Pre-conference workshops
There were pre-conference workshops on July 24, 2018.
Intensive Longitudinal Data Analysis / DSEM
Noémi Schuurman
Utrecht University, the Netherlands
Multilevel Structural Equation Modeling with Lavaan
Yves Rosseel
Ghent University, Belgium
The aim of this half-day workshop is to provide an introduction to multilevel structural equation modeling (SEM). After a brief history, and an overview of the different frameworks, we will focus on the two-level within/between approach that is most commonly used in the applied literature. Special focus will be given to the status/meaning of latent variables in a multilevel setting, and the distinction between observed and latent covariates. Several examples will be discussed, including the setup in lavaan. Finally, we will discuss several alternative approaches to multilevel SEM, and explain when they should be used.
Understanding SEM: Where do all the numbers come from?
Yves Rosseel
Ghent University, Belgium
This half-day workshop will offer a glimpse behind the scenes of SEM software. In the first part, it will be demonstrated how the model parameters in a SEM are estimated. We will set up a small function (in R) that takes a parameter vector as input, and computes the value of a suitable discrepancy function. We will then exploit the built-in optimizers of R (optim, nlminb) to find the best fitting parameters. In the second part, we will discuss the concept of information matrices, and we will explain how both standard and robust standard errors can be computed. Finally, in the third part, we will show how the so-called robust (Satorra-Bentler, Yuan-Bentler) test statistics are computed. All of this will be demonstrated with base R. This workshop aims to deepen your understanding of how Structural Equation Modeling works.
Hypothesis evaluation using the Bayes factor
Herbert Hoijtink
Utrecht University, the Netherlands
This workshop will introduce the participants to null hypothesis significance testing and its role in the replication crisis. Subsequently, an alternative, hypothesis evaluation using the Bayes factor will be introduced. It will be elaborated what the Bayes factor is, how it can be applied and should be interpreted. There will be attention for Bayesian updating (an alternative for power analysis), Bayesian (conditional) error probabilities, limitations of the approach, and software with which the Bayes factor can be computed.
Theory and analysis of conditional and average causal effects
Rolf Steyer
University of Jena, Germany

This short course is an introduction to the stochastic theory of causality, which is a generalization of the theory of causal effects in the tradition of J. Neyman and D. B. Rubin. In the course I will present the stochastic theory of causal effects and show how to use EffectLiteR for the analysis of conditional and average total effects.


  • Motivation: Simpson's paradox, non-orthogonal ANOVA
  • The scope of the theory: random experiments
  • The mathematical structure of causal models: causality space
  • True outcome variables, average and conditional causal effects
  • Prima facie effects
  • Sufficient conditions for unbiasedness
  • The role of randomization and other design techniques and strategies of data analysis
  • Estimating and testing average and conditional total effects via structural equation modeling (Applications using EffectLiteR) in some empirical examples

Department of Methodology and Evaluation Research

University of Jena

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