analysis plan in research methodology

How to Create a Data Analysis Plan: A Detailed Guide

by Barche Blaise | Aug 12, 2020 | Writing

If a good research question equates to a story then, a roadmap will be very vita l for good storytelling. We advise every student/researcher to personally write his/her data analysis plan before seeking any advice. In this blog article, we will explore how to create a data analysis plan: the content and structure.

This data analysis plan serves as a roadmap to how data collected will be organised and analysed. It includes the following aspects:

• Clearly states the research objectives and hypothesis
• Identifies the dataset to be used
• Inclusion and exclusion criteria
• Clearly states the research variables
• States statistical test hypotheses and the software for statistical analysis
• Creating shell tables

1. Stating research question(s), objectives and hypotheses:

All research objectives or goals must be clearly stated. They must be Specific, Measurable, Attainable, Realistic and Time-bound (SMART). Hypotheses are theories obtained from personal experience or previous literature and they lay a foundation for the statistical methods that will be applied to extrapolate results to the entire population.

2. The dataset:

The dataset that will be used for statistical analysis must be described and important aspects of the dataset outlined. These include; owner of the dataset, how to get access to the dataset, how the dataset was checked for quality control and in what program is the dataset stored (Excel, Epi Info, SQL, Microsoft access etc.).

3. The inclusion and exclusion criteria :

They guide the aspects of the dataset that will be used for data analysis. These criteria will also guide the choice of variables included in the main analysis.

4. Variables:

Every variable collected in the study should be clearly stated. They should be presented based on the level of measurement (ordinal/nominal or ratio/interval levels), or the role the variable plays in the study (independent/predictors or dependent/outcome variables). The variable types should also be outlined.  The variable type in conjunction with the research hypothesis forms the basis for selecting the appropriate statistical tests for inferential statistics. A good data analysis plan should summarize the variables as demonstrated in Figure 1 below.

5. Statistical software

There are tons of software packages for data analysis, some common examples are SPSS, Epi Info, SAS, STATA, Microsoft Excel. Include the version number,  year of release and author/manufacturer. Beginners have the tendency to try different software and finally not master any. It is rather good to select one and master it because almost all statistical software have the same performance for basic and the majority of advance analysis needed for a student thesis. This is what we recommend to all our students at CRENC before they begin writing their results section .

6. Selecting the appropriate statistical method to test hypotheses

Depending on the research question, hypothesis and type of variable, several statistical methods can be used to answer the research question appropriately. This aspect of the data analysis plan outlines clearly why each statistical method will be used to test hypotheses. The level of statistical significance (p-value) which is often but not always <0.05 should also be written.  Presented in figures 2a and 2b are decision trees for some common statistical tests based on the variable type and research question

A good analysis plan should clearly describe how missing data will be analysed.

7. Creating shell tables

Data analysis involves three levels of analysis; univariable, bivariable and multivariable analysis with increasing order of complexity. Shell tables should be created in anticipation for the results that will be obtained from these different levels of analysis. Read our blog article on how to present tables and figures for more details. Suppose you carry out a study to investigate the prevalence and associated factors of a certain disease “X” in a population, then the shell tables can be represented as in Tables 1, Table 2 and Table 3 below.

Table 1: Example of a shell table from univariate analysis

Table 2: Example of a shell table from bivariate analysis

Table 3: Example of a shell table from multivariate analysis

aOR = adjusted odds ratio

Now that you have learned how to create a data analysis plan, these are the takeaway points. It should clearly state the:

• Research question, objectives, and hypotheses
• Dataset to be used
• Variable types and their role
• Statistical software and statistical methods
• Shell tables for univariate, bivariate and multivariate analysis

Further readings

Creating a Data Analysis Plan: What to Consider When Choosing Statistics for a Study https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4552232/pdf/cjhp-68-311.pdf

Creating an Analysis Plan: https://www.cdc.gov/globalhealth/healthprotection/fetp/training_modules/9/creating-analysis-plan_pw_final_09242013.pdf

Data Analysis Plan: https://www.statisticssolutions.com/dissertation-consulting-services/data-analysis-plan-2/

Photo created by freepik – www.freepik.com

Dr Barche is a physician and holds a Masters in Public Health. He is a senior fellow at CRENC with interests in Data Science and Data Analysis.

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16 comments.

Thanks. Quite informative.

Educative write-up. Thanks.

Easy to understand. Thanks Dr

Very explicit Dr. Thanks

I will always remember how you help me conceptualize and understand data science in a simple way. I can only hope that someday I’ll be in a position to repay you, my dear friend.

Plan d’analyse

This is interesting, Thanks

Very understandable and informative. Thank you..

love the figures.

Nice, and informative

This is so much educative and good for beginners, I would love to recommend that you create and share a video because some people are able to grasp when there is an instructor. Lots of love

Thank you Doctor very helpful.

Educative and clearly written. Thanks

Well said doctor,thank you.But when do you present in tables ,bars,pie chart etc?

Very informative guide!

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Writing the Data Analysis Plan

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You and your project statistician have one major goal for your data analysis plan: You need to convince all the reviewers reading your proposal that you would know what to do with your data once your project is funded and your data are in hand. The data analytic plan is a signal to the reviewers about your ability to score, describe, and thoughtfully synthesize a large number of variables into appropriately-selected quantitative models once the data are collected. Reviewers respond very well to plans with a clear elucidation of the data analysis steps – in an appropriate order, with an appropriate level of detail and reference to relevant literatures, and with statistical models and methods for that map well into your proposed aims. A successful data analysis plan produces reviews that either include no comments about the data analysis plan or better yet, compliments it for being comprehensive and logical given your aims. This chapter offers practical advice about developing and writing a compelling, “bullet-proof” data analytic plan for your grant application.

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Panter, A.T. (2010). Writing the Data Analysis Plan. In: Pequegnat, W., Stover, E., Boyce, C. (eds) How to Write a Successful Research Grant Application. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-1454-5_22

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2.3 Data management and analysis

Learning objectives.

Learners will be able to…

• Define and construct a data analysis plan
• Define key quantitative data management terms—variable name, data dictionary, and observations/cases
• Differentiate between univariate and bivariate quantitative analysis
• Explain when we might use quantitative bivariate analysis in social work research
• Identify how your qualitative research question, research aim, and type of data may influence your choice of analytic methods
• Outline the steps you will take in preparation for conducting qualitative data analysis

After you have your raw data, whether this is secondary data or data you collected yourself, you will need to analyze it. While the specific steps to follow in quantitative or qualitative data analysis are beyond the scope of this chapter, we are going to address some basic concepts in this section to help you create a data analysis plan. A data analysis plan is an ordered outline that includes your research question, a description of the data you are going to use to answer it, and the exact step-by-step analyses that you plan to run to answer your research question. If you look back at Table 2.1, you will see that creating a data analysis plan is a part of the study design process. The data analysis plan flows from the research question, is integral to the study design, and should be well conceptualized prior to beginning data collection. In this section, we will walk through the basics of quantitative and qualitative data analysis to help you understand the fundamentals of creating a data analysis plan.

Quantitative Data: Management

When considering what data you might want to collect as part of your project, there are two important considerations that can create dilemmas for researchers. You might only get one chance to interact with your participants, so you must think comprehensively in your planning phase about what information you need and collect as much relevant data as possible. At the same time, though, especially when collecting sensitive information, you need to consider how onerous the data collection is for participants and whether you really need them to share that information. Just because something is interesting to us doesn’t mean it’s related enough to our research question to chase it down. Work with your research team and/or faculty early in your project to talk through these issues before you get to this point. And if you’re using secondary data, make sure you have access to all the information you need in that data before you use it.

Once you’ve collected your quantitative data, you need to make sure it is well-organized in a database in a way that’s actually usable. “Database” can be kind of a scary word, but really, it can be as simple as an Excel spreadsheet or a data file in whatever program you’re using to analyze your data.  You may want to avoid Excel and use a formal database such as Microsoft Access or MySQL if you’ve got a large or complicated data set. But if your data set is smaller and you plan to keep your analyses simple, you can definitely get away with Excel. A typical data set is organized with variables as columns and observations/cases as rows. For example, let’s say we did a survey on ice cream preferences and collected the following information in Table 2.3:

There are a few key data management terms to understand:

• Variable name : Just what it sounds like—the name of your variable. Make sure this is something useful, short and, if you’re using something other than Excel, all one word. Most statistical programs will automatically rename variables for you if they aren’t one word, but the names can be a little ridiculous and long.
• Observations/cases : The rows in your data set. In social work, these are often your study participants (people), but can be anything from census tracts to black bears to trains. When we talk about sample size, we’re talking about the number of observations/cases. In our mini data set, each person is an observation/case.
• Data dictionary (also called a code book or metadata) : This is the document where you list your variable names, what the variables actually measure or represent, what each of the values of the variable mean if the meaning isn’t obvious (i.e., if there are numbers assigned to gender), the level of measurement and anything special to know about the variables (for instance, the source if you mashed two data sets together). If you’re using secondary data, the researchers sharing the data should make the data dictionary available.

Let’s take that mini data set we’ve got up above and we’ll show you what your data dictionary might look like in Table 2.4.

Quantitative Data: Univariate Analysis

As part of planning for your research, you should come up with a data analysis plan. Remember, a data analysis plan is an ordered outline that includes your research question, a description of the data you are going to use to answer it, and the exact step-by-step analyses that you plan to run to answer your research question. A basic data analysis plan might look something like what you see in Table 2.5. Don’t panic if you don’t yet understand some of the statistical terms in the plan; we’re going to delve into some of them in this section, and others will be covered in more depth in your statistics courses. Note here also that this is what operationalizing your variables and moving through your research with them looks like on a basic level. We will cover operationalization in more depth in Chapter 10.

An important point to remember is that you should never get stuck on using a particular statistical method because you or one of your co-researchers thinks it’s cool or it’s the hot thing in your field right now. You should certainly go into your data analysis plan with ideas, but in the end, you need to let your research question guide what statistical tests you plan to use. Be prepared to be flexible if your plan doesn’t pan out because the data is behaving in unexpected ways.

You’ll notice that the first step in the quantitative data analysis plan is univariate and descriptive statistics.   Univariate data analysis is a quantitative method in which a variable is examined individually to determine its distribution , or the way the scores are distributed across the levels, or values, of that variable. When we talk about levels ,  what we are talking about are the possible values of the variable—like a participant’s age, income or gender. (Note that this is different from levels of measurement , which will be discussed in Chapter 11, but the level of measurement of your variables absolutely affects what kinds of analyses you can do with it.) Univariate analysis is non-relational , which just means that we’re not looking into how our variables relate to each other. Instead, we’re looking at variables in isolation to try to understand them better. For this reason, univariate analysis is used for descriptive research questions.

So when do you use univariate data analysis? Always! It should be the first thing you do with your quantitative data, whether you are planning to move on to more sophisticated statistical analyses or are conducting a study to describe a new phenomenon. You need to understand what the values of each variable look like—what if one of your variables has a lot of missing data because participants didn’t answer that question on your survey? What if there isn’t much variation in the gender of your sample? These are things you’ll learn through univariate analysis.

Quantitative Data: Bivariate Analysis

Did you know that ice cream causes shark attacks? It’s true! When ice cream sales go up in the summer, so does the rate of shark attacks. So you’d better put down that ice cream cone, unless you want to make yourself look more delicious to a shark.

Ok, so it’s quite obviously not true that ice cream causes shark attacks. But if you looked at these two variables and how they’re related, you’d notice that during times of the year with high ice cream sales, there are also the most shark attacks. This is a classic example of the difference between correlation and causation. Despite the fact that the conclusion we drew about causation was wrong, it’s nonetheless true that these two variables appear related, and researchers figured that out through the use of bivariate analysis.

Bivariate analysis consists of a group of statistical techniques that examine the association between two variables. We could look at how anti-depressant medications and appetite are related, whether there is a relation between having a pet and emotional well-being, or if a policy-maker’s level of education is related to how they vote on bills related to environmental issues.

Bivariate analysis forms the foundation of multivariate analysis, which we don’t get to in this book. All you really need to know here is that there are steps beyond bivariate analysis, which you’ve undoubtedly seen in scholarly literature already! But before we can move forward with multivariate analysis, we need to understand the associations between the variables in our study.

Throughout your PhD program, you will learn much more about quantitative data analysis techniques, including more sophisticated multivariate analysis methods. Hopefully this section has provided you with some initial insights into how data is analyzed, and the importance of creating a data analysis plan prior to collecting data. Next, we will discuss some basic strategies for creating a qualitative data analysis plan.

Resources for Quantitative Data Analysis

While you are affiliated with a university, it is likely that you will have access to some kind of commercial statistics software. Examples in the previous section uses SPSS, the most common one our authoring team has seen in social work education. Like its competitors SAS and STATA, SPSS is expensive and your license to the software must be renewed every year (like a subscription). Even if you are able to install commercial statistics software on your computer, once your license expires, your program will no longer work. We believe that forcing students to learn software they will never use is wasteful and contributes to the (accurate, in many cases) perception from students that research class is unrelated to real-world practice. SPSS is more accessible due to its graphical user interface and does not require researchers to learn basic computer programming, but it is prohibitively costly if a student wanted to use it to measure practice data in their agency post-graduation.

Instead, we suggest getting familiar with JASP Statistics , a free and open-source alternative to SPSS developed and supported by the University of Amsterdam. It has a similar user interface as SPSS, and should be similarly easy to learn. Moreover, usability upgrades from SPSS like generating APA formatted tables make it a compelling option. While a great many of my students will rely on statistical analyses of their programs and practices in reports to funders, it is unlikely that any will use SPSS. Browse JASP’s how-to guide or consult this textbook Learning Statistics with JASP: A Tutorial for Psychology Students and Other Beginners , written by  Danielle J. Navarro ,  David R. Foxcroft , and  Thomas J. Faulkenberry .

Another open source statistics software package is R (a.k.a. The R Project for Statistical Computing ). R uses a command line interface, so you will need some coding knowledge in order to use it. Luckily, R is the most commonly used statistics software in the world, and the community of support and guides for using R are omnipresent online. For beginning researchers, consult the textbook Learning Statistics with R: A tutorial for psychology students and other beginners by Danielle J. Navarro .

While statistics software is sometimes needed to perform advanced statistical tests, most univariate and bivariate tests can be performed in spreadsheet software like Microsoft Excel, Google Sheets, or the free and open source LibreOffice Calc . Microsoft includes a ToolPak to perform complex data analysis as an add-on to Excel. For more information on using spreadsheet software to perform statistics, the open textbook Collaborative Statistics Using Spreadsheets by Susan Dean, Irene Mary Duranczyk, Barbara Illowsky, Suzanne Loch, and Janet Stottlemyer.

