"A Causal Interpretation of Measurement Models in Psychology' by Riet Van Bork

ABSTRACT: Psychometrics the field that is concerned with measurement in psychology heavily relies on the use of statistical models to measure psychological attributes such as cognitive abilities, attitudes, personality traits and mental disorders. Latent variable theory and psychological network theory are two different psychometric theories that diverge in how observable behaviors are related to each other and to the attribute that is being measured. While the theories are different, the statistical models that are used in these two frameworks are statistically similar and in some cases even equivalent. A causal interpretation of these models can help disentangle network and latent variable models. To further the comparison of network models and latent variable models, I investigate different forms of model simplicity that not only consider their differences as statistical models, but also account for their differences as causal models.