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

Abstract

BackgroundThere is a paucity of empirical studies examining the latent structure of depression symptoms within clinical populations. ObjectiveThe 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. MethodsLatent class models, latent factor models, and factor mixture models were fit to data from 1165 patients currently undergoing online treatment for depression. ResultsModel 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. ConclusionsThe 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.