This Course will teach students to use multivariate statistics for the analysis of empirical data. At the end of the course students should be able to determine which statistical model should be used to investigate different types of research questions, e.g. regression, ANOVA, repeated measures and logistic regression analysis. Furthermore, the students should be able to execute the corresponding analyses using SPSS and interpret the results. Students have encountered most of these models in the bachelor programmes, but still have to develop confidence in applying these models and procedures while analysing empirical data. Special attention will be given to the practical issues: checking the model assumptions and dealing with violations. New topics are power, contrast testing, bootstrapping, missing data and imputation techniques, and a further advanced topic in multivariate statistics.
There are options to follow this course as a non-Research Master student (eg Elective student, PhD student etc). Please contact the teacher about these options on time, ie before July 15 (for courses in semester 1) / December 15 (for courses in semester 2). You will need written approval from the teacher (an email is sufficient) in order to register for this course at the Faculty’s Student Information Point (Faculty Students Desks). Note that for external parties, costs for participation may be involved.
Relation between tests and goals of course
1. Refreshing previously mastered knowledge of multivariate analyses.
2. Enhancing knowledge about multivariate analyses by studying and/or practicing topics not encountered in previous education (e.g. repeated measure analysis and logistic regression), and/or by studying related topics (e.g., contrast testing and multiple testing issues).
3. Apply knowledge of General Linear Model to design an appropriate statistical model for a given research question and dataset.
4. Being able to perform the analyses mentioned in the previous three points using SPSS and being able to interpret the results.
5. Knowing which statistical model should be used to investigate different types of research questions.
6. Acquire knowledge on new developments in statistics (e.g. Bootstrapping and the changing ideas on hypothesis testing).
7. Reporting skills in writing about design choice and choice for statistical test.
Presentation skills in criticizing existing article on logistic regression analysis.
Assignment 1: Short review and presentation, 20% of final grade.
Assignment 2: Data-analysis plan, 20% of final grade.
Assignment 3: Analysis and essay on an advanced topic in multivariate statistics, 20% of final grade.
Assignment 4: Presentation concerning logistic regression analysis, 20% of final grade.
Assignment 5: Reading questions handed in each week. One randomly chosen set of questions will be graded, 20% of final grade.
AimAssessment 1Assignment 1, 2 and 52Assignment 2, 4 and 53Assignment 24Assignment 3, 4 and 55Assignment 1 and 26Assignment 3 and 57Assignment 1 and 28Assignment 4