research philosophies

Research Philosophy

Research philosophy is a vast topic and here we will not be discussing this topic in great details. Research philosophy is associated with assumption, knowledge and nature of the study. It deals with the specific way of developing knowledge. This matter needs to be addressed because researchers may have different assumptions about the nature of truth and knowledge and philosophy helps us to understand their assumptions.

In business and economics dissertations at Bachelor’s level, you are not expected to discuss research philosophy in a great level of depth, and about one page in methodology chapter devoted to research philosophy usually suffices. For a business dissertation at Master’s level, on the other hand, you may need to provide more discussion of the philosophy of your study. But even there, about two pages of discussions are usually accepted as sufficient by supervisors.

Discussion of research philosophy in your dissertation should include the following:

• You need to specify the research philosophy of your study. Your research philosophy can be pragmatism , positivism , realism or interpretivism as discussed below in more details.
• The reasons behind philosophical classifications of the study need to be provided.
• You need to discuss the implications of your research philosophy on the research strategy in general and the choice of primary data collection methods in particular.

The Essence of Research Philosophy

Research philosophy deals with the source, nature and development of knowledge [1] . In simple terms, research philosophy is belief about the ways in which data about a phenomenon should be collected, analysed and used.

Although the idea of knowledge creation may appear to be profound, you are engaged in knowledge creation as part of completing your dissertation. You will collect secondary and primary data and engage in data analysis to answer the research question and this answer marks the creation of new knowledge.

In respect to business and economics philosophy has the following important three functions [2] :

• Demystifying : Exposing, criticising and explaining the unsustainable assumptions, inconsistencies and confusions these may contain.
• Informing : Helping researchers to understand where they stand in the wider field of knowledge-producing activities, and helping to make them aware of potentialities they might explore.
• Method-facilitating : Dissecting and better understanding the methods which economists or, more generally, scientists do, or could, use, and thereby to refine the methods on offer and/or to clarify their conditions of usage.

In essence, addressing research philosophy in your dissertation involves being aware and formulating your beliefs and assumptions.  As illustrated in figure below, the identification of research philosophy is positioned at the outer layer of the ‘research onion’. Accordingly it is the first topic to be clarified in research methodology chapter of your dissertation.

Research philosophy in the ‘research onion’ [2]

Each stage of the research process is based on assumptions about the sources and the nature of knowledge. Research philosophy will reflect the author’s important assumptions and these assumptions serve as base for the research strategy. Generally, research philosophy has many branches related to a wide range of disciplines. Within the scope of business studies in particular there are four main research philosophies:

• Interpretivism (Interpretivist)

The Choice of Research Philosophy

The choice of a specific research philosophy is impacted by practical implications. There are important philosophical differences between studies that focus on facts and numbers such as an analysis of the impact of foreign direct investment on the level of GDP growth and qualitative studies such as an analysis of leadership style on employee motivation in organizations.

The choice between positivist and interpretivist research philosophies or between quantitative and qualitative research methods has traditionally represented a major point of debate. However, the latest developments in the practice of conducting studies have increased the popularity of pragmatism and realism philosophies as well.

Moreover, as it is illustrated in table below, there are popular data collection methods associated with each research philosophy.

 Research philosophies and data collection methods [3]

My e-book,  The Ultimate Guide to Writing a Dissertation in Business Studies: a step by step assistance contains discussions of theory and application of research philosophy. The e-book also explains all stages of the  research process  starting from the  selection of the research area  to writing personal reflection. Important elements of dissertations such as  research philosophy ,  research approach ,  research design ,  methods of data collection  and  data analysis  are explained in this e-book in simple words.

John Dudovskiy

[1] Bajpai, N. (2011) “Business Research Methods” Pearson Education India

[2] Tsung, E.W.K. (2016) “The Philosophy of Management Research” Routledge

[3] Table adapted from Saunders, M., Lewis, P. & Thornhill, A. (2012) “Research Methods for Business Students” 6 th  edition, Pearson Education Limited

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.

Psst... there’s more!

