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

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

Importance of...

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

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

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

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

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

Descriptions of Possible Limitations

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

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

Possible Methodological Limitations

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

Possible Limitations of the Researcher

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

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

Structure and Writing Style

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

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

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

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

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

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

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

Writing Tip

Don't Inflate the Importance of Your Findings!

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

Another Writing Tip

Negative Results are Not a Limitation!

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

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

Yet Another Writing Tip

Sample Size Limitations in Qualitative Research

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

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

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How to Write Limitations of the Study (with examples)

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

Updated on August 24, 2023

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

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

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

What are limitations in research?

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

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

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

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

Why is identifying limitations important?

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

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

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

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

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

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

How to write limitations

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

Don’t hide your limitations

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

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

Writing limitations

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

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

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

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

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

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

Examples of common limitations

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

Methodology limitations

Methodology may include limitations due to:

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

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

Research process limitations

Limitations during the research process may arise from:

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

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

Final thoughts

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

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

Charla Viera, MS

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Social Research: Definitions, Types, Nature, and Characteristics

• First Online: 27 October 2022

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• Kanamik Kani Khan 4 &
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Social research is often defined as a study of mankind that helps to identify the relations between social life and social systems. This kind of research usually creates new knowledge and theories or tests and verifies existing theories. However, social research is a broad spectrum that requires a discursive understanding of its varied nature and definitions. This chapter aims to explain the multifarious definitions of social research given by different scholars. The information used in this chapter is solely based on existing literature regarding social research. There are various stages discussed regarding how social research can be effectively conducted. The types and characteristics of social research are further analysed in this chapter. Social research plays a substantial role in investigating knowledge and theories relevant to social problems. Additionally, social research is important for its contribution to national and international policymaking, which explains the importance of social research.

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• Published: 15 September 2022

Interviews in the social sciences

• Eleanor Knott   ORCID: orcid.org/0000-0002-9131-3939 1 ,
• Aliya Hamid Rao   ORCID: orcid.org/0000-0003-0674-4206 1 ,
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• Interdisciplinary studies

In-depth interviews are a versatile form of qualitative data collection used by researchers across the social sciences. They allow individuals to explain, in their own words, how they understand and interpret the world around them. Interviews represent a deceptively familiar social encounter in which people interact by asking and answering questions. They are, however, a very particular type of conversation, guided by the researcher and used for specific ends. This dynamic introduces a range of methodological, analytical and ethical challenges, for novice researchers in particular. In this Primer, we focus on the stages and challenges of designing and conducting an interview project and analysing data from it, as well as strategies to overcome such challenges.

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Introduction.

In-depth interviews are a qualitative research method that follow a deceptively familiar logic of human interaction: they are conversations where people talk with each other, interact and pose and answer questions 1 . An interview is a specific type of interaction in which — usually and predominantly — a researcher asks questions about someone’s life experience, opinions, dreams, fears and hopes and the interview participant answers the questions 1 .

Interviews will often be used as a standalone method or combined with other qualitative methods, such as focus groups or ethnography, or quantitative methods, such as surveys or experiments. Although interviewing is a frequently used method, it should not be viewed as an easy default for qualitative researchers 2 . Interviews are also not suited to answering all qualitative research questions, but instead have specific strengths that should guide whether or not they are deployed in a research project. Whereas ethnography might be better suited to trying to observe what people do, interviews provide a space for extended conversations that allow the researcher insights into how people think and what they believe. Quantitative surveys also give these kinds of insights, but they use pre-determined questions and scales, privileging breadth over depth and often overlooking harder-to-reach participants.

In-depth interviews can take many different shapes and forms, often with more than one participant or researcher. For example, interviews might be highly structured (using an almost survey-like interview guide), entirely unstructured (taking a narrative and free-flowing approach) or semi-structured (using a topic guide ). Researchers might combine these approaches within a single project depending on the purpose of the interview and the characteristics of the participant. Whatever form the interview takes, researchers should be mindful of the dynamics between interviewer and participant and factor these in at all stages of the project.

In this Primer, we focus on the most common type of interview: one researcher taking a semi-structured approach to interviewing one participant using a topic guide. Focusing on how to plan research using interviews, we discuss the necessary stages of data collection. We also discuss the stages and thought-process behind analysing interview material to ensure that the richness and interpretability of interview material is maintained and communicated to readers. The Primer also tracks innovations in interview methods and discusses the developments we expect over the next 5–10 years.

We wrote this Primer as researchers from sociology, social policy and political science. We note our disciplinary background because we acknowledge that there are disciplinary differences in how interviews are approached and understood as a method.

Experimentation

Here we address research design considerations and data collection issues focusing on topic guide construction and other pragmatics of the interview. We also explore issues of ethics and reflexivity that are crucial throughout the research project.

Research design

Participant selection.

Participants can be selected and recruited in various ways for in-depth interview studies. The researcher must first decide what defines the people or social groups being studied. Often, this means moving from an abstract theoretical research question to a more precise empirical one. For example, the researcher might be interested in how people talk about race in contexts of diversity. Empirical settings in which this issue could be studied could include schools, workplaces or adoption agencies. The best research designs should clearly explain why the particular setting was chosen. Often there are both intrinsic and extrinsic reasons for choosing to study a particular group of people at a specific time and place 3 . Intrinsic motivations relate to the fact that the research is focused on an important specific social phenomenon that has been understudied. Extrinsic motivations speak to the broader theoretical research questions and explain why the case at hand is a good one through which to address them empirically.

Next, the researcher needs to decide which types of people they would like to interview. This decision amounts to delineating the inclusion and exclusion criteria for the study. The criteria might be based on demographic variables, like race or gender, but they may also be context-specific, for example, years of experience in an organization. These should be decided based on the research goals. Researchers should be clear about what characteristics would make an individual a candidate for inclusion in the study (and what would exclude them).

The next step is to identify and recruit the study’s sample . Usually, many more people fit the inclusion criteria than can be interviewed. In cases where lists of potential participants are available, the researcher might want to employ stratified sampling , dividing the list by characteristics of interest before sampling.

When there are no lists, researchers will often employ purposive sampling . Many researchers consider purposive sampling the most useful mode for interview-based research since the number of interviews to be conducted is too small to aim to be statistically representative 4 . Instead, the aim is not breadth, via representativeness, but depth via rich insights about a set of participants. In addition to purposive sampling, researchers often use snowball sampling . Both purposive and snowball sampling can be combined with quota sampling . All three types of sampling aim to ensure a variety of perspectives within the confines of a research project. A goal for in-depth interview studies can be to sample for range, being mindful of recruiting a diversity of participants fitting the inclusion criteria.

Study design

The total number of interviews depends on many factors, including the population studied, whether comparisons are to be made and the duration of interviews. Studies that rely on quota sampling where explicit comparisons are made between groups will require a larger number of interviews than studies focused on one group only. Studies where participants are interviewed over several hours, days or even repeatedly across years will tend to have fewer participants than those that entail a one-off engagement.

Researchers often stop interviewing when new interviews confirm findings from earlier interviews with no new or surprising insights (saturation) 4 , 5 , 6 . As a criterion for research design, saturation assumes that data collection and analysis are happening in tandem and that researchers will stop collecting new data once there is no new information emerging from the interviews. This is not always possible. Researchers rarely have time for systematic data analysis during data collection and they often need to specify their sample in funding proposals prior to data collection. As a result, researchers often draw on existing reports of saturation to estimate a sample size prior to data collection. These suggest between 12 and 20 interviews per category of participant (although researchers have reported saturation with samples that are both smaller and larger than this) 7 , 8 , 9 . The idea of saturation has been critiqued by many qualitative researchers because it assumes that meaning inheres in the data, waiting to be discovered — and confirmed — once saturation has been reached 7 . In-depth interview data are often multivalent and can give rise to different interpretations. The important consideration is, therefore, not merely how many participants are interviewed, but whether one’s research design allows for collecting rich and textured data that provide insight into participants’ understandings, accounts, perceptions and interpretations.

Sometimes, researchers will conduct interviews with more than one participant at a time. Researchers should consider the benefits and shortcomings of such an approach. Joint interviews may, for example, give researchers insight into how caregivers agree or debate childrearing decisions. At the same time, they may be less adaptive to exploring aspects of caregiving that participants may not wish to disclose to each other. In other cases, there may be more than one person interviewing each participant, such as when an interpreter is used, and so it is important to consider during the research design phase how this might shape the dynamics of the interview.

Data collection

Semi-structured interviews are typically organized around a topic guide comprised of an ordered set of broad topics (usually 3–5). Each topic includes a set of questions that form the basis of the discussion between the researcher and participant (Fig.  1 ). These topics are organized around key concepts that the researcher has identified (for example, through a close study of prior research, or perhaps through piloting a small, exploratory study) 5 .

a | Elaborated topics the researcher wants to cover in the interview and example questions. b | An example topic arc. Using such an arc, one can think flexibly about the order of topics. Considering the main question for each topic will help to determine the best order for the topics. After conducting some interviews, the researcher can move topics around if a different order seems to make sense.

Topic guide

One common way to structure a topic guide is to start with relatively easy, open-ended questions (Table  1 ). Opening questions should be related to the research topic but broad and easy to answer, so that they help to ease the participant into conversation.

After these broad, opening questions, the topic guide may move into topics that speak more directly to the overarching research question. The interview questions will be accompanied by probes designed to elicit concrete details and examples from the participant (see Table  1 ).

Abstract questions are often easier for participants to answer once they have been asked more concrete questions. In our experience, for example, questions about feelings can be difficult for some participants to answer, but when following probes concerning factual experiences these questions can become less challenging. After the main themes of the topic guide have been covered, the topic guide can move onto closing questions. At this stage, participants often repeat something they have said before, although they may sometimes introduce a new topic.

Interviews are especially well suited to gaining a deeper insight into people’s experiences. Getting these insights largely depends on the participants’ willingness to talk to the researcher. We recommend designing open-ended questions that are more likely to elicit an elaborated response and extended reflection from participants rather than questions that can be answered with yes or no.

Questions should avoid foreclosing the possibility that the participant might disagree with the premise of the question. Take for example the question: “Do you support the new family-friendly policies?” This question minimizes the possibility of the participant disagreeing with the premise of this question, which assumes that the policies are ‘family-friendly’ and asks for a yes or no answer. Instead, asking more broadly how a participant feels about the specific policy being described as ‘family-friendly’ (for example, a work-from-home policy) allows them to express agreement, disagreement or impartiality and, crucially, to explain their reasoning 10 .

For an uninterrupted interview that will last between 90 and 120 minutes, the topic guide should be one to two single-spaced pages with questions and probes. Ideally, the researcher will memorize the topic guide before embarking on the first interview. It is fine to carry a printed-out copy of the topic guide but memorizing the topic guide ahead of the interviews can often make the interviewer feel well prepared in guiding the participant through the interview process.

Although the topic guide helps the researcher stay on track with the broad areas they want to cover, there is no need for the researcher to feel tied down by the topic guide. For instance, if a participant brings up a theme that the researcher intended to discuss later or a point the researcher had not anticipated, the researcher may well decide to follow the lead of the participant. The researcher’s role extends beyond simply stating the questions; it entails listening and responding, making split-second decisions about what line of inquiry to pursue and allowing the interview to proceed in unexpected directions.

Optimizing the interview

The ideal place for an interview will depend on the study and what is feasible for participants. Generally, a place where the participant and researcher can both feel relaxed, where the interview can be uninterrupted and where noise or other distractions are limited is ideal. But this may not always be possible and so the researcher needs to be prepared to adapt their plans within what is feasible (and desirable for participants).

Another key tool for the interview is a recording device (assuming that permission for recording has been given). Recording can be important to capture what the participant says verbatim. Additionally, it can allow the researcher to focus on determining what probes and follow-up questions they want to pursue rather than focusing on taking notes. Sometimes, however, a participant may not allow the researcher to record, or the recording may fail. If the interview is not recorded we suggest that the researcher takes brief notes during the interview, if feasible, and then thoroughly make notes immediately after the interview and try to remember the participant’s facial expressions, gestures and tone of voice. Not having a recording of an interview need not limit the researcher from getting analytical value from it.

As soon as possible after each interview, we recommend that the researcher write a one-page interview memo comprising three key sections. The first section should identify two to three important moments from the interview. What constitutes important is up to the researcher’s discretion 9 . The researcher should note down what happened in these moments, including the participant’s facial expressions, gestures, tone of voice and maybe even the sensory details of their surroundings. This exercise is about capturing ethnographic detail from the interview. The second part of the interview memo is the analytical section with notes on how the interview fits in with previous interviews, for example, where the participant’s responses concur or diverge from other responses. The third part consists of a methodological section where the researcher notes their perception of their relationship with the participant. The interview memo allows the researcher to think critically about their positionality and practice reflexivity — key concepts for an ethical and transparent research practice in qualitative methodology 11 , 12 .

Ethics and reflexivity

All elements of an in-depth interview can raise ethical challenges and concerns. Good ethical practice in interview studies often means going beyond the ethical procedures mandated by institutions 13 . While discussions and requirements of ethics can differ across disciplines, here we focus on the most pertinent considerations for interviews across the research process for an interdisciplinary audience.

Ethical considerations prior to interview

Before conducting interviews, researchers should consider harm minimization, informed consent, anonymity and confidentiality, and reflexivity and positionality. It is important for the researcher to develop their own ethical sensitivities and sensibilities by gaining training in interview and qualitative methods, reading methodological and field-specific texts on interviews and ethics and discussing their research plans with colleagues.

Researchers should map the potential harm to consider how this can be minimized. Primarily, researchers should consider harm from the participants’ perspective (Box  1 ). But, it is also important to consider and plan for potential harm to the researcher, research assistants, gatekeepers, future researchers and members of the wider community 14 . Even the most banal of research topics can potentially pose some form of harm to the participant, researcher and others — and the level of harm is often highly context-dependent. For example, a research project on religion in society might have very different ethical considerations in a democratic versus authoritarian research context because of how openly or not such topics can be discussed and debated 15 .

The researcher should consider how they will obtain and record informed consent (for example, written or oral), based on what makes the most sense for their research project and context 16 . Some institutions might specify how informed consent should be gained. Regardless of how consent is obtained, the participant must be made aware of the form of consent, the intentions and procedures of the interview and potential forms of harm and benefit to the participant or community before the interview commences. Moreover, the participant must agree to be interviewed before the interview commences. If, in addition to interviews, the study contains an ethnographic component, it is worth reading around this topic (see, for example, Murphy and Dingwall 17 ). Informed consent must also be gained for how the interview will be recorded before the interview commences. These practices are important to ensure the participant is contributing on a voluntary basis. It is also important to remind participants that they can withdraw their consent at any time during the interview and for a specified period after the interview (to be decided with the participant). The researcher should indicate that participants can ask for anything shared to be off the record and/or not disseminated.

In terms of anonymity and confidentiality, it is standard practice when conducting interviews to agree not to use (or even collect) participants’ names and personal details that are not pertinent to the study. Anonymizing can often be the safer option for minimizing harm to participants as it is hard to foresee all the consequences of de-anonymizing, even if participants agree. Regardless of what a researcher decides, decisions around anonymity must be agreed with participants during the process of gaining informed consent and respected following the interview.

Although not all ethical challenges can be foreseen or planned for 18 , researchers should think carefully — before the interview — about power dynamics, participant vulnerability, emotional state and interactional dynamics between interviewer and participant, even when discussing low-risk topics. Researchers may then wish to plan for potential ethical issues, for example by preparing a list of relevant organizations to which participants can be signposted. A researcher interviewing a participant about debt, for instance, might prepare in advance a list of debt advice charities, organizations and helplines that could provide further support and advice. It is important to remember that the role of an interviewer is as a researcher rather than as a social worker or counsellor because researchers may not have relevant and requisite training in these other domains.

Box 1 Mapping potential forms of harm

Social: researchers should avoid causing any relational detriment to anyone in the course of interviews, for example, by sharing information with other participants or causing interview participants to be shunned or mistreated by their community as a result of participating.

Economic: researchers should avoid causing financial detriment to anyone, for example, by expecting them to pay for transport to be interviewed or to potentially lose their job as a result of participating.

Physical: researchers should minimize the risk of anyone being exposed to violence as a result of the research both from other individuals or from authorities, including police.

Psychological: researchers should minimize the risk of causing anyone trauma (or re-traumatization) or psychological anguish as a result of the research; this includes not only the participant but importantly the researcher themselves and anyone that might read or analyse the transcripts, should they contain triggering information.

Political: researchers should minimize the risk of anyone being exposed to political detriment as a result of the research, such as retribution.

Professional/reputational: researchers should minimize the potential for reputational damage to anyone connected to the research (this includes ensuring good research practices so that any researchers involved are not harmed reputationally by being involved with the research project).

The task here is not to map exhaustively the potential forms of harm that might pertain to a particular research project (that is the researcher’s job and they should have the expertise most suited to mapping such potential harms relative to the specific project) but to demonstrate the breadth of potential forms of harm.

Ethical considerations post-interview

Researchers should consider how interview data are stored, analysed and disseminated. If participants have been offered anonymity and confidentiality, data should be stored in a way that does not compromise this. For example, researchers should consider removing names and any other unnecessary personal details from interview transcripts, password-protecting and encrypting files and using pseudonyms to label and store all interview data. It is also important to address where interview data are taken (for example, across borders in particular where interview data might be of interest to local authorities) and how this might affect the storage of interview data.

Examining how the researcher will represent participants is a paramount ethical consideration both in the planning stages of the interview study and after it has been conducted. Dissemination strategies also need to consider questions of anonymity and representation. In small communities, even if participants are given pseudonyms, it might be obvious who is being described. Anonymizing not only the names of those participating but also the research context is therefore a standard practice 19 . With particularly sensitive data or insights about the participant, it is worth considering describing participants in a more abstract way rather than as specific individuals. These practices are important both for protecting participants’ anonymity but can also affect the ability of the researcher and others to return ethically to the research context and similar contexts 20 .

