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The Four Types of Research Paradigms: A Comprehensive Guide

5-minute read

• 22nd January 2023

In this guide, you’ll learn all about the four research paradigms and how to choose the right one for your research.

Introduction to Research Paradigms

A paradigm is a system of beliefs, ideas, values, or habits that form the basis for a way of thinking about the world. Therefore, a research paradigm is an approach, model, or framework from which to conduct research. The research paradigm helps you to form a research philosophy, which in turn informs your research methodology.

Your research methodology is essentially the “how” of your research – how you design your study to not only accomplish your research’s aims and objectives but also to ensure your results are reliable and valid. Choosing the correct research paradigm is crucial because it provides a logical structure for conducting your research and improves the quality of your work, assuming it’s followed correctly.

Three Pillars: Ontology, Epistemology, and Methodology

Before we jump into the four types of research paradigms, we need to consider the three pillars of a research paradigm.

Ontology addresses the question, “What is reality?” It’s the study of being. This pillar is about finding out what you seek to research. What do you aim to examine?

Epistemology is the study of knowledge. It asks, “How is knowledge gathered and from what sources?”

Methodology involves the system in which you choose to investigate, measure, and analyze your research’s aims and objectives. It answers the “how” questions.

Let’s now take a look at the different research paradigms.

1.   Positivist Research Paradigm

The positivist research paradigm assumes that there is one objective reality, and people can know this reality and accurately describe and explain it. Positivists rely on their observations through their senses to gain knowledge of their surroundings.

In this singular objective reality, researchers can compare their claims and ascertain the truth. This means researchers are limited to data collection and interpretations from an objective viewpoint. As a result, positivists usually use quantitative methodologies in their research (e.g., statistics, social surveys, and structured questionnaires).

This research paradigm is mostly used in natural sciences, physical sciences, or whenever large sample sizes are being used.

2.   Interpretivist Research Paradigm

Interpretivists believe that different people in society experience and understand reality in different ways – while there may be only “one” reality, everyone interprets it according to their own view. They also believe that all research is influenced and shaped by researchers’ worldviews and theories.

As a result, interpretivists use qualitative methods and techniques to conduct their research. This includes interviews, focus groups, observations of a phenomenon, or collecting documentation on a phenomenon (e.g., newspaper articles, reports, or information from websites).

3.   Critical Theory Research Paradigm

The critical theory paradigm asserts that social science can never be 100% objective or value-free. This paradigm is focused on enacting social change through scientific investigation. Critical theorists question knowledge and procedures and acknowledge how power is used (or abused) in the phenomena or systems they’re investigating.

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Researchers using this paradigm are more often than not aiming to create a more just, egalitarian society in which individual and collective freedoms are secure. Both quantitative and qualitative methods can be used with this paradigm.

4.   Constructivist Research Paradigm

Constructivism asserts that reality is a construct of our minds ; therefore, reality is subjective. Constructivists believe that all knowledge comes from our experiences and reflections on those experiences and oppose the idea that there is a single methodology to generate knowledge.

This paradigm is mostly associated with qualitative research approaches due to its focus on experiences and subjectivity. The researcher focuses on participants’ experiences as well as their own.

Choosing the Right Research Paradigm for Your Study

Once you have a comprehensive understanding of each paradigm, you’re faced with a big question: which paradigm should you choose? The answer to this will set the course of your research and determine its success, findings, and results.

To start, you need to identify your research problem, research objectives , and hypothesis . This will help you to establish what you want to accomplish or understand from your research and the path you need to take to achieve this.

You can begin this process by asking yourself some questions:

• What is the nature of your research problem (i.e., quantitative or qualitative)?
• How can you acquire the knowledge you need and communicate it to others? For example, is this knowledge already available in other forms (e.g., documents) and do you need to gain it by gathering or observing other people’s experiences or by experiencing it personally?
• What is the nature of the reality that you want to study? Is it objective or subjective?

Depending on the problem and objective, other questions may arise during this process that lead you to a suitable paradigm. Ultimately, you must be able to state, explain, and justify the research paradigm you select for your research and be prepared to include this in your dissertation’s methodology and design section.

Using Two Paradigms

If the nature of your research problem and objectives involves both quantitative and qualitative aspects, then you might consider using two paradigms or a mixed methods approach . In this, one paradigm is used to frame the qualitative aspects of the study and another for the quantitative aspects. This is acceptable, although you will be tasked with explaining your rationale for using both of these paradigms in your research.

Choosing the right research paradigm for your research can seem like an insurmountable task. It requires you to:

●  Have a comprehensive understanding of the paradigms,

●  Identify your research problem, objectives, and hypothesis, and

●  Be able to state, explain, and justify the paradigm you select in your methodology and design section.

Although conducting your research and putting your dissertation together is no easy task, proofreading it can be! Our experts are here to make your writing shine. Your first 500 words are free !

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Research Philosophy & Paradigms

Positivism, Interpretivism & Pragmatism, Explained Simply

By: Derek Jansen (MBA) | Reviewer: Eunice Rautenbach (DTech) | June 2023

Research philosophy is one of those things that students tend to either gloss over or become utterly confused by when undertaking formal academic research for the first time. And understandably so – it’s all rather fluffy and conceptual. However, understanding the philosophical underpinnings of your research is genuinely important as it directly impacts how you develop your research methodology.

In this post, we’ll explain what research philosophy is , what the main research paradigms  are and how these play out in the real world, using loads of practical examples . To keep this all as digestible as possible, we are admittedly going to simplify things somewhat and we’re not going to dive into the finer details such as ontology, epistemology and axiology (we’ll save those brain benders for another post!). Nevertheless, this post should set you up with a solid foundational understanding of what research philosophy and research paradigms are, and what they mean for your project.

Overview: Research Philosophy

• What is a research philosophy or paradigm ?
• Positivism 101
• Interpretivism 101
• Pragmatism 101
• Choosing your research philosophy

What is a research philosophy or paradigm?

Research philosophy and research paradigm are terms that tend to be used pretty loosely, even interchangeably. Broadly speaking, they both refer to the set of beliefs, assumptions, and principles that underlie the way you approach your study (whether that’s a dissertation, thesis or any other sort of academic research project).

For example, one philosophical assumption could be that there is an external reality that exists independent of our perceptions (i.e., an objective reality), whereas an alternative assumption could be that reality is constructed by the observer (i.e., a subjective reality). Naturally, these assumptions have quite an impact on how you approach your study (more on this later…).

The research philosophy and research paradigm also encapsulate the nature of the knowledge that you seek to obtain by undertaking your study. In other words, your philosophy reflects what sort of knowledge and insight you believe you can realistically gain by undertaking your research project. For example, you might expect to find a concrete, absolute type of answer to your research question , or you might anticipate that things will turn out to be more nuanced and less directly calculable and measurable . Put another way, it’s about whether you expect “hard”, clean answers or softer, more opaque ones.

So, what’s the difference between research philosophy and paradigm?

Well, it depends on who you ask. Different textbooks will present slightly different definitions, with some saying that philosophy is about the researcher themselves while the paradigm is about the approach to the study . Others will use the two terms interchangeably. And others will say that the research philosophy is the top-level category and paradigms are the pre-packaged combinations of philosophical assumptions and expectations.

To keep things simple in this video, we’ll avoid getting tangled up in the terminology and rather focus on the shared focus of both these terms – that is that they both describe (or at least involve) the set of beliefs, assumptions, and principles that underlie the way you approach your study .

Importantly, your research philosophy and/or paradigm form the foundation of your study . More specifically, they will have a direct influence on your research methodology , including your research design , the data collection and analysis techniques you adopt, and of course, how you interpret your results. So, it’s important to understand the philosophy that underlies your research to ensure that the rest of your methodological decisions are well-aligned .

So, what are the options?

We’ll be straight with you – research philosophy is a rabbit hole (as with anything philosophy-related) and, as a result, there are many different approaches (or paradigms) you can take, each with its own perspective on the nature of reality and knowledge . To keep things simple though, we’ll focus on the “big three”, namely positivism , interpretivism and pragmatism . Understanding these three is a solid starting point and, in many cases, will be all you need.

Paradigm 1: Positivism

When you think positivism, think hard sciences – physics, biology, astronomy, etc. Simply put, positivism is rooted in the belief that knowledge can be obtained through objective observations and measurements . In other words, the positivist philosophy assumes that answers can be found by carefully measuring and analysing data, particularly numerical data .

As a research paradigm, positivism typically manifests in methodologies that make use of quantitative data , and oftentimes (but not always) adopt experimental or quasi-experimental research designs. Quite often, the focus is on causal relationships – in other words, understanding which variables affect other variables, in what way and to what extent. As a result, studies with a positivist research philosophy typically aim for objectivity, generalisability and replicability of findings.

Let’s look at an example of positivism to make things a little more tangible.

Assume you wanted to investigate the relationship between a particular dietary supplement and weight loss. In this case, you could design a randomised controlled trial (RCT) where you assign participants to either a control group (who do not receive the supplement) or an intervention group (who do receive the supplement). With this design in place, you could measure each participant’s weight before and after the study and then use various quantitative analysis methods to assess whether there’s a statistically significant difference in weight loss between the two groups. By doing so, you could infer a causal relationship between the dietary supplement and weight loss, based on objective measurements and rigorous experimental design.

As you can see in this example, the underlying assumptions and beliefs revolve around the viewpoint that knowledge and insight can be obtained through carefully controlling the environment, manipulating variables and analysing the resulting numerical data . Therefore, this sort of study would adopt a positivistic research philosophy. This is quite common for studies within the hard sciences – so much so that research philosophy is often just assumed to be positivistic and there’s no discussion of it within the methodology section of a dissertation or thesis.

Paradigm 2: Interpretivism

 If you can imagine a spectrum of research paradigms, interpretivism would sit more or less on the opposite side of the spectrum from positivism. Essentially, interpretivism takes the position that reality is socially constructed . In other words, that reality is subjective , and is constructed by the observer through their experience of it , rather than being independent of the observer (which, if you recall, is what positivism assumes).

The interpretivist paradigm typically underlies studies where the research aims involve attempting to understand the meanings and interpretations that people assign to their experiences. An interpretivistic philosophy also typically manifests in the adoption of a qualitative methodology , relying on data collection methods such as interviews , observations , and textual analysis . These types of studies commonly explore complex social phenomena and individual perspectives, which are naturally more subjective and nuanced.

Let’s look at an example of the interpretivist approach in action:

Assume that you’re interested in understanding the experiences of individuals suffering from chronic pain. In this case, you might conduct in-depth interviews with a group of participants and ask open-ended questions about their pain, its impact on their lives, coping strategies, and their overall experience and perceptions of living with pain. You would then transcribe those interviews and analyse the transcripts, using thematic analysis to identify recurring themes and patterns. Based on that analysis, you’d be able to better understand the experiences of these individuals, thereby satisfying your original research aim.

As you can see in this example, the underlying assumptions and beliefs revolve around the viewpoint that insight can be obtained through engaging in conversation with and exploring the subjective experiences of people (as opposed to collecting numerical data and trying to measure and calculate it). Therefore, this sort of study would adopt an interpretivistic research philosophy. Ultimately, if you’re looking to understand people’s lived experiences , you have to operate on the assumption that knowledge can be generated by exploring people’s viewpoints, as subjective as they may be.

Paradigm 3: Pragmatism

Now that we’ve looked at the two opposing ends of the research philosophy spectrum – positivism and interpretivism, you can probably see that both of the positions have their merits , and that they both function as tools for different jobs . More specifically, they lend themselves to different types of research aims, objectives and research questions . But what happens when your study doesn’t fall into a clear-cut category and involves exploring both “hard” and “soft” phenomena? Enter pragmatism…

As the name suggests, pragmatism takes a more practical and flexible approach, focusing on the usefulness and applicability of research findings , rather than an all-or-nothing, mutually exclusive philosophical position. This allows you, as the researcher, to explore research aims that cross philosophical boundaries, using different perspectives for different aspects of the study .

With a pragmatic research paradigm, both quantitative and qualitative methods can play a part, depending on the research questions and the context of the study. This often manifests in studies that adopt a mixed-method approach , utilising a combination of different data types and analysis methods. Ultimately, the pragmatist adopts a problem-solving mindset , seeking practical ways to achieve diverse research aims.

Let’s look at an example of pragmatism in action:

Imagine that you want to investigate the effectiveness of a new teaching method in improving student learning outcomes. In this case, you might adopt a mixed-methods approach, which makes use of both quantitative and qualitative data collection and analysis techniques. One part of your project could involve comparing standardised test results from an intervention group (students that received the new teaching method) and a control group (students that received the traditional teaching method). Additionally, you might conduct in-person interviews with a smaller group of students from both groups, to gather qualitative data on their perceptions and preferences regarding the respective teaching methods.

As you can see in this example, the pragmatist’s approach can incorporate both quantitative and qualitative data . This allows the researcher to develop a more holistic, comprehensive understanding of the teaching method’s efficacy and practical implications, with a synthesis of both types of data . Naturally, this type of insight is incredibly valuable in this case, as it’s essential to understand not just the impact of the teaching method on test results, but also on the students themselves!

Wrapping Up: Philosophies & Paradigms

Now that we’ve unpacked the “big three” research philosophies or paradigms – positivism, interpretivism and pragmatism, hopefully, you can see that research philosophy underlies all of the methodological decisions you’ll make in your study. In many ways, it’s less a case of you choosing your research philosophy and more a case of it choosing you (or at least, being revealed to you), based on the nature of your research aims and research questions .

• Research philosophies and paradigms encapsulate the set of beliefs, assumptions, and principles that guide the way you, as the researcher, approach your study and develop your methodology.
• Positivism is rooted in the belief that reality is independent of the observer, and consequently, that knowledge can be obtained through objective observations and measurements.
• Interpretivism takes the (opposing) position that reality is subjectively constructed by the observer through their experience of it, rather than being an independent thing.
• Pragmatism attempts to find a middle ground, focusing on the usefulness and applicability of research findings, rather than an all-or-nothing, mutually exclusive philosophical position.

If you’d like to learn more about research philosophy, research paradigms and research methodology more generally, be sure to check out the rest of the Grad Coach blog . Alternatively, if you’d like hands-on help with your research, consider our private coaching service , where we guide you through each stage of the research journey, step by step.

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13 Comments

was very useful for me, I had no idea what a philosophy is, and what type of philosophy of my study. thank you

Thanks for this explanation, is so good for me

You contributed much to my master thesis development and I wish to have again your support for PhD program through research.

the way of you explanation very good keep it up/continuous just like this

Very precise stuff. It has been of great use to me. It has greatly helped me to sharpen my PhD research project!

Very clear and very helpful explanation above. I have clearly understand the explanation.

Very clear and useful. Thanks

Thanks so much for your insightful explanations of the research philosophies that confuse me

I would like to thank Grad Coach TV or Youtube organizers and presenters. Since then, I have been able to learn a lot by finding very informative posts from them.

thank you so much for this valuable and explicit explanation,cheers

Hey, at last i have gained insight on which philosophy to use as i had little understanding on their applicability to my current research. Thanks

Tremendously useful

thank you and God bless you. This was very helpful, I had no understanding before this.

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Qualitative Interviewing The Art of Hearing Data

• Herbert J. Rubin - Northern Illinois University, USA
• Irene S. Rubin - Northern Illinois University, USA
• Description

Using in-depth qualitative interviews, authors Herbert J. Rubin and Irene S. Rubin have researched topics ranging from community redevelopment programs to the politics of budgeting and been energized by the depth, thoroughness, and credibility of what was revealed. They describe in-depth qualitative interviewing from beginning to end, from its underlying philosophy and assumptions to project design, analysis and write up.

"This book is exactly what I was looking for in that it covers interviewing and analysis in depth." —Daphne John, Oberlin College

" Students leave this book fully informed of the nuances and complexity of interviewing as well as excited about the promise interview research findings offer ." — Hannah Britton, University of Kansas

"The authors' focus on the reflective process, question development, and procedural steps associated with qualitative research is rich and thorough." — Tracy M. Lara, Kent State University

See what’s new to this edition by selecting the Features tab on this page. Should you need additional information or have questions regarding the HEOA information provided for this title, including what is new to this edition, please email [email protected] . Please include your name, contact information, and the name of the title for which you would like more information. For information on the HEOA, please go to http://ed.gov/policy/highered/leg/hea08/index.html .

For assistance with your order: Please email us at [email protected] or connect with your SAGE representative.

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“The book does a wonderful job of detailing how to develop questions, probes, analyze data, and organizing our data.”

“This book is exactly what I was looking for in that it covers interviewing and analysis in depth.”

“The Third Edition will be very useful for graduate students. It appears to seamlessly shift its lens from broad landscapes to close-ups without losing focus on the content. ”

“Students leave this book fully informed of the nuances and complexity of interviewing as well as excited about the promise interview research findings offer.”

“This text is well-written and easy to follow. It follows the natural flow of a qualitative project.”

