Research Methods in the Social Sciences features chapters that cover a wide range of concepts, methods, and theories. Each chapter begins with an introduction to a method, using real-world examples from a wide range of academic disciplines, before discussing the benefits and limitations of the approach, its current status in academic practice, and finally providing tips and advice on when and how to apply the method in research. The text covers both well-established concepts and emerging ideas, such as big data and network analysis, for qualitative and quantitative research methods.
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Book
Edited by Jean-Frédéric Morin, Christian Olsson, and Ece Özlem Atikcan
Chapter
Introduction. An Introduction to Research Methods in the Social Sciences
Ece Özlem Atikcan, Jean-Frédéric Morin, and Christian Olsson
Introducing research methods in the social sciences is not an easy task given how complex the subject matter is. Social sciences, like all sciences, can be divided into categories (disciplines). Disciplines are frequently defined according to what they study (their empirical object) and how they study it (their particular problematization of the object). They are, however, by no means unitary entities. Within each discipline, multiple theories typically contend over the ability to tell provisional truths about the world. They do so by building on specific visions of the nature of the world, reflections on how to generate scientific truth, systematic ways of collecting and analyzing data (methods) and of justifying these methods as part of a coherent research design (methodologies).
Chapter
Contextual Analysis
Putting Research into Context
Auke Willems
This chapter reflects on contextual analysis, which examines the environment in which a given phenomenon operates. Contextual analysis is used widely in social sciences, such as history, managerial and leadership studies, organizational theory, business studies, and political sciences. It is useful for identifying trends and topics within unstructured data (contexts). In a sense, contextual analysis helps create order out of chaos. The main aim of contextual analysis is to assess when and how contexts shape a social phenomenon and vice versa. Contexts can be, inter alia, historical, institutional, cultural, demographic, technological, psychological, ideological, ontological, and epistemological. A wide body of scholarship has developed on the topic of contextual analysis. The chapter reviews the literature briefly and identify clues and themes relevant to the social sciences.
Chapter
Literature Review
Mathieu Ouimet and Pierre-Olivier Bédard
This chapter highlights literature review. Reviewing the published literature is one of the key activities of social science research, as a way to position one’s academic contribution, but also to get a bird’s eye view of what the relevant literature says on a given topic or research question. Many guides have been created to assist academic researchers and students in conducting a literature review, but there is no consensus on the most appropriate method to do so. One of the reasons for this lack of consensus is the plurality of epistemological attitudes that coexist in the social sciences. Before initiating a literature review, the researcher should start by clarifying the need for and the purpose of the review. Once this has been clarified, the actual review protocol, tools, and databases to be used will need to be determined to strike a balance between the scope of the study and the depth of the review.
Chapter
Thematic Analysis
An Accessible and Flexible Approach for Qualitative Analysis
Jean-Frédéric Morin, Christian Olsson, and Ece Özlem Atikcan
This chapter evaluates thematic analysis (TA), which is one of the oldest and most widely used qualitative analytic method across the social sciences. TA is a flexible method for identifying and analysing patterns of meaning — ‘themes’ — in qualitative data, with wide-ranging applications. The method has a long, if indeterminate, history in the social sciences, but seems likely to have evolved from early forms of (qualitative) content analysis. TA is now more likely to be demarcated and acknowledged as a distinct method; however, confusion remains about what TA is. The popularity of TA as a distinct method received a considerable boost from the publication of Using Thematic Analysis in Psychology by social psychologists Virginia Braun and Victoria Clarke in 2006, which has become one of the most cited academic papers of recent decades.
Chapter
Formal Modelling
Érick Duchesne and Arthur Silve
This chapter focuses on formal modelling. A formal model is the mathematical exposition of reasoning. Its purpose is to formulate consistent and rigorously stated hypotheses, which often shed light on the causation of a particular social phenomenon. Often, in the social sciences, a formal model is valuable because it can accurately predict behaviour and describe an actual (although unobservable) causal mechanism. Thus, formal models also allow plenty of space for deductive reasoning. Whether they clarify hypotheses or describe a mechanism, the success of formal models remains a matter of debate. The chapter then presents a few examples of useful models and considers the most frequent criticisms of formal modelling in order to identify a series of good practices for its proper use.
