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Applied Multivariate Data Analysis (Fall)


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 prinicple 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 undertaking 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 programme (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) logistic regression, (2) item response theory, (3) principal component analysis, confirmatory factor analysis and structural equation modeling, (4) nonparametric regression and nonparametric principal components analysis (using optimal scaling) 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, 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 a 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

  • Lectures

  • Computer labs (supervised)

  • Independent study of the literature.

Assessment method

Assignments during the course.


During this course Blackboard will be used.

Reading list

Syllabus Applied Multivariate Data Analysis 2 – Fall semester.


Via uSis. Maximum of 45 students.

Please note that one single uSis registration is mandatory for the course.

Registration for each semester is possible from two months through one week before the first lecture at the latest.

Students who do not register cannot attend classes or take the (re)exam.

Contact information

  • Fall semester

  • Spring semester

    • Frank de Vos, MSc
    • “Dr. M.J. De Rooij”;


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