“The authors provide a clear and detailed illustration of the nuts and bolts of interviewing in qualitative research. The focus on the reflective process, question development, and procedural steps associated with qualitative research is rich and thorough.”

“This edition is at once simpler, and clearer yet more expansive and richer in content, examples and use.”

“[The book] is somehow both more concise and more comprehensive than the Second Edition , providing a rich discussion of philosophy as well as design and analytic methods. The authors also have a very pleasant writing style that is engaging to the reader, and provides both clarity of the concepts discussed as well as a sense of a strong knowledge through the use of personal narrative and sharing of experiences.”

This is a well laid out and focussed publication that not only guides the student to research method choices but takes them on the journey to completion once they have made the appropriate choice. Well recommended.

Adopted too many of your other books. Had to draw the line somewhere.

New to the Third Edition

• Provides greater clarity on a number of topics including how responsive interviewing relates to other in-depth models, how to use literature, and how to find and label concepts and themes
• Emphasizes the importance of choosing a research style in tune with one's own personality and the research situation at hand
• Offers advice on dealing with a variety of interviewing situations, such as cross-cultural interviews, interviews with several people, with special populations, and telephone and internet interviews

Key Features

• Offers a guide to responsive interviewing , showing readers how to respond to what they hear rather than relying exclusively on predetermined questions
• Shows how to perform analysis throughout the project , so that as they learn more, researchers can modify both the research problem they are exploring and the questions they ask

Sample Materials & Chapters

Chapter 1: Listening, Hearing, and Sharing

Chapter 2: Research Philosophy and Qualitative Interviews

For instructors

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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 ,
• Kate Summers   ORCID: orcid.org/0000-0001-9964-0259 1 &
• Chana Teeger   ORCID: orcid.org/0000-0002-5046-8280 1  

Nature Reviews Methods Primers volume  2 , Article number:  73 ( 2022 ) Cite this article

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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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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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29 Interviewing and Qualitative Field Methods: Pragmatism and Practicalities

Brian C. Rathbun is Professor of Political Science and International Relations at the University of Southern California.

• Published: 02 September 2009
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This article recommends the use of intensive, in-depth interviews which can help to establish motivations and preferences, even though they must deal with the perils of ‘strategic reconstruction’. The first section of this article makes the pragmatic case for interviewing. The second portion is devoted to assembling in one place the consensus in the literature on the basics of how to undertake interviews, including issues of how to build arguments using interview data, how to structure questionnaires, the proper role to adopt vis-à-vis respondents, and how to gain access to conversation partners. Doubts about the status of interview data and the reliability of respondents must be taken into account but can be addressed. These disadvantages rarely outweigh the unique advantages of interviewing.

Intensive interviewing is a powerful, but unfortunately underused tool in political science methodology. Where it is used, it is generally to add a little color to otherwise stiff accounts. Rarely do researchers talk to more than a handful of respondents. There are numerous practical reasons for this. Gaining access to interview subjects, particularly elites, is often difficult. Interviewing is costly as it often entails traveling great distances, sometimes across national borders. Interviewing often requires tremendous personal investment in language training that might not seem worth it. It is often a risky strategy. Even after the hurdles of access and travel are overcome, informants might reveal little. However, these obstacles cannot fully explain why more political scientists do not utilize interviewing in their research as a major source of data, or even as a supplement to quantitative analysis or archival records.

I maintain that there are two reasons why interviewing is often underused. First, interviewing often runs afoul of methodological tendencies in the discipline. Certain precepts of what I call the naive versions of behavioralism and rationalism make many skeptical about interviewing. Naive behavioralism objects to the status of data derived from interviewing as it is by nature subjective and imprecise, and therefore subject to multiple interpretations. Naive rationalism aims at a framework for understanding politics that privileges structure over agency and often assumes that the political actors of interest view the world objectively and respond the same way to the same stimuli. Both tendencies have the result of privileging some data over others. Most suspect is the kind of information that interviewing is best at ascertaining and that is crucial for many of the most interesting research questions in political science. To the extent that interviewing is endorsed, it is often with some questionable advice and presuppositions: that interview data should always be treated as opinion and not fact, that questions should be indirect and concrete rather than reflective and direct, and that dissembling on the part of respondents is endemic and irremediable. The first section of this article makes the pragmatic case for interviewing. Interviewing, despite its flaws, is often the best tool for establishing how subjective factors influence political decision‐making, the motivations of those involved, and the role of agency in events of interest. Behavioralism and rationalism alert us rightly to the importance of rigor in our analyses, but there are steps that can be taken to eliminate some of the concerns about reliability and validity. Skepticism should not be exaggerated.

The second reason why interviewing is often underused is that its practicalities are often not taught. The second portion of this chapter is therefore devoted to assembling in one place the consensus in the literature on the basics of how to undertake interviews, including issues of how to build arguments using interview data, how to structure questionnaires, the proper role to adopt vis‐à‐vis respondents, and how to gain access to conversation partners. Although advocates propose different techniques for interviewing, the differences are fairly trivial, often just the use of different terms or typologies to describe the same process. There is a striking degree of agreement among proponents once the initial issue of the worth of interviewing data is resolved.

Both tendencies are symbolized perhaps best by the scant attention devoted to interviewing in what is now possibly the most common methodological text in the political discipline, King, Keohane, and Verba's Designing Social Inquiry (1994) . This book devotes just a single footnote to the issue, a footnote that I argue points in many wrong directions as it is based on the application of the lessons of naive behavioralism to small‐N, intensive case‐study projects, those where interviewing can have the biggest impact. This chapter will focus on what has come to be known as “semistructured” interviewing as opposed to “open‐ended” interviews more common in ethnography or “close‐ended” interviews used in surveys ( Mishler 1986 ). Although ethnography and semi‐structured interviewing share a commitment to probing indepth the experiences of respondents and generally stress context over generalizability, induction over deduction, and complexity over parsimony, true ethnography is rare in modern political science for a variety of reasons. Political science generally involves the explanation of particular events. Participant observation, the most significant feature of ethnography, is generally not even possible as the outcome of interest has usually already taken place (although it can certainly be a fruitful source of new research questions and puzzles). Driven by explanation, political science generally involves the testing of hypotheses, even if they develop inductively over the course of the research process. This requires a more directed research strategy in which researchers seek to uncover some degree of objective truth. The ethnographic style begins with a clean slate and no such presuppositions and is associated with a more relativistic epistemology in which there is no real establishment of fact ( Leech 2002 , 665). Still there are lessons for ethnographers in the literature on semi‐structured interviewing.

1 Skepticism about Interviewing in Political Science

Methodological trends in modern political science, most notably behavioralism and more recently rational choice, have focused our attention on the importance of rigorous analysis based on principles drawn from the natural sciences. Most critical has been the value of objectivity and theory‐driven research. Early behavioralists stressed the importance of freeing explanation from the normative values of the researcher. Data should also be as objective as possible, so that different researchers could agree on the meaning of the same piece of data, the social scientific equivalent of replication. The explanation of political events should be pursued separately from normative theory. And lest personal values should nevertheless influence our results, pure research was given increasing priority over applied research. Quantification was not pursued for its own sake, but rather would improve the precision of data and facilitate objective evaluation. Numbers are a more universal language less prone to differences in interpretation ( Easton 1962 ; Somit and Tanenhaus 1967 ).

This led naturally to a tendency to focus on observed behavior, rather than unobservables such as mental processes that were more prone to subjective evaluation. Behavioralism of course did not completely eschew studies of political phenomena without observable behavioral implications. It did not go as far as the behaviorists in economics and psychology ( Merkl 1969 ). Nor did it abandon the examination of the effects of the subjective perceptions of political actors on political events. The advances made in public opinion research are an example of both. However, whenever feasible, the possible subjectivity of political actors was to be made more manageable, for instance by the deductive designation of a limited number of response categories in a survey so as to allow objective evaluation, precision, and comparison by the researcher. Behavioralists prefer “structured” interviews, in which the same questions are asked in the same order with a restricted number of answers ( Patton 1990 , 277–90). This of course also served the goal of capturing the generalizability of political phenomena. Intensive interviewing was simply too costly and time consuming if a survey researcher wanted to gain an overall perspective. A more in‐depth approach could be used, however, in early stages to make sure surveys were appropriately designed with questions that adequately measured variables of key concern.

Research was also to be driven by theory. This goal was juxtaposed unfavorably against traditional political science, which was marked by description. Without theory, political science is just journalism (and with a smaller readership). Theories were also to be of a particular sort—generalizable and parsimonious. Scholars should try to explain a lot with a little ( Easton 1962 ; Somit and Tanenhaus 1967 ). The turn towards generalization was generally associated with a focus on structure over agency. The more that human beings had choice and could change their environment, the less generalizable or parsimonious theories could be ( Almond and Genco 1977 ). Traditionalists responded that politics was far too complex and rich to be captured by general theories. Context mattered tremendously. Behavioralists replied that they missed the forest for the trees. A theoretical rather than an empiricist focus also meant that theories were best deduced from logical principles, rather than inductively built from the ground up with empirical data.

Interviewing does not always stand up well to these standards, as fruitful as they have been for modern political science. While behavioralism is well suited to large‐ N survey research, its principles have increasingly been applied in naive form to small‐ N case‐study research. Concerns about the scientific status of data drawn from interviews are rarely explicitly stated, but rather are more subtle. They are expressed in advice from faculty advisers to their graduate students or in skepticism about interview data in peer reviews of work submitted to academic journals. Interviewing's faults lie in its inherent emphasis on complexity and context to the detriment of objectivity, parsimony, and generalizability. The very purpose of interviewing is generally to go in‐depth in a way that secondhand sources, archives, or surveys do not allow ( Berry 2002 , 682). Interview subjects are often chosen because of their unique perspective on a particular phenomenon or event. Interviewing is of most use when interviewees have shaped the world around them, often undermining the goal of generalizability. Questions are not standardized across respondents, impeding comparability. The data, generally in the form of quotes or statements by the respondents, are prone to multiple, subjective interpretations by the researcher, making them less reliable. As a result, information culled from interviews has a dubious status, often described merely as the opinion of the respondent rather than fact. Because it cannot be objectively verified, it is less trustworthy. In any case, it is of little use in building the broader arguments that behavioralism is interested in. Interviewing is often regarded as the method used when social scientists do not have fully formed ideas or theories, leading to critiques of inductivism ( Mishler 1986 , 28).

The usefulness of interviewing has also come increasingly into question with the growing popularity of formal and rational choice frameworks for studying politics. Behavioralists did not deny the importance of unobservables such as motivations, psychology, norms, culture, learning, ethics, all of which might fall under the broader rubric of ideational factors. They merely prefer more observable factors because they lend themselves to more objective analysis akin to the natural sciences. These other factors are not denied, but ignored ( Almond and Genco 1977 ). Rationalists, in contrast, make particular ontological assumptions in their efforts to build general models of politics, most importantly the objectivity of political actors. While they might have varying preferences, all actors placed in the same situation view stimuli similarly, and if they share the same preferences, will respond identically ( Mercer 1996 ; Parsons 2006 ). The importance of objectivity is different than in behavioralism. For the latter, it is necessary for the researcher. For rationalists, it is necessary for political actors as well. If the situational incentives are the key to explanation, agency is limited, and perception is unproblematic, there is little need for interviewing. Rationalists “eliminate the mind” from analysis ( Mercer 2005 ). They restrict their ontology for the broader methodological goal of generalizable theory ( Fearon and Wendt 2002 ; Almond and Genco 1977 ). Given that interviewing is often used for deciphering what goes on between the ears, it serves less of a purpose for them. It might be of use for establishing preferences, but rationalists generally prefer the deduction of preferences from broader theories. They adopt the behavioral line about establishing motivations, as they are unobservable ( Frieden 1999 ).

The rising influence of rational choice theory also has a more subtle but perhaps even more important effect. Conceptualizing most political situations as games of strategic interaction has increasingly alerted political scientists to concerns about strategic reconstruction by respondents. Interview data are inherently faulty because respondents have incentives to dissemble. This raises the validity issue in interviewing ( Brenner 1985 ; Berry 2002 ). Respondents might seek to preserve their reputation and legacy or retain private information in an ongoing bargaining situation. When combined with tendencies discovered by psychologists to want to be perceived in a favorable light or justify one's actions to oneself, there are additional grounds not to trust interview data, not because of its epistemological status as in behavioralism, but because interviewees cannot be trusted. As argued below, this is a particularly vexing problem for students of political science. Politics involves power, which introduces unique dynamics. Political science is interested more than the other social sciences in explaining events, often those with broad social impact. In this sense it has more in common with history. For powerful individuals who often operate in an accountable and public realm, this introduces questions of how accounts are framed. Those formerly involved in politics are concerned about their legacy, those still involved about the public perception of their work. As such, political scientists must be particularly attuned to the issue of strategic reconstruction of events to suit the more vested interests of interviewees.

Paradoxically, the proponents of interviewing add fuel to this skeptical fire. There is a strange meeting of the minds between positivists and relativists. Many books on the method stress that it is not for the researcher to judge the correct view of events. Key proponents of the method discuss the difficulty of a true understanding between interviewer and interviewee because each unwittingly brings assumptions and biases to the conversation ( Mishler 1986 ; Rubin and Rubin 1995 ). Interviewee data are inherently subjective. This is undoubtedly true, but at the extreme, explanation, however difficult and incomplete, ceases to become the goal of social science. This tendency becomes more pronounced in the literature on ethnography as opposed to semi‐structured interviewing. When the research question is the attitudes or perspectives on politics of a particular group, the lack of an evidentiary standard might be appropriate. All are equally entitled to their opinion. But more often political scientists are interested in explaining outcomes, and must make judgements about the accuracy of conflicting accounts. Ironically, relativists end up sounding like pos‐ itivists when they stress that interview data are more opinion than fact, although they reach that conclusion from different angles. Positivists treat interview data skeptically because objective interpretation is difficult among researchers, relativists because interpretation is an inappropriate imposition of the researcher's subjective views and does not capture the true meaning of the responses. Rationalists assume complete objectivity among the political actors they study. Relativists do the opposite, presuming complete subjectivity to the degree that explanation becomes impossible.

2 The Pragmatic Case for Interviewing

Interviewers must be careful to separate fact from opinion, guard against phoney testimony by their conversation partners, should not let their own theoretical or political beliefs affect their interpretation, and cannot be satisfied with mere impressions. These are all valid concerns. But behavioralism and rationalism, taken to an extreme, can have the pernicious effect of discarding potentially valuable (and often invaluable) information. These rules limit the potential of interviewing significantly. Interviewing has flaws, but on pragmatic grounds, it is often the only means to obtain particular kinds of information. As discussed above, jettisoning interviewing often means restricting the set of independent variables to observable, structural factors. This impoverishes the study of politics to an unacceptable degree. Interviewing is often the best‐suited method for establishing the importance of agency or ideational factors such as culture, norms, ethics, perception, learning, and cognition ( Aberbach and Rockman 2002 , 673; Mishler 1986 , 279; Rubin and Rubin 1995, 19). Interviewing is often the most productive approach when influence over the outcome of interest was restricted to a few select decision‐makers, creating a bottleneck of political power that increases the importance of agency in the story (and also makes interviewing less costly and time consuming). In short, interviewing, whatever its flaws, is often the best‐suited method for gathering data on those characteristics of the social world that differentiate it from the natural world: human beings' effort to intentionally transform their environment on the basis of cognition, reflection, and learning ( Almond and Genco 1977 ).

Interviewing is often necessary for establishing motivations and preferences. Even for those approaches that focus on situational incentives, this is an absolutely critical element of any theory. Without an understanding of desires, even the most rigorous rationalist argument will not be falsifiable if it simply infers preferences from observable behavior and a posited set of situational constraints. It falls into the same trap as folk psychology ( Mercer 2005 ). Desires and beliefs must be measured independent of action. In some instances, interviewing is the only way to do so. For all its problems, interviewing is often more “scientific” than other methods.

Even while interviewing is well suited for discovering preferences and agency, this is not to say that it is not useful at establishing structural causes as well. Interviewing can help establish whether a political actor felt under pressure from forces beyond his or her control, and what those forces were, particularly when there are multiple independent variables in the theoretical mix. To the extent that one is interested, like rationalists, in showing the importance of structural or situational factors in explaining behavior, interviewing can help build the appropriate model. Strategic circumstances might be found by a model to provide the best account for particular action, but are only empirically useful if the model reflects more or less the actual circumstances that decision‐makers found themselves in. Interviewing is one, although certainly not the only, way of identifying that situation.

Other methods might provide some insight into these factors, such as archives or memoirs. But interviewing is unique in that it allows the interviewer to ask the questions that he or she wants answered. Memoirs and secondary accounts force the researcher to answer his or her key questions based on what others wanted to write about. Archives sometimes provide insights into the thinking of key individuals, but motivations and agency must be more indirectly established than in interviewing. They require considerable inference and interpretation, raising the reliability issue to a greater degree than in interviewing. Interviewing has the advantage of being perhaps the most directed and targeted method in the qualitative arsenal.