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

21 Comments

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Contemporary Research Methods in Hospitality and Tourism

ISBN : 978-1-80117-547-0 , eISBN : 978-1-80117-546-3

Publication date: 13 April 2022

Understanding the most appropriate research philosophy to underpin any piece of scholarly inquiry is crucial if one hopes to address research problems in a manner distinct from those already evidenced across extant literature. Distinct philosophical ideas and positions are often associated with specific research designs, therefore influencing the research approach adopted in any given study. Identifying an appropriate philosophical approach requires robust comprehension of how philosophical positions differ, alongside a reflective understanding of one's own perceptions and beliefs regarding what knowledge and reality “are” and how new knowledge is discovered, developed, and/or confirmed. This chapter therefore discusses different research paradigms and philosophies in order to identify core distinctions therein, highlighting the advantages and the challenges associated with different philosophical approaches to research along the way.

• Contemporary research
• Research philosophy
• Hospitality

Gannon, M.J. , Taheri, B. and Azer, J. (2022), "Contemporary Research Paradigms and Philosophies", Okumus, F. , Rasoolimanesh, S.M. and Jahani, S. (Ed.) Contemporary Research Methods in Hospitality and Tourism , Emerald Publishing Limited, Leeds, pp. 5-19. https://doi.org/10.1108/978-1-80117-546-320221002

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Research methods for social sciences, research philosophy.

• Literature Review
• Research Design
• Data Collection
• Data Analysis and Reporting
• Beyond the Traditional Methods
• Research Ethics

When conducting research, there are different philosophies, with their own assumptions and worldviews, that inform your decisions on selecting a method or design for your inquiry. Some common terms are:

• Constructivism
• Interpretivism
• Post-modernism

You may also heard the term research paradigm , which is the worldview from a specific philosophy that a researcher follows. For more information on different research philosophies and paradigms, see the suggested materials below.

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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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A research philosophy is a set of basic beliefs that guide the design and execution of a research study, and different research philosophies offer different ways of understanding scientific research. Qualitative research uses textual, audio, or visual data to understand the way that people experience a phenomenon and to understand the meanings that people attribute to their experiences. Research philosophies represent ‘a worldview that defines, for its holder, the nature of the “world,” the individual’s place in it, and the range of possible relationships to that world and its parts’. Post-positivism is the predominant philosophical position in which most researchers in sport and exercise psychology situate their studies. Research conducted from a constructivist philosophical position focuses on understanding the meanings people create for themselves and attribute to their experience. The notion of a subjective and transactional epistemology underlies the concept of co-constructing knowledge or meaning within qualitative research.

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Research Philosophy: Positivism, Interpretivism, and Pragmatism

Posted by Md. Harun Ar Rashid | Jun 28, 2023 | Research Methodology

Research philosophy refers to the set of beliefs, assumptions, and methodologies that guide the way researchers approach their investigations. It provides a framework for understanding the nature of knowledge, the role of the researcher, and the methods used to gather and interpret data. There are several research philosophies, but three of the most widely recognized and influential ones are positivism, interpretivism, and pragmatism. In this article, we will explore these three research philosophies in brief, providing a simple and accessible explanation of each.

1. Positivism:

Positivism is a research philosophy that originated in the natural sciences and gained prominence in the late 19th and early 20th centuries. It is based on the belief that scientific knowledge should be derived from empirical observation and objective measurement. Positivists assume that there is a single reality that exists independently of our perceptions and that this reality can be understood through systematic and rigorous scientific methods.

Key Principles:

a. Objectivity: Positivists emphasize the importance of objectivity in research. They argue that researchers should strive to eliminate bias and personal opinions from their investigations. Objectivity is achieved through careful design and execution of experiments, reliance on measurable and observable data, and the use of statistical analysis to draw conclusions.

b. Determinism: Positivism assumes that the social world operates according to universal laws that can be discovered through scientific inquiry. It suggests that human behavior is determined by external factors such as social structures, cultural norms, and economic conditions. This deterministic view implies that social phenomena can be predicted and explained using objective and generalizable laws.

c. Reductionism: Positivists often employ reductionism, which involves breaking down complex phenomena into smaller, more manageable parts. They believe that by studying the individual components of a system, they can gain a better understanding of the whole. Reductionism allows researchers to isolate specific variables and test their effects in controlled conditions.

d. Quantitative Methods: Positivism favors quantitative research methods, such as surveys, experiments, and statistical analysis. These methods provide numerical data that can be analyzed statistically to identify patterns, correlations, and cause-and-effect relationships. Positivists value the reliability and replicability of quantitative research, as it allows for precise measurement and comparison.