Chapter
Epistemology
Gianfranco Pellegrino
This chapter illustrates epistemology, which is the discipline devoted to the study of knowledge and justified belief. Epistemology concerns issues of the creation and dissemination of knowledge in particular fields of inquiry. Assuming that knowledge is distinguished from mere opinion for its being true — a common assumption in epistemology — truth is also connected to epistemology. In the social sciences, epistemology is a source for methodological criteria employed in research, as well as for guidance on ontological issues — such as the existence of theoretical entities and the relationships between the social and the natural world. The chapter then looks at the divide between scholars who understand social sciences as positive, objective disciplines, according to the model of natural sciences, and scholars who understand social sciences as less precise, more humanistic disciplines, with a looser standard of objectivity. This divide refers in its turn to an even broader topic, namely the discussion about when and whether objective knowledge can be obtained in the social sciences, and on the very meaning of ‘objectivity’ and ‘knowledge’. This topic belongs to the general field of epistemology.
Chapter
Mixed Methods
Combination of Quantitative and Qualitative Research Approaches
Manfredi Valeriani and Vicki L. Plano Clark
This chapter examines mixed-methods research, which is an approach that involves the integration of quantitative and qualitative methods at one or more stages of a research study. The central idea behind mixed-methods research is that the intentional combination of numeric-based methods with narrative-based methods can best provide answers to some research questions. The ongoing attempts to construct a simple and common conceptualization of mixed-methods provide a good indicator of the status of mixed-methods itself. mixed-methods research has emerged as a formalized methodology well suited to addressing complex problems, and is currently applied throughout the social sciences and beyond. Nowadays, researchers interested in combining quantitative and qualitative methods can benefit from the growing knowledge about the epistemological foundations, essential considerations, and rigorous designs that have been advanced for mixed-methods research.
Chapter
Concept Construction
Louis Bélanger Pierre-Marc Daigneault
This chapter highlights concept construction. All social sciences research projects, be they qualitative or quantitative, are dependent on concepts. The chapter first explains what concepts are and why social scientists should be self-conscious in the way they use them. It then describes the methodology of concept construction and presents three different ways to structure a concept. Finally, the chapter provides criteria to evaluate the quality of the concepts we have built ourselves or borrowed from others. Concept construction involves two basic operations beyond choosing a term to designate the concept: identifying the fundamental characteristics of the phenomenon of interest, and logically connecting these characteristics.
Chapter
Causation
Sierens Vivien and Ramona Coman
This chapter studies causation, which occupies a central place in the social sciences. In their attempts to understand and explain ‘why’ social, economic, and political phenomena occur, scholars have dealt with causality in many different ways. The way to define and observe causal relationships has always been at the heart of harsh academic debates in social as well as natural sciences. Drawing on distinctive ontological and epistemological standpoints, at least four different understandings of causation have emerged in political science. Most authors have adopted a correlational-probabilistic understanding of causation, but some have preferred a configurational one, while others have adopted a mechanistic or even a counterfactual understanding. To illustrate the concrete methodological challenges generated by this theoretical pluralism, the chapter discusses how scholars have dealt with causality to explain the impact of European integration on domestic policies and institutions.
Chapter
Factor Analysis
Uncovering Unobservable Constructs
Ulf Liebe
This chapter examines factor analysis, which is used to test whether a set of observable or manifest variables can measure one or more unobservable or latent constructs that they have in common. Such constructs are called factors. Factor analysis is therefore a data reduction method. In its foundation period, factor analysis was often applied to the study of general intelligence and mental abilities. Nowadays factor analysis is a workhorse for quantitative research in the social sciences, humanities, and natural sciences. There are two types of factor analysis: exploratory factor analysis and confirmatory factor analysis. Exploratory factor analysis is used for examining the underlying structures in a set of variables. Confirmatory factor analysis is used to test theoretical hypotheses; the researcher assumes that variables are interrelated in a specific way and uses factor analysis to find out whether the assumption is supported by the data — i.e. to what extent the data fits the predefined structure.