To the extent that interviewing is endorsed by many in the behavioralist and rationalist tradition, it comes with important provisos that are often ill advised and overly constricting. A single passage, taken from a footnote in Keohane, King, and Verba's guide to qualitative research (now standard reading in methodology classes in political science graduate courses), contains many of these:

Be as concrete as possible … In general and wherever possible, we must not ask an interviewee to do our work for us. It is best not to ask for estimates of causal effects. We must ask for measures of the explanatory and dependent variables, and estimate the causal effects ourselves. We must not ask for motivations, but rather for facts. This rule is not meant to imply that we should never ask people why they did something. Indeed, asking about motivations is often a productive means of generating hypotheses. Self‐reported motivations may also be a useful set of observable implications. However, the answers must be interpreted as the interviewee's response to the researcher's question, not necessarily as the correct answer. If questions such as these are to be of use, we should design research so that a particular answer given (with whatever justification, embellishments, lies or selective memories we may encounter) is an observable implication. (1994, 112 n.)

This quote raises four issues: the extent to which interview data have the status of fact; how tangible questions should be; whether data should be pursued directly; and whether respondents can be trusted to give accurate accounts.

According to these scholars, responses from interviewees cannot be taken as “the correct answer,” i.e. data with the status of fact, but rather should be treated as opinions. Motivations are juxtaposed antithetically to facts, indicating a bias in favor of behavior. We should ask for concrete actions, what an interviewee did, rather than his or her overall impression of a situation. But behavior rarely speaks for itself. There are often multiple possible causes and motivations for the same action and outcome. Research is often driven by the desire to explain an already determined and known behavioral outcome, likely due to its intrinsic importance. This means that social scientists will be trying to gauge the effect of competing independent variables with the same expected effect. In these cases behavior does not speak for itself, and the meaning of behavior must be established. There is no substitute for a good research design, but interviewing might often be the only means for establishing causation in these cases, particularly when the phenomenon of study is relatively recent and there are no reliable alternative sources for judging motivation. Although Keohane, King, and Verba (1994) caution us not to ask respondents to estimate causal effects or state their motivations, this seems unwise. Interviews often involve conversations with individuals in a unique position to gauge the importance of multiple and equally plausible causal factors, which any research question of interest generally suffers from. We need not instantly accept those estimates as fact, but when a consensus appears among those in a best position to know, it should be taken very seriously. Presuming that each account is equally valid, as many advocates of interviewing suggest, goes too far, however. Social science requires the researcher to weigh conflicting evidence and offer the best interpretation possible supported by the evidence. Understanding who was in a position to know the true facts of the outcome the researcher is trying to explain is a key factor in balancing different accounts, as it helps separate opinion from fact.

When Keohane et al. recommend concreteness, they mean that questions should be both tangible and indirect. Others do as well ( Mishler 1986 , 315). In terms of the former, more grounded questions about facts are less subject to multiple interpretations. Answers to concrete questions, such as the temporal sequence of particular actions, are often useful in reconstructing events and mediating between competing arguments. But questions that ask an interviewee to reflect more abstractly about the underlying causes and motivations behind his actions are just as important. In many instances, the researcher already knows about behavior because it has been a matter of public interest and record that led to the selection of the topic in the first place. It is the motivation that is missing. In practical terms, answers to less concrete questions are often easier to obtain, particularly as considerable time may have passed between the interview and the events or outcome in question. Contemporary newspaper accounts or even archives can often provide the facts about what happened on any particular day. To the extent that this is true, interviews should be devoted to allowing respondents to reflect on their experience, which they might only now have had an opportunity to do. Particularly when dealing with elites, respondents are perfectly able to contemplate the broader meaning of their actions just as well as a political scientist, although they do so in less self‐consciously theoretical terms. One revealing technique is to ask conversation partners to informally test competing arguments against one another. Rarely are political science theories too complicated to explain to an intelligent layman. The researcher can pose the question as, “Some say X , others say Y , how would you respond to that?” ( Mishler 1986 , 318). By posing such trade‐offs, researchers might encourage interviewees to draw evidence to support their account. This is the best of both worlds, for respondents to explain the broader significance of their actions with actual data about what they did. Another useful method is to ask counterfactuals in the form of “why didn't you do X ” or “what if you had done Y in situation Z ?” This is a very useful technique for establishing the extent to which structure or agency prevailed. The response might be that one path was more valued than another, or that one path was blocked by some constraint.

Being concrete also means questions should be indirect ( McCracken 1988 , 21; Brenner 1985 , 151). Keohane et al. (1994) recommend for instance not to ask a white conservative whether he is racist, but rather if he would mind if his daughter married a black man (1994, 112 n.). Indirect questions are particularly necessary in instances of what are known as “valence” issues, in which there is only one publicly acceptable position to have on an issue. Any outcome of interest in which politicians might have sought narrower parochial interests at the expense of the broader societal good, which is much of political science, might be so loaded and is a key place to worry about strategic reconstruction. On issues in which there are multiple, publicly acceptable positions, this is less of an issue. Skeptics of interviewing also worry that directed questions put words in the mouth of respondents and are therefore inappropriate. This is a potential worry, and researchers should indicate in their research, perhaps in a footnote, the question to which a quote corresponds if there is a fear of leading respondents.

However, as in journalism, interviewing's payoff is often in the quotation, getting someone on the record, even if it is nonattributable ( Mishler 1986 , 347). That is, direct statements are more valuable in terms of impact and credibility. And one must remember that interviews with subjects in political science often do have a strategic component, and interviewers must sometimes lead respondents into answering rather than dodging their questions, while simultaneously avoiding giving them the answers. Interviewers should proceed indirectly so as to get a sense of how far respondents will go, but ask follow‐up questions, trying to establish more directly what one wants to know. If the interviewee goes so far, one can then safely ask for a confirmation in the form of “I hear you saying that …,” without fears of leading the witness, as it were. Questions cannot be purely open ended and undirected. A couple of techniques might be helpful. In cases in which there is a publicly stated position for a particular behavioral outcome but the researcher or others have a suspicion that there was an ulterior motive not flattering to the interviewee, researchers can use presupposition questions in which they indicate their acceptance of the ulterior motive as perfectly natural, assumed, and justifiable ( Leech 2002 , 666; Mishler 1986 , 304). Interviewers might even note that their actions were perfectly understandable in light of the circumstances. They might use euphemisms that take the hard edges off that behavior ( Leech 2002 , 666). Or they might mention (only honestly!) that other interview subjects have indicated as much to them already. They should then look to see if the subject acquiesces or seeks to correct the assumption. The former is revealing if not concrete proof; the latter provides an opportunity for the interviewer to challenge the account and ask the conversation partner to provide evidence for it.

Although the concern about intentionally (or unintentionally) inaccurate answers is real, it is often overstated ( Rubin and Rubin 1995 , 225; Berry 2002 , 688). When Keohane et al. refer to “justification, embellishments, lies and selective memories,” there is almost a presumption of disingenuousness on the part of the respondent. But my experience with interviewing is that dishonesty is not the norm, even among the most high‐ranking officials. I was able to solicit evidence on recent issues in national security policy that did not cast subjects in a positive light ( Rathbun 2004 ). Most respondents would rather refuse an appointment than spend the time averting a difficult subject. Interviewees are more likely to commit sins of omission than commission, avoiding deliberate falsehoods and attempting to steer the conversation to other aspects of the subject. For someone who has prepared properly, this becomes obvious. Researchers are not nearly as threatening as journalists. An academic's audience is more limited and is certainly not the average man on the street reading tomorrow's newspaper. This creates difficulty in gaining access to important respondents, but once access is gained, they might be more forthcoming. Interviewees might welcome the chance to explain their actions more fully with fewer fears of being boxed in and quoted out of context ( Rubin and Rubin 1995 , 105). Those engaged in politics often believe in what they do and are proud of their accomplishments, even when they seem distasteful to some. Certainly the risk of being lied to is surely outweighed by the potential of new and exclusive data. Researchers should not presume interview subjects are telling the truth, but they should not assume they are lying either.

There are a number of other ways of avoiding the perils of strategic reconstruction. Some come from the common sense of journalism. Most simply, when possible, find multiple sources for the same data, either written or interview based ( Berry 2002 , 680; Rubin and Rubin 1995 , 225–7; Brenner 1985 , 155). If this is done with other interviews, this reinforces one's belief that what one is hearing is more than just opinion or that it is just a particular interpretation of ambiguous information. And it helps reduce worries that some respondents exaggerate the role they played in the process ( Berry 2002 , 680). Let them know you are an expert by your questioning, even if it means the gratuitous use of jargon. This tells them you are an informed observer who cannot be easily manipulated ( Rubin and Rubin 1995 , 197). Confront the respondent with data contrary to his or her point of view, even if you might believe he or she is being straightforward, as this might provide ideas about how to get leverage on multiple causal factors ( Rubin and Rubin 1995 , 223). The fact that much of the study of politics revolves around conflict in the public sphere also has advantages in gaining more accurate accounts. Interviewees can be challenged with data drawn from the other side of the political spectrum, a standard journalistic technique ( Rubin and Rubin 1995 , 68–70, 217). This makes the interviewee appear more neutral than if the challenge is issued directly, although interviewers should be careful not to give the impression that they are engaging in the same “gotcha” game as the media. If such an opponent is unavailable, researchers can explicitly refer themselves as a “devil's advocate” in order to be less provocative ( Rubin and Rubin 1995 , 215). Respondents might be asked to critique their own case, to explain why their adversaries do not accept their account ( Berry 2002 , 680).

When bias is expected, one can pose some preliminary questions unrelated to the subject that gauge a general ideological approach and allow the researcher to discount certain aspects of testimony, sometimes called “norming” ( Rubin and Rubin 1995 , 210). Understand the particular bureaucratic, political, or ideological position of your interviewees so you know what biases to expect. Know what the party line might be ( Rubin and Rubin 1995 , 221). This requires a familiarity with the public record. In improvisational questions, probe differences between the public account and the one you are hearing, but do not despair if they vary. On the contrary, generally this means that you are uncovering something new and exciting. When a researcher finds differences it can yield important new insights about the political context that respondents were negotiating. Follow up on this, as it is important for gauging the significance of structure and agency. Ask why particular arguments were instrumental and why this was necessary. If memories appear weak, ask basic questions about details that allow one to assess the overall credibility of the source ( Rubin and Rubin 1995 , 84). A researcher might make deliberate mistakes that should be corrected if memories are good ( Rubin and Rubin 1995 , 219). Finally, if the account has internal contradictions or inconsistencies, let these be known to the respondent and note the adjustments that he or she makes to the story.

3 The Consensus on the Practicalities of Interviewing

While there are often disagreements about whether interviews are worthwhile ventures, among those who undertake them there is a general consensus on most points. This section reviews those major points of agreement, proceeding chronologically from preparation to interpretation and writing.

Perhaps the most important lesson for any would‐be interviewer is not to begin intensive interviewing without being fully prepared ( Brenner 1985 , 152). As McCracken writes, “Every qualitative interview is, potentially, the victim of a shapeless inquiry. The scholar who does not control these data will surely sink without a trace” ( 1988 , 22). The interviewer should exhaust all of the secondary sources and publicly available primary sources before beginning ( Berry 2002 , 680). This allows him or her to work out a puzzle, if one isn't already evident; to figure out what is known and what is not known so questions can be more targeted and efficient; to understand how the debate is framed in different contexts (nation states, cultures, time periods, etc.) in the case that the issue involves political conflict, as it generally does in political science; and to develop expectations about what interview data would be evidence for his or her initial hypothesis or other competitors. In those instances in which research is being undertaken in a new area without a significant paper trail, a political scientist might consider a set of exploratory interviews to get a better sense of the interesting theoretical issues ( McCracken 1988 , 48), but these should be limited to lower‐ranking respondents or those with knowledge of a phenomenon but not directly involved, such as journalists. The researcher should not show up with a year's fellowship in a new place hoping that things will fall into place on the basis of ongoing conversations. They might, but luck will be necessary. An understanding of the literature will not only help the researcher eventually frame his argument in a broader context in the opening passages of an academic work but also provide a benchmark for what is surprising in the interview data he collects ( McCracken 1988 , 31). A good scholar will read not only the qualitative work in his field but also the quantitative even if he is not a statistical whiz, as it is a source of hypotheses and sometimes an indication of what is yet to be explored due to the inherent limitations of quantitative analyses, such as difficulties in establishing causation.

It is crucial to remember that the literature review, or one's own hypothesis, must be held at arm's length, so that researchers are open, in the true scientific sense, to other possibilities. McCracken calls this “manufacturing distance” ( 1988 , 23) This is particularly important in interviewing as the method is often used when there is a black hole in the evidence about something of theoretical interest, in which case assumptions would be inappropriate. And whereas quantitative analyses might be rerun or a secondary source reread, interviewers often only have one chance with their sources of data. If you are insistent on a particular hypothesis, even if it is not working out, you will waste an experience that cannot be repeated. “A good literature review makes the investigator the master, not the captive, of previous scholarship” ( McCracken 1988 , 31). Previous theories, or your own, might act as blinders ( Rubin and Rubin 1995 , 64). This is not only good social science, but any possibility of overturning conventional wisdom benefits the scholar's career!

After the literature review, researchers should establish contact with those who have a particular knowledge of the outcomes of interest. Exploratory interviews are often fine sources of such contacts. It is generally best to begin with low‐ranking members of the group you are interested in, whether it be a trade union, government bureaucracy, or social movement. The researcher should talk to anyone who will see him or her. This allows the social scientist to hone and try out different framing of questions, develop and accumulate contacts (sometimes known as “snowballing”), and gather basic information so as to be better prepared for more senior respondents who have less time and tolerance. Initial contact should not be a cold call if it can be avoided. Many recommend a professional letter ( Goldstein 2002 , 671; Aberbach and Rockman 2002 , 674; Peabody et al. 1990 , 453). 1 Letterhead conveys a sense of professionalism and status. In closed societies, it is often good to have a local affiliation at a research center. In more open societies, your own foreign university emblem might be better as it helps you stand out from the flood of mail that many of our conversation partners receive. Explain why the respondent might be of particular help for your project. This provides background but also gives them a sense of relevance that is inevitably flattering ( Rubin and Rubin 1995 , 103). Tell them they are not obligated to speak on the record or on an attributable basis. When possible, mention the person who referred you and others you have talked to, so as to give yourself a sense of credibility. Preferably the former should be on the same side of any political conflict as your contact, but often the latter can be someone opposed, as this might induce an interest on their part in equal time and setting the record straight that might make them more likely to meet you. Follow up this initial contact with a phone call to the individual or his or her appointments assistant. When possible, allow a large timeframe to set up a meeting, making it harder for the contact to refuse you and enabling the inevitable rescheduling. If possible, have a direct phone number, even a cell phone ( Goldstein 2002 , 671). Playing phone tag from your hotel room voice mail will prove difficult. Gather the names of the administrative staff as you set up the interview, keep them in your mind as you travel to the interview, and seek them out as you enter or leave the office to thank them directly by name.

Most advocates of interviewing recommend recording interviews ( Aberbach and Rockman 2002 , 674). Tell your interviewee that you would like to record the interview, and if it might make your work more credible later to identify him or her by name, that you would like for it to be on the record. If this is not possible, inform him or her of your preference for it to be nonattributable, in which you might quote, but not directly identify his or her name. Although recording on the record is often thought to make respondents more circumspect, this presumption is based on an assumption that respondents will not be forthcoming in the first place. As I think this is often overstated, I believe the advantages likely outweigh the disadvantages although every circumstance is different, and they obviously vary by the stakes involved and the local culture. “Senior official” does not have the same cachet in print as “Prime Minister Smith.” Having a recorded version of the conversation allows one to think through potential follow‐up questions when the conversation takes an interesting turn without worrying about taking down the exact text ( Brenner 1985 , 154; Berry 2002 , 682; Douglas 1985 , 83; McCracken 1988 , 41). Such a recording allows you to assemble a verbatim transcript that you can make available in an effort to remedy some of the subjective interpretation problems endemic to the method ( Brenner 1985 , 155; Mishler 1986 , 36; Rubin and Rubin 1995 , 85). This transparency helps avoid the accusation of massaging and spinning the data. Given that your research question or hypothesis will likely shift as you accumulate information, such a transcript is often invaluable as you have information on record that you might not have thought relevant and noted before in your notes. And given that interviewing is often used to identify the motivation and meaning behind otherwise indeterminate behavior, specific quotations are sometimes the only evidence possible or even moderately convincing. Merely stating your hypothesis and providing a footnote of “Interview with Mr X” is not sufficient. However, allow the interviewee the option of going off the record or not for attribution at any particular point in the interview. They often take it. Make sure you are clear on what you or they mean as “off the record” or “on background” ( Goldstein 2002 , 671). And take notes so that you do not have to transcribe every single recording, saving that valuable time for particularly evocative conversations.

