Multilevel Analysis of Longitudinal Data (MALD)


Dr. Frans Tan
Department of Methodology and Statistics (DEB1)
Phone: + 31 43 388 22 78
E-mail: frans.tan[at]maastrichtuniversity[dot]nl

Target group

All PhD students and researchers involved in studies that consider longitudinal data and multilevel data in epidemiological, social and behavioural research.
For a fruitful participation in the course, participants should be familiar with linear regression analysis at an intermediate level (e.g. PhD course Statistics part 2: regression analysis and SPSS). Without this basic knowledge, the course will be hard to follow. In any case the participant should be familiair with dummy variables, interpretation of the regression parameters and also has some experience working with SPSS (regression).

Study objectives

  • The participant learns how to model and analyse specific longitudinal data. 
  • The participant learns about the possibilities of SPSS procedures regarding the analysis of longitudinal data.
  • The participant learns in practice how to analyse longitudinal data using SPSS procedures.


Hox, Joop. (2002). Multilevel analysis. Techniques and applications. Lawrence Erlbaum: New Jersey.

Imbos Tj., Janssen MPE., Berger MPF (2001). Methodologie en statistiek I en II., Universitaire Pers, Maastricht.

Kleinbaum, Kupper, Muller, Nizam (1998). Applied regression analysis and multivariable methods. Duxbury press: London. 3rd edition.

Singer, J.D., Willett, J.B. (2003). Applied longitudinal data analysis: modelling change and event occurence. 

Snijders T., Bosker R. (1999). Multilevel analysis. An introduction to basic and advanced multilevel modeling. Sage: London (advanced).

Tan, Frans E.S. (2008). Best practices in analysis of longitudinal data: a multilevel approach. In J. Osborn (ed). Bestpractice in quantitative methods, chapter 30, 451-471.

Twisk, Jos W.R.(2003). Applied longitudinal data analysis for epidemiology. Cambridge University Press.

Verbeke & Molenberghs (2000) Linear mixed models for longitudinal data (advanced). Springer-Verlag: New York.  

Code: 0823

The course consists of 10 meetings of 2,5 hour each.


Min/Max PhD candidates: 
minimum 7, maximum 20




Dates Time Location
20/03/2018 10.40-13.10 UNS40-A0.731
22/03/2018 10.40-13.10 UNS40-A0.731
27/03/2018 10.40-13.10 UNS40-A0.737
29/03/2018 10.40-13.10 UNS40-A0.731
03/04/2018 10.40-13.10 UNS40-A.0737
05/04/2018 10.40-13.10 UNS40-C0.553
10/04/2018 10.40-13.10 UNS40-A0.737
12/04/2018 10.40-13.10 UNS40-A0.737
17/04/2018 10.40-13.10 UNS40-A0.737
19/04/2018 10.40-13.10 UNS40-C0.553

Course fees

PhD candidates (Promovendi) of FHML, MaCSBIO, M41 and Merln: no fee.
Others: €500,00.