Statistical analysis is performed in just about every discipline, and as a result, there are a lot of openly licensed, free resources to assist you with your data analysis. We have endeavored to provide you the basics in the past few chapters, but ultimately, you will likely need additional support in completing quantitative data analysis from an instructor, textbook, or other resource. Browse the Open Textbook Library for statistics resources or look for video tutorials from reputable instructors like this video textbook on statistics by Bryan Koenig .

Qualitative Data: Management

Qualitative research often involves human participants and qualitative data can include of recordings or transcripts of their words, photographs or images, or diaries and documents. The personal nature of qualitative data poses the challenge of recognizability of sensitive information on individuals, communities, and places. If you choose this methodology for your research, you should familiarize yourself with policies, procedures, and rules to ensure safety and security of data in the documentation and dissemination process.

In any research involving primary data, a researcher is not only entrusted with the responsibility of upholding privacy of their participants but also accountable to them, making confidentiality and human subjects’ protection front and center of qualitative data management. Data such as audiotapes, videotapes, transcripts, notes, and other records should be stored and secured in locations where only authorized persons have access to them.

Sometimes in qualitative research, you will learn intimate details about people’s lives. Often, qualitative data contain personal identifiers. A helpful practice to ensure that participants confidentiality is to replace personal information in transcripts with pseudonyms or descriptive language (e.g., “[the participant’s sister]” instead of the sister’s name). Once audio and video recordings have been accurately transcribed with the de-identification of personal identifiers, the original recordings should be destroyed.

Qualitative Data: Analysis

There are many different types of qualitative data, including transcripts of interviews and focus groups, observational data, documents and other artifacts, and more. Your qualitative data analysis plan should be anchored in the type of data collected and the purpose of your study. Qualitative research can serve a range of purposes. Below is a brief list of general purposes we might consider when using a qualitative approach.

• Are you trying to understand how a particular group is affected by an issue?
• Are you trying to uncover how people arrive at a decision in a given situation?
• Are you trying to examine different points of view on the impact of a recent event?
• Are you trying to summarize how people understand or make sense of a condition?
• Are you trying to describe the needs of your target population?

If you don’t see the general aim of your research question reflected in one of these areas, don’t fret! This is only a small sampling of what you might be trying to accomplish with your qualitative study. Whatever your aim, you need to have a plan for what you will do once you have collected your data.

Iterative or Linear

Some qualitative research is linear , meaning it follows more of a traditionally quantitative process: create a plan, gather data, and analyze data; each step is completed before we proceed to the next. You can think of this like how information is presented in this book. We discuss each topic, one after another.

However, many times qualitative research is iterative , or evolving in cycles. An iterative approach means that once we begin collecting data, we also begin analyzing data as it is coming in. This early and ongoing analysis of our (incomplete) data then impacts our continued planning, data gathering and future analysis. Again, coming back to this book, while it may be written linear, we hope that you engage with it iteratively as you design and conduct your own research. By this we mean that you will revisit previous sections so you can understand how they fit together and you are in continuous process of building and revising how you think about the concepts you are learning about.

As you may have guessed, there are benefits and challenges to both linear and iterative approaches. A linear approach is much more straightforward, each step being fairly defined. However, linear research being more defined and rigid also presents certain challenges. A linear approach assumes that we know what we need to ask or look for at the very beginning of data collection, which often is not the case. Figure 2.1 contrasts the two approaches.

With iterative research, we have more flexibility to adapt our approach as we learn new things. We still need to keep our approach systematic and organized, however, so that our work doesn’t become a free-for-all. As we adapt, we do not want to stray too far from the original premise of our study. It’s also important to remember with an iterative approach that we may risk ethical concerns if our work extends beyond the original boundaries of our informed consent and institutional review board agreement (IRB; see Chapter 3 for more on IRBs). If you feel that you do need to modify your original research plan in a significant way as you learn more about the topic, you can submit an addendum to modify your original application that was submitted. Make sure to keep detailed notes of the decisions that you are making and what is informing these choices. This helps to support transparency and your credibility throughout the research process.

Acquainting yourself with your data

As you begin your analysis, you need to get to know your data. This often means reading through your data prior to any attempt at breaking it apart and labeling it. You might read through a couple of times, in fact. This helps give you a more comprehensive feel for each piece of data and the data as a whole, again, before you start to break it down into smaller units or deconstruct it. This is especially important if others assisted us in the data collection process. We often gather data as part of team and everyone involved in the analysis needs to be very familiar with all of the data.

Capturing your emerging understanding of the data

During your reviewing you will start to develop and evolve your understanding of what the data means. Coding is a part of the qualitative data analysis process where we begin to interpret and assign meaning to the data. It represents one of the first steps as we begin to filter the data through our own subjective lens as the researcher. This understanding of the data should be dynamic and flexible, but you want to have a way to capture this understanding as it evolves. You may include this as part of your qualitative codebook where you are tracking the main ideas that are emerging and what they mean. Table 2.6 is an example of how your thinking might change about a code and how you can go about capturing it.

There are a variety of different approaches to qualitative analysis, including thematic analysis, content analysis, grounded theory, phenomenology, photovoice, and more. The specific steps you will take to code your qualitative data, and to generate themes from these codes, will vary based on the analytic strategy you are employing. In designing your qualitative study, you would identify an analytical approach as you plan out your project. The one you select would depend on the type of data you have and what you want to accomplish with it. In Chapter 19, we will go into more detail about various types of qualitative data analysis. Each qualitative approach has specific techniques and methods that take substantial study and practice to master.

Key Takeaways

• Getting organized at the beginning of your project with a data analysis plan will help keep you on track. Data analysis plans should include your research question, a description of your data, and a step-by-step outline of what you’re going to do with it. [chapter 14.1]
• Be flexible with your data analysis plan—sometimes data surprises us and we have to adjust the statistical tests we are using. [chapter 14.1]
• Always make a data dictionary or, if using secondary data, get a copy of the data dictionary so you (or someone else) can understand the basics of your data. [chapter 14.1]
• Bivariate analysis is a group of statistical techniques that examine the relationship between two variables. [chapter 15.1]
• You need to conduct bivariate analyses before you can begin to draw conclusions from your data, including in future multivariate analyses. [chapter 15.1]
• There are a lot of high quality and free online resources to learn and perform statistical analysis.
• Qualitative research analysis requires preparation and careful planning. You will need to take time to familiarize yourself with the data in a general sense before you begin analyzing. [chapter 19.3]
• The specific steps you will take to code your qualitative data and generate final themes will depend on the qualitative analytic approach you select.

TRACK 1 (IF YOU ARE CREATING A RESEARCH PROPOSAL FOR THIS CLASS)

• Make a data analysis plan for your project. Remember this should include your research question, a description of the data you will use, and a step-by-step outline of what you’re going to do with your data once you have it, including statistical tests (non-relational and relational) that you plan to use. You can do this exercise whether you’re using quantitative or qualitative data! The same principles apply.
• Make a data dictionary for the data you are proposing to collect as part of your study. You can use the example above as a template.

TRACK 2 (IF YOU  AREN’T CREATING A RESEARCH PROPOSAL FOR THIS CLASS)

You are researching the impact of your city’s recent harm reduction interventions for intravenous drug users (e.g., sterile injection kits, monitored use, overdose prevention, naloxone provision, etc.).

• Make a draft quantitative data analysis plan for your project. Remember this should include your research question, a description of the data you will use, and a step-by-step outline of what you’re going to do with your data once you have it, including statistical tests (non-relational and relational) that you plan to use. It’s okay if you don’t yet have a complete idea of the types of statistical analyses you might use.

An ordered outline that includes your research question, a description of the data you are going to use to answer it, and the exact analyses, step-by-step, that you plan to run to answer your research question.

The name of your variable.

The rows in your data set. In social work, these are often your study participants (people), but can be anything from census tracts to black bears to trains.

This is the document where you list your variable names, what the variables actually measure or represent, what each of the values of the variable mean if the meaning isn't obvious.

process by which researchers spell out precisely how a concept will be measured in their study

A group of statistical techniques that examines the relationship between at least three variables

Univariate data analysis is a quantitative method in which a variable is examined individually to determine its distribution.

the way the scores are distributed across the levels of that variable.

Chapter Outline

• Practical and ethical considerations ( 14 minute read)
• Raw data (10 minute read)
• Creating a data analysis plan (?? minute read)
• Critical considerations (3 minute read)

Content warning: Examples in this chapter discuss substance use disorders, mental health disorders and therapies, obesity, poverty, gun violence, gang violence, school discipline, racism and hate groups, domestic violence, trauma and triggers, incarceration, child neglect and abuse, bullying, self-harm and suicide, racial discrimination in housing, burnout in helping professions, and sex trafficking of indigenous women.

2.1 Practical and ethical considerations

Learners will be able to...

• Identify potential stakeholders and gatekeepers
• Differentiate between raw data and the results of scientific studies
• Evaluate whether you can feasibly complete your project

Pre-awareness check (Knowledge)

Similar to practice settings, research has ethical considerations that must be taken to ensure the safety of participants. What ethical considerations were relevant to your practice experience that may have impacted the delivery of services?

As a PhD student, you will have many opportunities to conduct research. You may be asked to be a part of a research team led by the faculty at your institution. You will also conduct your own research for your dissertation. As you will learn, research can take many forms. For example, you may want to focus qualitatively on individuals’ lived experiences, or perhaps you will quantitatively assess the impact of interventions on research subjects. You may work with large, already-existing datasets, or you may create your own data. Though social work research can vary widely from project to project, researchers typically follow the same general process, even if their specific research questions and methodologies differ. Table 2.1 outlines the major components of the research process covered in this textbook, and indicates the chapters where you will find more information on each subject. You will notice that your research paradigm is an organizing framework that guides each component of the research process.

Table 2.1 Components of the Research Process

Feasibility

Feasibility refers to whether you can practically conduct the study you plan to do, given the resources and ethical obligations you have. In this chapter, we will review some important practical and ethical considerations researchers should start thinking about from the beginning of a research project. These considerations apply to all research, but it is important to also consider the context of research and researchers when thinking about feasibility.

For example, as a doctoral student, you likely have a unique set of circumstances that inspire and constrain your research. Some students have the ability to engage in independent studies where they can gain skills and expertise in specialized research methods to prepare them for a research-intensive career. Others may have reasons, such as a limited amount of funding or family concerns, that encourage them to complete their dissertation research as quickly as possible. These circumstances relate to the feasibility of a research project. Regardless of the potential societal importance of a 10-year longitudinal study, it’s not feasible for a student to conduct it in time to graduate! Your dissertation chair, doctoral program director, and other faculty mentors can help you navigate the many decisions you will face as a doctoral student about conducting independent research or joining research projects.

The context and role of the researcher continue to affect feasibility even after a doctoral student graduates. Many will continue in their careers to become tenure track faculty with research expectations to obtain tenure. Some funders expect faculty members to have a track record of successful projects before trusting them to lead expensive or long-term studies.  Realistically, these expectations will influence what research is feasible for a junior faculty member to conduct. Just like for doctoral students, mentorship is incredibly valuable for junior faculty to make informed decisions about what research to conduct. Senior faculty, associate deans of research, chairs, and deans can help junior faculty decide what projects to pursue to ensure they meet the expectations placed on them without losing sight of the reasons they became a researcher in the first place.

As you read about other feasibility considerations such as gaining access, consent, and collecting data, consider the ways in which context and roles also influence feasibility.

Access, consent, and ethical obligations

One of the most important feasibility issues is gaining access to your target population. For example, let’s say you wanted to better understand middle-school students who engaged in self-harm behaviors. That is a topic of social importance, but what challenges might you face in accessing this population? Let's say you proposed to identify students from a local middle school and interview them about self-harm. Methodologically, that sounds great since you are getting data from those with the most knowledge about the topic, the students themselves. But practically, that sounds challenging. Think about the ethical obligations a social work practitioner has to adolescents who are engaging in self-harm (e.g., competence, respect). In research, we are similarly concerned mostly with the benefits and harms of what you propose to do as well as the openness and honesty with which you share your project publicly.

Gatekeepers

If you were the principal at your local middle school, would you allow researchers to interview kids in your schools about self-harm? What if the results of the study showed that self-harm was a big problem that your school was not addressing? What if the researcher's interviews themselves caused an increase in self-harming behaviors among the children? The principal in this situation is a gatekeeper . Gatekeepers are the individuals or organizations who control access to the population you want to study. The school board would also likely need to give consent for the research to take place at their institution. Gatekeepers must weigh their ethical questions because they have a responsibility to protect the safety of the people at their organization, just as you have an ethical obligation to protect the people in your research study.

For vulnerable populations, it can be a challenge to get consent from gatekeepers to conduct your research project. As a result, researchers often conduct research projects in places where they have established trust with gatekeepers. In the case where the population (children who self-harm) are too vulnerable, researchers may collect data from people who have secondary knowledge about the topic. For example, the principal may be more willing to let you talk to teachers or staff, rather than children.

Stakeholders

In some cases, researchers and gatekeepers partner on a research project. When this happens, the gatekeepers become stakeholders . Stakeholders are individuals or groups who have an interest in the outcome of the study you conduct. As you think about your project, consider whether there are formal advisory groups or boards (like a school board) or advocacy organizations who already serve or work with your target population. Approach them as experts and ask for their review of your study to see if there are any perspectives or details you missed that would make your project stronger.

There are many advantages to partnering with stakeholders to complete a research project together. Continuing with our example on self-harm in schools, in order to obtain access to interview children at a middle school, you will have to consider other stakeholders' goals. School administrators also want to help students struggling with self-harm, so they may want to use the results to form new programs. But they may also need to avoid scandal and panic if the results show high levels of self-harm. Most likely, they want to provide support to students without making the problem worse. By bringing in school administrators as stakeholders, you can better understand what the school is currently doing to address the issue and get an informed perspective on your project's questions. Negotiating the boundaries of a stakeholder relationship requires strong meso-level practice skills.

Of course, partnering with administrators probably sounds quite a bit easier than bringing on board the next group of stakeholders—parents. It's not ethical to ask children to participate in a study without their parents' consent. We will review the parameters of parental and child consent in Chapter 5 . Parents may be understandably skeptical of a researcher who wants to talk to their child about self-harm, and they may fear potential harm to the child and family from your study. Would you let a researcher you didn't know interview your children about a very sensitive issue?

