GESIS Training Courses

Scientific Coordination

Sebastian E. Wenz
Tel: +49 221 47694-159

Administrative Coordination

Loretta Langendörfer M.A.
Tel: +49 221 47694-143

Comparative Social Research with Multi-Group SEM

Course duration
10:00-17:00 CET
General Topics:
Course Level:
Software used:
5 days
Students: 500 €
Academics: 750 €
Commercial: 1500 €
Additional links
Lecturer(s): Daniel Seddig, Eldad Davidov, Peter Schmidt, Yannick Diehl

About the lecturer - Daniel Seddig

About the lecturer - Eldad Davidov

About the lecturer - Peter Schmidt

About the lecturer - Yannick Diehl

Course description

Determining whether people in certain countries, or at different time points score differently in measurements of interest, or whether constructs relate differently to each other across nations can indisputably assist in testing social sciences theories and advancing our knowledge. However, meaningful comparisons require equivalent measurements of these constructs. This is especially true for subjective attributes such as values, attitudes, perceptions, or opinions. In this course, we first discuss the meaning of cross-group measurement equivalence, look at possible sources of nonequivalence, and suggest ways to prevent it. Next, we examine the social science methodological literature for ways to empirically test for full or partial measurement equivalence using multiple group confirmatory factor analysis (MGCFA). In addition, we discuss how to test equivalence of regression coefficients and/or latent means and variances using MGCFA and multiple group structural equation modeling (MGSEM). We present such tests using the software environment R (e.g., lavaan). Furthermore, we consider what may be done when exact and partial equivalence is not supported by the data. We discuss strategies based on the less strict assumption of approximate equivalence, such as alignment optimization and the Bayesian estimation procedure. These methods offer exciting directions and solutions for future research in cross-group measurement equivalence assessment when exact equivalence is not supported by the data. Finally, we will address the analysis of categorical data. During the exercises, participants will have the opportunity to conduct these tests using data on human values from the European Social Survey, and if time allows data on biodiversity and intentions to behave in an environmentally friendly way with a reasoned action approach. We also encourage participants to bring their own data and apply the methods discussed in the course to their data.

Target group

Participants will find the course useful if they (intend to):
  • work with survey data;
  • compare groups (e.g., nations, cultures, but also within-country-groups like gender, age or education groups and even time points);
  • examine differences in means across groups;
  • examine differences in associations (regression coefficients, covariances) across group.
    In sum, participants may find the course relevant if they are interested in conducting meaningful comparative research across countries, over time or across other social groups such as natives and immigrants; and if they are interested in understanding potential problems in comparative research, and in studying and applying the methodological prerequisites for meaningful comparisons across groups.

    Learning objectives

    By the end of the course participants will:
  • know how to test for measurement invariance across groups using various types of multigroup confirmatory factor analyses with the R software (lavaan);
  • will be able to interpret the output (from lavaan in R) correctly;
  • have gained practical experience in testing for exact, partial, approximate measurement invariance and computing latent means for different groups (e.g., countries and time-points);
  • know how to deal with non-invariant measurements.
    Organizational structure of the course
    This is a five-day course with a total amount of 30 hours of class time. Participants can expect to take part in the following activities:
  • Individual and group work on lab exercises prepared in advance
  • Individual and group work on own data and models
  • Group discussion of in-class theoretical exercises
    On Monday to Thursday, course time will be split equally between teaching and tutorials.
    On Friday, students will present their own work with own data.
    The lecturers will be available for consultation on own projects, help in class assignments, and support when other theoretical or empirical questions may arise.


    The course assumes that participants
  • are familiar with the Linear Regression Model (OLS);
  • have basic knowledge of factor analysis and structural equation modeling;
  • have basic knowledge and some experience with the software R.
    Software and hardware requirements
    Participants will need to bring their own laptops with R ( and RStudio installed ( Both programs are free and open source. Ideally, the following R packages are also installed before the course: lavaan, blavaan, sirt, semTools. We expect participants to have basic knowledge and some experience with R.
    If you want to brush up your knowledge in R, you might be interested in also taking part in the online-workshop Introduction to R in the week before the Spring Seminar (22-24 Feb).

    Recommended readings