Reflexivity and positionality

Reflexivity and positionality mean considering the researcher’s role and assumptions in knowledge production 13 . A key part of reflexivity is considering the power relations between the researcher and participant within the interview setting, as well as how researchers might be perceived by participants. Further, researchers need to consider how their own identities shape the kind of knowledge and assumptions they bring to the interview, including how they approach and ask questions and their analysis of interviews (Box  2 ). Reflexivity is a necessary part of developing ethical sensibility as a researcher by adapting and reflecting on how one engages with participants. Participants should not feel judged, for example, when they share information that researchers might disagree with or find objectionable. How researchers deal with uncomfortable moments or information shared by participants is at their discretion, but they should consider how they will react both ahead of time and in the moment.

Researchers can develop their reflexivity by considering how they themselves would feel being asked these interview questions or represented in this way, and then adapting their practice accordingly. There might be situations where these questions are not appropriate in that they unduly centre the researchers’ experiences and worldview. Nevertheless, these prompts can provide a useful starting point for those beginning their reflexive journey and developing an ethical sensibility.

Reflexivity and ethical sensitivities require active reflection throughout the research process. For example, researchers should take care in interview memos and their notes to consider their assumptions, potential preconceptions, worldviews and own identities prior to and after interviews (Box  2 ). Checking in with assumptions can be a way of making sure that researchers are paying close attention to their own theoretical and analytical biases and revising them in accordance with what they learn through the interviews. Researchers should return to these notes (especially when analysing interview material), to try to unpack their own effects on the research process as well as how participants positioned and engaged with them.

Box 2 Aspects to reflect on reflexively

For reflexive engagement, and understanding the power relations being co-constructed and (re)produced in interviews, it is necessary to reflect, at a minimum, on the following.

Ethnicity, race and nationality, such as how does privilege stemming from race or nationality operate between the researcher, the participant and research context (for example, a researcher from a majority community may be interviewing a member of a minority community)

Gender and sexuality, see above on ethnicity, race and nationality

Social class, and in particular the issue of middle-class bias among researchers when formulating research and interview questions

Economic security/precarity, see above on social class and thinking about the researcher’s relative privilege and the source of biases that stem from this

Educational experiences and privileges, see above

Disciplinary biases, such as how the researcher’s discipline/subfield usually approaches these questions, possibly normalizing certain assumptions that might be contested by participants and in the research context

Political and social values

Lived experiences and other dimensions of ourselves that affect and construct our identity as researchers

In this section, we discuss the next stage of an interview study, namely, analysing the interview data. Data analysis may begin while more data are being collected. Doing so allows early findings to inform the focus of further data collection, as part of an iterative process across the research project. Here, the researcher is ultimately working towards achieving coherence between the data collected and the findings produced to answer successfully the research question(s) they have set.

The two most common methods used to analyse interview material across the social sciences are thematic analysis 21 and discourse analysis 22 . Thematic analysis is a particularly useful and accessible method for those starting out in analysis of qualitative data and interview material as a method of coding data to develop and interpret themes in the data 21 . Discourse analysis is more specialized and focuses on the role of discourse in society by paying close attention to the explicit, implicit and taken-for-granted dimensions of language and power 22 , 23 . Although thematic and discourse analysis are often discussed as separate techniques, in practice researchers might flexibly combine these approaches depending on the object of analysis. For example, those intending to use discourse analysis might first conduct thematic analysis as a way to organize and systematize the data. The object and intention of analysis might differ (for example, developing themes or interrogating language), but the questions facing the researcher (such as whether to take an inductive or deductive approach to analysis) are similar.

Preparing data

Data preparation is an important step in the data analysis process. The researcher should first determine what comprises the corpus of material and in what form it will it be analysed. The former refers to whether, for example, alongside the interviews themselves, analytic memos or observational notes that may have been taken during data collection will also be directly analysed. The latter refers to decisions about how the verbal/audio interview data will be transformed into a written form, making it suitable for processes of data analysis. Typically, interview audio recordings are transcribed to produce a written transcript. It is important to note that the process of transcription is one of transformation. The verbal interview data are transformed into a written transcript through a series of decisions that the researcher must make. The researcher should consider the effect of mishearing what has been said or how choosing to punctuate a sentence in a particular way will affect the final analysis.

Box  3 shows an example transcript excerpt from an interview with a teacher conducted by Teeger as part of her study of history education in post-apartheid South Africa 24 (Box  3 ). Seeing both the questions and the responses means that the reader can contextualize what the participant (Ms Mokoena) has said. Throughout the transcript the researcher has used square brackets, for example to indicate a pause in speech, when Ms Mokoena says “it’s [pause] it’s a difficult topic”. The transcription choice made here means that we see that Ms Mokoena has taken time to pause, perhaps to search for the right words, or perhaps because she has a slight apprehension. Square brackets are also included as an overt act of communication to the reader. When Ms Mokoena says “ja”, the English translation (“yes”) of the word in Afrikaans is placed in square brackets to ensure that the reader can follow the meaning of the speech.

Decisions about what to include when transcribing will be hugely important for the direction and possibilities of analysis. Researchers should decide what they want to capture in the transcript, based on their analytic focus. From a (post)positivist perspective 25 , the researcher may be interested in the manifest content of the interview (such as what is said, not how it is said). In that case, they may choose to transcribe intelligent verbatim . From a constructivist perspective 25 , researchers may choose to record more aspects of speech (including, for example, pauses, repetitions, false starts, talking over one another) so that these features can be analysed. Those working from this perspective argue that to recognize the interactional nature of the interview setting adequately and to avoid misinterpretations, features of interaction (pauses, overlaps between speakers and so on) should be preserved in transcription and therefore in the analysis 10 . Readers interested in learning more should consult Potter and Hepburn’s summary of how to present interaction through transcription of interview data 26 .

The process of analysing semi-structured interviews might be thought of as a generative rather than an extractive enterprise. Findings do not already exist within the interview data to be discovered. Rather, researchers create something new when analysing the data by applying their analytic lens or approach to the transcripts. At a high level, there are options as to what researchers might want to glean from their interview data. They might be interested in themes, whereby they identify patterns of meaning across the dataset 21 . Alternatively, they may focus on discourse(s), looking to identify how language is used to construct meanings and therefore how language reinforces or produces aspects of the social world 27 . Alternatively, they might look at the data to understand narrative or biographical elements 28 .

A further overarching decision to make is the extent to which researchers bring predetermined framings or understandings to bear on their data, or instead begin from the data themselves to generate an analysis. One way of articulating this is the extent to which researchers take a deductive approach or an inductive approach to analysis. One example of a truly inductive approach is grounded theory, whereby the aim of the analysis is to build new theory, beginning with one’s data 6 , 29 . In practice, researchers using thematic and discourse analysis often combine deductive and inductive logics and describe their process instead as iterative (referred to also as an abductive approach ) 30 , 31 . For example, researchers may decide that they will apply a given theoretical framing, or begin with an initial analytic framework, but then refine or develop these once they begin the process of analysis.

Box 3 Excerpt of interview transcript (from Teeger 24 )

Interviewer : Maybe you could just start by talking about what it’s like to teach apartheid history.

Ms Mokoena : It’s a bit challenging. You’ve got to accommodate all the kids in the class. You’ve got to be sensitive to all the racial differences. You want to emphasize the wrongs that were done in the past but you also want to, you know, not to make kids feel like it’s their fault. So you want to use the wrongs of the past to try and unite the kids …

Interviewer : So what kind of things do you do?

Ms Mokoena : Well I normally highlight the fact that people that were struggling were not just the blacks, it was all the races. And I give examples of the people … from all walks of life, all races, and highlight how they suffered as well as a result of apartheid, particularly the whites… . What I noticed, particularly my first year of teaching apartheid, I noticed that the black kids made the others feel responsible for what happened… . I had a lot of fights…. A lot of kids started hating each other because, you know, the others are white and the others were black. And they started saying, “My mother is a domestic worker because she was never allowed an opportunity to get good education.” …

Interviewer : I didn’t see any of that now when I was observing.

Ms Mokoena : … Like I was saying I think that because of the re-emphasis of the fact that, look, everybody did suffer one way or the other, they sort of got to see that it was everybody’s struggle … . They should now get to understand that that’s why we’re called a Rainbow Nation. Not everybody agreed with apartheid and not everybody suffered. Even all the blacks, not all blacks got to feel what the others felt . So ja [yes], it’s [pause] it’s a difficult topic, ja . But I think if you get the kids to understand why we’re teaching apartheid in the first place and you show the involvement of all races in all the different sides , then I think you have managed to teach it properly. So I think because of my inexperience then — that was my first year of teaching history — so I think I — maybe I over-emphasized the suffering of the blacks versus the whites [emphasis added].

Reprinted with permission from ref. 24 , Sage Publications.

From data to codes

Coding data is a key building block shared across many approaches to data analysis. Coding is a way of organizing and describing data, but is also ultimately a way of transforming data to produce analytic insights. The basic practice of coding involves highlighting a segment of text (this may be a sentence, a clause or a longer excerpt) and assigning a label to it. The aim of the label is to communicate some sort of summary of what is in the highlighted piece of text. Coding is an iterative process, whereby researchers read and reread their transcripts, applying and refining their codes, until they have a coding frame (a set of codes) that is applied coherently across the dataset and that captures and communicates the key features of what is contained in the data as it relates to the researchers’ analytic focus.

What one codes for is entirely contingent on the focus of the research project and the choices the researcher makes about the approach to analysis. At first, one might apply descriptive codes, summarizing what is contained in the interviews. It is rarely desirable to stop at this point, however, because coding is a tool to move from describing the data to interpreting the data. Suppose the researcher is pursuing some version of thematic analysis. In that case, it might be that the objects of coding are aspects of reported action, emotions, opinions, norms, relationships, routines, agreement/disagreement and change over time. A discourse analysis might instead code for different types of speech acts, tropes, linguistic or rhetorical devices. Multiple types of code might be generated within the same research project. What is important is that researchers are aware of the choices they are making in terms of what they are coding for. Moreover, through the process of refinement, the aim is to produce a set of discrete codes — in which codes are conceptually distinct, as opposed to overlapping. By using the same codes across the dataset, the researcher can capture commonalities across the interviews. This process of refinement involves relabelling codes and reorganizing how and where they are applied in the dataset.

From coding to analysis and writing

Data analysis is also an iterative process in which researchers move closer to and further away from the data. As they move away from the data, they synthesize their findings, thus honing and articulating their analytic insights. As they move closer to the data, they ground these insights in what is contained in the interviews. The link should not be broken between the data themselves and higher-order conceptual insights or claims being made. Researchers must be able to show evidence for their claims in the data. Figure  2 summarizes this iterative process and suggests the sorts of activities involved at each stage more concretely.

As well as going through steps 1 to 6 in order, the researcher will also go backwards and forwards between stages. Some stages will themselves be a forwards and backwards processing of coding and refining when working across different interview transcripts.

At the stage of synthesizing, there are some common quandaries. When dealing with a dataset consisting of multiple interviews, there will be salient and minority statements across different participants, or consensus or dissent on topics of interest to the researcher. A strength of qualitative interviews is that we can build in these nuances and variations across our data as opposed to aggregating them away. When exploring and reporting data, researchers should be asking how different findings are patterned and which interviews contain which codes, themes or tropes. Researchers should think about how these variations fit within the longer flow of individual interviews and what these variations tell them about the nature of their substantive research interests.

A further consideration is how to approach analysis within and across interview data. Researchers may look at one individual code, to examine the forms it takes across different participants and what they might be able to summarize about this code in the round. Alternatively, they might look at how a code or set of codes pattern across the account of one participant, to understand the code(s) in a more contextualized way. Further analysis might be done according to different sampling characteristics, where researchers group together interviews based on certain demographic characteristics and explore these together.

When it comes to writing up and presenting interview data, key considerations tend to rest on what is often termed transparency. When presenting the findings of an interview-based study, the reader should be able to understand and trace what the stated findings are based upon. This process typically involves describing the analytic process, how key decisions were made and presenting direct excerpts from the data. It is important to account for how the interview was set up and to consider the active part that the researcher has played in generating the data 32 . Quotes from interviews should not be thought of as merely embellishing or adding interest to a final research output. Rather, quotes serve the important function of connecting the reader directly to the underlying data. Quotes, therefore, should be chosen because they provide the reader with the most apt insight into what is being discussed. It is good practice to report not just on what participants said, but also on the questions that were asked to elicit the responses.

Researchers have increasingly used specialist qualitative data analysis software to organize and analyse their interview data, such as NVivo or ATLAS.ti. It is important to remember that such software is a tool for, rather than an approach or technique of, analysis. That said, software also creates a wide range of possibilities in terms of what can be done with the data. As researchers, we should reflect on how the range of possibilities of a given software package might be shaping our analytical choices and whether these are choices that we do indeed want to make.

Applications

This section reviews how and why in-depth interviews have been used by researchers studying gender, education and inequality, nationalism and ethnicity and the welfare state. Although interviews can be employed as a method of data collection in just about any social science topic, the applications below speak directly to the authors’ expertise and cutting-edge areas of research.

When it comes to the broad study of gender, in-depth interviews have been invaluable in shaping our understanding of how gender functions in everyday life. In a study of the US hedge fund industry (an industry dominated by white men), Tobias Neely was interested in understanding the factors that enable white men to prosper in the industry 33 . The study comprised interviews with 45 hedge fund workers and oversampled women of all races and men of colour to capture a range of experiences and beliefs. Tobias Neely found that practices of hiring, grooming and seeding are key to maintaining white men’s dominance in the industry. In terms of hiring, the interviews clarified that white men in charge typically preferred to hire people like themselves, usually from their extended networks. When women were hired, they were usually hired to less lucrative positions. In terms of grooming, Tobias Neely identifies how older and more senior men in the industry who have power and status will select one or several younger men as their protégés, to include in their own elite networks. Finally, in terms of her concept of seeding, Tobias Neely describes how older men who are hedge fund managers provide the seed money (often in the hundreds of millions of dollars) for a hedge fund to men, often their own sons (but not their daughters). These interviews provided an in-depth look into gendered and racialized mechanisms that allow white men to flourish in this industry.

Research by Rao draws on dozens of interviews with men and women who had lost their jobs, some of the participants’ spouses and follow-up interviews with about half the sample approximately 6 months after the initial interview 34 . Rao used interviews to understand the gendered experience and understanding of unemployment. Through these interviews, she found that the very process of losing their jobs meant different things for men and women. Women often saw job loss as being a personal indictment of their professional capabilities. The women interviewed often referenced how years of devaluation in the workplace coloured their interpretation of their job loss. Men, by contrast, were also saddened by their job loss, but they saw it as part and parcel of a weak economy rather than a personal failing. How these varied interpretations occurred was tied to men’s and women’s very different experiences in the workplace. Further, through her analysis of these interviews, Rao also showed how these gendered interpretations had implications for the kinds of jobs men and women sought to pursue after job loss. Whereas men remained tied to participating in full-time paid work, job loss appeared to be a catalyst pushing some of the women to re-evaluate their ties to the labour force.

In a study of workers in the tech industry, Hart used interviews to explain how individuals respond to unwanted and ambiguously sexual interactions 35 . Here, the researcher used interviews to allow participants to describe how these interactions made them feel and act and the logics of how they interpreted, classified and made sense of them 35 . Through her analysis of these interviews, Hart showed that participants engaged in a process she termed “trajectory guarding”, whereby they sought to monitor unwanted and ambiguously sexual interactions to avoid them from escalating. Yet, as Hart’s analysis proficiently demonstrates, these very strategies — which protect these workers sexually — also undermined their workplace advancement.

Drawing on interviews, these studies have helped us to understand better how gendered mechanisms, gendered interpretations and gendered interactions foster gender inequality when it comes to paid work. Methodologically, these studies illuminate the power of interviews to reveal important aspects of social life.

Nationalism and ethnicity

Traditionally, nationalism has been studied from a top-down perspective, through the lens of the state or using historical methods; in other words, in-depth interviews have not been a common way of collecting data to study nationalism. The methodological turn towards everyday nationalism has encouraged more scholars to go to the field and use interviews (and ethnography) to understand nationalism from the bottom up: how people talk about, give meaning, understand, navigate and contest their relation to nation, national identification and nationalism 36 , 37 , 38 , 39 . This turn has also addressed the gap left by those studying national and ethnic identification via quantitative methods, such as surveys.

Surveys can enumerate how individuals ascribe to categorical forms of identification 40 . However, interviews can question the usefulness of such categories and ask whether these categories are reflected, or resisted, by participants in terms of the meanings they give to identification 41 , 42 . Categories often pitch identification as a mutually exclusive choice; but identification might be more complex than such categories allow. For example, some might hybridize these categories or see themselves as moving between and across categories 43 . Hearing how people talk about themselves and their relation to nations, states and ethnicities, therefore, contributes substantially to the study of nationalism and national and ethnic forms of identification.

One particular approach to studying these topics, whether via everyday nationalism or alternatives, is that of using interviews to capture both articulations and narratives of identification, relations to nationalism and the boundaries people construct. For example, interviews can be used to gather self–other narratives by studying how individuals construct I–we–them boundaries 44 , including how participants talk about themselves, who participants include in their various ‘we’ groupings and which and how participants create ‘them’ groupings of others, inserting boundaries between ‘I/we’ and ‘them’. Overall, interviews hold great potential for listening to participants and understanding the nuances of identification and the construction of boundaries from their point of view.

Education and inequality

Scholars of social stratification have long noted that the school system often reproduces existing social inequalities. Carter explains that all schools have both material and sociocultural resources 45 . When children from different backgrounds attend schools with different material resources, their educational and occupational outcomes are likely to vary. Such material resources are relatively easy to measure. They are operationalized as teacher-to-student ratios, access to computers and textbooks and the physical infrastructure of classrooms and playgrounds.

Drawing on Bourdieusian theory 46 , Carter conceptualizes the sociocultural context as the norms, values and dispositions privileged within a social space 45 . Scholars have drawn on interviews with students and teachers (as well as ethnographic observations) to show how schools confer advantages on students from middle-class families, for example, by rewarding their help-seeking behaviours 47 . Focusing on race, researchers have revealed how schools can remain socioculturally white even as they enrol a racially diverse student population. In such contexts, for example, teachers often misrecognize the aesthetic choices made by students of colour, wrongly inferring that these students’ tastes in clothing and music reflect negative orientations to schooling 48 , 49 , 50 . These assessments can result in disparate forms of discipline and may ultimately shape educators’ assessments of students’ academic potential 51 .