Social work research must often satisfy multiple stakeholders. This is especially true if a researcher receives a grant to support the project, as the funder has goals it wants to accomplish by funding the research project. Your university is also a stakeholder in your project. When you conduct research, it reflects on your school. If you discover something of great importance, your school looks good. If you harm someone, they may be liable. Your university likely has opportunities for you to share your research with the campus community, and may have incentives or grant programs for researchers. Your school also provides you with support and access to resources like the library and data analysis software.

Target population

So far, we've talked about access in terms of gatekeepers and stakeholders. Let's assume all of those people agree that your study should proceed. But what about the people in the target population? They are the most important stakeholder of all! Think about the children in our proposed study on self-harm. How open do you think they would be to talking to you about such a sensitive issue? Would they consent to talk to you at all?

Maybe you are thinking about simply asking clients on your caseload. As we talked about before, leveraging existing relationships created through field work can help with accessing your target population. However, they introduce other ethical issues for researchers. Asking clients on your caseload or at your agency to participate in your project creates a dual relationship between you and your client. What if you learn something in the research project that you want to share with your clinical team? More importantly, would your client feel uncomfortable if they do not consent to your study? Social workers have power over clients, and any dual relationship would require strict supervision in the rare case it was allowed.

Resources and scope

Let's assume everyone consented to your project and you have adequately addressed any ethical issues with gatekeepers, stakeholders, and your target population. That means everything is ready to go, right? Not quite yet. As a researcher, you will need to carry out the study you propose to do. Depending on how big or how small your proposed project is, you’ll need a little or a lot of resources.

One thing that all projects need is raw data . Raw data can come in may forms. Very often in social science research, raw data includes the responses to a survey or transcripts of interviews and focus groups, but raw data can also include experimental results, diary entries, art, or other data points that social scientists use in analyzing the world. Primary data is data you have collected yourself. Sometimes, social work researchers do not collect raw data of their own, but instead use secondary data analysis to analyze raw data that has been shared by other researchers. Secondary data is data someone else has collected that you have permission to use in your research. For example, you could use data from a local probation program to determine if a shoplifting prevention group was reducing the rate at which people were re-offending. You would need data on who participated in the program and their criminal history six months after the end of their probation period. This is secondary data you could use to determine whether the shoplifting prevention group had any effect on an individual's likelihood of re-offending. Whether a researcher should use secondary data or collect their own raw data is an important choice which we will discuss in greater detail in section 2.2. Collecting raw data or obtaining secondary data can be time consuming or expensive, but without raw data there can be no research project.

Time is an important resource to consider when designing research projects. Make sure that your proposal won't require you to spend more time than you have to collect and analyze data. Think realistically about the timeline for your research project. If you propose to interview fifty mental health professionals in their offices in your community about your topic, make sure you can dedicate fifty hours to conduct those interviews, account for travel time, and think about how long it will take to transcribe and analyze those interviews.

• What is reasonable for you to do in your timeframe?
• How many hours each week can the research team dedicate to this project?

One thing that can delay a research project is receiving approval from the institutional review board (IRB), the research ethics committee at your university. If your study involves human subjects , you may have to formally propose your study to the IRB and get their approval before gathering your data. A well-prepared study is likely to gain IRB approval with minimal revisions needed, but the process can take weeks to complete and must be done before data collection can begin. We will address the ethical obligations of researchers in greater detail in Chapter 5 .

Most research projects cost some amount of money. Potential expenses include wages for members of the research team, incentives for research participants, travel expenses, and licensing costs for standardized instruments. Most researchers seek grant funding to support the research. Grant applications can be time consuming to write and grant funding can be competitive to receive.

Knowledge, competence, and skills

For social work researchers, the social work value of competence is key in their research ethics.

Clearly, researchers need to be skilled in working with their target population in order to conduct ethical research.  Some research addresses this challenge by collecting data from competent practitioners or administrators who have second-hand knowledge of target populations based on professional relationships. Members of the research team delivering an intervention also need to have training and skills in the intervention. For example, if a research study examines the effectiveness of dialectical behavioral therapy (DBT) in a particular context, the person delivering the DBT must be certified in DBT.  Another idea to keep in mind is the level of data collection and analysis skills needed to complete the project.  Some assessments require training to administer. Analyses may be complex or require statistical consultation or advanced training.

In summary, here are a few questions you should ask yourself about your project to make sure it's feasible. While we present them early on in the research process (we're only in Chapter 2), these are certainly questions you should ask yourself throughout the proposal writing process. We will revisit feasibility again in Chapter 9 when we work on finalizing your research question .

• Do you have access to the data you need or can you collect the data you need?
• Will you be able to get consent from stakeholders, gatekeepers, and your target population?
• Does your project pose risk to individuals through direct harm, dual relationships, or breaches in confidentiality?
• Are you competent enough to complete the study?
• Do you have the resources and time needed to carry out the project?
• People will have to say “yes” to your research project. Evaluate whether your project might have gatekeepers or potential stakeholders. They may control access to data or potential participants.
• Researchers need raw data such as survey responses, interview transcripts, or client charts. Your research project must involve more than looking at the analyses conducted by other researchers, as the literature review is only the first step of a research project.
• Make sure you have enough resources (time, money, and knowledge) to complete your research project.

Post-awareness check (Emotion)

What factors have created your passion toward assisting your target population? How can this connection enhance your ability to receive a “yes” from potential participants? What are the anticipated challenges to receiving a “yes” from potential participants?

Think about how you might answer your question by collecting your own data.

• Identify any gatekeepers and stakeholders you might need to contact.
• How can you increase the likelihood you will get access to the people or records you need for your study?

Describe the resources you will need for your project.

• Do you have concerns about feasibility?

TRACK 2 (IF YOU  AREN'T CREATING A RESEARCH PROPOSAL FOR THIS CLASS)

You are researching the impact of your city's recent harm reduction interventions for intravenous drug users (e.g., sterile injection kits, monitored use, overdose prevention, naloxone provision, etc.).

• Thinking about the services related to this issue in your own city, identify any gatekeepers and stakeholders you might need to contact.
• How might you approach these gatekeepers and stakeholders? How would you explain your study?

2.2 Raw data

• Identify potential sources of available data
• Weigh the challenges and benefits of collecting your own data

In our previous section, we addressed some of the challenges researchers face in collecting and analyzing raw data. Just as a reminder, raw data are unprocessed, unanalyzed data that researchers analyze using social science research methods. It is not just the statistics or qualitative themes in journal articles. It is the actual data from which those statistical outputs or themes are derived (e.g., interview transcripts or survey responses).

There are two approaches to getting raw data. First, students can analyze data that are publicly available or from agency records. Using secondary data like this can make projects more feasible, but you may not find existing data that are useful for answering your working question. For that reason, many students gather their own raw data. As we discussed in the previous section, potential harms that come from addressing sensitive topics mean that surveys and interviews of practitioners or other less-vulnerable populations may be the most feasible and ethical way to approach data collection.

Using secondary data

Within the agency setting, there are two main sources of raw data. One option is to examine client charts. For example, if you wanted to know if substance use was related to parental reunification for youth in foster care, you could look at client files and compare how long it took for families with differing levels of substance use to be reunified. You will have to negotiate with the agency the degree to which your analysis can be public. Agencies may be okay with you using client files for a class project but less comfortable with you presenting your findings at a city council meeting. When analyzing data from your agency, you will have to manage a stakeholder relationship.

Another great example of agency-based raw data comes from program evaluations. If you are working with a grant funded agency, administrators and clinicians are likely producing data for grant reporting. The agency may consent to have you look at the raw data and run your own analysis. Larger agencies may also conduct internal research—for example, surveying employees or clients about new initiatives. These, too, can be good sources of available data. Generally, if the agency has already collected the data, you can ask to use them. Again, it is important to be clear on the boundaries and expectations of the agency. And don't be angry if they say no!

Some agencies, usually government agencies, publish their data in formal reports. You could take a look at some of the websites for county or state agencies to see if there are any publicly available data relevant to your research topic. As an example, perhaps there are annual reports from the state department of education that show how seclusion and restraint is disproportionately applied to Black children with disabilities , as students found in Virginia. In another example, one student matched public data from their city's map of criminal incidents with historically redlined neighborhoods. For this project, she is using publicly available data from Mapping Inequality , which digitized historical records of redlined housing communities and the Roanoke, VA crime mapping webpage . By matching historical data on housing redlining with current crime records, she is testing whether redlining still impacts crime to this day.

Not all public data are easily accessible, though. The student in the previous example was lucky that scholars had digitized the records of how Virginia cities were redlined by race. Sources of historical data are often located in physical archives, rather than digital archives. If your project uses historical data in an archive, it would require you to physically go to the archive in order to review the data. Unless you have a travel budget, you may be limited to the archival data in your local libraries and government offices. Similarly, government data may have to be requested from an agency, which can take time. If the data are particularly sensitive or if the department would have to dedicate a lot of time to your request, you may have to file a Freedom of Information Act request. This process can be time-consuming, and in some cases, it will add financial cost to your study.

Another source of secondary data is shared by researchers as part of the publication and review process. There is a growing trend in research to publicly share data so others can verify your results and attempt to replicate your study. In more recent articles, you may notice links to data provided by the researcher. Often, these have been de-identified by eliminating some information that could lead to violations of confidentiality. You can browse through the data repositories in Table 2.1 to find raw data to analyze. Make sure that you pick a data set with thorough and easy to understand documentation. You may also want to use Google's dataset search which indexes some of the websites below as well as others in a very intuitive and easy to use way.

Ultimately, you will have to weigh the strengths and limitations of using secondary data on your own. Engel and Schutt (2016, p. 327) [1] propose six questions to ask before using secondary data:

• What were the agency’s or researcher’s goals in collecting the data?
• What data were collected, and what were they intended to measure?
• When was the information collected?
• What methods were used for data collection? Who was responsible for data collection, and what were their qualifications? Are they available to answer questions about the data?
• How is the information organized (by date, individual, family, event, etc.)? Are identifiers used to indicate different types of data available?
• What is known about the success of the data collection effort? How are missing data indicated and treated? What kind of documentation is available? How consistent are the data with data available from other sources?

In this section, we've talked about data as though it is always collected by scientists and professionals. But that's definitely not the case! Think more broadly about sources of data that are already out there in the world. Perhaps you want to examine the different topics mentioned in the past 10 State of the Union addresses by the President. Or maybe you want to examine whether the websites and public information about local health and mental health agencies use gender-inclusive language. People share their experiences through blogs, social media posts, videos, performances, among countless other sources of data. When you think broadly about data, you'll be surprised how much you can answer with available data.

Collecting your own raw data

The primary benefit of collecting your own data is that it allows you to collect and analyze the specific data you are looking for, rather than relying on what other people have shared. You can make sure the right questions are asked to the right people. Your early research projects may be smaller in scope. This isn't necessarily a limitation. Early projects are often the first step in a long research trajectory in which the same topic is studied in increasing detail and sophistication over time.

Student researchers often propose to survey or interview practitioners. The focus of these projects should be about the practice of social work and the study will uncover how practitioners understand what they do. Surveys of practitioners often test whether responses to questions are related to each other. For example, you could propose to examine whether someone's length of time in practice was related to the type of therapy they use or their level of burnout. Interviews or focus groups can also illuminate areas of practice. One student proposed to conduct focus groups of individuals in different helping professions in order to understand how they viewed the process of leaving an abusive partner. She suspected that people from different disciplines would make unique assumptions about the survivor's choices.

It's worth remembering here that you need to have access to practitioners, as we discussed in the previous section. Resourceful researchers will look at publicly available databases of practitioners, draw from agency and personal contacts, or post in public forums like Facebook groups. Consent from gatekeepers is important, and as we described earlier, you and your agency may be interested in collaborating on a project. Bringing your agency on board as a stakeholder in your project may allow you access to company email lists or time at staff meetings as well as access to practitioners. One student partnered with her internship placement at a local hospital to measure the burnout that nurses experienced in their department. Her project helped the agency identify which departments may need additional support.

Another possible way you could collect data is by partnering with your agency on evaluating an existing program. Perhaps they want you to evaluate the early stage of a program to see if it's going as planned and if any changes need to be made. Maybe there is an aspect of the program they haven't measured but would like to, and you can fill that gap for them. Collaborating with agency partners in this way can be a challenge, as you must negotiate roles, get stakeholder buy-in, and manage the conflicting time schedules of field work and research work. At the same time, it allows you to make your work immediately relevant to your specific practice and client population.

In summary, many early projects fall into one of the following categories. These aren't your only options! But they may be helpful in thinking about what research projects can look like.

• Analyzing charts or program evaluations at an agency
• Analyzing existing data from an agency, government body, or other public source
• Analyzing popular media or cultural artifacts
• Surveying or interviewing practitioners, administrators, or other less-vulnerable groups
• Conducting a program evaluation in collaboration with an agency
• All research projects require analyzing raw data.
• Research projects often analyze available data from agencies, government, or public sources. Doing so allows researchers to avoid the process of recruiting people to participate in their study. This makes projects more feasible but limits what you can study to the data that are already available to you.
• Think through the potential harm of discussing sensitive topics when surveying or interviewing clients and other vulnerable populations. Since many social work topics are sensitive, researchers often collect data from less-vulnerable populations such as practitioners and administrators.

Post-awareness check (Environment)

In what environment are you most comfortable in data collection (phone calls, face to face recruitment, etc)? Consider your preferred method of data collection that may align with both your personality and your target population.

• Describe the difference between raw data and the results of research articles.
• Consider browsing around the data repositories in Table 2.1.
• Identify a common type of project (e.g., surveys of practitioners) and how conducting a similar project might help you answer your working question.
• What kind of raw data might you collect yourself for your study?

2.3 Creating a data analysis plan

• Define and construct a data analysis plan.
• Define key quantitative data management terms—variable name, data dictionary, primary and secondary data, observations/cases.
• Differentiate between univariate and bivariate quantitative analysis.
• Explain when we might use quantitative bivariate analysis in social work research.
• Identify how your qualitative research question, research aim, and type of data may influence your choice of analytic methods.
• Outline the steps you will take in preparation for conducting qualitative data analysis.

After you have your raw data , whether this is secondary data or data you collected yourself, you will need to analyze it. While the specific steps to follow in quantitative or qualitative data analysis are beyond the scope of this chapter, we are going to address some basic concepts in this section to help you create a data analysis plan. A data analysis plan is an ordered outline that includes your research question, a description of the data you are going to use to answer it, and the exact step-by-step analyses that you plan to run to answer your research question. If you look back at Table 2.1, you will see that creating a data analysis plan is a part of the study design process. The data analysis plan flows from the research question, is integral to the study desig n, and should be well conceptualized prior to beginning data collection. In this section, we will walk through the basics of quantitative and qualitative data analysis to help you understand the fundamentals of creating a data analysis plan.

