Speaker

Dr. Yves Rosseel
Department of Data Analysis
Ghent University

Dr. Yves Rosseel was trained as a Theoretical and Experimental Psychologist in Ghent University, and obtained a PhD at the same university on a topic in the field of Mathematical Psychology. After some post-doctoral years in Warwick (UK) and Leuven, he joined the Department of Data Analysis in Ghent, where he is now an Associate Professor. Dr. Rosseel applies modern data-analytical techniques to answer research questions in Psychology and Educational Sciences. Since 2010, his main research interest is structural equation modeling. He has published widely on this topic and is well known as the developer of an open-source software package for structural equation modeling: the R package lavaan.

Title

The structural-after-measurement (SAM) approach to SEM

Abstract

In structural equation modeling (SEM), the measurement and structural parts ofthe model are usually estimated simultaneously. But already since the birth of SEM in the ’70s, various authors have advocated that we should first estimate the measurement part, and then estimate the structural part. We call this the Structural-After-Measurement (SAM) approach. In the first part of the presentation, I will give a brief historical overview of various SAM approaches, and discuss their advantages and disadvantages. Next, I will describe the so-called `local’ SAM method where the mean vector and variance–covariance matrix of the latent variables are expressed as a function of the observed summary statistics and the parameters of the measurement model. The method includes two-step corrected standard errors and local fit measures. In the second part of the presentation, I will discuss several recent developments that are based on the SAM approach, including the inclusion of latent quadratic and interaction terms, the use of non-iterative estimators for the measurement part of the model, small-sample corrections, and various approaches to study measurement invariance in the setting where the number of groups is very large. Finally, I will discuss a software implementation of the SAM approach that is available in the R package lavaan.