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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 discusses the principles of textual analysis as a means of gathering information and evidence in political research. Textual analysis has generated strong interest as a research method not only in Politics and International Relations, but also throughout the social sciences. In political research, two forms of textual analysis have become particularly prominent: discourse analysis and content analysis. The chapter examines discourse analysis and content analysis and explains the use of documents, archival sources, and historical writing as data. It considers the distinction between discourse analysis and content analysis, as well as the differences between qualitative and quantitative content analysis. It also describes the procedures that are involved in both quantitative and qualitative content analysis.

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.

This chapter deals with quantitative analysis, and especially description and inference. It introduces the reader to the principles of quantitative research and offers a step-by-step guide on how to use and interpret a range of commonly used techniques. The first part of the chapter considers the building blocks of quantitative analysis, with particular emphasis on different ways of summarizing data, both graphically and with tables, and ways of describing the distribution of one variable using univariate statistics. Two important measures are discussed: the mean and the standard deviation. After elaborating on descriptive statistics, the chapter explores inferential statistics and explains how to make generalizations. It also presents the concept of confidence intervals, more commonly known as the margin of error, and measures of central tendency.

This chapter examines qualitative comparative analysis (QCA), which strives to bridge the methodological rift between case study-based research and quantitative studies. QCA belongs to the broader family of configurational comparative methods (CCMs). From an analytical perspective, QCA can be distinguished from quantitative approaches. The emphasis shifts from covariance to the analysis of set relations. Being strongly tied to a profound theoretical and conceptual reasoning which is typical for comparison in general, the analysis of set relations is based on three steps: first, a score is attributed to a social phenomenon (representing either a dichotomous or a graded set membership), usually in relation to other phenomena. Second, necessary conditions are defined. Third, through the help of a truth table analysis, (combinations of) sufficient conditions are analysed.

This chapter highlights statistical significance. The key question in quantitative analysis is whether a pattern observed in a sample also holds for the population from which the sample was drawn. A positive answer to this question implies that the result is ‘statistically significant’ — i.e. it was not produced by a random variation from sample to sample, but, instead, reflects the pattern that exists in the population. The null hypothesis statistical test (NHST) has been a widely used approach for testing whether inference from a sample to the population is valid. Seeking to test whether valid inferences about the population could be made based on the results from a single sample, a researcher should consider a wide variety of approaches and take into the account not only p-values, but also sampling process, sample size, the quality of measurement, and other factors that may influence the reliability of estimates.