When considering what data you might want to collect as part of your project, there are two important considerations that can create dilemmas for researchers. You might only get one chance to interact with your participants, so you must think comprehensively in your planning phase about what information you need and collect as much relevant data as possible. At the same time, though, especially when collecting sensitive information, you need to consider how onerous the data collection is for participants and whether you really need them to share that information. Just because something is interesting to us doesn't mean it's related enough to our research question to chase it down. Work with your research team and/or faculty early in your project to talk through these issues before you get to this point. And if you're using secondary data, make sure you have access to all the information you need in that data before you use it.

Once you've collected your quantitative data, you need to make sure it is well- organized in a database in a way that's actually usable. "Database" can be kind of a scary word, but really, it can be as simple as an Excel spreadsheet or a data file in whatever program you're using to analyze your data.  You may want to avoid Excel and use a formal database such as Microsoft Access or MySQL if you've got a large or complicated data set. But if your data set is smaller and you plan to keep your analyses simple, you can definitely get away with Excel. A typical data set is organized with variables as columns and observations/cases as rows. For example, let's say we did a survey on ice cream preferences and collected the following information in Table 2.3:

• Variable name : Just what it sounds like—the name of your variable. Make sure this is something useful, short and, if you're using something other than Excel, all one word. Most statistical programs will automatically rename variables for you if they aren't one word, but the names can be a little ridiculous and long.
• Observations/cases : The rows in your data set. In social work, these are often your study participants (people), but can be anything from census tracts to black bears to trains. When we talk about sample size, we're talking about the number of observations/cases. In our mini data set, each person is an observation/case.
• Data dictionary (sometimes called a code book or metadata) : This is the document where you list your variable names, what the variables actually measure or represent, what each of the values of the variable mean if the meaning isn't obvious (i.e., if there are numbers assigned to gender), the level of measurement and anything special to know about the variables (for instance, the source if you mashed two data sets together). If you're using secondary data, the researchers sharing the data should make the data dictionary available .

Let's take that mini data set we've got up above and we'll show you what your data dictionary might look like in Table 2.4.

As part of planning for your research, you should come up with a data analysis plan. Remember, a data analysis plan is an ordered outline that includes your research question, a description of the data you are going to use to answer it, and the exact step-by-step analyses that you plan to run to answer your research question. A basic data analysis plan might look something like what you see in Table 2.5. Don't panic if you don't yet understand some of the statistical terms in the plan; we're going to delve into some of them in this section, and others will be covered in more depth in your statistics courses. Note here also that this is what operationalizing your variables and moving through your research with them looks like on a basic level. We will cover operationalization in more depth in Chapter 11.

An important point to remember is that you should never get stuck on using a particular statistical method because you or one of your co-researchers thinks it's cool or it's the hot thing in your field right now. You should certainly go into your data analysis plan with ideas, but in the end, you need to let your research question guide what statistical tests you plan to use. Be prepared to be flexible if your plan doesn't pan out because the data is behaving in unexpected ways.

You'll notice that the first step in the quantitative data analysis plan is univariate and descriptive statistics.   Univariate data analysis is a quantitative method in which a variable is examined individually to determine its distribution , or the way the scores are distributed across the levels, or values, of that variable. When we talk about levels ,  what we are talking about are the possible values of the variable—like a participant's age, income or gender. (Note that this is different from levels of measurement , which will be discussed in Chapter 11, but the level of measurement of your variables absolutely affects what kinds of analyses you can do with it.) Univariate analysis is n on-relational , which just means that we're not looking into how our variables relate to each other. Instead, we're looking at variables in isolation to try to understand them better. For this reason, univariate analysis is used for descriptive research questions.

So when do you use univariate data analysis? Always! It should be the first thing you do with your quantitative data, whether you are planning to move on to more sophisticated statistical analyses or are conducting a study to describe a new phenomenon. You need to understand what the values of each variable look like—what if one of your variables has a lot of missing data because participants didn't answer that question on your survey? What if there isn't much variation in the gender of your sample? These are things you'll learn through univariate analysis.

Did you know that ice cream causes shark attacks? It's true! When ice cream sales go up in the summer, so does the rate of shark attacks. So you'd better put down that ice cream cone, unless you want to make yourself look more delicious to a shark.

Ok, so it's quite obviously not true that ice cream causes shark attacks. But if you looked at these two variables and how they're related, you'd notice that during times of the year with high ice cream sales, there are also the most shark attacks. Despite the fact that the conclusion we drew about the relationship was wrong, it's nonetheless true that these two variables appear related, and researchers figured that out through the use of bivariate analysis. (You will learn about correlation versus causation in  Chapter 8 .)

Bivariate analysis consists of a group of statistical techniques that examine the association between two variables. We could look at how anti-depressant medications and appetite are related, whether there is a relation between having a pet and emotional well-being, or if a policy-maker's level of education is related to how they vote on bills related to environmental issues.

Bivariate analysis forms the foundation of multivariate analysis, which we don't get to in this book. All you really need to know here is that there are steps beyond bivariate analysis, which you've undoubtedly seen in scholarly literature already! But before we can move forward with multivariate analysis, we need to understand the associations between the variables in our study .

[MADE THIS UP] Throughout your PhD program, you will learn more about quantitative data analysis techniques. Hopefully this section has provided you with some initial insights into how data is analyzed, and the importance of creating a data analysis plan prior to collecting data. Next, we will discuss some basic strategies for creating a qualitative data analysis plan.

If you don't see the general aim of your research question reflected in one of these areas, don't fret! This is only a small sampling of what you might be trying to accomplish with your qualitative study. Whatever your aim, you need to have a plan for what you will do once you have collected your data.

Iterative or linear

Some qualitative research is linear , meaning it follows more of a tra ditionally quantitative process: create a plan, gather data, and analyze data; each step is completed before we proceed to the next. You can think of this like how information is presented in this book. We discuss each topic, one after another. 

However, many times qualitative research is iterative , or evolving in cycles. An iterative approach means that once we begin collecting data, we also begin analyzing data as it is coming in. This early and ongoing analysis of our (incomplete) data then impacts our continued planning, data gathering and future analysis. Again, coming back to this book, while it may be written linear, we hope that you engage with it iteratively as you design and conduct your own research. By this we mean that you will revisit previous sections so you can understand how they fit together and you are in continuous process of building and revising how you think about the concepts you are learning about. 

As you may have guessed, there are benefits and challenges to both linear and iterative approaches. A linear approach is much more straightforward, each step being fairly defined. However, linear research being more defined and rigid also presents certain challenges. A linear approach assumes that we know what we need to ask or look for at the very beginning of data collection, which often is not the case.

With iterative research, we have more flexibility to adapt our approach as we learn new things. We still need to keep our approach systematic and organized, however, so that our work doesn't become a free-for-all. As we adapt, we do not want to stray too far from the original premise of our study. It's also important to remember with an iterative approach that we may risk ethical concerns if our work extends beyond the original boundaries of our informed consent and institutional review board agreement (IRB; see Chapter 6 for more on IRBs). If you feel that you do need to modify your original research plan in a significant way as you learn more about the topic, you can submit an addendum to modify your original application that was submitted. Make sure to keep detailed notes of the decisions that you are making and what is informing these choices. This helps to support transparency and your credibility throughout the research process.

As y ou begin your analysis, y ou need to get to know your data. This often  means reading through your data prior to any attempt at breaking it apart and labeling it. You mig ht read through a couple of times, in fact. This helps give you a more comprehensive feel for each piece of data and the data as a whole, again, before you start to break it down into smaller units or deconstruct it. This is especially important if others assisted us in the data collection process. We often gather data as part of team and everyone involved in the analysis needs to be very familiar with all of the data. 

During your reviewing you will start to develop and evolve your understanding of what the data means. Coding is a part of the qualitative data analysis process where we begin to interpret and assign meaning to the data. It represents one of the first steps as we begin to filter the data through our own subjective lens as the researcher. This understanding of the data should be dynamic and flexible, but you want to have a way to capture this understanding as it evolves. You may include this as part of your qualitative codebook where you are tracking the main ideas that are emerging and what they mean. Figure 2.2 is an example of how your thinking might change about a code and how you can go about capturing it. 

There are a variety of different approaches to qualitative analysis, including thematic analysis, content analysis, grounded theory, phenomenology, photovoice, and more. The specific steps you will take to code your qualitative data, and to generate themes from these codes, will vary based on the analytic strategy you are employing. In designing your qualitative study, you would identify an analytical approach as you plan out your project. The one you select would depend on the type of data you have and what you want to accomplish with it.

• Getting organized at the beginning of your project with a data analysis plan will help keep you on track. Data analysis plans should include your research question, a description of your data, and a step-by-step outline of what you're going to do with it. [chapter 14.1]

Exercises [from chapter 14.1]

• Make a data analysis plan for your project. Remember this should include your research question, a description of the data you will use, and a step-by-step outline of what you're going to do with your data once you have it, including statistical tests (non-relational and relational) that you plan to use. You can do this exercise whether you're using quantitative or qualitative data! The same principles apply.
• Make a draft quantitative data analysis plan for your project. Remember this should include your research question, a description of the data you will use, and a step-by-step outline of what you're going to do with your data once you have it, including statistical tests (non-relational and relational) that you plan to use. It's okay if you don't yet have a complete idea of the types of statistical analyses you might use.

2.4 Critical considerations

• Critique the traditional role of researchers and identify how action research addresses these issues

So far in this chapter, we have presented the steps of research projects as follows:

• Find a topic that is important to you and read about it.
• Pose a question that is important to the literature and to your community.
• Propose to use specific research methods and data analysis techniques to answer your question.
• Carry out your project and report the results.

These were depicted in more detail in Table 2.1 earlier in this chapter. There are important limitations to this approach. This section examines those problems and how to address them.

Whose knowledge is privileged?

First, let's critically examine your role as the researcher. Following along with the steps in a research project, you start studying the literature on your topic, find a place where you can add to scientific knowledge, and conduct your study. But why are you the person who gets to decide what is important? Just as clients are the experts on their lives, members of your target population are the experts on their lives. What does it mean for a group of people to be researched on, rather than researched with? How can we better respect the knowledge and self-determination of community members?

A different way of approaching your research project is to start by talking with members of the target population and those who are knowledgeable about that community. Perhaps there is a community-led organization you can partner with on a research project. The researcher's role in this case would be more similar to a consultant, someone with specialized knowledge about research who can help communities study problems they consider to be important. The social worker is a co-investigator, and community members are equal partners in the research project. Each has a type of knowledge—scientific expertise vs. lived experience—that should inform the research process.

The community focus highlights something important: they are localized. These projects can dedicate themselves to issues at a single agency or within a service area. With a local scope, researchers can bring about change in their community. This is the purpose behind action research.

Action research

Action research   is research that is conducted for the purpose of creating social change. When engaging in action research, scholars collaborate with community stakeholders to conduct research that will be relevant to the community. Social workers who engage in action research don't just go it alone; instead, they collaborate with the people who are affected by the research at each stage in the process. Stakeholders, particularly those with the least power, should be consulted on the purpose of the research project, research questions, design, and reporting of results.

Action research also distinguishes itself from other research in that its purpose is to create change on an individual and community level. Kristin Esterberg puts it quite eloquently when she says, “At heart, all action researchers are concerned that research not simply contribute to knowledge but also lead to positive changes in people’s lives” (2002, p. 137). [2] Action research has multiple origins across the globe, including Kurt Lewin’s psychological experiments in the US and Paulo Friere’s literacy and education programs (Adelman, 1993; Reason, 1994). [3] Over the years, action research has become increasingly popular among scholars who wish for their work to have tangible outcomes that benefit the groups they study.

A traditional scientist might look at the literature or use their practice wisdom to formulate a question for quantitative or qualitative research, as we suggested earlier in this chapter. An action researcher, on the other hand, would consult with people in the target population and community to see what they believe the most pressing issues are and what their proposed solutions may be. In this way, action research flips traditional research on its head. Scientists are not the experts on the research topic. Instead, they are more like consultants who provide the tools and resources necessary for a target population to achieve their goals and to address social problems using social science research.

According to Healy (2001), [4] the assumptions of participatory-action research are that (a) oppression is caused by macro-level structures such as patriarchy and capitalism; (b) research should expose and confront the powerful; (c) researcher and participant relationships should be equal, with equitable distribution of research tasks and roles; and (d) research should result in consciousness-raising and collective action. Consistent with social work values, action research supports the self-determination of oppressed groups and privileges their voice and understanding through the conceptualization, design, data collection, data analysis, and dissemination processes of research. We will return to similar ideas in Part 4 of the textbook when we discuss qualitative research methods, though action research can certainly be used with quantitative research methods, as well.

• Traditionally, researchers did not consult target populations and communities prior to formulating a research question. Action research proposes a more community-engaged model in which researchers are consultants that help communities research topics of import to them.

Post- awareness check (Knowledge)

Based on what you know of your target population, what are a few ways to receive their “buy-in” to participate in your proposed research study?

• Apply the key concepts of action research to your project. How might you incorporate the perspectives and expertise of community members in your project?

The level that describes how data for variables are recorded. The level of measurement defines the type of operations can be conducted with your data. There are four levels: nominal, ordinal, interval, and ratio.

Referring to data analysis that doesn't examine how variables relate to each other.

a group of statistical techniques that examines the relationship between two variables

A research process where you create a plan, you gather your data, you analyze your data and each step is completed before you proceed to the next.

An iterative approach means that after planning and once we begin collecting data, we begin analyzing as data as it is coming in.  This early analysis of our (incomplete) data, then impacts our planning, ongoing data gathering and future analysis as it progresses.

Part of the qualitative data analysis process where we begin to interpret and assign meaning to the data.

A document that we use to keep track of and define the codes that we have identified (or are using) in our qualitative data analysis.

Doctoral Research Methods in Social Work Copyright © by Mavs Open Press. All Rights Reserved.

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Data Analysis in Research: Types & Methods

Content Index

Why analyze data in research?

Types of data in research, finding patterns in the qualitative data, methods used for data analysis in qualitative research, preparing data for analysis, methods used for data analysis in quantitative research, considerations in research data analysis, what is data analysis in research.

