Research: Merging Theory with Practice . New York: Guilford Press, 2010; Niglas, Katrin. “How the Novice Researcher Can Make Sense of Mixed Methods Designs.” International Journal of Multiple Research Approaches 3 (2009): 34-46; Onwuegbuzie, Anthony J. and Nancy L. Leech. “Linking Research Questions to Mixed Methods Data Analysis Procedures.” The Qualitative Report 11 (September 2006): 474-498; Tashakorri, Abbas and John W. Creswell. “The New Era of Mixed Methods.” Journal of Mixed Methods Research 1 (January 2007): 3-7; Zhanga, Wanqing. “Mixed Methods Application in Health Intervention Research: A Multiple Case Study.” International Journal of Multiple Research Approaches 8 (2014): 24-35 .

Observational Design

This type of research design draws a conclusion by comparing subjects against a control group, in cases where the researcher has no control over the experiment. There are two general types of observational designs. In direct observations, people know that you are watching them. Unobtrusive measures involve any method for studying behavior where individuals do not know they are being observed. An observational study allows a useful insight into a phenomenon and avoids the ethical and practical difficulties of setting up a large and cumbersome research project.

• Observational studies are usually flexible and do not necessarily need to be structured around a hypothesis about what you expect to observe [data is emergent rather than pre-existing].
• The researcher is able to collect in-depth information about a particular behavior.
• Can reveal interrelationships among multifaceted dimensions of group interactions.
• You can generalize your results to real life situations.
• Observational research is useful for discovering what variables may be important before applying other methods like experiments.
• Observation research designs account for the complexity of group behaviors.
• Reliability of data is low because seeing behaviors occur over and over again may be a time consuming task and are difficult to replicate.
• In observational research, findings may only reflect a unique sample population and, thus, cannot be generalized to other groups.
• There can be problems with bias as the researcher may only "see what they want to see."
• There is no possibility to determine "cause and effect" relationships since nothing is manipulated.
• Sources or subjects may not all be equally credible.
• Any group that is knowingly studied is altered to some degree by the presence of the researcher, therefore, potentially skewing any data collected.

Atkinson, Paul and Martyn Hammersley. “Ethnography and Participant Observation.” In Handbook of Qualitative Research . Norman K. Denzin and Yvonna S. Lincoln, eds. (Thousand Oaks, CA: Sage, 1994), pp. 248-261; Observational Research. Research Methods by Dummies. Department of Psychology. California State University, Fresno, 2006; Patton Michael Quinn. Qualitiative Research and Evaluation Methods . Chapter 6, Fieldwork Strategies and Observational Methods. 3rd ed. Thousand Oaks, CA: Sage, 2002; Payne, Geoff and Judy Payne. "Observation." In Key Concepts in Social Research . The SAGE Key Concepts series. (London, England: Sage, 2004), pp. 158-162; Rosenbaum, Paul R. Design of Observational Studies . New York: Springer, 2010;Williams, J. Patrick. "Nonparticipant Observation." In The Sage Encyclopedia of Qualitative Research Methods . Lisa M. Given, editor.(Thousand Oaks, CA: Sage, 2008), pp. 562-563.

Philosophical Design

Understood more as an broad approach to examining a research problem than a methodological design, philosophical analysis and argumentation is intended to challenge deeply embedded, often intractable, assumptions underpinning an area of study. This approach uses the tools of argumentation derived from philosophical traditions, concepts, models, and theories to critically explore and challenge, for example, the relevance of logic and evidence in academic debates, to analyze arguments about fundamental issues, or to discuss the root of existing discourse about a research problem. These overarching tools of analysis can be framed in three ways:

• Ontology -- the study that describes the nature of reality; for example, what is real and what is not, what is fundamental and what is derivative?
• Epistemology -- the study that explores the nature of knowledge; for example, by what means does knowledge and understanding depend upon and how can we be certain of what we know?
• Axiology -- the study of values; for example, what values does an individual or group hold and why? How are values related to interest, desire, will, experience, and means-to-end? And, what is the difference between a matter of fact and a matter of value?
• Can provide a basis for applying ethical decision-making to practice.
• Functions as a means of gaining greater self-understanding and self-knowledge about the purposes of research.
• Brings clarity to general guiding practices and principles of an individual or group.
• Philosophy informs methodology.
• Refine concepts and theories that are invoked in relatively unreflective modes of thought and discourse.
• Beyond methodology, philosophy also informs critical thinking about epistemology and the structure of reality (metaphysics).
• Offers clarity and definition to the practical and theoretical uses of terms, concepts, and ideas.
• Limited application to specific research problems [answering the "So What?" question in social science research].
• Analysis can be abstract, argumentative, and limited in its practical application to real-life issues.
• While a philosophical analysis may render problematic that which was once simple or taken-for-granted, the writing can be dense and subject to unnecessary jargon, overstatement, and/or excessive quotation and documentation.
• There are limitations in the use of metaphor as a vehicle of philosophical analysis.
• There can be analytical difficulties in moving from philosophy to advocacy and between abstract thought and application to the phenomenal world.

Burton, Dawn. "Part I, Philosophy of the Social Sciences." In Research Training for Social Scientists . (London, England: Sage, 2000), pp. 1-5; Chapter 4, Research Methodology and Design. Unisa Institutional Repository (UnisaIR), University of South Africa; Jarvie, Ian C., and Jesús Zamora-Bonilla, editors. The SAGE Handbook of the Philosophy of Social Sciences . London: Sage, 2011; Labaree, Robert V. and Ross Scimeca. “The Philosophical Problem of Truth in Librarianship.” The Library Quarterly 78 (January 2008): 43-70; Maykut, Pamela S. Beginning Qualitative Research: A Philosophic and Practical Guide . Washington, DC: Falmer Press, 1994; McLaughlin, Hugh. "The Philosophy of Social Research." In Understanding Social Work Research . 2nd edition. (London: SAGE Publications Ltd., 2012), pp. 24-47; Stanford Encyclopedia of Philosophy . Metaphysics Research Lab, CSLI, Stanford University, 2013.

Sequential Design

• The researcher has a limitless option when it comes to sample size and the sampling schedule.
• Due to the repetitive nature of this research design, minor changes and adjustments can be done during the initial parts of the study to correct and hone the research method.
• This is a useful design for exploratory studies.
• There is very little effort on the part of the researcher when performing this technique. It is generally not expensive, time consuming, or workforce intensive.
• Because the study is conducted serially, the results of one sample are known before the next sample is taken and analyzed. This provides opportunities for continuous improvement of sampling and methods of analysis.
• The sampling method is not representative of the entire population. The only possibility of approaching representativeness is when the researcher chooses to use a very large sample size significant enough to represent a significant portion of the entire population. In this case, moving on to study a second or more specific sample can be difficult.
• The design cannot be used to create conclusions and interpretations that pertain to an entire population because the sampling technique is not randomized. Generalizability from findings is, therefore, limited.
• Difficult to account for and interpret variation from one sample to another over time, particularly when using qualitative methods of data collection.

Betensky, Rebecca. Harvard University, Course Lecture Note slides; Bovaird, James A. and Kevin A. Kupzyk. "Sequential Design." In Encyclopedia of Research Design . Neil J. Salkind, editor. (Thousand Oaks, CA: Sage, 2010), pp. 1347-1352; Cresswell, John W. Et al. “Advanced Mixed-Methods Research Designs.” In Handbook of Mixed Methods in Social and Behavioral Research . Abbas Tashakkori and Charles Teddle, eds. (Thousand Oaks, CA: Sage, 2003), pp. 209-240; Henry, Gary T. "Sequential Sampling." In The SAGE Encyclopedia of Social Science Research Methods . Michael S. Lewis-Beck, Alan Bryman and Tim Futing Liao, editors. (Thousand Oaks, CA: Sage, 2004), pp. 1027-1028; Nataliya V. Ivankova. “Using Mixed-Methods Sequential Explanatory Design: From Theory to Practice.” Field Methods 18 (February 2006): 3-20; Bovaird, James A. and Kevin A. Kupzyk. “Sequential Design.” In Encyclopedia of Research Design . Neil J. Salkind, ed. Thousand Oaks, CA: Sage, 2010; Sequential Analysis. Wikipedia.

Systematic Review

• A systematic review synthesizes the findings of multiple studies related to each other by incorporating strategies of analysis and interpretation intended to reduce biases and random errors.
• The application of critical exploration, evaluation, and synthesis methods separates insignificant, unsound, or redundant research from the most salient and relevant studies worthy of reflection.
• They can be use to identify, justify, and refine hypotheses, recognize and avoid hidden problems in prior studies, and explain data inconsistencies and conflicts in data.
• Systematic reviews can be used to help policy makers formulate evidence-based guidelines and regulations.
• The use of strict, explicit, and pre-determined methods of synthesis, when applied appropriately, provide reliable estimates about the effects of interventions, evaluations, and effects related to the overarching research problem investigated by each study under review.
• Systematic reviews illuminate where knowledge or thorough understanding of a research problem is lacking and, therefore, can then be used to guide future research.
• The accepted inclusion of unpublished studies [i.e., grey literature] ensures the broadest possible way to analyze and interpret research on a topic.
• Results of the synthesis can be generalized and the findings extrapolated into the general population with more validity than most other types of studies .
• Systematic reviews do not create new knowledge per se; they are a method for synthesizing existing studies about a research problem in order to gain new insights and determine gaps in the literature.
• The way researchers have carried out their investigations [e.g., the period of time covered, number of participants, sources of data analyzed, etc.] can make it difficult to effectively synthesize studies.
• The inclusion of unpublished studies can introduce bias into the review because they may not have undergone a rigorous peer-review process prior to publication. Examples may include conference presentations or proceedings, publications from government agencies, white papers, working papers, and internal documents from organizations, and doctoral dissertations and Master's theses.

Denyer, David and David Tranfield. "Producing a Systematic Review." In The Sage Handbook of Organizational Research Methods .  David A. Buchanan and Alan Bryman, editors. ( Thousand Oaks, CA: Sage Publications, 2009), pp. 671-689; Foster, Margaret J. and Sarah T. Jewell, editors. Assembling the Pieces of a Systematic Review: A Guide for Librarians . Lanham, MD: Rowman and Littlefield, 2017; Gough, David, Sandy Oliver, James Thomas, editors. Introduction to Systematic Reviews . 2nd edition. Los Angeles, CA: Sage Publications, 2017; Gopalakrishnan, S. and P. Ganeshkumar. “Systematic Reviews and Meta-analysis: Understanding the Best Evidence in Primary Healthcare.” Journal of Family Medicine and Primary Care 2 (2013): 9-14; Gough, David, James Thomas, and Sandy Oliver. "Clarifying Differences between Review Designs and Methods." Systematic Reviews 1 (2012): 1-9; Khan, Khalid S., Regina Kunz, Jos Kleijnen, and Gerd Antes. “Five Steps to Conducting a Systematic Review.” Journal of the Royal Society of Medicine 96 (2003): 118-121; Mulrow, C. D. “Systematic Reviews: Rationale for Systematic Reviews.” BMJ 309:597 (September 1994); O'Dwyer, Linda C., and Q. Eileen Wafford. "Addressing Challenges with Systematic Review Teams through Effective Communication: A Case Report." Journal of the Medical Library Association 109 (October 2021): 643-647; Okoli, Chitu, and Kira Schabram. "A Guide to Conducting a Systematic Literature Review of Information Systems Research."  Sprouts: Working Papers on Information Systems 10 (2010); Siddaway, Andy P., Alex M. Wood, and Larry V. Hedges. "How to Do a Systematic Review: A Best Practice Guide for Conducting and Reporting Narrative Reviews, Meta-analyses, and Meta-syntheses." Annual Review of Psychology 70 (2019): 747-770; Torgerson, Carole J. “Publication Bias: The Achilles’ Heel of Systematic Reviews?” British Journal of Educational Studies 54 (March 2006): 89-102; Torgerson, Carole. Systematic Reviews . New York: Continuum, 2003.

• << Previous: Purpose of Guide
• Next: Design Flaws to Avoid >>
• Last Updated: Sep 4, 2024 9:40 AM
• URL: https://libguides.usc.edu/writingguide

Chapter 2. Research Design

Getting started.

When I teach undergraduates qualitative research methods, the final product of the course is a “research proposal” that incorporates all they have learned and enlists the knowledge they have learned about qualitative research methods in an original design that addresses a particular research question. I highly recommend you think about designing your own research study as you progress through this textbook. Even if you don’t have a study in mind yet, it can be a helpful exercise as you progress through the course. But how to start? How can one design a research study before they even know what research looks like? This chapter will serve as a brief overview of the research design process to orient you to what will be coming in later chapters. Think of it as a “skeleton” of what you will read in more detail in later chapters. Ideally, you will read this chapter both now (in sequence) and later during your reading of the remainder of the text. Do not worry if you have questions the first time you read this chapter. Many things will become clearer as the text advances and as you gain a deeper understanding of all the components of good qualitative research. This is just a preliminary map to get you on the right road.

Research Design Steps

Before you even get started, you will need to have a broad topic of interest in mind. [1] . In my experience, students can confuse this broad topic with the actual research question, so it is important to clearly distinguish the two. And the place to start is the broad topic. It might be, as was the case with me, working-class college students. But what about working-class college students? What’s it like to be one? Why are there so few compared to others? How do colleges assist (or fail to assist) them? What interested me was something I could barely articulate at first and went something like this: “Why was it so difficult and lonely to be me?” And by extension, “Did others share this experience?”

Once you have a general topic, reflect on why this is important to you. Sometimes we connect with a topic and we don’t really know why. Even if you are not willing to share the real underlying reason you are interested in a topic, it is important that you know the deeper reasons that motivate you. Otherwise, it is quite possible that at some point during the research, you will find yourself turned around facing the wrong direction. I have seen it happen many times. The reason is that the research question is not the same thing as the general topic of interest, and if you don’t know the reasons for your interest, you are likely to design a study answering a research question that is beside the point—to you, at least. And this means you will be much less motivated to carry your research to completion.

Researcher Note

Why do you employ qualitative research methods in your area of study? What are the advantages of qualitative research methods for studying mentorship?

Qualitative research methods are a huge opportunity to increase access, equity, inclusion, and social justice. Qualitative research allows us to engage and examine the uniquenesses/nuances within minoritized and dominant identities and our experiences with these identities. Qualitative research allows us to explore a specific topic, and through that exploration, we can link history to experiences and look for patterns or offer up a unique phenomenon. There’s such beauty in being able to tell a particular story, and qualitative research is a great mode for that! For our work, we examined the relationships we typically use the term mentorship for but didn’t feel that was quite the right word. Qualitative research allowed us to pick apart what we did and how we engaged in our relationships, which then allowed us to more accurately describe what was unique about our mentorship relationships, which we ultimately named liberationships ( McAloney and Long 2021) . Qualitative research gave us the means to explore, process, and name our experiences; what a powerful tool!

How do you come up with ideas for what to study (and how to study it)? Where did you get the idea for studying mentorship?

Coming up with ideas for research, for me, is kind of like Googling a question I have, not finding enough information, and then deciding to dig a little deeper to get the answer. The idea to study mentorship actually came up in conversation with my mentorship triad. We were talking in one of our meetings about our relationship—kind of meta, huh? We discussed how we felt that mentorship was not quite the right term for the relationships we had built. One of us asked what was different about our relationships and mentorship. This all happened when I was taking an ethnography course. During the next session of class, we were discussing auto- and duoethnography, and it hit me—let’s explore our version of mentorship, which we later went on to name liberationships ( McAloney and Long 2021 ). The idea and questions came out of being curious and wanting to find an answer. As I continue to research, I see opportunities in questions I have about my work or during conversations that, in our search for answers, end up exposing gaps in the literature. If I can’t find the answer already out there, I can study it.

—Kim McAloney, PhD, College Student Services Administration Ecampus coordinator and instructor

When you have a better idea of why you are interested in what it is that interests you, you may be surprised to learn that the obvious approaches to the topic are not the only ones. For example, let’s say you think you are interested in preserving coastal wildlife. And as a social scientist, you are interested in policies and practices that affect the long-term viability of coastal wildlife, especially around fishing communities. It would be natural then to consider designing a research study around fishing communities and how they manage their ecosystems. But when you really think about it, you realize that what interests you the most is how people whose livelihoods depend on a particular resource act in ways that deplete that resource. Or, even deeper, you contemplate the puzzle, “How do people justify actions that damage their surroundings?” Now, there are many ways to design a study that gets at that broader question, and not all of them are about fishing communities, although that is certainly one way to go. Maybe you could design an interview-based study that includes and compares loggers, fishers, and desert golfers (those who golf in arid lands that require a great deal of wasteful irrigation). Or design a case study around one particular example where resources were completely used up by a community. Without knowing what it is you are really interested in, what motivates your interest in a surface phenomenon, you are unlikely to come up with the appropriate research design.

These first stages of research design are often the most difficult, but have patience . Taking the time to consider why you are going to go through a lot of trouble to get answers will prevent a lot of wasted energy in the future.

There are distinct reasons for pursuing particular research questions, and it is helpful to distinguish between them.  First, you may be personally motivated.  This is probably the most important and the most often overlooked.   What is it about the social world that sparks your curiosity? What bothers you? What answers do you need in order to keep living? For me, I knew I needed to get a handle on what higher education was for before I kept going at it. I needed to understand why I felt so different from my peers and whether this whole “higher education” thing was “for the likes of me” before I could complete my degree. That is the personal motivation question. Your personal motivation might also be political in nature, in that you want to change the world in a particular way. It’s all right to acknowledge this. In fact, it is better to acknowledge it than to hide it.

There are also academic and professional motivations for a particular study.  If you are an absolute beginner, these may be difficult to find. We’ll talk more about this when we discuss reviewing the literature. Simply put, you are probably not the only person in the world to have thought about this question or issue and those related to it. So how does your interest area fit into what others have studied? Perhaps there is a good study out there of fishing communities, but no one has quite asked the “justification” question. You are motivated to address this to “fill the gap” in our collective knowledge. And maybe you are really not at all sure of what interests you, but you do know that [insert your topic] interests a lot of people, so you would like to work in this area too. You want to be involved in the academic conversation. That is a professional motivation and a very important one to articulate.

Practical and strategic motivations are a third kind. Perhaps you want to encourage people to take better care of the natural resources around them. If this is also part of your motivation, you will want to design your research project in a way that might have an impact on how people behave in the future. There are many ways to do this, one of which is using qualitative research methods rather than quantitative research methods, as the findings of qualitative research are often easier to communicate to a broader audience than the results of quantitative research. You might even be able to engage the community you are studying in the collecting and analyzing of data, something taboo in quantitative research but actively embraced and encouraged by qualitative researchers. But there are other practical reasons, such as getting “done” with your research in a certain amount of time or having access (or no access) to certain information. There is nothing wrong with considering constraints and opportunities when designing your study. Or maybe one of the practical or strategic goals is about learning competence in this area so that you can demonstrate the ability to conduct interviews and focus groups with future employers. Keeping that in mind will help shape your study and prevent you from getting sidetracked using a technique that you are less invested in learning about.

STOP HERE for a moment

I recommend you write a paragraph (at least) explaining your aims and goals. Include a sentence about each of the following: personal/political goals, practical or professional/academic goals, and practical/strategic goals. Think through how all of the goals are related and can be achieved by this particular research study . If they can’t, have a rethink. Perhaps this is not the best way to go about it.

You will also want to be clear about the purpose of your study. “Wait, didn’t we just do this?” you might ask. No! Your goals are not the same as the purpose of the study, although they are related. You can think about purpose lying on a continuum from “ theory ” to “action” (figure 2.1). Sometimes you are doing research to discover new knowledge about the world, while other times you are doing a study because you want to measure an impact or make a difference in the world.

Basic research involves research that is done for the sake of “pure” knowledge—that is, knowledge that, at least at this moment in time, may not have any apparent use or application. Often, and this is very important, knowledge of this kind is later found to be extremely helpful in solving problems. So one way of thinking about basic research is that it is knowledge for which no use is yet known but will probably one day prove to be extremely useful. If you are doing basic research, you do not need to argue its usefulness, as the whole point is that we just don’t know yet what this might be.

Researchers engaged in basic research want to understand how the world operates. They are interested in investigating a phenomenon to get at the nature of reality with regard to that phenomenon. The basic researcher’s purpose is to understand and explain ( Patton 2002:215 ).

Basic research is interested in generating and testing hypotheses about how the world works. Grounded Theory is one approach to qualitative research methods that exemplifies basic research (see chapter 4). Most academic journal articles publish basic research findings. If you are working in academia (e.g., writing your dissertation), the default expectation is that you are conducting basic research.

Applied research in the social sciences is research that addresses human and social problems. Unlike basic research, the researcher has expectations that the research will help contribute to resolving a problem, if only by identifying its contours, history, or context. From my experience, most students have this as their baseline assumption about research. Why do a study if not to make things better? But this is a common mistake. Students and their committee members are often working with default assumptions here—the former thinking about applied research as their purpose, the latter thinking about basic research: “The purpose of applied research is to contribute knowledge that will help people to understand the nature of a problem in order to intervene, thereby allowing human beings to more effectively control their environment. While in basic research the source of questions is the tradition within a scholarly discipline, in applied research the source of questions is in the problems and concerns experienced by people and by policymakers” ( Patton 2002:217 ).

Applied research is less geared toward theory in two ways. First, its questions do not derive from previous literature. For this reason, applied research studies have much more limited literature reviews than those found in basic research (although they make up for this by having much more “background” about the problem). Second, it does not generate theory in the same way as basic research does. The findings of an applied research project may not be generalizable beyond the boundaries of this particular problem or context. The findings are more limited. They are useful now but may be less useful later. This is why basic research remains the default “gold standard” of academic research.

Evaluation research is research that is designed to evaluate or test the effectiveness of specific solutions and programs addressing specific social problems. We already know the problems, and someone has already come up with solutions. There might be a program, say, for first-generation college students on your campus. Does this program work? Are first-generation students who participate in the program more likely to graduate than those who do not? These are the types of questions addressed by evaluation research. There are two types of research within this broader frame; however, one more action-oriented than the next. In summative evaluation , an overall judgment about the effectiveness of a program or policy is made. Should we continue our first-gen program? Is it a good model for other campuses? Because the purpose of such summative evaluation is to measure success and to determine whether this success is scalable (capable of being generalized beyond the specific case), quantitative data is more often used than qualitative data. In our example, we might have “outcomes” data for thousands of students, and we might run various tests to determine if the better outcomes of those in the program are statistically significant so that we can generalize the findings and recommend similar programs elsewhere. Qualitative data in the form of focus groups or interviews can then be used for illustrative purposes, providing more depth to the quantitative analyses. In contrast, formative evaluation attempts to improve a program or policy (to help “form” or shape its effectiveness). Formative evaluations rely more heavily on qualitative data—case studies, interviews, focus groups. The findings are meant not to generalize beyond the particular but to improve this program. If you are a student seeking to improve your qualitative research skills and you do not care about generating basic research, formative evaluation studies might be an attractive option for you to pursue, as there are always local programs that need evaluation and suggestions for improvement. Again, be very clear about your purpose when talking through your research proposal with your committee.

Action research takes a further step beyond evaluation, even formative evaluation, to being part of the solution itself. This is about as far from basic research as one could get and definitely falls beyond the scope of “science,” as conventionally defined. The distinction between action and research is blurry, the research methods are often in constant flux, and the only “findings” are specific to the problem or case at hand and often are findings about the process of intervention itself. Rather than evaluate a program as a whole, action research often seeks to change and improve some particular aspect that may not be working—maybe there is not enough diversity in an organization or maybe women’s voices are muted during meetings and the organization wonders why and would like to change this. In a further step, participatory action research , those women would become part of the research team, attempting to amplify their voices in the organization through participation in the action research. As action research employs methods that involve people in the process, focus groups are quite common.

If you are working on a thesis or dissertation, chances are your committee will expect you to be contributing to fundamental knowledge and theory ( basic research ). If your interests lie more toward the action end of the continuum, however, it is helpful to talk to your committee about this before you get started. Knowing your purpose in advance will help avoid misunderstandings during the later stages of the research process!

The Research Question

Once you have written your paragraph and clarified your purpose and truly know that this study is the best study for you to be doing right now , you are ready to write and refine your actual research question. Know that research questions are often moving targets in qualitative research, that they can be refined up to the very end of data collection and analysis. But you do have to have a working research question at all stages. This is your “anchor” when you get lost in the data. What are you addressing? What are you looking at and why? Your research question guides you through the thicket. It is common to have a whole host of questions about a phenomenon or case, both at the outset and throughout the study, but you should be able to pare it down to no more than two or three sentences when asked. These sentences should both clarify the intent of the research and explain why this is an important question to answer. More on refining your research question can be found in chapter 4.

Chances are, you will have already done some prior reading before coming up with your interest and your questions, but you may not have conducted a systematic literature review. This is the next crucial stage to be completed before venturing further. You don’t want to start collecting data and then realize that someone has already beaten you to the punch. A review of the literature that is already out there will let you know (1) if others have already done the study you are envisioning; (2) if others have done similar studies, which can help you out; and (3) what ideas or concepts are out there that can help you frame your study and make sense of your findings. More on literature reviews can be found in chapter 9.

In addition to reviewing the literature for similar studies to what you are proposing, it can be extremely helpful to find a study that inspires you. This may have absolutely nothing to do with the topic you are interested in but is written so beautifully or organized so interestingly or otherwise speaks to you in such a way that you want to post it somewhere to remind you of what you want to be doing. You might not understand this in the early stages—why would you find a study that has nothing to do with the one you are doing helpful? But trust me, when you are deep into analysis and writing, having an inspirational model in view can help you push through. If you are motivated to do something that might change the world, you probably have read something somewhere that inspired you. Go back to that original inspiration and read it carefully and see how they managed to convey the passion that you so appreciate.

At this stage, you are still just getting started. There are a lot of things to do before setting forth to collect data! You’ll want to consider and choose a research tradition and a set of data-collection techniques that both help you answer your research question and match all your aims and goals. For example, if you really want to help migrant workers speak for themselves, you might draw on feminist theory and participatory action research models. Chapters 3 and 4 will provide you with more information on epistemologies and approaches.

Next, you have to clarify your “units of analysis.” What is the level at which you are focusing your study? Often, the unit in qualitative research methods is individual people, or “human subjects.” But your units of analysis could just as well be organizations (colleges, hospitals) or programs or even whole nations. Think about what it is you want to be saying at the end of your study—are the insights you are hoping to make about people or about organizations or about something else entirely? A unit of analysis can even be a historical period! Every unit of analysis will call for a different kind of data collection and analysis and will produce different kinds of “findings” at the conclusion of your study. [2]

Regardless of what unit of analysis you select, you will probably have to consider the “human subjects” involved in your research. [3] Who are they? What interactions will you have with them—that is, what kind of data will you be collecting? Before answering these questions, define your population of interest and your research setting. Use your research question to help guide you.

Let’s use an example from a real study. In Geographies of Campus Inequality , Benson and Lee ( 2020 ) list three related research questions: “(1) What are the different ways that first-generation students organize their social, extracurricular, and academic activities at selective and highly selective colleges? (2) how do first-generation students sort themselves and get sorted into these different types of campus lives; and (3) how do these different patterns of campus engagement prepare first-generation students for their post-college lives?” (3).

Note that we are jumping into this a bit late, after Benson and Lee have described previous studies (the literature review) and what is known about first-generation college students and what is not known. They want to know about differences within this group, and they are interested in ones attending certain kinds of colleges because those colleges will be sites where academic and extracurricular pressures compete. That is the context for their three related research questions. What is the population of interest here? First-generation college students . What is the research setting? Selective and highly selective colleges . But a host of questions remain. Which students in the real world, which colleges? What about gender, race, and other identity markers? Will the students be asked questions? Are the students still in college, or will they be asked about what college was like for them? Will they be observed? Will they be shadowed? Will they be surveyed? Will they be asked to keep diaries of their time in college? How many students? How many colleges? For how long will they be observed?

