Once students have developed an idea, outlined a rationale for their research, and found the relevant literature, they then need to start mapping out what their project will look like. To do this, they will need to make some decisions about how they will answer their research questions. Research can be approached and conducted in many different ways. Broadly speaking, there are four interrelated stages of building a social science dissertation: research strategy: the type of data under investigation (qualitative, quantitative, or mixed methods); research design: the framework through which that data will be collected; research methods: the methods associated with collecting the type of data selected; and type of analysis: the techniques through which the data will be analysed. This chapter focuses on the decisions that students can make in relation to the first two stages: research strategy and research design.
Edited by Jean-Frédéric Morin, Christian Olsson, and Ece Özlem Atikcan
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.
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.
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).
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.
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.
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.
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.
Putting Research into Context
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.
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.
This chapter focuses on behaviourism, which is a methodological approach that involves the observable measurement of individual behaviour. It is closely related to the epistemology of positivism and empiricism, which emphasize the observation and verifiability of individual or social phenomena to generate knowledge. Hence, behaviourists focus on the study of perceptible reactions of humans or animals to different situations. Behaviour is understood as reflexive or conscious reactions to different stimuli and does not presume an underlying rationality. Ultimately, behaviourism follows the logic of the natural sciences, by relying on objective, observable information based on sensory experiences. The chapter then traces the origins of behaviourism and its use across disciplines.
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’.
On the Rise
Leeann Bass and Holli A. Semetko
This chapter explains content analysis, which is a social science research method that involves the systematic analysis of text, media, communication, or information. The source, the message, the receiver, the medium, and the influence of the message are all topics that have been studied using content analysis and in combination with other methods. There are deductive and inductive approaches to content analysis. Two widely cited studies using content analysis take a deductive approach: using predefined categories and variables based on findings and best practices from prior research. Studies taking an inductive approach to content analysis, by contrast, have an open view of the content, usually involve a small-N sample, and are often based on a qualitative approach. Meanwhile, much has been written on methods and approaches to measuring reliability with human coders. Traditional content analysis uses human coders, whereas a variety of software has emerged that can be used to download and score or code vast amounts of textual news data. The chapter then identifies key benefits and challenges associated with new computational social science tools such as text analysis.
This chapter addresses determinism, which has been the predominant mode of perceiving the universe in modern sciences. The basic assumption is that any event is the effect of an external cause. Generally speaking, biological determinism focuses on the biological causes of events, whereas social sciences focus on the social causes. This mode of perceiving the social universe is typically associated with positivism and, more specifically, social naturalism — or the idea that there is no significant difference between social phenomena and natural phenomena. In this logic, it is assumed that social scientists can and should discover ‘social laws’ — or universal relations of causality between a social cause and a social effect. However, determinism in the social sciences has been criticized since its very beginning. In response to these critiques, many social scientists have adopted various forms of ‘soft’ determinism. The chapter then considers social predictions and probabilism.
Céline C. Cocq and Ora Szekely
This chapter illustrates comparative analysis, which is simply defined as comparing and contrasting two or more phenomena in order to better understand them. Comparative analysis plays an important role in both academic and policy-related circles and can be useful in many different ways. While in the hard sciences it is possible to conduct experiments under controlled laboratory conditions, this is often impossible in social science. Social scientists must therefore find other ways of isolating and testing the impact of variables and understanding the relationships between them. Accordingly, the goal of comparative analysis is the comparison of phenomena — whether that means comparison within individual cases, among a small group of cases, or the analysis of large amounts of data — to identify key independent variable(s) and establish what link, if any, exists between them and the dependent variable(s). Comparative analysis can also be useful in establishing the nature of that relationship, assessing whether it is necessary, sufficient, or both. Moreover, cross-case comparison allows social scientists to build broad theories that are applicable in different contexts.
Louis M. Imbeau, Sule Tomkinson, and Yasmina Malki
This chapter assesses descriptive, explanatory, and interpretive approaches. ‘Description’, ‘explanation’, and ‘interpretation’ are distinct stages of the research process. Description makes the link between what is to be described and a concept and its empirical referent. It defines a way to understand empirical reality, as variations, significations, or processes. Description refers to the ‘what’ question, as the first step towards explanation. When it comes to answering the ‘why’ and ‘how’ questions, some social scientists differentiate between explanation and interpretation. For them, the aim of social sciences is to ‘understand’, that is, to uncover the meanings of individuals’ or groups’ actions through the interpretation of their beliefs and discourses, whereas the aim of natural sciences is to ‘explain’, that is, to establish causality and general laws. The chapter presents an approach which offers a broader perspective for the social sciences, advocating an explanatory pluralism that allows for a more ecumenical approach.
Breaking Down Ideational Boundaries in the Social Sciences
Elisa Narminio and Caterina Carta
This chapter describes discourse analysis. In linguistics, discourse is generally defined as a continuous expression of connected written or spoken language that is larger than a sentence. However, as a method in the social sciences, discourse analysis (DA) gave rise to diatribes about where to set the borders of discourse. As language constitutes the very entry point to the world, some discourse analysts argue that all that exists acquires meaning through language. Does this mean that discourse constitutes reality? Is there anything outside text and discourse? Or is discourse one among many means of social construction? The evolution of DA in social science unearths an ontological debate between ‘realists’ and ‘nominalists’, which eventually reverberates in epistemological strategies.
Uncovering Unobservable Constructs
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.
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.
Theory and Methodology of Interpretation
This chapter assesses hermeneutics, which can refer to an art, a methodological paradigm, or a philosophical movement. In its primary sense, hermeneutics is the art of interpreting texts correctly. Today, hermeneutic method is practised across the human sciences and applied to the study of all types of written texts, actions, and other meaningful material. The chapter then focuses on the relationship between hermeneutics and human sciences. It also examines hermeneutics as a methodological approach used in legal research and the practice of law. There are two broad notions of legal interpretation. According to the first — the prevailing and most traditional notion — the interpretation of the sources of law is knowledge-based; the interpreter’s task is to extract the pre-existing meaning of a legal text, as set out by its author. According to the second notion of legal interpretation, the activity involves the interpreter’s will. The interpreter’s task is to attribute meaning to a text, by choosing from several possible meanings.