Definition of research in data analysis: According to LeCompte and Schensul, research data analysis is a process used by researchers to reduce data to a story and interpret it to derive insights. The data analysis process helps reduce a large chunk of data into smaller fragments, which makes sense. 

Three essential things occur during the data analysis process — the first is data organization . Summarization and categorization together contribute to becoming the second known method used for data reduction. It helps find patterns and themes in the data for easy identification and linking. The third and last way is data analysis – researchers do it in both top-down and bottom-up fashion.

LEARN ABOUT: Research Process Steps

On the other hand, Marshall and Rossman describe data analysis as a messy, ambiguous, and time-consuming but creative and fascinating process through which a mass of collected data is brought to order, structure and meaning.

We can say that “the data analysis and data interpretation is a process representing the application of deductive and inductive logic to the research and data analysis.”

Researchers rely heavily on data as they have a story to tell or research problems to solve. It starts with a question, and data is nothing but an answer to that question. But, what if there is no question to ask? Well! It is possible to explore data even without a problem – we call it ‘Data Mining’, which often reveals some interesting patterns within the data that are worth exploring.

Irrelevant to the type of data researchers explore, their mission and audiences’ vision guide them to find the patterns to shape the story they want to tell. One of the essential things expected from researchers while analyzing data is to stay open and remain unbiased toward unexpected patterns, expressions, and results. Remember, sometimes, data analysis tells the most unforeseen yet exciting stories that were not expected when initiating data analysis. Therefore, rely on the data you have at hand and enjoy the journey of exploratory research. 

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Every kind of data has a rare quality of describing things after assigning a specific value to it. For analysis, you need to organize these values, processed and presented in a given context, to make it useful. Data can be in different forms; here are the primary data types.

• Qualitative data: When the data presented has words and descriptions, then we call it qualitative data . Although you can observe this data, it is subjective and harder to analyze data in research, especially for comparison. Example: Quality data represents everything describing taste, experience, texture, or an opinion that is considered quality data. This type of data is usually collected through focus groups, personal qualitative interviews , qualitative observation or using open-ended questions in surveys.
• Quantitative data: Any data expressed in numbers of numerical figures are called quantitative data . This type of data can be distinguished into categories, grouped, measured, calculated, or ranked. Example: questions such as age, rank, cost, length, weight, scores, etc. everything comes under this type of data. You can present such data in graphical format, charts, or apply statistical analysis methods to this data. The (Outcomes Measurement Systems) OMS questionnaires in surveys are a significant source of collecting numeric data.
• Categorical data: It is data presented in groups. However, an item included in the categorical data cannot belong to more than one group. Example: A person responding to a survey by telling his living style, marital status, smoking habit, or drinking habit comes under the categorical data. A chi-square test is a standard method used to analyze this data.

Learn More : Examples of Qualitative Data in Education

Data analysis in qualitative research

Data analysis and qualitative data research work a little differently from the numerical data as the quality data is made up of words, descriptions, images, objects, and sometimes symbols. Getting insight from such complicated information is a complicated process. Hence it is typically used for exploratory research and data analysis .

Although there are several ways to find patterns in the textual information, a word-based method is the most relied and widely used global technique for research and data analysis. Notably, the data analysis process in qualitative research is manual. Here the researchers usually read the available data and find repetitive or commonly used words. 

For example, while studying data collected from African countries to understand the most pressing issues people face, researchers might find  “food”  and  “hunger” are the most commonly used words and will highlight them for further analysis.

LEARN ABOUT: Level of Analysis

The keyword context is another widely used word-based technique. In this method, the researcher tries to understand the concept by analyzing the context in which the participants use a particular keyword.  

For example , researchers conducting research and data analysis for studying the concept of ‘diabetes’ amongst respondents might analyze the context of when and how the respondent has used or referred to the word ‘diabetes.’

The scrutiny-based technique is also one of the highly recommended  text analysis  methods used to identify a quality data pattern. Compare and contrast is the widely used method under this technique to differentiate how a specific text is similar or different from each other. 

For example: To find out the “importance of resident doctor in a company,” the collected data is divided into people who think it is necessary to hire a resident doctor and those who think it is unnecessary. Compare and contrast is the best method that can be used to analyze the polls having single-answer questions types .

Metaphors can be used to reduce the data pile and find patterns in it so that it becomes easier to connect data with theory.

Variable Partitioning is another technique used to split variables so that researchers can find more coherent descriptions and explanations from the enormous data.

LEARN ABOUT: Qualitative Research Questions and Questionnaires

There are several techniques to analyze the data in qualitative research, but here are some commonly used methods,

• Content Analysis:  It is widely accepted and the most frequently employed technique for data analysis in research methodology. It can be used to analyze the documented information from text, images, and sometimes from the physical items. It depends on the research questions to predict when and where to use this method.
• Narrative Analysis: This method is used to analyze content gathered from various sources such as personal interviews, field observation, and  surveys . The majority of times, stories, or opinions shared by people are focused on finding answers to the research questions.
• Discourse Analysis:  Similar to narrative analysis, discourse analysis is used to analyze the interactions with people. Nevertheless, this particular method considers the social context under which or within which the communication between the researcher and respondent takes place. In addition to that, discourse analysis also focuses on the lifestyle and day-to-day environment while deriving any conclusion.
• Grounded Theory:  When you want to explain why a particular phenomenon happened, then using grounded theory for analyzing quality data is the best resort. Grounded theory is applied to study data about the host of similar cases occurring in different settings. When researchers are using this method, they might alter explanations or produce new ones until they arrive at some conclusion.

LEARN ABOUT: 12 Best Tools for Researchers

Data analysis in quantitative research

The first stage in research and data analysis is to make it for the analysis so that the nominal data can be converted into something meaningful. Data preparation consists of the below phases.

Phase I: Data Validation

Data validation is done to understand if the collected data sample is per the pre-set standards, or it is a biased data sample again divided into four different stages

• Fraud: To ensure an actual human being records each response to the survey or the questionnaire
• Screening: To make sure each participant or respondent is selected or chosen in compliance with the research criteria
• Procedure: To ensure ethical standards were maintained while collecting the data sample
• Completeness: To ensure that the respondent has answered all the questions in an online survey. Else, the interviewer had asked all the questions devised in the questionnaire.

Phase II: Data Editing

More often, an extensive research data sample comes loaded with errors. Respondents sometimes fill in some fields incorrectly or sometimes skip them accidentally. Data editing is a process wherein the researchers have to confirm that the provided data is free of such errors. They need to conduct necessary checks and outlier checks to edit the raw edit and make it ready for analysis.

Phase III: Data Coding

Out of all three, this is the most critical phase of data preparation associated with grouping and assigning values to the survey responses . If a survey is completed with a 1000 sample size, the researcher will create an age bracket to distinguish the respondents based on their age. Thus, it becomes easier to analyze small data buckets rather than deal with the massive data pile.

LEARN ABOUT: Steps in Qualitative Research

After the data is prepared for analysis, researchers are open to using different research and data analysis methods to derive meaningful insights. For sure, statistical analysis plans are the most favored to analyze numerical data. In statistical analysis, distinguishing between categorical data and numerical data is essential, as categorical data involves distinct categories or labels, while numerical data consists of measurable quantities. The method is again classified into two groups. First, ‘Descriptive Statistics’ used to describe data. Second, ‘Inferential statistics’ that helps in comparing the data .

Descriptive statistics

This method is used to describe the basic features of versatile types of data in research. It presents the data in such a meaningful way that pattern in the data starts making sense. Nevertheless, the descriptive analysis does not go beyond making conclusions. The conclusions are again based on the hypothesis researchers have formulated so far. Here are a few major types of descriptive analysis methods.

Measures of Frequency

• Count, Percent, Frequency
• It is used to denote home often a particular event occurs.
• Researchers use it when they want to showcase how often a response is given.

Measures of Central Tendency

• Mean, Median, Mode
• The method is widely used to demonstrate distribution by various points.
• Researchers use this method when they want to showcase the most commonly or averagely indicated response.

Measures of Dispersion or Variation

• Range, Variance, Standard deviation
• Here the field equals high/low points.
• Variance standard deviation = difference between the observed score and mean
• It is used to identify the spread of scores by stating intervals.
• Researchers use this method to showcase data spread out. It helps them identify the depth until which the data is spread out that it directly affects the mean.

Measures of Position

• Percentile ranks, Quartile ranks
• It relies on standardized scores helping researchers to identify the relationship between different scores.
• It is often used when researchers want to compare scores with the average count.

For quantitative research use of descriptive analysis often give absolute numbers, but the in-depth analysis is never sufficient to demonstrate the rationale behind those numbers. Nevertheless, it is necessary to think of the best method for research and data analysis suiting your survey questionnaire and what story researchers want to tell. For example, the mean is the best way to demonstrate the students’ average scores in schools. It is better to rely on the descriptive statistics when the researchers intend to keep the research or outcome limited to the provided  sample  without generalizing it. For example, when you want to compare average voting done in two different cities, differential statistics are enough.

Descriptive analysis is also called a ‘univariate analysis’ since it is commonly used to analyze a single variable.

Inferential statistics

Inferential statistics are used to make predictions about a larger population after research and data analysis of the representing population’s collected sample. For example, you can ask some odd 100 audiences at a movie theater if they like the movie they are watching. Researchers then use inferential statistics on the collected  sample  to reason that about 80-90% of people like the movie. 

Here are two significant areas of inferential statistics.

• Estimating parameters: It takes statistics from the sample research data and demonstrates something about the population parameter.
• Hypothesis test: I t’s about sampling research data to answer the survey research questions. For example, researchers might be interested to understand if the new shade of lipstick recently launched is good or not, or if the multivitamin capsules help children to perform better at games.

These are sophisticated analysis methods used to showcase the relationship between different variables instead of describing a single variable. It is often used when researchers want something beyond absolute numbers to understand the relationship between variables.

Here are some of the commonly used methods for data analysis in research.

• Correlation: When researchers are not conducting experimental research or quasi-experimental research wherein the researchers are interested to understand the relationship between two or more variables, they opt for correlational research methods.
• Cross-tabulation: Also called contingency tables,  cross-tabulation  is used to analyze the relationship between multiple variables.  Suppose provided data has age and gender categories presented in rows and columns. A two-dimensional cross-tabulation helps for seamless data analysis and research by showing the number of males and females in each age category.
• Regression analysis: For understanding the strong relationship between two variables, researchers do not look beyond the primary and commonly used regression analysis method, which is also a type of predictive analysis used. In this method, you have an essential factor called the dependent variable. You also have multiple independent variables in regression analysis. You undertake efforts to find out the impact of independent variables on the dependent variable. The values of both independent and dependent variables are assumed as being ascertained in an error-free random manner.
• Frequency tables: The statistical procedure is used for testing the degree to which two or more vary or differ in an experiment. A considerable degree of variation means research findings were significant. In many contexts, ANOVA testing and variance analysis are similar.
• Analysis of variance: The statistical procedure is used for testing the degree to which two or more vary or differ in an experiment. A considerable degree of variation means research findings were significant. In many contexts, ANOVA testing and variance analysis are similar.
• Researchers must have the necessary research skills to analyze and manipulation the data , Getting trained to demonstrate a high standard of research practice. Ideally, researchers must possess more than a basic understanding of the rationale of selecting one statistical method over the other to obtain better data insights.
• Usually, research and data analytics projects differ by scientific discipline; therefore, getting statistical advice at the beginning of analysis helps design a survey questionnaire, select data collection methods , and choose samples.

LEARN ABOUT: Best Data Collection Tools

• The primary aim of data research and analysis is to derive ultimate insights that are unbiased. Any mistake in or keeping a biased mind to collect data, selecting an analysis method, or choosing  audience  sample il to draw a biased inference.
• Irrelevant to the sophistication used in research data and analysis is enough to rectify the poorly defined objective outcome measurements. It does not matter if the design is at fault or intentions are not clear, but lack of clarity might mislead readers, so avoid the practice.
• The motive behind data analysis in research is to present accurate and reliable data. As far as possible, avoid statistical errors, and find a way to deal with everyday challenges like outliers, missing data, data altering, data mining , or developing graphical representation.

LEARN MORE: Descriptive Research vs Correlational Research The sheer amount of data generated daily is frightening. Especially when data analysis has taken center stage. in 2018. In last year, the total data supply amounted to 2.8 trillion gigabytes. Hence, it is clear that the enterprises willing to survive in the hypercompetitive world must possess an excellent capability to analyze complex research data, derive actionable insights, and adapt to the new market needs.

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How to write a research plan: Step-by-step guide

Last updated

30 January 2024

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Today’s businesses and institutions rely on data and analytics to inform their product and service decisions. These metrics influence how organizations stay competitive and inspire innovation. However, gathering data and insights requires carefully constructed research, and every research project needs a roadmap. This is where a research plan comes into play.

Read this step-by-step guide for writing a detailed research plan that can apply to any project, whether it’s scientific, educational, or business-related.

• What is a research plan?

A research plan is a documented overview of a project in its entirety, from end to end. It details the research efforts, participants, and methods needed, along with any anticipated results. It also outlines the project’s goals and mission, creating layers of steps to achieve those goals within a specified timeline.

Without a research plan, you and your team are flying blind, potentially wasting time and resources to pursue research without structured guidance.

The principal investigator, or PI, is responsible for facilitating the research oversight. They will create the research plan and inform team members and stakeholders of every detail relating to the project. The PI will also use the research plan to inform decision-making throughout the project.

• Why do you need a research plan?

Create a research plan before starting any official research to maximize every effort in pursuing and collecting the research data. Crucially, the plan will model the activities needed at each phase of the research project .

Like any roadmap, a research plan serves as a valuable tool providing direction for those involved in the project—both internally and externally. It will keep you and your immediate team organized and task-focused while also providing necessary definitions and timelines so you can execute your project initiatives with full understanding and transparency.

External stakeholders appreciate a working research plan because it’s a great communication tool, documenting progress and changing dynamics as they arise. Any participants of your planned research sessions will be informed about the purpose of your study, while the exercises will be based on the key messaging outlined in the official plan.

Here are some of the benefits of creating a research plan document for every project:

Project organization and structure

Well-informed participants

All stakeholders and teams align in support of the project

Clearly defined project definitions and purposes

Distractions are eliminated, prioritizing task focus

Timely management of individual task schedules and roles

Costly reworks are avoided

• What should a research plan include?

The different aspects of your research plan will depend on the nature of the project. However, most official research plan documents will include the core elements below. Each aims to define the problem statement , devising an official plan for seeking a solution.

