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Applied Multivariate Data Analysis (Fall) - Education and Child Studies


NB Language of instruction is English

Admission requirements

The research master course is taught at a level that is suitable for both research master students and PhD candidates, and is in principle not open to regular master students.. Students with a good background in elementary statistics and methods of research at the bachelor level may apply to take part. Other interested persons have to apply explicitly, also if they intend to follow a single topic (which is not encouraged).


The complete course Applied Multivariate Data Analysis which runs during an entire calendar year is a joint enterprise of the Institute of Psychology (responsible for the Spring semester) and the Institute of Education and Child Studies (responsible for the Fall semester). Participants can start either in the spring or the fall. The course is part of the Research Master program (in Educational Sciences).

Each part has meetings once a week during a whole semester (see the Course Schedules for precise details). Both parts consist of a series of topics each of which include “when and why to use a special statistical technique”, “how to use a special technique” and “how to interpret the results”.

Topics of AMDA – Fall semester include (1) principal component analysis and confirmatory factor analysis (with a short extension to structural equation modeling), (2) logistic regression, (3) nonparametric regression and nonparametric principal components analysis (using optimal scaling), (4) item response theory and (5) meta-analysis.

Topics of AMDA – Spring semester include (1) quasi-experimental design, (2) multilevel analysis including longitudinal analysis, (3) cluster analysis, (4) mediation and moderation, (5) missing data.

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, wherever 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 specialised software) designed to carry out the analyses.

  • Participants have to hand in written work (assignments), on which grades are based. The assignments consist of a data analysis with the technique discussed in the current topic.

  • Students need to have a passing grade on all assignments. The final grade for each part will be an average of all individual grades. Grades can only be awarded to participants who have handed all assignments of a semester. Thus separate grades are awarded for each semester.

Course objectives

After completion of this course, the student
1 Has a basic understanding of the techniques discussed in the different topics
2 Is able to use different software packages to perform statistical analyses for the techniques discussed
3 Is able to write a result section of a scientific manuscript on the basis of the output of the statistical analysis
4 Has the ability to choose the appropriate technique for a given research question with data
5 Knows which assumptions are made in the different analyses and is able to validate the analysis.




Mode of instruction

  • Semester I (Fall): 10 lectures of two hours and 15 computer labs (supervised) of two hours

  • Semester II (Spring): 11 lectures of two hours and 14 computer labs (supervised) of two hours

  • Independent study of the literature.

Assessment method

Participants have to hand in assignments. The assignments consist of a data analysis with the technique discussed in the current topic. Each assignment will be graded. The final grade for each semester will be a (weighted) average of the individual grades with the additional requirement that each assignment should be graded with at least a 4.

In semester I there are 5 assignments.

In semester II there are 4 assignments.

The Faculty of Social Sciences has instituted that instructors use a software programme for the systematic detection of plagiarism in students’ written work. In case of fraud disciplinary actions will be taken. Please see the information concerning fraud


During this course Blackboard will be used.

Reading list

Syllabus and selected materials Applied Multivariate Data Analysis through Blackboard.


Please note that separate uSis registration is mandatory for lectures, seminars, exams and re-exams. Student who do not register, cannot attend courses or take exams.


  • Registration for the lectures of the course is possible as of 100 calendar days through 10 calendar days before the first lecture at the latest;

  • Registration for the seminars of the course is possible as of 100 calendar days through 10 calendar days before the first seminar at the latest.


Student must register for each exam through uSis. This is only possible until 10 calendar days before the exam. More information on exam registration

Contact information


The course is a collaboration between the Institute of Education and Child Studies and the Institute of Psychology.