Applied Statistics I

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The course consists of eights units.

In the first four units, students will be given an in-depth training in the following standard statistical methods: factorial ANOVA for between-subject designs, analysis of covariance (ANCOVA), multivariate ANOVA (MANOVA), discriminant analysis and multiple linear regression. Students are assumed to have background knowledge of balanced two-way factorial ANOVA and multiple regression. These methods will be briefly reviewed. The following advanced topics will then be covered: unbalanced factorial designs, contrast analysis, interaction in multiple regression, simple slope analysis, dummy coding, centering covariates, different coding schemes, collinearity and residuals checks and data transformation.

The second half of the core course consists of four units, two on repeated measures ANOVA and two on mixed linear regression for repeated measures. The first two units cover classical repeated measures ANOVA for the one- and two-way within-subject design and the split-plot (between x within) design. Special attention is given to: a) the choice between multivariate and univariate data formats and method of analysis, and the sphericity assumption; b) the distinction between the within-subjects and between-subjects part of a split-plot ANOVA, and how to obtain both using regression analysis;
Subsequently, two units are devoted to mixed (multilevel) regression for repeated measures. This starts with a unit on marginal models for repeated measures as an alternative to repeated measures ANOVA in cases of missing data and/or of within-subject covariates. Students are shown the pros and cons of various models for the correlational structure of repeated measures, such as compound symmetry and AR1. The second unit covers the random intercept and random slope model for repeated measures as a method to include individual effects into models for longitudinal data (growth curves) or single trial analyses of lab data (response times, ERP, fMRI). Students learn how this can be combined with e.g. ARMA modelling to distinguish between inter-personal and intra-personal outcome variation.