Critiques and Limitations:

Critics argue that positivism has several limitations. They claim that the positivist approach overlooks the subjective experiences and meanings that individuals attach to their actions. Additionally, positivism’s focus on observable and measurable phenomena may neglect important contextual factors that influence human behavior. Critics also argue that the notion of objective reality is problematic, as individuals’ perceptions and interpretations shape their understanding of the world.

2. Interpretivism:

Interpretivism, also known as constructivism or hermeneutics, emerged as a response to the limitations of positivism in the social sciences. It emphasizes the subjective nature of human experience and focuses on understanding social phenomena through the meanings and interpretations that individuals assign to them. Interpretivists believe that social reality is socially constructed and context-dependent and that it cannot be reduced to objective laws or generalizations.

a. Subjectivity: Interpretivism acknowledges that individuals have unique experiences, perspectives, and interpretations of the world. Researchers aim to understand these subjective meanings by engaging in dialogue and interaction with research participants. They seek to uncover the complex and diverse ways in which individuals create and attribute meaning to their actions and social interactions.

b. Social and Historical Context: Interpretivists emphasize the importance of understanding social phenomena within their specific social and historical contexts. They recognize that individuals’ beliefs, values, and behaviors are shaped by their cultural, historical, and institutional backgrounds. Researchers employ qualitative methods, such as interviews, observations, and textual analysis, to capture the richness and complexity of these contextual factors.

c. Reflexivity: Interpretivism promotes reflexivity, which involves acknowledging and reflecting upon the influence of the researcher’s own background, biases, and assumptions on the research process. Researchers recognize that their interpretations are inherently subjective and influenced by their own perspectives. Reflexivity helps researchers identify and address potential biases and enhances the credibility of their findings.

d. Inductive Reasoning: Interpretivists often employ inductive reasoning, which involves deriving general conclusions from specific observations. They emphasize the importance of exploring and discovering patterns and themes in qualitative data, rather than starting with preconceived theories or hypotheses. This approach allows for the emergence of new insights and theories grounded in the data.

Critics argue that interpretivism lacks objectivity and generalizability, as its focus on subjective meanings and context-specific interpretations makes it difficult to draw universal conclusions. They also claim that interpretive research can be overly reliant on the researcher’s interpretive skills, potentially introducing bias and subjectivity into the analysis. Additionally, some argue that interpretivism may prioritize understanding over explanation, limiting its ability to generate causal explanations or predict behavior.

3. Pragmatism:

Pragmatism is a research philosophy that seeks to bridge the gap between positivism and interpretivism. It emphasizes the practical consequences of knowledge and encourages researchers to adopt a flexible and problem-solving approach. Pragmatists believe that the value of knowledge lies in its usefulness and its ability to address real-world problems.

• Practicality: Pragmatism prioritizes the practical applications of knowledge. Researchers are encouraged to focus on solving real-world problems and addressing the needs of individuals and communities. Pragmatists believe that research should have practical implications and be relevant to the concerns of society.
• Pluralism: Pragmatism recognizes that different research methods and approaches can be useful in different contexts. It promotes a pluralistic view that values the integration of multiple perspectives and methods. Researchers are encouraged to select the most appropriate methods based on the research question, context, and desired outcomes.
• Mixed Methods: Pragmatists often employ mixed methods, combining both qualitative and quantitative approaches, to gain a more comprehensive understanding of research phenomena. They believe that this integration of methods can provide a more holistic and nuanced perspective, drawing on the strengths of each approach.
• Pragmatic Truth: Pragmatism defines truth in terms of its practical consequences. Rather than seeking absolute or universal truth, pragmatists focus on the usefulness and effectiveness of knowledge in solving problems and improving outcomes. Truth is seen as a dynamic and evolving concept that is subject to revision based on new evidence and experiences.

Critics argue that pragmatism can be seen as a compromise that sacrifices depth and rigor for practicality. They claim that the integration of different methods can be challenging and may lead to a superficial treatment of complex research phenomena. Additionally, some argue that the emphasis on practicality and problem-solving may overshadow the critical examination of underlying power structures and social inequalities.

