Factor mixture analysis of DSM-IV symptoms of major depression in a treatment seeking clinical population

Abstract

Background: There is a paucity of empirical studies examining the latent structure of depression symptoms within clinical populations.

Objective: The current study aimed to evaluate the latent structure of DSM-IV major depression utilising dimensional, categorical, and hybrid models of dimensional and categorical latent variables in a large treatment-seeking population.

Methods: Latent class models, latent factor models, and factor mixture models were fit to data from 1165 patients currently undergoing online treatment for depression.

Results: Model fit statistics indicated that a two-factor model fit the data the best when compared to a one-factor model, latent class models, and factor mixture models.

Conclusions: The current study suggests that the structure of depression consists of two underlying dimensions of depression severity when compared to categorical or a mixture of both categorical and dimensional structures. For clinical samples, the two latent factors represent psychological and somatic symptoms.