Chapter
I. Interdisciplinarity
The Interaction of Different Disciplines for Understanding Common Problems
Roberto Carrillo and Lidia Núñez
This chapter describes interdisciplinary, a term which refers to a mode of conducting research that ‘integrates information, data, techniques, tools, perspectives, concepts, and/or theories from two or more disciplines or bodies of specialized knowledge to advance fundamental understanding or to solve problems whose solutions are beyond the scope of a single discipline or area of research practice’. Therefore, it is a way of conducting research that goes beyond the frontiers of traditional disciplines. The chapter provides an overview of the main features of how interdisciplinarity is applied in the social sciences. It defines the concept and traces its origins and evolution, as well as the interrelationship between interdisciplinary studies, society, and the development of public policies. The chapter then discusses the measurement and analysis of interdisciplinarity. Finally, it presents the main criticisms of interdisciplinarity and its use in the social sciences.
Chapter
Operationalization
From the Concepts to the Data
Anne-Laure Mahé and Theodore McLauchlin
This chapter describes operationalization, which refers to the intellectual operations the researcher undertakes to decide how to observe a concept in reality. This is a crucial step of the research process, as many concepts in the social sciences are too abstract to be immediately observed. The most important criteria of a successful operationalization are consequently the consistency between each step of the research design, from theory formation to data collection, and the degree to which the indicators effectively allow the researcher to gather observations that work well in the context under study. One way to synthesize these points is that operationalization should enable the researcher to respect the principle of double adequacy. First, the researcher’s conceptual argument and the operationalized data should correspond. Second, there is a need for adequacy between those data and the ‘reference reality’.
Chapter
Bias
Unavoidable Subjectivity?
Aysel Küçüksu and Stephanie Anne Shelton
This chapter looks at bias, a term which refers to an uninvited, but inevitable aspect of conducting research. It is usually equated with subjectivity, the distortion and manipulation of data, or a lack of objectivity, which undermines the credibility of the research. Bias comes in many forms and the chapter discusses the two that are the most common in the literature: gender bias and confirmation bias. The long-standing positivist interpretation of bias considers that it is an inherently problematic ‘ethical issue’. Yet, contemporary research has called for a ‘reconceptualization’ of this perception of bias in order to encourage a more nuanced view. In the social sciences, bias is a manifestation of how cultural and political standing affects our approach to science. Bias should be acknowledged early on to ensure that both researchers and readers have the critical tools necessary to recognize it and evaluate its influences. This approach originated in anthropology and is known as ‘positionality’.
Chapter
Positivism, Post-positivism, and Social Science
Patrick Thaddeus Jackson and Lucas Dolan
This chapter highlights positivism and post-positivism in the social sciences. ‘Post-positivism’, much like ‘positivism’, is a notoriously imprecise term that nonetheless does significantly effective work in shaping academic controversies. Post-positivist approaches are loosely organized around a common rejection of the notion that the social sciences should take the natural sciences as their epistemic model. This rejection, which is a dissent from the naturalist position that all the sciences belong together and produce the same kind of knowledge in similar ways, often also includes a rejection of what are taken to be the central components of a natural-scientific approach: a dualist separation of knowing subjects from their objects of study, and a limitation of knowledge to the tangible and measurable. To get a handle on ‘post-positivism’, the chapter discusses these three rejections (naturalism, dualism, and empiricism) in turn.