Choosing Your Research Philosophy:

Selecting a research philosophy is a crucial step in the research process, as it provides a framework for understanding the nature of knowledge, the role of the researcher, and the methods used to gather and interpret data. The choice of research philosophy depends on several factors, including the research question, the nature of the study, and the researcher’s epistemological and ontological beliefs. In this section, we will discuss important considerations when choosing your research philosophy.

A. Research Question: The research question serves as the starting point for selecting a research philosophy. Consider the nature of your research question and the type of knowledge you seek to generate. Is your research question focused on understanding the subjective experiences and meanings of individuals? Or does it aim to identify general patterns and causal relationships? The research question will help guide your choice between interpretivism and positivism.

• If your research question focuses on exploring subjective meanings, social interactions, and cultural context, interpretivism may be a suitable choice. Interpretive research allows for in-depth exploration of individuals’ perspectives and the contextual factors that shape their experiences.
• On the other hand, if your research question aims to establish general laws, predict behavior, or test cause-and-effect relationships, positivism may be more appropriate. Positivist research emphasizes objectivity, quantifiable data, and the identification of universal patterns.

B. Nature of the Study: Consider the nature of your study, including its scope, context, and practical implications. This consideration can guide your choice of research philosophy.

• If your study is exploratory, seeks to generate new theories or concepts, or focuses on understanding complex social phenomena, interpretivism may be suitable. Interpretive research methods, such as interviews, observations, and qualitative analysis, allow for a deep exploration of individual experiences and the context in which they occur.
• If your study is more applied, aims to solve practical problems, or requires numerical data for statistical analysis, pragmatism may be a viable option. Pragmatic research emphasizes practicality, mixed methods, and the integration of different perspectives and approaches to address real-world issues.

C. Epistemological and Ontological Beliefs: Epistemology and ontology refer to your beliefs about the nature of knowledge and reality, respectively. These philosophical perspectives influence your choice of research philosophy.

• Epistemological Beliefs: Consider whether you believe knowledge is objective and can be discovered through empirical observation (positivism) or whether it is subjective and constructed through interpretation and social interactions (interpretivism). If you value subjective experiences, interpretations, and context-dependency of knowledge, interpretivism may align with your epistemological beliefs.
• Ontological Beliefs: Reflect on your beliefs about the nature of reality and whether you think it is deterministic (positivism) or socially constructed and context-dependent (interpretivism). If you believe that reality is objective, exists independently of human perception, and can be reduced to observable and measurable phenomena, positivism may align with your ontological beliefs. If you believe that reality is socially constructed and shaped by human experiences, interpretations, and cultural context, interpretivism may resonate with your ontological beliefs.

D. Practical Considerations: Practical considerations, such as available resources, time constraints, and the feasibility of data collection and analysis methods, should also be taken into account when selecting a research philosophy.

• Resource Availability: Consider the resources available to you, including funding, equipment, and access to research participants. Certain research methods and approaches may require more resources than others. For example, positivist research often requires larger sample sizes and sophisticated data collection tools, while interpretive research may rely on in-depth interviews or participant observation.
• Time Constraints: Consider the time available for conducting your research. Some research philosophies, such as positivism, often require structured data collection methods and statistical analysis, which can be time-consuming. Conversely, interpretive research may involve prolonged engagement with research participants and iterative data analysis processes.
• Feasibility of Methods: Evaluate the feasibility of different research methods within the constraints of your study. Consider the compatibility of your research question with various data collection methods (e.g., surveys, interviews, observations) and analysis techniques (e.g., statistical analysis, content analysis). Choose a research philosophy that allows you to collect and analyze data effectively within the limitations of your study.

In summary, choosing a research philosophy involves considering the research question, the nature of the study, your epistemological and ontological beliefs, and practical considerations. By carefully evaluating these factors, you can select a research philosophy that aligns with your research goals, enhances the validity of your findings, and contributes to the advancement of knowledge in your field.

Finally, we can say that positivism, interpretivism, and pragmatism represent distinct research philosophies, each with its own set of assumptions, principles, and methodologies. Positivism emphasizes objectivity, determinism, and quantitative methods, seeking to discover general laws that govern the social world. Interpretivism focuses on subjective meanings, social and historical context, and qualitative methods, aiming to understand the complexities of human experiences and social interactions. Pragmatism emphasizes practicality, pluralism, and mixed methods, seeking to address real-world problems and generate useful knowledge. While these research philosophies have their respective strengths and limitations, researchers often adopt a philosophy based on the nature of their research questions, the context of the study, and their own epistemological and ontological beliefs. By understanding these philosophies, researchers can make informed choices about their research approaches and contribute to the advancement of knowledge in their respective fields.