Recommendation

Take a moment and write down suggestions for Benson and Lee before continuing on to what they actually did.

Have you written down your own suggestions? Good. Now let’s compare those with what they actually did. Benson and Lee drew on two sources of data: in-depth interviews with sixty-four first-generation students and survey data from a preexisting national survey of students at twenty-eight selective colleges. Let’s ignore the survey for our purposes here and focus on those interviews. The interviews were conducted between 2014 and 2016 at a single selective college, “Hilltop” (a pseudonym ). They employed a “purposive” sampling strategy to ensure an equal number of male-identifying and female-identifying students as well as equal numbers of White, Black, and Latinx students. Each student was interviewed once. Hilltop is a selective liberal arts college in the northeast that enrolls about three thousand students.

How did your suggestions match up to those actually used by the researchers in this study? It is possible your suggestions were too ambitious? Beginning qualitative researchers can often make that mistake. You want a research design that is both effective (it matches your question and goals) and doable. You will never be able to collect data from your entire population of interest (unless your research question is really so narrow to be relevant to very few people!), so you will need to come up with a good sample. Define the criteria for this sample, as Benson and Lee did when deciding to interview an equal number of students by gender and race categories. Define the criteria for your sample setting too. Hilltop is typical for selective colleges. That was a research choice made by Benson and Lee. For more on sampling and sampling choices, see chapter 5.

Benson and Lee chose to employ interviews. If you also would like to include interviews, you have to think about what will be asked in them. Most interview-based research involves an interview guide, a set of questions or question areas that will be asked of each participant. The research question helps you create a relevant interview guide. You want to ask questions whose answers will provide insight into your research question. Again, your research question is the anchor you will continually come back to as you plan for and conduct your study. It may be that once you begin interviewing, you find that people are telling you something totally unexpected, and this makes you rethink your research question. That is fine. Then you have a new anchor. But you always have an anchor. More on interviewing can be found in chapter 11.

Let’s imagine Benson and Lee also observed college students as they went about doing the things college students do, both in the classroom and in the clubs and social activities in which they participate. They would have needed a plan for this. Would they sit in on classes? Which ones and how many? Would they attend club meetings and sports events? Which ones and how many? Would they participate themselves? How would they record their observations? More on observation techniques can be found in both chapters 13 and 14.

At this point, the design is almost complete. You know why you are doing this study, you have a clear research question to guide you, you have identified your population of interest and research setting, and you have a reasonable sample of each. You also have put together a plan for data collection, which might include drafting an interview guide or making plans for observations. And so you know exactly what you will be doing for the next several months (or years!). To put the project into action, there are a few more things necessary before actually going into the field.

First, you will need to make sure you have any necessary supplies, including recording technology. These days, many researchers use their phones to record interviews. Second, you will need to draft a few documents for your participants. These include informed consent forms and recruiting materials, such as posters or email texts, that explain what this study is in clear language. Third, you will draft a research protocol to submit to your institutional review board (IRB) ; this research protocol will include the interview guide (if you are using one), the consent form template, and all examples of recruiting material. Depending on your institution and the details of your study design, it may take weeks or even, in some unfortunate cases, months before you secure IRB approval. Make sure you plan on this time in your project timeline. While you wait, you can continue to review the literature and possibly begin drafting a section on the literature review for your eventual presentation/publication. More on IRB procedures can be found in chapter 8 and more general ethical considerations in chapter 7.

Once you have approval, you can begin!

Research Design Checklist

Before data collection begins, do the following:

• Write a paragraph explaining your aims and goals (personal/political, practical/strategic, professional/academic).
• Define your research question; write two to three sentences that clarify the intent of the research and why this is an important question to answer.
• Review the literature for similar studies that address your research question or similar research questions; think laterally about some literature that might be helpful or illuminating but is not exactly about the same topic.
• Find a written study that inspires you—it may or may not be on the research question you have chosen.
• Consider and choose a research tradition and set of data-collection techniques that (1) help answer your research question and (2) match your aims and goals.
• Define your population of interest and your research setting.
• Define the criteria for your sample (How many? Why these? How will you find them, gain access, and acquire consent?).
• If you are conducting interviews, draft an interview guide.
•  If you are making observations, create a plan for observations (sites, times, recording, access).
• Acquire any necessary technology (recording devices/software).
• Draft consent forms that clearly identify the research focus and selection process.
• Create recruiting materials (posters, email, texts).
• Apply for IRB approval (proposal plus consent form plus recruiting materials).
• Block out time for collecting data.
• At the end of the chapter, you will find a " Research Design Checklist " that summarizes the main recommendations made here ↵
• For example, if your focus is society and culture , you might collect data through observation or a case study. If your focus is individual lived experience , you are probably going to be interviewing some people. And if your focus is language and communication , you will probably be analyzing text (written or visual). ( Marshall and Rossman 2016:16 ). ↵
• You may not have any "live" human subjects. There are qualitative research methods that do not require interactions with live human beings - see chapter 16 , "Archival and Historical Sources." But for the most part, you are probably reading this textbook because you are interested in doing research with people. The rest of the chapter will assume this is the case. ↵

One of the primary methodological traditions of inquiry in qualitative research, ethnography is the study of a group or group culture, largely through observational fieldwork supplemented by interviews. It is a form of fieldwork that may include participant-observation data collection. See chapter 14 for a discussion of deep ethnography. 

A methodological tradition of inquiry and research design that focuses on an individual case (e.g., setting, institution, or sometimes an individual) in order to explore its complexity, history, and interactive parts.  As an approach, it is particularly useful for obtaining a deep appreciation of an issue, event, or phenomenon of interest in its particular context.

The controlling force in research; can be understood as lying on a continuum from basic research (knowledge production) to action research (effecting change).

In its most basic sense, a theory is a story we tell about how the world works that can be tested with empirical evidence.  In qualitative research, we use the term in a variety of ways, many of which are different from how they are used by quantitative researchers.  Although some qualitative research can be described as “testing theory,” it is more common to “build theory” from the data using inductive reasoning , as done in Grounded Theory .  There are so-called “grand theories” that seek to integrate a whole series of findings and stories into an overarching paradigm about how the world works, and much smaller theories or concepts about particular processes and relationships.  Theory can even be used to explain particular methodological perspectives or approaches, as in Institutional Ethnography , which is both a way of doing research and a theory about how the world works.

Research that is interested in generating and testing hypotheses about how the world works.

A methodological tradition of inquiry and approach to analyzing qualitative data in which theories emerge from a rigorous and systematic process of induction.  This approach was pioneered by the sociologists Glaser and Strauss (1967).  The elements of theory generated from comparative analysis of data are, first, conceptual categories and their properties and, second, hypotheses or generalized relations among the categories and their properties – “The constant comparing of many groups draws the [researcher’s] attention to their many similarities and differences.  Considering these leads [the researcher] to generate abstract categories and their properties, which, since they emerge from the data, will clearly be important to a theory explaining the kind of behavior under observation.” (36).

An approach to research that is “multimethod in focus, involving an interpretative, naturalistic approach to its subject matter.  This means that qualitative researchers study things in their natural settings, attempting to make sense of, or interpret, phenomena in terms of the meanings people bring to them.  Qualitative research involves the studied use and collection of a variety of empirical materials – case study, personal experience, introspective, life story, interview, observational, historical, interactional, and visual texts – that describe routine and problematic moments and meanings in individuals’ lives." ( Denzin and Lincoln 2005:2 ). Contrast with quantitative research .

Research that contributes knowledge that will help people to understand the nature of a problem in order to intervene, thereby allowing human beings to more effectively control their environment.

Research that is designed to evaluate or test the effectiveness of specific solutions and programs addressing specific social problems.  There are two kinds: summative and formative .

Research in which an overall judgment about the effectiveness of a program or policy is made, often for the purpose of generalizing to other cases or programs.  Generally uses qualitative research as a supplement to primary quantitative data analyses.  Contrast formative evaluation research .

Research designed to improve a program or policy (to help “form” or shape its effectiveness); relies heavily on qualitative research methods.  Contrast summative evaluation research

Research carried out at a particular organizational or community site with the intention of affecting change; often involves research subjects as participants of the study.  See also participatory action research .

Research in which both researchers and participants work together to understand a problematic situation and change it for the better.

The level of the focus of analysis (e.g., individual people, organizations, programs, neighborhoods).

The large group of interest to the researcher.  Although it will likely be impossible to design a study that incorporates or reaches all members of the population of interest, this should be clearly defined at the outset of a study so that a reasonable sample of the population can be taken.  For example, if one is studying working-class college students, the sample may include twenty such students attending a particular college, while the population is “working-class college students.”  In quantitative research, clearly defining the general population of interest is a necessary step in generalizing results from a sample.  In qualitative research, defining the population is conceptually important for clarity.

A fictional name assigned to give anonymity to a person, group, or place.  Pseudonyms are important ways of protecting the identity of research participants while still providing a “human element” in the presentation of qualitative data.  There are ethical considerations to be made in selecting pseudonyms; some researchers allow research participants to choose their own.

A requirement for research involving human participants; the documentation of informed consent.  In some cases, oral consent or assent may be sufficient, but the default standard is a single-page easy-to-understand form that both the researcher and the participant sign and date.   Under federal guidelines, all researchers "shall seek such consent only under circumstances that provide the prospective subject or the representative sufficient opportunity to consider whether or not to participate and that minimize the possibility of coercion or undue influence. The information that is given to the subject or the representative shall be in language understandable to the subject or the representative.  No informed consent, whether oral or written, may include any exculpatory language through which the subject or the representative is made to waive or appear to waive any of the subject's rights or releases or appears to release the investigator, the sponsor, the institution, or its agents from liability for negligence" (21 CFR 50.20).  Your IRB office will be able to provide a template for use in your study .

An administrative body established to protect the rights and welfare of human research subjects recruited to participate in research activities conducted under the auspices of the institution with which it is affiliated. The IRB is charged with the responsibility of reviewing all research involving human participants. The IRB is concerned with protecting the welfare, rights, and privacy of human subjects. The IRB has the authority to approve, disapprove, monitor, and require modifications in all research activities that fall within its jurisdiction as specified by both the federal regulations and institutional policy.

Introduction to Qualitative Research Methods Copyright © 2023 by Allison Hurst is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License , except where otherwise noted.

Have a language expert improve your writing

Run a free plagiarism check in 10 minutes, automatically generate references for free.

• Knowledge Base
• Methodology

Types of Research Designs Compared | Examples

Published on 5 May 2022 by Shona McCombes . Revised on 10 October 2022.

When you start planning a research project, developing research questions and creating a  research design , you will have to make various decisions about the type of research you want to do.

There are many ways to categorise different types of research. The words you use to describe your research depend on your discipline and field. In general, though, the form your research design takes will be shaped by:

• The type of knowledge you aim to produce
• The type of data you will collect and analyse
• The sampling methods , timescale, and location of the research

This article takes a look at some common distinctions made between different types of research and outlines the key differences between them.

Table of contents

Types of research aims, types of research data, types of sampling, timescale, and location.

The first thing to consider is what kind of knowledge your research aims to contribute.

Prevent plagiarism, run a free check.

The next thing to consider is what type of data you will collect. Each kind of data is associated with a range of specific research methods and procedures.

Finally, you have to consider three closely related questions: How will you select the subjects or participants of the research? When and how often will you collect data from your subjects? And where will the research take place?

Choosing among all these different research types is part of the process of creating your research design , which determines exactly how the research will be conducted. But the type of research is only the first step: next, you have to make more concrete decisions about your research methods and the details of the study.

Read more about creating a research design

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the ‘Cite this Scribbr article’ button to automatically add the citation to our free Reference Generator.

McCombes, S. (2022, October 10). Types of Research Designs Compared | Examples. Scribbr. Retrieved 3 September 2024, from https://www.scribbr.co.uk/research-methods/types-of-research-designs/

Is this article helpful?

Shona McCombes

Other students also liked, sampling methods | types, techniques, & examples, qualitative vs quantitative research | examples & methods, between-subjects design | examples, pros & cons.

• University Libraries
• Research Guides
• Topic Guides
• Research Methods Guide
• Research Design & Method

Research Methods Guide: Research Design & Method

• Introduction
• Survey Research
• Interview Research
• Data Analysis
• Resources & Consultation

Tutorial Videos: Research Design & Method

Research Methods (sociology-focused)

Qualitative vs. Quantitative Methods (intro)

Qualitative vs. Quantitative Methods (advanced)

FAQ: Research Design & Method

What is the difference between Research Design and Research Method?

Research design is a plan to answer your research question.  A research method is a strategy used to implement that plan.  Research design and methods are different but closely related, because good research design ensures that the data you obtain will help you answer your research question more effectively.

Which research method should I choose ?

It depends on your research goal.  It depends on what subjects (and who) you want to study.  Let's say you are interested in studying what makes people happy, or why some students are more conscious about recycling on campus.  To answer these questions, you need to make a decision about how to collect your data.  Most frequently used methods include:

• Observation / Participant Observation
• Focus Groups
• Experiments
• Secondary Data Analysis / Archival Study
• Mixed Methods (combination of some of the above)

One particular method could be better suited to your research goal than others, because the data you collect from different methods will be different in quality and quantity.   For instance, surveys are usually designed to produce relatively short answers, rather than the extensive responses expected in qualitative interviews.

What other factors should I consider when choosing one method over another?

Time for data collection and analysis is something you want to consider.  An observation or interview method, so-called qualitative approach, helps you collect richer information, but it takes time.  Using a survey helps you collect more data quickly, yet it may lack details.  So, you will need to consider the time you have for research and the balance between strengths and weaknesses associated with each method (e.g., qualitative vs. quantitative).

• << Previous: Introduction
• Next: Survey Research >>
• Last Updated: Aug 21, 2023 10:42 AM

What is Research Design? Characteristics, Types, Process, & Examples

Link Copied

Share on Facebook

Share on Twitter

Share on LinkedIn

Your search has come to an end!

Ever felt like a hamster on a research wheel fast, spinning with a million questions but going nowhere? You've got your topic; you're brimming with curiosity, but... what next? So, forget the research rut and get your papers! This ultimate guide to "what is research design?" will have you navigating your project like a pro, uncovering answers and avoiding dead ends. Know the features of good research design, what you mean by research design, elements of research design, and more.

What is Research Design?

Before starting with the topic, do you know what is research design? Research design is the structure of research methods and techniques selected to conduct a study. It refines the methods suited to the subject and ensures a successful setup. Defining a research topic clarifies the type of research (experimental, survey research, correlational, semi-experimental, review) and its sub-type (experimental design, research problem, descriptive case-study).

There are three main types of designs for research:

1. Data Collection

2. Measurement

3. Data Analysis

Elements of Research Design 

Now that you know what is research design, it is important to know the elements and components of research design. Impactful research minimises bias and enhances data accuracy. Designs with minimal error margins are ideal. Key elements include:

1. Accurate purpose statement

2. Techniques for data collection and analysis

3. Methods for data analysis

4. Type of research methodology

5. Probable objections to research

6. Research settings

7. Timeline

8. Measurement of analysis

Got a hang of research, now book yout student accommodation with one click!

Book through amber today!

Characteristics of Research Design

Research design has several key characteristics that contribute to the validity, reliability, and overall success of a research study. To know the answer for what is research design, it is important to know the characteristics. These are-

1. Reliability

A reliable research design ensures that each study’s results are accurate and can be replicated. This means that if the research is conducted again under the same conditions, it should yield similar results.

2. Validity

A valid research design uses appropriate measuring tools to gauge the results according to the research objective. This ensures that the data collected and the conclusions drawn are relevant and accurately reflect the phenomenon being studied.

3. Neutrality

A neutral research design ensures that the assumptions made at the beginning of the research are free from bias. This means that the data collected throughout the research is based on these unbiased assumptions.

4. Generalizability

A good research design draws an outcome that can be applied to a large set of people and is not limited to the sample size or the research group.

Research Design Process

What is research design? A good research helps you do a really good study that gives fair, trustworthy, and useful results. But it's also good to have a bit of wiggle room for changes. If you’re wondering how to conduct a research in just 5 mins , here's a breakdown and examples to work even better.

1. Consider Aims and Approaches

Define the research questions and objectives, and establish the theoretical framework and methodology.

2. Choose a Type of Research Design

Select the suitable research design, such as experimental, correlational, survey, case study, or ethnographic, according to the research questions and objectives.

3. Identify Population and Sampling Method

Determine the target population and sample size, and select the sampling method, like random, stratified random sampling, or convenience sampling.

4. Choose Data Collection Methods

Decide on the data collection methods, such as surveys, interviews, observations, or experiments, and choose the appropriate instruments for data collection.

5. Plan Data Collection Procedures

Create a plan for data collection, detailing the timeframe, location, and personnel involved, while ensuring ethical considerations are met.

6. Decide on Data Analysis Strategies

Select the appropriate data analysis techniques, like statistical analysis, content analysis, or discourse analysis, and plan the interpretation of the results.

What are the Types of Research Design?

A researcher must grasp various types to decide which model to use for a study. There are different research designs that can be broadly classified into quantitative and qualitative.

Qualitative Research

Qualitative research identifies relationships between collected data and observations through mathematical calculations. Statistical methods validate or refute theories about natural phenomena. This research method answers "why" a theory exists and explores respondents' perspectives.

Quantitative Research

Quantitative research is essential when statistical conclusions are needed to gather actionable insights. Numbers provide clarity for critical business decisions. This method is crucial for organizational growth, with insights from complex numerical data guiding future business decisions.

Qualitative Research vs Quantitative Research

While researching, it is important to know the difference between qualitative and quantitative research. Here's a quick difference between the two:

The research types can be further divided into 5 categories:

1. Descriptive Research

Descriptive research design focuses on detailing a situation or case. It's a theory-driven method that involves gathering, analysing, and presenting data. This approach offers insights into the reasons and mechanisms behind a research subject, enhancing understanding of the research's importance. When the problem statement is unclear, exploratory research can be conducted.

2. Experimental Research

Experimental research design investigates cause-and-effect relationships. It’s a causal design where the impact of an independent variable on a dependent variable is observed. For example, the effect of price on customer satisfaction. This method efficiently addresses problems by manipulating independent variables to see their effect on dependent variables. Often used in social sciences, it involves analysing human behaviour by studying changes in one group's actions and their impact on another group.

3. Correlational Research

Correlational research design is a non-experimental technique that identifies relationships between closely linked variables. It uses statistical analysis to determine these relationships without assumptions. This method requires two different groups. A correlation coefficient between -1 and +1 indicates the strength and direction of the relationship, with +1 showing a positive correlation and -1 a negative correlation.

4. Diagnostic Research

Diagnostic research design aims to identify the underlying causes of specific issues. This method delves into factors creating problematic situations and has three phases: 

• Issue inception
• Issue diagnosis
• Issue resolution

5. Explanatory Research

Explanatory research design builds on a researcher’s ideas to explore theories further. It seeks to explain the unexplored aspects of a subject, addressing the what, how, and why of research questions.

Benefits of Research Design

After learning about what is research design and the process, it is important to know the key benefits of a well-structured research design:

1. Minimises Risk of Errors: A good research design minimises the risk of errors and reduces inaccuracy. It ensures that the study is carried out in the right direction and that all the team members are on the same page.

2. Efficient Use of Resources: It facilitates a concrete research plan for the efficient use of time and resources. It helps the researcher better complete all the tasks, even with limited resources.

3. Provides Direction: The purpose of the research design is to enable the researcher to proceed in the right direction without deviating from the tasks. It helps to identify the major and minor tasks of the study.

4. Ensures Validity and Reliability: A well-designed research enhances the validity and reliability of the findings and allows for the replication of studies by other researchers. The main advantage of a good research design is that it provides accuracy, reliability, consistency, and legitimacy to the research.

5. Facilitates Problem-Solving: A researcher can easily frame the objectives of the research work based on the design of experiments (research design). A good research design helps the researcher find the best solution for the research problems.

6. Better Documentation: It helps in better documentation of the various activities while the project work is going on.

That's it! You've explored all the answers for what is research design in research? Remember, it's not just about picking a fancy method – it's about choosing the perfect tool to answer your burning questions. By carefully considering your goals and resources, you can design a research plan that gathers reliable information and helps you reach clear conclusions. 

Frequently Asked Questions

What are the key components of a research design, how can i choose the best research design for my study, what are some common pitfalls in research design, and how can they be avoided, how does research design impact the validity and reliability of a study, what ethical considerations should be taken into account in research design.

Your ideal student home & a flight ticket awaits

Follow us on :

Related Posts

Top 10 Film Schools in New York in 2024!

Explore 15 Best Short Certificate Programs That Pay Well in 2024!

Education As An Investment: 12 Key Reasons to Consider in 2024

amber © 2024. All rights reserved.

4.8/5 on Trustpilot

Rated as "Excellent" • 4800+ Reviews by students

Rated as "Excellent" • 4800+ Reviews by Students

• Skip to main content
• Skip to primary sidebar
• Skip to footer
• QuestionPro

• Solutions Industries Gaming Automotive Sports and events Education Government Travel & Hospitality Financial Services Healthcare Cannabis Technology Use Case AskWhy Communities Audience Contactless surveys Mobile LivePolls Member Experience GDPR Positive People Science 360 Feedback Surveys
• Resources Blog eBooks Survey Templates Case Studies Training Help center

Home Market Research Research Tools and Apps

Research Design: What it is, Elements & Types

Can you imagine doing research without a plan? Probably not. When we discuss a strategy to collect, study, and evaluate data, we talk about research design. This design addresses problems and creates a consistent and logical model for data analysis. Let’s learn more about it.

What is Research Design?

Research design is the framework of research methods and techniques chosen by a researcher to conduct a study. The design allows researchers to sharpen the research methods suitable for the subject matter and set up their studies for success.

Creating a research topic explains the type of research (experimental,  survey research ,  correlational , semi-experimental, review) and its sub-type (experimental design, research problem , descriptive case-study). 

There are three main types of designs for research:

• Data collection
• Measurement
• Data Analysis

The research problem an organization faces will determine the design, not vice-versa. The design phase of a study determines which tools to use and how they are used.

The Process of Research Design

The research design process is a systematic and structured approach to conducting research. The process is essential to ensure that the study is valid, reliable, and produces meaningful results.

• Consider your aims and approaches: Determine the research questions and objectives, and identify the theoretical framework and methodology for the study.
• Choose a type of Research Design: Select the appropriate research design, such as experimental, correlational, survey, case study, or ethnographic, based on the research questions and objectives.
• Identify your population and sampling method: Determine the target population and sample size, and choose the sampling method, such as random , stratified random sampling , or convenience sampling.
• Choose your data collection methods: Decide on the data collection methods , such as surveys, interviews, observations, or experiments, and select the appropriate instruments or tools for collecting data.
• Plan your data collection procedures: Develop a plan for data collection, including the timeframe, location, and personnel involved, and ensure ethical considerations.
• Decide on your data analysis strategies: Select the appropriate data analysis techniques, such as statistical analysis , content analysis, or discourse analysis, and plan how to interpret the results.

The process of research design is a critical step in conducting research. By following the steps of research design, researchers can ensure that their study is well-planned, ethical, and rigorous.

Research Design Elements

Impactful research usually creates a minimum bias in data and increases trust in the accuracy of collected data. A design that produces the slightest margin of error in experimental research is generally considered the desired outcome. The essential elements are:

• Accurate purpose statement
• Techniques to be implemented for collecting and analyzing research
• The method applied for analyzing collected details
• Type of research methodology
• Probable objections to research
• Settings for the research study
• Measurement of analysis

Characteristics of Research Design

A proper design sets your study up for success. Successful research studies provide insights that are accurate and unbiased. You’ll need to create a survey that meets all of the main characteristics of a design. There are four key characteristics:

• Neutrality: When you set up your study, you may have to make assumptions about the data you expect to collect. The results projected in the research should be free from research bias and neutral. Understand opinions about the final evaluated scores and conclusions from multiple individuals and consider those who agree with the results.
• Reliability: With regularly conducted research, the researcher expects similar results every time. You’ll only be able to reach the desired results if your design is reliable. Your plan should indicate how to form research questions to ensure the standard of results.
• Validity: There are multiple measuring tools available. However, the only correct measuring tools are those which help a researcher in gauging results according to the objective of the research. The  questionnaire  developed from this design will then be valid.
• Generalization:  The outcome of your design should apply to a population and not just a restricted sample . A generalized method implies that your survey can be conducted on any part of a population with similar accuracy.

The above factors affect how respondents answer the research questions, so they should balance all the above characteristics in a good design. If you want, you can also learn about Selection Bias through our blog.

Research Design Types

A researcher must clearly understand the various types to select which model to implement for a study. Like the research itself, the design of your analysis can be broadly classified into quantitative and qualitative.

Qualitative research

Qualitative research determines relationships between collected data and observations based on mathematical calculations. Statistical methods can prove or disprove theories related to a naturally existing phenomenon. Researchers rely on qualitative observation research methods that conclude “why” a particular theory exists and “what” respondents have to say about it.

Quantitative research

Quantitative research is for cases where statistical conclusions to collect actionable insights are essential. Numbers provide a better perspective for making critical business decisions. Quantitative research methods are necessary for the growth of any organization. Insights drawn from complex numerical data and analysis prove to be highly effective when making decisions about the business’s future.

Qualitative Research vs Quantitative Research

Here is a chart that highlights the major differences between qualitative and quantitative research:

In summary or analysis , the step of qualitative research is more exploratory and focuses on understanding the subjective experiences of individuals, while quantitative research is more focused on objective data and statistical analysis.

You can further break down the types of research design into five categories:

1. Descriptive: In a descriptive composition, a researcher is solely interested in describing the situation or case under their research study. It is a theory-based design method created by gathering, analyzing, and presenting collected data. This allows a researcher to provide insights into the why and how of research. Descriptive design helps others better understand the need for the research. If the problem statement is not clear, you can conduct exploratory research. 

2. Experimental: Experimental research establishes a relationship between the cause and effect of a situation. It is a causal research design where one observes the impact caused by the independent variable on the dependent variable. For example, one monitors the influence of an independent variable such as a price on a dependent variable such as customer satisfaction or brand loyalty. It is an efficient research method as it contributes to solving a problem.

The independent variables are manipulated to monitor the change it has on the dependent variable. Social sciences often use it to observe human behavior by analyzing two groups. Researchers can have participants change their actions and study how the people around them react to understand social psychology better.

3. Correlational research: Correlational research  is a non-experimental research technique. It helps researchers establish a relationship between two closely connected variables. There is no assumption while evaluating a relationship between two other variables, and statistical analysis techniques calculate the relationship between them. This type of research requires two different groups.

A correlation coefficient determines the correlation between two variables whose values range between -1 and +1. If the correlation coefficient is towards +1, it indicates a positive relationship between the variables, and -1 means a negative relationship between the two variables. 

4. Diagnostic research: In diagnostic design, the researcher is looking to evaluate the underlying cause of a specific topic or phenomenon. This method helps one learn more about the factors that create troublesome situations. 

This design has three parts of the research:

• Inception of the issue
• Diagnosis of the issue
• Solution for the issue

5. Explanatory research : Explanatory design uses a researcher’s ideas and thoughts on a subject to further explore their theories. The study explains unexplored aspects of a subject and details the research questions’ what, how, and why.

Benefits of Research Design

There are several benefits of having a well-designed research plan. Including:

• Clarity of research objectives: Research design provides a clear understanding of the research objectives and the desired outcomes.
• Increased validity and reliability: To ensure the validity and reliability of results, research design help to minimize the risk of bias and helps to control extraneous variables.
• Improved data collection: Research design helps to ensure that the proper data is collected and data is collected systematically and consistently.
• Better data analysis: Research design helps ensure that the collected data can be analyzed effectively, providing meaningful insights and conclusions.
• Improved communication: A well-designed research helps ensure the results are clean and influential within the research team and external stakeholders.
• Efficient use of resources: reducing the risk of waste and maximizing the impact of the research, research design helps to ensure that resources are used efficiently.