Further, teachers and administrators tend to view the appropriate relationship between home and school in ways that resonate with white middle-class parents 52 . These parents are then able to advocate effectively for their children in ways that non-white parents are not 53 . In-depth interviews are particularly good at tapping into these understandings, revealing the mechanisms that confer privilege on certain groups of students and thereby reproduce inequality.

In addition, interviews can shed light on the unequal experiences that young people have within educational institutions, as the views of dominant groups are affirmed while those from disadvantaged backgrounds are delegitimized. For example, Teeger’s interviews with South African high schoolers showed how — because racially charged incidents are often framed as jokes in the broader school culture — Black students often feel compelled to ignore and keep silent about the racism they experience 54 . Interviews revealed that Black students who objected to these supposed jokes were coded by other students as serious or angry. In trying to avoid such labels, these students found themselves unable to challenge the racism they experienced. Interviews give us insight into these dynamics and help us see how young people understand and interpret the messages transmitted in schools — including those that speak to issues of inequality in their local school contexts as well as in society more broadly 24 , 55 .

The welfare state

In-depth interviews have also proved to be an important method for studying various aspects of the welfare state. By welfare state, we mean the social institutions relating to the economic and social wellbeing of a state’s citizens. Notably, using interviews has been useful to look at how policy design features are experienced and play out on the ground. Interviews have often been paired with large-scale surveys to produce mixed-methods study designs, therefore achieving both breadth and depth of insights.

In-depth interviews provide the opportunity to look behind policy assumptions or how policies are designed from the top down, to examine how these play out in the lives of those affected by the policies and whose experiences might otherwise be obscured or ignored. For example, the Welfare Conditionality project used interviews to critique the assumptions that conditionality (such as, the withdrawal of social security benefits if recipients did not perform or meet certain criteria) improved employment outcomes and instead showed that conditionality was harmful to mental health, living standards and had many other negative consequences 56 . Meanwhile, combining datasets from two small-scale interview studies with recipients allowed Summers and Young to critique assumptions around the simplicity that underpinned the design of Universal Credit in 2020, for example, showing that the apparently simple monthly payment design instead burdened recipients with additional money management decisions and responsibilities 57 .

Similarly, the Welfare at a (Social) Distance project used a mixed-methods approach in a large-scale study that combined national surveys with case studies and in-depth interviews to investigate the experience of claiming social security benefits during the COVID-19 pandemic. The interviews allowed researchers to understand in detail any issues experienced by recipients of benefits, such as delays in the process of claiming, managing on a very tight budget and navigating stigma and claiming 58 .

These applications demonstrate the multi-faceted topics and questions for which interviews can be a relevant method for data collection. These applications highlight not only the relevance of interviews, but also emphasize the key added value of interviews, which might be missed by other methods (surveys, in particular). Interviews can expose and question what is taken for granted and directly engage with communities and participants that might otherwise be ignored, obscured or marginalized.

Reproducibility and data deposition

There is a robust, ongoing debate about reproducibility in qualitative research, including interview studies. In some research paradigms, reproducibility can be a way of interrogating the rigour and robustness of research claims, by seeing whether these hold up when the research process is repeated. Some scholars have suggested that although reproducibility may be challenging, researchers can facilitate it by naming the place where the research was conducted, naming participants, sharing interview and fieldwork transcripts (anonymized and de-identified in cases where researchers are not naming people or places) and employing fact-checkers for accuracy 11 , 59 , 60 .

In addition to the ethical concerns of whether de-anonymization is ever feasible or desirable, it is also important to address whether the replicability of interview studies is meaningful. For example, the flexibility of interviews allows for the unexpected and the unforeseen to be incorporated into the scope of the research 61 . However, this flexibility means that we cannot expect reproducibility in the conventional sense, given that different researchers will elicit different types of data from participants. Sharing interview transcripts with other researchers, for instance, downplays the contextual nature of an interview.

Drawing on Bauer and Gaskell, we propose several measures to enhance rigour in qualitative research: transparency, grounding interpretations and aiming for theoretical transferability and significance 62 .

Researchers should be transparent when describing their methodological choices. Transparency means documenting who was interviewed, where and when (without requiring de-anonymization, for example, by documenting their characteristics), as well as the questions they were asked. It means carefully considering who was left out of the interviews and what that could mean for the researcher’s findings. It also means carefully considering who the researcher is and how their identity shaped the research process (integrating and articulating reflexivity into whatever is written up).

Second, researchers should ground their interpretations in the data. Grounding means presenting the evidence upon which the interpretation relies. Quotes and extracts should be extensive enough to allow the reader to evaluate whether the researcher’s interpretations are grounded in the data. At each step, researchers should carefully compare their own explanations and interpretations with alternative explanations. Doing so systematically and frequently allows researchers to become more confident in their claims. Here, researchers should justify the link between data and analysis by using quotes to justify and demonstrate the analytical point, while making sure the analytical point offers an interpretation of quotes (Box  4 ).

An important step in considering alternative explanations is to seek out disconfirming evidence 4 , 63 . This involves looking for instances where participants deviate from what the majority are saying and thus bring into question the theory (or explanation) that the researcher is developing. Careful analysis of such examples can often demonstrate the salience and meaning of what appears to be the norm (see Table  2 for examples) 54 . Considering alternative explanations and paying attention to disconfirming evidence allows the researcher to refine their own theories in respect of the data.

Finally, researchers should aim for theoretical transferability and significance in their discussions of findings. One way to think about this is to imagine someone who is not interested in the empirical study. Articulating theoretical transferability and significance usually takes the form of broadening out from the specific findings to consider explicitly how the research has refined or altered prior theoretical approaches. This process also means considering under what other conditions, aside from those of the study, the researcher thinks their theoretical revision would be supported by and why. Importantly, it also includes thinking about the limitations of one’s own approach and where the theoretical implications of the study might not hold.

Box 4 An example of grounding interpretations in data (from Rao 34 )

In an article explaining how unemployed men frame their job loss as a pervasive experience, Rao writes the following: “Unemployed men in this study understood unemployment to be an expected aspect of paid work in the contemporary United States. Robert, a white unemployed communications professional, compared the economic landscape after the Great Recession with the tragic events of September 11, 2001:

Part of your post-9/11 world was knowing people that died as a result of terrorism. The same thing is true with the [Great] Recession, right? … After the Recession you know somebody who was unemployed … People that really should be working.

The pervasiveness of unemployment rendered it normal, as Robert indicates.”

Here, the link between the quote presented and the analytical point Rao is making is clear: the analytical point is grounded in a quote and an interpretation of the quote is offered 34 .

Limitations and optimizations

When deciding which research method to use, the key question is whether the method provides a good fit for the research questions posed. In other words, researchers should consider whether interviews will allow them to successfully access the social phenomena necessary to answer their question(s) and whether the interviews will do so more effectively than other methods. Table  3 summarizes the major strengths and limitations of interviews. However, the accompanying text below is organized around some key issues, where relative strengths and weaknesses are presented alongside each other, the aim being that readers should think about how these can be balanced and optimized in relation to their own research.

Breadth versus depth of insight

Achieving an overall breadth of insight, in a statistically representative sense, is not something that is possible or indeed desirable when conducting in-depth interviews. Instead, the strength of conducting interviews lies in their ability to generate various sorts of depth of insight. The experiences or views of participants that can be accessed by conducting interviews help us to understand participants’ subjective realities. The challenge, therefore, is for researchers to be clear about why depth of insight is the focus and what we should aim to glean from these types of insight.

Naturalistic or artificial interviews

Interviews make use of a form of interaction with which people are familiar 64 . By replicating a naturalistic form of interaction as a tool to gather social science data, researchers can capitalize on people’s familiarity and expectations of what happens in a conversation. This familiarity can also be a challenge, as people come to the interview with preconceived ideas about what this conversation might be for or about. People may draw on experiences of other similar conversations when taking part in a research interview (for example, job interviews, therapy sessions, confessional conversations, chats with friends). Researchers should be aware of such potential overlaps and think through their implications both in how the aims and purposes of the research interview are communicated to participants and in how interview data are interpreted.

Further, some argue that a limitation of interviews is that they are an artificial form of data collection. By taking people out of their daily lives and asking them to stand back and pass comment, we are creating a distance that makes it difficult to use such data to say something meaningful about people’s actions, experiences and views. Other approaches, such as ethnography, might be more suitable for tapping into what people actually do, as opposed to what they say they do 65 .

Dynamism and replicability

Interviews following a semi-structured format offer flexibility both to the researcher and the participant. As the conversation develops, the interlocutors can explore the topics raised in much more detail, if desired, or pass over ones that are not relevant. This flexibility allows for the unexpected and the unforeseen to be incorporated into the scope of the research.

However, this flexibility has a related challenge of replicability. Interviews cannot be reproduced because they are contingent upon the interaction between the researcher and the participant in that given moment of interaction. In some research paradigms, replicability can be a way of interrogating the robustness of research claims, by seeing whether they hold when they are repeated. This is not a useful framework to bring to in-depth interviews and instead quality criteria (such as transparency) tend to be employed as criteria of rigour.

Accessing the private and personal

Interviews have been recognized for their strength in accessing private, personal issues, which participants may feel more comfortable talking about in a one-to-one conversation. Furthermore, interviews are likely to take a more personable form with their extended questions and answers, perhaps making a participant feel more at ease when discussing sensitive topics in such a context. There is a similar, but separate, argument made about accessing what are sometimes referred to as vulnerable groups, who may be difficult to make contact with using other research methods.

There is an associated challenge of anonymity. There can be types of in-depth interview that make it particularly challenging to protect the identities of participants, such as interviewing within a small community, or multiple members of the same household. The challenge to ensure anonymity in such contexts is even more important and difficult when the topic of research is of a sensitive nature or participants are vulnerable.

Increasingly, researchers are collaborating in large-scale interview-based studies and integrating interviews into broader mixed-methods designs. At the same time, interviews can be seen as an old-fashioned (and perhaps outdated) mode of data collection. We review these debates and discussions and point to innovations in interview-based studies. These include the shift from face-to-face interviews to the use of online platforms, as well as integrating and adapting interviews towards more inclusive methodologies.

Collaborating and mixing

Qualitative researchers have long worked alone 66 . Increasingly, however, researchers are collaborating with others for reasons such as efficiency, institutional incentives (for example, funding for collaborative research) and a desire to pool expertise (for example, studying similar phenomena in different contexts 67 or via different methods). Collaboration can occur across disciplines and methods, cases and contexts and between industry/business, practitioners and researchers. In many settings and contexts, collaboration has become an imperative 68 .

Cheek notes how collaboration provides both advantages and disadvantages 68 . For example, collaboration can be advantageous, saving time and building on the divergent knowledge, skills and resources of different researchers. Scholars with different theoretical or case-based knowledge (or contacts) can work together to build research that is comparative and/or more than the sum of its parts. But such endeavours also carry with them practical and political challenges in terms of how resources might actually be pooled, shared or accounted for. When undertaking such projects, as Morse notes, it is worth thinking about the nature of the collaboration and being explicit about such a choice, its advantages and its disadvantages 66 .

A further tension, but also a motivation for collaboration, stems from integrating interviews as a method in a mixed-methods project, whether with other qualitative researchers (to combine with, for example, focus groups, document analysis or ethnography) or with quantitative researchers (to combine with, for example, surveys, social media analysis or big data analysis). Cheek and Morse both note the pitfalls of collaboration with quantitative researchers: that quality of research may be sacrificed, qualitative interpretations watered down or not taken seriously, or tensions experienced over the pace and different assumptions that come with different methods and approaches of research 66 , 68 .

At the same time, there can be real benefits of such mixed-methods collaboration, such as reaching different and more diverse audiences or testing assumptions and theories between research components in the same project (for example, testing insights from prior quantitative research via interviews, or vice versa), as long as the skillsets of collaborators are seen as equally beneficial to the project. Cheek provides a set of questions that, as a starting point, can be useful for guiding collaboration, whether mixed methods or otherwise. First, Cheek advises asking all collaborators about their assumptions and understandings concerning collaboration. Second, Cheek recommends discussing what each perspective highlights and focuses on (and conversely ignores or sidelines) 68 .

A different way to engage with the idea of collaboration and mixed methods research is by fostering greater collaboration between researchers in the Global South and Global North, thus reversing trends of researchers from the Global North extracting knowledge from the Global South 69 . Such forms of collaboration also align with interview innovations, discussed below, that seek to transform traditional interview approaches into more participatory and inclusive (as part of participatory methodologies).

Digital innovations and challenges

The ongoing COVID-19 pandemic has centred the question of technology within interview-based fieldwork. Although conducting synchronous oral interviews online — for example, via Zoom, Skype or other such platforms — has been a method used by a small constituency of researchers for many years, it became (and remains) a necessity for many researchers wanting to continue or start interview-based projects while COVID-19 prevents face-to-face data collection.

In the past, online interviews were often framed as an inferior form of data collection for not providing the kinds of (often necessary) insights and forms of immersion face-to-face interviews allow 70 , 71 . Online interviews do tend to be more decontextualized than interviews conducted face-to-face 72 . For example, it is harder to recognize, engage with and respond to non-verbal cues 71 . At the same time, they broaden participation to those who might not have been able to access or travel to sites where interviews would have been conducted otherwise, for example people with disabilities. Online interviews also offer more flexibility in terms of scheduling and time requirements. For example, they provide more flexibility around precarious employment or caring responsibilities without having to travel and be away from home. In addition, online interviews might also reduce discomfort between researchers and participants, compared with face-to-face interviews, enabling more discussion of sensitive material 71 . They can also provide participants with more control, enabling them to turn on and off the microphone and video as they choose, for example, to provide more time to reflect and disconnect if they so wish 72 .

That said, online interviews can also introduce new biases based on access to technology 72 . For example, in the Global South, there are often urban/rural and gender gaps between who has access to mobile phones and who does not, meaning that some population groups might be overlooked unless researchers sample mindfully 71 . There are also important ethical considerations when deciding between online and face-to-face interviews. Online interviews might seem to imply lower ethical risks than face-to-face interviews (for example, they lower the chances of identification of participants or researchers), but they also offer more barriers to building trust between researchers and participants 72 . Interacting only online with participants might not provide the information needed to assess risk, for example, participants’ access to a private space to speak 71 . Just because online interviews might be more likely to be conducted in private spaces does not mean that private spaces are safe, for example, for victims of domestic violence. Finally, online interviews prompt further questions about decolonizing research and engaging with participants if research is conducted from afar 72 , such as how to include participants meaningfully and challenge dominant assumptions while doing so remotely.

A further digital innovation, modulating how researchers conduct interviews and the kinds of data collected and analysed, stems from the use and integration of (new) technology, such as WhatsApp text or voice notes to conduct synchronous or asynchronous oral or written interviews 73 . Such methods can provide more privacy, comfort and control to participants and make recruitment easier, allowing participants to share what they want when they want to, using technology that already forms a part of their daily lives, especially for young people 74 , 75 . Such technology is also emerging in other qualitative methods, such as focus groups, with similar arguments around greater inclusivity versus traditional offline modes. Here, the digital challenge might be higher for researchers than for participants if they are less used to such technology 75 . And while there might be concerns about the richness, depth and quality of written messages as a form of interview data, Gibson reports that the reams of transcripts that resulted from a study using written messaging were dense with meaning to be analysed 75 .

Like with online and face-to-face interviews, it is important also to consider the ethical questions and challenges of using such technology, from gaining consent to ensuring participant safety and attending to their distress, without cues, like crying, that might be more obvious in a face-to-face setting 75 , 76 . Attention to the platform used for such interviews is also important and researchers should be attuned to the local and national context. For example, in China, many platforms are neither legal nor available 76 . There, more popular platforms — like WeChat — can be highly monitored by the government, posing potential risks to participants depending on the topic of the interview. Ultimately, researchers should consider trade-offs between online and offline interview modalities, being attentive to the social context and power dynamics involved.

The next 5–10 years

Continuing to integrate (ethically) this technology will be among the major persisting developments in interview-based research, whether to offer more flexibility to researchers or participants, or to diversify who can participate and on what terms.

Pushing the idea of inclusion even further is the potential for integrating interview-based studies within participatory methods, which are also innovating via integrating technology. There is no hard and fast line between researchers using in-depth interviews and participatory methods; many who employ participatory methods will use interviews at the beginning, middle or end phases of a research project to capture insights, perspectives and reflections from participants 77 , 78 . Participatory methods emphasize the need to resist existing power and knowledge structures. They broaden who has the right and ability to contribute to academic knowledge by including and incorporating participants not only as subjects of data collection, but as crucial voices in research design and data analysis 77 . Participatory methods also seek to facilitate local change and to produce research materials, whether for academic or non-academic audiences, including films and documentaries, in collaboration with participants.

In responding to the challenges of COVID-19, capturing the fraught situation wrought by the pandemic and the momentum to integrate technology, participatory researchers have sought to continue data collection from afar. For example, Marzi has adapted an existing project to co-produce participatory videos, via participants’ smartphones in Medellin, Colombia, alongside regular check-in conversations/meetings/interviews with participants 79 . Integrating participatory methods into interview studies offers a route by which researchers can respond to the challenge of diversifying knowledge, challenging assumptions and power hierarchies and creating more inclusive and collaborative partnerships between participants and researchers in the Global North and South.

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Acknowledgements

The authors are grateful to the MY421 team and students for prompting how best to frame and communicate issues pertinent to in-depth interview studies.

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A pre-written interview outline for a semi-structured interview that provides both a topic structure and the ability to adapt flexibly to the content and context of the interview and the interaction between the interviewer and participant. Others may refer to the topic guide as an interview protocol.

Here we refer to the participants that take part in the study as the sample. Other researchers may refer to the participants as a participant group or dataset.

This involves dividing a population into smaller groups based on particular characteristics, for example, age or gender, and then sampling randomly within each group.

A sampling method where the guiding logic when deciding who to recruit is to achieve the most relevant participants for the research topic, in terms of being rich in information or insights.

