This chapter considers the main types of data used in Politics and International Relations, as well as the main criteria by which to judge whether the data collected is good or not. It first describes the steps involved in the process of thinking about what data or evidence is relevant to answering a research question before discussing the importance of addressing issues of validity and reliability in research. Some of these issues are illustrated by referring to recent attempts to measure corruption, a major topic of interest in Politics and International Relations. The chapter also examines the issue of case selection as well as the collection of qualitative and quantitative data using methods such as interviewing and observation. Finally, it analyses the so-called ‘big data’ revolution in data collection and analysis, and provides a data quality checklist.
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Chapter
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.
Chapter
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’.
Chapter
Todd Landman and Larissa C. S. K. Kersten
This chapter focuses on the measurement and monitoring of human rights. It explains the purpose, challenges, and types of human rights measures and discusses the main content of human rights that ought to be measured, including the different categories and dimensions of human rights. It also considers the different ways that human rights have been measured using various kinds of data and measurement strategies, such as events-based data, standards-based data, survey data, and socio-economic and administrative statistics. Furthermore, it looks at new trends in human rights measurement, with a focus on new ways to measure economic and social rights, ‘open source’, and ‘big’ data, and the mapping and visualization of human rights data. The chapter concludes by discussing the remaining challenges for human rights measurement and monitoring, including biased reporting, incomplete source material, and the importance of continued dialogue between different academic disciplines on the need for measurement.
Chapter
Although much of the data used in social science dissertation projects is produced by interviews, surveys, and participant observation, there are other forms of data that can be used for the purposes of social science. This chapter explores some of this ‘documentary’ data and how to use it for the purposes of research. Documentary forms of data have some significant advantages that make them particularly useful for student research projects. This does not mean that they are without problems, but the chapter provides a practical guide for those who are prepared to look beyond familiar horizons. It makes the case for using documents; explores what can be included under the broad heading of documents; and introduces both quantitative and qualitative content analysis as a means to analyse documents.
Book
Tom Clark, Liam Foster, and Alan Bryman
How to do your Social Research Project or Dissertation looks to help readers to navigate research for a project or dissertation. It starts with an introduction to the research process and how to get started. It examines the process of developing an idea. It reviews the available literature. It then considers how to build upon the project idea, the ethical issues, and how to write a proposal. Next it considers sampling, and collecting and analyzing quantitative and qualitative data. Finally, it describes how to evaluate the project and the process of writing up.
Chapter
The literature review is a key component of a dissertation. It serves to contextualize the aims and objectives of the project, and in terms of the research process it helps to sensitize issues of interest that the student might want to direct their attention towards when they begin collecting and analysing data. This chapter provides an introduction to the literature review and examines its purpose in relation to the research process. Beginning with a short exploration of the nature of a literature review and its relationship with theory, the chapter goes on to examine the different types of review before detailing the key content. By the end of the chapter, students should have a good understanding of the role of the literature review in research and how it informs every aspect of the research process.
Chapter
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
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.
Chapter
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.
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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
This text provides readers with the analytic skills and resources they need to evaluate research findings in political research, as well as the practical skills for conducting their own independent inquiry. It shows that empirical research and normative research are not independent of each other and explains the distinction between positivism and interpretivism, and between quantitative and qualitative research. Part 1 of this edition discusses key issues in the philosophy of social science, while Part 2 presents a ‘nuts and bolts’ or ‘how to’ guide to research design, such as how to find and formulate a research question. Part 3 evaluates different methods of data collection and analysis that can be used to answer research questions, along with the variety of considerations and decisions that researchers must confront when using different methods.