To the degree possible, the social scientist should inform himself of the role played by that individual in the phenomenon of interest. Let that preparation be known in a brief introduction of yourself and your general research interests. This helps break the ice ( Leech 2002 , 666; Peabody et al. 1990 , 652). Yet the researcher does better not to specifically identify his or her own working hypothesis, whether it be a fully formed argument or just a hunch, as the respondent might be tempted inappropriately to help gather information on behalf of the interviewer or presumptuously dismiss it (and you) ( McCracken 1988 , 27–8; Aberbach and Rockman 2002 , 673). In this sense, Keohane et al. are correct that interviewees should not do our work for us. Use specialized language or acronyms to demonstrate your competence and prompt them to avoid needless background that wastes valuable time ( Rubin and Rubin 1995 , 77–9). When possible, interview in the language of the interviewee. All of this demonstrates professionalism and gives the sense that the conversation partner is truly valued and important. While politicians might be accustomed to this treatment, other (even powerful) figures might not. The social scientist must strike a balance between formality on the one hand and rapport on the other. Professionalism and a certain distance ensure that the interviewer retains control of the questioning and is taken seriously. Yet too cold a demeanor can inhibit respondents from trusting the interviewer with often sensitive information ( McCracken 1988 , 26–7). Questioning should be sympathetic, respectful, and nonargumentative, although this does not imply unchallenging, as discussed earlier ( Brenner 1985 , 157). Yet political science interviewing differs from journalism in that it must be less adversarial. Interviewees might have an obligation to the public interest to sit for an interview with a journalist and they can hardly call it off midstream. That is not the case with academics. Some suggest that an academic demonstrate less of a command of the subject matter than he or she actually has so that respondents do not feel inferior, but this runs the risk of making some feel they are wasting their time ( McCracken 1988 ). Leech's advice to appear knowledgeable, but not as knowledgeable as the interviewee, is probably better ( 2002 , 665).

Researchers should bring a questionnaire to their interviews specifically designed around the unique role played by their particular conversation partner. This helps separate fact from opinion as well as not wasting time with information that could be gathered from other sources ( Leech 2002 , 666). Interviews should not be entirely improvisational because researchers might forget important questions and not have an opportunity to fill those holes later. But they cannot be too rigidly designed so as to inhibit adaptation. After all, the goal of interviews is rarely simply confirmatory. Social scientists want to be surprised. They are often interviewing in the first place because there are certain things that are just not known. The questionnaire should be a general set of themes, and specifically worded questions that have been found to elicit better responses than others. Do not begin with challenging questions as they might put the respondent on guard and heighten sensitivity to the running tape recorder ( McCracken 1988 , 38; Mishler 1986 , 294; Douglas 1985 , 138; Rubin and Rubin 1995 , 223). They should be reserved until later after a sense of rapport has been established (but don't wait too long and run out of time) ( Leech 2002 , 666). But beginning questions should not be tedious either. The interviewer might first try to elicit facts and then as the interviewee loosens up begin with more complex matters of interpretation, motivation, etc., particularly as the former often jogs the memory of events long past ( Peabody et al. 1990 , 452; Rubin and Rubin 1995 , 210). If time is limited, however, focus first on the absolute essentials and then work towards other less important matters ( Rubin and Rubin 1995 , 200). In these instances, do not ask questions for which information can be obtained from other less busy sources or that you should know the answer to already, thus undermining your credibility ( Peabody et al. 1990 , 452). Learn the value of nonverbal communication. If an answer is not complete, the researcher can indicate this by remaining silent and nodding his head, both signaling an interest in further explication. If a respondent avoids a question or simply misses it, note this and return to the subject later from a different angle. Have at your disposal some “bridges” that enable you to return to the area of interest in a respectful way ( Berry 2002 , 681).

When leaving, always ask the conversation partner for other suggestions about whom to talk to. Those directly involved are often in the best position to know who would be uniquely knowledgeable on some aspect of your research topic and can be used as a reference in your request. One should never finish an interview without knowing the next person one wants to talk to. An interviewer should never have crossed off all the names of possible subjects. Also, always ask if he or she would be available for additional questions, either in person, e‐mail, or by phone. This is a promise that can be included in a follow‐up letter requesting more information that has a better chance of getting past the secretary, especially if you have been previously cordial in your interactions ( Rubin and Rubin 1995 , 138).

Successive interviews should build on each other. The researcher should constantly be looking over his notes, looking for sources of agreement and disagreement, new themes and causal factors previously untheorized or not mentioned in the literature. Over time, interviews will naturally become less exploratory or inductive and more deductive ( Rubin and Rubin 1995 , 43; Brenner 1985 , 152; McCracken 1988 , 45). Scholars will begin to develop more precise hypotheses that they seek to confirm or test the limits of. Questions will become more honed and precise and fewer topics will need to be explored as a narrative emerges. As with all research there will be a point of diminishing returns in which later interviews generate less new information than previous ones, although interviewers should be aware of premature closure ( McCracken 1988 , 45). This will guide the social scientist in his effort at organizing notes and thoughts once interviewing is complete. With the proper legwork before interviewing and continual reappraisals of the data during the process, researchers can end the data collection phase of research with a well‐formed albeit sometimes preliminary idea of the final product.

4 Conclusion

Interviewing is a potentially invaluable tool for collecting data that researchers should approach pragmatically, both in terms of how, but also whether, it is conducted. Its financial costs and methodological difficulties are real, but if a political scientist believes there might be some aspect of a phenomenon best captured by directly talking to participants, he or she should not hesitate. Doubts about the status of interview data and the reliability of respondents must be taken into account but can be addressed. These disadvantages rarely outweigh the unique advantages of interviewing: the ability to target questions directly to actual participants and push them for responses in a way that archival or other qualitative research never allows. Although most would agree that interviewing is a kind of art that takes some practice, there is a solid consensus on the practicalities of interviewing. It is no substitute for careful research and investigation of secondary and written primary sources, however. Systematic analysis and meticulous preparation are a must in any kind of analysis.

Aberbach, J. D. and Rockman, B. A.   2002 . Conducting and coding elite interviews.   Political Science and Politics , 35: 673–4.

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Some note that in developing societies requests for interviews should be made in person if possible, for both cultural and technical reasons. See Rivera, Kozyreva, and Sarovskii (2002) .

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Qualitative Interviewing The Art of Hearing Data

• Herbert J. Rubin - Northern Illinois University, USA
• Irene S. Rubin - Northern Illinois University, USA
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“The book does a wonderful job of detailing how to develop questions, probes, analyze data, and organizing our data.”

“This book is exactly what I was looking for in that it covers interviewing and analysis in depth.”

“The Third Edition will be very useful for graduate students. It appears to seamlessly shift its lens from broad landscapes to close-ups without losing focus on the content. ”

“Students leave this book fully informed of the nuances and complexity of interviewing as well as excited about the promise interview research findings offer.”

“This text is well-written and easy to follow. It follows the natural flow of a qualitative project.”

“The authors provide a clear and detailed illustration of the nuts and bolts of interviewing in qualitative research. The focus on the reflective process, question development, and procedural steps associated with qualitative research is rich and thorough.”

“This edition is at once simpler, and clearer yet more expansive and richer in content, examples and use.”

“[The book] is somehow both more concise and more comprehensive than the Second Edition , providing a rich discussion of philosophy as well as design and analytic methods. The authors also have a very pleasant writing style that is engaging to the reader, and provides both clarity of the concepts discussed as well as a sense of a strong knowledge through the use of personal narrative and sharing of experiences.”

This is a well laid out and focussed publication that not only guides the student to research method choices but takes them on the journey to completion once they have made the appropriate choice. Well recommended.

Adopted too many of your other books. Had to draw the line somewhere.

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Chapter 1: Listening, Hearing, and Sharing

Chapter 2: Research Philosophy and Qualitative Interviews

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At Risk of Deprivation pp 57–89 Cite as

Research Philosophy, Methodological Implications, and Research Design

• Jonas Bergmann 6  
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Part of the book series: Studien zur Migrations- und Integrationspolitik ((SZMI))

In this chapter, I explain the choices for the layers of the research approach applied in this book. Chiefly, I used a critical realist research stance and analyzed both qualitative case studies as well as survey data in a mixed methods approach. For the central qualitative research, I collected data through 81 problem-centered interviews, one focus group with 12 affected people, and discussions with over 60 experts. I analyzed the data through Qualitative Text Analysis to examine effects, mechanisms, social system dynamics, and structures. For the parallel quantitative study on the Coastal El Niño, I assessed extensive survey data through regression models. To evaluate differential displacement risk, I used a dataset collected by Peru’s National Institute of Statistics and Informatics directly after the disaster with close to 190,000 affected adults spread across all of Peru. Additionally, to identify the effects of displacement on well-being, I applied a customized, merged dataset of that survey and the National Census collected later in the same year. The chapter discusses the used data as well as the strengths and limitations of all chosen methods.

You have full access to this open access chapter,  Download chapter PDF

In this chapter, I explain the choices for the four different layers of the research approach applied in this dissertation, which are illustrated in Figure  3.1 (Saunders et al. 2011). I used a critical realist research stance, applied retroduction and abduction as modes of reasoning, and analyzed both qualitative case studies and survey data in a mixed methods approach.

Layers of research and application in this study. (Note: Blue boxes indicate the choices made in this study. Reproduced from Saunders et al. (2019: 130) and edited by the author.)

1 Critical Realist Stance and Implications

First, the philosophical (or research ) stance as the outermost layer refers to assumptions about the nature of reality (ontology), valid knowledge and knowing (epistemology), and values and aims of research (axiology). Few migration studies explicate their philosophical stance (Castles 2012; Iosifides 2012). This dissertation is based on critical realism, which combines a realist ontology and relativist epistemology. This research philosophy has gained a standing in social sciences and serves this study for four reasons (Maxwell & Mittapalli 2010). First, its ontology allows for a complex analysis of why well-being changes in (im)mobilities occur, and what role structure and agency play therein. Second, its epistemology favors diversity in research perspectives, methods, and data, which is useful for examining alternative explanations of well-being impacts. Third, the stance makes it possible to incorporate the role of the human mind into research, for example, the insight that biases may lead migrants to misinterpret their well-being situations. Finally, critical realism has a strong value orientation, which is, in my view, essential when studying well-being in the context of climate injustices and in an unequal society such as Peru (Iosifides 2012; Maxwell 2012; World Bank 2021b).

Second, philosophic stances come with different approaches to theories or modes of reasoning. Theories are “the analysis and statement of how and why a set of facts relates to each other” (Kumar 2011: 21). Approaches to theories refer to different mental operations to construct order and logic in data and to connect data with theory. The critical realist goal is to develop hypothetical models for the mechanisms and structures behind empirically observed phenomena and build theories of them with multiple viewpoints (Lawson et al. 2009). Realism permits both using existing theories and provides guidance on how theory can be developed. Induction (going from data to broader theory) and deduction (testing theory-derived hypotheses with data) serve as the foundation for abduction and retroduction (Hartwig 2007). Footnote 1 In other words, I will “continue to ask the question why?” (Easton 2010: 124), use counterfactual thinking, study extreme or surprising cases, and compare cases to identify generative mechanisms (Danermark et al. 2002).

Third, methodology applies the research stance and modes of reasoning systematically to the research (Castles 2012). It discusses how scholars can retrieve and produce knowledge about the social world and why which type of methods can provide valid data (Teddlie & Tashakkori 2010). Given the critical realist premises, both quantitative and qualitative research methods can produce valid knowledge under certain conditions (Iosifides 2012; Maxwell & Mittapalli 2010). Footnote 2 Given these complementarities of qualitative and quantitative approaches, it is methodologically sensible to combine them (Sayer 1992; Seawright 2016). Qualitative methods can discern social action, intentions, and meanings around (im)mobilities and well-being. They can address context, complexity, and diversity, and shed light on generative mechanisms. Conversely, quantitative methods are valuable to systematically inquire diversity and regularities in well-being effects; to scale and compare scales; to measure the strength of influences; and to test and refine hypotheses about mechanisms. Various prior mixed methods studies have used critical realism (Shannon-Baker 2016) and several authors have called for mixed methods to study migration (Castles 2012; Iosifides 2012, 2017). I explain the research design in detail in section  3.2 .

Before, I close with discussing how the critical realist value base shapes this research. Critical realist research aims at reducing domination and expanding freedom or flourishing (Maxwell 2012). Footnote 3 It is based on a similar argument as critical social theories that theory should serve emancipation and not mere knowledge creation (Horkheimer 1982; Lawson et al. 2009). I agree that in a world where migration offers opportunities for few wealthier people while many marginalized groups confront restrictions and control, “realist explanatory critiques of social relations of injustice and of their effects and consequences are urgently needed” (Iosifides 2012: 47). All knowledge generation is a social practice with impacts; it should aim to inform those affected by domination and inequality to empower them in their struggles for self-determination. In this study, I attempt to do so by revealing structures of domination, control, oppression, and exclusion before and after people leave areas facing climate hazards; how these structures shape the uneven distribution of opportunities to migrate in the first place and under humane conditions; and how they shape chances to preserve well-being. I also attempt to expose mechanisms behind well-being changes of migrants and stayers, and how climate (im)mobilities modify, reduce, reproduce, or reinforce such structural inequalities. In doing so, I refrain from dominant discourses of managing and controlling migrants.

Finally, I also attempt to approach the subjects of inquiry (self-)critically. Producing knowledge is a social practice shaped by politics, power, and by researchers themselves. Evaluating knowledge requires awareness that it is produced by communication, which, in turn, typically occurs in unequal social settings that favor certain narratives. My own values, socialization, and biases can have influenced this research. As a relatively young, male, white academic from the Global North, my socialization is different from that of most interviewees. I interviewed people of all different ages, also much older ones; of different ethnicities and religions; as well as with a different upbringing and socioeconomic situation. While I have studied and lived in Latin America, speak Spanish, and prepared scientifically and culturally for the fieldwork, these differences have shaped interviews, the analysis, and interpretations. I am aware that relationships with the respondents were often unequal. Lastly, while I have tried to be as impartial as possible, I acknowledge that all collected data on empirical events linked to concepts like (im)mobilities and well-being are value-laden and do not represent one objective truth.

2 Mixed Methods Research Design

After having explained the three outer layers of this research approach, I turn to discuss the concrete choices for the research design and methods in the next section. Methods are the procedures and practices chosen to collect and analyze data, as justified by the methodology (Castles 2012).

To study the well-being impacts of climate (im)mobilities in Peru in this dissertation, I use an ex-post-facto, convergent parallel design with qualitative methods weighted more heavily than quantitative ones (Creswell & Clark 2017). An ex-post design is appropriate here given the absence of experimental options, which could have reduced the influence of unobserved third factors, such as self-selection of migrants (McKenzie et al. 2010; McKenzie & Yang 2010; Stillman et al. 2015). This choice was most realistic for the time and resource horizon of this study and is in line with prior guidance for studies in this research field (Banerjee et al. 2013; Melde et al. 2017; Milan & Gioli 2015). I partially address the lack of experimental setup through method and results triangulation.

The chosen critical realist stance favors mixed methods approaches, which bring several benefits for studying climate (im)mobilities and well-being. Foremost, social science studies apply mixed methods to use strengths of both qualitative and quantitative strands while reducing their individual limitations (Kelle 2014; Teddlie & Tashakkori 2010). Beyond, mixed methods allow testing whether both components produce convergent results (corroboration); shedding light on respective blind spots (completeness); and raising the integrity of findings (credibility) (Bryman 2006; Kelle 2014; Schoonenboom & Johnson 2017; Tashakkori & Teddlie 2010). These advantages lead eminent scholars like Stephen Castles to argue that “most forms of migration research are likely to require ‘mixed-methods approaches’” (2012: 21; Fauser 2018).

In mixed methods designs, qualitative and quantitative strands can be weighted differently and integrated at different points (Kelle 2014; Schoonenboom & Johnson 2017). In this study, I prioritized the qualitative component due to its unique adeptness to assess the meaning of people’s climate-related experiences in the social world (Nature Climate Change 2021). For comparability, I conducted the same qualitative methods in all three large zones of Peru (highlands, rainforest, and coast). Moreover, data and time constraints allowed for one additional quantitative analysis of the Coastal El Niño case. I performed both components concurrently but separately to preserve data independence and triangulation options, and integrated them later through meta-inferences (Tashakkori & Teddlie 2010). This approach is coined convergent parallel or parallel mixed design (Creswell & Clark 2017; Schoonenboom & Johnson 2017). Figure  3.2 provides an overview of the applied research design.