Researchers ask participants to introduce the researcher to others who meet the study’s inclusion criteria.

Similar to stratified sampling, but participants are not necessarily randomly selected. Instead, the researcher determines how many people from each category of participants should be recruited. Recruitment can happen via snowball or purposive sampling.

A method for developing, analysing and interpreting patterns across data by coding in order to develop themes.

An approach that interrogates the explicit, implicit and taken-for-granted dimensions of language as well as the contexts in which it is articulated to unpack its purposes and effects.

A form of transcription that simplifies what has been said by removing certain verbal and non-verbal details that add no further meaning, such as ‘ums and ahs’ and false starts.

The analytic framework, theoretical approach and often hypotheses, are developed prior to examining the data and then applied to the dataset.

The analytic framework and theoretical approach is developed from analysing the data.

An approach that combines deductive and inductive components to work recursively by going back and forth between data and existing theoretical frameworks (also described as an iterative approach). This approach is increasingly recognized not only as a more realistic but also more desirable third alternative to the more traditional inductive versus deductive binary choice.

A theoretical apparatus that emphasizes the role of cultural processes and capital in (intergenerational) social reproduction.

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Knott, E., Rao, A.H., Summers, K. et al. Interviews in the social sciences. Nat Rev Methods Primers 2 , 73 (2022). https://doi.org/10.1038/s43586-022-00150-6

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Limited by our limitations

Paula t. ross.

Medical School, University of Michigan, Ann Arbor, MI USA

Nikki L. Bibler Zaidi

Study limitations represent weaknesses within a research design that may influence outcomes and conclusions of the research. Researchers have an obligation to the academic community to present complete and honest limitations of a presented study. Too often, authors use generic descriptions to describe study limitations. Including redundant or irrelevant limitations is an ineffective use of the already limited word count. A meaningful presentation of study limitations should describe the potential limitation, explain the implication of the limitation, provide possible alternative approaches, and describe steps taken to mitigate the limitation. This includes placing research findings within their proper context to ensure readers do not overemphasize or minimize findings. A more complete presentation will enrich the readers’ understanding of the study’s limitations and support future investigation.

Introduction

Regardless of the format scholarship assumes, from qualitative research to clinical trials, all studies have limitations. Limitations represent weaknesses within the study that may influence outcomes and conclusions of the research. The goal of presenting limitations is to provide meaningful information to the reader; however, too often, limitations in medical education articles are overlooked or reduced to simplistic and minimally relevant themes (e.g., single institution study, use of self-reported data, or small sample size) [ 1 ]. This issue is prominent in other fields of inquiry in medicine as well. For example, despite the clinical implications, medical studies often fail to discuss how limitations could have affected the study findings and interpretations [ 2 ]. Further, observational research often fails to remind readers of the fundamental limitation inherent in the study design, which is the inability to attribute causation [ 3 ]. By reporting generic limitations or omitting them altogether, researchers miss opportunities to fully communicate the relevance of their work, illustrate how their work advances a larger field under study, and suggest potential areas for further investigation.

Goals of presenting limitations

Medical education scholarship should provide empirical evidence that deepens our knowledge and understanding of education [ 4 , 5 ], informs educational practice and process, [ 6 , 7 ] and serves as a forum for educating other researchers [ 8 ]. Providing study limitations is indeed an important part of this scholarly process. Without them, research consumers are pressed to fully grasp the potential exclusion areas or other biases that may affect the results and conclusions provided [ 9 ]. Study limitations should leave the reader thinking about opportunities to engage in prospective improvements [ 9 – 11 ] by presenting gaps in the current research and extant literature, thereby cultivating other researchers’ curiosity and interest in expanding the line of scholarly inquiry [ 9 ].

Presenting study limitations is also an ethical element of scientific inquiry [ 12 ]. It ensures transparency of both the research and the researchers [ 10 , 13 , 14 ], as well as provides transferability [ 15 ] and reproducibility of methods. Presenting limitations also supports proper interpretation and validity of the findings [ 16 ]. A study’s limitations should place research findings within their proper context to ensure readers are fully able to discern the credibility of a study’s conclusion, and can generalize findings appropriately [ 16 ].

Why some authors may fail to present limitations

As Price and Murnan [ 8 ] note, there may be overriding reasons why researchers do not sufficiently report the limitations of their study. For example, authors may not fully understand the importance and implications of their study’s limitations or assume that not discussing them may increase the likelihood of publication. Word limits imposed by journals may also prevent authors from providing thorough descriptions of their study’s limitations [ 17 ]. Still another possible reason for excluding limitations is a diffusion of responsibility in which some authors may incorrectly assume that the journal editor is responsible for identifying limitations. Regardless of reason or intent, researchers have an obligation to the academic community to present complete and honest study limitations.

A guide to presenting limitations

The presentation of limitations should describe the potential limitations, explain the implication of the limitations, provide possible alternative approaches, and describe steps taken to mitigate the limitations. Too often, authors only list the potential limitations, without including these other important elements.

Describe the limitations

When describing limitations authors should identify the limitation type to clearly introduce the limitation and specify the origin of the limitation. This helps to ensure readers are able to interpret and generalize findings appropriately. Here we outline various limitation types that can occur at different stages of the research process.

Study design

Some study limitations originate from conscious choices made by the researcher (also known as delimitations) to narrow the scope of the study [ 1 , 8 , 18 ]. For example, the researcher may have designed the study for a particular age group, sex, race, ethnicity, geographically defined region, or some other attribute that would limit to whom the findings can be generalized. Such delimitations involve conscious exclusionary and inclusionary decisions made during the development of the study plan, which may represent a systematic bias intentionally introduced into the study design or instrument by the researcher [ 8 ]. The clear description and delineation of delimitations and limitations will assist editors and reviewers in understanding any methodological issues.

Data collection

Study limitations can also be introduced during data collection. An unintentional consequence of human subjects research is the potential of the researcher to influence how participants respond to their questions. Even when appropriate methods for sampling have been employed, some studies remain limited by the use of data collected only from participants who decided to enrol in the study (self-selection bias) [ 11 , 19 ]. In some cases, participants may provide biased input by responding to questions they believe are favourable to the researcher rather than their authentic response (social desirability bias) [ 20 – 22 ]. Participants may influence the data collected by changing their behaviour when they are knowingly being observed (Hawthorne effect) [ 23 ]. Researchers—in their role as an observer—may also bias the data they collect by allowing a first impression of the participant to be influenced by a single characteristic or impression of another characteristic either unfavourably (horns effect) or favourably (halo effort) [ 24 ].

Data analysis

Study limitations may arise as a consequence of the type of statistical analysis performed. Some studies may not follow the basic tenets of inferential statistical analyses when they use convenience sampling (i.e. non-probability sampling) rather than employing probability sampling from a target population [ 19 ]. Another limitation that can arise during statistical analyses occurs when studies employ unplanned post-hoc data analyses that were not specified before the initial analysis [ 25 ]. Unplanned post-hoc analysis may lead to statistical relationships that suggest associations but are no more than coincidental findings [ 23 ]. Therefore, when unplanned post-hoc analyses are conducted, this should be clearly stated to allow the reader to make proper interpretation and conclusions—especially when only a subset of the original sample is investigated [ 23 ].

Study results

The limitations of any research study will be rooted in the validity of its results—specifically threats to internal or external validity [ 8 ]. Internal validity refers to reliability or accuracy of the study results [ 26 ], while external validity pertains to the generalizability of results from the study’s sample to the larger, target population [ 8 ].

Examples of threats to internal validity include: effects of events external to the study (history), changes in participants due to time instead of the studied effect (maturation), systematic reduction in participants related to a feature of the study (attrition), changes in participant responses due to repeatedly measuring participants (testing effect), modifications to the instrument (instrumentality) and selecting participants based on extreme scores that will regress towards the mean in repeat tests (regression to the mean) [ 27 ].

Threats to external validity include factors that might inhibit generalizability of results from the study’s sample to the larger, target population [ 8 , 27 ]. External validity is challenged when results from a study cannot be generalized to its larger population or to similar populations in terms of the context, setting, participants and time [ 18 ]. Therefore, limitations should be made transparent in the results to inform research consumers of any known or potentially hidden biases that may have affected the study and prevent generalization beyond the study parameters.

Explain the implication(s) of each limitation

Authors should include the potential impact of the limitations (e.g., likelihood, magnitude) [ 13 ] as well as address specific validity implications of the results and subsequent conclusions [ 16 , 28 ]. For example, self-reported data may lead to inaccuracies (e.g. due to social desirability bias) which threatens internal validity [ 19 ]. Even a researcher’s inappropriate attribution to a characteristic or outcome (e.g., stereotyping) can overemphasize (either positively or negatively) unrelated characteristics or outcomes (halo or horns effect) and impact the internal validity [ 24 ]. Participants’ awareness that they are part of a research study can also influence outcomes (Hawthorne effect) and limit external validity of findings [ 23 ]. External validity may also be threatened should the respondents’ propensity for participation be correlated with the substantive topic of study, as data will be biased and not represent the population of interest (self-selection bias) [ 29 ]. Having this explanation helps readers interpret the results and generalize the applicability of the results for their own setting.

Provide potential alternative approaches and explanations

Often, researchers use other studies’ limitations as the first step in formulating new research questions and shaping the next phase of research. Therefore, it is important for readers to understand why potential alternative approaches (e.g. approaches taken by others exploring similar topics) were not taken. In addition to alternative approaches, authors can also present alternative explanations for their own study’s findings [ 13 ]. This information is valuable coming from the researcher because of the direct, relevant experience and insight gained as they conducted the study. The presentation of alternative approaches represents a major contribution to the scholarly community.

Describe steps taken to minimize each limitation

No research design is perfect and free from explicit and implicit biases; however various methods can be employed to minimize the impact of study limitations. Some suggested steps to mitigate or minimize the limitations mentioned above include using neutral questions, randomized response technique, force choice items, or self-administered questionnaires to reduce respondents’ discomfort when answering sensitive questions (social desirability bias) [ 21 ]; using unobtrusive data collection measures (e.g., use of secondary data) that do not require the researcher to be present (Hawthorne effect) [ 11 , 30 ]; using standardized rubrics and objective assessment forms with clearly defined scoring instructions to minimize researcher bias, or making rater adjustments to assessment scores to account for rater tendencies (halo or horns effect) [ 24 ]; or using existing data or control groups (self-selection bias) [ 11 , 30 ]. When appropriate, researchers should provide sufficient evidence that demonstrates the steps taken to mitigate limitations as part of their study design [ 13 ].

In conclusion, authors may be limiting the impact of their research by neglecting or providing abbreviated and generic limitations. We present several examples of limitations to consider; however, this should not be considered an exhaustive list nor should these examples be added to the growing list of generic and overused limitations. Instead, careful thought should go into presenting limitations after research has concluded and the major findings have been described. Limitations help focus the reader on key findings, therefore it is important to only address the most salient limitations of the study [ 17 , 28 ] related to the specific research problem, not general limitations of most studies [ 1 ]. It is important not to minimize the limitations of study design or results. Rather, results, including their limitations, must help readers draw connections between current research and the extant literature.

The quality and rigor of our research is largely defined by our limitations [ 31 ]. In fact, one of the top reasons reviewers report recommending acceptance of medical education research manuscripts involves limitations—specifically how the study’s interpretation accounts for its limitations [ 32 ]. Therefore, it is not only best for authors to acknowledge their study’s limitations rather than to have them identified by an editor or reviewer, but proper framing and presentation of limitations can actually increase the likelihood of acceptance. Perhaps, these issues could be ameliorated if academic and research organizations adopted policies and/or expectations to guide authors in proper description of limitations.

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

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

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

Table of Contents

What Are the Limitations of a Research Study?

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

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

Why and Where Should You Include the Research Limitations?

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

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

1. Common Limitations of the Researchers

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

2. Limited Access to Information

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

3. Limited Time

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

4. Conflict over Biased Views and Personal Issues

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

Types of Limitations of Research

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

1. Research Design Limitations

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

2. Impact Limitations

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

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

3. Data or statistical limitations

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

How to Correctly Structure Your Research Limitations?

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

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

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

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

1. Formulating your goals and objectives

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

2. Application of your data collection methods in research

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

3. Sample sizes

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

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

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

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

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

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

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

Frequently Asked Questions

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

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

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

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

Excellent article ,,,it has helped me big

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

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Last Updated on November 2, 2023 by Karl Thompson

Social Surveys are a quantitative, positivist research method consisting of structured questionnaires and interviews. This post considers the theoretical, practical and ethical advantages and disadvantages of using social surveys in social research. 

The strengths and limitations below are mainly based around surveys administered as self-completion questionnaires.

Theoretical Factors

Theoretical strengths of social surveys

Detachment, objectivity and validity.

Positivists favour questionnaires because they are a detached and objective (unbiased) method, where the sociologist’s personal involvement with respondents is kept to a minimum.

Hypothesis Testing

Questionnaires are particularly useful for testing hypotheses about cause and effect relationships between different variables, because the fact that they are quantifiable allows us to find correlations.

For example, based on government statistics on educational achievement we know that white boys on Free School Meals achieve at a significantly lower level than Chinese girls on Free School Meals. We reasonably hypothesise that this is because differences in parental attitudes – Chinese parents may value education more highly, and they may be stricter with their children when it comes to homework compared to white parents. Good questionnaire design and appropriate sampling would enable us to test out this hypothesis. Good sampling would further allow us to see if those white working class boys who do well have parents with similar attitudes to those Chinese girls who do well.

Representativeness

Questionnaires allow the researcher to collect information from a large number of people, so the results should be more representative of the wider population than with more qualitative methods. However, this all depends on appropriate sampling techniques being used and the researchers having knowledge of how actually completes the questionnaire.

Reliability

Questionnaires are generally seen as one of the more reliable methods of data collection – if repeated by another researcher, then they should give similar results. There are two main reasons for this:

When the research is repeated, it is easy to use the exact same questionnaire meaning the respondents are asked the exact same questions in the same order and they have the same choice of answers.

With self-completion questions, especially those sent by post, there is no researcher present to influence the results.

The reliability of questionnaires means that if we do find differences in answers, then we can be reasonably certain that this is because the opinions of the respondents have changed over time. For this reason, questionnaires are a good method for conducting longitudinal research where change over time is measured.

Theoretical Limitations

Issues affecting validity – Interpretivists make a number of criticisms of questionnaires .

The Imposition Problem

The imposition problem is when the researcher chooses the questions, they are deciding what is important rather than the respondent, and with closed ended questions the respondent has to fit their answers into what’s on offer. The result is that the respondent may not be able to express themselves in the way that want to. The structure of the questionnaire thus distorts the respondents’ meanings and undermines the validity of the data.

Misinterpetation of questions

Interpretivists argue that the detached nature of questionnaires and the lack of close contact between researcher and respondent means that there is no way to guarantee that the respondents are interpreting the questions in the same way as the researcher. This is especially true where very complex topics are involved – If I tick ‘yes’ that I am Christian’ – this could mean a range of things – from my being baptised but not practising or really believing to being a devout Fundamentalist. For this reason Interpretivists typically prefer qualitative methods where researchers are present to clarify meanings and probe deeper.

Researchers may not be present to check whether respondents are giving s ocially desirable answers , or simply lying, or even to check who is actually completing the questionnaire. At least with interviews researchers are present to check up on these problems (by observing body language or probing further for example).

Issues affecting representativeness

Postal questionnaires in particular can suffer from a low response rate. For example, Shere Hite’s (1991) study of ‘love, passion, and emotional violence’ in the America sent out 100, 000 questionnaires but only 4.5% of them were returned.

All self-completion questionnaires also suffer from the problem of a self-selecting sample which makes the research unrepresentative – certain types of people are more likely to complete questionnaires – literate people for example, people with plenty of time, or people who get a positive sense of self-esteem when completing questionnaires.

Practical Factors

Practical Strengths of Social Surveys

Questionnaires are a quick and cheap means of gathering large amounts of data from large numbers of people, even if they are widely dispersed geographically if the questionnaire is sent by post or conducted online. It is difficult to see how any other research method could provide 10s of millions of responses as is the case with the UK national census.

In the context of education, Connor and Dewson (2001) posted nearly 4000 questionnaires to students at 14 higher education institutions in their study of the factors which influenced working class decisions to attend university.

With self-completion questionnaires there is no need to recruit and train interviewers, which reduces cost.

The data is quick to analyse once it has been collected. With online questionnaires, pre-coded questions can be updated live.

Practical Limitations

The fact that questionnaires need to be brief means you can only ever get relatively superficial data from them, thus for many topics, they will need to be combined with more qualitative methods to achieve more insight.

Although questionnaires are a relatively cheap form of gathering data, it might be necessary to offer incentives for people to return them.

Structured Interviews are also considerably more expensive than self-completion questionnaires.

Ethical Factors

Ethical strengths of surveys

When a respondent is presented with a questionnaire, it is fairly obvious that research is taken place, so informed consent isn’t normally an issue as long as researchers are honest about the purpose of the research.

It is also a relatively unobtrusive method, given the detachment of the researcher, and it is quite an easy matter for respondents to just ignore questionnaires if they don’t want to complete them.

Ethical Limitations

They are best avoided when researching sensitive topics.

Related Posts 

An Introduction to Social Surveys – Definition and Basic Types of Survey

Positivism, Sociology and Social Research – Positivists like the survey method.

Please click here for more posts on research methods .

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Limitations in Research – Types, Examples and Writing Guide

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Limitations in Research

Limitations in research refer to the factors that may affect the results, conclusions , and generalizability of a study. These limitations can arise from various sources, such as the design of the study, the sampling methods used, the measurement tools employed, and the limitations of the data analysis techniques.

Types of Limitations in Research

Types of Limitations in Research are as follows:

Sample Size Limitations

This refers to the size of the group of people or subjects that are being studied. If the sample size is too small, then the results may not be representative of the population being studied. This can lead to a lack of generalizability of the results.

Time Limitations

Time limitations can be a constraint on the research process . This could mean that the study is unable to be conducted for a long enough period of time to observe the long-term effects of an intervention, or to collect enough data to draw accurate conclusions.

Selection Bias

This refers to a type of bias that can occur when the selection of participants in a study is not random. This can lead to a biased sample that is not representative of the population being studied.

