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Multilevel regression analysis with R (RSS2.15) - Confirmed

The course covers multilevel regression analysis with R for two types of data; individuals nested within social contexts (e.g. cross-national surveys), and repeated observations from individuals (e.g. longitudinal panel studies).

Duration: one-week.



    The application deadline has passed, applying is no longer possible

    The course introduces multilevel regression analysis for researchers featuring models for nested or hierarchical 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. Every course day starts with a lecture followed by a computer exercise in which you complete an assignment. 

    During the computer exercises various aspects of multilevel modeling will be trained using the R software. In completing the assignments you will work with cross-national and longitudinal datasets. 

    The day-to-day content includes, on day 1, discussion of the 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. 

    Topics covered on day 2 are fixed effects, random slopes and significance testing, and you discuss level-1 (X) and level-2 (Z) predictors variables, types of regression effects (fixed, random), and null hypothesis tests. 

    On day 3 the issues covered include cross-level interaction and proportion explained variance. Here you 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, and fixed effects models. 

    On day 5 the multilevel longitudinal model is juxtaposed with Generalized Least Squares followed by a discussion of multilevel logistic regression.


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    Starting date

    24 June 2024, 9 am
    Educational method
    Main Language
    24 June 2024, 9 am - 28 June 2024, 5 pm
    Unique code


    Type of education
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    See the requirements in cost and admission
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    Rob Eisinga

    Rob Eisinga

    Rob Eisinga is a professor of quantitative research methods at the Radboud University Nijmegen, where he teaches, among others, regression analysis to bachelor students and multilevel analysis to research master students and PhDs. His substantive interests concern the analysis of social and political change, including electoral and religious behavior, and the obesity epidemic. He combines this interest with a main interest in quantitative research methods and survey methodology. His studies were published in a wide array of journals, ranging from Political Analysis, Demography, and International Journal of Epidemiology to Appetite, Annals of Anatomy, and Bioinformatics. His current methodological interest is in the analysis of rank data and their null distribution in particular. Some key publications are available at

    The application deadline has passed, applying is no longer possible



    • Regular: €1049 (application deadline 13th of May)
    • Student & PhD's: €699 (application deadline 13th of May)

    Includes: your course, short morning and late afternoon courses, coffee and tea during breaks, a warm lunch every day, Official Opening, MethodsNET Café (including some drinks and snacks) Official Closing (with some drinks and snacks) and a 1-year (2024 calendar year) free membership as MethodsNET regular member.

    Excludes: transport, accommodation, social events and other costs. 

    Discounts and Scholarships


    Level of participant: 

    • Master
    • PhD
    • Postdoc
    • Professional

    Admission requirements: 

    Participants should have a basic knowledge of social science statistics, including analysis of variance and - most important - 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: