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.
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.
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.
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.