Confounding Variables

Confounding variables are factors that can influence the outcome of a study, but are not being measured or controlled for. These can lead to inaccurate conclusions or a lack of clarity in the results.

Measurement Error

This refers to inaccuracies in the measurement of variables, such as using a faulty instrument or scale. This can lead to inaccurate results or a lack of validity in the study.

Ethical Limitations

Ethical limitations refer to the ethical constraints placed on research studies. For example, certain studies may not be allowed to be conducted due to ethical concerns, such as studies that involve harm to participants.

Examples of Limitations in Research

Some Examples of Limitations in Research are as follows:

Research Title: “The Effectiveness of Machine Learning Algorithms in Predicting Customer Behavior”

Limitations:

• The study only considered a limited number of machine learning algorithms and did not explore the effectiveness of other algorithms.
• The study used a specific dataset, which may not be representative of all customer behaviors or demographics.
• The study did not consider the potential ethical implications of using machine learning algorithms in predicting customer behavior.

Research Title: “The Impact of Online Learning on Student Performance in Computer Science Courses”

• The study was conducted during the COVID-19 pandemic, which may have affected the results due to the unique circumstances of remote learning.
• The study only included students from a single university, which may limit the generalizability of the findings to other institutions.
• The study did not consider the impact of individual differences, such as prior knowledge or motivation, on student performance in online learning environments.

Research Title: “The Effect of Gamification on User Engagement in Mobile Health Applications”

• The study only tested a specific gamification strategy and did not explore the effectiveness of other gamification techniques.
• The study relied on self-reported measures of user engagement, which may be subject to social desirability bias or measurement errors.
• The study only included a specific demographic group (e.g., young adults) and may not be generalizable to other populations with different preferences or needs.

How to Write Limitations in Research

When writing about the limitations of a research study, it is important to be honest and clear about the potential weaknesses of your work. Here are some tips for writing about limitations in research:

• Identify the limitations: Start by identifying the potential limitations of your research. These may include sample size, selection bias, measurement error, or other issues that could affect the validity and reliability of your findings.
• Be honest and objective: When describing the limitations of your research, be honest and objective. Do not try to minimize or downplay the limitations, but also do not exaggerate them. Be clear and concise in your description of the limitations.
• Provide context: It is important to provide context for the limitations of your research. For example, if your sample size was small, explain why this was the case and how it may have affected your results. Providing context can help readers understand the limitations in a broader context.
• Discuss implications : Discuss the implications of the limitations for your research findings. For example, if there was a selection bias in your sample, explain how this may have affected the generalizability of your findings. This can help readers understand the limitations in terms of their impact on the overall validity of your research.
• Provide suggestions for future research : Finally, provide suggestions for future research that can address the limitations of your study. This can help readers understand how your research fits into the broader field and can provide a roadmap for future studies.

Purpose of Limitations in Research

There are several purposes of limitations in research. Here are some of the most important ones:

• To acknowledge the boundaries of the study : Limitations help to define the scope of the research project and set realistic expectations for the findings. They can help to clarify what the study is not intended to address.
• To identify potential sources of bias: Limitations can help researchers identify potential sources of bias in their research design, data collection, or analysis. This can help to improve the validity and reliability of the findings.
• To provide opportunities for future research: Limitations can highlight areas for future research and suggest avenues for further exploration. This can help to advance knowledge in a particular field.
• To demonstrate transparency and accountability: By acknowledging the limitations of their research, researchers can demonstrate transparency and accountability to their readers, peers, and funders. This can help to build trust and credibility in the research community.
• To encourage critical thinking: Limitations can encourage readers to critically evaluate the study’s findings and consider alternative explanations or interpretations. This can help to promote a more nuanced and sophisticated understanding of the topic under investigation.

When to Write Limitations in Research

Limitations should be included in research when they help to provide a more complete understanding of the study’s results and implications. A limitation is any factor that could potentially impact the accuracy, reliability, or generalizability of the study’s findings.

It is important to identify and discuss limitations in research because doing so helps to ensure that the results are interpreted appropriately and that any conclusions drawn are supported by the available evidence. Limitations can also suggest areas for future research, highlight potential biases or confounding factors that may have affected the results, and provide context for the study’s findings.

Generally, limitations should be discussed in the conclusion section of a research paper or thesis, although they may also be mentioned in other sections, such as the introduction or methods. The specific limitations that are discussed will depend on the nature of the study, the research question being investigated, and the data that was collected.

Examples of limitations that might be discussed in research include sample size limitations, data collection methods, the validity and reliability of measures used, and potential biases or confounding factors that could have affected the results. It is important to note that limitations should not be used as a justification for poor research design or methodology, but rather as a way to enhance the understanding and interpretation of the study’s findings.

Importance of Limitations in Research

Here are some reasons why limitations are important in research:

• Enhances the credibility of research: Limitations highlight the potential weaknesses and threats to validity, which helps readers to understand the scope and boundaries of the study. This improves the credibility of research by acknowledging its limitations and providing a clear picture of what can and cannot be concluded from the study.
• Facilitates replication: By highlighting the limitations, researchers can provide detailed information about the study’s methodology, data collection, and analysis. This information helps other researchers to replicate the study and test the validity of the findings, which enhances the reliability of research.
• Guides future research : Limitations provide insights into areas for future research by identifying gaps or areas that require further investigation. This can help researchers to design more comprehensive and effective studies that build on existing knowledge.
• Provides a balanced view: Limitations help to provide a balanced view of the research by highlighting both strengths and weaknesses. This ensures that readers have a clear understanding of the study’s limitations and can make informed decisions about the generalizability and applicability of the findings.

Advantages of Limitations in Research

Here are some potential advantages of limitations in research:

• Focus : Limitations can help researchers focus their study on a specific area or population, which can make the research more relevant and useful.
• Realism : Limitations can make a study more realistic by reflecting the practical constraints and challenges of conducting research in the real world.
• Innovation : Limitations can spur researchers to be more innovative and creative in their research design and methodology, as they search for ways to work around the limitations.
• Rigor : Limitations can actually increase the rigor and credibility of a study, as researchers are forced to carefully consider the potential sources of bias and error, and address them to the best of their abilities.
• Generalizability : Limitations can actually improve the generalizability of a study by ensuring that it is not overly focused on a specific sample or situation, and that the results can be applied more broadly.

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Muhammad Hassan

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Social Science Works

Center of Deliberation

The Limits Of Survey Data: What Questionnaires Can’t Tell Us

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All research methodologies have their limitations, as many authors have pointed before (see for example Visser, Krosnick and Lavrakas, 2000). From the generalisabilty of data to the nitty-gritty of bias and question wording, every method has its flaws. In fact, the in-fighting between methodological approaches is one of social science’s worst kept secrets: the hostility between quantitative and qualitative data scholars knows almost no bounds (admittedly that’s ‘almost no bounds’ within the polite world of academic debate) and doesn’t look set to be resolved any time soon. That said, there are some methods that are better suited than others to certain types of studies. This article will examine the role of survey data in values studies and argue that it is a blunt tool for this kind of research and that qualitative study methods, particularly via deliberation, are more appropriate. This article will do so via an examination of a piece of 2016 research published by the German ministry for migrants and refugees (the BAMF) which explored both the demographics and the social values held by refugees that have arrived in Germany in the last three years. This article will argue that surveys are unfit to get at the issues that are most important to people.

The Good, The Bad & The Survey

Germany has been Europe’s leading figure as the refugee crisis has deepened worldwide following the collapse of government in Syria and the rise of ISIS. Today, there are 65.3 million displaced people from across the world and 21.3 million refugees (UNHCR, 2016) , a number that surpasses even the number of refugees following the Second World War. The exact number of refugees living in Germany (official statistics typically count all migrants seeking protection as refugees, although there is some difference between the various legal statuses) is not entirely clear and the figure is unstable. And while this figure still lags behind the efforts made by countries like Turkey and Jordan, this represents the highest total number of refugees in a European country and matches the pro capita efforts of Sweden. Meanwhile, there are signs that Germany’s residents do not always welcome their new neighbours. For example, in 2016, there were almost 2,000 reported attacks on refugees and refugee homes (Antonio-Amedeo Stiftung, 2017) a similar trend was established by Benček and Strasheim (2016), and the rise of the far-right and anti-migrant party, the AfD in local elections last year points to unresolved resentment towards the newcomers.

In this context then, it makes sense for the BAMF ( Bundesamt für Migration und Flüchtlinge ) , the ministry responsible for refugees and migrants in Germany, to respond to pressure in the media and from politicians to get a better overall picture about the kinds of people the refugees to Germany are. As such, their 2016 paper: “Survey of refugees – flight, arrival in Germany and first steps of integration” [1] details a host of information about newcomers in Germany. The study, which relied on questionnaires given by BAMF officials in a number of languages, and a face-to-face or online format (BAMF, 2016, 11), asked questions of 4,500 refugee respondents. For the most part, the study offers excellent insight into the demographic history of refugees to Germany and will be helpful for policymakers looking to ensure that efforts to help settles refugees are appropriately targeted. For example, the study detailed the relatively high level of education enjoyed by typical refugees to Germany (an average between 10 and 11 years of schooling) (ibid., 37) and some of the specific difficulties this group have in successfully navigating the job market (ibid., 46) and where this group turns to for help for this.

In addition to offering the most up-to-date information about refugees’ home countries and their path into Germany, the study is extremely helpful for politicians and scholars looking to enhance their understanding of logistical and practical issues facing migrants; for example, who has access to integration courses? How many unaccompanied children are in Germany? How many men and how many women fled to Germany? Here, the study is undoubtedly helpful.  However, the latter stages of the report purport to examine the social values held by refugees, and it is this part of the study that this article takes issue with.

Respondents were asked to answer questions about their values. The topics included: the right form a government should take, the role of democracy, voting rights of women, the role of religion in the state, men and women’s equality in a marriage, and perceived difference between the values of refugees and Germans among others. While this article doesn’t take issue with the veracity of the findings reported in the article, it does argue that the methods used here are inappropriate for the task at hand. Consider first the questions relating to refugees’ attitudes toward democracy and government. The report found that 96% of refugee respondents agreed with the statement: “One should have a democratic system” [2] compared with 95% of the German control group (ibid., 52). This finding was picked up in the liberal media and heralded as a sign that refugees share central German social values. It is entirely possible that this is true. However, it isn’t difficult to see the ways in which this number might have been accidentally manufactured and should hence be treated with considerable caution.

To do so, one must first consider the circumstances of the interview or questionnaire. As a refugee in Germany, you are confronted with the authority of the BAMF regularly, and you are also likely aware that it is representatives from this organization that ultimately decide  on you and your family’s status in Germany and whether you will have the right to stay or not. You are then asked for detailed information about your family history, your education and your participation in integration courses by a representative from this institution. Finally, the interviewer asks what your views are on democracy, women’s rights and religion. Is it too much of a jump to suggest that someone who has had to flee their home and take the extraordinarily dangerous trip to Europe is savvy enough to spot a potential trap here? In these circumstances, there is a tendency to give the answer the interviewer wants to hear. This interviewer bias effect is not a problem exclusive to surveys of refugees’ social values (Davis, 2013), however the power imbalance in these interactions exacerbates the effect. The argument advanced here is not that refugees do not hold a positive view of democracy, but that the trying to find out their views via a survey of this sort is flawed. In fact, the report doesn’t find any significant points of departure between Germans and refugees on any of the major values other than the difficulties presented by women earning more money than their husbands and its potential to cause marital difficulties (ibid., 54).

Asking Questions About Essential Contested Concepts

Beyond the serious power imbalance noted above, another key issue not addressed in the BAMF study is the question of contested concepts. Essential contested concepts, an idea first advanced by W.B. Gallie in 1956, are the big topics like art, beauty, fairness and trust. These big topics, which also include traditionally social scientific and political topics like democracy and equality, are defined as ‘essentially contested’ when the premise of the concept – for example ‘freedom’ – is widely accepted, but how best to realise freedom is disputed (Hart, 1961, 156). The BAMF survey uses these big topics without offering a definition to go with them. What do people mean when they say that ‘men and women should have equal rights [3] ’ (BAMF, 2016, 52)? What does equality mean in this context? There are of course many different ways that ‘equality’ between men and women can be interpreted. For example, many conservative Catholic churches argue that men and women are ‘equal’ but different, and have clear family roles for men and women. Likewise, participants could equally mean to say that they believe that men and women should have equal, shared family responsibilities, there is no way to know this from this study. Hence, it is difficult to know how best to interpret these kinds of statistics without considerable context.

As part of the work undertaken by Social Science Works, the team are regularly confronted by these kinds of questions via deliberative workshops with Germans and refugees. In these workshops the team ask questions like “What is democracy?”, “What is freedom?”, “What is equality?”. In doing so, the aim of the workshops is to build a consensus together by formulating and reformulating possible definitions [4] , finding common ground between conflicting perspectives and ultimately defining the concepts as a group. What is among the most striking things about these meetings is the initial reluctance of participants to volunteer answers – there is a real lack of certainty about what these kinds of words mean in practice, even among participants who, for example, have studied social and political sciences or work in politics. With the benefit of hindsight, workshop participants have acknowledged these problems in dealing with essentially contested concepts, participants have commented :

“Social Science Works has encouraged me to question my own views and views more critically and to develop a more precise concept for large and often hard to grasp terms such as “democracy”, “freedom” or “equality”. This experience has shown me how complicated it is for me – as someone who I really felt proficient in these questions – to formulate such ideas concretely.”   (German participant from the 2016 series of workshops) “The central starting point for the training was, for me, the common notion of understanding of democracy and freedom. In the intensive discussion, I realized that these terms, which seem self-evident, are anything but.” (German participant from the 2016 series of workshops).

In attempting to talk about these big issues, it become clear just how little consensus there is on these kinds of topics. The participants quoted here work and volunteer in the German social sector and hence confront these kinds of ideas implicitly on a daily basis. The level of uncertainty pointed at here, and from Social Science Works’ wider experience working with volunteers, social workers and refugees suggests that the lack of fluency in essentially contested concepts is a wider problem. In the context of the BAMF research then, it is clear that readers ought to take the chapter detailing the ‘values’ of refugees and Germans with a generous pinch of salt.

Building Consensus & Moving Forward

This article does not seek to suggest that there is no role for survey data in helping to answer questions relating to refugees in Germany. For the most part, the BAMF research offers excellent data on key questions relating to demographics and current social conditions. Hence, the study ought to make an excellent tool of policy makers seeking to better target their support of refugees. However, it is equally clear that to discuss essentially contested concepts like democracy and equality, a survey is a very blunt tool, and here the BAMF study fails to convince. The study seeks to make clear that the social and political values between Germans and refugees are similar and the differences are minimal. The experience in the deliberative workshops hosted by Social Science Works suggests that this is probably true, insofar as both groups find these concepts difficult to define and have to wrestle to make sense of them. This is not something articulated in the BAMF research, however.

Our collective lack of fluency in these topics, even among social and political scholars, has long roots best described another time. However, if we are to improve our abilities to discuss these kinds of topics and build collective ideas for social change and cohesion, there are much better places to begin than a questionnaire. If we are to build a collective understanding of our political structures and our social values, we need to address this lack of fluency by engaging in discussions with diverse groups and together building a coherent idea about social and political ideas.

[1]  Original German: “Befragung von Geflüchteten – Flucht, Ankunft in Deutschland und erste Schritte der Integration“

[2] Original German: „Man sollte ein demokratisches System haben.“

[3] Original German: „Frauen haben die gleichen Rechte wie Männer“

[4] For a more detailed overview of the deliberative method in these workshops, see Blokland, 2016.

Amadeu Antonio Foundation (2016), Hate Speech Against Refugees , Amadeu Antonio Foundation, Berlin.

Benček, D. and Strasheim, J. (2016), Refugees Welcome? Introducing a New Dataset on Anti-Refugee Violence in Germany, 2014–2015 , Working Paper No. 2032, University of Kiel.

Davis, R. E.; et al. (Feb 2010). Interviewer effects in public health surveys , Health Education Research, Oxford University Press, Oxford.

Hart, H.L.A., (1961),  The Concept of Law , Oxford University Press, Oxford.

IAB-BAMF-SOEP (2016), B efragung von Geflüchteten – Flucht, Ankunft in Deutschland und erste Schritte der Integration , BAMF-Forschungsbericht 29, Nürnberg: Bundesamt für Migration und Flüchtlinge.

UNHCR (2016), Global Trends: Forced Displacement in 2015 , UNHCR, New York.

Visser, P. S., Krosnick, J. A., & Lavrakas, P. (2000), Survey research , in H. T. Reis & C. M. Judd (Eds.), Handbook of research methods in social psychology , New York: Cambridge University Press.

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Qualitative Research: Definition, Methodology, Limitation & Examples

Qualitative research is a method focused on understanding human behavior and experiences through non-numerical data. Examples of qualitative research include:

• One-on-one interviews,
• Focus groups, Ethnographic research,
• Case studies,
• Record keeping,
• Qualitative observations

In this article, we’ll provide tips and tricks on how to use qualitative research to better understand your audience through real world examples and improve your ROI. We’ll also learn the difference between qualitative and quantitative data.

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Marketers often seek to understand their customers deeply. Qualitative research methods such as face-to-face interviews, focus groups, and qualitative observations can provide valuable insights into your products, your market, and your customers’ opinions and motivations. Understanding these nuances can significantly enhance marketing strategies and overall customer satisfaction.

What is Qualitative Research

Qualitative research is a market research method that focuses on obtaining data through open-ended and conversational communication. This method focuses on the “why” rather than the “what” people think about you. Thus, qualitative research seeks to uncover the underlying motivations, attitudes, and beliefs that drive people’s actions. 

Let’s say you have an online shop catering to a general audience. You do a demographic analysis and you find out that most of your customers are male. Naturally, you will want to find out why women are not buying from you. And that’s what qualitative research will help you find out.

In the case of your online shop, qualitative research would involve reaching out to female non-customers through methods such as in-depth interviews or focus groups. These interactions provide a platform for women to express their thoughts, feelings, and concerns regarding your products or brand. Through qualitative analysis, you can uncover valuable insights into factors such as product preferences, user experience, brand perception, and barriers to purchase.