Chapter
This chapter considers different types and forms of interviewing, including focus groups, and how they should be conducted. Interviews are a popular method of data collection in political research. They share similarities with surveys, but these similarities relate mostly to structured interviews. The chapter focuses on semi-structured interviews, including focus groups, the emphasis of which is to get the interviewee to open up and discuss something of relevance to the research question. After describing the different types and forms of interview, the chapter explains how interview data can be used to confirm or disconfirm a hypothesis or argument. It also shows how to plan and carry out an interview and how the type and wording of questions, as well as the order in which they are asked, affect the responses you get. Finally, it examines the interviewing skills that will ensure a more successful outcome to an interview.
Chapter
Jean-Frédéric Morin, Christian Olsson, and Ece Özlem Atikcan
This chapter addresses the unit of analysis and observation. Each empirical social or behavioural science study typically includes the identification of one or more units of analysis. The unit is the entity, element, or grouping that constitutes the focus of the study’s analyses, and multiple cases of this unit are analysed. The unit of analysis is of primary importance, as this is the unit that is referred to in hypotheses or research questions and therefore the unit that is the focus of data analyses that address these hypotheses or research questions. However, there are two other types of units that need to be considered. In sum, the three types of units in any empirical study are the unit of sampling, the unit of observation or measurement (sometimes called the unit of inquiry or unit of data collection), and the unit of analysis.
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
Jacob A. Hasselbalch and Leonard Seabrooke
This chapter discusses prosopography, which is defined as the investigation of the common background characteristics of a group of actors in history by means of a collective study of their lives. The etymology of the word suggests that prosopography is about describing or recording a person’s appearance or life, but prosopography differs from biography in that it analyses structured biographical data of groups of individuals that have something in common. Prosopography emerged primarily as a method for historical research. Outside of historical research, it is more commonly known as ‘group biography’ or ‘career-path analysis’. Prosopography has also been a key element of ‘field-based’ research on social groups and the sociology of professions, and is more of an approach than a method sui generis: it implies the systematic organization of data in such a way that connections and patterns that influence historical processes are revealed. The chapter then details the five stages of prosopography.
Chapter
Sampling Techniques
Sample Types and Sample Size
Emilie van Haute
This chapter assesses sampling techniques. Researchers may restrict their data collection to a sample of a population for convenience or necessity if they lack the time and resources to collect data for the entire population. Therefore, a sample is any subset of units collected from a population. Research sampling techniques refer to case selection strategy — the process and methods used to select a subset of units from a population. While sampling techniques reduce the costs of data collection, they induce a loss in terms of comprehensiveness and accuracy, compared to working on the entire population. The data collected are subject to errors or bias. Two main decisions determine the size or margin of error and whether the results of a sample study can be generalized and applied to the entire population with accuracy: the choice of sample type and the sample size.
Chapter
Jean-Frédéric Morin, Christian Olsson, and Ece Özlem Atikcan
This chapter discusses survey research. Surveys are a very common method of data collection used by many social researchers. As such, they are used in public opinion polls to gauge political trends and trait, but also in marketing research examining consumer behaviour and feedback. Surveys are also a common data collection method in many social research projects. They are further used to evaluate needs, processes, and outcomes. Importantly, surveys are a unidirectional communication approach to collect data, which is very different from observational methods, semi-structured and structured interviews, or other types of data collection where the researcher takes an active role. Specifically, using surveys, participants are presented with a set of instructions and predetermined questions. The researcher is not expected to engage in any participatory interaction or in-depth conversation with participants.
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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
This chapter discusses the principles of ethnography and participant observation: what they are, how (if) they became standardized as a research method, what form of evidence they constitute, and what place they occupy in the study of Politics. Participant observation has emerged as a popular research tool across the social sciences. In particular, political ethnographies are now widely carried out in a broad variety of contexts, from the study of political institutions and organizations to the investigation of social movements and informal networks, such as terrorist groups and drugs cartels. Political ethnography is also becoming a research method of choice in the field of International Relations. The chapter examines the strengths of ethnographic fieldwork, focusing on issues relating to sampling, access, key informants, and collecting observational data. It also addresses the weaknesses of ethnography, especially issues of subjectivity, reliability, and generalizability.
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