What is research philosophy?

Research philosophy refers to the set of beliefs, assumptions, and methodologies that guide the way researchers approach their investigations. It provides a framework for understanding the nature of knowledge, the role of the researcher, and the methods used to gather and interpret data.

What is positivism?

Positivism is a research philosophy that originated in the natural sciences and emphasizes the importance of empirical observation and objective measurement. Positivists believe in a single, objective reality that exists independently of our perceptions and can be understood through systematic and rigorous scientific methods.

What is interpretivism?

Interpretivism, also known as constructivism or hermeneutics, is a research philosophy that emphasizes the subjective nature of human experience and focuses on understanding social phenomena through the meanings and interpretations that individuals assign to them. It recognizes that social reality is socially constructed and context-dependent.

What is pragmatism?

What is the significance of choosing a research philosophy?

Choosing a research philosophy is significant because it provides a framework for understanding the nature of knowledge, the role of the researcher, and the methods used to gather and interpret data. It guides the entire research process, from formulating research questions to selecting appropriate methodologies.

How do I choose the right research philosophy?

Choosing the right research philosophy involves considering several factors:

• Research question: Consider the nature of your research question and the type of knowledge you seek to generate. Is it focused on subjective experiences or general patterns? This will guide your choice between interpretivism and positivism.
• Nature of the study: Evaluate the scope, context, and practical implications of your study. Does it require in-depth exploration or solving real-world problems? This can guide you towards interpretivism or pragmatism, respectively.
• Epistemological and ontological beliefs : Reflect on your beliefs about the nature of knowledge and reality. Do you value subjective interpretations or objective observations? This will align with interpretivism or positivism, respectively.
• Practical considerations: Consider the available resources, time constraints, and feasibility of methods. Choose a philosophy that suits the resources at your disposal and aligns with the practical requirements of your study.

Can I mix research philosophies?

Yes, it is possible to mix research philosophies, and this approach is known as methodological pluralism. Pragmatism, in particular, promotes the integration of multiple perspectives and methods. Researchers may combine qualitative and quantitative approaches or draw from different philosophies depending on the research question and objectives.

What are the advantages of each research philosophy?

• Positivism: Positivism provides objectivity and allows for generalizable findings. It emphasizes empirical evidence and can establish causal relationships.
• Interpretivism: Interpretivism enables a deep understanding of subjective experiences and social contexts. It allows researchers to explore complex social phenomena and capture rich qualitative data.
• Pragmatism: Pragmatism focuses on practical applications and problem-solving. It allows researchers to integrate various methods, gaining a comprehensive understanding of research phenomena.

Are there any limitations to each research philosophy?

• Positivism: Positivism may overlook subjective experiences and contextual factors. It can be limited in its ability to capture complex social phenomena and may neglect the influence of social and cultural factors.
• Interpretivism: Interpretivism lacks objectivity and generalizability. The subjective interpretation of data can introduce researcher bias. It may also prioritize understanding over explanation or prediction.
• Pragmatism: Pragmatism may sacrifice depth and rigor for practicality. Integrating different methods can be challenging, and the focus on problem-solving may overshadow the critical examination of underlying social issues.

Can I change my research philosophy during a study?

While it is possible to change research philosophies during a study, it should be done cautiously and with valid reasons. Changing the research philosophy may affect the research design, data collection, and interpretation of findings. Consult with experienced researchers or your academic advisor before making such a change.