Types of Qualitative Research Methods

Qualitative research methods are designed in a manner that helps reveal the behavior and perception of a target audience regarding a particular topic.

The most frequently used qualitative analysis methods are one-on-one interviews, focus groups, ethnographic research, case study research, record keeping, and qualitative observation.

1. One-on-one interviews

Conducting one-on-one interviews is one of the most common qualitative research methods. One of the advantages of this method is that it provides a great opportunity to gather precise data about what people think and their motivations.

Spending time talking to customers not only helps marketers understand who their clients are, but also helps with customer care: clients love hearing from brands. This strengthens the relationship between a brand and its clients and paves the way for customer testimonials.

• A company might conduct interviews to understand why a product failed to meet sales expectations.
• A researcher might use interviews to gather personal stories about experiences with healthcare.

These interviews can be performed face-to-face or on the phone and usually last between half an hour to over two hours. 

When a one-on-one interview is conducted face-to-face, it also gives the marketer the opportunity to read the body language of the respondent and match the responses.

2. Focus groups

Focus groups gather a small number of people to discuss and provide feedback on a particular subject. The ideal size of a focus group is usually between five and eight participants. The size of focus groups should reflect the participants’ familiarity with the topic. For less important topics or when participants have little experience, a group of 10 can be effective. For more critical topics or when participants are more knowledgeable, a smaller group of five to six is preferable for deeper discussions.

The main goal of a focus group is to find answers to the “why”, “what”, and “how” questions. This method is highly effective in exploring people’s feelings and ideas in a social setting, where group dynamics can bring out insights that might not emerge in one-on-one situations.

• A focus group could be used to test reactions to a new product concept.
• Marketers might use focus groups to see how different demographic groups react to an advertising campaign.

One advantage that focus groups have is that the marketer doesn’t necessarily have to interact with the group in person. Nowadays focus groups can be sent as online qualitative surveys on various devices.

Focus groups are an expensive option compared to the other qualitative research methods, which is why they are typically used to explain complex processes.

3. Ethnographic research

Ethnographic research is the most in-depth observational method that studies individuals in their naturally occurring environment.

This method aims at understanding the cultures, challenges, motivations, and settings that occur.

• A study of workplace culture within a tech startup.
• Observational research in a remote village to understand local traditions.

Ethnographic research requires the marketer to adapt to the target audiences’ environments (a different organization, a different city, or even a remote location), which is why geographical constraints can be an issue while collecting data.

This type of research can last from a few days to a few years. It’s challenging and time-consuming and solely depends on the expertise of the marketer to be able to analyze, observe, and infer the data.

4. Case study research

The case study method has grown into a valuable qualitative research method. This type of research method is usually used in education or social sciences. It involves a comprehensive examination of a single instance or event, providing detailed insights into complex issues in real-life contexts.  

• Analyzing a single school’s innovative teaching method.
• A detailed study of a patient’s medical treatment over several years.

Case study research may seem difficult to operate, but it’s actually one of the simplest ways of conducting research as it involves a deep dive and thorough understanding of the data collection methods and inferring the data.

5. Record keeping

Record keeping is similar to going to the library: you go over books or any other reference material to collect relevant data. This method uses already existing reliable documents and similar sources of information as a data source.

• Historical research using old newspapers and letters.
• A study on policy changes over the years by examining government records.

This method is useful for constructing a historical context around a research topic or verifying other findings with documented evidence.

6. Qualitative observation

Qualitative observation is a method that uses subjective methodologies to gather systematic information or data. This method deals with the five major sensory organs and their functioning, sight, smell, touch, taste, and hearing.

• Sight : Observing the way customers visually interact with product displays in a store to understand their browsing behaviors and preferences.
• Smell : Noting reactions of consumers to different scents in a fragrance shop to study the impact of olfactory elements on product preference.
• Touch : Watching how individuals interact with different materials in a clothing store to assess the importance of texture in fabric selection.
• Taste : Evaluating reactions of participants in a taste test to identify flavor profiles that appeal to different demographic groups.
• Hearing : Documenting responses to changes in background music within a retail environment to determine its effect on shopping behavior and mood.

Below we are also providing real-life examples of qualitative research that demonstrate practical applications across various contexts:

Qualitative Research Real World Examples

Let’s explore some examples of how qualitative research can be applied in different contexts.

1. Online grocery shop with a predominantly male audience

Method used: one-on-one interviews.

Let’s go back to one of the previous examples. You have an online grocery shop. By nature, it addresses a general audience, but after you do a demographic analysis you find out that most of your customers are male.

One good method to determine why women are not buying from you is to hold one-on-one interviews with potential customers in the category.

Interviewing a sample of potential female customers should reveal why they don’t find your store appealing. The reasons could range from not stocking enough products for women to perhaps the store’s emphasis on heavy-duty tools and automotive products, for example. These insights can guide adjustments in inventory and marketing strategies.

2. Software company launching a new product

Method used: focus groups.

Focus groups are great for establishing product-market fit.

Let’s assume you are a software company that wants to launch a new product and you hold a focus group with 12 people. Although getting their feedback regarding users’ experience with the product is a good thing, this sample is too small to define how the entire market will react to your product.

So what you can do instead is holding multiple focus groups in 20 different geographic regions. Each region should be hosting a group of 12 for each market segment; you can even segment your audience based on age. This would be a better way to establish credibility in the feedback you receive.

3. Alan Pushkin’s “God’s Choice: The Total World of a Fundamentalist Christian School”

Method used: ethnographic research.

Moving from a fictional example to a real-life one, let’s analyze Alan Peshkin’s 1986 book “God’s Choice: The Total World of a Fundamentalist Christian School”.

Peshkin studied the culture of Bethany Baptist Academy by interviewing the students, parents, teachers, and members of the community alike, and spending eighteen months observing them to provide a comprehensive and in-depth analysis of Christian schooling as an alternative to public education.

The study highlights the school’s unified purpose, rigorous academic environment, and strong community support while also pointing out its lack of cultural diversity and openness to differing viewpoints. These insights are crucial for understanding how such educational settings operate and what they offer to students.

Even after discovering all this, Peshkin still presented the school in a positive light and stated that public schools have much to learn from such schools.

Peshkin’s in-depth research represents a qualitative study that uses observations and unstructured interviews, without any assumptions or hypotheses. He utilizes descriptive or non-quantifiable data on Bethany Baptist Academy specifically, without attempting to generalize the findings to other Christian schools.

4. Understanding buyers’ trends

Method used: record keeping.

Another way marketers can use quality research is to understand buyers’ trends. To do this, marketers need to look at historical data for both their company and their industry and identify where buyers are purchasing items in higher volumes.

For example, electronics distributors know that the holiday season is a peak market for sales while life insurance agents find that spring and summer wedding months are good seasons for targeting new clients.

5. Determining products/services missing from the market

Conducting your own research isn’t always necessary. If there are significant breakthroughs in your industry, you can use industry data and adapt it to your marketing needs.

The influx of hacking and hijacking of cloud-based information has made Internet security a topic of many industry reports lately. A software company could use these reports to better understand the problems its clients are facing.

As a result, the company can provide solutions prospects already know they need.

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Qualitative Research Approaches

Once the marketer has decided that their research questions will provide data that is qualitative in nature, the next step is to choose the appropriate qualitative approach.

The approach chosen will take into account the purpose of the research, the role of the researcher, the data collected, the method of data analysis , and how the results will be presented. The most common approaches include:

• Narrative : This method focuses on individual life stories to understand personal experiences and journeys. It examines how people structure their stories and the themes within them to explore human existence. For example, a narrative study might look at cancer survivors to understand their resilience and coping strategies.
• Phenomenology : attempts to understand or explain life experiences or phenomena; It aims to reveal the depth of human consciousness and perception, such as by studying the daily lives of those with chronic illnesses.
• Grounded theory : investigates the process, action, or interaction with the goal of developing a theory “grounded” in observations and empirical data. 
• Ethnography : describes and interprets an ethnic, cultural, or social group;
• Case study : examines episodic events in a definable framework, develops in-depth analyses of single or multiple cases, and generally explains “how”. An example might be studying a community health program to evaluate its success and impact.

How to Analyze Qualitative Data

Analyzing qualitative data involves interpreting non-numerical data to uncover patterns, themes, and deeper insights. This process is typically more subjective and requires a systematic approach to ensure reliability and validity. 

1. Data Collection

Ensure that your data collection methods (e.g., interviews, focus groups, observations) are well-documented and comprehensive. This step is crucial because the quality and depth of the data collected will significantly influence the analysis.

2. Data Preparation

Once collected, the data needs to be organized. Transcribe audio and video recordings, and gather all notes and documents. Ensure that all data is anonymized to protect participant confidentiality where necessary.

3. Familiarization

Immerse yourself in the data by reading through the materials multiple times. This helps you get a general sense of the information and begin identifying patterns or recurring themes.

Develop a coding system to tag data with labels that summarize and account for each piece of information. Codes can be words, phrases, or acronyms that represent how these segments relate to your research questions.

• Descriptive Coding : Summarize the primary topic of the data.
• In Vivo Coding : Use language and terms used by the participants themselves.
• Process Coding : Use gerunds (“-ing” words) to label the processes at play.
• Emotion Coding : Identify and record the emotions conveyed or experienced.

5. Thematic Development

Group codes into themes that represent larger patterns in the data. These themes should relate directly to the research questions and form a coherent narrative about the findings.

6. Interpreting the Data

Interpret the data by constructing a logical narrative. This involves piecing together the themes to explain larger insights about the data. Link the results back to your research objectives and existing literature to bolster your interpretations.

7. Validation

Check the reliability and validity of your findings by reviewing if the interpretations are supported by the data. This may involve revisiting the data multiple times or discussing the findings with colleagues or participants for validation.

8. Reporting

Finally, present the findings in a clear and organized manner. Use direct quotes and detailed descriptions to illustrate the themes and insights. The report should communicate the narrative you’ve built from your data, clearly linking your findings to your research questions.

Limitations of qualitative research

The disadvantages of qualitative research are quite unique. The techniques of the data collector and their own unique observations can alter the information in subtle ways. That being said, these are the qualitative research’s limitations:

1. It’s a time-consuming process

The main drawback of qualitative study is that the process is time-consuming. Another problem is that the interpretations are limited. Personal experience and knowledge influence observations and conclusions.

Thus, qualitative research might take several weeks or months. Also, since this process delves into personal interaction for data collection, discussions often tend to deviate from the main issue to be studied.

2. You can’t verify the results of qualitative research

Because qualitative research is open-ended, participants have more control over the content of the data collected. So the marketer is not able to verify the results objectively against the scenarios stated by the respondents. For example, in a focus group discussing a new product, participants might express their feelings about the design and functionality. However, these opinions are influenced by individual tastes and experiences, making it difficult to ascertain a universally applicable conclusion from these discussions.

3. It’s a labor-intensive approach

Qualitative research requires a labor-intensive analysis process such as categorization, recording, etc. Similarly, qualitative research requires well-experienced marketers to obtain the needed data from a group of respondents.

4. It’s difficult to investigate causality

Qualitative research requires thoughtful planning to ensure the obtained results are accurate. There is no way to analyze qualitative data mathematically. This type of research is based more on opinion and judgment rather than results. Because all qualitative studies are unique they are difficult to replicate.

5. Qualitative research is not statistically representative

Because qualitative research is a perspective-based method of research, the responses given are not measured.

Comparisons can be made and this can lead toward duplication, but for the most part, quantitative data is required for circumstances that need statistical representation and that is not part of the qualitative research process.

While doing a qualitative study, it’s important to cross-reference the data obtained with the quantitative data. By continuously surveying prospects and customers marketers can build a stronger database of useful information.

Quantitative vs. Qualitative Research

Image source

Quantitative and qualitative research are two distinct methodologies used in the field of market research, each offering unique insights and approaches to understanding consumer behavior and preferences.

As we already defined, qualitative analysis seeks to explore the deeper meanings, perceptions, and motivations behind human behavior through non-numerical data. On the other hand, quantitative research focuses on collecting and analyzing numerical data to identify patterns, trends, and statistical relationships.  

Let’s explore their key differences: 

Nature of Data:

• Quantitative research : Involves numerical data that can be measured and analyzed statistically.
• Qualitative research : Focuses on non-numerical data, such as words, images, and observations, to capture subjective experiences and meanings.

Research Questions:

• Quantitative research : Typically addresses questions related to “how many,” “how much,” or “to what extent,” aiming to quantify relationships and patterns.
• Qualitative research: Explores questions related to “why” and “how,” aiming to understand the underlying motivations, beliefs, and perceptions of individuals.

Data Collection Methods:

• Quantitative research : Relies on structured surveys, experiments, or observations with predefined variables and measures.
• Qualitative research : Utilizes open-ended interviews, focus groups, participant observations, and textual analysis to gather rich, contextually nuanced data.

Analysis Techniques:

• Quantitative research: Involves statistical analysis to identify correlations, associations, or differences between variables.
• Qualitative research: Employs thematic analysis, coding, and interpretation to uncover patterns, themes, and insights within qualitative data.

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• Last modified: January 3, 2023
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• Meaning, Functions or Uses and Limitations of Social Science Research

MEANING OF SOCIAL SCIENCE RESEARCH: “Social science research is a systematic method o exploring, analysing and conceptualizing human life in order to extend, correct or verify knowledge of human behaviour and social life.” In other words, social science research seek to find explanations to unexplained social phenomena, to clarify the doubtful and correct the misconceived facts of social life. FUNCTIONS OR USES OF SOCIAL SCIENCE RESEARCH: 1 Discovery of facts and their Interpretation: Research provides answer to questions of what, where, when, how and why of man, social life and institutions. They are half-truths pseudo truths and superstitions. Discovery of facts and their interpretation help us discard such distortions and thus enlighten us and contribute to our understanding of social reality research strengthens our desire for truth and opens up before our eyes, hidden social mysteries. 2 Diagnosis of problems and their analysis: The developing courtiers have innumerable problems such as poverty, unemployment, economic imbalance, economic inequality, social tension, low productivity, technological backwardness, etc. The nature and dimensions of such problems have to be diagnosed and analysed; social science research plays a significant role in this respect. An analysis of problems leads to an identification of appropriate remedial actions. 3 Systematization of knowledge: The facts discovered through research are systematized and the body of knowledge is developed. Thus research contributes to the growth of various social sciences and theory building. 4 Control over social phenomena: Research in social science areas equips us with first-hand knowledge about the organizing and working of the society and its institutions. This knowledge gives us a greater power of control over the social phenomena. 5 Prediction: Research aims at findings an order among social facts and their casual relation. This affords a sound basis for prediction in several cases. Although the predictions cannot be perfect because of the inherent limitations of social sciences, they will be fairly useful for better social planning and control. 6 Development planning: Planning for socio-economic development calls for baseline data on the various aspects of our society and economy, resource endowment, peoples needs and aspirations, etc. systematic research can give us the required data base for planning and designing developmental schemes and programmes. Analytical studies can illuminate critical areas of policy and testing the validity of planning assumptions. Evaluation studies point the impact of plan, policies and programmes and throw out suggestions for their proper reformulation. 7 Social Welfare: Social research can unfold and identify the causes of social evils problems. It can thus help in taking appropriate remedial actions. It can also give us sound guidelines for appropriate positive measures of reform and social welfare. LIMITATIONS OF SOCIAL SCIENCES RESEARCH Research in social sciences has certain limitations and problems when compared with research in physical sciences. They are discussed below: a)      Scientists a part of what is studied: The fact that social scientist is part of the human society which he studies gives rise to certain limitations. Man must have to be his won guinea pig. as pointed out by Jalian Huxley. This has a number of methodological consequences. For example, it restricts the scope for controlled experiments. It limits the scope for objectivity in social science research. b)      Complexity of the subject matter: The subject matter of research in social science, viz. human society and human behaviour is too complex varied and changing to yield to the scientific categorization, measurement, analysis and prediction. The multiplicity and complexity of causation make it difficult to apply the technique of experimentation. Human behaviour can be studied only be other human beings, and this always distorts fundamentally the facts being studied so that there can be no objective procedure for achieving the truth. c)      Human Problems: A social scientist faces certain human problems, which the natural scientist is sparred. These problems are varied and include refusal of respondents improper understanding of questions by them their loss of memory, their reluctance to furnish certain information, etc. All these problems cause biases and invalidate the research findings and conclusions. d)      Personal Values: Subjects and clients, as well as investigators, have personal values, which are apt to become involved in the research process. One should not assume that these are freely exploitable. The investigator must have respect for the client’s values. e)      Anthropomorphization: Another hazard of social science research is the danger of the temptation to anthropomorphize about humans, it results in using observation obtained by sheer intuition or empathy in conceptualizing in anthropomorphic manner. f)       Wrong Decisions: The quality of research findings depends upon the soundness of decisions made by the social scientist on such crucial stages of his research process as definition of the unit of study operationalization of concepts, selection of sampling techniques and statistical techniques. Any mistake in any of these decisions will vitiate the validity of his findings. Reference : Research Methodology by C R Kothari Research Methodology   - Pondicherry University Research Methodology   - Calicut University Other Sources - Internet

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US State and Regional Energy Innovation Index

Vibrant regional energy innovation ecosystems are important for any national net-zero strategy. But to understand the potential contributions they can make to the price and performance of clean energy technologies, we must first benchmark the resources they bring to bear.

KEY TAKEAWAYS

Key takeaways.