A well-designed research plan is essential for successful research, providing clear and meaningful insights and ensuring that resources are practical.

QuestionPro offers a comprehensive solution for researchers looking to conduct research. With its user-friendly interface, robust data collection and analysis tools, and the ability to integrate results from multiple sources, QuestionPro provides a versatile platform for designing and executing research projects.

Our robust suite of research tools provides you with all you need to derive research results. Our online survey platform includes custom point-and-click logic and advanced question types. Uncover the insights that matter the most.

LEARN MORE         FREE TRIAL

MORE LIKE THIS

Interactive Forms: Key Features, Benefits, Uses + Design Tips

Sep 4, 2024

Closed-Loop Management: The Key to Customer Centricity

Sep 3, 2024

Net Trust Score: Tool for Measuring Trust in Organization

Sep 2, 2024

Why You Should Attend XDAY 2024

Aug 30, 2024

Other categories

• Academic Research
• Artificial Intelligence
• Assessments
• Brand Awareness
• Case Studies
• Communities
• Consumer Insights
• Customer effort score
• Customer Engagement
• Customer Experience
• Customer Loyalty
• Customer Research
• Customer Satisfaction
• Employee Benefits
• Employee Engagement
• Employee Retention
• Friday Five
• General Data Protection Regulation
• Insights Hub
• Life@QuestionPro
• Market Research
• Mobile diaries
• Mobile Surveys
• New Features
• Online Communities
• Question Types
• Questionnaire
• QuestionPro Products
• Release Notes
• Research Tools and Apps
• Revenue at Risk
• Survey Templates
• Training Tips
• Tuesday CX Thoughts (TCXT)
• Uncategorized
• What’s Coming Up
• Workforce Intelligence

Experimental Research Design — 6 mistakes you should never make!

Since school days’ students perform scientific experiments that provide results that define and prove the laws and theorems in science. These experiments are laid on a strong foundation of experimental research designs.

An experimental research design helps researchers execute their research objectives with more clarity and transparency.

In this article, we will not only discuss the key aspects of experimental research designs but also the issues to avoid and problems to resolve while designing your research study.

Table of Contents

What Is Experimental Research Design?

Experimental research design is a framework of protocols and procedures created to conduct experimental research with a scientific approach using two sets of variables. Herein, the first set of variables acts as a constant, used to measure the differences of the second set. The best example of experimental research methods is quantitative research .

Experimental research helps a researcher gather the necessary data for making better research decisions and determining the facts of a research study.

When Can a Researcher Conduct Experimental Research?

A researcher can conduct experimental research in the following situations —

• When time is an important factor in establishing a relationship between the cause and effect.
• When there is an invariable or never-changing behavior between the cause and effect.
• Finally, when the researcher wishes to understand the importance of the cause and effect.

Importance of Experimental Research Design

To publish significant results, choosing a quality research design forms the foundation to build the research study. Moreover, effective research design helps establish quality decision-making procedures, structures the research to lead to easier data analysis, and addresses the main research question. Therefore, it is essential to cater undivided attention and time to create an experimental research design before beginning the practical experiment.

By creating a research design, a researcher is also giving oneself time to organize the research, set up relevant boundaries for the study, and increase the reliability of the results. Through all these efforts, one could also avoid inconclusive results. If any part of the research design is flawed, it will reflect on the quality of the results derived.

Types of Experimental Research Designs

Based on the methods used to collect data in experimental studies, the experimental research designs are of three primary types:

1. Pre-experimental Research Design

A research study could conduct pre-experimental research design when a group or many groups are under observation after implementing factors of cause and effect of the research. The pre-experimental design will help researchers understand whether further investigation is necessary for the groups under observation.

Pre-experimental research is of three types —

• One-shot Case Study Research Design
• One-group Pretest-posttest Research Design
• Static-group Comparison

2. True Experimental Research Design

A true experimental research design relies on statistical analysis to prove or disprove a researcher’s hypothesis. It is one of the most accurate forms of research because it provides specific scientific evidence. Furthermore, out of all the types of experimental designs, only a true experimental design can establish a cause-effect relationship within a group. However, in a true experiment, a researcher must satisfy these three factors —

• There is a control group that is not subjected to changes and an experimental group that will experience the changed variables
• A variable that can be manipulated by the researcher
• Random distribution of the variables

This type of experimental research is commonly observed in the physical sciences.

3. Quasi-experimental Research Design

The word “Quasi” means similarity. A quasi-experimental design is similar to a true experimental design. However, the difference between the two is the assignment of the control group. In this research design, an independent variable is manipulated, but the participants of a group are not randomly assigned. This type of research design is used in field settings where random assignment is either irrelevant or not required.

The classification of the research subjects, conditions, or groups determines the type of research design to be used.

Advantages of Experimental Research

Experimental research allows you to test your idea in a controlled environment before taking the research to clinical trials. Moreover, it provides the best method to test your theory because of the following advantages:

• Researchers have firm control over variables to obtain results.
• The subject does not impact the effectiveness of experimental research. Anyone can implement it for research purposes.
• The results are specific.
• Post results analysis, research findings from the same dataset can be repurposed for similar research ideas.
• Researchers can identify the cause and effect of the hypothesis and further analyze this relationship to determine in-depth ideas.
• Experimental research makes an ideal starting point. The collected data could be used as a foundation to build new research ideas for further studies.

6 Mistakes to Avoid While Designing Your Research

There is no order to this list, and any one of these issues can seriously compromise the quality of your research. You could refer to the list as a checklist of what to avoid while designing your research.

1. Invalid Theoretical Framework

Usually, researchers miss out on checking if their hypothesis is logical to be tested. If your research design does not have basic assumptions or postulates, then it is fundamentally flawed and you need to rework on your research framework.

2. Inadequate Literature Study

Without a comprehensive research literature review , it is difficult to identify and fill the knowledge and information gaps. Furthermore, you need to clearly state how your research will contribute to the research field, either by adding value to the pertinent literature or challenging previous findings and assumptions.

3. Insufficient or Incorrect Statistical Analysis

Statistical results are one of the most trusted scientific evidence. The ultimate goal of a research experiment is to gain valid and sustainable evidence. Therefore, incorrect statistical analysis could affect the quality of any quantitative research.

4. Undefined Research Problem

This is one of the most basic aspects of research design. The research problem statement must be clear and to do that, you must set the framework for the development of research questions that address the core problems.

5. Research Limitations

Every study has some type of limitations . You should anticipate and incorporate those limitations into your conclusion, as well as the basic research design. Include a statement in your manuscript about any perceived limitations, and how you considered them while designing your experiment and drawing the conclusion.

6. Ethical Implications

The most important yet less talked about topic is the ethical issue. Your research design must include ways to minimize any risk for your participants and also address the research problem or question at hand. If you cannot manage the ethical norms along with your research study, your research objectives and validity could be questioned.

Experimental Research Design Example

In an experimental design, a researcher gathers plant samples and then randomly assigns half the samples to photosynthesize in sunlight and the other half to be kept in a dark box without sunlight, while controlling all the other variables (nutrients, water, soil, etc.)

By comparing their outcomes in biochemical tests, the researcher can confirm that the changes in the plants were due to the sunlight and not the other variables.

Experimental research is often the final form of a study conducted in the research process which is considered to provide conclusive and specific results. But it is not meant for every research. It involves a lot of resources, time, and money and is not easy to conduct, unless a foundation of research is built. Yet it is widely used in research institutes and commercial industries, for its most conclusive results in the scientific approach.

Have you worked on research designs? How was your experience creating an experimental design? What difficulties did you face? Do write to us or comment below and share your insights on experimental research designs!

Frequently Asked Questions

Randomization is important in an experimental research because it ensures unbiased results of the experiment. It also measures the cause-effect relationship on a particular group of interest.

Experimental research design lay the foundation of a research and structures the research to establish quality decision making process.

There are 3 types of experimental research designs. These are pre-experimental research design, true experimental research design, and quasi experimental research design.

The difference between an experimental and a quasi-experimental design are: 1. The assignment of the control group in quasi experimental research is non-random, unlike true experimental design, which is randomly assigned. 2. Experimental research group always has a control group; on the other hand, it may not be always present in quasi experimental research.

Experimental research establishes a cause-effect relationship by testing a theory or hypothesis using experimental groups or control variables. In contrast, descriptive research describes a study or a topic by defining the variables under it and answering the questions related to the same.

good and valuable

Very very good

Good presentation.

Rate this article Cancel Reply

Your email address will not be published.

Enago Academy's Most Popular Articles

• Promoting Research

Graphical Abstracts Vs. Infographics: Best practices for using visual illustrations for increased research impact

Dr. Sarah Chen stared at her computer screen, her eyes staring at her recently published…

• Publishing Research

10 Tips to Prevent Research Papers From Being Retracted

Research paper retractions represent a critical event in the scientific community. When a published article…

• Industry News

Google Releases 2024 Scholar Metrics, Evaluates Impact of Scholarly Articles

Google has released its 2024 Scholar Metrics, assessing scholarly articles from 2019 to 2023. This…

Ensuring Academic Integrity and Transparency in Academic Research: A comprehensive checklist for researchers

Academic integrity is the foundation upon which the credibility and value of scientific findings are…

• Reporting Research

How to Optimize Your Research Process: A step-by-step guide

For researchers across disciplines, the path to uncovering novel findings and insights is often filled…

Choosing the Right Analytical Approach: Thematic analysis vs. content analysis for…

Comparing Cross Sectional and Longitudinal Studies: 5 steps for choosing the right…

Sign-up to read more

Subscribe for free to get unrestricted access to all our resources on research writing and academic publishing including:

• 2000+ blog articles
• 50+ Webinars
• 10+ Expert podcasts
• 50+ Infographics
• 10+ Checklists
• Research Guides

We hate spam too. We promise to protect your privacy and never spam you.

• AI in Academia
• Career Corner
• Diversity and Inclusion
• Infographics
• Expert Video Library
• Other Resources
• Enago Learn
• Upcoming & On-Demand Webinars
• Peer Review Week 2024
• Open Access Week 2023
• Conference Videos
• Enago Report
• Journal Finder
• Enago Plagiarism & AI Grammar Check
• Editing Services
• Publication Support Services
• Research Impact
• Translation Services
• Publication solutions
• AI-Based Solutions
• Thought Leadership
• Call for Articles
• Call for Speakers
• Author Training
• Edit Profile

I am looking for Editing/ Proofreading services for my manuscript Tentative date of next journal submission:

In your opinion, what is the most effective way to improve integrity in the peer review process?

• Basics of Research Process
• Methodology
• Research Design: Definition, Types, Characteristics & Study Examples
• Speech Topics
• Basics of Essay Writing
• Essay Topics
• Other Essays
• Main Academic Essays
• Research Paper Topics
• Basics of Research Paper Writing
• Miscellaneous
• Chicago/ Turabian
• Data & Statistics
• Admission Writing Tips
• Admission Advice
• Other Guides
• Student Life
• Studying Tips
• Understanding Plagiarism
• Academic Writing Tips
• Basics of Dissertation & Thesis Writing

• Essay Guides
• Research Paper Guides
• Formatting Guides
• Admission Guides
• Dissertation & Thesis Guides

Research Design: Definition, Types, Characteristics & Study Examples

Table of contents

Use our free Readability checker

A research design is the blueprint for any study. It's the plan that outlines how the research will be carried out. A study design usually includes the methods of data collection, the type of data to be gathered, and how it will be analyzed. Research designs help ensure the study is reliable, valid, and can answer the research question.

Behind every groundbreaking discovery and innovation lies a well-designed research. Whether you're investigating a new technology or exploring a social phenomenon, a solid research design is key to achieving reliable results. But what exactly does it means, and how do you create an effective one? Stay with our paper writers and find out:

• Detailed definition
• Types of research study designs
• How to write a research design
• Useful examples.

Whether you're a seasoned researcher or just getting started, understanding the core principles will help you conduct better studies and make more meaningful contributions.

What Is a Research Design: Definition

Research design is an overall study plan outlining a specific approach to investigating a research question . It covers particular methods and strategies for collecting, measuring and analyzing data. Students  are required to build a study design either as an individual task or as a separate chapter in a research paper , thesis or dissertation .

Before designing a research project, you need to consider a series aspects of your future study:

• Research aims What research objectives do you want to accomplish with your study? What approach will you take to get there? Will you use a quantitative, qualitative, or mixed methods approach?
• Type of data Will you gather new data (primary research), or rely on existing data (secondary research) to answer your research question?
• Sampling methods How will you pick participants? What criteria will you use to ensure your sample is representative of the population?
• Data collection methods What tools or instruments will you use to gather data (e.g., conducting a survey , interview, or observation)?
• Measurement  What metrics will you use to capture and quantify data?
• Data analysis  What statistical or qualitative techniques will you use to make sense of your findings?

By using a well-designed research plan, you can make sure your findings are solid and can be generalized to a larger group.

Research design example

You are going to investigate the effectiveness of a mindfulness-based intervention for reducing stress and anxiety among college students. You decide to organize an experiment to explore the impact. Participants should be randomly assigned to either an intervention group or a control group. You need to conduct pre- and post-intervention using self-report measures of stress and anxiety.

What Makes a Good Study Design? 

To design a research study that works, you need to carefully think things through. Make sure your strategy is tailored to your research topic and watch out for potential biases. Your procedures should be flexible enough to accommodate changes that may arise during the course of research. 

A good research design should be:

• Clear and methodologically sound
• Feasible and realistic
• Knowledge-driven.

By following these guidelines, you'll set yourself up for success and be able to produce reliable results.

Research Study Design Structure

A structured research design provides a clear and organized plan for carrying out a study. It helps researchers to stay on track and ensure that the study stays within the bounds of acceptable time, resources, and funding.

A typical design includes 5 main components:

• Research question(s): Central research topic(s) or issue(s).
• Sampling strategy: Method for selecting participants or subjects.
• Data collection techniques: Tools or instruments for retrieving data.
• Data analysis approaches: Techniques for interpreting and scrutinizing assembled data.
• Ethical considerations: Principles for protecting human subjects (e.g., obtaining a written consent, ensuring confidentiality guarantees).

Research Design Essential Characteristics

Creating a research design warrants a firm foundation for your exploration. The cost of making a mistake is too high. This is not something scholars can afford, especially if financial resources or a considerable amount of time is invested. Choose the wrong strategy, and you risk undermining your whole study and wasting resources. 

To avoid any unpleasant surprises, make sure your study conforms to the key characteristics. Here are some core features of research designs:

• Reliability   Reliability is stability of your measures or instruments over time. A reliable research design is one that can be reproduced in the same way and deliver consistent outcomes. It should also nurture accurate representations of actual conditions and guarantee data quality.
• Validity For a study to be valid , it must measure what it claims to measure. This means that methodological approaches should be carefully considered and aligned to the main research question(s).
• Generalizability Generalizability means that your insights can be practiced outside of the scope of a study. When making inferences, researchers must take into account determinants such as sample size, sampling technique, and context.
• Neutrality A study model should be free from personal or cognitive biases to ensure an impartial investigation of a research topic. Steer clear of highlighting any particular group or achievement.

Key Concepts in Research Design

Now let’s discuss the fundamental principles that underpin study designs in research. This will help you develop a strong framework and make sure all the puzzles fit together.

Primary concepts

Types of Approaches to Research Design

Study frameworks can fall into 2 major categories depending on the approach to compiling data you opt for. The 2 main types of study designs in research are qualitative and quantitative research. Both approaches have their unique strengths and weaknesses, and can be utilized based on the nature of information you are dealing with. 

Quantitative Research  

Quantitative study is focused on establishing empirical relationships between variables and collecting numerical data. It involves using statistics, surveys, and experiments to measure the effects of certain phenomena. This research design type looks at hard evidence and provides measurements that can be analyzed using statistical techniques. 

Qualitative Research 

Qualitative approach is used to examine the behavior, attitudes, and perceptions of individuals in a given environment. This type of study design relies on unstructured data retrieved through interviews, open-ended questions and observational methods. 

If you need your study done yesterday, leave StudyCrumb a “ write my research paper for me ” notice and have your project completed by experts.

Types of Research Designs & Examples

Choosing a research design may be tough especially for the first-timers. One of the great ways to get started is to pick the right design that will best fit your objectives. There are 4 different types of research designs you can opt for to carry out your investigation:

• Experimental
• Correlational
• Descriptive
• Diagnostic/explanatory.

For more advanced studies, you can even combine several types. Mixed-methods research may come in handy when exploring complex phenomena that cannot be adequately captured by one method alone.

Below we will go through each type and offer you examples of study designs to assist you with selection.

1. Experimental

In experimental research design , scientists manipulate one or more independent variables and control other factors in order to observe their effect on a dependent variable. This type of research design is used for experiments where the goal is to determine a causal relationship. 

Its core characteristics include:

• Randomization
• Manipulation
• Replication.

A pharmaceutical company wants to test a new drug to investigate its effectiveness in treating a specific medical condition. Researchers would randomly assign participants to either a control group (receiving a placebo) or an experimental group (receiving the new drug). They would rigorously control all variables (e.g, age, medical history) and manipulate them to get reliable results.

2. Correlational

Correlational study is used to examine the existing relationships between variables. In this type of design, you don’t need to manipulate other variables. Here, researchers just focus on observing and measuring the naturally occurring relationship.

Correlational studies encompass such features: 

• Data collection from natural settings
• No intervention by the researcher
• Observation over time.

A research team wants to examine the relationship between academic performance and extracurricular activities. They would observe students' performance in courses and measure how much time they spend engaging in extracurricular activities.

3. Descriptive 

Descriptive research design is all about describing a particular population or phenomenon without any interruption. This study design is especially helpful when we're not sure about something and want to understand it better.

Descriptive studies are characterized by such features:

• Random and convenience sampling
• Observation
• No intervention.

A psychologist wants to understand how parents' behavior affects their child's self-concept. They would observe the interaction between children and their parents in a natural setting. Gathered information will help her get an overview of this situation and recognize some patterns.

4. Diagnostic

Diagnostic or explanatory research is used to determine the cause of an existing problem or a chronic symptom. Unlike other types of design, here scientists try to understand why something is happening. 

Among essential hallmarks of explanatory studies are: 

• Testing hypotheses and theories
• Examining existing data
• Comparative analysis.

A public health specialist wants to identify the cause of an outbreak of water-borne disease in a certain area. They would inspect water samples and records to compare them with similar outbreaks in other areas. This will help to uncover reasons behind this accident.

How to Design a Research Study: Step-by-Step Process

When designing your research don't just jump into it. It's important to take the time and do things right in order to attain accurate findings. Follow these simple steps on how to design a study to get the most out of your project.

1. Determine Your Aims 

The first step in the research design process is figuring out what you want to achieve. This involves identifying your research question, goals and specific objectives you want to accomplish. Think whether you want to explore a specific issue or develop a new theory? Setting your aims from the get-go will help you stay focused and ensure that your study is driven by purpose. 

Once  you are clear with your goals, you need to decide on the main approach. Will you use qualitative or quantitative methods? Or perhaps a mixture of both?

2. Select a Type of Research Design

Choosing a suitable design requires considering multiple factors, such as your research question, data collection methods, and resources. There are various research design types, each with its own advantages and limitations. Think about the kind of data that would be most useful to address your questions. Ultimately, a well-devised strategy should help you gather accurate data to achieve your objectives.

3. Define Your Population and Sampling Methods

To design a research project, it is essential to establish your target population and parameters for selecting participants. First, identify a cohort of individuals who share common characteristics and possess relevant experiences. 

For instance, if you are researching the impact of social media on mental health, your population could be young adults aged 18-25 who use social media frequently.

With your population in mind, you can now choose an optimal sampling method. Sampling is basically the process of narrowing down your target group to only those individuals who will participate in your study. At this point, you need to decide on whether you want to randomly choose the participants (probability sampling) or set out any selection criteria (non-probability sampling). 

To examine the influence of social media on mental well-being, we will divide a whole population into smaller subgroups using stratified random sampling . Then, we will randomly pick participants from each subcategory to make sure that findings are also true for a broader group of young adults.

4. Decide on Your Data Collection Methods

When devising your study, it is also important to consider how you will retrieve data.  Depending on the type of design you are using, you may deploy diverse methods. Below you can see various data collection techniques suited for different research designs. 

Data collection methods in various studies

Additionally, if you plan on integrating existing data sources like medical records or publicly available datasets, you want to mention this as well. 

5. Arrange Your Data Collection Process

Your data collection process should also be meticulously thought out. This stage involves scheduling interviews, arranging questionnaires and preparing all the necessary tools for collecting information from participants. Detail how long your study will take and what procedures will be followed for recording and analyzing the data. 

State which variables will be studied and what measures or scales will be used when assessing each variable.

Measures and scales 

Measures and scales are tools used to quantify variables in research. A measure is any method used to collect data on a variable, while a scale is a set of items or questions used to measure a particular construct or concept. Different types of scales include nominal, ordinal, interval, or ratio , each of which has distinct properties

Operationalization 

When working with abstract information that needs to be quantified, researchers often operationalize the variable by defining it in concrete terms that can be measured or observed. This allows the abstract concept to be studied systematically and rigorously. 

Operationalization in study design example

If studying the concept of happiness, researchers might operationalize it by using a scale that measures positive affect or life satisfaction. This allows us to quantify happiness and inspect its relationship with other variables, such as income or social support.

Remember that research design should be flexible enough to adjust for any unforeseen developments. Even with rigorous preparation, you may still face unexpected challenges during your project. That’s why you need to work out contingency plans when designing research.

6. Choose Data Analysis Techniques

It’s impossible to design research without mentioning how you are going to scrutinize data. To select a proper method, take into account the type of data you are dealing with and how many variables you need to analyze. 

Qualitative data may require thematic analysis or content analysis.

Quantitative data, on the other hand, could be processed with more sophisticated statistical analysis approaches such as regression analysis, factor analysis or descriptive statistics.

Finally, don’t forget about ethical considerations. Opt for those methods that minimize harm to participants and protect their rights.

Research Design Checklist

Having a checklist in front of you will help you design your research flawlessly.

• checkbox I clearly defined my research question and its significance.
• checkbox I considered crucial factors such as the nature of my study, type of required data and available resources to choose a suitable design.
• checkbox A sample size is sufficient to provide statistically significant results.
• checkbox My data collection methods are reliable and valid.
• checkbox Analysis methods are appropriate for the type of data I will be gathering.
• checkbox My research design protects the rights and privacy of my participants.
• checkbox I created a realistic timeline for research, including deadlines for data collection, analysis, and write-up.
• checkbox I considered funding sources and potential limitations.

Bottom Line on Research Design & Study Types

Designing a research project involves making countless decisions that can affect the quality of your work. By planning out each step and selecting the best methods for data collection and analysis, you can ensure that your project is conducted professionally.

We hope this article has helped you to better understand the research design process. If you have any questions or comments, ping us in the comments section below.

Entrust us your study and we will find the best research paper writer to complete your project. Count on a unique work and fast turnaround.

FAQ About Research Study Designs

1. what is a study design.

Study design, or else called research design, is the overall plan for a project, including its purpose, methodology, data collection and analysis techniques. A good design ensures that your project is conducted in an organized and ethical manner. It also provides clear guidelines for replicating or extending a study in the future.

2. What is the purpose of a research design?

The purpose of a research design is to provide a structure and framework for your project. By outlining your methodology, data collection techniques, and analysis methods in advance, you can ensure that your project will be conducted effectively.

3. What is the importance of research designs?

Research designs are critical to the success of any research project for several reasons. Specifically, study designs grant:

• Clear direction for all stages of a study
• Validity and reliability of findings
• Roadmap for replication or further extension
• Accurate results by controlling for potential bias
• Comparison between studies by providing consistent guidelines.

By following an established plan, researchers can be sure that their projects are organized, ethical, and reliable.

4. What are the 4 types of study designs?

There are generally 4 types of study designs commonly used in research:

• Experimental studies: investigate cause-and-effect relationships by manipulating the independent variable.
• Correlational studies: examine relationships between 2 or more variables without intruding them.
• Descriptive studies: describe the characteristics of a population or phenomenon without making any inferences about cause and effect.
• Explanatory studies: intended to explain causal relationships.

Joe Eckel is an expert on Dissertations writing. He makes sure that each student gets precious insights on composing A-grade academic writing.

You may also like

MBA Knowledge Base

Business • Management • Technology

Home » Research Methodology » Sample Design – Meaning, Steps, Criteria and Characteristics

Sample Design – Meaning, Steps, Criteria and Characteristics

A sample design is a definite plan for obtaining a sample from a given population . It refers to the technique or the procedure the researcher would adopt in selecting items for the sample. Sample design also leads to a procedure to tell the number of items to be included in the sample i.e., the size of the sample. Hence, sample design is determined before the collection of data. Among various types of sample design technique, the researcher should choose that samples which are reliable and appropriate for his research study.

Steps in Sample Design

There are various steps which the researcher should follow. Those are;

• Type of universe: In the first step the researcher should clarify and should be expert in the study of universe. The universe may be finite (no of items are know) or Infinite (numbers of items are not know).
• Sampling unit: A decision has to be taken concerning a sampling unit before selecting a sample. Sampling unit may be a geographical one such as state, district, village etc., or construction unit such as house, flat, etc., or it may be a social unit such as family, club, school etc., or it may be an individual.
• Source list: Source list is known as ‘sampling frame’ from which sample is to be drawn. It consists the names of all items of a universe. Such a list would be comprehensive, correct, reliable and appropriate and the source list should be a representative of the population.
• Size of sample: Size of sample refers to the number of items to be selected from the universe to constitute a sample. Selection of sample size is a headache to the researcher. The size should not be too large or too small rather it should be optimum. An optimum sample is one which fulfills the requirements of efficiency, representativeness, reliability and flexibility. The parameters of interest in a research study must be kept in view, while deciding the size of the sample. Cost factor i.e., budgetary conditions should also be taken into consideration.
• Sampling procedure: In the final step of the sample design, a researcher must decide the type of the sample s/he will use i.e., s/he must decide about the techniques to be used in selecting the items for the sample.

Criteria for Sample Design Selection

While selecting samples a researcher must remember that the procedure of sampling analysis involves two costs viz., (i) the cost of collecting the data and (ii) the cost of an incorrect inferences resulting from the data. So, far as the cost of collecting data is concerned, it completely depends on the researcher to reduce it and to some extent it is within the control of the researcher. But the real problem arises while taking into account about the cost of incorrect inferences which is again of two types,

• Systematic bias and
• Sampling error.

Systematic bias results from errors in the sampling procedures , and it cannot be reduced or eliminated by increasing the sample size. It can be eliminated by eliminating and correcting the causes which are responsible for its occurrence. Following are some causes of the occurrence of systematic bias which requires concern to the researcher.

• Inappropriate sampling frame: If the sampling frame is inappropriate i.e., a biased representation of the universe, then it will result in a systematic bias.
• Defective measuring device: The second cause of occurrence of systematic bias is the selection of defective measuring devices. The measuring devices may be the interviewers; the questionnaire or other instrument used to collect data or may be physical measuring devices. If the questionnaire or the interviewer is biased and/or if the physical measuring device is defective this will lead to the occurrence of systematic bias.
• Non-respondents: If the researcher is unable to sample all the individuals initially included in the sample, there may arise a systematic bias. The reason is that in such a situation the likelihood of establishing correct or receiving a response from an individual is often corrected with the measure of what is to be estimated.
• Natural bias in the reporting of data: There is usually a downward bias in the individual income data collected by the income tax department where as an upward bias is found in the income data collected by some social organizations. People give less income data when asked for income tax but they overstate when asked for social status.
• Indeterminacy principle: Same times a researcher finds that individuals act differently when kept under observations than what they do when kept in non-observed situation.