Specific project goals and individual objectives

Ideal strategies or methods for reaching those goals

Required resources

Descriptions of the target audience, sample sizes , demographics, and scopes

Key performance indicators (KPIs)

Project background

Research and testing support

Preliminary studies and progress reporting mechanisms

Cost estimates and change order processes

Depending on the research project’s size and scope, your research plan could be brief—perhaps only a few pages of documented plans. Alternatively, it could be a fully comprehensive report. Either way, it’s an essential first step in dictating your project’s facilitation in the most efficient and effective way.

• How to write a research plan for your project

When you start writing your research plan, aim to be detailed about each step, requirement, and idea. The more time you spend curating your research plan, the more precise your research execution efforts will be.

Account for every potential scenario, and be sure to address each and every aspect of the research.

Consider following this flow to develop a great research plan for your project:

Define your project’s purpose

Start by defining your project’s purpose. Identify what your project aims to accomplish and what you are researching. Remember to use clear language.

Thinking about the project’s purpose will help you set realistic goals and inform how you divide tasks and assign responsibilities. These individual tasks will be your stepping stones to reach your overarching goal.

Additionally, you’ll want to identify the specific problem, the usability metrics needed, and the intended solutions.

Know the following three things about your project’s purpose before you outline anything else:

What you’re doing

Why you’re doing it

What you expect from it

Identify individual objectives

With your overarching project objectives in place, you can identify any individual goals or steps needed to reach those objectives. Break them down into phases or steps. You can work backward from the project goal and identify every process required to facilitate it.

Be mindful to identify each unique task so that you can assign responsibilities to various team members. At this point in your research plan development, you’ll also want to assign priority to those smaller, more manageable steps and phases that require more immediate or dedicated attention.

Select research methods

Once you have outlined your goals, objectives, steps, and tasks, it’s time to drill down on selecting research methods . You’ll want to leverage specific research strategies and processes. When you know what methods will help you reach your goals, you and your teams will have direction to perform and execute your assigned tasks.

Research methods might include any of the following:

User interviews : this is a qualitative research method where researchers engage with participants in one-on-one or group conversations. The aim is to gather insights into their experiences, preferences, and opinions to uncover patterns, trends, and data.

Field studies : this approach allows for a contextual understanding of behaviors, interactions, and processes in real-world settings. It involves the researcher immersing themselves in the field, conducting observations, interviews, or experiments to gather in-depth insights.

Card sorting : participants categorize information by sorting content cards into groups based on their perceived similarities. You might use this process to gain insights into participants’ mental models and preferences when navigating or organizing information on websites, apps, or other systems.

Focus groups : use organized discussions among select groups of participants to provide relevant views and experiences about a particular topic.

Diary studies : ask participants to record their experiences, thoughts, and activities in a diary over a specified period. This method provides a deeper understanding of user experiences, uncovers patterns, and identifies areas for improvement.

Five-second testing: participants are shown a design, such as a web page or interface, for just five seconds. They then answer questions about their initial impressions and recall, allowing you to evaluate the design’s effectiveness.

Surveys : get feedback from participant groups with structured surveys. You can use online forms, telephone interviews, or paper questionnaires to reveal trends, patterns, and correlations.

Tree testing : tree testing involves researching web assets through the lens of findability and navigability. Participants are given a textual representation of the site’s hierarchy (the “tree”) and asked to locate specific information or complete tasks by selecting paths.

Usability testing : ask participants to interact with a product, website, or application to evaluate its ease of use. This method enables you to uncover areas for improvement in digital key feature functionality by observing participants using the product.

Live website testing: research and collect analytics that outlines the design, usability, and performance efficiencies of a website in real time.

There are no limits to the number of research methods you could use within your project. Just make sure your research methods help you determine the following:

What do you plan to do with the research findings?

What decisions will this research inform? How can your stakeholders leverage the research data and results?

Recruit participants and allocate tasks

Next, identify the participants needed to complete the research and the resources required to complete the tasks. Different people will be proficient at different tasks, and having a task allocation plan will allow everything to run smoothly.

Prepare a thorough project summary

Every well-designed research plan will feature a project summary. This official summary will guide your research alongside its communications or messaging. You’ll use the summary while recruiting participants and during stakeholder meetings. It can also be useful when conducting field studies.

Ensure this summary includes all the elements of your research project . Separate the steps into an easily explainable piece of text that includes the following:

An introduction: the message you’ll deliver to participants about the interview, pre-planned questioning, and testing tasks.

Interview questions: prepare questions you intend to ask participants as part of your research study, guiding the sessions from start to finish.

An exit message: draft messaging your teams will use to conclude testing or survey sessions. These should include the next steps and express gratitude for the participant’s time.

Create a realistic timeline

While your project might already have a deadline or a results timeline in place, you’ll need to consider the time needed to execute it effectively.

Realistically outline the time needed to properly execute each supporting phase of research and implementation. And, as you evaluate the necessary schedules, be sure to include additional time for achieving each milestone in case any changes or unexpected delays arise.

For this part of your research plan, you might find it helpful to create visuals to ensure your research team and stakeholders fully understand the information.

Determine how to present your results

A research plan must also describe how you intend to present your results. Depending on the nature of your project and its goals, you might dedicate one team member (the PI) or assume responsibility for communicating the findings yourself.

In this part of the research plan, you’ll articulate how you’ll share the results. Detail any materials you’ll use, such as:

Presentations and slides

A project report booklet

A project findings pamphlet

Documents with key takeaways and statistics

Graphic visuals to support your findings

• Format your research plan

As you create your research plan, you can enjoy a little creative freedom. A plan can assume many forms, so format it how you see fit. Determine the best layout based on your specific project, intended communications, and the preferences of your teams and stakeholders.

Find format inspiration among the following layouts:

Written outlines

Narrative storytelling

Visual mapping

Graphic timelines

Remember, the research plan format you choose will be subject to change and adaptation as your research and findings unfold. However, your final format should ideally outline questions, problems, opportunities, and expectations.

• Research plan example

Imagine you’ve been tasked with finding out how to get more customers to order takeout from an online food delivery platform. The goal is to improve satisfaction and retain existing customers. You set out to discover why more people aren’t ordering and what it is they do want to order or experience. 

You identify the need for a research project that helps you understand what drives customer loyalty . But before you jump in and start calling past customers, you need to develop a research plan—the roadmap that provides focus, clarity, and realistic details to the project.

Here’s an example outline of a research plan you might put together:

Project title

Project members involved in the research plan

Purpose of the project (provide a summary of the research plan’s intent)

Objective 1 (provide a short description for each objective)

Objective 2

Objective 3

Proposed timeline

Audience (detail the group you want to research, such as customers or non-customers)

Budget (how much you think it might cost to do the research)

Risk factors/contingencies (any potential risk factors that may impact the project’s success)

Remember, your research plan doesn’t have to reinvent the wheel—it just needs to fit your project’s unique needs and aims.

Customizing a research plan template

Some companies offer research plan templates to help get you started. However, it may make more sense to develop your own customized plan template. Be sure to include the core elements of a great research plan with your template layout, including the following:

Introductions to participants and stakeholders

Background problems and needs statement

Significance, ethics, and purpose

Research methods, questions, and designs

Preliminary beliefs and expectations

Implications and intended outcomes

Realistic timelines for each phase

Conclusion and presentations

How many pages should a research plan be?

Generally, a research plan can vary in length between 500 to 1,500 words. This is roughly three pages of content. More substantial projects will be 2,000 to 3,500 words, taking up four to seven pages of planning documents.

What is the difference between a research plan and a research proposal?

A research plan is a roadmap to success for research teams. A research proposal, on the other hand, is a dissertation aimed at convincing or earning the support of others. Both are relevant in creating a guide to follow to complete a project goal.

What are the seven steps to developing a research plan?

While each research project is different, it’s best to follow these seven general steps to create your research plan:

Defining the problem

Identifying goals

Choosing research methods

Recruiting participants

Preparing the brief or summary

Establishing task timelines

Defining how you will present the findings

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Data analysis

• Initial data analysis
• Post-hoc & sensitivity analyses
• Data analysis documentation
• Handling missing data

To promote structured targeted data analysis.

Requirements

An analysis plan should be created and finalized prior to the data analyses.

Documentation

The analysis plan (Guidelines per study type are provided below)

Responsibilities

• Executing researcher: To create the analysis plan prior to the data analyses, containing a description of the research question and what the various steps in the analysis are going to be. This should also be signed and dated by the PI.
• Project leaders: To inform the executing researcher about setting up the analysis plan before analyses are undertaken.
• Research assistant: N.a.

An analysis plan should be created and finalized (signed and dated by PI) prior to the data analyses. The analysis plan contains a description of the research question and what the various steps in the analysis are going to be. It also contains an exploration of literature (what is already know? What will this study add?) to make sure your research question is relevant (see Glasziou et al. Lancet 2014 on avoiding research waste).The analysis plan is intended as a starting point for the analysis. It ensures that the analysis can be undertaken in a targeted manner, and promotes research integrity.

If you will perform an exploratory study you can adjust your analysis based on the data you find; this may be useful if not much is known about the research subject, but it is considered as relatively low level evidence and it should be clearly mentioned in your report that the presented study is exploratory. If you want to perform an hypothesis-testing study (be it interventional or using observational data) you need to pre-specify the analyses you intend to do prior to performing the analysis, including the population, subgroups, stratifications and statistical tests. If deviations from the analysis plan are made during the study this should be documented in the analysis plan and stated in the report (i.e. post-hoc tests). If you intend to do hypothesis-free research with multiple testing you should pre-specify your threshold for statistical significance according to the number of analyses you will perform. Lastly, if you intend to perform an RCT, the analysis plan is practically set in stone. (Also see ICH E9 - statistical principles for clinical trials )

If needed, an exploratory analysis may be part of the analysis plan, to inform the setting up of the final analysis (see initial data analysis ). For instance, you may want to know distributions of values in order to create meaningful categories, or determine whether data are normally distributed. The findings and decisions made during these preliminary exploratory analyses should be clearly documented, preferably in a version two of the analysis plan, and made reproducible by providing the data analysis syntax (in SPSS, SAS, STATA, R) (see guideline Documentation of data analysis ).

The concrete research question needs to be formulated firstly within the analysis plan following the literature review; this is the question intended to be answered by the analyses. Concrete research questions may be defined using the acronym PICO: Population, Intervention, Comparison, Outcomes. An example of a concrete question could be: “Does frequent bending at work lead to an elevated risk of lower back pain occurring in employees?” (Population = Employees; Intervention = Frequent bending; Comparison = Infrequent bending; Outcome = Occurrence of back pain). Concrete research questions are essential for determining the analyses required.

The analysis plan should then describe the primary and secondary outcomes, the determinants and data needed, and which statistical techniques are to be used to analyse the data. The following issues need to be considered in this process and described where applicable:

• In case of a trial: is the trial a superiority, non-inferiority or equivalence trial.
• Superiority: treatment A is better than the control.
• Non-inferiority: treatment A is not worse than treatment B.
• Equivalence: testing similarity using a tolerance range.

In other studies: what is the study design (case control, longitudinal cohort etc).

• Which (subgroup of the) population is to be included in the analyses? Which groups will you compare?;
• What are the primary and secondary endpoints? Which data from which endpoint (T1, T2, etc.) will be used?;
• Which (dependent and independent) variables are to be used in the analyses and how are the variables to be analysed (e.g. continuous or in categories)?;
• Which variables are to be investigated as potential confounders or effect modifiers (and why) and how are these variables to be analysed? There are different ways of dealing with confounders. We distinguish the following: 1) correct for all potential confounders (and do not concern about the question whether or not a variable is a ‘real’ confounder). Mostly, confounders are split up in little groups (demographic factors, clinical parameters, etc.). As a result you get corrected model 1, corrected model 2, etc. However, pay attention to collinearity and overcorrection if confounders coincide too much with primary determinants. 2) if the sample size is not big enough relative to the number of potential confounders,  you may consider to only correct for those confounders that are relevant for the association between determinant and outcome. To select the relevant confounders, mostly a forward selection procedure is performed. In this case the confounders are added to the model one by one (the confounder that is associated strongest first). Subsequently, consider to what extent the effect of the variable of interest is changed. Then first choose the strongest confounder in the model. Subsequently, repeat this procedure untill no confounder has a relevant effect (<10% change in regression coefficient). Alternatively, you can select the confounders that univariately change the point estimate of the association with >10%. 3) Another option is to set up a Directed Acyclic Graph (DAG), to determine which confounders should be added to the model. Please see http://www.dagitty.net/ for more information.
• How to deal with missing values? (see chapter on handeling missing data );
• Which analyses are to be carried out in which order (e.g. univariable analyses, multivariable analyses, analysis of confounders, analysis of interaction effects, analysis of sub-populations, etc.)?; Which sensitivity analyses will be performed?
• Do the data meet the criteria for the specific statistical technique?

A statistician may need to be consulted regarding the choice of statistical techniques (also see this intanetpage on statistical analysis plan ).

It is recommended to already design  the empty tables to be included in the article prior to the start of data analysis. This is often very helpful in deciding which analyses are exactly required in order to analyse the data in a targeted manner.

You may consider to make your study protocol including the (statistical) analysis plan public, either by placing in on a publicly accessible website (Concept Paper/Design paper) or by uploading it in an appropriate studies register (for human trials: NTR / EUDRACT / ClinicalTrials.gov , for non-/preclinicaltrials: preclinicaltrials.eu ).

Check the reporting guidelines when writing an analysis plan . These will help increase the quality of your research and guide you.

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• BMC Med Res Methodol

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

Lawrence mbuagbaw.

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

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

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

Daeria O. Lawson

Livia puljak.

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

David B. Allison

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

Lehana Thabane

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

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

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

Associated Data

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

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

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

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

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

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

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

epidemiological study” in PubMed.

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

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

What is a methodological study?

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

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

When should we conduct a methodological study?

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

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

How often are methodological studies conducted?

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

Why do we conduct methodological studies?

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

Where can we find methodological studies?

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

Some frequently asked questions about methodological studies

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

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

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

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

Q: How many databases should I search?

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

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

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

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

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

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

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

Q: Should I justify a sample size?

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

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

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

Q: What should I call my study?

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

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

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

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

Q: Should I extract data in duplicate?

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

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

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

Q: What variables are relevant to methodological studies?

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

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

Q: Should I focus only on high impact journals?

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

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

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

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

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

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

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

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

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

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

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

A proposed framework

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

• What is the aim?

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

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

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

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

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

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

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

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

• 2. What is the design?

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

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

• 3. What is the sampling strategy?

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

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

• 4. What is the unit of analysis?

