In the previous chapter, students learned how to compare the means of two groups with the t-test and U test. In Chapter 9, students are taught how to statistically compare three or more groups/levels among one or two independent variables. Students are also coached through three different kinds of ANOVAs: between-subjects, within-subjects, and between-within methods. The assumptions for these ANOVAs are reviewed and step-by-step instructions are given in SPSS and R. Student researchers are also taught how to interpret the results for testing hypotheses that use an ANOVA.

Students are guided through learning about comparing the means of two groups / levels using a t-test. The differences between a paired samples t-test and an independent samples t-test are reviewed along with the statistics’ assumptions. When two independent groups do not have equal variances, students are coached through completing a Mann–Whitney U test. Students are also guided through creating charts that can accompany their results in SPSS or R.

In previous chapters, student researchers were guided through the creation of basic graphs and charts to help visualize results. In this chapter, Mayer’s principals of multimedia learning are reviewed as they pertain to presenting information using graphics. Readers are brought through best practices with how to visually display information and facilitate an accurate transfer of meaning. Students are shown how to modify the charts and graphs that were presented in previous chapters are reviewed with step-by-step instructions in SPSS and R. Additionally, students are taught how to create more detailed charts in R.

The appropriate method of data analysis depends upon a variety of factors that have been specified in the research question and as part of the research design. One key issue is whether the data are qualitative or quantitative, and this depends upon the underlying research approach. If the research approach is deductive, then most of the data are likely to be expressed as numbers and the key issue will be selecting the appropriate statistical techniques for describing and analysing the data. In this chapter, we will concentrate on techniques for describing quantitative data and for providing simple preliminary analyses.

The appropriate method of data analysis depends upon a variety of factors that have been specified in the research question and as part of the research design. One key issue is whether the data are qualitative or quantitative, and this depends upon the underlying research approach. If the research approach is deductive, then most of the data are likely to be expressed as numbers and the key issue will be selecting the appropriate statistical techniques for describing and analysing the data. In this chapter, we will concentrate on techniques for describing quantitative data and for providing simple preliminary analyses.