Interpreting psychometric models

Abstract

The field of psychometrics aims to develop theories on how to measure psychological constructs through observable behavior. This dissertation focuses on two psychometric theories that differ in how the psychological construct is related to observable behaviors. Latent trait theory understands psychological constructs as underlying common causes of observed behavior that explain the associations between certain behaviors. Alternatively, in the psychological network theory, behaviors correlate because they mutually reinforce each other and the psychological construct refers to the resulting cluster of associated behaviors. These different theories about how to conceptualize psychological constructs and how to relate these constructs to observable behavior can be formally defined in a set of equations and assumptions that make up a psychometric model. The chapters in this dissertation focus on two types of psychometric models: Latent variable models and network models. Part I of the dissertation focuses on the interpretation of the latent variable model. Part II of the dissertation makes a comparison between latent variable models and network models. While psychometric models can be interpreted as representations of a theory about the data-generating mechanism, this is not necessary. Psychometric models are often viewed as mere descriptions of data. This dissertation shows the importance of thinking through the choice of interpreting psychometric models either as a representation of a causal mechanism or as a description of the data and provides insights in the implications of that choice.