References:

• Creswell, J. W. (2014). Research Design: Qualitative, Quantitative, and Mixed Methods Approaches. Sage Publications.
• Guba, E. G., & Lincoln, Y. S. (1994). Competing paradigms in qualitative research. In N. K. Denzin & Y. S. Lincoln (Eds.), Handbook of qualitative research (pp. 105-117). Sage Publications.
• Morgan, D. L. (2007). Paradigms lost and pragmatism regained: Methodological implications of combining qualitative and quantitative methods. Journal of Mixed Methods Research, 1(1), 48-76.
• Ponterotto, J. G. (2005). Qualitative research in counseling psychology: A primer on research paradigms and philosophy of science. Journal of Counseling Psychology, 52(2), 126-136.
• Smith, J. A., Flowers, P., & Larkin, M. (2009). Interpretative phenomenological analysis: Theory, method, and research. Sage Publications.
• Smith, M. J., & Heshusius, L. (2005). Closing the gap: From evidence to action. Heinemann.
• Tashakkori, A., & Teddlie, C. (Eds.). (2003). Handbook of mixed methods in social and behavioral research. Sage Publications.
• Teddlie, C., & Tashakkori, A. (2009). Foundations of mixed methods research: Integrating quantitative and qualitative approaches in the social and behavioral sciences. Sage Publications.
• Yin, R. K. (2018). Case study research and applications: Design and methods. Sage Publications.

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Spirituality

The mental health of the "spiritual but not religious", surprisingly, people who identify as spiritual tend to have worse mental health..

Posted August 27, 2024 | Reviewed by Michelle Quirk

• Many people today report having a spiritual life while disavowing any particular religious practice.
• Research shows that being spiritual but not religious significantly predicts mental distress.
• Modes of evaluation that commend a spiritual life without due reflection should be regarded with caution.

There is a long tradition of wondering about the mental health implications of religious practice. The psychiatrist and psychoanalyst Carl Jung famously claimed to have seen almost no practicing Catholics in decades of clinical practice. Others have failed to replicate this result, but the idea that religious practice has some meaningful impact on mental health persists.

For Jung, speaking in 1939, the world could be divided into two categories: those who practiced a religion (which for Europeans of Jung's era primarily included Catholicism, Protestantism, and Judaism) and those who did not. Any serious contemporary consideration of this question, however, would need to introduce a third category. Many people today reject "organized religion," but do not quite identify as secular either. They report having a spiritual life while disavowing any particular religious practice. They are, in a phrase, " spiritual but not religious ."

This fact introduces a new question for psychology: What are the mental health benefits of this spiritual attitude? One might reasonably suppose that they are positive. After all, many people who take this attitude engage in practices that are widely held to be beneficial to mental health, such as meditation , even if they do not accept the background theology of Buddhism or other major religions that encourage meditative practices. This spiritual orientation is also a part of 12-step programs that encourage individuals to find their own "higher power," outside the bounds of traditional religious belief. So, one might think that this kind of spiritual orientation to the world is associated with positive mental health.

Mixed Research Results

The empirical literature on this question, however, is decidedly more mixed. Consider an important 2013 study in the British Journal of Psychiatry . The authors consider data from approximately 7,400 individuals in England. Of these, most identify as either religious or as non-religious and non-spiritual, but about a fifth (19 percent) identify as spiritual but not religious. The prevalence of mental disorders in the first two groups (the religious and the non-religious non-spiritual) is roughly the same, but the spiritual but not religious are different: Among other things, they are significantly more likely to have phobias, anxiety , and neurotic disorders generally. In short, being spiritual but not religious is a significant predictor of mental distress, compared to the general population.

This correlation between spirituality without religiosity ought to give us pause, in part because it is confirmed by subsequent studies. For example, one more recent study (Vittengl, 2018) finds that people who are more spiritual than they are religious are at greater risk for the development of depressive disorders. As I said, all this is very puzzling. What explains these somewhat dispiriting findings? And what lessons should we draw from it?

Three Caveats

To begin with, we should note three caveats or complications.

First, as the authors emphasize, these findings say nothing about cause and effect. It could be that spiritual practices outside of traditional religion are a cause of mental distress. Or it equally well could be that people in mental distress seek out spiritual but non-religious practices. Or it could be that these two phenomena—being spiritual but not religious and experiencing mental distress—are common effects of some shared cause.

Second, many people do not seek their spiritual orientation, in the first place, because of its mental health benefits. People who are drawn to spirituality while rejecting traditional religious frameworks are in the first place pursuing their own spiritual values, rather than seeking mental health. So these correlations should not, on their own, lead anyone to doubt their own spiritual convictions.