Overview of the applied mixed methods design. (Note: Created by the author)

In this paragraph, I briefly outline the applied methods before I explain them in detail below. I started the central qualitative research with a review of the evidence (see chapter  4 ). Afterwards, I collected data through 81 problem-centered interviews (Witzel & Reiter 2012), one focus group with 12 affected people (Morgan 1999b), and discussions with over 60 experts. Next, I analyzed the data through Qualitative Text Analysis to examine effects, mechanisms, social system dynamics, and structures (Kuckartz & Rädiker 2019). For the parallel quantitative study on the Coastal El Niño, I assessed extensive survey data through regression models. To evaluate differential displacement risk, I used a dataset collected by Peru’s National Institute of Statistics and Informatics (INEI) Footnote 4 directly after the disaster with close to 190,000 affected adults spread across all of Peru. Additionally, INEI on request created a customized, merged dataset of that survey and the National Census collected later in the same year, which I analyzed to identify the effects of displacement on well-being.

2.1 Qualitative Methods

I mainly used qualitative methods to analyze affected people’s narratives on the experienced well-being changes and underlying mechanisms of action. I collected data during several weeks of research in the Peruvian communities of interest during three visits in 2018 and 2019. Another scheduled visit in 2020 to present results and liaise with stakeholders was held virtually due to COVID-19 restrictions. The collected data included (a) problem-centered interviews with 81 (36 m / 45f) migrants and family members to explore their perceptions on hazards and well-being impacts of (im)mobilities (Witzel & Reiter 2012); (b) one focus group with 12 (3 m / 9f) pupils in a sending community to cover an important group underrepresented in the interviews (Vogl 2014); and (c) more than 60 discussions with experts such as policy makers, researchers, and practitioners to gain background insights into structural conditions that shape well-being effects (Gläser & Laudel 2010; Helfferich 2014).

The qualitative strand is case-oriented and uses the comparative method for “rich descriptions of a few instances” of typical cases of villages of departure or immobility and areas of arrival, focusing on “context, complexity and difference” in the chosen cases (Della Porta 2008: 216, 221). The dense knowledge created in this small-N case comparison is useful for discovering well-being effects and mechanisms. While the three cases in Peru are distinctively configured in space and time, the knowledge gained in these in-depth studies can help to build more generalized concepts “that transcend the validity of individual cases” (Della Porta 2008: 206). I explain the site selection below.

2.1.1 Site Selection

I collected data from Peru’s three major regions to cover the following cases (Figure  3.3 ):

Long-distance rural-to-urban migration from two villages in the highlands of the Lima Region and immobility in these areas, influenced by gradual glacier recession and rainfall changes;

short-distance, attempted planned relocation (community-wide migration) of two villages in the rainforest Region of San Martín due to abrupt floods, resulting in entrapment and only one eventual relocation; and

short-distance displacement ( acute , forced migration) from several villages in the coastal Region of Piura, forced mainly by abrupt flooding.

The three Regions for the qualitative data collection in Peru. (Note: The map on the left displays Peru’s location in Latin America, the one on the right the Regions within Peru where qualitative data was collected. Created by the author using paintmaps.com © and mapchart.net © and edited subsequently)

Figure  3.4 below specifies the distribution of these villages across Peru’s three large natural zones.

Sites for qualitative data collection across Peru’s three large regions. (Note: To protect the respondents, the pins indicate approximate locations only. Created by the author, based on CIA (1970))

I selected the areas of origin of migrants (and the homes of stayers) with a view to match three criteria:

Rural villages with similar, locally typical subsistence livelihood systems that

have experienced impacts of water-related climate hazards typical for Peru’s three large topographical zones ( highlands, rainforest, and coast), which

have influenced (im)mobilities in forms characteristic for these hazards, but varied across cases, resulting in diverse well-being conditions.

First, I selected areas with livelihoods—and by extension with (im)mobility patterns—susceptible to climate hazards. The chosen villages primarily use ecosystem-based livelihoods and are typically home to smallholder subsistence farmers with low levels of income, education, and health, who tend to be among the groups most vulnerable to climate impacts (Cohn et al. 2017; Donatti et al. 2019; Niles & Salerno 2018). Selecting villages with these similar livelihood features reduced the number of confounding variables and facilitated better insights into well-being mechanisms; nonetheless, even similar villages are never the same and keeping all contextual variables constant is impossible.

Second, I chose home villages of migrants and stayers affected by either gradual or abrupt water-related hazards, which were either directly related to climate change or provided temporal analogs. To begin with, I set the focus on water (and related hazards) because it is one of Peru’s adaptation priorities in its Nationally Determined Contributions (NDCs) and National Adaptation Plan (NAP) (GoP 2015; MINAM 2021), while global reviews highlight its role in climate (im)mobilities (Nagabhatla et al.; Wrathall et al. 2018). Next, the ex-post design required to select areas where people could notice physical (for example, glacier retreat) or temporal effects of hazards (for example, changes in rainfall timing) which influence (im)mobilities (Laczko & Aghazarm 2009). I selected three hazard dynamics that the systematic review for this study identified as the most typical influences on (im)mobility patterns in Peru’s three large topographical zones: glacier recession (alongside rainfall changes) in the highlands ( Sierra ); floods in the rainforest ( Selva ); and El Niño events in the coastal zone ( Costa ) (Bergmann et al. 2021a; see also reviews in results chapters  5 – 7 ). On the one hand, I selected Sierra villages harmed by gradual hazards directly attributable to climate change, namely glacier recession (Seehaus et al. 2019) and changes in the rainfall regime (Heidinger et al. 2018). Studies demonstrate that both such glacier retreat (e.g. Alata et al. 2018; Altamirano Rua 2021; Figueiredo et al. 2019; Heikkinen 2017; Wrathall et al. 2014) and rainfall changes (e.g. Hook & Snyder 2021; Lennox 2015; Milan 2016; Milan & Ho 2014) can alter migration in the Sierra . On the other hand, I chose villages affected by two types of abrupt hazards for which climate change attribution is not as clear, but which provide temporal analogs for future climate impacts. Footnote 5 To begin with, I selected two Selva villages harmed by floods, which periodically affect (im)mobilities in this region (e.g. Hofmeijer et al. 2013; Langill 2018; List 2016; Sherman et al. 2016). When habitability is threatened, the state has occasionally attempted to relocate entire communities (Bernales 2019; Desmaison et al. 2018; Estrada et al. 2018; Lopez 2018; Pittaluga 2019). While extreme floods have already increased in the Selva (Barichivich et al. 2018; Gloor et al. 2013; Marengo & Espinoza 2016), it remains unclear how much more likely climate change made the specific floods analyzed in this study. Yet, given that extreme floods have increased in this region overall, and climate change is projected to raise them further (Duffy et al. 2015; Langerwisch et al. 2013; Zulkafli et al. 2016), the cases do provide valuable insights into a dynamic with increasing importance. Moreover, I selected sites on the Costa harmed by the 2017 Coastal El Niño (CEN) floods. Peru’s coast is periodically affected by severe flooding due to El Niño events (Sanabria et al. 2018), which are among the main drivers of acute migration in this zone (Bayer et al. 2014; Ferradas 2015; French & Mechler 2017; Venkateswaran et al. 2017). Climate change made the specific 2017 CEN analyzed here at least 1.5 times more likely (Christidis et al. 2019). Even independently of the exact climate attribution for this event, the analysis of the 2017 CEN sheds light on a type of phenomenon that Peru will face more often due to climate change (Cai et al. 2015; IPCC 2019a; Peng et al. 2019). (Lastly, choosing one case per zone also did justice to Peru’s diverse topography and made the findings relevant for national policymakers, who typically think in these boundaries.)

Third, I selected departure and arrival points of diverse spatial and temporal forms of migration to observe varied conditions for well-being changes. Migration was either propelled suddenly (coast and rainforest) or driven over longer time frames (highlands), as shaped by the abrupt and gradual hazards discussed above. Moreover, I sought to investigate various forms of (im)mobilities along the spectrum of more voluntary (some cases from the highlands) and forced instances (highlands, coast, and rainforest). I also chose (im)mobilities involving different numbers of people, from individuals to households (highlands and some coastal cases) and entire communities (coast and rainforest). These choices intended to satisfy quality criteria for case selections (Gerring & Cojocaru 2016; Seawright & Gerring 2008). Footnote 6

The local partners facilitating the selection of cases included the Mountain Institute for the highlands; San Martín’s Regional Office of Security and National Defense and the Peruvian National Center for Disaster Risk Estimation, Prevention and Reduction (CENEPRED) for the rainforest cases; as well as Caritas and the student group CIMA at the University of Piura for the coast. Footnote 7 Gaining access to the research sites and subjects is a key task of empirical research, and these partners allowed me to enter the villages together with local experts who had known the respondents for years. This approach is common in studies on hard-to-reach migrant populations (Bloch 2007; Ho & Milan 2012). Once the sites were determined, sampling and interviewing followed to gather the qualitative data.

2.1.2 Data Collection

The analytical units were individual migrants and members of migrant households who either accompanied these migrants or stayed at home (stayers). I targeted the heads of migrant households, and occasionally additional household members like spouses, to gain insights into their experiences related to hazards, (im)mobilities, and well-being. For families of migrant members who had moved away, I attempted to interview the new head of household in the village of origin.

I used non-probabilistic, iterative sampling orientated at contrasts, which some authors coin as theoretical sampling . I selected this strategy to systematically contrast cases and reveal themes, connections, and divergences; to compare the mechanisms which express themselves in the different cases; and to illustrate the diversity of well-being constellations, similar as in grounded theory (Corbin & Strauss 2014; Przyborski & Wolhrab-Sahr 2014; Strübing 2014). Footnote 8 After interviews, I iteratively read through notes to find incipient patterns and themes around well-being effects and mechanisms, which guided the selection of new interviewees until returns of further interviews diminished and saturation was reached, which was the case after 81 interviews. Footnote 9 The sampling differed slightly in the three cases. Migrants from the villages in the Selva and Costa moved in large clusters and over short distances, so that they could be readily tracked in destinations. Accompanied by local partners, I spent several days in these sites and went from home to home to select and interview migrants until saturation was reached. By contrast, sampling longer-distance migrants from the Sierra required two steps. I started by interviewing households in the Andean home villages affected by hazards, and then used snowball (or chain referral) sampling to trace migrants in urban areas. Footnote 10 Regarding destinations, I focused on Junín’s Regional capital Huancayo and the national capital Lima for two reasons. First, interviewees in the villages observed that these were the main destinations. Second, both cities featured migrant hometown associations from the Province of origin, which organized events that offered chances to meet migrants. I conducted all interviews in Spanish without interpreters. As all inhabitants in the study areas spoke Spanish, no exclusions due to language had to be made.

For conducting the interviews, techniques with varying premises exist (Hopf 2015; Lamnek & Krell 2016). Broadly speaking, they are either like structured mining for information or narrative travelling (Kvale & Brinkmann 2009: 48–50). Footnote 11 I decided that combining structured and narrative interviewing served the research interest here best for two reasons. First, it puts researchers in an active position so they can use scientific research knowledge to structure key topics in the interview. Yet, second, it does not limit the proper local perspectives of respondents or impede the chance of discovering novel aspects. To this end, I used elements of the problem-centered method (Kurz et al. 2000; Witzel & Reiter 2012), Footnote 12 which brings together the knowledge of the researcher and respondents in a dialogue. Interviewees are competent (but partially biased) insider experts of their lives. Researchers enter as well-informed travelers with scientific knowledge to openly learn, and at once, to assist in reconstructing the meaning of the insider knowledge regarding the research interests.

Accordingly, a prerequisite for this research was compiling information on the interviewee’s living conditions. I had gathered this knowledge in a preliminary sensitizing framework that defined the direction of interest and initial priorities. Later, during the interviews, I assessed and situated new empirical observations by continuously mentally referring to this knowledge. Based on the framework, I developed a topical guide with a road map of key interview topics (Figure  3.5 and Electronic Supplementary Material). The guide provided structure and enabled me to re-center on the research interest during interviews, although the relevance and sequence of topics depended on respondents’ accounts and the guide was adjusted to new data received. In this way, the guide also ensured comparability across interviews by establishing similar topical complexes in each dialogue.

Topical guide and topical complexes. (Note: Created by the author)

Conducting the interview proceeded in several stages (Witzel & Reiter 2012). Bearing in mind that the questions were personal and partially sensitive, I left it to the respondents to decide on a setting in which they felt most comfortable to speak (and which still permitted decent recording). Often, we spoke at their homes but when outside, I asked to talk at a small distance from other people (Figure  3.6 ). Afterwards, a warming up phase with informal conversations with respondents followed to build a relationship. Then, I briefly explained the research project and answered initial questions. Afterwards, I provided an introductory explanation for the interview, including ethical and data protection information as well as a request for permission to record (see Electronic Supplementary Material). Opening questions followed to facilitate narrative accounts by the respondents; they prompted interviewees to tell me the story of how their lives and well-being had changed since they had migrated or stayed. These narrative accounts provided cues for the follow-up conversation on well-being effects and their causes. Next, I asked follow-up questions to encourage additional narrative accounts and to stimulate self-reflection, sporadically providing imaginative prompts or pre-interpretations. I also used strategies to improve understanding where suitable. When topics from the topical guide were omitted, I asked ad-hoc questions on them, usually toward the end. Closing the interview involved various steps. First, I collected data on age, gender, livelihoods, occupation, and other factors to compare profiles. The recordings stopped here. Second, I debriefed respondents and thanked them for the insights shared. I invited final questions or thoughts and provided information on how to contact me. Third, after leaving the interview site, I wrote postscripts that captured key information for self-debriefing, as sketches of the interviews with first interpretations and cues that would later support the analysis of the data.

Photo of an interview with an affected farmer. (Note: Photo taken by colleagues from the Mountain Institute )

Besides individual interviews, I convened one focus group with adolescents, as they were previously underrepresented in the data (Figure  3.7 ). This method brings together people from a target group to engage in a moderated discussion and interaction, which provides different types of insights than individual interviews (Krueger & King 1999; Morgan 1999b). Twelve pupils (3 m / 9f) aged 14 to 16 years old participated. The sampling was purposive: through local partners in the school, pupils in the final classes before graduating from school—and thus facing the decision whether to stay or migrate—were invited. Questions followed the topical guide for the interviews in a discreetly structured approach. I allowed participants to open their own directions but also applied moderation tools to refocus group dynamics on the research interests. To this end, I used a funnel approach, moving from initially broader, open-ended questions encouraging narration to the central topics, and finally, to specific questions on the research interests (Krueger 1999; Morgan 1999a).

Photo of the focus group with pupils in a study site in the highlands. (Note: Photo by the author)

The charts in Figure  3.8 below summarize key data of the 93 affected people. The tables in the respective results chapters  5 – 7 provide information disaggregated by regions. They illustrate that while most interviewees were at working age, I also covered younger and older groups. Women are slightly overrepresented in the data. While most respondents were mestizo, I was able to sample one indigenous village. Primarily, most interviewees worked in agriculture, and almost all households were agricultural. Finally, across Peru’s three large zones, I interviewed similar shares of migrants, displaced persons, relocatees and those trapped but aspiring to relocate, as well as other stayers.

Qualitative data profiles of 93 affected people. (Note: The graphs illustrate the profiles of 81 interviewees and 12 focus group participants. Created by the author)

I also conducted discussions with experts for background context on the larger structural factors and processes behind the well-being effects of (im)mobilities in Peru. I identified the experts through desk research and referral from authorities, civil society, and international organizations working on related topics. They included experts at higher state levels, such as staff in national ministries, and at the local level, such as village heads. In total, I discussed with more than 60 policy makers, officials, practitioners, academics, and activists working in diverse entities (Figure  3.9 ).

Experts consulted across administrative scales and fields of expertise. (Note: Boxes colored beige indicate discussions with experts from the Costa , gray from the Sierra , and green from the Selva . V1 and V2 = village 1 and village 2 in the Sierra; H and L = Huancayo and Lima; V3 and V4 = village 3 and 4 in the Selva ; LP and UP = Lower and Upper Piura on the Costa. Created by the author)

Discussions with experts are not a method as such; rather, they are defined by the target group of respondents, namely experts (or key informant), and their special knowledge, position, and access to information about climate change, (im)mobilities, and well-being (Witzel & Reiter 2012). While the interviews with affected people aimed at distilling their subjectivity, discussions with experts intended to find more neutral views on the effects of (im)mobilities held by people who are not research objects themselves (Bogner & Menz 2009; Gläser & Laudel 2010; Helfferich 2014). Footnote 13 To this end, I used elements of the problem-centered method (Witzel & Reiter 2012). Footnote 14 These discussions fed into the analysis via field notes taken and were not recorded or transcribed.

