This chapter discusses the analysis of qualitative material. There are many types of qualitative analysis. Some approaches are related to specific forms of data, whereas others are more generic in nature. There can also be considerable differences between some forms of qualitative analysis to the extent that they have very little in common with one another. Given this diversity, it is not possible adequately to address every type of analysis, or provide highly detailed instructions for the more common techniques. Hence, the chapter introduces the iterative processes of coding and categorization as well as some of the major types of qualitative analysis. It shows how to identify key concepts in data, and how those concepts can be connected to theory.
This chapter discusses two approaches to analyse quantitative data: descriptive statistics and inferential statistics. Descriptive statistics allows the student to summarize and describe data, whereas inferential statistics enables him or her to infer answers from the data using hypotheses. Both approaches can be extremely useful. The chapter details some of the techniques associated with these approaches, and helps the student to choose which techniques are appropriate for the data collected. In showing how to present data appropriately, it also explores ways to discuss findings in an evidence-informed manner. By introducing some of the basic ideas involved in quantitative analysis, the chapter provides students with skills that can be employed in a quantitative dissertation.
This chapter examines five main approaches in comparative politics that represent important contributions: old and new institutional analysis, interest approach, ideas approach, individual approach, and the influence of the international environment. The role of ‘interaction’ is also explored. After explaining the use of theory in comparative political analysis, the chapter considers structural functionalism, systems theory, Marxism, corporatism, institutionalism, governance, and comparative political economy. It also discusses behavioural and rational choice approaches, how political culture helps to understand political behaviour in different countries, self-interest in politics, and the implications of globalization for comparative politics. The chapter concludes by assessing the importance of looking at political processes and of defining what the ‘dependent variables’ are.
This chapter discusses archival research, starting with a description of archives. The various types of archives constitute a precious resource for researchers, and in many disciplines, such as sociology, political sciences, law, or history, archival research occupies a central place. Depending on the field of research, analysis may be limited to a single archive or require a comprehensive comparative analysis of various archives, taking into account their diverse nature and purposes. Archival research can be defined as finding, using, and correlating information within primary and secondary sources. Primary sources are sources created by persons directly involved in the event, reflecting their point of view, such as personal diaries or letters. In contrast, secondary sources are sources not based on a direct observation of an event, or on evidence directly associated with the subject, and they rely on pre-existing primary sources. The chapter then details the main steps of archival research and looks at the development of online archives and databases.
4. Asking Questions: How to Find and Formulate Research Questions
This chapter deals with the first step of the research process: the formulation of a well-crafted research question. It explains why political research should begin with a research question and how a research question structures the research process. It discusses the difference between a topic or general question, on the one hand, and a focused research question, on the other. It also considers the question of where to find and how to formulate research questions, the various types of questions scholars ask, and the role of the ‘literature review’ as a source and rationale for research questions. Finally, it describes a tool called the ‘research vase’ that provides a visualization of the research process, along with different types of questions: descriptive, explanatory, predictive, prescriptive, and normative.
For many years, the concept of an authoritarian regime was considered to be one large category, with little understanding of how these regimes differed. The study of authoritarian regimes has come a long way since. Though all authoritarian regimes share in common that there is no turnover in power of the executive, there are considerable differences that distinguish autocracies. Authoritarian regimes today are increasingly attempting to use ‘democratic’ institutions to prolong their rule. This has led to a rise in competitive authoritarian regimes, or hybrid regimes. In spite of these changes, authoritarian regimes are more robust than ever. This chapter explains the different ways in which authoritarian regimes are categorized. The chapter then explains how the different types of authoritarian regimes perform, and what factors make them more durable. As the chapter demonstrates, autocratic regimes have become increasingly better equipped to maintain themselves.