Sampling errors on the other hand, is the random variations in the sample estimated around the true population parameters. Since they occur randomly and are equally likely to be in either direction, their nature happens to be of compensatory type and the expected value of such errors happens to be equal to zero. Sampling error decreases with the increase in sample size and it happens to be a smaller magnitude in case where the population is characterized as homogeneous. Sampling error can be measured for a given sampling design and size which is called as ‘a precision of the sampling plan’. If the sample size is increased, the precision can be improved but increase in sample size causes limitations like cost of collecting data, and also increases the systematic bias. Thus the effective way to increase the precision is usually to select a better sampling design which has a smaller sampling error for a given sample size at a given cost. Therefore, it shows that while selecting a sampling procedure the researcher must ensure that the procedure causes a relatively small sampling error and helps to control the systematic bias in a better way.

Characteristics of a Good Sample Design

The characteristics of a good sample as follows;

• Sample design must result in a truly representative sample,
• Sample design must be such which results in a small sampling error,
• Sampling design must be viable in the context of funds available for the research study,
• Sample design must be such that systematic bias can be controlled in a better way, and
• Sample should be such that the results of the sample study can be applied, in general, for the universe with a reasonable level of confidence.

Related posts:

• Meaning of Sampling and Steps in Sampling Process
• Data Mining – Meaning, Steps and Business Applications
• Organizational Design and Structure – Meaning and Importance
• Job Design – Meaning, Importance and Methods
• Social Research – Definition, Steps and Objectives
• Steps Involved in Drafting a Research Report
• Decision Making: Meaning and Characteristics
• Non-Profit Organization: Meaning and Characteristics

Leave a Reply Cancel reply

Your email address will not be published. Required fields are marked *

• Open access
• Published: 05 September 2024

Household economic burden of type-2 diabetes and hypertension comorbidity care in urban-poor Ghana: a mixed methods study

• Samuel Amon 1 , 2 ,
• Moses Aikins 2 ,
• Hassan Haghparast-Bidgoli 3 ,
• Irene Akwo Kretchy 4 ,
• Daniel Kojo Arhinful 1 ,
• Leonard Baatiema 2 , 5 ,
• Raphael Baffour Awuah 6 ,
• Vida Asah-Ayeh 1 ,
• Olutobi Adekunle Sanuade 7 ,
• Sandra Boatemaa Kushitor 8 , 9 ,
• Sedzro Kojo Mensah 1 ,
• Mawuli Komla Kushitor 3 , 10 , 11 ,
• Carlos Grijalva-Eternod 3 , 11 ,
• Ann Blandford 12 ,
• Hannah Jennings 13 , 14 ,
• Kwadwo Koram 1 ,
• Publa Antwi 13 ,
• Ethan Gray 3 , 12 &
• Edward Fottrell 3  

BMC Health Services Research volume  24 , Article number:  1028 ( 2024 ) Cite this article

Metrics details

Non-communicable diseases (NCDs) predispose households to exorbitant healthcare expenditures in health systems where there is no access to effective financial protection for healthcare. This study assessed the economic burden associated with the rising burden of type-2 diabetes (T2D) and hypertension comorbidity management, and its implications for healthcare seeking in urban Accra.

A convergent parallel mixed-methods study design was used. Quantitative sociodemographic and cost data were collected through survey from a random community-based sample of 120 adults aged 25 years and older and living with comorbid T2D and hypertension in Ga Mashie, Accra, Ghana in November and December 2022. The monthly economic cost of T2D and hypertension comorbidity care was estimated using a descriptive cost-of-illness analysis technique from the perspective of patients. Thirteen focus group discussions (FGDs) were conducted among community members with and without comorbid T2D and hypertension. The FGDs were analysed using deductive and inductive thematic approaches. Findings from the survey and qualitative study were integrated in the discussion.

Out of a total of 120 respondents who self-reported comorbid T2D and hypertension, 23 (19.2%) provided complete healthcare cost data. The direct cost of managing T2D and hypertension comorbidity constituted almost 94% of the monthly economic cost of care, and the median direct cost of care was US$19.30 (IQR:10.55–118.88). Almost a quarter of the respondents pay for their healthcare through co-payment and insurance jointly, and 42.9% pay out-of-pocket (OOP). Patients with lower socioeconomic status incurred a higher direct cost burden compared to those in the higher socioeconomic bracket. The implications of the high economic burden resulting from self-funding of healthcare were found from the qualitative study to be: 1) poor access to quality healthcare; (2) poor medication adherence; (3) aggravated direct non-medical and indirect cost; and (4) psychosocial support to help cope with the cost burden.

The economic burden associated with healthcare in instances of comorbid T2D and hypertension can significantly impact household budget and cause financial difficulty or impoverishment. Policies targeted at effectively managing NCDs should focus on strengthening a comprehensive and reliable National Health Insurance Scheme coverage for care of chronic conditions.

Peer Review reports

Introduction

Globally, non-communicable diseases (NCDs) lead to about 15 million premature deaths annually [ 1 , 2 ], and about eight in every ten deaths occur in low-and-middle-income countries (LMICs) [ 3 ]. World Health Organization (WHO) has projected that by 2025, NCDs will account for over 70% of all deaths globally, with more than 80% of the death occurring in developing countries [ 4 ]. Developing countries will incur NCDs related economic losses of US$21.3 trillion over the next two decades [ 5 ]. Existing literature indicates that diabetes, cancer, chronic lung diseases and cardiovascular diseases (CVD), alongside mental health, will cumulatively pose a global economic loss of 47 trillion US$ by 2030. This estimate is about 75% of the global gross domestic product (GDP) [ 6 ], which is projected to have disproportionate impacts on LMICs due to their fragile health systems. Approximately 10% of households globally are faced with high healthcare spending, of which the situation is projected to be worse in African countries [ 7 ]. In addition to Africa battling the attainment of universal health coverage (UHC) and financial risk protection schemes, over 2 billion people lack efficient, equitable and adequately funded healthcare systems [ 8 ]. Compared to high-income countries (HIC), the household financial burden of NCDs care in LMICs is much higher [ 9 , 10 ].

Evidence suggests that NCDs predispose households to a higher risk of health expenditure [ 11 ]. For instance, the mean household total costs per year in LMICs of CVD, cancers and diabetes were US$6055.99, US$3303.81 and US$1017.05 respectively [ 9 ]. The mean annual financial cost of managing one diabetic case at the outpatient clinic in Ghana was estimated at US$194.09 [ 12 ] and the mean healthcare management cost was US$38.68 [ 13 ]. Also, uncontrolled hypertension was found to be independent predictor of a higher cost of treatment in patients who died compared to those who survive in urban Ghana [ 14 ]. Excessive out-of-pocket (OOP) spending on healthcare services weakens households financially by wiping out savings and other durable resources, thereby plunging families into poverty [ 15 ]. Poor and vulnerable groups are least likely to obtain treatment for NCDs due to the high impact of OOP spending [ 16 , 17 ]. Meanwhile, there is growing evidence that governments’ expenditures on healthcare in SSA rarely focus on NCDs, suggesting that the costs of healthcare are passed on to patients [ 18 , 19 ]. Also, available evidence suggests there is poor coverage of NCD care by National Health Insurance Schemes [ 20 ], including Ghana. These phenomena hamper progress towards the attainment of UHC [ 11 ].

Comorbidity (co-existence of two or more conditions within an individual) is a growing public health challenge globally [ 21 ], substantially effecting individuals, carers and society [ 22 ]. Meanwhile, healthcare models in many LMICs have been designed to manage single health conditions rather than multiple conditions. Comparatively, individuals with comorbid chronic conditions often suffer higher rates of unplanned hospitalizations and frequent use of emergency services than those with single conditions [ 23 ]. In healthcare systems similar to Ghana where health insurance is ineffective and out of pocket payment as well as co-payments for healthcare is high, comorbidity exert more catastrophic healthcare expenditure on households [ 23 , 24 ]. Although the Ghana National Health Insurance Scheme (NHIS) benefit package is supposed to cover essential services like lab diagnosis and medicines, these often are not accessible to patients. The benefit routinely ends at catering for consultation fee. Consequently, most individuals with multiple chronic conditions become economically dependent on their relatives and support networks [ 23 , 24 ]. Also, the high healthcare cost drive people with NCDs to seek relatively more affordable alternative means of treatment (i.e., herbal and spiritual) to complement or completely replace orthodox medication [ 25 , 26 ].

There is a dearth of research on the effects of the healthcare-related economic burden of NCDs comorbidity on patients in Africa [ 27 , 28 ]. Although NCDs multimorbidity cause high financial burdens on households [ 29 , 30 ], the full extent of the economic burden that patients endure while seeking and receiving care is seldom reported. Costs incurred at each stage of the cascade of care (i.e., screening and diagnosis, treatment, management, and palliative care) include direct medical and non-medical costs, as well as indirect costs. These costs have implications for healthcare for people with NCDs, including comorbid T2D and hypertension [ 31 ]. Another major limitation in the literature is that, despite increasing scholarship on the economic burden caused by NCDs globally, most of the existing literature is from high-income countries and is disease specific [ 32 , 33 , 34 ].

As part of the ‘Contextual Awareness, Response and Evaluation: Diabetes in Ghana’ (CARE-Diabetes) project [ 35 ] (a mixed-methods study to generate a contextual understanding of T2D in an urban poor population), this study estimated the economic burden associated with T2D and hypertension multimorbidity in urban Ghana and discussed implications for interventions targeted at improving financial risk protection in vulnerable population in Ghana and other similar LMICs.

Study design

A convergent parallel mixed-methods study design was used. Quantitative and qualitative data were concurrently collected independently and analysed to assess the burden imposed by T2D and hypertension comorbidity, and its implications for healthcare. A descriptive cost-of-illness (COI) approach was used to estimate the economic burden of managing comorbid T2D and hypertension. The COI is a study method used to evaluate the economic burden imposed by an illness on individuals, institutions and/or society as a whole [ 36 ]. We further conducted focus group discussions (FGDs) to explore the cost burden implications for healthcare. Given that the CARE-Diabetes study focused on T2D, only the participants that self-reported an earlier diagnosis of T2D (index case) and co-occurrence of hypertension were used in this study.

Study setting

The study was carried out in Ga Mashie, a densely populated impecunious urban setting comprising two indigenous communities, namely James Town and Ussher Town, located in the Greater Accra Region of Ghana. The mean monthly household income in the study setting is USD78.83, and about three-quarters of the population have attained up to Junior High School (or middle school) education and above [ 37 ]. The twin towns, i.e., James Town and Ussher Town, are indigenous communities with fishing, petty trading and other fishing-related activities being the main economic activities and primary sources of livelihood for community members. Health services are provided mainly by government hospitals including Ussher Town Polyclinic and the Korle-Bu Teaching Hospital, a tertiary-level healthcare facility located close by. Also, there are few private hospitals offering healthcare services to the residents. More details of the study settings can be found elsewhere [ 35 ].

Sample size and sampling

Quantitative study.

This study was part of the CARE-Diabetes project[ 35 ], which had a target sample size of 1,242 adults aged ≥ 25 years within 959 households across 80 enumeration areas (EAs) of Ga Mashie. The sample size was determined on the ability to estimate the prevalence of T2D, and the sample was randomly selected from the 2021 population census [ 38 ]. The study excluded pregnant women or those who had given birth within the past six months as well as individuals who were unable to provide informed consent or had difficulty completing the survey, including those who were mentally incapacitated. All participants (n = 120) who self-reported T2D and hypertension were included in the present analysis.

Qualitative study

Likewise, the qualitative study used data from the CARE-Diabetes project. This study used 13 focus group discussions (FGDs) with community members. The participants included men and women with T2D and hypertension comorbidity, and people caring for relatives with the comorbid conditions. The respondents were enlisted using three sampling techniques. Firstly, relying on T2D patients scheduled for appointment on NCD clinic day at the Ussher Hospital (the main public health facility serving the people of Ga Mashie), we identified people with T2D and recruited them for FGD on the first day of data collection. Secondly, using the people with T2D identified from the hospital as index, a snowball technique was used to identify and recruit community members with comorbid T2D and hypertension. The snowball process continued until the required number of participants for the 5 FGDs was reached. Thirdly, participant (caregivers) without comorbid T2D and hypertension (n = 8) were recruited using convenient sampling technique, whereby a community liaison guided the research team to select potential participants from across the community.

Data collection

Quantitative.

Forty enumerators were recruited and trained to gather survey data on Open Data Kit (ODK) using mobile tablets in November and December 2022 [ 35 ]. Prior to data collection, the survey questionnaire was pretested in a different community outside Ga Mashie. Overall, 854 individuals completed the survey for the CARE-Diabetes project. Of this number, 120 (14%) self-reported co-morbid hypertension and T2D, all of whom were included in the present analysis.

Qualitative

Using pretested FGD guides, a total of 13 FGDs among community members with and without T2D and hypertension comorbidity were conducted from November to December, 2022 in the two predominant local dialects (Ga and Twi). The participants were different from those who participated in the survey. The topic guides were developed based on a literature review, and used to gather information on social norms, experiences, and attitudes regarding prevention, control, and care-seeking for T2D and hypertension comorbidity. Prior to the data collection, the topic guide was pretested in a different community. Copies of the FGD topic guides are attached to this manuscript as Supplementary files . The FGDs were led by trained research assistants. The training focused on the study guides and standard operating procedures (SOPs) for qualitative interviews. The total number of FGDs was considered sufficient for thematic saturation (i.e., no new information could be harnessed from interviews) [ 39 ]. The FGDs lasted for approximately one hour and were recorded digitally and detailed notes of the interactions were taken.

Data analyses

Quantitative analysis.

We generate a household wealth index using Principal Components Analysis (PCA) [ 40 ]. For the PCA, we selected and inputted into the model 15 out of the 23 assets, because they were reported to be owned by ≥ 5% but ≤ 95% of households. We also inputted into the PCA model whether the household had access to improved sources of drinking water, toilet facilities, gas or electricity as cooking fuels, and a separate room for the kitchen and the number of rooms in the household. We categorised the generated household wealth index into tertiles, specifically as ‘most poor’, ‘poor,’ and ‘least poor’.

Direct and indirect cost analyses were conducted using Microsoft Excel and STATA version 17. We adjusted for cluster and unequal probability survey design in the analysis by weighting. Direct medical cost was estimated by summing total cost incurred by people with comorbid T2D and hypertension on consultation, diagnostics and medication. Non-medical was estimated by summing the total cost of travel to and from hospital for comorbid T2D and hypertension medical care during the past one month. Total direct cost was estimated by summing the total direct medical and non-medical costs. The median and interquartile range were estimated. Indirect cost was estimated using the human capital approach (HCA). The HCA is a method commonly used to estimate lost productivity that results from disease, disability or premature death—which is an important component of the economic burden of chronic conditions [ 41 ]. Indirect cost was estimated by multiplying total productive hours lost (i.e., seeking comorbid T2D and hypertension care by patient and their caregiver). The national minimum wage per day of GHS13.53 for Ghana (US$1.00 equivalent to GHS8.58 (Bank of Ghana mean monthly interbank exchange rate, December 2022) was used to estimate value lost to productivity (Ministry of Finance, December 2022). The ratio of direct cost to income, by sex and socioeconomic status, was analysed. The mean economic cost of managing comorbid T2D and hypertension was estimated by dividing the sum of direct and indirect costs by the total participants. The robustness of cost estimates was tested through one-way and multi-way sensitivity analyses. This was done by varying critical cost components of the data which lacked certainty (i.e., medications and wages) by 3%, 8%, and 10% [ 42 ].

Qualitative analysis

All FGDs were transcribed and translated into English by trained fieldworkers who also conducted/facilitated the interviews. Transcripts were analysed thematically using the framework approach [ 43 ]. By this, a deductive coding framework was developed jointly by three of the authors based on existing literature on the consequences of the direct cost of managing comorbid T2D and hypertension for healthcare [ 44 ]. The framework was expanded when new codes or themes emerged through joint deliberation and review of the transcripts by the three authors (inductive approach). All transcripts were loaded into QSR NVIVO Version 11 to facilitate data coding and analysis. The thematic coding was done by the first author (who was part of the joint review and has extensive experience in qualitative thematic analysis). One person did the coding because the involvement of three authors in the development of the coding framework allowed for consensus building on all the codes relative to its alignments with the respective themes . After coding, the three authors jointly reviewed the output, and resolved any discordance between codes and themes. The coding exclusively focused on the consequences of direct OOP cost in the management of T2D and hypertension comorbidity on patients’ healthcare. Data are reported following the Consolidated Criteria for Reporting Qualitative Research (COREQ) [ 45 ].

The findings from the qualitative and quantitative works were synthesized by categorizing the findings to identify complementary themes that correspond with the research questions about the economic cost burden (direct and indirect cost) and its consequences for healthcare for people with T2D and hypertension co-morbidity [ 46 ].

Findings from the quantitative study

Survey data were gathered from 854 individuals in 629 households (household response rate of 66%; individual response rate of 69%). Of the 854 individuals who completed the survey, 120 (14%) self-reported comorbid T2D and hypertension, all of whom were included in the present analysis. However, the cost analysis included 23/120 (19.2%) comorbid T2D and hypertension individuals that provided completed healthcare cost data. Individuals who could not provide complete set of direct and indirect cost data were excluded in the economic burden analysis. As shown in Table  1 , many of the survey respondents were women (81.7%). More than half were ≥ 60 years, and most were unemployed (51.7%). Almost a quarter of the respondents reported that their healthcare was funded by co-payment and insurance jointly. A third reported funding their healthcare by insurance, whereas 42.9% reported funding solely out-of-pocket (OOP). Of the 94 participants of the FGDs, most were females (52.1%), almost two-third were widowed/single, and more than 56% were aged 25–49.

As presented in Table  2 , over 80% of the survey participants who provided complete direct and indirect costs information and were actually included in the economic cost analysis were females. The majority of the participants (60.9%) were employed, and most paid directly out-of-pocket for health care (42.9%).

As shown in Table  3. , the direct cost of managing T2D and hypertension comorbidity constituted almost 94% of the total economic cost of care, and the median monthly direct household cost of care was US$19.30 (IQR:10.55–118.88).

Further analysis of the proportion of direct cost to income, by patients’ socioeconomic status and sex, are presented in Table  4 . The absolute value of the mean direct cost for the poorest tertile was higher than the absolute value of the mean direct costs for the other wealth tertiles, although our sample size was too small to assess for statistical differences among groups. Also, men reported spending 122% of their income on healthcare compared to women (76.5%), although our sample size was too small to assess for statistical differences among groups. Furthermore, patients that paid for healthcare directly out of pocket spent over 100% of their income on care.

Findings from the qualitative study

The findings presented above on the proportion of the income expended on the direct cost of healthcare demonstrate the huge cost burden posed on people with comorbid T2D and hypertension. The remaining results sections focus on the implications of this cost burden on healthcare seeking, from the perspectives of patients and their caregivers (those without T2D and hypertension).

Implications of economic burden of managing T2D and hypertension comorbidity on healthcare seeking

The possible implications of the economic burden imposed by comorbid T2D and hypertension are classified into four broad themes and further elucidated in the subsequent sections of the results. These were: 1) poor access to quality healthcare; (2) poor medication adherence; (3) direct non-medical and indirect treatment cost aggravating burden; and (4) psychosocial support helps to cope with economic burden.

High treatment cost impacts access to quality healthcare

The high cost of managing T2D and hypertension comorbidity posed a huge burden for people living with these conditions. Most of the study respondents emphasized that availability and quality of healthcare were not a problem; however, affordability was a major hindrance to access. Thus, obtaining quality treatment was tied to the patient’s ability to pay for health services. Meanwhile, the extent of healthcare services offered depended on the patient’s ability to pay OOP at the point of seeking care. Even with the National Health Insurance Scheme (NHIS), patients were denied medication when they could not afford to pay OOP. The cost of healthcare services including labs, diagnostic tests, and certain medications often deter healthcare utilisation. Scheduled appointments were not adhered to due to the cost of health services.

“The healthcare provision is good, but it all depends on money. Treatment is not free, even though the health insurance covers part of the treatments, it does not cover most of the labs done by people living with T2D and hypertension.” (Man with comorbid T2D and hypertension )

“The main obstacle to accessing the services is the cost…The cost of the services, including lab, diagnostic tests, and medications, can be prohibitive. It prevents people from getting the care they need, even when they have an appointment scheduled.” (Woman with comorbid T2D and hypertension)

The inability to afford quality biomedical care led to plurality of healthcare, further complications and deteriorated conditions of patients. Some respondents shared experiences of the devastating consequences of their inability to meet the financial strains posed by direct and indirect costs of care. Due to the cost barrier to approved biomedical care, comorbid patients resorted to inferior treatment from multiple sources, which often worsen cost burden and health outcomes. That said, some patients noted that the use of complementary alternative medicines was also not cheap.

“They gave me the excuses that the health insurance does not cover the bills of the lab test. I resorted to using herbal medicine and going for prayers at different churches. After two years, I went to checkup on the same issue again at the hospital, and they realized the illness has worsened.” ( Woman with comorbid T2D and hypertension )

“Using Korle Bu hospital as an example, if you or any member of your family is admitted and you do not have the financial means to cater for the bills, I am sorry you will die. I have had a personal experience with them when my wife was admitted... Meanwhile herbal medicine is also not cheap” ( Man with comorbid T2D and hypertension )

Furthermore, the limited and unreliable NHIS coverage contributes to the direct cost burden. This is mainly because of a lack of knowledge on NHIS coverage by people with T2D and hypertension. Whereas some respondents believed that T2D and hypertension services were supposed to be free under the NHIS, others believed just a portion was covered. There was a widely held view among respondents that treatments for NCDs, particularly T2D and hypertension are supposed to be free under the NHIS. However, most medicines and services such as laboratory investigations were paid OOP.

“We were told that T2D and hypertension medicine is supposed to be free. All the health facilities in this community charge us for the service they render to us, none is free.” (Woman with comorbid T2D and hypertension)

“…we are told that insurance doesn’t cover the labs we do, and so we must pay. But it is through the lab result that diagnosis can be made, so they must review that aspect for us.” (Woman with comorbid T2D and hypertension

“The health insurance covers some of the diabetic’s drugs such as metformin, and some hypertensive drugs. But if the doctor prescribes specific one for you, you would be told it’s not available unless you pay out of pocket.” (Woman with comorbid T2D and hypertension)

According to some of the respondents with comorbid T2D and hypertension, the NHIS helped cover part of their hospital bills. However, patients bemoaned the limited and unreliable operations of the NHIS. They observed that medicines which were supposed to be free under the insurance were routinely sold to NHIS subscribers. The consequences were often devastating for those unable to co-pay. About three-quarters of the respondents (both those with and without comorbidity) accentuated the limited coverage of the NHIS and wondered what the relevance of subscribing to the NHIS was if their health needs could not freely or significantly be catered for.

“I heard the medication for T2D and hypertension was not to be sold, but right now if you don’t have money and you go to the hospital, you will die.” ( Man with comorbid T2D and hypertension )

“…We need a lot of medications, and they are expensive. If I don’t have money, I wouldn’t go to the hospital even though I have insurance… Last week I heard someone also confirm that the national health insurance is not working. (Woman with comorbid T2D and hypertension)

Cost affects adherence to medication

Even with the NHIS, patients with comorbid T2D and hypertension could not always get prescribed medications, even if they are supposedly entitled to them. People with T2D and hypertension comorbidity were compelled to pay a portion of the cost (i.e., co-payment) before being served with medication. Inability to afford healthcare results in patients not being attended to, affecting medication adherence. Thus, the cost of medication affects adherence to treatment regimens, as most patients manage their condition by heavily relying on financial support. The erratic financial support system for people with T2D and hypertension comorbidity led to non-adherence to treatment schedules. All respondents acknowledged that non-adherence to medication due to cost often led to dire complications like foot ulcers and cardiovascular diseases.

“…if you don’t have money, they will not sell the medicine to you, but in the health insurance it is supposed to be free, but they tell us it is not free, you must pay something. If you are not able to do so, your prescription will be given back to you.” ( Woman with comorbid T2D and hypertension )

“…My brother for instance takes injections twice a day; these drugs are very expensive…If he doesn’t get financial help, he skips the appointment. When he goes later after the default, he is sacked.” (Female without comorbid T2D and hypertension)

“Financial issues worry us a lot... When I run out of insulin, my legs will get swollen within four to five days and I will become very lean, which means the condition is becoming serious. Then my blood pressure will rise” (Man with comorbid T2D and hypertension)

Direct non-medical and indirect care cost adds to the burden

Some caregivers highlighted the additional burden imposed by the indirect cost of managing T2D and hypertension on their relatives. This mainly relates to the special diets recommended by healthcare specialists. Furthermore, the devastating nature of comorbid T2D and hypertension rendered most patients incapacitated for productive ventures. A respondent with T2D and hypertension comorbidity observed that the negative effects of the conditions on work and productivity plunged most people living with the conditions into impoverishment, thereby affecting their livelihood as well as their dependents.

“I also think money is the only solution to their problem because they need to eat certain meals which are different from what everyone else in the family eats. So, they need money to be able to afford that kind of life.” ( Woman without comorbid T2D and hypertension)

“This disease causes one to spend a lot of money. Lacking financial means when one develops this disease renders the victim’s life miserable. Say you are the breadwinner of the family; developing this illness hinders you from working hence bring about hunger in your home.” ( Man with comorbid T2D and hypertension)

Psychosocial support helps to cope with economic burden

All study respondents emphasized the importance of social support in the management of their T2D and hypertension comorbidity. Specifically, the inability of family and friends to financially and emotionally support healthcare for people with comorbid T2D and hypertension resulted in non-adherence to the treatment regimen, thereby causing significant emotional and psychosocial burden, for example depression, anxiety, frustration, and confusion. The study respondents reiterated that there was no way they could have solely managed their comorbid condition without psychosocial and physical support from family and friends.

“If maybe I need money and family and friends do not have money to help, it makes me overthink, depressed, anxious, worried, unhappy, frustrated and confusion . I am told not to overthink, but it is something that has been disturbing me.” (Man with comorbid T2D and hypertension)

“…in fact, if you don’t have a strong family support, you would be humiliated because everything about diabetes and hypertension involve money…if you don’t have anyone in the family to support and always be close to you, you will deteriorate. Because at a point, if you don’t get support financially and physically, you will die from stress and depression.” (Man with comorbid T2D and hypertension)

“Sometimes my siblings help me, sometimes too they don’t help, so there are times I am not able to afford my medication. The Country’s economy is in bad state, so you cannot burden people with your financial challenges because they also have responsibilities.” (Woman with comorbid T2D and hypertension)

This study sought to understand and add to the limited literature available on the economic cost associated with the rising burden of T2D and hypertension comorbidity in the economically disadvantaged urban setting of Ga Mashie Accra and its implications for seeking healthcare. The study found a significant economic cost burden associated with management of T2D and hypertension comorbidity. Patients spent excessively more than their income on healthcare. Our findings are consistent with those of previous studies conducted in SSA that have reported high direct costs of managing chronic diseases [ 10 , 47 ], most specifically, T2D [ 48 , 49 , 50 , 51 ], hypertension [ 52 ], and comorbid T2D and hypertension [ 53 ].

Like other studies conducted in Ghana [ 13 , 54 ], evidence from this study emphasizes that the cost of managing T2D and hypertension comorbidity is high. Other studies in Ghana have reported that the cost of managing T2D can lead to catastrophic healthcare spending [ 49 , 55 ]. Although the estimated mean economic cost of managing comorbid T2D and hypertension [US$63.08 (95% CI:0.00- 145.35)] was analysed from a patient perspective, the cost is comparable to that reported in urban Kenya (US$38) which was analysed from a societal perspective [ 53 ]. This implies a higher burden of managing the comorbid condition in Ga Mashie compared to Kenya since the societal perspective estimates economic cost from a broader perspective comprising both patient and institutional costs. Overall, individuals with the comorbid condition spent almost 81% of their income on healthcare. This can be attributed to the poor healthcare seeking behaviour of people with NCDs in poverty-stricken urban communities of Ghana, whereby individuals seek healthcare in a worsened state and thus incur high cost of care [ 56 ].