2.1.3 Transcription and Text Analysis

The next step for analyzing the information contained in the recorded interviews with affected people was transcribing them into text. Transcription is an integral part of qualitative analysis processes because it requires selective decisions that imply a first sampling and analysis of the oral material, and results in interpretive constructions (Davidson 2009; Kvale 2007; Sandelowski 1994; Wellard & McKenna 2001). To guarantee careful transcription, the EPICC project at PIK hired a Peruvian student assistant who typed the Spanish transcriptions manually. I provided the assistant with detailed notation, confidentiality, and data protection instructions as well as information on the study purpose, as recommended by the literature (Stuckey 2014; Wellard & McKenna 2001). The transcriptions are based on intelligent verbatim guidelines, with cues of some nonverbal behavior, an approach which can increase reliability, dependability, and trustworthiness of the results (Easton et al. 2000; Stuckey 2014). In this way, the assistant only discreetly adjusted information for readability, without changing the core of what was said. Finally, the assistant proofread all transcripts and I checked and listened to some of the transcribed tapes for quality control (MacLean et al. 2004). After transcription, I deleted any data that could identify the interviewee, such as names, workplaces, and specific positions (Stuckey 2014). The transcription guidelines are in the Electronic Supplementary Material.

Amon the many approaches used for analyzing qualitative data (Flick 2009; Gläser & Laudel 2010; Mayring 2014), I selected thematic and evaluative Qualitative Text Analysis (QTA) after Kuckartz (2010, 2014b; Kuckartz & Rädiker 2019) as the central method for analyzing the transcribed interviews in this dissertation. Thematic (or content-related) analysis enables “identifying, systematizing, and analyzing topics and subtopics and how they are related”, while evaluative analysis is about “assessing, classifying, and evaluating content” (Kuckartz 2014b: 68). I used this combination to understand factual changes in well-being as well as underlying processes. Footnote 15 The QTA followed a five-step approach with reference to the research questions (Kuckartz 2014b) (Figure  3.10 ).

The analytical process of Qualitative Text Analysis. (Note: Reproduced and edited by the author, based on Kuckartz (2014b: 40))

Using MaxQDA software, first I added several variables to the cases for comparative analysis later (age; gender; interview site; occupations; and (im)mobility status). Then, I systematically read entire interviews with a view to understanding their meanings for the research questions (Kuckartz 2014b).

Afterwards, I created a combination of thematic and evaluative categories in a mixed, concept- and data-driven approach. Footnote 16 In a first step, I derived concept-driven, thematic and evaluative categories and sub-categories from the research questions, central concepts, theories, and topical guide in this study. For example, for categories on objective well-being, I adjusted and extended previous findings from ressearch with deprived groups in Peru (Copestake 2008c) (see section  2.3 ). Initial categories also evolved from the topical interview guide, for example, on migration capabilities, aspirations, and drivers. The coding started with these categories. Second, while coding the first 30% of all interviews, I added new, data-driven categories using a subsumption strategy (Kuckartz 2014b; Mayring 2010): I probed all text step by step to find new topics around the research questions. Then I subsumed aspects already covered by existing categories under those. Finally, I created new (sub-)categories for new aspects. For evaluative categories (such as well-being changes in health), I defined three ordinal levels: positive/improving, neutral, or negative/deteriorating. While coding the first 30% of the material, I also adjusted the concept-driven categories as needed. Third, I compiled all text segments for each category, developed category definitions and anchor examples (and differentiation from other codes, where needed), and fixed the category system. Finally, I used this system to code the whole material. The category system is detailed in the Electronic Supplementary Material.

Subsequently, I used three tools to analyze the data based on these categories (Kuckartz 2014b; Kuckartz & Rädiker 2019). First, I focused on topics and sub-topics , analyzing each main category regarding what was discussed and what was omitted or evaded, as well as what tendencies and singularities emerged across cases. I thereby aimed to account for the criticism that QTA tends to overstress frequently mentioned topics, reproduce mainstream and dominant narratives, and suppress or deny other contents and their absence (George 1959). Second, I examined relationships between main categories and their sub-categories. For example, I analyzed how well-being components within the category development from a secure base (livelihoods, education, health and food security) related to each other, and also how this main category related to the other three main categories. Third, I examined trends across groups , for example by comparing views of people engaging in varied types of (im)mobilities, driven by either abrupt or gradual hazards. Building on these tools, I drew conclusions on the research questions and identified new questions arising from the analysis.

2.1.4 Ethical Considerations and Data Protection

Research with human subjects must address ethical challenges (Friedrichs 2014), especially when asking migrants or stayers, some of whom in vulnerable situations, about sensitive topics (van Iiempt & Bilger 2012). Ethics require taking responsibility for the researchers’ actions as well as providing accountability and redress options (Dench et al. 2004). The principle of “do no harm” is key for qualitative studies, which imply personal and little standardized interactions. Guidelines and regulations commonly highlight the Belmont principles. Footnote 17 The German Professional Association of Sociologists and the American Sociological Association share similar criteria (Friedrichs 2014).

To comply with these standards, I asked respondents for their written informed consent to participate in interviews (see Electronic Supplementary Material). Footnote 18 Further, I explained which information would be collected and how it would be used. I also detailed the research procedures and products as well as related potential benefits and risks. As migration research often influences real policies, scholars need to be aware of possible impacts on their respondents and reflect on which data truly needs to be collected (van Iiempt & Bilger 2012). Footnote 19 Next, prior to the interviews, I explained that confidential information would be treated as such, and that the data would not be used in ways that could compromise respondents. I stressed that I would never reveal people’s clear names or the names of their hometowns, and since I interviewed respondents from small settlements, I carefully assessed if they could be identified despite the deletion of these names. In the analysis, I use a numbering system (for example, V1–4 for respondent 4 from village 1) and broad categories (such as age group) to refer to interviewees. Then, I asked for written permission to record, transcribe, and use the information academically. Finally, I restricted access to recordings and transcripts to myself and the student hired for transcription, under strict data protection policies. Focus groups require equal attention to ethical principles (Morgan 1999b), especially as the one conducted here was with adolescents. Footnote 20 One overarching ethical challenge in the qualitative part was dealing with inequalities in the relationship with the interviewees (Lammers 2007). I, as a foreign, privileged researcher, met people in often-vulnerable situations in which power relations, hierarchies, and strong socio-economic differences were salient. I attempted to be aware of these factors to avoid that people participated against their will, for example, due to social pressure or fear of negative consequences, and bearing in mind that there might be personal reasons to participate (Glazer 1982). I emphasized that the interviews had academic character and would not entail financial compensation, which was key as many deprived respondents hoped for support. Footnote 21 Ethical considerations also applied to the time after collecting the qualitative data. (Most of these considerations also applied to the quantitative strand discussed further below). Footnote 22

2.1.5 Limitations

The research design offered various strengths—which are discussed in the conclusions (chapter  9 )—but also implied limitations. First, the site selection was strongly shaped by what local partners suggested as accessible locations. Although I chose sites representing diverse conditions, partners did not propose areas that would be too dangerous for an outsider. Thus, the study might not cover well-being processes of people in insecure vicinities. I attempted to compensate for this possible limitation through discussions with experts and the quantitative strand, which provides data for all settings.

Second, not all migrants of interest could be sampled and interviewed. For example, men, adolescents, and older adults are underrepresented in the data, and I did not interview children due to ethical concerns. In particular, the snowballing technique applied for tracing migrants from the Sierra might have created biases and blind spots (Jacobsen & Landau 2003). People without close contacts in their villages of origin were possibly not reached and respondents’ personal situations might have further shaped the reach. For example, some migrants may have declined interviews as they were either ashamed of their situations or doing so well that they did not care to spend time with an outsider. In addition, not all migrants came to hometown association meetings where most interviews took place, some possibly because they lacked money for the necessary travel or time due to their hard work. Nevertheless, snowballing was the most robust option available for the set-up of this study and built upon prior studies in this field (Koubi et al. 2016; Laczko & Aghazarm 2009). In research with hard-to-reach populations, accurate sampling frames tend to be unavailable or too expensive to create, as was the case here (Bloch 2007). In such cases, chain referral through intermediaries, service providers, and local organizations—such as the migrant hometown associations here—is common. In addition, as the new respondents often have friendly and trusted ties with the chain referrers, such sampling can build more motivation and higher response rates among otherwise hard-to-reach groups than other methods (Bloch 2007; Faugier & Sargeant 1997). Building such access and personal relationships is key for interviewing people who may be otherwise reluctant to participate and allows for an efficient use of time and resources (Atkinson & Flint 2001; Heckathorn 2002; Rodgers 2004).

Third, in some cases it was not possible to follow through with the interview techniques suggested by the problem-centered method. Respondents were often on the move or occupied, so that conversational instead of overly formalized approaches were required. Moreover, many respondents did not provide long narrative accounts in response to opening questions or further prompts, which led to some situations where question-response schemes prevailed. In addition, as interviews mostly took place in places familiar for respondents, occasionally, more people joined in and created small group discussions. These additional accounts often opened new views, but occasionally, they also changed the conversation dynamics. In such situations, social desirability, hierarchies, and fear of over-disclosure may have shaped the main respondents’ answers (Reczek 2014).

Third, I initially had envisaged more focus groups, yet time, resource, and later COVID-19 constraints impeded this goal. The focus group in the Sierra provided valuable insights and might have been usefully replicated in other settings to explore narratives of other specific groups. For example, distilling female group views would have been interesting to contrast male narratives, since gender aspects are often salient in rural areas in Peru (Milan 2016). However, with around 60% of the interviewees being women, female views are still duly accounted for. Valuable insights could also have been gained through additional focus groups with members of receiving communities or with groups divided between migrants faring better and those faring worse in destinations. I accounted for this change in plans by considering results across varied sub-groups of respondents in the analysis.

Fourth, Qualitative Text Analysis also implied certain limitations. To start with, additional coders or reviewers could have increased the reliability and quality of the category system (Kuckartz & Rädiker 2019) but were not available due to resource constraints. Beyond, QTA alone may not pierce through the surface of all interview content (Rosenthal 2018), and as a code-based analysis, it risks detaching text from the original context (Hitzler & Honer 1997). I countered this constraint by accounting for the sequential structure and Gestalt of key cases, which raised the understanding of the meaning of the texts and their contexts (Hopf 1995; Hopf & Hopf 1997; Hopf & Schmidt 1993). Finally, because I met several experts in the context of work trips for the EPICC project at PIK, some of the discussions were infused with discussions around project needs and results, which occasionally conflicted with a structured interview approach. For this reason and due to time and resource constraints, these discussions with experts were not recorded or transcribed; rather, I used notes taken from the conversations with experts mostly as contextual information for the analysis.

Lastly, while several features of the study design raised the validity of results—including in-depth interviews with affected people and triangulation with experts—findings should still be read with two limitations in mind. First, the cross-sectional data may mask longer-term changes in OWB and SWB or lagged interactions. Intergenerational and life-course views would provide additional value for time-dependent effects (Dustmann & Glitz 2011; Singh et al. 2019) and longitudinal data could provide supplementary insights (KNOMAD 2015). For example, the lack of long-term data impeded an evaluation of possible long-term, positive side-effects of the 2017 CEN on the Costa (such as more pasture, planting areas, and forests, which were witnessed in prior events (Sperling et al. 2008)), which could influence people’s well-being. Finally, for the Selva and Costa cases, limits of temporal analogs must be kept in mind, so that the results of this study may be transferable to a large degree to future El Niño events or rainforest floods, but not fully (Berrang-Ford et al. 2011; Ford et al. 2010). As just one example, governance strongly shapes the emergence of disasters (e.g. Ahrens & Rudolph 2006; UNDRR 2020) and strongly affected the well-being effects for displaced persons and relocatees in this study; however, it remains unclear how Peruvian institutions, policies, and governance may change in the future, and how these changes would affect well-being outcomes in turn.

2.2 Quantitative Methods

This section explains the data and methods used in the statistical analyses to study differential displacement risk and the effects of displacement on people’s well-being after the Coastal El Niño (CEN) floods in March 2017. I give additional details in the full empirical case study in chapter  7 .

The quantitative analyses make use of two datasets compiled by INEI. First, INEI collected data from households and public buildings in areas affected by the CEN through a survey conducted between mid-April and end of April 2017. Through this “CEN Survey” Footnote 23 , it aimed to improve the understanding of damages and the characteristics of affected people, their dwellings, and public infrastructure. To gather the data, INEI asked local authorities in the 892 districts declared in a state of emergency due to the CEN (Table  3.1 ) to identify all affected rural villages as well as the affected blocks in urban areas, in which enumerators then recorded data from all heads of households and information about all public buildings (INEI 2017a, 2017c). Footnote 24 Altogether, the CEN Survey registered 398,148 persons in 199,938 dwellings and 2,615 public buildings. This analysis focuses on the 186,437 adult respondents whose homes where directly affected and experienced at least minor damages. Footnote 25 The extensive CEN Survey provides a first valuable data point about the most affected areas in Peru shortly after the main floods had affected Peru in March 2017.

The second dataset is the Peruvian National Census 2017 (INEI 2018c), Footnote 26 which was by chance enumerated seven months after the CEN disaster and thus six months after the CEN Survey. To support this research, INEI searched for the 398,148 respondents of the CEN Survey of April 2017 among the 29.4 million entries of the National Census collected on the 22 nd October 2017 (INEI 2018c). Footnote 27 INEI found 342,009 CEN respondents in the Census (87.2%), whereas 49,933 persons (12.7%) could not be cross identified. The well-being analysis here focuses on the 186,437 adult CEN Survey respondents with affected homes, of whom 164,084 (88%) could and 22,353 (12%) could not be cross-identified in the National Census data. This attrition could be due to various reasons. For example, persons surveyed in the CEN could have passed away, moved abroad, lived in areas that could not be surveyed, or refused to cooperate in the enumeration. However, because the differences between the identified and non-identified groups are not large, they should not lead to a strong systematic attrition bias in the analyses. The summary statistics for the CEN Survey respondents with homes affected by the disaster demonstrate that the respondents who could not be identified in the National Census did not differ substantially from the cross-identified population regarding key social factors (Table  3.2 ). The two groups had almost identical rates of secondary education, civil status, and disabilities. In the group cross-identified in the Census, approximately five percentage points less respondents lived in small rural villages and around five percentage points more were unemployed or female compared to the non-matched group.

2.2.2 Regression Models

These datasets were then used to analyze the research questions explained above through several regression models. The first analysis estimated how different environmental, socioeconomic, and demographic factors influenced the displacement risk of the households. Because the outcome is binary coded, the estimation was completed with logistic regression models. Model 1 considered only the influence of exogenous environmental factors, such as topographical and rainfall data. Footnote 28 This baseline model was then gradually extended by including further information on household composition and demographic characteristics (model 2) as well as on livelihood factors and wealth (model 3). I detail the model parameters in the empirical section  7.3 .

The second analysis centered on how displacement affected people’s well-being. It started by comparing the well-being of the displaced households to those whose houses were affected but who could remain at home directly after the disaster, based on summary statistics of the CEN Survey. Because this exceptional sample covers close to the full affected population, summary statistics render robust results on people’s well-being outcomes. Then, five linear regression models were specified to explore the impact of the displacement on well-being seven months after the CEN under control of a broad set of environmental, demographic, and socioeconomic variables. The sample in this part of the analysis were the affected adult CEN Survey respondents who could be tracked in the National Census. To understand the displacement effects, a well-being index based on indicators available in the data was built, mainly using items for a space to live better and, to some degree, items for development from a secure base (see section  7.3 for details). The impacts on well-being were then analyzed through various models. Baseline model 1 comprised displacement as the only parameter. The next models added gradually more control variables for environmental factors (model 2), household composition and demographics (model 3), livelihood characteristics and wealth (model 4), and individual characteristics (model 5). The models thereby control for the potential non-randomness of the displacement risk. People do not randomly migrate or flee but factors such as age, sex, and well-being can systematically shape the probability of movement (Aksoy & Poutvaara 2021; Borjas et al. 1992; Kaestner & Malamud 2014). The controls are needed since the observed well-being outcomes might therefore not be due to the displacement itself, but due to pre-movement factors that made displacement more likely in the first place.

2.2.3 Limitations

The quantitative work allowed for a novel analysis of differential displacement risk and well-being impacts in an extensive sample of affected people from all of Peru. Thereby, the work complemented the in-depth qualitative analysis of well-being effects and mechanisms in coastal Piura usefully. Despite generating this added value, the results should be read with the following limitations in mind.

First, because data was not available for all parameters, the analyses operated with a subsample of the respondents (see section  7.3 ). Data was not consistently enlisted for those households whose homes had remained unaffected by the disaster. Therefore, the analyses focused on the respondents who had indicated that the disaster had affected their houses negatively, and for whom data was available. The differences in displacement risk and well-being might be even larger if compared to the unaffected. In addition, as the study excludes respondents below 18 years to avoid double counting and due to missing data, it allows insights into children’s situation by extension only.

Second, because the CEN Survey did not contain an explicit question on displacement status, the analysis is based on proxies that may be noisy. The assumption that uninhabitable homes equaled displacement (see section  7.3 ) is a plausible basis for the analysis. However, people with intact homes could still have fled, for example, because they were afraid of the disaster, had lost their livelihoods or health, or complied with the issued early warnings. Conversely, respondents whose homes were destroyed could still have decided to remain in place. Additionally, while the data on habitability allowed to infer that people were displaced one month after the CEN, information was missing if they had returned or remained in displacement due to the event seven months later.

