424301 :Causal Analysis in Data Science


Voertaal Engels
Werkvorm: Lectures, practicals (Collegerooster)
Tentamenvorm: Written exam (Tentamenrooster)
Studielast:6 ECTS credits
Inschrijving:Inschrijven via COMAP.
Blackboard informatieLink to Blackboard (Als u de melding 'Guest are not allowed in this course' krijgt, dient u nog bij Blackboard in te loggen)


dr. G.B.D. Moors (Coordinator)

prof. dr. J.P.T.M. Gelissen (Lecturer)

Doel van de cursus (alleen in het Engels beschikbaar)


After taking this course, students:

  1. are able to choose the appropriate analysis technique for answering a specific research problem from the range of techniques that are covered in the course .
  2. can clarify the statistical and/or methodological assumptions that apply to the techniques that are discussed in this course.
  3. can carry out adequate data analyses of social scientific or behavioral data with the techniques that are discussed in the course, using appropriate software.
  4. can give correct interpretations of the output of such analyses.
  5. can indicate the limitations of the techniques that are discussed in the course.

Inhoud van de cursus (alleen in het Engels beschikbaar)


In this course the following techniques will be discussed:


  1. Module 1 (Moors): Mediation analysis, moderation analysis, and conditional process analysis (mediated moderation/moderated mediation).
  2. Module 2 (Gelissen): Multilevel Modeling for continuous dependent variables.


The course will have a strong emphasis on the correct interpretation of research findings and understanding of the statistical and methodological properties of the techniques.


Bijzonderheden (alleen in het Engels beschikbaar)

Every module of the course constitutes an equal part in the exam. The exam consists of two parts.

A student passes the course if the final course grade >= 5.5.

The resit covers all modules of the course. Resit of a single module is not possible.

Verplichte literatuur

  1. Will be announced via Blackboard at the beginning of the course..

Gewenste voorkennis

Knowledge of regession analysis; practical knowledge SPSS

Mogelijk interessant voor

  • Data Science: Business and Governance ( 2015, 2016 )
  • Data Science: Business and Governance (voorjaar) ( 2017 )