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

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

This framework is outlined in Fig.  2 .

A proposed framework for methodological studies

Conclusions

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

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

Acknowledgements

Abbreviations.

Authors’ contributions

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

This work did not receive any dedicated funding.

Availability of data and materials

Ethics approval and consent to participate.

Not applicable.

Consent for publication

Competing interests.

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

Publisher’s Note

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

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Here's What You Need to Understand About Research Methodology

Table of Contents

Research methodology involves a systematic and well-structured approach to conducting scholarly or scientific inquiries. Knowing the significance of research methodology and its different components is crucial as it serves as the basis for any study.

Typically, your research topic will start as a broad idea you want to investigate more thoroughly. Once you’ve identified a research problem and created research questions , you must choose the appropriate methodology and frameworks to address those questions effectively.

What is the definition of a research methodology?

Research methodology is the process or the way you intend to execute your study. The methodology section of a research paper outlines how you plan to conduct your study. It covers various steps such as collecting data, statistical analysis, observing participants, and other procedures involved in the research process

The methods section should give a description of the process that will convert your idea into a study. Additionally, the outcomes of your process must provide valid and reliable results resonant with the aims and objectives of your research. This thumb rule holds complete validity, no matter whether your paper has inclinations for qualitative or quantitative usage.

Studying research methods used in related studies can provide helpful insights and direction for your own research. Now easily discover papers related to your topic on SciSpace and utilize our AI research assistant, Copilot , to quickly review the methodologies applied in different papers.

The need for a good research methodology

While deciding on your approach towards your research, the reason or factors you weighed in choosing a particular problem and formulating a research topic need to be validated and explained. A research methodology helps you do exactly that. Moreover, a good research methodology lets you build your argument to validate your research work performed through various data collection methods, analytical methods, and other essential points.

Just imagine it as a strategy documented to provide an overview of what you intend to do.

While undertaking any research writing or performing the research itself, you may get drifted in not something of much importance. In such a case, a research methodology helps you to get back to your outlined work methodology.

A research methodology helps in keeping you accountable for your work. Additionally, it can help you evaluate whether your work is in sync with your original aims and objectives or not. Besides, a good research methodology enables you to navigate your research process smoothly and swiftly while providing effective planning to achieve your desired results.

What is the basic structure of a research methodology?

Usually, you must ensure to include the following stated aspects while deciding over the basic structure of your research methodology:

1. Your research procedure

Explain what research methods you’re going to use. Whether you intend to proceed with quantitative or qualitative, or a composite of both approaches, you need to state that explicitly. The option among the three depends on your research’s aim, objectives, and scope.

2. Provide the rationality behind your chosen approach

Based on logic and reason, let your readers know why you have chosen said research methodologies. Additionally, you have to build strong arguments supporting why your chosen research method is the best way to achieve the desired outcome.

3. Explain your mechanism

The mechanism encompasses the research methods or instruments you will use to develop your research methodology. It usually refers to your data collection methods. You can use interviews, surveys, physical questionnaires, etc., of the many available mechanisms as research methodology instruments. The data collection method is determined by the type of research and whether the data is quantitative data(includes numerical data) or qualitative data (perception, morale, etc.) Moreover, you need to put logical reasoning behind choosing a particular instrument.

4. Significance of outcomes

The results will be available once you have finished experimenting. However, you should also explain how you plan to use the data to interpret the findings. This section also aids in understanding the problem from within, breaking it down into pieces, and viewing the research problem from various perspectives.

5. Reader’s advice

Anything that you feel must be explained to spread more awareness among readers and focus groups must be included and described in detail. You should not just specify your research methodology on the assumption that a reader is aware of the topic.  

All the relevant information that explains and simplifies your research paper must be included in the methodology section. If you are conducting your research in a non-traditional manner, give a logical justification and list its benefits.

6. Explain your sample space

Include information about the sample and sample space in the methodology section. The term "sample" refers to a smaller set of data that a researcher selects or chooses from a larger group of people or focus groups using a predetermined selection method. Let your readers know how you are going to distinguish between relevant and non-relevant samples. How you figured out those exact numbers to back your research methodology, i.e. the sample spacing of instruments, must be discussed thoroughly.

For example, if you are going to conduct a survey or interview, then by what procedure will you select the interviewees (or sample size in case of surveys), and how exactly will the interview or survey be conducted.

7. Challenges and limitations

This part, which is frequently assumed to be unnecessary, is actually very important. The challenges and limitations that your chosen strategy inherently possesses must be specified while you are conducting different types of research.

The importance of a good research methodology

You must have observed that all research papers, dissertations, or theses carry a chapter entirely dedicated to research methodology. This section helps maintain your credibility as a better interpreter of results rather than a manipulator.

A good research methodology always explains the procedure, data collection methods and techniques, aim, and scope of the research. In a research study, it leads to a well-organized, rationality-based approach, while the paper lacking it is often observed as messy or disorganized.

You should pay special attention to validating your chosen way towards the research methodology. This becomes extremely important in case you select an unconventional or a distinct method of execution.

Curating and developing a strong, effective research methodology can assist you in addressing a variety of situations, such as:

• When someone tries to duplicate or expand upon your research after few years.
• If a contradiction or conflict of facts occurs at a later time. This gives you the security you need to deal with these contradictions while still being able to defend your approach.
• Gaining a tactical approach in getting your research completed in time. Just ensure you are using the right approach while drafting your research methodology, and it can help you achieve your desired outcomes. Additionally, it provides a better explanation and understanding of the research question itself.
• Documenting the results so that the final outcome of the research stays as you intended it to be while starting.

Instruments you could use while writing a good research methodology

As a researcher, you must choose which tools or data collection methods that fit best in terms of the relevance of your research. This decision has to be wise.

There exists many research equipments or tools that you can use to carry out your research process. These are classified as:

a. Interviews (One-on-One or a Group)

An interview aimed to get your desired research outcomes can be undertaken in many different ways. For example, you can design your interview as structured, semi-structured, or unstructured. What sets them apart is the degree of formality in the questions. On the other hand, in a group interview, your aim should be to collect more opinions and group perceptions from the focus groups on a certain topic rather than looking out for some formal answers.

In surveys, you are in better control if you specifically draft the questions you seek the response for. For example, you may choose to include free-style questions that can be answered descriptively, or you may provide a multiple-choice type response for questions. Besides, you can also opt to choose both ways, deciding what suits your research process and purpose better.

c. Sample Groups

Similar to the group interviews, here, you can select a group of individuals and assign them a topic to discuss or freely express their opinions over that. You can simultaneously note down the answers and later draft them appropriately, deciding on the relevance of every response.

d. Observations

If your research domain is humanities or sociology, observations are the best-proven method to draw your research methodology. Of course, you can always include studying the spontaneous response of the participants towards a situation or conducting the same but in a more structured manner. A structured observation means putting the participants in a situation at a previously decided time and then studying their responses.

Of all the tools described above, it is you who should wisely choose the instruments and decide what’s the best fit for your research. You must not restrict yourself from multiple methods or a combination of a few instruments if appropriate in drafting a good research methodology.

Types of research methodology

A research methodology exists in various forms. Depending upon their approach, whether centered around words, numbers, or both, methodologies are distinguished as qualitative, quantitative, or an amalgamation of both.

1. Qualitative research methodology

When a research methodology primarily focuses on words and textual data, then it is generally referred to as qualitative research methodology. This type is usually preferred among researchers when the aim and scope of the research are mainly theoretical and explanatory.

The instruments used are observations, interviews, and sample groups. You can use this methodology if you are trying to study human behavior or response in some situations. Generally, qualitative research methodology is widely used in sociology, psychology, and other related domains.

2. Quantitative research methodology

If your research is majorly centered on data, figures, and stats, then analyzing these numerical data is often referred to as quantitative research methodology. You can use quantitative research methodology if your research requires you to validate or justify the obtained results.

In quantitative methods, surveys, tests, experiments, and evaluations of current databases can be advantageously used as instruments If your research involves testing some hypothesis, then use this methodology.

3. Amalgam methodology

As the name suggests, the amalgam methodology uses both quantitative and qualitative approaches. This methodology is used when a part of the research requires you to verify the facts and figures, whereas the other part demands you to discover the theoretical and explanatory nature of the research question.

The instruments for the amalgam methodology require you to conduct interviews and surveys, including tests and experiments. The outcome of this methodology can be insightful and valuable as it provides precise test results in line with theoretical explanations and reasoning.

The amalgam method, makes your work both factual and rational at the same time.

Final words: How to decide which is the best research methodology?

If you have kept your sincerity and awareness intact with the aims and scope of research well enough, you must have got an idea of which research methodology suits your work best.

Before deciding which research methodology answers your research question, you must invest significant time in reading and doing your homework for that. Taking references that yield relevant results should be your first approach to establishing a research methodology.

Moreover, you should never refrain from exploring other options. Before setting your work in stone, you must try all the available options as it explains why the choice of research methodology that you finally make is more appropriate than the other available options.

You should always go for a quantitative research methodology if your research requires gathering large amounts of data, figures, and statistics. This research methodology will provide you with results if your research paper involves the validation of some hypothesis.

Whereas, if  you are looking for more explanations, reasons, opinions, and public perceptions around a theory, you must use qualitative research methodology.The choice of an appropriate research methodology ultimately depends on what you want to achieve through your research.

Frequently Asked Questions (FAQs) about Research Methodology

1. how to write a research methodology.

You can always provide a separate section for research methodology where you should specify details about the methods and instruments used during the research, discussions on result analysis, including insights into the background information, and conveying the research limitations.

2. What are the types of research methodology?

There generally exists four types of research methodology i.e.

• Observation
• Experimental
• Derivational

3. What is the true meaning of research methodology?

The set of techniques or procedures followed to discover and analyze the information gathered to validate or justify a research outcome is generally called Research Methodology.

4. Where lies the importance of research methodology?

Your research methodology directly reflects the validity of your research outcomes and how well-informed your research work is. Moreover, it can help future researchers cite or refer to your research if they plan to use a similar research methodology.

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Data analysis plan

Data analysis plan refers to a roadmap for how the data will be organized and analyzed and how results will be presented. A data analysis plan should be established when planning a research study (i.e., before data collection begins). Among other things, the data analysis plan should describe: (a) the data to be collected; (b) the analyses to be conducted to address the research objectives, including assumptions required by said analyses; (c) data cleaning and management procedures; (d) data transformations, if applicable; and (e) how the study results will be presented (e.g., graphs, tables).

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Home » Data Analysis – Process, Methods and Types

Data Analysis – Process, Methods and Types

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Data Analysis

Definition:

Data analysis refers to the process of inspecting, cleaning, transforming, and modeling data with the goal of discovering useful information, drawing conclusions, and supporting decision-making. It involves applying various statistical and computational techniques to interpret and derive insights from large datasets. The ultimate aim of data analysis is to convert raw data into actionable insights that can inform business decisions, scientific research, and other endeavors.

Data Analysis Process

The following are step-by-step guides to the data analysis process:

Define the Problem

The first step in data analysis is to clearly define the problem or question that needs to be answered. This involves identifying the purpose of the analysis, the data required, and the intended outcome.

Collect the Data

The next step is to collect the relevant data from various sources. This may involve collecting data from surveys, databases, or other sources. It is important to ensure that the data collected is accurate, complete, and relevant to the problem being analyzed.

Clean and Organize the Data

Once the data has been collected, it needs to be cleaned and organized. This involves removing any errors or inconsistencies in the data, filling in missing values, and ensuring that the data is in a format that can be easily analyzed.

Analyze the Data

The next step is to analyze the data using various statistical and analytical techniques. This may involve identifying patterns in the data, conducting statistical tests, or using machine learning algorithms to identify trends and insights.

Interpret the Results

After analyzing the data, the next step is to interpret the results. This involves drawing conclusions based on the analysis and identifying any significant findings or trends.

Communicate the Findings

Once the results have been interpreted, they need to be communicated to stakeholders. This may involve creating reports, visualizations, or presentations to effectively communicate the findings and recommendations.

Take Action

The final step in the data analysis process is to take action based on the findings. This may involve implementing new policies or procedures, making strategic decisions, or taking other actions based on the insights gained from the analysis.

Types of Data Analysis

Types of Data Analysis are as follows:

Descriptive Analysis

This type of analysis involves summarizing and describing the main characteristics of a dataset, such as the mean, median, mode, standard deviation, and range.

Inferential Analysis

This type of analysis involves making inferences about a population based on a sample. Inferential analysis can help determine whether a certain relationship or pattern observed in a sample is likely to be present in the entire population.

Diagnostic Analysis

This type of analysis involves identifying and diagnosing problems or issues within a dataset. Diagnostic analysis can help identify outliers, errors, missing data, or other anomalies in the dataset.

Predictive Analysis

This type of analysis involves using statistical models and algorithms to predict future outcomes or trends based on historical data. Predictive analysis can help businesses and organizations make informed decisions about the future.

Prescriptive Analysis

This type of analysis involves recommending a course of action based on the results of previous analyses. Prescriptive analysis can help organizations make data-driven decisions about how to optimize their operations, products, or services.

Exploratory Analysis

This type of analysis involves exploring the relationships and patterns within a dataset to identify new insights and trends. Exploratory analysis is often used in the early stages of research or data analysis to generate hypotheses and identify areas for further investigation.

Data Analysis Methods

Data Analysis Methods are as follows:

Statistical Analysis

This method involves the use of mathematical models and statistical tools to analyze and interpret data. It includes measures of central tendency, correlation analysis, regression analysis, hypothesis testing, and more.

Machine Learning

This method involves the use of algorithms to identify patterns and relationships in data. It includes supervised and unsupervised learning, classification, clustering, and predictive modeling.

Data Mining

This method involves using statistical and machine learning techniques to extract information and insights from large and complex datasets.

Text Analysis

This method involves using natural language processing (NLP) techniques to analyze and interpret text data. It includes sentiment analysis, topic modeling, and entity recognition.

Network Analysis

This method involves analyzing the relationships and connections between entities in a network, such as social networks or computer networks. It includes social network analysis and graph theory.

Time Series Analysis

This method involves analyzing data collected over time to identify patterns and trends. It includes forecasting, decomposition, and smoothing techniques.

Spatial Analysis

This method involves analyzing geographic data to identify spatial patterns and relationships. It includes spatial statistics, spatial regression, and geospatial data visualization.

Data Visualization

This method involves using graphs, charts, and other visual representations to help communicate the findings of the analysis. It includes scatter plots, bar charts, heat maps, and interactive dashboards.

