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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 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 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.