Key Takeaways 1

Introduction . 2

Regional Innovation Ecosystems: Engines of the Energy Transition . 3

From TBED to CEBED: The Regional Moment in U.S. Energy and Climate Innovation Policy 5

Measuring State and Regional Energy Innovation Ecosystems 7

The Index 11

Conclusions and Recommendations 18

Appendix 1: Indicators and Weights 20

Appendix 2: Methodology and Sources 20

Appendix 3: Search Strategies 20

Endnotes 21

Introduction

The United States, along with the rest of the world, has embarked on a transition to clean energy. The transition’s ultimate endpoint is net-zero greenhouse gas (GHG) emissions to limit the impact of climate change. Energy security, human health, local environmental protection, and economic opportunity also motivate the global community to pursue this important objective. But the path to net zero is strewn with obstacles. Many of the technologies the world needs to stay on it are too expensive, perform too poorly, or are simply unavailable right now. Innovation should therefore be a major focus of any net-zero strategy. [1]

Regional energy innovation ecosystems have great potential to contribute to such strategies. Geographically concentrated networks of technology and service firms, research institutions, and nonprofit and public sector entities could drive price and performance improvements in a diverse array of clean energy sources and uses. This report assesses the potential of energy innovation ecosystems across the United States to contribute to this important mission, drawing on a wide range of data, such as federal and private funding, publications and patents, and state and regional policies and public opinion, covering nine categories of innovation system functions, to compile an index of this potential. Fourteen technology-specific indices, which draw on subsets of the main database, complement the main index and highlight regional diversity.

The index, while inevitably imperfect, provides a baseline against which to measure the future impact of recent federal legislation. Landmark bills passed by Congress in 2021 and 2022 support states and regions that seek to strengthen their energy innovation ecosystems. Quite a few states and regions had already begun to do so before the new federal programs were created, and many more are now responding to these opportunities. The report concludes by offering broad suggestions for sustaining this momentum and improving the odds that the new policies will succeed.

Explore the five accompanying data visualizations for detailed profiles of states , regions , metropolitan statistical areas , combined statistical areas , and metropolitan divisions , respectively:

Regional Innovation Ecosystems: Engines of the Energy Transition

Abundant, affordable, reliable energy is a fundamental requirement of a high standard of living. A small handful of individuals, following Thoreau, may choose a life of voluntary simplicity, but the vast majority of the world’s population seeks the comforts and opportunities that are widely available in high-income countries. While these need not be supplied as wastefully as they are now, especially in the United States, they intrinsically demand substantial energy inputs.

The Industrial Revolution, which brought for the first time a measure of comfort and opportunity to a large proportion of the population in the places it swept through, rested on energy from fossil fuels. That pattern continues today, with these same fuels providing about 80 percent of global primary energy. They remain abundant and reasonably affordable and reliable, but the social costs of burning them have mounted. Most notably, fossil fuel combustion accounts for about 75 percent of the GHG emissions that are driving catastrophic storms, wildfires, and other symptoms of global climate change. [2]

The challenge facing human civilization, then, is to enable all those who desire to live at a high standard to have the quantity and quality of energy they need to do so, while simultaneously and dramatically reducing the harm that would cause. As Gaster, Atkinson, and Righter argue, new and improved energy technologies that emit far fewer GHGs, while matching (or nearly so) the price and performance of the incumbents, lie at the core of any strategy with any chance of surmounting this monumental challenge. [3]

A diverse array of such innovations are needed. Some, such as solar panels and heat pumps, are well advanced, though still capable of significant improvement. Many others, such as green steel and carbon dioxide (CO 2 ) removal, are early in their development. Many of these new technologies are complex systems themselves, and nearly all must be further integrated with even more complex systems, such as the power grid. [4]

Energy innovation is a subject of discussion in international climate talks and figures into many national policies. Some national governments are making important contributions by funding clean energy research, development, and demonstration, fostering climate-tech venture investments, and the like. But the innovation rubber really hits the energy transition road at the regional level. That’s because innovation, especially innovation in complex systems, accelerates most quickly when dense networks of firms and supporting institutions, clustered in relatively compact geographic areas, pursue it. [5]

The concept of regional innovation ecosystems is an old one, dating back to the 19th century economist Alfred Marshall, who noted “something in the air” in places such as Sheffield, where Britain’s pioneering cutlery makers were concentrated. Modern research has revealed that “something” to have many elements: When working effectively, regional innovation ecosystems foster knowledge exchange, attract specialized labor, facilitate infrastructure investment, and encourage entrepreneurship, among other things. Regions diverge economically in large part because of these ecosystems. Some are home to innovative industries that serve growing markets beyond the regions in which they are located, while others rely on stagnant or shrinking sectors. Silicon Valley and Detroit epitomize these extremes in the public mind. [6]

Digitalization might have been expected to undermine these dynamics, but, as many analysts have noted, “the death of distance has been greatly exaggerated.” Van der Wouden and Youn, for instance, find that while the geographical distance between research collaborators grew substantially between 1975 and 2015, so had the “learning premium” associated with geographical proximity. Those who collaborated locally were far more likely to enter new fields and build their own capabilities than those who collaborated long distance. The effect was especially strong in STEM (science, technology, engineering, and math) disciplines, such as chemistry, materials science, and engineering, which are particularly important in energy innovation. [7]

The systemic nature of energy innovation heightens the importance of collaboration within regions. Innovative low-carbon power, transportation, and industrial systems typically involve diverse components that must be integrated carefully to optimize performance and minimize cost and emissions. These integration processes, in turn, often require learning-by-doing and learning-by-using across organizational and institutional boundaries. Geographic proximity is likely to ease them by facilitating hands-on and face-to-face interactions. [8]

The importance of regional energy innovation ecosystems in the coming decades will be heightened by the vulnerability to disruption of places dependent today on fossil-fuel-based industries. Wyoming’s coal mines, Houston’s petrochemical plants, and Detroit’s auto factories are among those at risk. Hanson, a co-author of 2016’s “The China Shock” paper (a belated recognition of that epochal impact by neoclassical economists) wrote that “the energy transition … is a shock foretold” for such regions. [9]

Whether such “brownfield” regions are willing and able to repurpose their existing assets or build new ones to seize the opportunities presented by the transition will go a long way toward determining their future economic dynamism in a low-carbon world. Wyoming’s effort to position itself as a leader in nuclear power and carbon capture, Houston’s push to become a hydrogen hub, and Detroit’s emerging shift to electric vehicles illustrate these dynamics. Of course, such retooling regions must frequently compete with “greenfield” locations elsewhere, domestically and globally. [10]

That competition has important consequences for the energy transition. If regional innovation ecosystems are able to lower the cost and improve the performance of emissions-reducing technologies, their uptake will expand, feeding ideas and resources back to the regions that make them. This virtuous cycle extending beyond the region will be enhanced and enabled by international agreements and national policies, but ultimately depends on positive feedbacks within the region among laboratories, factories, testbeds, and related facilities, organizations, and institutions.

From TBED to CEBED: The Regional Moment in U.S. Energy and Climate Innovation Policy

Some regional innovation ecosystems specializing in low-carbon technology have emerged relatively spontaneously. Wind energy innovation revitalized Denmark’s central Jutland region, for instance, repurposing older manufacturing assets beginning in the 1970s and later fending off higher-tech competitors elsewhere. Others have been built up more deliberately. The solar power manufacturing cluster in China’s Yangtze River Delta was created in the 2000s in large measure by targeted local, provincial, and national policies. [11]

The deliberate approach to building such ecosystems is likely to dominate going forward, as the need for energy innovation, and the extra-regional export opportunities created by the energy transition, are increasingly evident to policymakers worldwide. China’s success in solar manufacturing is part of a broader strategy to dominate emerging clean technologies. The European Union is pursuing a “smart specialization strategy” with an increasingly green tilt to diversify its regional economies and move them “up the ladder of higher knowledge complexity and value creation.” [12]

Some state and local governments in the United States adopted such strategies in the 2010s. New York has sought to establish its southern tier as a global center for energy storage manufacturing, while Colorado’s Front Range region has become a hub for cleantech start-ups. Until recently, however, the U.S. federal government has not kept pace with its global competitors in this regard. [13]

That changed with the passage of major legislation by the 117th Congress (2021–2022). New programs supported regional innovation ecosystems and technology-based economic development (TBED) across all industries, encouraging many states and regions to propose initiatives focusing on clean energy technologies. Five out of 21 regional coalitions that won the Build Back Better Regional Challenge, funded by the Department of Commerce (DOC) under the 2021 American Rescue Plan, focused on energy innovation. So did 7 of DOC’s 31 regional tech hubs designees and 7 of its 18 regional strategy development grantees, a program authorized by the 2022 CHIPS and Science Act. (See box 1 for a brief description of this program.) Six of the 10 winners of regional “engine” grants selected by the National Science Foundation (NSF) (also under CHIPS and Science) are seeking to drive sustainable energy or climate-related innovation as well. [14]

In addition, the Bipartisan Infrastructure Law and Inflation Reduction Act established programs and funding streams specifically to catalyze regional energy innovation. The new DOE Office of Clean Energy Demonstrations (OCED), for instance, is implementing an $8 billion program to create regional hubs for clean hydrogen production, distribution, and use. OCED has roughly $20 billion more for large-scale demonstration projects in other technology areas, including $6.3 billion for industrial decarbonization. DOE’s Office of Fossil Energy and Carbon Management has received an additional $3.5 billion to fund direct air capture hubs. More broadly, Congress has explicitly tasked DOE with responsibility for fostering regional competitiveness through clean energy innovation, and given preference to fossil-fuel-dependent communities in many of these programs. [15]

The response to these bills indicates that an increasing number of states and regions in the United States are seeking to enhance their competitive advantages in a world striving for net-zero emissions. (Box 2 briefly describes a regional strategy and box 3 a state strategy.) Their efforts fold into a broader discourse around TBED and “place-based” policies. Best practice in these domains rests on a grounded assessment of existing state and regional assets that allows identification of “adjacent possible” sectors. These are sectors with a realistic potential for future export growth rather than fantastic dreams of building the next Silicon Valley. [16]

This report advances the movement toward Clean Energy Based Economic Development (CEBED) by applying insights from the large corpus of analytical work that underpins TBED. We have compiled a wide range of indicators that measure how well a region’s energy innovation system is functioning today. We hope the findings will inform strategies to build a more prosperous and cleaner future.

Federal Regional Technology and Innovation Hub Program (Tech Hubs)

The Tech Hubs program, initially proposed by ITIF, was authorized by the 2022 CHIPS and Science Act. It seeks to enable regions (Metropolitan Statistical Areas (MSAs) or closely connected MSAs and nearby micropolitan statistical areas) to become globally competitive in “industries of the future.” Such industries lie within the ambit of 10 broad technology areas laid out in the act, including “advanced energy and industrial efficiency” as well as “disaster prevention or mitigation.” Congress authorized $10 billion for the program and appropriated $500 million through fiscal year 2023. [17]

Regional consortia seeking Tech Hubs grants from the Economic Development Administration (EDA), a unit of DOC, must include an institution of higher education; state, local, or tribal governments; industry; labor; and economic development organizations. These consortia must set forth a compelling narrative that describes a region’s potential to achieve world-class status, the barriers that impede its achievement, and projects that would address those barriers. Projects may advance innovation, strengthen the workforce, develop business and entrepreneurship opportunities, and build infrastructure. [18]

In October 2023, EDA designated 31 consortia as eligible for 5 to 10 grants of $50 million to $75 million. It also awarded 29 strategy development grants of roughly $500,000, 11 to consortia eligible now and 18 to consortia that may become eligible in future phases of the program. In addition to EDA funding, Tech Hubs will receive preferential treatment in a variety of other federal programs, such as those supporting foreign direct investment and providing export assistance. [19]

Seven of the eligible consortia fall within the categories of “Accelerating America’s Clean Energy Transition” and “Strengthening Our Critical Minerals Supply Chain”:

§ Louisiana: offshore wind and renewable energy

§ Idaho and Wyoming: small modular reactors (SMR) and advanced nuclear

§ South Carolina: exportable electricity technologies

§ Florida: sustainable and resilient infrastructure

§ New York: batteries

§ Nevada: lithium

§ Missouri: battery materials

Several others will contribute less directly to energy innovation, such as gallium nitride technology (Vermont), which underpins power system electronics. [20]

The governing statute for the program enumerates 13 considerations for selecting hubs, which EDA has distilled into 7 broad criteria: project quality and ability to execute, impact on economic and national security, investment and policy commitments, workforce, capital, equity and diversity, and governance. A consortium’s plan to leverage existing innovation assets is included in the first, fourth, and fifth criteria, while its forecast for the targeted technology’s impact and prospects for retaining manufacturing are incorporated into the second. [21]

Measuring State and Regional Energy Innovation Ecosystems

Energy innovation ecosystems are made up of complex networks of actors, institutions, and resources that contribute to the generation, development, diffusion, and use of innovative energy products and services. To be effective, such systems must perform a broad range of functions, including mobilizing resources, developing and diffusing new scientific and technical knowledge, facilitating experimentation by entrepreneurs, facilitating the formation of supply chains and new markets, legitimizing new technologies in society, guiding the search for new knowledge in certain directions, and guiding its spillover into other related industries. [22]

Our index is built from the following four subindices that seek to capture distinct groups of these functions:

▪ Knowledge development and diffusion

▪ Entrepreneurial experimentation

▪ Supply chain and market formation

▪ Social legitimation

In this section, we briefly review the categories and indicators included in each of the four subindices. Most indicators are available at the county level and are aggregated to the regional and state levels.

In addition to the main index, our work provides insights into regional technological specializations, which vary greatly across the United States. (See figure 1 for a comparison of Massachusetts and South Carolina.) Fourteen technology areas, each of which is covered by an index that draws on a subset of the main database and is constructed in the same fashion, are listed at the end of this section.

A very detailed account of sources and methods can be found in appendix 2.

Subindex: Knowledge Development and Diffusion

Knowledge development and diffusion activities comprise the first subindex. Unless new scientific and technical knowledge is developed and diffused, no new clean energy innovations will emerge, and there will be nothing to scale up. The subindex consists of three categories of indicators.

Category: Research and Development

Mobilization of resources to fund research and development (R&D) activities performed by companies, government laboratories, and academic institutions lies at the base of this subindex. The public sector plays a larger role in energy R&D than in many other sectors, in large part because the transition to clean energy is being driven by the environmental threat of climate change, and markets have not been responsive to it. The category focuses on federal low-carbon energy R&D spending, which far outpaces state and local investments, assessing the ability of states and regions to garner federal awards.

Category: Knowledge

R&D funding contributes to scientific discoveries. The quality of this new knowledge varies considerably. Most discoveries end up having little scientific or commercial value, while highly valued knowledge is ultimately recognized by and diffused through networks of academic and professional peers. We use data on publications as a proxy for new discoveries and data on publication citations to estimate their quality and extent of diffusion.

Category: Invention

R&D funding also to contributes to the development of technical know-how and the generation of new inventions. Like new knowledge, the quality and commercial viability of inventions varies considerably. We use data on patents as a proxy for new inventions and data on patent citations to estimate their quality and extent of diffusion.

Subindex: Entrepreneurial Experimentation

Entrepreneurial experimentation activities comprise the second subindex. These activities largely involve a different set of actors, institutions, and processes than those involved in knowledge development and diffusion, whose aim is to test and demonstrate the commercial viability of new technological innovations in niche markets.

Category: Demonstration

Technology demonstration projects seek to establish the market viability of new clean energy innovations. The public sector plays a larger role in energy demonstration projects than in many other sectors due to the high-risk nature and long development horizons of many emerging energy technologies. We use federal spending data to assess the ability of states and regions to garner federal awards for energy demonstration projects.

Category: Entrepreneurship

Entrepreneurs create new ventures that carry out the high-risk technological, business, and social experiments that must be performed before innovative energy products and services can join the mainstream. These new ventures may receive support from venture capitalists and federal grants and, when successful, scale up by exiting through acquisitions or initial public offerings (IPOs). We use data on federal seed investments, venture capital investments, and successful company exits to assess state and regional contributions to the entrepreneurship function.

Subindex: Supply Chain and Market Formation

Supply chain and market formation activities comprise the third subindex. Successful scale-up of innovations, whether carried out by a new or established business, depends on the availability of inputs at a competitive cost and on a growing array of buyers who find value in deploying these innovations. Some supply chains and markets may lie within the state or region where an innovation is made, although these functions frequently extend beyond these boundaries. Proximity to suppliers and customers can provide valuable feedback as innovations bridge from early adoption to mass markets.

Category: Industry

A central goal of CEBED is to create jobs and steady employment in clean energy industries. We use data on low-carbon energy employment to assess the ability of states and regions to create such jobs and strengthen state and regional supply chains.

Category: Technology Adoption

A long-term CEBED strategy ultimately depends on generating an abundant and reliable supply of low-carbon energy resources to power industrial activities and ensure sustainable economic development. We use data on the supply of low-carbon electricity generation and energy storage resources to assess the ability of states and regions to mobilize resources and facilitate market formation for building clean energy infrastructure.

Subindex: Social Legitimation

Social legitimation activities comprise the fourth subindex. Innovation is an intrinsically social process. Incumbent energy technologies are often buttressed by political, legal, and regulatory mechanisms and embedded in supportive state and regional cultures. The more innovations disrupt legacy systems, the more effort is required for them to break through to widespread adoption.

Category: Public Goals and Strategies

Social legitimation of innovations and CEBED depends on the goals and strategies of policymakers. We use data on published public policy and strategy documents to assess the degree to which states and regions have adopted CEBED strategies.

Category: Social Values

In a democracy, social legitimation and CEBED policies ultimately depend on the values of the general public. We use data on public opinion about clean energy R&D and climate action to assess the extent to which the citizens of states and regions value clean energy innovation and CEBED.

Technological Specialization

A function of energy innovation ecosystems that adds significant depth to the index is guidance on the direction of the search for new technologies, and ultimately, CEBED. The clean energy transition is a deliberate and purposeful attempt to guide the economy away from dependence on unabated fossil fuels and toward a sustainable path of low-carbon energy production and use. Within that overarching framework, energy innovation ecosystems may also be guided toward specific technology areas. The index seeks to capture these technological specializations at the state and regional level. These are measured by subindices covering fourteen technology areas:

1. Advanced energy materials

2. Bioenergy

3. Carbon capture, utilization, and storage (CCUS)

4. Clean energy manufacturing

5. Clean energy transportation

6. Energy efficiency

7. Energy storage

8. Geothermal energy

9. Grid technologies

10. Hydrogen and fuel cells

11. Nuclear energy

12. Solar energy

13. Water energy

14. Wind energy

Limitations

Our measures of state and regional energy innovation ecosystems are imperfect. For instance, private R&D spending is a very important input to these ecosystems, but it is not measured adequately enough to incorporate into the index. Data constraints also limit our visibility into clean energy employment and state and regional clean energy innovation policies in the third and fourth subindices. In the final section of this report, we recommend that federal agencies invest in improved measurement systems so that state and regional economic development strategists can become better informed.