The burden is aggravated by the fact that most comorbid T2D and hypertension patients are unemployed and rely heavily on financial and social support systems within the already impoverished community where income levels are generally low [ 37 ]. Hence, the economic cost burden imposed by the condition transcends the individual suffering from the disease. As shown by this study, the economic burden has far-reaching effects on healthcare. From the qualitative study, we found four main possible implications of the high economic burden on individual’s healthcare. Firstly, the cost burden affected access to care and treatment quality; secondly, the high cost affected medication adherence; thirdly, direct non-medical and indirect treatment cost add to the economic burden; and finally, lack of psychosocial support aggravates the economic burden. These themes are discussed below.

High economic burden impacts access to care and treatment quality

Firstly, the high healthcare cost impacts access to T2D and hypertension care and treatment quality among the poor urban community of Ga Mashie. In this study, the high-cost burden imposed by approved sources of care (health facilities) coupled with low socioeconomic status are barriers to access to quality comorbid T2D and hypertension care. Other studies conducted in Africa have reported the association between low socioeconomic status and limited access to treatment due to high cost [ 50 , 57 ], likewise other regions of the world [ 58 , 59 ].

Similar to available evidence on NCDs care and management across Africa [ 60 ], there are three main means through which people with T2D and hypertension in Ga Mashie seek healthcare and manage their condition. These are biomedical, ethnomedical (herbal) and faith/spiritual treatments. Often, biomedical treatment sources like government and private health facilities serve as the first point of call to persons with T2D and hypertension for diagnosis and medical education by health professionals. However, many comorbid T2D and hypertension patients in Ga Mashie consider biomedical treatment very expensive. The expenses incurred include consultation, diagnosis, medication, and other hospital bills. Meanwhile, evidence on biomedical therapy for NCDs globally indicates that most patients must take medication for the rest of their lives and on a regular basis [ 61 , 62 ]. Hence, borne out of desperation to lessen the economic burden through cheaper sources that promise rapid and permanent cure, patients resort to pluralistic means of combining biomedical, ethnomedical (herbal) and/or spiritual care, thereby compromising treatment quality.

A further possible implication of the high economic cost of biomedical treatment is that, not only does it serve as a barrier to accessing quality care but also to accessing biomedically approved medications, as people seek alternative means (i.e., herbal and spiritual) of treatment to complement or completely replace orthodox medication. Herbal drugs are perceived to be relatively more affordable than pharmaceutical drugs. This confirms the findings of other studies conducted in the African region [ 25 , 26 ]. Also, it is common in SSA that due to the high economic burden associated with managing T2D and hypertension, some people with T2D in poverty-stricken urban communities like Ga Mashie typically combine biomedical therapy with spiritual therapy, whereas others solely depend on spiritual/faith healing therapy as a cost-effective rapid measure to manage their T2D [ 63 , 64 ].

The economic burden of managing T2D in Ga Mashie is untenable for most of the patients in need of care. Bekele et al. reported that having health insurance is a strong predictor of access to screening of T2D and effective biomedical care [ 65 ]. In Ghana, the NHIS is the main strategy for delivering social protection. The NHIS Act (Act 850, 2012) exempts children under 18 years, lactating mothers, and the elderly over 70 years from premium payments. The exemptions aim to support the management of various ill-health conditions including NCDs. Although the NHIS targets everybody, principally the vulnerable, there is a plethora of evidence to show that due to the inability to afford premiums because of low socioeconomic status, segments of the population are not covered [ 66 , 67 ]. Our findings show low confidence in the NHIS due to its erratic and unreliable operations as well as inconsistent information on the insurance coverage. This pushes patients to seek healthcare outside the approved biomedical care system. The consequence of the cost barrier to reliable access to approved biomedical care is the inferiority of treatment sought from multiple sources often leading to an exacerbated cost burden and poor health outcomes.

Cost affects medication adherence

Our findings are consistent with those of other studies that have found that non-adherence to treatment schedule and medication is endemic among people with T2D [ 68 ] and hypertension [ 69 ] in Ghana. They also corroborate other studies on diabetes in SSA that highlighted the high cost of biomedical medication, the absence of reliable health insurance cover for diabetes care [ 70 ], and the inability of patients to afford consultation fees and laboratory services [ 71 ] creating health system barriers for medical adherence among T2D patients. The cost barrier is fundamental to the non-adherence to prescribed medications among study participants. Thus, this study found that non-adherence to T2D medication occurs mainly because of patients' inability to afford direct medical and/or non-medical costs of treatment. Affordability is a real problem partly because most comorbid T2D and hypertension patients were found to be elderly and, thus, were not productively engaged for financial income. Hence, the majority of T2D patients rely heavily on social support for their healthcare needs.

Adherence to medication and treatment plans for patients in Ga Mashie critically depends on financial and social support from relatives and friends [ 72 , 73 ]. Our findings show that comorbid T2D and hypertension patients rely heavily on relatives to pay for direct medical and non-medical costs associated with care. Relatives support direct medical cost expenses like consultation, laboratory diagnosis, medication and other healthcare costs. Likewise, relatives and friends assist with non-medical expenses like transportation to and from the healthcare facilities as well as other subsistence costs. Consequently, erratic financial support from relatives and friends has implications for adherence to the systematic plan for their treatment therapy and, ultimately, health outcomes. Furthermore, adherence to biomedical treatment among T2D and hypertension comorbidity patients in poor urban communities like Ga Mashie depends on the type of treatment and cost [ 74 , 75 ]. By this, care providers routinely compromise healthcare quality to meet the financial strength of patients. Patients cannot afford the right dosage of medication required for effective management of their condition, hence the need to modify the treatment regimen.

Non-medical and indirect treatment cost adds to the burden

Besides the direct medical cost of comorbid T2D and hypertension treatment, there are other costs which are often not extensively considered in the economic burden of NCD dialogues. These are direct non-medical (e.g., transportation costs to and from healthcare facilities and cost of dietary and nutritional therapy) and indirect costs (i.e., productive workdays lost due to health-seeking or health condition) of care. Akin to a study in south-eastern Tanzania that reported lived experiences of diabetes management among adults [ 75 ], this study found that the cost of transportation to and from health facilities imposes an additional cost burden on patients.

Similar to some studies in SSA [ 65 , 76 ], we found changes in the pattern of diet and nutritional arrangements for persons with T2D and hypertension comorbidity recommended by dieticians. It was widely observed among this study's respondents that adherence to dietary changes is an integral factor in the management of T2D and hypertension comorbidity due to its vital contribution to blood pressure and glycaemic control. However, the cost of purchasing suitable foods regularly is problematic, thereby preventing strict adherence to the recommended dietary patterns. Literature in Africa supports the observation made by this study that comorbid T2D and hypertension patients of low socioeconomic status find it challenging to adhere to recommended dietary plans because of the associated cost burden [ 77 ].

Furthermore, although the findings of this study show a minimal contribution of indirect cost to the cost profile, the far-reaching impact on patients’ livelihoods is devastating. The health condition of most people with comorbid T2D and hypertension prevented them from engaging in any meaningful productive work, thereby indirectly worsening the cost burden. Consequently, patients mostly rely on the benevolence of family and friends for the management of their illness and general subsistence. Given the low socioeconomic status of the people of Ga Mashie coupled with the catastrophic direct medical cost of treatment, these direct non-medical and indirect costs exacerbate the burden on patients.

Psychosocial support helps to cope with the economic burden

The significant psychosocial burden imposed on people with NCDs cannot be underestimated [ 78 , 79 ]. Patients' inability to independently or substantially cater for themselves often poses psychological stress on them and their caregivers [ 60 ]. Like findings of a systematic review of experiences of people living with NCDs in Africa [ 60 ], the psychological changes T2D and hypertension comorbidity patients in Ga Mashie go through include depression, stress, guilt, anxiety, anger, confusion frustration, and fear of death. These adverse psychosocial experiences intangibly contribute to the cost burden and physical deterioration in underprivileged communities like Ga Mashie. This happens partly because the psychosocial burden imposed by the disease is often overlooked by health professionals notwithstanding its overwhelming consequences [ 80 ]. Social support is therefore the most viable option available for people living with the disease in Ga Mashie.

Consistent with prior literature on the experiences of people living with NCDs in Africa, the findings of this study show that primary caregivers and other family members as well as friends play significant roles in the healthcare and management of comorbid T2D and hypertension [ 65 , 81 , 82 ]. Particularly among the aged, there is always active support from partners, children, caregivers, and other family members in the management of the disease. The main psychosocial support provided includes financial, biological, emotional, spiritual, cultural, social, and mental. The support includes accompanying patients to health facilities and ensuring medical and dietary adherence. Respondents have attributed any semblance of good quality of life among people with T2D and hypertension comorbidity in Ga Mashie to the unwavering financial support from their families [ 83 ]. However, in the long run, the huge healthcare cost burden, loss of caregivers' productive hours, and disruption in family members’ routine socioeconomic activities lead to neglect of patients in a poor urban setting like Ga Mashie [ 60 ].

Policy and practice implications

Although the NHIS coverage has greatly expanded in Ghana over the years, the current modalities still offer limited protection against high healthcare expenditure for patients with comorbid T2D and hypertension. To address the high-cost burden of managing T2D and hypertension comorbidity, population-based interventions aimed at eliminating the catastrophic healthcare expenditure and strengthening health systems for the provision of effective biomedical care for those affected are essential. Policies should crucially consider reform of the NHIS benefits packages for NCDs to improve its potency for financial risk protection and reliability of biomedical care, particularly for people with T2D and hypertension comorbidity. These should consider subsidies/exemptions on medication and sensitization on the consequences of medical pluralism and NHIS coverage.

Study strengths and limitations

The major strength of this study is the triangulation of quantitative and qualitative data source that promoted a richer understanding of the findings. However, the small sample of respondents who provided complete cost data for the quantitative analysis is a limitation which may have reduced the precision of our cost estimates, and hinders generalizability of the findings. Future studies intent on measuring the economic cost of comorbid NCDs should consider larger sample sizes. Also, although the CARE-Diabetes project’s survey participants were selected using rigorous multi-stage sampling approach, females constituted over 80% of the subset data used for this analysis, suggesting likelihood of a highly biased sampling method. However, this may also be ascribed to women being more conscious of their health status – as cases of comorbid T2D and hypertension were self-reported. For the qualitative study, the thematic coding was done by one person—an approach which may have compromised the analysis. However, we made cautious efforts to maintain the internal validity of the data by having three of the authors check the transcripts to resolve any discordance between codes and global/organizing themes. Furthermore, there may not necessarily be a direct relationship between the qualitative and quantitative results presented due to the different populations (of living with T2D and hypertension) used, and thus possible variations in the degree of disease burden across the two groups.

The economic burden of managing T2D and hypertension comorbidity is significant in deprived urban Ghana. The burden weighs heavily on household budgets, thereby negatively affecting health and healthcare seeking patterns of patients. To alleviate the economic burden of medical care and promote appropriate therapy, the NHIS should prioritize free/affordable medical care for patients with NCDs to facilitate the effective management of T2D and hypertension comorbidity. Future research should consider using a larger sample size for the cost analysis and consider assessing the catastrophic health expenditure associated with healthcare (proportion of healthcare expenditure to household monthly food and non-food spending).

Availability of data and materials

The data and materials that support the findings of this study are available from the authors upon reasonable request.

Riley L, Gouda H, Cowan M. The non-communicable disease progress monitor. World Health Organization. 2017. Available: http://apps.who.int/iris/bitstream/10665/258940/1/9789241513029-eng.pdf .

World Health Organization. Noncommunicable diseases Country profile 2018. World Health Organization (WHO): Institutional Repository for Information Sharing. 2018. Available: https://iris.who.int/handle/10665/274512 .

Allen L, Cobiac L, Townsend N. Quantifying the global distribution of premature mortality from non-communicable diseases. J Pub Health. 2017;39(4):698–703. https://doi.org/10.1093/pubmed/fdx008 .

Article   Google Scholar  

World Health Organization. Noncommunicable diseases progress monitor. Geneva: World Health Organization; 2015.

Google Scholar  

Ganju A, Goulart AC, Ray A, Majumdar A, Jeffers BW, Llamosa G, et al. Systemic solutions for addressing non-communicable diseases in low-and middle-income countries. J Multidiscip Healthc. 2020;Volume 13:693–707.

NCD Alliance. The financial burden of NCDs. 2020. Available: https://ncdalliance.org/why-ncds/financing-ncds .

Marshall DC, Al Omari O, Goodall R, Shalhoub J, Adcock IM, Chung KF, et al. Trends in prevalence, mortality, and disability-adjusted life-years relating to chronic obstructive pulmonary disease in Europe: an observational study of the global burden of disease database, 2001–2019. BMC Pulm Med. 2022;22(1):1–11.

Escobar M, Griffin E, Shaw. The impact of health insurance in low-and middle-income countries. Washington DC: Brookings Institution Press; 2010. Available: https://www.brookings.edu/wp-content/uploads/2016/07/theimpactofhealthinsurance_fulltext.pdf .

Kazibwe J, Tran PB, Annerstedt KS. The household financial burden of non-communicable diseases in low-and middle-income countries: a systematic review. Health Res Policy Syst. 2021;19(1):96.

Article   PubMed   PubMed Central   Google Scholar  

Chuma J, Maina T. Catastrophic health care spending and impoverishment in Kenya. BMC Health Serv Res. 2012;12(1): 413.

Adeniji FIP, Obembe TA. Cardiovascular disease and its implication for higher catastrophic health expenditures among households in sub-Saharan Africa. J Health Econ Outcomes Res. 2023;10(1):59.

Gad M, Kazibwe J, Abassah-Konadu E, Amankwah I, Owusu R, Gulbi G, et al. The epidemiological and economic burden of diabetes in Ghana: a scoping review to inform health technology assessment. medRxiv. 2023:2023.04:19.23288806. Available: https://doi.org/10.1101/2023.04.19.23288806 .

Amon SK, Aikins MKS. Economic burden of type 2 diabetes mellitus complications among patients in the eastern region of Ghana: a descriptive cross-sectional cost-of-illness study. Diabetes Management. 2017;7(5):367–76.

Appiah LT, Nguah SB, Opare-Addo PA, Fugar S, Holu JM, Commodore-Mensah Y, et al. Cost and outcomes of acute cardiovascular disease hospitalizations in a tertiary hospital in Ghana. Alexandria Journal of Medicine. 2023;59(1):42–7.

Kitole FA, Lihawa RM, Mkuna E. Comparative analysis on communicable and non-communicable diseases on catastrophic spending and impoverishment in Tanzania. Glob Soc Welf. 2024;11:123–34. https://doi.org/10.1007/s40609-022-00241-y00241-y .

Kankeu HT, Saksena P, Xu K, Evans DB. The financial burden from non-communicable diseases in low- and middle-income countries: a literature review. Health Res Policy Syst. 2013;11:31.

Murphy A, Palafox B, O’Donnell O, Stuckler D, Perel P, AlHabib KF, et al. Inequalities in the use of secondary prevention of cardiovascular disease by socioeconomic status: evidence from the PURE observational study. Lancet Glob Health. 2018;6(3):e292–301.

Jailobaeva K, Falconer J, Loffreda G, Arakelyan S, Witter S, Ager A. An analysis of policy and funding priorities of global actors regarding noncommunicable disease in low-and middle-income countries. Glob Health. 2021;17(1):1–15.

Institute for Health Metrics and Evaluation (IHME). Financing global health 2020. University of Washington. 2020. Available from: https://www.healthdata.org/sites/default/files/files/policy_report/FGH/2021/FGH_2020_full-report.pdf .

Christmals CD, Aidam K. Implementation of the National Health Insurance Scheme (NHIS) in Ghana: lessons for South Africa and low- and middle-income countries. Risk Manag Healthc Policy. 2020;13:1879–904.

Academy of Medical Sciences. Multimorbidity: a priority for global health research. Academy of Medical Sciences; 2018. Available: https://www.georgeinstitute.org/sites/default/files/multimorbidity-report-2018.pdf .

Skou ST, Mair FS, Fortin M, Guthrie B, Nunes BP, Miranda JJ, et al. Multimorbidity. Nature Reviews Disease Primers. 2022;8(1):48.

Mercer S, Furler J, Moffat K, Fischbacher-Smith D, Sanci L. Multimorbidity: technical series on safer primary care. Geneva: World Health Organization; 2016.

Goryakin Y, Suhrcke M. The prevalence and determinants of catastrophic health expenditures attributable to non-communicable diseases in low-and middle-income countries: a methodological commentary. International journal for equity in health. 2014;13:1–5.

Ozioma E-OJ, Chinwe OAN. Herbal medicines in African traditional medicine. Herbal medicine. 2019;10:191–214.

Suglo JN, Evans C. Factors influencing self-management in relation to type 2 diabetes in Africa: a qualitative systematic review. PLoS ONE. 2020;15(10): e0240938.

Article   CAS   PubMed   PubMed Central   Google Scholar  

Jaspers L, Colpani V, Chaker L, van der Lee SJ, Muka T, Imo D, et al. The global impact of non-communicable diseases on households and impoverishment: a systematic review. Eur J Epidemiol. 2015;30(3):163–88.

Article   PubMed   Google Scholar  

Tsolekile L, Murphy A, Palafox B. The household economic burden of non-communicable diseases in 18 countries. 2020.

Vandenberghe D, Albrecht J. The financial burden of non-communicable diseases in the European Union: a systematic review. Eur J Pub Health. 2020;30(4):833–9.

Murphy A, Palafox B, Walli-Attaei M, Powell-Jackson T, Rangarajan S, Alhabib KF, et al. The household economic burden of non-communicable diseases in 18 countries. BMJ Glob Health. 2020;5(2): e002040.

Karinja M, Pillai G, Schlienger R, Tanner M, Ogutu B. Care-seeking dynamics among patients with diabetes mellitus and hypertension in selected rural settings in Kenya. Int J Environ Res Public Health. 2019;16(11): 2016.

Barrett AM, Colosia A, Boye KS, Oyelowo O. PSY27 burden of obesity: 10-year review of published literature on direct and indirect costs in nine countries. Value in Health. 2008;3(11):A160.

Lin J-MS, Resch SC, Brimmer DJ, Johnson A, Kennedy S, Burstein N, et al. The economic impact of chronic fatigue syndrome in Georgia: direct and indirect costs. Cost Effectiveness and Resource Allocation. 2011;9(1):1–12.

Schofield D, Passey M, Percival R, Shrestha R, Callander E, Kelly S. Retiring early with cardiovascular disease—impact on individual’s financial assets. Int J Cardiol. 2011;146(1):125–6.

Lule SA, Kushitor SB, Grijalva-Eternod CS, Adjaye-Gbewonyo K, Sanuade OA, Kushitor MK, et al. The contextual awareness, response and evaluation (CARE) diabetes project: study design for a quantitative survey of diabetes prevalence and non-communicable disease risk in Ga Mashie, Accra, Ghana. Glob Health Action. 2024;17(1): 2297513.

Schattenberg JM, Lazarus JV, Newsome PN, Serfaty L, Aghemo A, Augustin S, et al. Disease burden and economic impact of diagnosed non-alcoholic steatohepatitis in five European countries in 2018: a cost-of-illness analysis. Liver Int. 2021;41(6):1227–42.

de‐Graft Aikins A, Kushitor M, Kushitor SB, Sanuade O, Asante PY, Sakyi L, et al. Building cardiovascular disease competence in an urban poor Ghanaian community: a social psychology of participation approach. J Commun Appl Soc Psychol. 2020;30(4):419–40.

Asamoah-Boaheng M, Sarfo-Kantanka O, Tuffour AB, Eghan B, Mbanya JC. Prevalence and risk factors for diabetes mellitus among adults in Ghana: a systematic review and meta-analysis. Int Health. 2019;11(2):83–92.

Saunders B, Sim J, Kingstone T, Baker S, Waterfield J, Bartlam B, et al. Saturation in qualitative research: exploring its conceptualization and operationalization. Qual Quant. 2018;52:1893–907.

Vyas S, Kumaranayake L. Constructing socio-economic status indices: how to use principal components analysis. Health Policy Plan. 2006;21(6):459–68.

Pike J, Grosse SD. Friction cost estimates of productivity costs in cost-of-illness studies in comparison with human capital estimates: a review. Appl Health Econ Health Policy. 2018;16(6):765–78.

Kenton W, Kindness D, Velasquez V. Sensitivity analysis definition. New York: Investopedia; 2022.

Braun V, Clarke V. Using thematic analysis in psychology. Qual Res Psychol. 2006;3(2):77–101.

Haque MS. Inductive and/or deductive research designs. In: Principles of social research methodology. Singapore: Springer Nature. 2022. p. 59–71. https://doi.org/10.1007/978-981-19-5441-2 .

Tong A, Sainsbury P, Craig J. Consolidated criteria for reporting qualitative research (COREQ): a 32-item checklist for interviews and focus groups. Int J Qual Health Care. 2007;19(6):349–57.

Mukumbang FC, Orth Z, van Wyk B. What do the implementation outcome variables tell us about the scaling-up of the antiretroviral treatment adherence clubs in South Africa? A document review. Health research policy and systems. 2019;17(1):1–12.

Hjelm K, Atwine F. Health-care seeking behaviour among persons with diabetes in Uganda: an interview study. BMC Int Health Hum Rights. 2011;11(1): 11.

Bommer C, Sagalova V, Heesemann E, Manne-Goehler J, Atun R, Bärnighausen T, et al. Global economic burden of diabetes in adults: projections from 2015 to 2030. Diabetes Care. 2018;41(5):963–70.

Mapa-Tassou C, Katte JC, Mba Maadjhou C, Mbanya JC. Economic impact of diabetes in Africa. Curr Diab Rep. 2019;19:1–8.

Moucheraud C, Lenz C, Latkovic M, Wirtz VJ. The costs of diabetes treatment in low-and middle-income countries: a systematic review. BMJ Glob Health. 2019;4(1): e001258.

Williams R, Karuranga S, Malanda B, Saeedi P, Basit A, Besançon S, et al. Global and regional estimates and projections of diabetes-related health expenditure: results from the International Diabetes Federation Diabetes Atlas. Diabetes Res Clin Pract. 2020;162: 108072.

Gad M, Lord J, Chalkidou K, Asare B, Lutterodt MG, Ruiz F. Supporting the development of evidence-informed policy options: an economic evaluation of hypertension management in Ghana. Value in Health. 2020;23(2):171–9.

Tarus AJ. Economic costs of hypertension-diabetes mellitus comorbidity in primary public health facilities in Kiambu County, Kenya. Univ Nairobi Res Archive. 2022. Available: http://erepository.uonbi.ac.ke/handle/11295/162331 .

de-Graft Aikins A, Boynton P, Atanga LL. Developing effective chronic disease interventions in Africa: insights from Ghana and Cameroon. Globalization and Health. 2010;6(1):6.

Annani-Akollor ME, Addai-Mensah O, Fondjo LA, Sallah L, Owiredu E-W, Acheampong E, et al. Predominant complications of type 2 diabetes in kumasi: a 4-year retrospective cross-sectional study at a teaching hospital in Ghana. Medicina. 2019;55(5): 125.

Yarney L, Adjei-Mensah E, Darko J, Frempong ANA. Health-seeking behaviour among Ghanaian urban residents: a quantitative exploratory case study. Res J Med Health Sci. 2022;3(1):12–24.

Misra A, Gopalan H, Jayawardena R, Hills AP, Soares M, Reza-Albarrán AA, et al. Diabetes in developing countries. J Diabetes. 2019;11(7):522–39.

Elhussein A, Anderson A, Bancks MP, Coday M, Knowler WC, Peters A, et al. Racial/ethnic and socioeconomic disparities in the use of newer diabetes medications in the Look AHEAD study. Lancet Reg Health Am. 2022;6:6.

Sumarsono A, Buckley LF, Machado SR, Wadhera RK, Warraich HJ, Desai RJ, et al. Medicaid expansion and utilization of antihyperglycemic therapies. Diabetes Care. 2020;43(11):2684–90.

Klu D, Alhassan R, Dalaba M, Doegah P. Trajectories of lived experiences of people living with diabetes mellitus in Africa: evidence mapping of conceptual framework. African Journal of Diabetes. 2022;30(1):1–10.

Fekadu G, Bula K, Bayisa G, Turi E, Tolossa T, Kasaye HK. Challenges and factors associated with poor glycemic control among type 2 diabetes mellitus patients at Nekemte Referral Hospital. Western Ethiopia J Multidiscip Healthc. 2019;2019(12):963–74. https://doi.org/10.2147/JMDH.S232691 .

Mogre V, Johnson NA, Tzelepis F, Paul C. Barriers to diabetic self-care: a qualitative study of patients’ and healthcare providers’ perspectives. J Clin Nurs. 2019;28(11–12):2296–308.

Kasole R, Martin HD, Kimiywe J. Traditional medicine and its role in the management of diabetes mellitus: “patients’ and herbalists’ perspectives.” Evid Based Complement Alternat Med. 2019;2019:2835691.

Tsabang N, Ngah N, Estella F, Ga A, editors. Herbal medicine and treatment of diabetes in Africa: case study in Cameroon. 2017. https://doi.org/10.4172/2572-5629.1000112 .

Bekele H, Asefa A, Getachew B, Belete AM. Barriers and strategies to lifestyle and dietary pattern interventions for prevention and management of type-2 diabetes in Africa, systematic review. Journal of Diabetes Research. 2020;2020:7948712.

Ayanore MA, Pavlova M, Kugbey N, Fusheini A, Tetteh J, Ayanore AA, et al. Health insurance coverage, type of payment for health insurance, and reasons for not being insured under the National Health Insurance Scheme in Ghana. Heal Econ Rev. 2019;9:1–15.

Kwarteng A, Akazili J, Welaga P, Dalinjong PA, Asante KP, Sarpong D, et al. The state of enrollment on the National Health Insurance Scheme in rural Ghana after eight years of implementation. International journal for equity in health. 2020;19(1):1–14.

Afaya RA, Bam V, Azongo TB, Afaya A, Kusi-Amponsah A, Ajusiyine JM, et al. Medication adherence and self-care behaviours among patients with type 2 diabetes mellitus in Ghana. PLoS ONE. 2020;15(8): e0237710.

Christian AK, Sanuade OA, Kushitor SB, Kushitor M, Kretchy I, Agyemang C, et al. Metabolic syndrome among individuals living with hypertension in Accra, Ghana. PLoS ONE. 2021;16(10): e0253837.

Pourhabibi N, Mohebbi B, Sadeghi R, Shakibazadeh E, Sanjari M, Tol A, et al. Determinants of poor treatment adherence among patients with type 2 diabetes and limited health literacy: a scoping review. Journal of Diabetes Research. 2022;2022:2980250.

Yasmin F, Nahar N, Banu B, Ali L, Sauerborn R, Souares A. The influence of mobile phone-based health reminders on patient adherence to medications and healthy lifestyle recommendations for effective management of diabetes type 2: a randomized control trial in Dhaka, Bangladesh. BMC Health Serv Res. 2020;20(1):520.

Abdulrehman MS, Woith W, Jenkins S, Kossman S, Hunter GL. Exploring cultural influences of self-management of diabetes in coastal Kenya: an ethnography. Glob Qual Nurs Res. 2016;3:2333393616641825.

PubMed   PubMed Central   Google Scholar  

Matima R, Murphy K, Levitt NS, BeLue R, Oni T. A qualitative study on the experiences and perspectives of public sector patients in Cape Town in managing the workload of demands of HIV and type 2 diabetes multimorbidity. PLoS ONE. 2018;13(3): e0194191.

Atwine F, Hultsjö S, Albin B, Hjelm K. Health-care seeking behaviour and the use of traditional medicine among persons with type 2 diabetes in south-western Uganda: a study of focus group interviews. Pan Afr Med J. 2015;20:76.

Metta E, Haisma H, Kessy F, Geubbels E, Hutter I, Bailey A. “It is the medicines that keep us alive”: lived experiences of diabetes medication use and continuity among adults in Southeastern Tanzania. BMC Health Serv Res. 2015;15(1):111.

