When presented with data and questions, you show the ability to statistically analyse, report and interpret data from research with more than two groups or variables. Background knowledge of the functions and problems of such research. Skilful choice of the correct analysis method and in the use of SPSS.

1. Variation analysis: expansion of the t-tests to designs with more than two groups or variables
2. Generalisability theory: specification of the degree of “agreement” between different observers or measuring instruments
3. Dealing with SPSS for Windows and choosing the right analysis method
4. Multiple regression: expansion of regression and correlation to designs with more than two variables
5. GLM/MANOVA: variation analysis for designs with multiple types of dependent or independent variables
6. Non-parametric tests: expansions of all previous tests for variables that are abnormally distributed, including variables that are qualitative or have only been assigned an order.

• Two “closed-book” written partial examinations
• SPSS assignment, to be handed in three days before the second examination
• Two assignments (optional), one for each examination: theses may lead to higher partial examination marks (conditions that apply; see Brightspace).
• A passing mark for both partial examinations and for the SPSS assignment is required. If this requirement is met, the mark will be the average of the score for the two partial examinations, both counting for 50%.
• In the second examination a number of questions count as 'knockout' criteria. See Brightspace.