RSS02.D7 Multilevel regression analysis with R

The course introduces multilevel regression analysis for researchers featuring models for hierarchical or nested social science data. Models and examples are discussed in as non-technical a way as possible; the emphasis is on understanding the methodological and statistical issues involved in application of the models. Following the day 1 introductory meeting, course days 2 and 3 are devoted to the analysis of (two-level) cross-sectional data, day 4 to the analysis of panel data, with repeated measures nested in individuals, and day 5 to multilevel logistic regression.
Every course day starts with a lecture followed by a computer exercise in which participants complete an exercise. During the computer exercises various aspects of multilevel modeling will be trained using the R software. In completing the assignments participants will work with cross-national survey and longitudinal datasets.
The day-to-day content includes:
  • Day 1, discussion of the unique features of multilevel data and their consequences for statistical analysis, including number of effective observations, intra-class correlation, null and random intercept model, and the R software itself.
  • Topics covered on day 2 are fixed effects, random slopes and significance testing, and we discuss level-1 (X) and level-2 (Z) predictors variables, types of regression effects (fixed, random), and null hypothesis tests (Wald test, likelihood ratio test, Satterthwaite df).
  • On day 3 the issues covered include cross-level interaction and proportion explained variance. Here we will discuss interaction of X and Z variables, R2 measures, and within and between regression.
  • On day 4 the attention shifts to longitudinal data. The topics discussed are wide vs long data files, fixed and random parts of multilevel longitudinal models, time-constant and time-varying predictor variables, within and between effects of time-varying variables, and fixed effects models.
  • On day 5 the multilevel mixed effects model is briefly juxtaposed with Generalized Least Squares followed by a discussion of multilevel logistic regression.


26 June 2023 - 30 June 2023
Course Fee

Regular: €995
Students & PhD's: €645

Early Bird Regular: €895 (application deadline* April 1st) 
Early Bird Students & PhD's: €580,50 (application deadline* April 1st)

Scholarships and discounts Find more information here
Application deadline

April 15th

*Your application is only completed when the course fee has been paid

Course leader Rob Eisinga
Level of participant
  • Master
  • PhD
  • PostDoc
  • Professional
Admission requirements ​Participants should have a basic knowledge of social science statistics, including analysis of variance and multiple regression analysis. Computer exercises will be done in R but familiarity with R is not required. Participants need to bring their laptop computer. The R software provided by CRAN should be installed on the participant’s device. It is also recommended to install RStudio
Admission documents
  • To get the student/PhD discount you need to upload a copy of your Student card or other proof of registration
  • If you are not a student/PhD, you can upload an empty document under 'Student Card'.
Mode of Study On Campus
ECTS 2 or 4 Find more information here
Location Radboud University