Third, a possible attrition bias must be discussed for the well-being analysis because 12% of the subsample of interest was lost when merging the surveys. The remaining sample is still large, but if the attrition is not random, then the differences between the dropped-out and the remaining respondents could introduce a bias into the results and decrease the internal validity of the study (the identified relationships between variables). Yet, the summary statistics document that the differences between the remaining and the dropped-out respondents are marginal (Table  3.2 ). Additionally, the attrition affects the external validity less (the generalizability to the original population) as the sample still includes almost the entire possible population of the Peruvian households affected by the CEN.

Fourth, the surveys collected by INEI could not reflect the full range of well-being indicators of interest in this dissertation (see framework developed in section  2.3 ). Primarily, the data did not contain indicators on social relatedness and subjective well-being . While more data was available for the components development from a secure base and a space to live better , information was missing for several key subitems of these components, such as education or physical security. Therefore, the quantitative well-being analysis offers a robust indication of the life situations of a large group of affected people, but the scope of well-being which could be analyzed was limited. The qualitative analysis was a critical complement to understand the broader range of well-being changes of interest.

Fifth, there might be additional, district- or community-level factors that this analysis could not control for, but which could have influenced the well-being results. Examples include the quality of community networks and support, social participation, neighborhood infrastructure, local leadership and governance, and resource equity (Berkes & Ross 2013; Koliou et al. 2018). While the statistical analysis could not rule out indirect effects through these factors, the qualitative analysis partially compensates for this lack of data and offers insights into some of the possible influences.

Finally, the survey data offered two data points for up to seven months after the CEN, but neither allowed for insights into people’s gradual development of well-being nor into the outcomes over the long term. Given that many persons displaced by the CEN have remained in prolonged displacement (AFP 2021; IOM 2017c, 2018), it would have been interesting to see how different groups have recuperated over time, and which factors have aided or impeded recovery. The qualitative data collected one year after the Census helped to discern some of these longer-term phenomena.

Despite these limitations, the analyses of the secondary quantitative data provide extensive information on the differential displacement risk and well-being of a large group of affected people across the entire country, which usefully complements the analysis of the primary qualitative data.

Abduction means “a theoretical redescription of events, phenomena and processes using certain conceptual schemes and frameworks” (see Iosifides (2012: 43)), with the aim of re-describing and re-contextualizing the observed by relating it to a rule, see Danermark et al. (2002)). Retroduction refers to “identifying the necessary conditions for the occurrence of certain events, processes or phenomena” (see Iosifides (2012: 43)) and asking about the more fundamental “transfactual conditions, structures and mechanisms” that must exist for something to be possible, see Danermark et al. (2002: 80).

Quantitative methods can discern persistent regularities (or semi-regularities), patterns, and effect distributions; they allow investigating “formal relations of similarity” and find “descriptive, representative generalizations” (Danermark et al. 2002: 165). Nonetheless, quantitative research is limited to observable and quantifiable objects, while quantification and aggregation, in turn, can lead to simplistic representations of the social world that can ignore diversity, context, and outliers, and may lead to basic, biased models (Maxwell & Mittapalli 2010). While quantitative research uses an established range of methods to distinguish correlation from causation, it is often not sufficient for understanding generative mechanisms, which depend on process, contexts, and underlying conditions (Cook et al. 2002). Conversely, qualitative methods can shed light on generative mechanisms behind quantitatively observed regularities by elucidating contexts and tracing in detail how processes materialize in specific cases (Iosifides 2012). Nevertheless, small qualitative samples cannot adequately represent the full diversity of a setting or population, so that care must be taken to avoid “simplistic generalizations” (Maxwell & Mittapalli 2010: 160).

Critics raise that using these goals as the value base of research results in bias, see Hammersley (2009). I argue that all research is value-laden, even allegedly value-free positivist studies. The latter only do not expose the values that underlie research. Applying values in research always implies judgement, but such judgement calls are justified if research explores and exposes social structures of inequality, as is the case here.

Original Spanish name: Instituto Nacional de Estadística e Informática (INEI).

The temporal analog approach is useful to infer possible future impacts of climate change, related (im)mobilities, and well-being implications, see Smit & Wandel (2006). How a system reacts to hazards now can shed light on possible interactions in another time or area for a similarly structured and organized system, see Ford et al. (2010). While systems are never identical and analogs cannot echo future situations perfectly, they can provide a useful empirical starting point and are often employed in research on human dimensions of climate change, see McLeman & Hunter (2010); Sherman et al. (2015). The specific conditions for climate impacts in selected sites provide overarching insights for similar localities that can expect to see more frequent and severe hazards of the same type, see Berrang-Ford et al. (2011). Therefore, notwithstanding causal attribution to climate change, the analogs can provide insights for the future.

For comparability , I only selected rural agrarian villages with mainly poor subsistence farmers as sending communities. To ensure plentitude , I visited at least two areas per region and several villages in each region. To guarantee sufficient variation , I selected a range of values for the variables of interest, namely various forms of (im)mobilities leading to diverse conditions for well-being changes. For independence , I chose villages across Peru’s three large areas that are spatially and socioeconomically distinct. To raise representativeness , I discussed the case selection with local experts in ministries, academia, and civil society so that they would reflect properties of a larger number of cases. These discussions also served to do justice to the boundedness criterion.

I reiterate my deep gratefulness to these partners. Their original Spanish names are: Instituto de Montaña ; Oficina Regional de Seguridad y Defensa Nacional de San Martín ; and Centro Nacional de Estimación, Prevención y Reducción del Riesgo de Desastres; Cima (Grupo de Formación en Ciencias del Medio Ambiente) de la Universidad de Piura .

Such sampling does not aim to saturate existing categories (such as age or gender, as in selective sampling ), but rather to select cases that shed light on the key topics for the research questions, see (Witzel & Reiter (2012). Thus, I recruited participants with varied well-being paths or with similar outcomes despite dissimilar conditions.

Some authors argue that one interview can be enough, others indicate that saturation can be reached after six to twelve interviews, or between 20 and 50 interviews, see Baker et al. (2012); Guest et al. (2006); Guest et al. (2017).

In snowball sampling, initial respondents refer researchers to others with similar backgrounds in their networks, see Biernacki & Waldorf (1981); Sadler et al. (2010). After identifying and interviewing migrant households in the villages of origin, I asked whether they could connect me with the absent relatives and other migrants. I repeated this step until saturation was reached for the family interviews and sufficient contacts to urban migrant were identified. In the next step, I contacted and visited the migrants in the cities for interviews, and asked each one of them for additional contacts to migrants from their villages until saturation was reached.

Mining allows searching for specific information through pre-defined interests and standardized questions. Yet, it provides limited opportunities to discover new or unanticipated aspects, and changing prearranged criteria is difficult. By contrast, travelling is like open wandering in the interviewee’s experiences. This form facilitates an unprejudiced view of how interviewees construct their subjectivity, but is less goal-orientated, and can require substantive time and resources.

The German term Problem underscores the focus on a societal issue with practical relevance for the respondent, in this case, the impacts of (im)mobilities (the Problemstellung ). Centering means that researcher and respondent jointly establish a focus on the research subject of interest. See Witzel & Reiter (2012).

Nevertheless, expert knowledge is also formed by personal experiences, aspirations, and socialization.

The method stipulates that discussions with experts require significant prior knowledge so that the researcher can structure the conversation and reconstruct knowledge. Researchers thus become co-experts, but still aim for a dialogue with narrations and moderately re-center on the research interests along the topical guide.

Beyond, I also explored the use of fine structure and system analyses for a deeper level of understanding, see Froschauer & Lueger (2003). However, the preconceived ideas of the German discussants and their social conditions (foreign, white, academic, wealthy, researchers) were too different from the Peruvian context. To apply this method, the original Spanish texts would have also required a translation into English or German that would have further blurred words and meanings.

Categories (sometimes labelled codes ) in social research are “a term, a heading, a label that designates something similar under certain aspects”, see Kuckartz & Rädiker (2019: 184). They depict commonalities. As umbrella terms, they are based on criteria that allow subsuming common features to lower complexity and sort information on research interests. They are the central analytical tool in QTA, which “stands or falls by its categories”, see Berelson (1952: 147). All categories together form the category system .

Respect for human dignity, justice, and beneficence, which are to be fulfilled through four strategies: “informed consent, non-deception, privacy and confidentiality, and accuracy”, see Christians (2005: 144).

Receiving genuine consent from respondents who are in vulnerable situations can be challenging, as they may consent due to power dynamics, see Mackenzie et al. (2007). Therefore, I stressed that participation was voluntary, and that refusal to participate or to answer specific questions would not result in negative consequences.

Although I strived to understand possible risks for the respondents, I recognize that I am an outsider without full knowledge of their social circumstances and cannot rule out all negative impacts.

I highlighted voluntary participation and the right to refuse to answer questions, assured confidentiality, and informed participants that what they said would never be quoted with their names. Participants were free to take breaks or leave at any point. Especially when discussing potentially sensitive or stressful aspects, I set limits for the discussion and tried to avoid over-disclosure by participants which they might regret later, or which might expose them.

In some cases, respondents still approached me for non-financial help. No official guidelines exist on adequate reactions to such requests, and according to van Iiempt and Bilger, they constitute “an ethical challenge that is open for debate and strongly influenced by one’s personal views” (2012: 461). In the critical realist view taken here, practical help and advocacy were desirable whenever feasible without compromising the research quality, if respondents explicitly requested and agreed to such non-financial support. Interviewing requires building trustworthy relationships and a reciprocal process of giving and receiving, and research with people in vulnerable positions must go beyond doing no harm toward reciprocal benefits: “when a human being is in need and the researcher is in a position to respond to that need, non-intervention in the name of ‘objective’ research is unethical”, see Mackenzie et al. (2007: 316). In my view, researchers are often well-positioned and may even have a duty to speak on behalf of their respondents if the latter lack voice to speak for themselves.

Storing and cleaning the data from the fieldwork involved a strict protocol for data protection, such as encrypted storage in a separate virtual partition with password protection and saving identifying information separately from files with substantive responses. The student assistant hired for transcription signed a contract with strict data protection requirements. When analyzing data and formulating interpretations, I considered potential risks and benefits for the respondents, especially when dealing with inconsistencies in the primary data, for example, what respondents revealed and what they seemed to adapt, distort, or conceal, see van Iiempt & Bilger (2012). Before disseminating the findings through this dissertation and other publications, I took care to review how the outputs could affect respondents first. Finally, since affected people shared their time and information with me, I also attempted to share the results of this study with them in reciprocity. However, since COVID-19 made traveling to Peru difficult, I could not share the results directly on-site as originally planned.

Census of Population, Housing, and Public Infrastructure Affected by El Niño Costero 2017, original Spanish name: Censo de Población, Vivienda e Infraestructura Pública Afectadas por El Niño Costero 2017.

For more technical details regarding sampling and enumeration, refer to INEI (2017c).

The analysis excludes children below 18 years to avoid double counting and because the survey did not contain relevant data for them on key well-being items, such as employment.

Original Spanish name: Censos Nacionales 2017: XII de Población, VII de Vivienday III de Comunidades Indígenas .

I would like to reiterate my gratitude to the colleagues at INEI for supporting this research. They first cleaned the CEN Survey data and removed entries without information on surnames, which left 391,942 records. Afterwards, it identified the CEN Survey respondents in the Census based on identical names, surnames, dates of birth, sex, districts of residence, and identity document numbers through a deterministic linking application in SQL Server. Entries with a similarity of more than 85% were selected. INEI then tracked further cases through probabilistic linking with names, surnames, and similar ages, as well as through visual review. Duplicates were removed.

The rainfall data was drawn from the MERRA-2 dataset, which is based on GPM satellite data and provided by NASA. Riccardo Biella helped with the data extraction and resampling to a 1 km resolution using bilinear interpolation. The topographical data (maximum elevation and elevation range in the districts as well as average distance to inland water bodies) were distilled from the GTOPO30 digital elevation model, which has a 30 arcsec resolution.

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Bergmann, J. (2024). Research Philosophy, Methodological Implications, and Research Design. In: At Risk of Deprivation. Studien zur Migrations- und Integrationspolitik. Springer VS, Wiesbaden. https://doi.org/10.1007/978-3-658-42298-1_3

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Understanding the basics: what is research philosophy.

Research philosophy is the backbone of any academic study, yet many students struggle to grasp its importance and relevance. In this blog post, we will delve into the fundamentals of research philosophy, demystifying its complexities and shedding light on why it is crucial for producing high-quality research.

Introduction to Research Philosophy

Research philosophy is a crucial aspect of any research study. It refers to the set of beliefs, principles, and assumptions that guide the researcher’s approach and methods in conducting their study. In simple terms, it is the framework that shapes how a researcher sees and understands the world around them.

There are three main research philosophies: positivism, interpretivism, and critical realism. Each of these has its own unique perspective on reality, knowledge, and methodology. Let us explore each one in more detail.

Definition and Importance of Research Philosophy

Research philosophy is an essential aspect of any research study, as it provides the foundation for how knowledge is created and understood. Simply put, research philosophy refers to the beliefs, values, and assumptions that guide a researcher’s approach to their study.

There are three main types of research philosophies: positivism, interpretivism, and critical theory. Positivist researchers believe in the existence of an objective reality that can be studied through scientific methods. They aim to uncover universal laws and establish causal relationships between variables. On the other hand, interpretivist researchers view reality as subjective and socially constructed. They focus on understanding individual experiences and interpreting the meanings attributed by individuals to their actions.

Critical theory takes a more critical stance towards society and aims to challenge power imbalances and social inequalities through research. It views knowledge as being shaped by societal structures and seeks to empower marginalized groups.

The choice of research philosophy depends on the nature of the research question, the subject being studied, and the researcher’s personal beliefs and values. Each philosophy has its strengths and limitations; therefore, it is crucial for researchers to understand their underlying assumptions before embarking on a study.

The importance of research philosophy lies in its ability to shape every aspect of a study, from choosing a methodology to analyzing data, ultimately influencing the outcomes of the research. It also helps researchers make decisions about what information should be collected, how it should be collected, and how it should be interpreted.

Types of Research Philosophies 

Research philosophy is the guiding framework that underpins a researcher’s approach to conducting their study. It shapes the way they think about and understand the world, as well as the methods and techniques they use to gather and analyze data. There are various research philosophies, but the most commonly discussed ones are positivism, interpretivism, and pragmatism.

Positivism is a philosophical approach that believes in the existence of an objective reality that can be studied through scientific methods. Proponents of this philosophy argue that knowledge should be based on observable facts and empirical evidence rather than personal opinions or subjective experiences. Positivists view reality as being independent of human perception and believe that it can be understood through cause-and-effect relationships. They also prioritize quantitative data over qualitative data, emphasizing statistical analysis to establish generalizable laws or theories.

On the other hand, interpretivism is a research philosophy that focuses on understanding social phenomena from the perspective of those involved in them. It recognizes that individuals have their own unique experiences and interpretations of reality, shaped by their cultural backgrounds, values, beliefs, and emotions. Therefore, interpretivists emphasize qualitative methods such as interviews, observations, and document analysis to uncover these underlying meanings and subjective perspectives.

Pragmatism combines elements from both positivism and interpretivism but puts more emphasis on practical applicability than theoretical debates. This research philosophy considers multiple perspectives while also acknowledging the importance of real-world implications for decision-making. Pragmatists value mixing qualitative and quantitative methods to gain a comprehensive understanding of a phenomenon.

Each research philosophy has its strengths and limitations, depending on the nature of the study being conducted. For instance, positivist approaches are ideal for studies seeking generalizability or causality; however, they may not capture complex social phenomena or individual experiences adequately. In contrast, interpretive approaches excel at exploring complex issues but may lack generalizability due to their small sample sizes.

It is essential to note that research philosophy is not a rigid framework, and researchers can adopt different philosophies at different stages of their study. For instance, a study may begin with a positivist approach to establish generalizability, then use an interpretive approach to gain a deeper understanding of individual experiences.

Key Characteristics and Differences Between Each Philosophy

When it comes to research philosophy, there are three main categories that are often used to define and guide the approach of a study: positivism, interpretivism, and pragmatism. Each of these philosophies has its own unique characteristics and differences that can greatly impact the design and execution of a research project.

Positivism is based on the belief that reality exists independently of our perceptions or interpretations. This philosophy emphasizes objectivity, quantifiable data, and scientific methods in order to discover universal laws and patterns. Positivists argue that through empirical observation and experimentation, researchers can uncover objective truths about the world.

On the other hand, interpretivism rejects the idea of an objective reality and instead focuses on understanding subjective experiences and meanings attributed to individuals. This philosophy places importance on qualitative data such as personal narratives, observations, and interviews in order to gain a deeper understanding of human behavior. Interpretivists believe that meaning is constructed through social interactions and cultural contexts.

