Advanced statistical methods and models in experimental design

The course assumes students have the basic knowledge of statistical analysis in empirical sciences (as well as some experience with R) and, based on it, introduces more advanced statistical methods used in cognitive research. The course provides students with hands-on experience with real data analysis using R, a cutting-edge statistical environment.

The course assumes students have the basic knowledge of statistical analysis in behavioural sciences, including the understanding of the logic

of statistical inference and the knowledge of classical statistical tests (test, chi-square test etc.). Based on these foundations, students in this

course learn statistical methods stemming from the General Linear Model (linear regression, analysis of variance) and from its extensions (e.g., logistic regression, hierarchical models). They learn how to apply those methods to experimental data, how to prepare data, if necessary, for the

analysis and how to make statistical inferences in complex experimental designs. The course leans towards practice rather than theory and provides students with hands-on experience with real data analysis using R.

Students understand the basics of the General Linear Model and know statistical methods based on it and its generalisations (K_W03).

Students know the main statistical methods used to analyse experimental data (K_W03).

Abilities:

Students can use the programming language of R to perform analyses of experimental data (K_U03, K_U04).

Students are able to choose the right statistical method and use it to analyse a particular dataset (K_U04).

Students can properly report results of their statistical analyses (K_U04, K_U06).

Students are able to draw valid conclusions from statistical analyses they perform (K_U04).