Chapter
Triangulation
Jean-Frédéric Morin, Christian Olsson, and Ece Özlem Atikcan
This chapter looks at triangulation, which is classically defined as looking at one research object from different perspectives. However, this large and consensual definition masks different approaches to triangulation and ignores its historical evolution since its emergence in social sciences literature. To gain a better insight into its current definitions, the chapter first proposes a brief historical overview and highlight its different meanings. It then illustrates how triangulation can be used in a research design in order to gain extra knowledge. Finally, the chapter talks about mixed-methods research and its relationship with triangulation. In the context of the tensions opposing qualitative and quantitative research, triangulation is used by mixed-methods research to justify that qualitative and quantitative methods should systematically be articulated.
Chapter
Big Data
Yannick Dufresne and Brittany I. Davidson
This chapter assesses big data. Within the social sciences, big data could refer to an emerging field of research that brings together academics from a variety of disciplines using and developing tools to widen perspective, to utilize latent data sets, as well as for the generation of new data. Another way to define big data in the social sciences refers to data corresponding to at least one of the three s of big data: volume, variety, or velocity.. These characteristics are widely used by researchers attempting to define and distinguish new types of data from conventional ones. However, there are a number of ethical and consent issues with big data analytics. For example, many studies across the social sciences utilize big data from the web, from social media, online communities, and the darknet, where there is a question as to whether users provided consent to the reuse of their posts, profiles, or other data shared when they signed up, knowing their profiles and information would be public. This has led to a number of issues regarding algorithms making decisions that cannot be explained. The chapter then considers the opportunities and pitfalls that come along with big data.
Chapter
Sequence Analysis
Being Earnest with Time
Thomas Collas and Philippe Blanchard
This chapter explores sequence analysis (SA), which conceives the social world as happening in processes, in series of events experienced by social entities. SA refers to a set of tools used to summarize, represent, and compare sequences — i.e. ordered lists of items. Job careers (succession of job positions) are typical examples of sequences. Various other topics have been studied through SA, such as steps in traditional English dances, country-level adoption of welfare policies over one century, or individual and family time-diaries. Andrew Abbott played a pioneering role in the diffusion of SA. With colleagues, Abbott introduced optimal matching analysis (OMA) in the social sciences, a tool to compare sequences borrowed from computer science and previously adapted to DNA sequences. Abbott’s work on SA was part of a wider methodological thinking on social processes. The chapter then looks at the most common type of sequences in social science: categorical time series — i.e. successions of states with a duration defined on a more or less refined chronological scale.
Chapter
Systems Analysis
Jean-Frédéric Morin, Christian Olsson, and Ece Özlem Atikcan
This chapter examines systems analysis, which broadly refers to the theories and methods used in the study of interdependent elements forming a complex whole. Proponents of systems analysis hold that interacting systems exhibit properties that one cannot understand by only looking at their individual parts. Complexity science notably aims to explain the properties that govern complex systems such as non-linearity, emergence, self-regulation, and adaptation. In both natural and social sciences, the systems view of life has gained traction in recent years; the number of studies adopting a systemic lens is increasing. Yet, systems analysis remains relatively marginal. The goal of systems analysis is to understand how interactions between individual parts give rise to properties that cannot be explained by looking at them separately.
Chapter
Case Study
Jessica Luciano Gomes and Miriam Gomes Saraiva
This chapter explores the case study, which is a very common research method in the field of social sciences. Case studies are important because they provide the examination of samples of a larger atmosphere, therefore enabling researchers with a variety of possibilities: to deepen the analysis of a particular occurrence in the world, to contribute to an existing theoretical framework, and to serve as an instrument of comparative analysis. Although it might sound simplistic, the research framework for case studies usually has to satisfy a few key points. Case studies can be divided into separate categories: exploratory, descriptive, and explanatory. They are also directly related to the type of research question being posed from the traditional five types of survey questions: ‘who’, ‘what’, ‘where’, ‘how’, and ‘why’. One can often find case studies among both qualitative and quantitative approaches, focusing on a case study per se or on cross-case method.
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