Mohammed AS, Adem F, Tadiwos Y, Woldekidan NA, Degu A. Level of adherence to the dietary recommendation and glycemic control among patients with type 2 diabetes mellitus in Eastern Ethiopia: a cross-sectional study. Diabetes Metab Syndr Obes. 2020;2020(13):2605–12. https://doi.org/10.2147/DMSO.S256738 .

Okop KJ, Ndayi K, Tsolekile L, Sanders D, Puoane T. Low intake of commonly available fruits and vegetables in socio-economically disadvantaged communities of South Africa: influence of affordability and sugary drinks intake. BMC Public Health. 2019;19(1):940.

Chilunga FP, Schwerzel PS, Meeks KA, Beune E, Bahendeka S, Mockenhaupt F, et al. Associations of psychosocial stress with type 2 diabetes and glycaemic control among Ghanaians: the RODAM study. Diabet Med. 2023;40(1): e15006.

Hapunda G. Coping strategies and their association with diabetes specific distress, depression and diabetes self-care among people living with diabetes in Zambia. BMC Endocr Disord. 2022;22(1):215.

Dhada B, Blackbeard D. Caregivers of children with diabetes mellitus: challenges of caring for and perceptions of consultations in a South African public sector context. South African Family Practice. 2019;61(4):117–35.

Hushie M. Exploring the barriers and facilitators of dietary self-care for type 2 diabetes: a qualitative study in Ghana. Health promotion perspectives. 2019;9(3):223.

Werfalli MM, Kalula SZ, Manning K, Levitt NS. Does social support effect knowledge and diabetes self-management practices in older persons with type 2 diabetes attending primary care clinics in Cape Town, South Africa? PLoS ONE. 2020;15(3): e0230173.

Fatusin A, Agboola SM, Shabi OM. Relationship between family support and quality of life of type-2 diabetes mellitus patients attending family medicine clinic, federal medical centre, Ido–Ekiti. Afr J Fam Pract. 2016;7(2):3–11.

Download references

Acknowledgements

The work was supported by the Medical Research Council (MRC) through the United Kingdom Research and Innovation (UKRI), grant number MR/T029919/1. We are grateful to members of the CARE-Diabetes project team who helped execute this research work.

This research was funded by the United Kingdom Research and Innovation (UKRI)—Medical Research Council (MRC) through a Grant [reference MR/T029919/1]. The funder of the study had no role in the study design, data collection, data analysis, data interpretation, or writing of this manuscript.

Author information

Authors and affiliations.

Noguchi Memorial Institute for Medical Research, University of Ghana, Accra, Ghana

Samuel Amon, Daniel Kojo Arhinful, Vida Asah-Ayeh, Sedzro Kojo Mensah & Kwadwo Koram

Department of Health Policy, Planning and Management, School of Public Health, University of Ghana, P.O. Box LG 13, Legon, Accra, Ghana

Samuel Amon, Moses Aikins & Leonard Baatiema

Institute for Global Health, University College London, London, UK

Hassan Haghparast-Bidgoli, Mawuli Komla Kushitor, Carlos Grijalva-Eternod, Ethan Gray & Edward Fottrell

Department of Pharmacy Practice and Clinical Pharmacy, School of Pharmacy, University of Ghana, Accra, Ghana

Irene Akwo Kretchy

Center for Tropical Medicine and Global Health Research, Nuffield Department of Medicine, University of Oxford, Oxford, UK

Leonard Baatiema

Regional Institute for Population Studies, University of Ghana, Accra, Ghana

Raphael Baffour Awuah

Department of Population Health Sciences, Division of Health System Innovation and Research, Spencer Fox Eccles School of Medicineat the , University of Utah, Salt Lake City, USA

Olutobi Adekunle Sanuade

Department of Community Health, Ensign Global College, Kpong, Ghana

Sandra Boatemaa Kushitor

Department of Food Science and Centre for Sustainability Studies, Stellenbosch University, Stellenbosch, South Africa

Department of Health Policy, Planning and Management (HPPM), Fred N. Binka School of Public Health (HPPM), University of Health and Allied Sciences (UHAS), Ho, Volta Region, Ghana

Mawuli Komla Kushitor

London School of Hygiene and Tropical Medicine, London, UK

Mawuli Komla Kushitor & Carlos Grijalva-Eternod

UCL Interaction Centre (UCLIC), University College London, London, UK

Ann Blandford & Ethan Gray

Department of Health Sciences, University of York, York, UK

Hannah Jennings & Publa Antwi

Hull York Medical School, University of York, York, UK

Hannah Jennings

You can also search for this author in PubMed   Google Scholar

Contributions

S,A., MA and H.H-B conceived the study. S.A., M.A., H.H-B and E.F. contributed to the methodology of the study. S.A., L.B., R.B.A., I.A.K., K.K., V.A-A, S.B.K., H.J., P.A., E.G. and D.K.A. contributed to the implementation of the study. SA and MA led the analyses with support from HHB, SKM and CGE. SA drafted the original manuscript with significant revisions from M.A., H.H-B, L.O., I.A.K., R.B.A., O.A.S., E.F., S.B.K., A.B., C.G-E, D.K.A., S.K.M., H.J., P.A., E.G. and K.K. All authors reviewed the final draft of the manuscript.

Corresponding author

Correspondence to Samuel Amon .

Ethics declarations

Ethics approval and consent to participate.

Ethical approval was obtained from the Ethical Review Committee of the Ghana Health Service (GHS-ERC: 017/02/22), Noguchi Memorial Institute for Medical Research (NMIMR-IRB CPN 060/21-22) and the Research Ethics Committee at University College London (ID:21541/001). Permission from the Metropolitan Assembly was obtained before data collection. Written informed consent was obtained from participants before interviews.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Additional information

Publisher's note.

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

Supplementary Information

Supplementary material 1., rights and permissions.

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/ .

Reprints and permissions

About this article

Cite this article.

Amon, S., Aikins, M., Haghparast-Bidgoli, H. et al. Household economic burden of type-2 diabetes and hypertension comorbidity care in urban-poor Ghana: a mixed methods study. BMC Health Serv Res 24 , 1028 (2024). https://doi.org/10.1186/s12913-024-11516-9

Download citation

Received : 22 May 2024

Accepted : 30 August 2024

Published : 05 September 2024

DOI : https://doi.org/10.1186/s12913-024-11516-9

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Non-communicable diseases
• Comorbidity
• Economic burden

BMC Health Services Research

ISSN: 1472-6963

• General enquiries: [email protected]

Working together, we can reimagine medicine to improve and extend people’s lives.

Principal Scientist with PhenoCycler Fusion experience (PhD)

About the role.

Internal Job Title: Principal Scientist I/II

Position Location: Cambridge, MA, onsite

About the Role:

We are seeking a highly motivated individual passionate about cutting-edge technology to explore single cell multiplex spatial proteomics. This role involves working with the latest generation PhenoCycler Fusion instrument and collaborating with translational immunologists, cancer biologists, and other researchers to advance our understanding of cellular processes in complex tissues and their application to drug development. This role offers exciting opportunities for career development, enhancing leadership skills and influencing collaborative efforts within various disease areas.

Key Responsibilities:

• Operate, maintain, and utilize the PhenoCycler Fusion (formerly CODEX).
• Build and optimize antibody panels.
• Conjugate and perform quality control of reagents.
• Consult with users on potential projects, including sample accessibility and experimental design.
• Optimize procedures, design panels, and provide data analysis consultation.
• Conduct multiplex imaging experiments.
• Perform basic data quality evaluation.
• Analyze data using licensed software.
• Maintain records of procedures and resultant data, both manually and on the computer.

Knowledge, Skills, and Abilities:

• Serve as a leader in spatial proteomic single cell biology and translational research applications, focusing on new targets, biomarkers/patient population selection, and treatment strategies.
• Focus efforts in priority application areas in Biomedical Research (BR) at Novartis to deliver impactful results through matrix collaboration with DA teams.
• Building on success from initial focused efforts, develop broader application strategies at BR in translational and reverse translation research, with support from leaders in Discovery Science, Disease Areas and Biomedical Research.
• Strong interpersonal and communication skills for close collaboration with team members.
• Ability to work effectively in a fast-paced, diverse environment.
• Good judgment, technical problem-solving, and analytical skills.
• Flexibility and adaptability as technology evolves.
• Prior experience in imaging techniques and applications in biological research.
• General lab skills and knowledge of lab safety and infection control.

Qualifications:

• Ph.D. in immunology, biological sciences, biochemistry, or a related field, and 2+ years of related postgraduate work experience
• Other technical and academic degrees will be considered with relevant research experience.
• 3+ years of demonstrated skill and experience using CODEX/PhenoCycler.
• Possess deep knowledge and expertise in immunology, biology, and multi-omics applications in translational research across various disease areas such as oncology (ONC), immuno-oncology (IO), immunity-driven diseases, and related treatment strategies.
• Understanding sample preparation, instrument optimization, and data analysis.
• Interest in bioinformatics and experience with software.
• Ability to identify and troubleshoot critical issues.
• Detail-orientated

Why Novartis: Our purpose is to reimagine medicine to improve and extend people’s lives and our vision is to become the most valued and trusted medicines company in the world. How can we achieve this? With our people. It is our associates that drive us each day to reach our ambitions. Be a part of this mission and join us! Learn more here: https://www.novartis.com/about/strategy/people-and-culture

You’ll receive: You can find everything you need to know about our benefits and rewards in the Novartis Life Handbook: https://www.novartis.com/careers/benefits-rewards

Commitment to Diversity and Inclusion / EEO: The Novartis Group of Companies are Equal Opportunity Employers and take pride in maintaining a diverse environment. We do not discriminate in recruitment, hiring, training, promotion or other employment practices for reasons of race, color, religion, gender, national origin, age, sexual orientation, gender identity or expression, marital or veteran status, disability, or any other legally protected status. We are committed to building diverse teams, representative of the patients and communities we serve, and we strive to create an inclusive workplace that cultivates bold innovation through collaboration and empowers our people to unleash their full potential.

Novartis Compensation and Benefit Summary: The pay range for this position at commencement of employment is expected to be between $112,800 to $186,000/year; however, while salary ranges are effective from 1/1/24 through 12/31/24, fluctuations in the job market may necessitate adjustments to pay ranges during this period. Further, final pay determinations will depend on various factors, including, but not limited to geographical location, experience level, knowledge, skills, and abilities. The total compensation package for this position may also include other elements, including a sign-on bonus, restricted stock units, and discretionary awards in addition to a full range of medical, financial, and/or other benefits (including 401(k) eligibility and various paid time off benefits, such as vacation, sick time, and parental leave), dependent on the position offered. Details of participation in these benefit plans will be provided if an employee receives an offer of employment. If hired, employee will be in an “at-will position” and the Company reserves the right to modify base salary (as well as any other discretionary payment or compensation program) at any time, including for reasons related to individual performance, Company or individual department/team performance, and market factors.

Join our Novartis Network: If this role is not suitable to your experience or career goals but you wish to stay connected to hear more about Novartis and our career opportunities, join the Novartis Network here: https://talentnetwork.novartis.com/network

Commitment to Diversity and Inclusion: Novartis is committed to building an outstanding, inclusive work environment and diverse teams' representative of the patients and communities we serve.

Why Novartis: Helping people with disease and their families takes more than innovative science. It takes a community of smart, passionate people like you. Collaborating, supporting and inspiring each other. Combining to achieve breakthroughs that change patients’ lives. Ready to create a brighter future together? https://www.novartis.com/about/strategy/people-and-culture

Join our Novartis Network: Not the right Novartis role for you? Sign up to our talent community to stay connected and learn about suitable career opportunities as soon as they come up: https://talentnetwork.novartis.com/network

Benefits and Rewards: Read our handbook to learn about all the ways we’ll help you thrive personally and professionally: https://www.novartis.com/careers/benefits-rewards

EEO Statement:

The Novartis Group of Companies are Equal Opportunity Employers who are focused on building and advancing a culture of inclusion that values and celebrates individual differences, uniqueness, backgrounds and perspectives. We do not discriminate in recruitment, hiring, training, promotion or other employment practices for reasons of race, color, religion, sex, national origin, age, sexual orientation, gender identity or expression, marital or veteran status, disability, or any other legally protected status. We are committed to fostering a diverse and inclusive workplace that reflects the world around us and connects us to the patients, customers and communities we serve.

Accessibility & Reasonable Accommodations

The Novartis Group of Companies are committed to working with and providing reasonable accommodation to individuals with disabilities. If, because of a medical condition or disability, you need a reasonable accommodation for any part of the application process, or to perform the essential functions of a position, please send an e-mail to [email protected] or call +1(877)395-2339 and let us know the nature of your request and your contact information. Please include the job requisition number in your message.

Have a language expert improve your writing

Run a free plagiarism check in 10 minutes, generate accurate citations for free.

• Knowledge Base

Methodology

• Sampling Methods | Types, Techniques & Examples

Sampling Methods | Types, Techniques & Examples

Published on September 19, 2019 by Shona McCombes . Revised on June 22, 2023.

When you conduct research about a group of people, it’s rarely possible to collect data from every person in that group. Instead, you select a sample . The sample is the group of individuals who will actually participate in the research.

To draw valid conclusions from your results, you have to carefully decide how you will select a sample that is representative of the group as a whole. This is called a sampling method . There are two primary types of sampling methods that you can use in your research:

• Probability sampling involves random selection, allowing you to make strong statistical inferences about the whole group.
• Non-probability sampling involves non-random selection based on convenience or other criteria, allowing you to easily collect data.

You should clearly explain how you selected your sample in the methodology section of your paper or thesis, as well as how you approached minimizing research bias in your work.

Table of contents

Population vs. sample, probability sampling methods, non-probability sampling methods, other interesting articles, frequently asked questions about sampling.

First, you need to understand the difference between a population and a sample , and identify the target population of your research.

• The population is the entire group that you want to draw conclusions about.
• The sample is the specific group of individuals that you will collect data from.

The population can be defined in terms of geographical location, age, income, or many other characteristics.

It is important to carefully define your target population according to the purpose and practicalities of your project.

If the population is very large, demographically mixed, and geographically dispersed, it might be difficult to gain access to a representative sample. A lack of a representative sample affects the validity of your results, and can lead to several research biases , particularly sampling bias .

Sampling frame

The sampling frame is the actual list of individuals that the sample will be drawn from. Ideally, it should include the entire target population (and nobody who is not part of that population).

Sample size

The number of individuals you should include in your sample depends on various factors, including the size and variability of the population and your research design. There are different sample size calculators and formulas depending on what you want to achieve with statistical analysis .

Prevent plagiarism. Run a free check.

Probability sampling means that every member of the population has a chance of being selected. It is mainly used in quantitative research . If you want to produce results that are representative of the whole population, probability sampling techniques are the most valid choice.

There are four main types of probability sample.

1. Simple random sampling

In a simple random sample, every member of the population has an equal chance of being selected. Your sampling frame should include the whole population.

To conduct this type of sampling, you can use tools like random number generators or other techniques that are based entirely on chance.

2. Systematic sampling

Systematic sampling is similar to simple random sampling, but it is usually slightly easier to conduct. Every member of the population is listed with a number, but instead of randomly generating numbers, individuals are chosen at regular intervals.

If you use this technique, it is important to make sure that there is no hidden pattern in the list that might skew the sample. For example, if the HR database groups employees by team, and team members are listed in order of seniority, there is a risk that your interval might skip over people in junior roles, resulting in a sample that is skewed towards senior employees.

3. Stratified sampling

Stratified sampling involves dividing the population into subpopulations that may differ in important ways. It allows you draw more precise conclusions by ensuring that every subgroup is properly represented in the sample.

To use this sampling method, you divide the population into subgroups (called strata) based on the relevant characteristic (e.g., gender identity, age range, income bracket, job role).

Based on the overall proportions of the population, you calculate how many people should be sampled from each subgroup. Then you use random or systematic sampling to select a sample from each subgroup.

4. Cluster sampling

Cluster sampling also involves dividing the population into subgroups, but each subgroup should have similar characteristics to the whole sample. Instead of sampling individuals from each subgroup, you randomly select entire subgroups.

If it is practically possible, you might include every individual from each sampled cluster. If the clusters themselves are large, you can also sample individuals from within each cluster using one of the techniques above. This is called multistage sampling .

This method is good for dealing with large and dispersed populations, but there is more risk of error in the sample, as there could be substantial differences between clusters. It’s difficult to guarantee that the sampled clusters are really representative of the whole population.

In a non-probability sample, individuals are selected based on non-random criteria, and not every individual has a chance of being included.

This type of sample is easier and cheaper to access, but it has a higher risk of sampling bias . That means the inferences you can make about the population are weaker than with probability samples, and your conclusions may be more limited. If you use a non-probability sample, you should still aim to make it as representative of the population as possible.

Non-probability sampling techniques are often used in exploratory and qualitative research . In these types of research, the aim is not to test a hypothesis about a broad population, but to develop an initial understanding of a small or under-researched population.

1. Convenience sampling

A convenience sample simply includes the individuals who happen to be most accessible to the researcher.

This is an easy and inexpensive way to gather initial data, but there is no way to tell if the sample is representative of the population, so it can’t produce generalizable results. Convenience samples are at risk for both sampling bias and selection bias .

2. Voluntary response sampling

Similar to a convenience sample, a voluntary response sample is mainly based on ease of access. Instead of the researcher choosing participants and directly contacting them, people volunteer themselves (e.g. by responding to a public online survey).

Voluntary response samples are always at least somewhat biased , as some people will inherently be more likely to volunteer than others, leading to self-selection bias .

3. Purposive sampling

This type of sampling, also known as judgement sampling, involves the researcher using their expertise to select a sample that is most useful to the purposes of the research.

It is often used in qualitative research , where the researcher wants to gain detailed knowledge about a specific phenomenon rather than make statistical inferences, or where the population is very small and specific. An effective purposive sample must have clear criteria and rationale for inclusion. Always make sure to describe your inclusion and exclusion criteria and beware of observer bias affecting your arguments.

4. Snowball sampling

If the population is hard to access, snowball sampling can be used to recruit participants via other participants. The number of people you have access to “snowballs” as you get in contact with more people. The downside here is also representativeness, as you have no way of knowing how representative your sample is due to the reliance on participants recruiting others. This can lead to sampling bias .

5. Quota sampling

Quota sampling relies on the non-random selection of a predetermined number or proportion of units. This is called a quota.

You first divide the population into mutually exclusive subgroups (called strata) and then recruit sample units until you reach your quota. These units share specific characteristics, determined by you prior to forming your strata. The aim of quota sampling is to control what or who makes up your sample.

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.

• Student’s  t -distribution
• Normal distribution
• Null and Alternative Hypotheses
• Chi square tests
• Confidence interval
• Quartiles & Quantiles
• Cluster sampling
• Stratified sampling
• Data cleansing
• Reproducibility vs Replicability
• Peer review
• Prospective cohort study

Research bias

• Implicit bias
• Cognitive bias
• Placebo effect
• Hawthorne effect
• Hindsight bias
• Affect heuristic
• Social desirability bias

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.

Samples are used to make inferences about populations . Samples are easier to collect data from because they are practical, cost-effective, convenient, and manageable.

Probability sampling means that every member of the target population has a known chance of being included in the sample.

Probability sampling methods include simple random sampling , systematic sampling , stratified sampling , and cluster sampling .

In non-probability sampling , the sample is selected based on non-random criteria, and not every member of the population has a chance of being included.

Common non-probability sampling methods include convenience sampling , voluntary response sampling, purposive sampling , snowball sampling, and quota sampling .

In multistage sampling , or multistage cluster sampling, you draw a sample from a population using smaller and smaller groups at each stage.

This method is often used to collect data from a large, geographically spread group of people in national surveys, for example. You take advantage of hierarchical groupings (e.g., from state to city to neighborhood) to create a sample that’s less expensive and time-consuming to collect data from.

Sampling bias occurs when some members of a population are systematically more likely to be selected in a sample than others.

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the “Cite this Scribbr article” button to automatically add the citation to our free Citation Generator.

McCombes, S. (2023, June 22). Sampling Methods | Types, Techniques & Examples. Scribbr. Retrieved September 4, 2024, from https://www.scribbr.com/methodology/sampling-methods/

Is this article helpful?

Shona McCombes

Other students also liked, population vs. sample | definitions, differences & examples, simple random sampling | definition, steps & examples, sampling bias and how to avoid it | types & examples, what is your plagiarism score.

• Open access
• Published: 31 August 2024

Workplace gossip erodes proactive work behavior: anxiety and neuroticism as underlying mechanisms

• Chengyin Gao 1 , 2 ,
• Sadia Shaheen 3 &
• Muhammad Waseem Bari 3  

BMC Psychology volume  12 , Article number:  464 ( 2024 ) Cite this article

7 Altmetric

Metrics details

Services organizations highly value proactive employees. Managers are interested in promoting frontline employees’ proactive behavior because proactivity is crucial for organizational success. The mechanism of negative workplace gossip on workplace prosocial behavior is unclear. This research investigates the factors hindering this valuable behavior, specifically focusing on negative workplace gossip and employee anxiety, through the lens of the conservation of resources theory.

Design/methodology/approach

Data were collected from a sample of 352 female frontline employees across diverse service organizations using a two-wave design. Statistical analyses were conducted using appropriate software (e.g., SPSS, AMOS) to test the hypothesized relationships.

The study’s findings reveal that negative workplace gossip reduces employees’ proactive work behavior, and anxiety mediates the relationship between NWGS and proactive work behavior. Further, Neuroticism strengthens the relationship between NWGS and anxiety. These results offer a novel perspective on the detrimental consequences of gossip in services sector.

Practical implications

Originality/value While research on negative gossip exists, this study specifically examines its impact on frontline service employees, a crucial but under-studied group in service organizations.

Peer Review reports

Introduction

Today’s business environment is constantly evolving, requiring employees to take initiative and drive positive changes in their work. By being proactive, employees can better manage their growing workload and seize new opportunities as they arise [ 1 , 2 ]. Proactive work behavior means taking initiative and challenging the current situation in an anticipatory manner rather than a passive manner [ 3 , 4 , 5 ]. Essentially, it’s a self-centered behavior that employees exhibit without the instructions of the supervisor in order to change the status quo [ 4 ]. Proactive work behavior enhances individual as well as organizational performance [ 6 ]. Thus, it is essential to investigate the predictors of proactive work behavior in order to enhance organizational performance (such as growth, image, and profitability as well as employee outcomes such as (satisfaction, engagement, and career growth) [ 6 , 7 ]. However, available research has depicted that negative workplace events reduce proactive work behavior [ 8 , 9 ].

Multiple antecedents of PWB have been examined in the current literature such as individual factors (e.g. high negativity effect, personality types (McCormick et al., 2019; Parker et al., 2019), and organizational factors (e.g., role stressors (He et al., 2022), time pressure (Sonnentag & Spychala, 2012) organizational climate (Caniëls & Baaten, 2019), abusive supervision (Ouyang et al., 2015)), and contextual factors includes leadership styles, job design, and autonomy (Nurjaman et al., 2019; Permata & Mangundjaya, 2021).

Despite the fact that gossip is a widespread issue in workplaces, the negative side of gossip, particularly among frontline service employees, remains under-investigated. In services sectors employee proactive work behavior helps in improving customers’ views about the quality of the services, satisfying customers’ needs, and increasing organizational performance [ 2 , 9 ]. Employees’ proactive work behavior is not only necessary for production organizations but is equally important for service organizations. A supportive working environment boosts employee energy and they come up with the motivation to perform their duties in an efficient manner [ 7 ]. But workplace stressors such as negative workplace gossip reduce employees’ energies and enhance negative emotions at the workplace such as emotional exhaustion and anxiety [ 10 ].

Gossip is considered as ever-present in the workplace because 90% of the dialogues consisted of gossip [ 11 ]. Gossips defined as colloquial and judgmental conservation about someone who is not present [ 5 ]. Gossip damages the mutual relationship between all the colleagues who are involved in spreading gossip (Liu et al. 2020). Additionally, it can also harm employee attachment to the organization [ 12 ]. Several studies have highlighted its adverse consequences in the workplace. For instance, workplace gossip can have a negative impact on knowledge sharing [ 13 ], employee satisfaction [ 14 ], commitment to the organization [ 15 ] and employee creativity [ 13 , 16 ]. However, little is known in the service sector context, particularly where frontline employees continuously have to serve and maintain harmonious relationships with customers or clients. For instance, in the nursing profession, nurses have to serve patients in a timely manner when they need assistance, treatment, and other help. The same is true for other frontline employees such as female frontline workers working in salons, and serving as bus hostesses referred as pink color jobs). In such types of professions, frontline employees have to maintain a high level of interaction and collaboration with the customers as well as coworkers. Due to this reason, we particularly focus on the services sector to investigate the impact of workplace gossip on the proactive behavior of frontline employees. Gossip can be categorized as positive workplace gossips and negative workplace gossip [ 16 ]. We are particularly focusing on the negative workplace gossip that is receiving considerable attention from academic researchers and practitioners. Negative gossip often spreads faster and has a stronger influence on others than positive rumors [ 17 ]. Despite its prevalence, the impact of negative workplace gossip (NGW) on frontline employees’ proactive work behavior within the service sector remains under investigation. NWG can have an adverse impact on employee’s emotions, perceptions, and behavior [ 18 ]. When employees find themselves as a victim of NGW it can cause them to go through distress and psychological unrest [ 19 ]. Consequently, NWG hinders the ability of employees to focus on core responsibilities due to psychological unrest and stress.

Employee personality traits, like neuroticism, might influence how negative workplace gossip (NWG) indirectly affects proactive work behavior through anxiety. Studies have shown a positive connection between exposure to negative workplace gossip (NWG) and destructive behaviors among employees high in neuroticism [ 20 ]. Employees with high neurotic personalities react more toward negative events in contrast to those employees who score low in neuroticism [ 21 ]. Researchers agree that neuroticism aggravates the connection between NWG and negative emotional outcomes such as frustration and envy [ 21 ]. Neuroticism also results in depressive symptoms in employees. In a similar vein, the connection between unpleasant events and negative outcomes is stronger for employees who are high in neuroticism [ 20 ]. Therefore, we propose, that neuroticism stronger the impact of NWG on anxiety, which results in decreasing their proactive work behavior.

To explain how NWG and proactive work behavior are linked, we rely on the conservation of resource theory [ 22 ]. Negative workplace gossip can be viewed as a resource threat [ 23 ]. It can damage one’s reputation, social standing, and psychological well-being, thereby depleting personal resources [ 24 ]. When exposed to negative gossip, individuals may experience increased anxiety. This emotional response can further deplete personal resources, making it difficult to engage in proactive work behaviors. Thus, we tried to contribute to the current literature in several ways. First, this research aims to expand the current understanding of negative workplace gossip (NWG) by examining its impact on employee proactiveness. We propose that NWG not only fosters negative employee behaviors like deviance but also has the potential to deplete positive behaviors like proactive work behaviors. Second, while prior research has explored the link between negative workplace gossip (NWG) and employee proactiveness through emotional responses, this study sheds light on employee anxiety as a potential, yet unexplored, mediating factor in this relationship Third, this research delves specifically into how neuroticism might amplify the effect of negative workplace gossip on employee anxiety. This clarifies how negative workplace gossip (NWG) is particularly problematic for employees who are more sensitive to stressors due to their personality traits characterized by higher levels of neuroticism. We prioritize in-depth exploration of how these factors (neuroticism and anxiety) influence the behavioral consequences of negative gossip, rather than simply examining a wider range of potential effects.

The manuscript follows an academic structure. It begins with a literature review, followed by a methods section. The results of the study are then presented, followed by a discussion of their implications section. The paper concludes with limitations and suggestions for future research.

Theory and hypothesis

Impact of workplace negative gossip and proactive work behavior.

