There were pre-conference workshops on July 24, 2018.
Intensive Longitudinal Data Analysis / DSEM
Multilevel Structural Equation
Modeling with Lavaan
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
Understanding SEM: Where do all
the numbers come from?
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
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
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