In Chapter 2, simple methods to analyse longitudinal data are introduced. First, methods for analysing the change over time for longitudinal studies with two repeated measurements. Second, methods for analysing the change over time for longitudinal studies with more than two repeated measurements. And finally, methods for analysing the difference in the change over time between groups for longitudinal studies with both two and more than two repeated measurements. Although both the parametric and non-parametric versions of these methods are discussed, a very detailed explanation of GLM for repeated measures is provided. All methods are accompanied by extensive real-life data examples.

The two-way ANOVA , is applied to data with a factorial arrangement (and its extensions to more factors), and is an important tool for analysing data from experimental studies. We start by characterising the properties of a factorial design and compare it with a hierarchical design. We introduce two important experimental concepts here - the ideas of a balanced design and of a proportional design. We then describe the two-way ANOVA model, including an explanation of the interaction term and its use in ANOVA models. We outline some basic types of correct experimental designs, including complete randomised blocks, and contrast them with incorrect designs resulting in pseudo-replicated observations. Separate sections deal with ANOVA model specification for randomised blocks and Latin square designs, and with the specific issues of the multiple comparisons procedure in ANOVA models with multiple factors. A nonparametric counterpart of the randomised complete block ANOVA - the Friedman test - is also introduced. The methods described in this chapter are accompanied by a carefully-explained guide to the R code needed for their use, including the multcomp package.