The aim of this study is to explore negative workplace gossip from the perspective of the gossipers. This viewpoint is closely linked to workplace victimization [ 25 ], where the target perceives themselves as a victim. Negative workplace gossip influences workplace attitudes and behaviors in various ways. Employees can often sense when they are the subject of gossip due to noticeable changes in the environment and the suspicious behavior of others [ 26 ]. For example, colleagues may stop talking when the target approaches or avoid making eye contact [ 27 ]. Conversely, some individuals may inform the target about the negative evaluations made by others [ 8 ]. Negative workplace gossip often involves hostile assessments of the target and is considered an informal conversation that can damage the target’s image and reputation (Fay & Urbach, 2023).

The most common topics which can be discussed about the victim contain affairs, divorce, job titles, etc. [ 8 ]. These types the topics are commonly discussed about the frontline females who regularly interact with the customers. The nature of negative workplace gossip depends upon the situation and nature of the relationship with the victim. Research suggests that negative workplace gossip (NWG) can have detrimental effects on employees. It can erode their confidence, weaken their motivation to work, decrease their overall engagement, and hinder their proactiveness [ 7 ]. Employees tend to involve in proactive work behavior when they found support from the work environment [ 28 ]. Workplace events and situational factors are essential components of employees’ proactive work behavior [ 7 ]. On the contrary workplace stressor and unpleasant situations hinder employees’ proactive work behavior [ 29 ]. NWG acts as a stressor and influences employees’ positive work behavior. Thus, to cope with such stressors the victim needs to utilize his essential psychological resources. According to the COR theory, the depletion of employee psychological resources leads to lower performance and difficulties in handling workplace situations [ 30 , 31 ]. Therefore, employees safeguard their resources by not utilizing them at the workplace. Proactive work behavior is not a mandatory behavior of employees and it is out of the punishment and rewards parameters. Thus, employees who deplete their resources due to workplace stressors (such as NWG) are less likely involved in proactive work behavior.

We therefore hypothesized.

NWG is negatively linked with employee proactive work behavior.

The mediating role of anxiety in the relationship between workplace negative gossip and proactive work behavior

Research indicates that workplace stressors, such as negative workplace gossip (NWGS), can drain employees’ psychological and social resources, increasing the likelihood of undesirable workplace behaviors. These behaviors may include deviant actions (e.g., sabotage, theft), withdrawal behaviors (e.g., absenteeism, reduced communication), and diminished work engagement [ 17 , 32 ]. Numerous researchers have found that stressful situations lead to tension, frustration, and exhaustion, which impair employees’ ability to perform their tasks proactively [ 33 ]. NWGS, as a workplace stressor, causes the victim to feel depressed and experience negative emotions. According to Conservation of Resources (COR) theory, negative evaluations by others, such as negative gossip, can result in frustration, stress, and anxiety, weakening employees’ competence to perform their daily tasks proactively (Hobfoll, 2011a; Malik, 2023). This study suggests that NWGS may deplete employees’ emotional resources, leading to feelings of frustration and anxiety [ 34 ]. These negative emotions can, in turn, hinder job performance by reducing concentration and increasing the likelihood of errors.

Particularly, when the victim is unable to respond back to the gossiper he became a victim of anxiety. Under high stress, employees may struggle to manage their energy and resources, potentially leading to performance decline. Proactive work behavior is defined as anticipatory, self-started, persistent, and future-oriented behavior that beats the mandatory requirements of one’s job [ 35 ]. Due to the frequent nature of problems faced by frontline employees, a proactive approach is crucial. By anticipating and addressing potential issues, they can prevent them from recurring in the future. The researcher described proactive behavior at the organizational level, team level, and individual level [ 36 ]. But the focus of this study is individual frontline employees’ proactive work behavior. Employees need a great amount of energy and support from the work environment in order to exhibit proactive work behavior [ 37 ]. Effective proactive work behavior requires a future-focused mindset. By analyzing the current situation and anticipating potential needs, employees can plan and take action to ensure successful task completion [ 38 ]. Therefore, employees who exhibit proactive work behavior need energy, support, and a compassionate work environment. Thus, a proactive employee needs extra physical as well as psychological resources at the workplace so that he can perform in a proactive manner [ 39 ]. In a situation where employees suffer from any type of stress such as the workplace gossip employees suffer from anxiety which depletes their valuable resources [ 32 ]. Thus, the employees who became victims of gossip remained less interested in exhibiting proactive work behavior. But they tried to restore their resources by avoiding any exceptional work such as proactive work behavior. Proactivity occurs only in a situation when an employee is fully motivated, enthusiastic, and energetic [ 40 ]. Therefore, employees who are suffering from stressful situations protect their resources by not engaging in proactive work behavior.

Anxiety mediates the relationship between NWG and Proactive work behavior.

The moderating role of neuroticism in the connection between workplace negative gossip and anxiety and proactive work behavior

Neuroticism is characterized as a negative personality trait in employees, leading to emotions such as frustration, mood swings, envy, and jealousy, which hinder their ability to cope with stressful situations like negative workplace gossip (NWGS) (Roelofs et al., 2024; Zellars et al., 2002). Studies indicate that neurotic employees are more reactive to stress compared to those with lower levels of neuroticism (Wang et al., 2015). Employees with high neuroticism exhibit less emotional stability, making them more susceptible to stressful events such as NWGS (Bowling et al., 2005; Tian et al., 2019). These employees, prone to experiencing negative emotions and anxiety, often show lower levels of positive organizational behaviors during stressful situations. This tendency is due to their focus on conserving resources as a coping mechanism, prioritizing the protection of existing resources over proactive work behaviors or exceeding expectations. Research shows a positive correlation between negative workplace events and neuroticism, with highly neurotic employees being more vulnerable to stress and less capable of performing tasks proactively. Drawing on the conservation of resource theory, employees who score high in neuroticism react to stressful situations more aggressively and exhibit negative emotions such as anxiety in a contrast to employees who score low in neuroticism. The employees who are emotionally stable have plentiful psychological resources thus they react less toward negative situations such as NWGS, and they perform their tasks in an above-average manner.

Neuroticism moderates the relationship between NWG and proactive work behavior such that the relationship will be stronger in the presence of high neuroticism in contrast to low neuroticism.

Neuroticism moderates the mediated relationship between NWG and proactive work behavior such that the relationship will be weaken in the presence of high neuroticism in contrast to low neuroticism. Figure 1 explains the study framework and hypotheses relationships. 

Research framework

Methodology

We choose a quantitative design and a time lag data collection method. A quantitative study design is best suited for data collection from a larger population and enriches the generalizability of the findings. Furthermore, a time lag data collection method is best suitable to study the temporal effects of variables (e.g., negative workplace gossip, proactive work behavior, and anxiety), it also benefits to investigate the causal relationships and helps to minimize the common method bias.

Due to the informal nature of pink-collar employees in Pakistan and the difficulties associated with the approachability of the respondents we preferred to choose the convenience sampling technique. The duty schedule of these workers usually may not be fixed, such as nurses’ childcare workers and bus hostesses. Additionally, we do not have a complete list of the population, therefore, we used non-probability sampling techniques. Convenience sampling techniques benefit us to collect the data from those employees who are available at the time of the data collection, as well as it also supports to coordinate with the participants.

We recruited participants in various ways. First, we targeted those service organizations where the majority of pink-collar workers are serving such as salons, bus hostesses, nurses, and childcare organizations. Then we contacted the managers/owners of those organizations through emails and personal contacts. We also used the available references such as references of the students, friends, and family members. First of all, the objective of this study was elaborated to the managers and owners of the organizations. Then after getting permission from the management of the services organizations, the participants were approached and contacted through emails, WhatsApp, and by physically distributing the questionnaire. Before data collection written informed consent was taken from all the participants and it was assured to them that there is no right and wrong answer of the given questions. We only want to record your valuable opinion regarding this study. It was also assured to them that they are fully free to quit this study at any point of time without bearing any penalty.

We collected data from female employees working at various service organizations such as beauty salons, bus hostesses, nurses, and child care centers also known as pink collar workers. We selected the above-mentioned organization believing that most of the female in Pakistan works in these organizations. All the protocols of the research were applied before data collection. Ethical approval was obtained from the Ethics Committee of Lyallpur Business School during their 6th meeting of board of studies. The board is affiliated with Government College University in Faisalabad. A written informed consent was taken from the participant before data collection. It was elaborated to all of them there is no right and wrong answer and they are totally free to leave the study whenever they want.

Data were collected by personal visits and with the reference of friends, students, and colleagues. Additionally, to alleviate the issue of common method bias data were collected in two times lags. In lag 1, data were collected on independent variable (negative workplace gossip), moderator (neuroticism), and mediator anxiety. After four weeks’ interval data were collected on the dependent variable (proactive work behavior). The objective of this study is to particularly focus on the pink-collar workers in a developing country. The experiences of females regarding negative work place gossips may differ significantly as compared to male workers due to the collectivist and masculine nature of culture. Female workers particularly those doing lower-level jobs are more sensitive to negative workplace gossips as contrast to males. However, a robust study can be done by doing a comparison between male and female experiences regarding negative workplace gossip, anxiety, neuroticism, and proactive work behavior. therefore, we have highlighted this point in the future research directions.

All the variables were measured on five-point Likert scale ranging from 1 = strongly disagree and 5 = strongly agree.

Workplace negative gossips

We adapted a three-item scale to measure Workplace negative gossips from Chandra and Robinson (2010). The sample items include “At work others (e.g., coworkers/supervisor) made false allegations about me (α = 0.87).

6-items anxiety scale was adapted from [ 41 ]. The sample items include “tense”, “uneasy”, and “worried” (α = 0.94).

• Neuroticism

Neuroticism 8 items scale was adapted from [ 42 ]. The sample items of the scale include “Do you tend to say what is in your mind?” “Do you sometimes feel lonely?” (α = 0.95).

• Proactive work behavior

Proactive work behavior 3 items scale was adapted form [ 43 ] further validated by [ 44 ]. The sample items include “Initiated better ways of doing your core tasks” “Come up with ideas to improve the way in which your core tasks are done”. (α = 0.92)

Data has been analyzed by using AMOS.21 and SPSS. First, we conduct the confirmatory factor analysis by using AMOS 21. Then we checked the hypothesized model by using PROCESS macro by Hayes. We used PROCESS model 4 for mediation and model-7 for moderated mediation analysis.

Measurement model

We used confirmatory factor analysis to test the measurement model. There were four latent variables in the measurement model such as negative workplace gossip, anxiety, proactive work behavior, and neuroticism. According to the results of the measurement model, all the fit indices are within the acceptable range such as (χ2 = 362.376, df = 154, p  < .001, CFI = 0.97, TLI = 0.96, IFI = 0.97 and RMSEA = 0.06) all the yielded results depict a good fit. (Please see Table 1 )

Table  2 represents the mean, standard deviation, CR, α, AVE, and the square root of AVE. We test the convergent and discriminant validity of the proposed model. The statistical results of AVE prove the convergent validity of the model because all the values are greater than the cutoff point which is 0.5 (see Table  2 ). The discriminant validity of the model is also established according to the statistical evidence because the square root of AVE is greater than their correlations (see Table  2 , the square root of AVE is shown in diagonal). Thus both convergent and discriminant validity is proved. Additionally, the CR and α values of negative workplace gossip, neuroticism, anxiety, and proactive work behavior are meeting the threshold criteria which is 0.6 (see Table  2 ).

We also presented a correlation analysis of the proposed model. The correlation analysis shows negative workplace gossip is positively related to neuroticism ( r  = .73, P  < .01), employee anxiety ( r  = .769, P  < .01), and negatively relayed to proactive work behavior ( r − .752 =, P  < .01). Neuroticism is positively related to employee anxiety (r 0.789=, P  < .01) and negatively related to proactive work behavior (r-0.737 =, P  < .01). Employee anxiety is negatively related to employee proactive work behavior (r-0.780 =, P  < .01) (see Table  2 ).

Hypothesis analysis

We test the proposed model by using the PROCESS macro by Hayes. We applied model 7 to test the moderated mediation and previously a number of researchers used this model to test the same type of model such as [ 45 , 46 , 47 ]. Therefore, we strongly believe that model 7 is perfectly suitable to test the hypothesized relationships of our proposed model. For clarity first, we present the result of the mediation analysis in Table  3 . According to the proposed model negative workplace gossip is negatively related to employee proactive work behavior. The obtained results support this expectation (β = −0.139, p  < .05) therefore, hypothesis 1 is accepted. Hypothesis 2 states, employee anxiety mediates the relationship between negative workplace gossip and proactive work behavior which has been proved with the help of statistical data as depicted by the 95% Bootstrapped confidence interval which has no zero [-0.513; − 0.268]. The direction of the UL and LL support that there is a mediation effect of employee anxiety in the connection between negative workplace gossip and employee proactive work behavior.

According to hypothesis 3, neuroticism moderates the relationship between workplace negative gossip and anxiety proved by the statistical results (β = − 0.057, p  < .05). According to the expectation, the connection between proactive work behavior and anxiety is stronger when a person is high in neuroticism (see Table  4 ).

In the current study, we test the impact of negative workplace gossips on proactive work behavior through employee anxiety. Additionally, the moderating role of neuroticism in the relationship between negative workplace gossips and anxiety is also tested. Data were collected from only female workers, working in different service sectors such as nursing, hotels, working as bus hostesses, and working in salons.

Females who are high in neuroticism deplete their psychological, emotional, and physical resources in stressful situations (e.g., NWG) more frequently in contrast to those who are low in neuroticism. High neurotic employees need more energy to manage negative workplace gossip when they experience negative gossip from coworkers and society. Consequently, the drain of energy in managing negative gossip, they remained less involved in proactive work behavior. For instance, preparing themselves for challenging goals, thinking of new ideas for improvement, and being vigorous and responsive at the workplace. The results of the study are verified by [ 6 , 8 , 20 ] as well as COR theory [ 48 ].

Our findings provide strong support for all hypothesized relationships. Notably, negative workplace gossip was found to significantly elevate employee anxiety in a collectivist cultural context. This heightened anxiety, in turn, appears to be associated with decreased proactive work behavior.

This study contributes valuable insights into the dynamics of negative workplace gossip within collectivist societies. Furthermore, by focusing on female employees, the research highlights a potential vulnerability specific to this demographic. In collectivist cultures, women may be disproportionately targeted by negative gossip, particularly when their work roles are traditionally considered less prestigious compared to their male counterparts.

The findings of the moderated mediation analysis shed light on the underlying mechanisms that contribute to lower levels of attentiveness, energy, and passion among female employees in these service sector jobs. This study contributes to the literature on the service sector in Pakistan by providing a deeper understanding of the root causes associated with reduced proactive behavior among blue-collar female workers.

Although this study particularly deals with negative workplace gossip, however, any type of personal mistreatment enhances employee anxiety and consequently reduces proactive work behavior. Based on recent research different types of personal mistreatment such as bullying, abusive supervision, ostracism, and undermining have resource depletion effects and reduce proactive work behavior [ 20 ].

Theoretical implications

This study has numerous contributions. First, this study enhances our knowledge regarding negative workplace gossips by investigating proactive work behavior in services sector. Existing research on job performance [ 49 ] and organizational citizenship behavior [ 50 ] provides us a theoretical support to understand the effect of negative workplace gossips on proactive work behavior of employees. Existing studies on negative workplace gossips has not focused on the blue-collar worker’s job outcomes (e.g., proactive work behavior). Leaving promising research gap in the current literature.

Second, this paper breaks new ground in the study of workplace gossip by exploring its impact on employee proactivity through the lens of anxiety. This nuanced approach deepens our understanding of how negative rumors can hinder employee’s proactive work behavior. While prior research has centered on how workplace gossip shapes employee psychology, emotions (Spoelma & Hetrick, 2021; Guo et al., 2022; Sun et al., 2023), and attitudes (Brady et al., 2017; Chen, 2018; Zhou et al., 2021), this study takes a different angle, exploring how these internal shifts ultimately influence employee behavior. Taking the research on negative gossip one step further, we explored how they make people less willing to be proactive at work.

Third, while understanding individual emotional responses to gossip is valuable, a crucial next step is exploring how it shapes workplace behavior, particularly for women in collectivist societies. This study pioneers this investigation, specifically delving into how female workers navigate the implications of negative gossip in such cultural contexts.

Forth, building on previous research by Nhu et al. (2021) who called for more studies on what influences employees proactive work behavior, this study examines how negative gossip at work can discourage employees in the service sector from going the extra mile. To get a complete understanding of how people behave at work, we need to consider all the factors that influence them, and that definitely includes negative work place gossips.

This study offers several practical implications for managers as well as for organizations in services sectors. In the services sector, employees’ proactive behavior is very essential to serving customers in an adequate and timely manner. In services sector employees need to be attentive, energetic and prepared to deal every type of customer. But negative workplace gossips can drain their energies which push them towards anxiety particularly for high neurotic employees. Therefore, they invest their energies to manage negative gossips and anxiety which reduces their attentiveness and proactivity at the workplace.

This study contributes to the understanding of fostering service employee proactivity by proposing several interventions for managers in the service sector. Firstly, implementing recognition programs, such as appreciation ceremonies, could acknowledge the value of blue-collar employees and contribute to a more positive work environment. Secondly, offering targeted counseling sessions could help blue-collar employees understand the significance of their role and how their contributions impact the organization’s success. More importantly, proactive measures are necessary to address negative workplace gossip. Managers can implement educational programs to equip employees with the skills to identify and effectively deal with such behaviors. Negative workplace gossip represents a critical and detrimental phenomenon that can significantly hinder employee performance [ 51 ]. These training initiatives should raise awareness about the importance of eradicating such detrimental behaviors. Training programs can range from formal, off-site workshops to informal, on-the-job training sessions.

Thirdly, our result stated that negative work place gossips influence more to high neurotic employees, thus it is necessary to find out the employees who are high in neuroticism and managers should find out the ways to mitigate the effect of negative workplace gossips for neurotic employees. Managers need to do personality tests before hiring a blue-collar employee and should avoid those employees who are high in neuroticism. The managers should also arrange training sessions for high neurotic employee and train them how they can deal with uneven situations. Hence, organizations should pay more attention to those employees who are high in neuroticism. The organization should create a culture of social support and friendly environment. So, employees can restore their energies by sharing their problems with each other.

Limitations and future research directions

This study is not without limitations. First, a potential limitation of this study is its focus solely on the influence of negative workplace gossip on proactive work behavior. Future research could explore the potentially contrasting role of positive workplace gossip in promoting employee proactivity. Examining the impact of both positive and negative gossip on employee behavior would provide a more comprehensive understanding of this dynamic.

Second, this study’s generalizability may be limited due to the inclusion of only female service sector workers. Future research should aim to compare the reactions of male and female employees to negative workplace gossip to explore potential gender differences in this response. Third, we collect data from collectivist society the study can be replicate on individualistic cultures for better generalizability. Furthermore, this study employed a single moderator variable. However, it is important to acknowledge that other personality traits or dispositions, such as extraversion, trait emotional exhaustion, and attribution style, could also potentially moderate the relationship between negative workplace gossip and employee proactive work behavior.

Data availability

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Aboramadan M, Turkmenoglu MA, Dahleez KA, Cicek B. File:///D:/F/Dr Sehar Zulfiqar/PWB paper/Proactive WB Paper 2021/Neuroticism articles/PWB definition.pdf. Int J Contemp Hosp Manag. 2021;33(2):428–47.

Article   Google Scholar  

Wang Q, Jiang H, Lixin W, Ziyi J, Serena L, Lyu C. Leader humor, workplace gossip, and employee authentic self – expression: implications for employee proactive behaviors. Curr Psychol. 2023;(0123456789). https://doi.org/10.1007/s12144-023-05443-x

Crant JM. Proactive behavior in organizations. J Manage. 2000;26(3):435–62.

Google Scholar  

Rank J, Carsten JM, Unger JM, Spector PE. File:///D:/F/Dr Sehar Zulfiqar/PWB paper/Proactive WB Paper 2021/PWB articles/PWB def 2.Pdf. Hum Perform. 2007;20(4):363–90.

Parker SK, Williams HM, Turner N. Modeling the antecedents of proactive behavior at work. J Appl Psychol. 2006;91(3):636–52.

Article   PubMed   Google Scholar  

Tian Qtao, Song Y, Kwan HK, Li X. Workplace gossip and frontline employees’ proactive service performance. Serv Ind J. 2019;39(1):25–42. https://doi.org/10.1080/02642069.2018.1435642

Fay D, Urbach T. Creating meaning by taking initiative: Proactive work behavior fosters work meaningfulness. 2023;(August 2021):506–34.

Wu X, Kwan HK, Wu LZ, Ma J. The Effect of Workplace negative gossip on employee proactive behavior in China: the moderating role of Traditionality. J Bus Ethics. 2018;148(4):801–15.

Singh A, Rangnekar S. Empowering leadership in hospital employees. 2020;69(7):1497–519.

Liu XY, Kwan HK, Zhang X. Introverts maintain creativity: a resource depletion model of negative workplace gossip. Asia Pac J Manag. 2020;37(1):325–44.

Joe G, Ellwardt L, Joe G, Wittek R. Who are the objects of positive and negative gossip at work ? Publisher ’ s PDF, also known as Version of record Publication date: who are the objects of positive and negative gossip at work? A social network perspective on workplace gossip. Soc Networks. 2012;34(2):193–205. https://doi.org/10.1016/j.socnet.2011.11.003

Lyu B. Is not workplace gossip bad ? The effect of positive workplace gossip on employee innovative behavior. 2022;(November).

Cheng B, Peng Y, Shaalan A, Tourky M. The Hidden Costs of Negative Workplace Gossip: Its Effect on Targets ’ Behaviors, the Mediating Role of Guanxi Closeness, and the Moderating Effect of Need for Affiliation. J Bus Ethics. 2023;182(1):287–302. https://doi.org/10.1007/s10551-021-04994-y

Liu T, Liu L, Cafferkey K, Jia Y. Assessing the impact of negative workplace gossip on family satisfaction: Evidence from employees in China. Curr Psychol. 2022;(0123456789). https://doi.org/10.1007/s12144-022-03241-5

Xiaolei S, Siliang Guo. The impact of negative workplace gossip on employees ’ organizational self-esteem in a Differential Atmosphere negative workplace gossip and. 2022;13(June).

Guang X, Shan L, Xue Z, Haiyan Y. Does negative evaluation make you lose yourself ? Effects of negative workplace gossip on workplace prosocial behavior of employee. 2023.

Babalola MT, Ren S, Kobinah T, Qu YE, Garba OA, Guo L. Negative workplace gossip: Its impact on customer service performance and moderating roles of trait mindfulness and forgiveness. Int J Hosp Manag. 2019;80(February):136–43. https://doi.org/10.1016/j.ijhm.2019.02.007

Akgunduz Y. The background of restaurant employees ’ revenge intention: supervisor incivility, organizational gossip, and blaming others. 2023.

Zhong R, Tang PM, Lee SH. The Gossiper ’ s high and low: Investigating the impact of negative gossip about the supervisor on work engagement. 2022;(December):1–29.

Jahanzeb S, Fatima T, De Clercq D. When workplace bullying spreads workplace deviance through anger and neuroticism. Int J Organ Anal. 2020;29(4):1074–90.

Zellars L. Burnout in Health Care: The Role of the Five Factors of Personality. 2000;1570–98.

Hobfoll SE. The influence of culture, community, and the nested-self in the stress process: advancing conservation of resources theory. Appl Psychol. 2001.

Yao Z, Luo J, Zhang X. Gossip is a fearful thing: the impact of negative workplace gossip on knowledge hiding. J Knowl Manag. 2020;24(7):1755–75.

Pheko MM. Rumors and gossip as tools of social undermining and social dominance in workplace bullying and mobbing practices: A closer look at perceived perpetrator motives. J Hum Behav Soc Environ. 2018;28(4):449–65. https://doi.org/10.1080/10911359.2017.1421111

Aquino K. Dominating Interpersonal Behavior and Perceived Victimization in Groups: Evidence for a Curvilinear Relationship. 2002;28(1):69–87.

Kaya C, Ataman G, Yener Aydin B. Workplace Ostracism and Work Engagement: the moderating role of Neuroticism. Int J Bus Manag. 2017;V(2).

Cheng B, Peng Y, Zhou X, Shaalan A, Tourky M. Negative workplace gossip and targets ’ subjective well-being: a moderated mediation model. Int J Hum Resour Manag. 2023;34(9):1757–81. https://doi.org/10.1080/09585192.2022.2029931

Malik P. Measuring the impact of learning organization on proactive work behavior: mediating role of employee resilience. 2023;15(3):325–44.

Zacher H, Rudolph CW. Dynamic effects of personal initiative on engagement and exhaustion: The role of mood, autonomy, and support. 2019;(February 2018):38–58.

Gorgievski MJ, Hobfoll SE. Work can burn us out and fire us up. Handb Stress Burn Heal Care. 2008.

Aliza K, Shaheen S, Malik MJ, Zulfiqar S, Batool SA, Ahmad-ur-Rehman M et al. Linking ostracism with employee negligence behavior: a moderated mediation model. Serv Ind J. 2022;42(11–12):872–96. https://doi.org/10.1080/02642069.2021.1933456

Hobfoll SE. Conservation of resource caravans and engaged settings. 2011;116–22.

Schmitt A, Hartog DN, Den, Belschak FD. Transformational leadership and proactive work behaviour: a moderated mediation model including work engagement and job strain. 2016;588–610.

Liu T, Wu L, Yang Y, Jia Y. Work-to-Family Spillover effects of Workplace negative gossip: a mediated moderation model. 2020;11(July):1–12.

Griffin MA, Parker SK, New model of work role performance. A New Model of Work Role Performance: Positive Behavior in Uncertain and Interdependent Contexts A: Positive Behavior in Uncertain and Interdependent Contexts. 2007;(June 2014).

Zhou X, Fan L, Cheng C, Fan Y. When and Why Do Good People Not Do Good Deeds? Third-Party Observers’ Unfavorable Reactions to Negative Workplace Gossip. J Bus Ethics. 2021;171(3):599–617. https://doi.org/10.1007/s10551-020-04470-z

Caesens G, Marique G, Hanin D. The relationship between perceived organizational support and proactive behaviour directed towards the organization. Eur J Work Organ Psychol. 2016;25(3):398–411. https://doi.org/10.1080/1359432X.2015.1092960

James R. Repatriates ’ Work Engagement: Proactive Behavior, Perceived Support, and Adjustment. 2019;1–15.

Maan AT, Abid G, Butt TH, Ashfaq F, Ahmed S. Perceived organizational support and job satisfaction: a moderated mediation model of proactive personality and psychological empowerment. Futur Bus J. 2020;6(1):1–10. https://doi.org/10.1186/s43093-020-00027-8

Pingel R, Fay D, Urbach T. A resources perspective on when and how proactive work behaviour leads to employee withdrawal. 2019;410–35.

Warr P, Downing J. Learning strategies, learning anxiety and knowledge acquisition. Br J Psychol. 2000;91(3):311–33.

Otonari J, Nagano J, Morita M, Budhathoki S. Neuroticism and extraversion personality traits, health behaviours, and subjective well-being: the Fukuoka Study (Japan). 2012;1847–55.

Griffin MA, Parker SK, Neil A, A New model of work, role performance: positive behavior in uncertain and interdependent contexts the University of Queensland. Acad Manag J. 2007;50(2):327–47.

Strauss K, Parker SK, Shea DO, WHEN DOES PROACTIVITY HAVE A COST? MOTIVATION AT WORK MODERATES, THE EFFECTS OF PROACTIVE WORK BEHAVIOR ON EMPLOYEE JOB STRAIN. J Vocat Behav. 2017; https://doi.org/10.1016/j.jvb.2017.02.001

Shaheen S, Zulfiqar S, Saleem S, Shehazadi G. Does organizational cronyism lead to Lower Employee performance? Examining the Mediating Role of Employee Engagement and moderating role of Islamic Work Ethics. Front Psychol. 2020;11(October):1–14.

