ABSTRACT

This chapter introduces inferential statistics, i.e. those statistics that enable researchers to make inferences about the wider population. It focuses on difference tests, what they are, what they do and how to use them with different kinds (scales) of data. The chapter addresses: measures of difference between groups; the t-test (a test of difference for parametric data); analysis of variance (ANOVA) – one-way and two-way – and MANOVA (tests of difference for parametric data); the chi-square test (a test of difference and a test of goodness of fit for non parametric data); degrees of freedom (a statistic used in calculating statistical significance in difference tests); the Mann-Whitney and Wilcoxon tests (tests of difference for non-parametric data); the Kruskal-Wallis and Friedman tests (tests of difference for non-parametric data). The chapter indicates when these statistics can and cannot be used, how to decide which statistics to use, and the ‘safety checks’ required before proceeding to use each specific statistic. It provides worked examples of each of these statistics, together with the SPSS command sequences for working with the statistics included, worked examples of how to analyse SPSS tables of output, and examples of how to report results using the different statistics in this chapter.