Sample (material)

Construct (python library)

Population

Mathematics

Latent variable

Categorical variable

Statistics

Mathematical analysis

Econometrics

Cognitive psychology

Psychology

Variable (mathematics)

Chemistry

Programming language

Computer science

Cluster analysis

Chromatography

Mixture model

Latent variable model

Structural equation modeling

Medicine

Environmental health

Psychological Medicine

Taxometric procedures, model-based clustering and latent variable mixture modeling (LVMM) are statistical methods that use the inter-relationships of observed symptoms or questionnaire items to investigate empirically whether the underlying psychiatric or psychological construct is dimensional or categorical. In this review we show why the results of such an investigation depend on the characteristics of the observed symptoms (e.g. symptom prevalence in the sample) and of the sample (e.g. clinical, population sample). Furthermore, the three methods differ with respect to their assumptions and therefore require different types of a priori knowledge about the observed symptoms and their inter-relationships. We argue that the choice of method should optimally match and make use of the existing knowledge about the data that are analyzed.

Does nature have joints worth carving? A discussion of taxometrics, model-based clustering and latent variable mixture modeling