Multilevel Analysis of Longitudinal Data (MALD)

Multilevel models are a class of regression models for data that have a hierarchical or nested structure. Repeated measurements in longitudinal studies or patients nested within hospitals are examples of data with a hierarchical structure.

Standard regression models such as ordinary least squares that ignore such hierarchical structures can produce invalid results leading to incorrect conclusions. Mixed-effects regression models instead take into account this hierarchical structure when analyzing the data.   

When the registration list is full, you can register for the waiting list by filling out the application form on this website.

Please send this to:


Dr. Shahab Jolani
Department of Methodology and Statistics (DEB1)
Phone: + 31 43 388 24 34

Target group

All PhD students and researchers involved in studies that consider longitudinal and multilevel data in medical, epidemiological, and social and behavioral research.

Required prior knowledge

Participants should be familiar with standard linear regression analysis at an intermediate level (e.g., successfully completed the courses Introduction to Statistics Part 1 and Regression Analysis, Statistics Part 2). Without this basic knowledge, the course will be hard to follow. In any case, the participant should be familiar with dummy variables, interpretation of the regression parameters as well as experience working with SPSS (or R if it is preferred).

Study objectives

After completing this course, participants will have knowledge of and insight into:

  • What multilevel models are

  • How to model and analyze specific longitudinal data

  • What steps are required to analyze longitudinal data in practice

  • What possibilities exist in SPSS (or R) procedures regarding the analysis of longitudinal data

It also offers participants the opportunity to practice these methods using SPSS (or R).


  • Standard linear regression model and its shortcoming

  • Examples of multilevel designs

  • Mixed-effects regression models including random intercept model, random intercept and slope model, models with serial correlations, and marginal models

  • Basic principles of model selection in multilevel models

  • Analysis of pre-post measurement designs

  • Analysis of longitudinal experimental studies

  • Missing observations in longitudinal studies

Course material

  • Tan, F.E.S. and Jolani, S. (2022). Applied linear regression for longitudinal data: with an emphasis on missing observations (1st ed.). Chapmen and Hall/CRC. It is highly recommended to purchase the book, as it is the main study material of the course.
  • handouts provided by the tutor

Course fees

PhD candidates (Promovendi) of FHML, MaCSBIO, M4I and MERLN: no fee

These courses are free of charge in case you are employed or registered as FHML PhD candidate. 
Master students: no fee (*)
Others: €500,00

(*) PhD candidates and other participants are given preference. If some spots are still available, then Master students can apply.

Course dates March/April 2023

Dates Time Location
12/03/2024 10.45-13.15 uur UNS 60 M0.12
15/03/2024 10.45-13.15 uur UNS 60 M0.12
19/03/2024 10.45-13.15 uur UNS 60 M0.12
22/03/2024 10.45-13.15 uur UNS 60 M0.12
26/03/2024 10.45-13.15 uur UNS 60 M0.12
02/04/2024 10.45-13.15 uur UNS 60 M0.12
05/04/2024 10.45-13.15 uur UNS 60 M0.12
09/04/2024 10.45-13.15 uur UNS 60 M0.12
12/04/2024 10.45-13.15 uur UNS 60 M0.12
16/04/2024 10.45-13.15 uur UNS 60 M0.12


PhD secretary
Available Monday until Thursday: 9 am – 5 pm
Friday morning: 9 am – 1 pm

 +31 43 387 28 44
 Visitors address: Fac.Bur. FHML, P.Debeyelaan 15/ Dr. Van Kleeftoren, 2N2.004