NB Language of instruction is English
The course runs during the first (second semester) and the second year (first semester) of the Research Master programme (in Educational Sciences); each semester for 16 weeks. Each part consists of a series of mini-courses where each topic includes “when and why to use a special statistical technique”, “how to use a special technique” and “how to interpret the results”. Topics include factor analysis and principal component analysis, nonlinear analysis of categorical data, (principal components analysis, regression analysis, (multiple) correspondence analysis), multidimensional scaling & cluster analysis, models for binary data, and missing data analysis (in the first semester) and applied regression & analysis of variance, quasi-experimental designs, multilevel analysis, and structural equation modeling in the second semester. The treatment of each topic in the course will have a similar structure, in particular:
Exposition of the situations in which a particular technique should be used and why, illustrated with an example from actual research, where possible.
A summary exposition of the basic principles and the working of the technique and how it can be applied to real data.
Discussion of the output of computer programs (mostly SPSS, sometimes specialized software) designed to carry out the analyses.
The participants have to hand in assignments. The assignment consists of a data analysis with the technique in question.
Each assignment will be graded, and students will have to have a passing grade on all assignments. The final grade will be an average of all individual grades. Grades can only be awarded to participants who take part in the entire course.
After completion of this course, participants should have:
A thorough understanding of the various theories and methodological approaches (data analysis techniques included) which are commonly used in the research programs that are central to this research master (achievement level 2).
The ability to independently formulate, perform and assess scientific research at a level suitable to preparing scientific publications (7).
The ability to write scientific reports in English (8).
Advanced, up to date knowledge of quantitative and qualitative research methodology (9).
Mode of instruction
11 Computer labs
Independent study of the literature.
During this course Blackboard will be used.
- Syllabus Applied Multivariate Data Analysis 2.
Please note that separate uSis registration is mandatory for lectures, seminars, exam and re-exam.
Registration for the lectures of the course is possible as of two months through one week before the first lecture at the latest;
Registration for the seminars of the course is possible as of two months through one week before the first lecture at the latest;
Registration for the exam is possible as of two months through one week before the exam at the latest;
Registration for the re-exam is possible as of two months through one week before the re-exam at the latest.
Students who don’t register cannot attend classes or take the (re)exam.
The course is a collaboration between the Institute of Education and Child Studies and the Institute of Psychology.
The course is taught at a level that is suitable for both research master students and PhD candidates, and is suitable for participants with a good background in elementary statistics and methods of research at the bachelor level.