New Energy New York

New York State’s Southern Tier, an eight-county region bordering Pennsylvania, was a thriving center of manufacturing in the first half of the 20th century. Major U.S. firms such as IBM and General Electric called the Southern Tier home. While the region’s strength in electronics manufacturing cushioned the blow, the Southern Tier suffered a long decline in the second half of the 20th century, which has worsened since then. [23]

New Energy New York (NENY) is a regional initiative led by Binghamton University that seeks to help revive the area by creating a globally competitive battery technology development and manufacturing hub. The NENY coalition includes state and local government agencies along with universities and a variety of community and nonprofit organizations. The initiative’s key elements include technology prototyping, supplier identification and certification, workforce development, and start-up incubation, with attention to equity across diverse populations throughout. [24]

The initiative emerged from a longer-term effort by both Binghamton to develop its innovation capacity in the wake of deindustrialization and by the state to target clean energy industries for economic development. A series of grants from federal and state sources, capped by a New York State Energy Research and Development Authority (NYSERDA)-funded clean energy incubator, put Binghamton in position to compete effectively in the new federal grant programs. M. Stanley Whittingham, a Binghamton University distinguished professor who won the Nobel Prize for his contributions to the invention of the lithium-ion battery, played a foundational role in establishing NENY’s credibility. A battery “gigafactory” being developed by iM3NY, on a site where IBM manufactured products from 1911 to 2002, is another anchor asset. [25]

With strong support from the state’s congressional delegation and significant state investments, NENY has run the table in federal grant competitions. It won $63.7 million In EDA’s Build Back Better Regional Challenge to construct a technology and manufacturing development center equipped with state-of-the-art manufacturing lines for the production of full-size battery cells. It was designated as an EDA Tech Hub, enabling it to compete for $50 million to $75 million in the next phase of the program and benefit from preferential treatment in other federal programs. In early 2024, it took home an NSF Regional Engine award worth $15 million over the next two years and up to $160 million in the next decade to carry out R&D, technology translation, and workforce development for the battery industry. NENY and its partners must now execute the challenging commitments they have made to secure these investments. [26]

This section reports illustrative results from the ITIF State and Regional Energy Innovation Index. The weighting scheme used to compile the index is set forth in appendix 1. The full results and the underlying database, which cover all 50 states and the District of Columbia and up to 935 regions (Core-Based Statistical Areas, as defined by the Office of Management and Budget), can be accessed through the ITIF Center for Clean Energy Innovation website. The website allows users to find scores for the overall index, four subindices, and nine functional categories for user-specified states or regions for the years 2016 to 2021. Users can also find the 14 technology-specific versions of these scores and generate charts displaying a location’s functional and technological strengths and weaknesses. The site also features national heat maps of this data.

Table 1 reports the top five and bottom five states in the 2021 Index and their strongest and weakest functional categories and technology areas. States with small populations take the top slots, perhaps because many index categories are scaled by the size of the state population or economy. Nonetheless, the index reveals important strengths and weaknesses. For example, while the Index’s top-ranked state, Vermont, ranks well across most categories, it is especially strong in start-ups (measured by federal Small Business Innovation Research (SBIR) grants, private venture capital investments, and successful company exits). The #2 state, South Dakota, by contrast, does well in technology adoption, thanks to the importance of wind power there, but does relatively poorly in generating and diffusing original research through scientific publications. Neighboring North Dakota, which ranks fifth overall, shows an even sharper contrast, capturing a disproportionate share of federal R&D spending for its size but coming in 48th in the social legitimation subindex due to very low public support for low-carbon energy research and climate action. The technology specializations reveal similar divergences. Hawaii, for instance, ranks last in grid technologies but sixth in solar energy.

Table 1: Top and bottom states and their strengths and weaknesses in the 2021 index

Table 2 reports the top 5 and bottom 5 out of 382 MSAs in the 2021 Index and their strongest and weakest functional categories and technology areas. Like the state index, the regional index reveals important strengths and weaknesses. The top region, which is in central Virginia, for instance, is at the top of the supply chain and market formation subindex, which includes clean energy employment, but only 123rd in the entrepreneurial experimentation subindex. The bottom region, Rome, Georgia, actually matches the top region in the entrepreneurship ranking, but is pulled down by extreme weakness in all the other subindices. Among larger, better-known metro regions, the San Francisco metropolitan region ranks 79th, Chicago 269th, Atlanta 293rd, and New York City 295th out of the 382 MSAs.

Table 2: Top and bottom regions and their strengths and weaknesses in the 2021 Index

Table 3 and table 4 report illustrative results for 5 of the 14 technology areas at the state and regional levels, respectively, for 2021. Vermont’s top ranking in the overall index is reflected in its high ranks in four of these five areas, while Rhode Island, which ranked 34th overall, leads in wind energy technological innovation. Similarly, among MSA regions, Bangor, Maine, ranks 1st in wind energy technological innovation (and 2nd in water technological innovation, which is not shown), but 25th overall and as low as 221st in hydrogen and 230th in nuclear energy.

Table 3: Top ten states across five technology areas in 2021

Table 4: Top ten regions across five technology areas in 2021

Finally, figure 1 and figure 2 compare two states in the middle of the rankings, Massachusetts (ranked 25th) and South Carolina (ranked 26th) to illustrate their functional and technological similarities and differences. Massachusetts outshines South Carolina in entrepreneurship and societal values, while South Carolina displays greater strength in clean energy employment (industry) and technology adoption. Across technological areas, Massachusetts ranks in the top 10 states across most, but in the bottom third in transportation and hydrogen. South Carolina’s top area is nuclear energy, where it ranks 4th, while its worst showing is in energy efficiency, where it ranks 28th.

Figure 1: Functional comparison of Massachusetts and South Carolina

Figure 2: Technological comparison of Massachusetts and South Carolina

South Carolina Nexus for Advanced Resilient Energy

The state of South Carolina entered the modern manufacturing economy in the early 1990s when German automaker BMW sited a new campus there. The auto plant and the industrial ecosystem that grew up around it took the place of a textile industry in decline. A decade later, Boeing began building parts of its 787 Dreamliner in the state, which is now the sole assembly site for the plane. A sprawling network of suppliers grew up around these anchor facilities. Manufacturing production and employment surged as the sector regained its role as a pillar of the state economy. [27]

When the federal Tech Hubs program was announced, the state’s economic development agency had completed a roadmapping exercise that identified further diversification of manufacturing as a key strategy. Burgeoning global markets in fields such as electric vehicles, nuclear power, and renewables beckoned. Tech Hub’s “advanced energy” key technology focus area aligned with this strategy. [28]

The state assembled a broad cross-sectoral coalition to support its Tech Hub proposal, including manufacturers such as Rolls Royce and Westinghouse, utilities, educational institutions, and DOE’s Savannah River National Laboratory, along with numerous state agencies. The South Carolina Nexus for Advanced Resilient Energy (SC Nexus) seeks to create a “globally leading hub driving innovation in core technologies that enable an end-to-end resilient, sustainable energy ecosystem across clean-electricity generation, distribution, and grid-scale storage.” [29] The proposal targets manufacturing of components for nuclear, offshore wind, hydrogen, and solar photovoltaic systems; the creation of a battery innovation and testing ecosystem; and power grid re-engineering. It includes a plan to establish an incubator to support the state’s advanced energy entrepreneurs. [30]

SC Nexus’s designation as a Tech Hub in October 2023 allows it to compete for a phase 2 award of $50 million to $75 million. Its phase 2 application, submitted in February 2024, focuses on manufacturing distributed energy resource systems and enabling their innovative use. It includes testbeds and simulation resources for improving grid operations and security, drawing on DOE and Department of Defense as well as academic capabilities, and a new enegy storage institute that aims to commercialize new technologies for grid-scale use. Whether or not the state wins this award, it plans to continue with the SC Nexus strategy. [31]

Conclusions and Recommendations

Regional innovation ecosystems have the potential to become vital engines of the global transition to low-carbon energy. The creation and strengthening of agile, geographically proximate learning networks of research institutions, suppliers, and producers, loosely coordinated by public and nonprofit regional organizations, offers a promising pathway to drive price and performance improvements in many specialized domains of clean-tech production and use.

The United States ought to be home to many of these ecosystems. As the world’s largest historic source of emissions, it has an obligation to contribute to climate solutions; as the world’s leader in science, technology, and innovation, it has tremendous potential to do so.

The ITIF State and Regional Energy Innovation Index provides a comprehensive map of that potential. This report summarizes indicators that seek to measure a wide range of energy innovation ecosystem functions including knowledge discovery and dissemination, entrepreneurial experimentation, supply chains and market formation, and social legitimation. These indicators are available at multiple geographical levels, including states and metropolitan regions, and cover 14 technological domains.

Economic development organizations in the United States are increasingly cognizant of the potential benefits of clean energy innovation. Recent federal legislation has amplified that awareness and provided resources to act on it. This index provides a baseline against which to measure the impacts of federal programs growing from that legislation in the coming years.

These prospective impacts would be enhanced by sustaining and improving key features of the new programs. We offer several recommendations to this end.

▪ The federal government should continue to support the development and implementation of innovation-based state and regional development strategies, including those relying on clean energy innovation. The economic development programs created by Congress over the last three years are fundamentally sound and long overdue. The CHIPS and Science Act provides the authority to expand several of them substantially. While fiscal conditions may not allow fully authorized levels to be reached for some time, moderate growth is necessary to sustain the institutional momentum that these programs have created at the state and regional levels. The strong bottom-up interest in clean energy innovation ensures that it will have a robust place in state and local strategies as long as federal resources continue to flow. [32]

▪ Federal programs supporting state and regional economic development strategies should continue to use evaluation criteria that enable clean energy innovation. The new programs generally mandate that federal grants address critical national challenges. The Tech Hubs program, for example, includes “advanced energy” as one of its key technology focus areas that may be tackled by applicants. Both the broad requirement to address national challenges and the specific inclusion of clean energy innovation within it are appropriate. Energy security, reliability, and affordability, and limiting the impact of climate change, are long-term, large-scale challenges to which clean energy innovation, rooted in regional industrial clusters, is an essential response. [33]

▪ Federal agencies should support data collection and related research that enable state and regional economic development strategists to make better-informed decisions about the growth potential and resource and asset requirements of industries drawing on clean energy innovation. A major difficulty in devising economic development strategies is that the industries of the future may not look like industries of the past. The infrastructure, skill requirements, supply chains, and technological foundations will evolve and may even transform. The difficulty is particularly acute for clean energy innovation because unabated fossil fuel combustion is so deeply embedded in the core technologies of many legacy sectors. Electric vehicles are very different from conventional cars, and green steelmaking processes look nothing like blast furnaces. While uncertainty about the future cannot be eliminated, a concerted national research program would help reduce it as well as help align expectations across regions about opportunities and threats posed by the energy transition. [34]

▪ Federal programs should continue to support state and regional capacity-building for clean energy innovation so that bottom-up strategies stand a better chance of success. States and regions vary in their sophistication about economic development and administrative capacity to execute strategies. Congress and federal implementing agencies impose uniform requirements that are challenging for a significant fraction of state and regional applicants to fulfill. For instance, the NSF Regional Engines program requires cross-sectoral partnerships that can translate new research into tangible economic outcomes, which many regions lack. The program recognizes that applicants do not start on a level playing field, and it prioritizes “regions … without well-established innovation ecosystems.” [35] For this approach to succeed, the agency will need to be patient, recognize potential as well as achievement in evaluating proposals, and cultivate that potential in the post-award period by encouraging awardees to build capacity.

▪ Federal programs supporting state and regional economic development strategies should strengthen coordination among themselves to reduce the administrative burdens on applicants to these programs and to ensure the programs are mutually complementary. A common theme in the discourse among participants in state and regional economic development policy is application fatigue. Applications for federal funds are lengthy and complex, and are not uniform across agencies. Congressional mandates bind federal agencies to some degree, but agencies have discretion to make the process easier without sacrificing either its legality or effectiveness. Federal program managers are aware of this challenge and have taken steps to address it. NSF and EDA have entered into a formal memorandum of understanding, for instance. They are collaborating to make their place-based grants with overlapping focus areas and regions of service “stackable” and exploring joint reporting, among other things. [36] DOE’s technology-specific programs seem to be less engaged in these interagency processes.

The U.S. economy’s ability to adapt to changing geopolitical, environmental, social, and technological circumstances has been an enduring strength throughout its history. The nation’s regional economies, individually and collectively, are a key element of this strength. This strength will be tested again by the energy transition and global climate change. Public policy at all levels of governance can and should foster regional clean energy innovation ecosystems to enable the nation to pass this latest test.

Appendix 1: Indicators and Weights

(See the PDF, pages 20–42 .)

Appendix 2: Methodology and Sources

(See the PDF, pages 43–59 .)

Appendix 3: Search Strategies

(See the PDF, pages 60–68 .)

Acknowledgments

The authors would like to thank Rob Atkinson, Robin Gaster, and Erica Schaffer of ITIF, Lachlan Carey and colleagues from RMI’s Accelerating Clean Regional Economies initiative, and numerous interviewees for sharing their ideas and experiences with us.

About the Authors

Chad A. Smith is a doctoral student in public policy at George Mason University’s Schar School of Policy and Government

David M. Hart is a professor at George Mason University’s Schar School of Policy and Government. He is a senior fellow at ITIF and the former director of ITIF’s Center for Clean Energy Innovation. Prof. Hart co-authored Energizing America (Columbia University Center for Global Energy Policy, 2020), Unlocking Energy Innovation (MIT Press, 2012), and numerous ITIF reports.

The Information Technology and Innovation Foundation (ITIF) is an independent 501(c)(3) nonprofit, nonpartisan research and educational institute that has been recognized repeatedly as the world’s leading think tank for science and technology policy. Its mission is to formulate, evaluate, and promote policy solutions that accelerate innovation and boost productivity to spur growth, opportunity, and progress. For more information, visit itif.org/about .

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[18] .   EDA, “Notice of Funding Opportunity,” https://www.eda.gov/sites/default/files/2023-10/Tech_Hubs_NOFO_2_FINAL.pdf .

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[21] .   EDA, “Notice of Funding Opportunity,” 33–37.

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[23] .   Office of the State Comptroller, “The Changing Manufacturing Sector in Upstate New York,” June 2010, https://www.osc.ny.gov/files/local-government/publications/pdf/manufacturingreport.pdf ; Susanne Craig, “New York’s Southern Tier: Once a Home for Big Business, Is Struggling, September 30, 2015, https://www.nytimes.com/2015/09/30/nyregion/new-yorks-southern-tier-once-a-home-for-big-business-is-struggling.html .

[24] .   “New Energy New York Coalition Members” accessed April 5, 2024, https://newenergynewyork.com/#coalition ; American Jobs Project, “The New York Jobs Project,” December 2018, http://americanjobsproject.us/wp/wp-content/uploads/2018/12/The-New-York-Jobs-Project.pdf .

[25] .   Per Stromberg, interview, March 5, 2024; BingUNews, “This Is a Big Deal,” February 24, 2022, https://www.binghamton.edu/news/story/3495/this-is-a-big-deal-new-energy-new-york-stakeholders-meet-to-discuss-lithium-ion-battery-manufacturing-proposal ; Spectrum News, “Endicott Prepares for Resurgence,” September 26, 2022, https://spectrumlocalnews.com/nys/binghamton/news/2022/09/25/endicott-prepares-for-resurgence--here-s-how-they-got-here .

[26] .   “New Energy New York: Overarching Narrative,” https://www.eda.gov/sites/default/files/2022-09/New_Energy_New_York.pdf ; New Energy New York, “Battery Tech Hub,” https://www.eda.gov/sites/default/files/2023-11/New_Energy_New_York_Battery_Tech_Hub.pdf ; New Energy New York, “NSF Engines: Upstate New York Energy Storage Engine,” https://newenergynewyork.com/nsf-engine-upstate-ny-energy-storage-engine/ .

[27] .   Krys Merryman, “BMW’s $26B Impact on South Carolina Economy Still Growing,” SC Biz News, March 22, 2023 https://scbiznews.com/bmws-26b-impact-on-south-carolina-economy-still-growing/ ; Business Facilities, “Boeing To Consolidate 787 Production in South Carolina in 2021, October 6, 2021, https://businessfacilities.com/boeing-to-consolidate-787-production-in-south-carolina-in-2021 ; South Carolina Manufacturers Alliance, “The Impact of Manufacturing in South Carolina,” April 2021, https://scfuturemakers.com/wp-content/uploads/2021/04/SCManufacturingEconomicImpact.pdf .  

[28] .   Harry Lightsey, interview, February 20, 2024.

[29] .   SC Nexus for Advanced Resilient Energy, “Tech Hub Designation Application,” https://www.eda.gov/sites/default/files/2023-11/SC_Nexus_for_Advanced_Resilient_Energy.pdf .

[30] .   Ibid.

[31] .   “SC Nexus Webinar,” January 3, 2024, https://www.sccommerce.com/sites/default/files/2024-01/20240103_SC%20NEXUS_Webinar_vS_0.pdf ; Lightsey interview.

[32] .   Congressional Research Service, “Regional Innovation: Federal Programs and Issues for Consideration,” April 3, 2023, https://crsreports.congress.gov/product/pdf/R/R47495 .

[33] .   EDA, “Notice of Funding Opportunity,”

[34] .   RMI, “Accelerating Clean Regional Economies: A Great Lakes Investment Strategy,” September 2023, https://rmi.org/accelerating-clean-regional-economies-a-great-lakes-investment-strategy/ .

[35] .   NSF, “Regional Innovation Engines Broad Agency Announcement,” May 3, 2022, https://new.nsf.gov/funding/initiatives/regional-innovation-engines/updates/funding-opportunity-nsf-regional-innovation .

[36] .   Scott Andes and Alex Jones, interview, February 14, 2024; Joda Thognopnua, interview, February 22, 2024.

Editors’ Recommendations

May 28, 2024

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