Third, as all of the authors discussed above acknowledge, these correlations remain very poorly understood. This is partly because we are stuck in a dichotomous way of thinking about spirituality—on which people are religious or not religious—that the introduction of a third category remains something of a novelty. Furthermore, this third category remains poorly understood, in part because "spirituality" itself admits so many different understandings.

With those qualifications in place, however, I think these correlations ought to be better known and recognized by practicing clinicians. Many clinicians will see the development of a spiritual life in a client, outside the bounds of traditional religion, as a sign of psychological growth. And, indeed, it is often that. But it is, at the same time, something of a risk factor for many mental health disorders, and so is not exactly an unalloyed good.

Here as elsewhere, there are few unambiguous goods in therapy , and what may be good for one person may be concerning in another. Modes of evaluation that commend a spiritual life, without due reflection on the role and structures involved in that life, should be regarded with some caution. The empirical evidence, such as it is, suggests that being "spiritual but not religious" is a more ambivalent state than it is usually taken to be.

King M, Marston L, McManus S, Brugha T, Meltzer H, Bebbington P (2013) Religion, spirituality and mental health: results from a national study of English households. British Journal of Psychiatry . 202(1):68–73.

Vittengl JR (2018) A lonely search?: Risk for depression when spirituality exceeds religiosity. Journal of Nervous and Mental Disease 206:386–389.

John T. Maier, Ph.D., MSW , is a psychotherapist in private practice in Cambridge, Massachusetts.

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Professor of Philosophy

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The highly ranked Department of Philosophy is seeking excellent junior candidates for multiple tenure/tenure-track faculty positions. The selected candidate will mount a vigorous research program while making significant contributions to the department's teaching mission. The area of research is open and specialization is open. The successful candidate will advance the educational mission of the College of Letters & Science, that values, prioritizes, and actualizes evidence-based and student-centered teaching and undergraduate student mentoring. They will contribute to an inclusive, fair, and equitable environment that fosters engagement and a sense of belonging for faculty, staff, students and members of the broader community.

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Successful applicants will teach graduate and undergraduate classes, mentor students, conduct scholarly research, and provide service to the department, college, university, and academic community nationally or internationally. The general teaching load is 4 courses per year, 2 in one semester and 2 in the other. The successful candidate, as a member of the College of L&S, will proactively contribute to, support, and advance the college's commitment to equity among all aspects of their teaching, mentoring, research, and service.

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Diversity is a source of strength, creativity, and innovation for UW-Madison. We value the contributions of each person and respect the profound ways their identity, culture, background, experience, status, abilities, and opinion enrich the university community. We commit ourselves to the pursuit of excellence in teaching, research, outreach, and diversity as inextricably linked goals. The University of Wisconsin-Madison fulfills its public mission by creating a welcoming and inclusive community for people from every background - people who as students, faculty, and staff serve Wisconsin and the world. For more information on diversity and inclusion on campus, please visit: Diversity and Inclusion

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Candidates should demonstrate evidence of creativity and excellence in teaching and scholarly research. In addition, the successful candidate will demonstrate experience with fostering or the ability to foster an inclusive and equity-centered teaching, learning, departmental, and research environment where all can thrive.

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The Department of Philosophy at UW-Madison is highly rated and has department strengths in traditional areas of Philosophy as well as a variety of subdisciplines, including Philosophy of Science, Philosophy of Education, and AI Ethics. Our department is multidisciplinary and has a highly collaborative environment. Madison is the state's capitol city and is well known for offering a small town feel in a medium sized city. It is a great place to raise a family and offers an ideal combination of natural beauty, stimulating cultural events, outstanding schools and outdoor recreation. The College of Letters & Science is committed to creating an inclusive environment in which all of us - students, staff, and faculty - can thrive. Ours is a community in which we all are welcome. Most importantly, we strive to build a community in which all of us feel a great sense of belonging. There is no excellence without diversity in all its forms; diverse teams are more creative and successful than homogeneous ones. We are better when we are diverse and when we acknowledge, celebrate and honor our diversity. In acknowledging and honoring our diversity, we also assume a responsibility to support and stand up for each other.

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Research Philosophy, Methodological Implications, and Research Design

• Open Access
• First Online: 20 October 2023

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• Jonas Bergmann 6  

Part of the book series: Studien zur Migrations- und Integrationspolitik ((SZMI))

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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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