Qualitative Analysis

This method involves analyzing non-numeric data such as interviews, observations, and open-ended survey responses. It includes thematic analysis, content analysis, and grounded theory.

Multi-criteria Decision Analysis

This method involves analyzing multiple criteria and objectives to support decision-making. It includes techniques such as the analytical hierarchy process, TOPSIS, and ELECTRE.

Data Analysis Tools

There are various data analysis tools available that can help with different aspects of data analysis. Below is a list of some commonly used data analysis tools:

• Microsoft Excel: A widely used spreadsheet program that allows for data organization, analysis, and visualization.
• SQL : A programming language used to manage and manipulate relational databases.
• R : An open-source programming language and software environment for statistical computing and graphics.
• Python : A general-purpose programming language that is widely used in data analysis and machine learning.
• Tableau : A data visualization software that allows for interactive and dynamic visualizations of data.
• SAS : A statistical analysis software used for data management, analysis, and reporting.
• SPSS : A statistical analysis software used for data analysis, reporting, and modeling.
• Matlab : A numerical computing software that is widely used in scientific research and engineering.
• RapidMiner : A data science platform that offers a wide range of data analysis and machine learning tools.

Applications of Data Analysis

Data analysis has numerous applications across various fields. Below are some examples of how data analysis is used in different fields:

• Business : Data analysis is used to gain insights into customer behavior, market trends, and financial performance. This includes customer segmentation, sales forecasting, and market research.
• Healthcare : Data analysis is used to identify patterns and trends in patient data, improve patient outcomes, and optimize healthcare operations. This includes clinical decision support, disease surveillance, and healthcare cost analysis.
• Education : Data analysis is used to measure student performance, evaluate teaching effectiveness, and improve educational programs. This includes assessment analytics, learning analytics, and program evaluation.
• Finance : Data analysis is used to monitor and evaluate financial performance, identify risks, and make investment decisions. This includes risk management, portfolio optimization, and fraud detection.
• Government : Data analysis is used to inform policy-making, improve public services, and enhance public safety. This includes crime analysis, disaster response planning, and social welfare program evaluation.
• Sports : Data analysis is used to gain insights into athlete performance, improve team strategy, and enhance fan engagement. This includes player evaluation, scouting analysis, and game strategy optimization.
• Marketing : Data analysis is used to measure the effectiveness of marketing campaigns, understand customer behavior, and develop targeted marketing strategies. This includes customer segmentation, marketing attribution analysis, and social media analytics.
• Environmental science : Data analysis is used to monitor and evaluate environmental conditions, assess the impact of human activities on the environment, and develop environmental policies. This includes climate modeling, ecological forecasting, and pollution monitoring.

When to Use Data Analysis

Data analysis is useful when you need to extract meaningful insights and information from large and complex datasets. It is a crucial step in the decision-making process, as it helps you understand the underlying patterns and relationships within the data, and identify potential areas for improvement or opportunities for growth.

Here are some specific scenarios where data analysis can be particularly helpful:

• Problem-solving : When you encounter a problem or challenge, data analysis can help you identify the root cause and develop effective solutions.
• Optimization : Data analysis can help you optimize processes, products, or services to increase efficiency, reduce costs, and improve overall performance.
• Prediction: Data analysis can help you make predictions about future trends or outcomes, which can inform strategic planning and decision-making.
• Performance evaluation : Data analysis can help you evaluate the performance of a process, product, or service to identify areas for improvement and potential opportunities for growth.
• Risk assessment : Data analysis can help you assess and mitigate risks, whether it is financial, operational, or related to safety.
• Market research : Data analysis can help you understand customer behavior and preferences, identify market trends, and develop effective marketing strategies.
• Quality control: Data analysis can help you ensure product quality and customer satisfaction by identifying and addressing quality issues.

Purpose of Data Analysis

The primary purposes of data analysis can be summarized as follows:

• To gain insights: Data analysis allows you to identify patterns and trends in data, which can provide valuable insights into the underlying factors that influence a particular phenomenon or process.
• To inform decision-making: Data analysis can help you make informed decisions based on the information that is available. By analyzing data, you can identify potential risks, opportunities, and solutions to problems.
• To improve performance: Data analysis can help you optimize processes, products, or services by identifying areas for improvement and potential opportunities for growth.
• To measure progress: Data analysis can help you measure progress towards a specific goal or objective, allowing you to track performance over time and adjust your strategies accordingly.
• To identify new opportunities: Data analysis can help you identify new opportunities for growth and innovation by identifying patterns and trends that may not have been visible before.

Examples of Data Analysis

Some Examples of Data Analysis are as follows:

• Social Media Monitoring: Companies use data analysis to monitor social media activity in real-time to understand their brand reputation, identify potential customer issues, and track competitors. By analyzing social media data, businesses can make informed decisions on product development, marketing strategies, and customer service.
• Financial Trading: Financial traders use data analysis to make real-time decisions about buying and selling stocks, bonds, and other financial instruments. By analyzing real-time market data, traders can identify trends and patterns that help them make informed investment decisions.
• Traffic Monitoring : Cities use data analysis to monitor traffic patterns and make real-time decisions about traffic management. By analyzing data from traffic cameras, sensors, and other sources, cities can identify congestion hotspots and make changes to improve traffic flow.
• Healthcare Monitoring: Healthcare providers use data analysis to monitor patient health in real-time. By analyzing data from wearable devices, electronic health records, and other sources, healthcare providers can identify potential health issues and provide timely interventions.
• Online Advertising: Online advertisers use data analysis to make real-time decisions about advertising campaigns. By analyzing data on user behavior and ad performance, advertisers can make adjustments to their campaigns to improve their effectiveness.
• Sports Analysis : Sports teams use data analysis to make real-time decisions about strategy and player performance. By analyzing data on player movement, ball position, and other variables, coaches can make informed decisions about substitutions, game strategy, and training regimens.
• Energy Management : Energy companies use data analysis to monitor energy consumption in real-time. By analyzing data on energy usage patterns, companies can identify opportunities to reduce energy consumption and improve efficiency.

Characteristics of Data Analysis

Characteristics of Data Analysis are as follows:

• Objective : Data analysis should be objective and based on empirical evidence, rather than subjective assumptions or opinions.
• Systematic : Data analysis should follow a systematic approach, using established methods and procedures for collecting, cleaning, and analyzing data.
• Accurate : Data analysis should produce accurate results, free from errors and bias. Data should be validated and verified to ensure its quality.
• Relevant : Data analysis should be relevant to the research question or problem being addressed. It should focus on the data that is most useful for answering the research question or solving the problem.
• Comprehensive : Data analysis should be comprehensive and consider all relevant factors that may affect the research question or problem.
• Timely : Data analysis should be conducted in a timely manner, so that the results are available when they are needed.
• Reproducible : Data analysis should be reproducible, meaning that other researchers should be able to replicate the analysis using the same data and methods.
• Communicable : Data analysis should be communicated clearly and effectively to stakeholders and other interested parties. The results should be presented in a way that is understandable and useful for decision-making.

Advantages of Data Analysis

Advantages of Data Analysis are as follows:

• Better decision-making: Data analysis helps in making informed decisions based on facts and evidence, rather than intuition or guesswork.
• Improved efficiency: Data analysis can identify inefficiencies and bottlenecks in business processes, allowing organizations to optimize their operations and reduce costs.
• Increased accuracy: Data analysis helps to reduce errors and bias, providing more accurate and reliable information.
• Better customer service: Data analysis can help organizations understand their customers better, allowing them to provide better customer service and improve customer satisfaction.
• Competitive advantage: Data analysis can provide organizations with insights into their competitors, allowing them to identify areas where they can gain a competitive advantage.
• Identification of trends and patterns : Data analysis can identify trends and patterns in data that may not be immediately apparent, helping organizations to make predictions and plan for the future.
• Improved risk management : Data analysis can help organizations identify potential risks and take proactive steps to mitigate them.
• Innovation: Data analysis can inspire innovation and new ideas by revealing new opportunities or previously unknown correlations in data.

Limitations of Data Analysis

• Data quality: The quality of data can impact the accuracy and reliability of analysis results. If data is incomplete, inconsistent, or outdated, the analysis may not provide meaningful insights.
• Limited scope: Data analysis is limited by the scope of the data available. If data is incomplete or does not capture all relevant factors, the analysis may not provide a complete picture.
• Human error : Data analysis is often conducted by humans, and errors can occur in data collection, cleaning, and analysis.
• Cost : Data analysis can be expensive, requiring specialized tools, software, and expertise.
• Time-consuming : Data analysis can be time-consuming, especially when working with large datasets or conducting complex analyses.
• Overreliance on data: Data analysis should be complemented with human intuition and expertise. Overreliance on data can lead to a lack of creativity and innovation.
• Privacy concerns: Data analysis can raise privacy concerns if personal or sensitive information is used without proper consent or security measures.

About the author

Muhammad Hassan

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Methodology

Research Methods | Definitions, Types, Examples

Research methods are specific procedures for collecting and analyzing data. Developing your research methods is an integral part of your research design . When planning your methods, there are two key decisions you will make.

First, decide how you will collect data . Your methods depend on what type of data you need to answer your research question :

• Qualitative vs. quantitative : Will your data take the form of words or numbers?
• Primary vs. secondary : Will you collect original data yourself, or will you use data that has already been collected by someone else?
• Descriptive vs. experimental : Will you take measurements of something as it is, or will you perform an experiment?

Second, decide how you will analyze the data .

• For quantitative data, you can use statistical analysis methods to test relationships between variables.
• For qualitative data, you can use methods such as thematic analysis to interpret patterns and meanings in the data.

Table of contents

Methods for collecting data, examples of data collection methods, methods for analyzing data, examples of data analysis methods, other interesting articles, frequently asked questions about research methods.

Data is the information that you collect for the purposes of answering your research question . The type of data you need depends on the aims of your research.

Qualitative vs. quantitative data

Your choice of qualitative or quantitative data collection depends on the type of knowledge you want to develop.

For questions about ideas, experiences and meanings, or to study something that can’t be described numerically, collect qualitative data .

If you want to develop a more mechanistic understanding of a topic, or your research involves hypothesis testing , collect quantitative data .

You can also take a mixed methods approach , where you use both qualitative and quantitative research methods.

Primary vs. secondary research

Primary research is any original data that you collect yourself for the purposes of answering your research question (e.g. through surveys , observations and experiments ). Secondary research is data that has already been collected by other researchers (e.g. in a government census or previous scientific studies).

If you are exploring a novel research question, you’ll probably need to collect primary data . But if you want to synthesize existing knowledge, analyze historical trends, or identify patterns on a large scale, secondary data might be a better choice.

Descriptive vs. experimental data

In descriptive research , you collect data about your study subject without intervening. The validity of your research will depend on your sampling method .

In experimental research , you systematically intervene in a process and measure the outcome. The validity of your research will depend on your experimental design .

To conduct an experiment, you need to be able to vary your independent variable , precisely measure your dependent variable, and control for confounding variables . If it’s practically and ethically possible, this method is the best choice for answering questions about cause and effect.

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Your data analysis methods will depend on the type of data you collect and how you prepare it for analysis.

Data can often be analyzed both quantitatively and qualitatively. For example, survey responses could be analyzed qualitatively by studying the meanings of responses or quantitatively by studying the frequencies of responses.

Qualitative analysis methods

Qualitative analysis is used to understand words, ideas, and experiences. You can use it to interpret data that was collected:

• From open-ended surveys and interviews , literature reviews , case studies , ethnographies , and other sources that use text rather than numbers.
• Using non-probability sampling methods .

Qualitative analysis tends to be quite flexible and relies on the researcher’s judgement, so you have to reflect carefully on your choices and assumptions and be careful to avoid research bias .

Quantitative analysis methods

Quantitative analysis uses numbers and statistics to understand frequencies, averages and correlations (in descriptive studies) or cause-and-effect relationships (in experiments).

You can use quantitative analysis to interpret data that was collected either:

• During an experiment .
• Using probability sampling methods .

Because the data is collected and analyzed in a statistically valid way, the results of quantitative analysis can be easily standardized and shared among researchers.

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.

• Chi square test of independence
• Statistical power
• Descriptive statistics
• Degrees of freedom
• Pearson correlation
• Null hypothesis
• Double-blind study
• Case-control study
• Research ethics
• Data collection
• Hypothesis testing
• Structured interviews

Research bias

• Hawthorne effect
• Unconscious bias
• Recall bias
• Halo effect
• Self-serving bias
• Information bias

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

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

In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .

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.

The research methods you use depend on the type of data you need to answer your research question .

• If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts and meanings, use qualitative methods .
• If you want to analyze a large amount of readily-available data, use secondary data. If you want data specific to your purposes with control over how it is generated, collect primary data.
• If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

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

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

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

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

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Comprehensive guidelines for appropriate statistical analysis methods in research

Affiliations.

• 1 Department of Anesthesiology and Pain Medicine, Daegu Catholic University School of Medicine, Daegu, Korea.
• 2 Department of Medical Statistics, Daegu Catholic University School of Medicine, Daegu, Korea.
• PMID: 39210669
• DOI: 10.4097/kja.24016

Background: The selection of statistical analysis methods in research is a critical and nuanced task that requires a scientific and rational approach. Aligning the chosen method with the specifics of the research design and hypothesis is paramount, as it can significantly impact the reliability and quality of the research outcomes.

Methods: This study explores a comprehensive guideline for systematically choosing appropriate statistical analysis methods, with a particular focus on the statistical hypothesis testing stage and categorization of variables. By providing a detailed examination of these aspects, this study aims to provide researchers with a solid foundation for informed methodological decision making. Moving beyond theoretical considerations, this study delves into the practical realm by examining the null and alternative hypotheses tailored to specific statistical methods of analysis. The dynamic relationship between these hypotheses and statistical methods is thoroughly explored, and a carefully crafted flowchart for selecting the statistical analysis method is proposed.

Results: Based on the flowchart, we examined whether exemplary research papers appropriately used statistical methods that align with the variables chosen and hypotheses built for the research. This iterative process ensures the adaptability and relevance of this flowchart across diverse research contexts, contributing to both theoretical insights and tangible tools for methodological decision-making.

Conclusions: This study emphasizes the importance of a scientific and rational approach for the selection of statistical analysis methods. By providing comprehensive guidelines, insights into the null and alternative hypotheses, and a practical flowchart, this study aims to empower researchers and enhance the overall quality and reliability of scientific studies.

Keywords: Algorithms; Biostatistics; Data analysis; Guideline; Statistical data interpretation; Statistical model..

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