GSMS Course Registration

All GSMS courses

Only GSMS PhD students (including BCN PhD students) can participate in these courses. Use your P-number to register (ask your Personnel Office).

For registration please go to Upcoming courses.

Course: Applied Longitudinal Data Analysis: modelling change over time

This course focuses on the analysis of longitudinal data  with continuous outcome variables. Step by step, we will be developing longitudinal data analysis techniques by extending the well-known multiple linear regression model for studies with repeated observations on the same respondents. This will result in the presentation of the mixed effects model (also known as multilevel model, random-effects model, hierarchical linear model, ...), allowing the analysis of change over time (such as change of test-scores over time, growth of any kind).

Throughout the course lectures, the emphasis will be on understanding the why and how of these models by explaining the underlying theory of these multilevel analyses using lots of examples. The application and interpretation of outcome of these techniques will be demonstrated in SPSS. The lectures will follow topics and theory along the lines of the first half of the book Applied Longitudinal Data Analysis by J.D. Singer and J.B. Willet.  

In the computer practicals, students will analyse data from example datasets using SPSS. In the book and online material which accompany this course, scripts and data for these examples can also be found for other software packages, such as R, SAS and Stata (and in lesser extent: for HLM, MLwiN and Mplus).

Students entering this course should have knowledge of and experience in using basic statistical concepts and techniques, including multiple linear regression analysis and analysis of variance. This basic knowledge is provided by the Basic Medical Statistics course. For more in-depth model building (and techniques for non-continuous outcome variables), the use of Generalized Linear Mixed models and GEE, see also the course Mixed Models.

Learning outcomes

  • Explore (graphically and numerically) longitudinal data and recognize the need for mixed effects models (multilevel models)
  • Understand the theory behind the multilevel model for change
  • Build, examine, interpret, expand and compare mixed effects models
  • Perform all described techniques using SPSS (or other major statistical software packages, see above)

Basic Medical Statistics (or an equivalent course, see above)

Hand-outs of the lecture-slides and exercises for the computer practicals will be provided in the lectures.

The book Applied Longitudinal Data Analysis by J.D. Singer and J.B. Willet (2003, ISBN-139780195152968) is recommended, but optional.

For those interested in being graded the option of a written assignment can be provided

EC (without exam)


EC (with exam)


Course coordinators

  • Sacha la Bastide, PhD
  • Christine zu Eulenburg, PhD



Contact person

Renate Kroese,

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