Jahanzeb S, Fatima T, Javed B, Giles JP. Can mindfulness overcome the effects of workplace ostracism on job performance ? J Soc Psychol. 2020;160(5):589–602. https://doi.org/10.1080/00224545.2019.1707465

Bs AS, Mba JK, Muhammad L, Namra A, Bba M, Jaafar LM. Relationship between organisational dehumanization and nurses ’ deviant behaviours: a moderated mediation model. 2021;(December 2020):1036–45.

Hobfoll SE. Conservation of resource caravans and engaged settings. J Occup Organ Psychol. 2011.

Cheng B, School of Business Administration F of BA, Southwestern University of Finance and Economics, Chengdu C, Peng Y, School of Management, Xiamen University, Xiamen C, Tian J et al. How negative workplace gossip undermines employees ’ career growth: from a reputational perspective. 2023.

Martinescu E, Beersma B. Negative gossip decreases targets ’ organizational citizenship behavior by decreasing social inclusion. A Multi- Method Approach; 2021.

Sun T, Schilpzand P, Liu Y. Workplace gossip: an integrative review of its antecedents, functions, and consequences. J Organ Behav. 2023;44(2):311–34.

Download references

Acknowledgements

Not applicable.

The authors declare that no funding is associated with this study.

Author information

Authors and affiliations.

School of Accounting, Hainan Vocational University of Science and Technology, Haikou, China

Chengyin Gao

Lincoln University College, Petaling Jaya, Selangor, Malaysia

Lyallpur Business School, Government College University Faisalabad, Jinnah Block, New Campus Jhang Road, Faisalabad, Pakistan

Sadia Shaheen & Muhammad Waseem Bari

You can also search for this author in PubMed   Google Scholar

Contributions

Conceptualization: C.G, S S: formal analysis: MWB writing—originaldraft: SS software: C.G, SS writing—review and editing: MWB. All authors haveread and agreed to the published version of the manuscript.

Corresponding author

Correspondence to Sadia Shaheen .

Ethics declarations

Ethics approval and consent to participate.

This study was performed in line with the principles of the Declaration of Helsinki. Ethical approval was obtained from the Ethics Committee of Lyallpur Business School during their 6th meeting of board of studies. The board is affiliated with Government College University in Faisalabad. All methods were carried out in accordance with relevant guidelines and regulations. The participants provided their written informed consent to participate in this study.

Consent for publication

Competing interests.

The authors declare no competing interests.

Additional information

Publisher’s note.

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

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/ .

Reprints and permissions

About this article

Cite this article.

Gao, C., Shaheen, S. & Bari, M.W. Workplace gossip erodes proactive work behavior: anxiety and neuroticism as underlying mechanisms. BMC Psychol 12 , 464 (2024). https://doi.org/10.1186/s40359-024-01966-5

Download citation

Received : 15 May 2024

Accepted : 23 August 2024

Published : 31 August 2024

DOI : https://doi.org/10.1186/s40359-024-01966-5

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Negative workplace gossip
• Employee anxiety
• Conservation of resource theory

BMC Psychology

ISSN: 2050-7283

• General enquiries: [email protected]

• Open access
• Published: 28 August 2024

Determinants of survival time for HIV/AIDS patients in the pastoralist region of Borena: a study at Yabelo General Hospital, South East Ethiopia

• Galgalo Jaba Nura 1 ,
• Kumbi Sara Wario 2 &
• Markos Abiso Erango 3  

AIDS Research and Therapy volume  21 , Article number:  58 ( 2024 ) Cite this article

Metrics details

Introduction

HIV/AIDS is one of the most dangerous diseases globally, impacting public health, economics, society, political issues, and communities. As of 2023, the World Health Organization estimates that 40.4 million people are living with HIV/AIDS. This study aimed to identify the determinants of survival time for HIV/AIDS patients in the pastoralist region of Borena at Yabelo General Hospital.

The study design was a retrospective cohort study, with a sample size of 293 individuals living with HIV/AIDS, based on recorded data. This research utilized survival model analysis, employing Kaplan-Meier plots, the log-rank test, and Cox proportional hazard model analysis.

Out of the total sample size, 179 (61.1%) were female and 114 (38.1%) were male. Among these males, 36 (31.6%) were deceased. The analysis using the Cox proportional hazard model revealed that the following variables were significantly associated with the survival time of HIV/AIDS patients: gender, educational status, area of residence, tuberculosis (TB), and opportunistic infections.

Conclusions

We concluded that individuals living with HIV/AIDS in urban areas have a lower risk of death compared to those in rural areas, indicating that rural residents have a reduced survival probability. Therefore, the Borena zone administration should focus on adult patients to enhance life expectancy.

The human immunodeficiency virus (HIV) is the world’s most critical public health issue. According to estimates from the World Health Organization, approximately 40.4 million people were living with HIV by mid-2023. In the African region, an estimated 25.6 million individuals had HIV by that time, as reported by the WHO. In 2022, over 20.9 million people received antiretroviral treatment. That same year, an estimated 660,000 individuals acquired HIV, and by mid-2023, the rate of new HIV infections across all ages had decreased to 0.57 per 1,000, although the uninfected population had declined from 1.75 in 2010 [ 1 ].

Survival patterns among African communities following HIV infection before the introduction of ART served as an initial benchmark for assessing the future viability of intervention initiatives [ 2 ]. Since the advent of antiretroviral therapy (ART), HIV infection has transitioned from a severe condition to a chronic illness [ 3 ]. In Ethiopia, current estimates indicate a slight decline in PLWH, from 610,350 in 2022 to 603,537 in 2023. Reported prevalence shows that the number of PLHIV in the Oromia region gradually decreased, from 158,152 in 2022 to 156,184 in 2023 [ 4 ].

The Borena community pastoralists have long existed under the Gada society’s cultural, social, community, and political organization, led by the Abba Gada or elders of Borena. Following 1950, the modern education system in Borena began, but the Gada system’s structure has been in place since around the 14th century, resulting in a lack of contemporary education. According to a report from the Ethiopia Public Health Institute [ 5 ], 2,600 adult Borena individuals are living with HIV infection, indicating that many pastoralists remain unaware of disease transmission. This vulnerability to the disease is prevalent throughout all areas of the Borena pastoralist community. Consequently, numerous individuals have been infected, primarily due to insufficient protective measures and insufficient education.

In addition, concurrent extramarital sexual activities, polygamy, and marrying a deceased wife’s sister have been identified as risk factors for HIV infection. Although not extensively documented, the practice of maintaining extramarital sexual partners by both men and women, widow inheritance, and polygamy appears to have decreased, although it continues to occur in secret [ 6 , 7 ]. Despite the lack of studies on vulnerability within the Borana population, a few behavioral and biological studies indicate a very high HIV prevalence in the region compared to similar contexts [ 8 , 9 ]. The researcher aimed to determine the survival time for HIV/AIDS patients in the pastoralist region of Borena at Yabelo General Hospital from January 2016 to December 2019. The results will provide information about the determinants of survival time for people living with HIV/AIDS in the pastoralist region of Borena.

Methods and materials

The study was conducted at Yabelo General Hospital, situated in Yabelo town, Borena Zone. This zone is one of twenty-one zones in the Oromia Region. In 2010, the hospital was upgraded from a Health Center to a general hospital. It provides various services to the residents of Borena Zone and other Ethiopian ethnic groups. Currently, the zone comprises ten rural pastoralist woredas and one town administration, Yabelo, which has a state function. The zone is located in the southern part of the Oromia region. It shares borders with the West Guji Zone to the north, the South Nations, Nationalities, and Peoples region to the west, the Somali region to the southeast, and an international boundary with Kenya to the south (as shown in the geographical map below, Fig.  1 ).

Map of Borena zone

According to the 2023 report from the Borena Zone Administration Office, over 1.4 million people reside in the zone, with a male-to-female ratio of 1:1. This suggests significant variation in settlement patterns from district to district. Approximately 89% of the population inhabits the rural pastoralist areas of the zone [ 10 ]. The Borana Zone is one of the most pastoralist regions in Ethiopia, primarily relying on livestock rearing. The livestock population in Borena includes 1,482,053 goats, 1,179,645 sheep, 637,632 horses, 2,222 mules, 5,525 donkeys, 68,799 camels, and 185,382 cattle [ 11 ].

Source of data and study population

The study is a retrospective cohort analysis, indicating that all events and exposures detailed in the review subjects’ patient cards and information sheets occurred in the past. All individuals diagnosed with HIV at Yabelo General Hospital and receiving ART were included in the study at regular intervals. Based on the inclusion and exclusion criteria, 293 adult HIV/AIDS patients were selected from their medical records. Participants in this study were HIV-positive individuals receiving follow-up antiretroviral therapy during the study intervals. This study encompassed all adult HIV-positive patients who visited the hospital for treatment three or more times, as well as adult HIV/AIDS patients who initiated treatment between January 2016 and December 2019. According to hospital records, 1,147 HIV patients underwent ART treatment and were assessed for baseline CD4 count cells during the study periods.

Sample size determination

The researcher was able to obtain statistically significant results by employing the formula for calculating the required sample size [ 12 ]. According to [ 13 ], the sample size was determined by analyzing the mortality rates in two groups of HIV-positive individuals on ART, categorized by their WHO clinical stage as exposure status. Consequently, the sample size for this current study has reached 293 HIV/AIDS-positive subjects, taking into account the inclusion criteria (further calculations are available in the supplementary material).

Variables of the study

The outcome variable for survival analysis is the survival time and/or time to death of patients under follow-up among HIV-infected adults. The predictors included in this study were gender, age, marital status, educational status, place of residence, WHO stages, TB, adherence to ART treatment, functional status, family history, and opportunistic infectious diseases.

These are the clinical stages of patients based on CD4 values, classified into four stages: stage I, stage II, stage III, and stage IV.

Tuberculosis (TB)

Individuals with HIV and weakened immune systems are at a higher risk of contracting tuberculosis compared to those with typical immune systems.

Family History

This refers to the previous occurrences of HIV/AIDS disease or past incidences among family members.

Opportunistic infectious diseases

These are infections that occur more frequently and are more severe in individuals with declining immune systems.

Functional status

Working: able to perform usual work in or out of the house; Ambulatory: able to carry out activities of daily living; Bedridden: unable to perform activities of daily living [ 14 ].

Adherence was categorized as good if patients adhered to at least 95% of the prescribed medication, fair if they adhered between 85% and 95%, and poor if they adhered to less than 85% of the prescribed medication [ 15 ].

Method of analysis

The analysis was conducted using R software version 4.3.1. It includes descriptive statistics of variables, the Kaplan-Meier method, the log-rank test, and the Cox proportional hazards model for the time-to-event data from the survival datasets.

Survival analysis model

Survival analysis is a branch of statistics that investigates the anticipated duration until one or more events take place [ 16 ]. This data shows that not all patients experience the event by the conclusion of the observation period; thus, the actual survival times for some individuals living with HIV/AIDS remain unknown, a phenomenon referred to as censoring, which must be accounted for in the study to yield meaningful results [ 17 , 18 ].

Kaplan - Meier estimator

The Kaplan-Meier estimator [ 19 ] provides a non-parametric maximum likelihood estimate of the survival function.

Cox proportional hazards model

The basic model for survival analysis is investigated under the Cox proportional hazard model, a model originated by Cox [ 16 ]. In a model, the unique effect of a unit increase in a covariate is multiplicative in terms of the hazard rate. Its covariates can be time-independent. This model implies that the hazard function \(\:{\lambda\:}_{\:}\) (t, X,) \(\:\beta\:\) is connected to the covariates as a product of a baseline hazard \(\:{{\lambda\:}}_{0}\left(\text{t}\right)\) and a function of covariates.

In this study, records of 293 individuals living with HIV/AIDS were included; of this total, 179 (61.1%) were female. Among these females, 33 (18.4%) had died, while the others were censored. Among the male patients, 36 (31.6%) were deceased. Of the total samples, 83 (28.3%) were related to tuberculosis. Among the tuberculosis (TB) patients, 34 (41.0%) died, whereas 35 (16.7%) of the non-tuberculosis patients died. Regarding functional status, 221 (75.4%) of the patients were working, 27 (9.2%) were bedridden, and 45 (15.4%) were ambulatory. Among those who were working, 50 (22.6%) patients died.

In the baseline test results, 201 (68.6%) of the patients had no family members related to this disease (none related to HIV/AIDS previously), while the remaining 92 (31.4%) were suffering from opportunistic infections of another disease, with 35 (38.0%) of these patients having died from their opportunistic infections (Table  1 ).

Survival analysis

Comparison of survival grouped data.

The survival data for these studies consists of baseline information extracted from the entire sample patient set. The significant difference in group variables was determined using Kaplan-Meier plots and a log-rank test. Figure  2 below illustrates a significant difference between the categorical groups, as shown in the Kaplan-Meier plot. Female patients had slightly higher survival rates than males from the beginning to the end. Based on place of residence, patients from urban areas exhibited a higher survival probability than those from rural areas regarding survival time. The log-rank test for these variables indicates a statistically significant difference between patients from urban areas and those from rural areas (Supplementary Table 1 ).

When comparing the different educational statuses of patients, a Kaplan-Meier plot for this variable is presented in Fig.  2 . It is evident that there is no significant difference between the groups in the plot. In comparing the categories, primary and secondary education displayed similar patterns, while not formally educated and tertiary groups also showed similar trends, though not statistically supported. A statistical test using the log-rank method reveals a statistically significant difference ( P  = 0.02) among not formally educated, primary, secondary, and tertiary groups concerning survival time in months.

Among tuberculosis (TB) patients, the Kaplan-Meier estimate plot indicates that individuals living with HIV/AIDS who did not have TB were more likely to survive than those who had TB, in terms of survival time in months. The log-rank test for these variables also demonstrates a statistically significant difference between patients with TB and those without (Supplementary Table 1 ).

Kaplan-Meier plots of different categorical variables

Assumption checking

The results of the covariates and the global test for the proportionality assumption of the Cox proportional hazards model are presented. The p-values for the covariate terms and the global test are insignificant at the 5% level, indicating that the proportional hazards assumptions are not violated. In the Schoenfeld residual plot, no patterns are observed between the variables and time. The assumption of proportional hazards has been satisfied for both methods (Supplementary Tables 2 and Supplementary Fig.  1 ).

Multivariate analysis of the Cox-PH model

Variables such as gender, educational status, place of residence, tuberculosis, family history, and opportunistic infections were significantly associated with the survival time of adults living with HIV/AIDS undergoing ART treatment at the 5% level of significance. According to the adjusted hazard ratio, male HIV-infected patients were 1.69 times more likely to die than their female counterparts (HR = 1.69, p-value = 0.036). This indicates that male patients faced a 69% higher risk of experiencing an event compared to female patients (Table  2 ).

It has been estimated that patients educated at the secondary level have a hazard rate of 0.31, indicating a 0.31-fold lower risk of death compared to non-formally educated patients (HR = 0.31, p-value = 0.028). There was a 1.72 times greater mortality risk for HIV-infected adults with TB compared to those without TB. The results indicate that 72% of TB patients face an increased risk of death compared to those without TB.

Regarding the family history of HIV patients, families with a history of the disease were at 1.66 times higher risk of death than those without a family history of HIV/AIDS (HR = 1.66, p-value = 0.047). Concerning opportunistic infections, patients with a risk of opportunistic infections had a 2.30 times higher risk of death than patients without such a risk (HR = 2.30, p-value = 0.002). However, marital status and WHO stages do not significantly affect the survival time to death in HIV patients.

Discussions

This study aimed to identify factors affecting the survival time of adult HIV/AIDS patients in the pastoralist area of Borena at Yabelo General Hospital from January 2016 to December 2019. In the current study, the gender variable is significantly associated with survival time until death, consistent with several other studies [ 20 , 21 , 22 ]. The mortality risk for adult male patients was higher than that for adult female patients, suggesting that female patients are more likely to know their HIV status at an earlier stage and to start ART with higher CD4 counts than males [ 20 ]. According to other studies, gender status was not associated with survival time until HIV/AIDS-related risks [ 23 , 24 , 25 , 26 ].

The findings of this study revealed that individuals living with HIV/AIDS who had a secondary educational status had a lower hazard ratio of death than those with no formal education. Various studies supported the notion that secondary educational status was linked to a lower risk of mortality among HIV-infected antiretroviral therapy users, indicating significant effects on the survival time of adult patients [ 25 , 27 , 28 , 29 , 30 , 31 ].

A patient living in urban areas has a 0.46 times lower death rate than a patient living in rural areas, indicating that patients from urban areas are more likely to survive than those in rural regions. Similarly, the study at Debre Tabor Referral Hospital suggests that patients in urban areas had significantly higher survival rates compared to those from rural areas [ 32 ]. In a study examining the impact of the “universal test and treat” program on HIV treatment in the Gurage Zone, it was found that rural patients had significantly better survival rates than urban patients [ 33 ]. Possible reasons include better drug adherence, improved access to services, closer proximity to health centers, superior care provided, and varying levels of knowledge.

According to the findings of this study, patients with tuberculosis (TB) and HIV faced 1.72 times the risk of dying from the disease compared to patients without TB. Therefore, patients without coinfection diseases have a better survival rate than those with them. A similar study conducted at Goba Hospital in Southeast Ethiopia found that TB coinfection at the start of ART was strongly associated with increased mortality risks among ART patients [ 26 , 33 ]. However, other study results did not demonstrate any association between baseline TB infection and the death hazard rate [ 23 ].

People living with HIV/AIDS who have opportunistic infections are linked to an increase in HIV-infected patients, according to our study. It has been estimated that patients with opportunistic infections alongside other diseases face a higher risk of death compared to those without such infections. Various studies support the notion that opportunistic infections are significantly associated with the survival and mortality of HIV-infected patients [ 23 , 25 ].

The main objective of this study was to determine the survival time for HIV/AIDS patients in the pastoralist region of Borena at Yabelo General Hospital from January 2016 to December 2019. In this study, a total of 293 adults living with HIV/AIDS were analyzed. According to the Cox-PH model, covariates such as gender, educational status, place of residence, TB, family history, and opportunistic infections were identified as factors affecting the survival time of HIV-infected individuals. Patients residing in urban areas have a lower risk of death than those living in rural areas, indicating that rural patients have a lower survival probability compared to their urban counterparts. Therefore, the Borena zone administration should pay special attention to adult patients to enhance life expectancy.

Data availability

after getting acceptance.

Abbreviations

Ethiopian Public Health Institute

Proportional Hazard

Tuberculosis

Joint United Nations Programme on HIV/AIDS

World Health Organization

The path that ends AIDS: UNAIDS Global AIDS Update. 2023. Geneva: Joint United Nations Programme on HIV/AIDS, 2023. License: CC BY-NC-SA 3.0 IGO.

US Department of Health and Human Services. Guidelines for the use of antiretroviral agents in HIV-1-infected adults and adolescents. http://aidsinfo.nih.gov/OrderPublication/OrderPubsBrowseSearchResultsTable . aspx? 2009, ID = 115.

EPHI. (2023). HIV-Related Estimates and Projections in Ethiopia for the Year 2022–2023, Addis Ababa.

The Ethiopia Public Health Institute. (EPHI, 2023). HIV Related Estimates and Projections in Ethiopia for the Year 2022–2023. May 2023, Addis Ababa. https://ephi.gov.et/wp-content/uploads/2021/02/HIV-Estimates-and-projection-for-the-year-2022-and-2023.pdf

Mirgissa K, Ibrahim A, Damen HM. Extramarital sexual practices and perceived association with HIV infection among the Borana pastoral community. Ethiop J Health Dev. 2013;27(1):25–32.

Google Scholar  

Miz-Hasab Research Centre. HIV/AIDS and gender in Ethiopia: the case of ten Weredas in Oromia and Southern Nations and Nationalities people’s region. Addis Ababa: Miz-Hasab research center; 2004.

Tefera B, Ahmed Y. Contribution of the anti HIV/AIDS community conversation programs in preventing and controlling the spread of HIV/ AIDS. Ethiop J Health Dev. 2013;27(3):216–29.

Mela Research. Know Your HIV Epidemic/Know Your HIV Response (KYE/KYR) Synthesis in Oromia, Ethiopia. Addis Ababa, Ethiopia; 2014.

Collett D. Modeling survival data in medical research. Chapman and Hall/CRC; 2023.

Borena. (2023). Borena zone administration office report on the population severe drought effects in 2023.unpublished document.

Fenetahun Y, Fentahun T. Socio-economic profile of arid and semi-arid agro-pastoral region of Borana rangeland Southern Ethiopia. MOJ Eco Environ Sci. 2020;5(3):113–22.

Gebrerufael GG, Asfaw ZG, Chekole DM. The effect of longitudinal body weight and CD4 cell progression for the survival of HIV/AIDS patients. Cogent Med. 2021;8(1):1986269.

Article   Google Scholar  

Cox DR. Regression models and life-tables. J Roy Stat Soc: Ser B (Methodol). 1972;34(2):187–202.

Tsegaye E, Worku A. Assessment of antiretroviral treatment outcome in public hospitals, South nations Nationalities and Peoples Region, Ethiopia. Ethiop J Health Dev. 2011;25:102–9.

Abbastabar H, Rezaianzadeh A, Rajaeefard A, Ghaem H, Motamedifar M, Kazeroon PA. 2016. Determining factors of CD4 cell count in HIV patients: in a historical cohort study. International Journal of Life Science and Pharma Research, 2016, 93–101.

Schober P, Vetter TR. Survival analysis and interpretation of time-to-event data: the tortoise and the hare. Anesth Analgesia. 2018;127(3):792–8.

George B, Seals S, Aban I. Survival analysis and regression models. J Nuclear Cardiol. 2014;21(4):686–94.

Kaplan EL, Meier P. Nonparametric estimation from incomplete observations. J Am Stat Assoc. 1958;53(282):457–81.

Mageda K, Leyna GH, Mmbaga EJ. High initial HIV/AIDS-Related mortality and predictors among patients on antiretroviral therapy in the Kagera Region of Tanzania: a five-year retrospective cohort study. AIDS Res Treat. 2012;2012(1):843598.

PubMed   PubMed Central   Google Scholar  

Zheng H, Wang L, Huang P, Norris J, Wang Q, Guo W, Peng Z, Yu R, Wang N. Incidence and risk factors for AIDS-related mortality in HIV patients in China: a cross-sectional study. BMC Public Health. 2014;14:1–9.

Article   CAS   Google Scholar  

Mengesha S, Belayihun B, Kumie A. Predictors of survival in HIV-infected patients after initiation of HAART in Zewditu Memorial Hospital, Addis Ababa, Ethiopia. Int Sch Res Notices. 2014;2014(1):250913.

Seyoum D, Degryse JM, Kifle YG, Taye A, Tadesse M, Birlie B, Banbeta A, Rosas-Aguirre A, Duchateau L, Speybroeck N. Risk factors for mortality among adult HIV/AIDS patients following antiretroviral therapy in Southwestern Ethiopia: an assessment through survival models. Int J Environ Res Public Health. 2017;14(3):296.

Article   PubMed   PubMed Central   Google Scholar  

Tegegne AS, Ndlovu P, Zewotir T. Determinants of CD4 cell count change and time-to default from HAART; a comparison of separate and joint models. BMC Infect Dis. 2018;18:1–1.

Setegn T, Takele A, Gizaw T, Nigatu D, Haile D. Predictors of mortality among adult antiretroviral therapy users in southeastern Ethiopia: retrospective cohort study. AIDS Res Treat. 2015;2015(1):148769.

Hassan AS, Mwaringa SM, Ndirangu KK, Sanders EJ, de Wit TF, Berkley JA. Incidence and predictors of attrition from antiretroviral care among adults in a rural HIV clinic in Coastal Kenya: a retrospective cohort study. BMC Public Health. 2015;15:1–9.

Tadesse K, Haile F, Hiruy N. Predictors of mortality among patients enrolled on antiretroviral therapy in Aksum hospital, northern Ethiopia: a retrospective cohort study. PLoS ONE. 2014;9(1):e87392.

Bello SI, Itiola OA. Drug adherence amongst tuberculosis patients in the University of Ilorin Teaching Hospital, Ilorin, Nigeria. Afr J Pharm Pharmacol. 2010;4(3):109–14.

Jarrin I, Lumbreras B, Ferrero I, Pérez-Hoyos S, Hurtado I, Hernández-Aguado I. Effect of education on overall and cause-specific mortality in injecting drug users, according to HIV and introduction of HAART. Int J Epidemiol. 2007;36(1):187–94.

Article   CAS   PubMed   Google Scholar  

Seid A, Getie M, Birlie B, Getachew Y. Joint modeling of longitudinal CD4 cell counts and time-to-default from HAART treatment: a comparison of separate and joint models. Electron J Appl Stat Anal. 2014;7(2):292–314.

Kebede MM, Zegeye DT, Zeleke BM. Predictors of CD4 count changes after initiation of antiretroviral treatment in University of Gondar Hospital, Gondar in Ethiopia. Clin Res HIV/AIDS. 2015;1(2):1–5.

Birhan H, Seyoum A, Derebe K, Muche S, Wale M, Sisay S. Joint clinical and socio-demographic determinants of CD4 cell count and body weight in HIV/TB co-infected adult patients on HAART. Sci Afr. 2022;18:e01396.

Girum T, Yasin F, Wasie A, Shumbej T, Bekele F, Zeleke B. The effect of the universal test and treat program on HIV treatment outcomes and patient survival among a cohort of adults taking antiretroviral treatment (ART) in low-income settings of Gurage Zone, South Ethiopia. AIDS Res Therapy. 2020;17:1–9.

Ayalew J, Moges H, Worku A. Identifying factors related to the survival of AIDS patients under the follow-up of antiretroviral therapy (ART): the case of South Wollo. Int J Data Envelopment Anal Oper Res. 2014;1:21–7.

Download references

Acknowledgements

First and foremost, I would like to thank the almighty God for being there with me in every step of my life. Next, I would like to express my grateful and sincere gratitude to my principal advisor Dr. Markos Abiso (PhD).

The authors received no specific funding for this work.

Author information

Authors and affiliations.

Borena Zone Labour and Social Affairs Office, Borena, Oromia, Ethiopia

Galgalo Jaba Nura

Department of Economics, Borena University, Borena, Ethiopia

Kumbi Sara Wario

Department of Statistics, Arba Minch University, Arba Minch, Ethiopia

Markos Abiso Erango

You can also search for this author in PubMed   Google Scholar

Contributions

Conceptualization: Galgalo Jaba Nura, Markos Abiso Erango.Data curation: Galgalo Jaba Nura, Kumbi Sara Wario. Formal analysis: Galgalo Jaba Nura. Investigation: Galgalo Jaba Nura, Kumbi Sara Wario, Markos Abiso Erango.Methodology: Galgalo Jaba Nura, Markos Abiso Erango.Project administration: Markos Abiso Erango.Software: Galgalo Jaba Nura, Markos Abiso Erango. Supervision: Markos Abiso Erango.Validation: Markos Abiso Erango. Writing – original draft: Kumbi Sara Wario, Markos Abiso Erango. Writing – review & editing: Kumbi Sara Wario, Markos Abiso Erango.

Corresponding author

Correspondence to Galgalo Jaba Nura .

Ethics declarations

Competing interests.

The authors declare no competing interests.

Additional information

Publisher’s note.

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

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1

Supplementary material 2, rights and permissions.

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/ .

Reprints and permissions

About this article

Cite this article.

Nura, G.J., Wario, K.S. & Erango, M.A. Determinants of survival time for HIV/AIDS patients in the pastoralist region of Borena: a study at Yabelo General Hospital, South East Ethiopia. AIDS Res Ther 21 , 58 (2024). https://doi.org/10.1186/s12981-024-00644-1

Download citation

Received : 03 July 2024

Accepted : 09 August 2024

Published : 28 August 2024

DOI : https://doi.org/10.1186/s12981-024-00644-1

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Survival time
• Yabelo General Hospital

AIDS Research and Therapy

ISSN: 1742-6405

• Submission enquiries: [email protected]