Automated Text Analysis
The Application of Automatic Text Processing in the Social Sciences
This chapter examines automated text analysis (ATA), which describes the different methodologies that can be applied in order to perform text analysis with the use of computer software. ATA is a computer-assisted method for analysing text, whenever the analysis would be prohibitively labour-intensive due to the volume of texts to be analysed. ATA methods have become more popular due to current interest in big data, taking into account the volume of textual content that is made easily accessible by the digitization of human activity. Key to ATA is the notion of corpus, which is a collection of texts. A necessary step before starting any analysis is to collect together the necessary documents and construct the corpora that will be used. Which texts need to be included in this step is dictated by the research question. After text collection, some processing steps need to be taken before the analysis starts, for example tokenization and part-of-speech tagging. Tokenization is the process of splitting a text into its constituent words, also called tokens, whereas part-of-speech tagging assigns each word a label that indicates the respective part-of-speech.
This chapter evaluates Bayesian inference, which refers to the Bayesian statistical method for estimating the parameters of a model and for testing a hypothesis. It relies on subjective statistics and extensively uses Bayes’s theorem. In the early 1990s, Bayesian statistics boomed with the emergence of sampling techniques. These new tools rely on the computational power to sample from (rather than evaluate) the posterior probability. However, the main drawback of the Bayesian approach lies in the computation of the posterior probability. The analytical computation of the posterior probability is a complex problem for any application, and this has limited Bayesian statistics for years.
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.
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’.
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.
This chapter addresses Boolean algebra, which is based on Boolean logic. In the social sciences, Boolean algebra comes under different labels. It is often used in set-theoretic and qualitative comparative analysis to assess complex causation that leads to particular outcomes involving different combinations of conditions. The basic features of Boolean algebra are the use of binary data, combinatorial logic, and Boolean minimization to reduce the expressions of causal complexity. By calculating the intersection between the final Boolean equation and the hypotheses formulated in Boolean terms, three subsets of causal combinations emerge: hypothesized and empirically confirmed; hypothesized, but not detected within the empirical evidence; and causal configurations found empirically, but not hypothesized. This approach is both holistic and analytic because it examines cases as a whole and in parts.
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.
This chapter describes case selection, which is a crucial component of designing social research. Its importance can hardly be overstated because the cases you choose affect the answers you get. However, how should researchers select their cases? A careful inspection of the research question, the study’s objective, should be the starting point. The research question typically anchors the study in a research area, specifies the universe of cases, and guides its engagement with theory. Ideally, case selection is solely driven by methodology; however, practicality and feasibility considerations frequently make adjustments to the design necessary. Such considerations concern, for instance, the costs of data collection. The chapter introduces a few commonly used case selection strategies as well as two hotly debated topics in the literature on case selection: selecting on the dependent variable and random case selection.
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.
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.
This chapter deals with qualitative data. While everyone is familiar with the idea of interviewing and observing, actually collecting qualitative data is not as easy as it might first appear to be. In fact, when doing qualitative work, it is easy to become overwhelmed by the amount of information collected. However, with some purposeful planning, piloting, and practice, the student can avoid some of the pitfalls associated with qualitative data collection. Focusing on qualitative interviews and participant observation, the chapter introduces some of the common issues that arise when gathering qualitative data and offers useful advice concerning the planning and practice of collecting data ‘in the field’.
This chapter discusses the basics of collecting quantitative material. It outlines the nature of quantitative data in the context of the research process, before exploring the differences between primary and secondary data. In doing so, it highlights some of the benefits of using secondary data sets for the purposes of dissertation-based research. The chapter then examines the relationship between research questions, concepts, and variables, before exploring how quantitative data can be measured at different levels. Finally, it offers some useful tips and advice concerning one technique that is particularly common in student projects — the questionnaire — and demonstrates the different ways in which questionnaires can be developed and administered.
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.
Comparative Politics provides an introduction to the field. Comparative politics is an empirical science that deals primarily with domestic politics. It is one of the three main subfields of political science, alongside international relations and political theory. The text provides a comprehensive introduction to comparative politics. It includes three chapters dedicated to familiarizing readers with the comparative approach, discussing substance as well as method. It then guides readers through a thematically organized, comprehensive analysis of the core methods, theories, and concepts in comparative politics. Empirical data is drawn on to demonstrate key similarities and differences of political systems in practice. Increased focus is given to the Global South and its path towards democratization. At the end of each chapter, there are questions designed to encourage critical thinking. The six sections of the work deal with: theories and methods; the historical context; structures and institutions; actors and processes; public policies; and beyond the nation-state.