Pragmatism takes a more practical approach by combining elements from both positivism and interpretivism. It recognizes the importance of both quantitative and qualitative data in research but also values real-world application. Pragmatists emphasize the use of mixed methods (both qualitative and quantitative) to gather comprehensive data for problem-solving purposes.

One key difference between these philosophies lies in their view of causality. Positivists assume a cause-and-effect relationship between variables, while interpretivists focus more on understanding how certain factors influence behaviors or events than establishing causation. Pragmatists take a middle ground by acknowledging both perspectives but ultimately prioritize finding solutions over identifying causes.

Another important distinction is their attitudes towards subjectivity vs. objectivity. While positivists strive for objectivity through unbiased observations, interpretivists embrace subjectivity as they seek to understand individual experiences within specific contexts. Pragmatism acknowledges the role of subjectivity but emphasizes using objective measures when possible.

The choice of research philosophy ultimately depends on the nature of the research question, the goals of the study, and the researcher’s personal beliefs. Each philosophy has its own strengths and limitations, and it is important to carefully consider which approach best aligns with your research objectives .

Positivism focuses on discovering universal truths through objective observation and experimentation; interpretivism emphasizes understanding subjective experiences and meanings within social contexts; and pragmatism takes a practical approach by combining elements from both philosophies in order to find solutions to real-world problems. Understanding these key characteristics and differences between each philosophy can help researchers make informed decisions when designing their studies.

How Research Philosophies Influence Methodology and Data Collection

Research philosophy is the framework that guides a researcher’s understanding and approach to conducting their study. It is a crucial aspect of any research as it influences the methodology and data collection process. In this section, we will delve deeper into how research philosophies impact these two elements of the research process.

Methodology refers to the overall strategy or plan for conducting a study. It outlines the procedures and techniques that will be used to gather and analyze the data. The choice of methodology depends on the researcher’s philosophical perspective, which can broadly be classified into three categories: positivism, interpretivism, and realism.

Positivism is based on the idea that knowledge can only be gained through empirical evidence and objective observations. Researchers who adopt this philosophy believe in using quantitative methods such as surveys, experiments, and statistical analysis to collect and analyze data. The focus is on producing generalizable results that are free from personal biases or interpretations.

On the other hand, interpretivism advocates for a subjective understanding of reality through qualitative methods like interviews, observations, and document analysis. This approach acknowledges that individuals have different perspectives and experiences that shape their understanding of the world. Therefore, researchers adopting this philosophy aim to understand complex social phenomena by interpreting people’s thoughts, feelings, and behaviors.

Realism falls somewhere in between positivism and interpretivism, as it recognizes both objective facts (positivist) and subjective interpretations (interpretivist). Realists believe in studying social structures objectively while also considering individual perceptions to gain a more comprehensive understanding of reality. They use mixed-methods approaches that combine both qualitative and quantitative techniques.

Criticisms and Limitations of Research Philosophy

While research philosophy can be a powerful tool in guiding the design and execution of a research project, it is not without its criticisms and limitations. In this section, we will explore some of the common critiques of research philosophy and its inherent limitations.

1. Subjectivity: One of the main criticisms of research philosophy is that it relies heavily on subjective interpretation and personal biases. Since each individual researcher has their own set of beliefs, values, and assumptions, their chosen research philosophy may influence the way they approach a study. This can lead to biased or limited findings, as different researchers with different philosophies may come to conflicting conclusions about the same topic.

2. Generalizability: Another limitation of research philosophy is its potential lack of generalizability. As research philosophies are often shaped by specific cultural, social, and historical contexts, their applicability to other settings may be limited. For example, a phenomenological approach to studying mental health in Western societies may not be applicable in an Eastern context due to cultural differences in perceptions and attitudes towards mental health.

3. Time-consuming: The process of identifying and selecting a suitable research philosophy can be time-consuming, as it involves deep introspection and critical thinking. Furthermore, adhering strictly to one’s chosen philosophical stance throughout the entire research process can also be time-consuming, as it requires constant reflection on how one’s beliefs influence every stage, from formulating questions to analyzing data.

4. Narrow focus: Some critics argue that adhering too closely to a particular research philosophy can result in a narrow focus on certain aspects while neglecting others that may also be relevant. For instance, taking an interpretivist perspective might prioritize understanding individuals’ lived experiences but overlook broader structural factors that could impact those experiences.

5. Lack of flexibility: Another limitation is that once researchers have committed themselves to a particular philosophical stance at the start of their study, it can be challenging to deviate from it. This lack of flexibility may hinder researchers’ ability to adapt their approach if they encounter unexpected challenges or new information during the research process.

Understanding research philosophy is crucial for conducting high-quality research. It provides a framework and underlying principles that guide the entire research process, from the formulation of research questions to the interpretation of findings. By clearly defining one’s research philosophy, researchers can ensure that their study is grounded in a solid theoretical foundation and follows a rigorous and systematic approach.

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Stanford University

The Cyber Policy Center is a joint initiative of the  Freeman Spogli Institute for International Studies and Stanford Law School .

APRIL 30 DEADLINE | CALL FOR PROPOSALS: 2024 TRUST & SAFETY RESEARCH CONFERENCE

How to fix the online child exploitation reporting system.

• Stanford Internet Observatory
• The CyberTipline is enormously valuable, and leads to the rescue of children and prosecution of offenders.
• Many online platforms submit low-quality reports. 
• NCMEC has faced challenges in rapidly implementing technological improvements that would aid law enforcement in triage.
• Legal constraints on NCMEC and U.S. law enforcement have implications for efficiency.
• These issues would be best addressed by a concerted effort to massively uplift NCMEC's technical and analytical capabilities, which will require the cooperation of platforms, NCMEC, law enforcement and, importantly, the U.S. Congress.
• DOWNLOAD REPORT

The CyberTipline is the main line of defense for children who are exploited on the internet. It leads to the rescue of children and the arrest of abusers. Yet after 26 years many believe the entire system is not living up to its potential. A new Stanford Internet Observatory report examines issues in the reporting system and what the technology industry, the nonprofit that runs the tipline, and the U.S. Congress must do to fix it.

If online platforms in the U.S. become aware of child sexual abuse material (CSAM), federal law requires that they report it to the CyberTipline. This centralized system for reporting online child exploitation is operated by the National Center for Missing and Exploited Children (NCMEC), a nonprofit organization. NCMEC attempts to identify the location of the users who sent and received the abuse content, and may attempt to locate the victim. These reports are then sent to local or national law enforcement agencies in the U.S. and abroad.

The report is based on interviews with 66 respondents across industry, law enforcement, and civil society. Researchers also visited NCMEC’s headquarters for three days of extensive interviews.

While our research focuses on CyberTipline challenges, we want to note that many respondents highlighted that the entire CyberTipline process is enormously valuable and the fact that U.S. platforms are required to report CSAM is a strength of the system. “The system is worth nurturing, preserving, and securing,” one respondent said.

Law enforcement officers are overwhelmed by the high volume of CyberTipline reports they receive. However, we find that the core issue extends beyond volume: officers struggle to triage and prioritize these reports to identify offenders and reach children who are in harm. An officer might examine two CyberTipline reports – each documenting an individual uploading a single piece of CSAM – yet, upon investigation, one report might lead nowhere, while the other could uncover ongoing child abuse by the uploader. Nothing in the reports would have indicated which should be prioritized.

We identify three key challenges for law enforcement to prioritize reports for investigation.

First, while some tech companies are known for providing careful and detailed CyberTipline reports, many reports are low quality. Executives may be unwilling to dedicate engineering resources to ensure the accurate completion of fields within the reporting API. Trust and safety staff turnover and a lack of documentation on reporting best practices cause knowledge gaps in consistency and effective reporting. This is especially true for platforms that make fewer reports. That said, submitting a high volume of reports is not necessarily correlated with submitting high quality reports.

Second, NCMEC has faced challenges in rapidly implementing technological improvements that would aid law enforcement in triage. NCMEC faces resource constraints and lower salaries, leading to difficulties in retaining personnel who are often poached by industry trust and safety teams. While there has been progress in report deconfliction—identifying connections between reports, such as identical offenders—the pace of improvement has been considered slow. Additionally, varied case management interfaces used by law enforcement to process CyberTipline reports make it difficult to ensure linked reports are displayed. Integration difficulties with external data sources, which could enrich reports and facilitate triage, are partly attributed to the sensitive nature of CyberTipline data and potentially staffing constraints for technical infrastructure upgrades. Legal restrictions on NCMEC’s use of cloud services hampers their ability to leverage advanced machine learning tools, although opinions vary on the appropriateness of cloud storage for their sensitive data.

Third, there are legal constraints on NCMEC’s and law enforcement’s roles. A federal appeals court held in 2016 that NCMEC is a governmental entity or agent, meaning its actions are subject to Fourth Amendment rules. As a result, NCMEC may not tell platforms what to look for or report, as that risks turning them into government agents too , converting what once were voluntary private searches into warrantless government searches (which generally requires suppression of evidence in court). Consequently, NCMEC is hesitant to put best practices in writing. Instead, many trust and safety staff who are new to the CyberTipline process must learn from more established platforms or industry coalitions. 

Another federal appeals court held in 2021 that the government must get a warrant before opening a reported file unless the platform viewed that file before submitting the report.  Platforms often do not indicate whether content has been viewed; if they have not so indicated, then NCMEC, like law enforcement, cannot open those files. Platforms may automate reports to the CyberTipline on the basis of a hash match hit to known CSAM instead of having staff view each file, whether due to limited review capacity or not wanting to expose staff to harmful content. Where reported files weren’t viewed by the platform, law enforcement may need a warrant to investigate those reports, and NCMEC currently cannot help with an initial review. 

This review process makes it difficult to process the high volume of reported viral and meme content. Such content commonly gets shared widely, for example out of outrage or a misguided attempt at humor; nevertheless, if it meets the definition of CSAM, it is still illegal and must be reported. Platform staff don’t always review meme content (to avoid repeated unnecessary exposure to known material), but if these reports with unviewed files are submitted without checking the CyberTipline report form’s box for memes, it creates an enormous amount of work for law enforcement to close out these unactionable reports. Meanwhile, since platforms are required to preserve reported material for only 90 days, the time it takes to process a report means preserved content has often been deleted by the time law enforcement follows up with the platform in actionable cases.

Recommendations

• Online platforms should  prioritize child safety staffing with expertise for in-depth investigations that proactively identify and address child sexual abuse and exploitation to stay ahead of measures taken by bad actors to avoid detection.
• Platforms should invest dedicated engineering resources in  implementing the NCMEC reporting API . Ensure there is an accurate and (where possible) automated process for completing all relevant fields Our interviews suggest reports are more actionable when they provide offender information (including location information, particularly an upload IP address), victim information (including location information), the associated file (a hash alone is insufficient) or chat, and the time of the incident (including a description of how the platform defines the incident time). 
• To avoid state actor concerns, an NGO that is not NCMEC should  publish the key CyberTipline form fields that platforms should complete to increase the likelihood that law enforcement will be able to investigate their reports.
• Congress should  increase NCMEC’s budget  to enable it to hire more competitively in the technical division, and to dedicate more resources to CyberTipline technical infrastructure development. This funding should not be taken out of the budget for Internet Crimes Against Children Task Forces.  
• NCMEC should prioritize  investment in technical staff and the technical infrastructure of the CyberTipline to speed up implementation of their technical roadmap.
• NCMEC and Internet Crimes Against Children Task Forces should  partner with researchers to bring insights into the CyberTipline reporting flow along with the relationship between CyberTipline reports, arrests, and victim identification.
• Congress should pass legislation that  extends the required preservation period to at least 180 days, but preferably one year.
• The U.S. Supreme Court should  resolve the split in authority over whether the private search doctrine requires human review by platform personnel in order for law enforcement to open a file without a warrant, or whether the doctrine is satisfied where a reported file is a hash match for a previously-viewed file. 

Investigation Finds AI Image Generation Models Trained on Child Abuse

Addressing child exploitation on federated social media, an update on the sg-csam ecosystem.

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The Widest-Ever Global Coral Crisis Will Hit Within Weeks, Scientists Say

Rising sea temperatures around the planet have caused a bleaching event that is expected to be the most extensive on record.

By Catrin Einhorn

The world’s coral reefs are in the throes of a global bleaching event caused by extraordinary ocean temperatures, the National Oceanic and Atmospheric Administration and international partners announced Monday.

It is the fourth such global event on record and is expected to affect more reefs than any other. Bleaching occurs when corals become so stressed that they lose the symbiotic algae they need to survive. Bleached corals can recover, but if the water surrounding them is too hot for too long, they die.

Coral reefs are vital ecosystems: limestone cradles of marine life that nurture an estimated quarter of ocean species at some point during their life cycles, support fish that provide protein for millions of people and protect coasts from storms. The economic value of the world’s coral reefs has been estimated at $2.7 trillion annually .

For the last year, ocean temperatures have been off the charts .

“This is scary, because coral reefs are so important,” said Derek Manzello, the coordinator of NOAA’s Coral Reef Watch program, which monitors and predicts bleaching events.

The news is the latest example of climate scientists’ alarming predictions coming to pass as the planet heats. Despite decades of warnings from scientists and pledges from leaders, nations are burning more fossil fuels than ever and greenhouse gas emissions continue to rise .

Substantial coral death has been confirmed around Florida and the Caribbean, particularly among staghorn and elk horn species, but scientists say it’s too soon to estimate what the extent of global mortality will be.

To determine a global bleaching event, NOAA and the group of global partners, the International Coral Reef Initiative, use a combination of sea surface temperatures and evidence from reefs. By their criteria, all three ocean basins that host coral reefs — the Pacific, Indian and Atlantic — must experience bleaching within 365 days, and at least 12 percent of the reefs in each basin must be subjected to temperatures that cause bleaching.

Currently, more than 54 percent of the world’s coral area has experienced bleaching-level heat stress in the past year, and that number is increasing by about 1 percent per week, Dr. Manzello said.

He added that within a week or two, “this event is likely to be the most spatially extensive global bleaching event on record.”

Each of the three previous global bleaching events has been worse than the last. During the first, in 1998, 20 percent of the world’s reef areas suffered bleaching-level heat stress. In 2010, it was 35 percent. The third spanned 2014 to 2017 and affected 56 percent of reefs.

The current event is expected to be shorter-lived, Dr. Manzello said, because El Niño, a natural climate pattern associated with warmer oceans, is weakening and forecasters predict a cooler La Niña period to take hold by the end of the year.

Bleaching has been confirmed in 54 countries, territories and local economies, as far apart as Florida , Saudi Arabia and Fiji. The Great Barrier Reef in Australia is suffering what appears to be its most severe bleaching event; about a third of the reefs surveyed by air showed prevalence of very high or extreme bleaching, and at least three quarters showed some bleaching.

“I do get depressed sometimes, because the feeling is like, ‘My God, this is happening,’” said Ove Hoegh-Guldberg, a professor of marine studies at the University of Queensland who published early predictions about how global warming would be catastrophic for coral reefs.

“Now we’re at the point where we’re in the disaster movie,” he said.

The most recent confirmation of widespread bleaching, prompting Monday’s announcement, came from the Western Indian Ocean, including Tanzania, Kenya, Mauritius, Seychelles and off the western coast of Indonesia.

Swaleh Aboud, a coral reef scientist at CORDIO East Africa, a research and conservation nonprofit group based in Kenya and focused on the Indian Ocean, said coral species that are known to be thermally resistant are bleaching, as are reefs in a cooler area considered to be a climate refuge.

Recently he visited a fishing community in Kenya called Kuruwitu that has worked to revive its reef. Many of the restored coral colonies had turned ghostly white. Others were pale, apparently on their way.

“Urgent global action is necessary to reduce future bleaching events, primarily driven by carbon emissions,” Mr. Aboud said.

Scientists are still learning about corals’ ability to adapt to climate change. Efforts are underway to breed coral that tolerate higher temperatures. In a few places, including Australia and Japan, coral appear to be migrating poleward, beginning to occupy new places. But scientists say a variety of factors, such as how much light penetrates the water and the topography of the sea floor, make such migration limited or unlikely in much of the world. Plus there’s the problem of ocean acidification; as seawater absorbs carbon dioxide from the atmosphere, it becomes more acidic, making it harder for coral to build and maintain reefs.

Dr. Hoegh-Guldberg, who has studied the impact of climate change on coral reefs for more than three decades, was an author of a 2018 report from the Intergovernmental Panel on Climate Change that found the world would lose the vast majority of its coral reefs at 1.5 degrees Celsius of warming, and virtually all at 2 degrees. Current pledges by nations put the Earth on track for about 2.5 degrees by 2100. Still, he has not lost hope.

“I think we will solve the problem if we get up and fight to solve the problem,” Dr. Hoegh-Guldberg said. “If we continue to pay lip service but not get on with the solutions, then we’re kidding ourselves.”

Catrin Einhorn covers biodiversity, climate and the environment for The Times. More about Catrin Einhorn

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