Factor mixture analysis of DSM-IV symptoms of major depression in a treatment seeking clinical population.
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.
School of Psychiatry, University of New South Wales, Sydney, Australia. matthews@unsw.edu.au
Matthew
M
Sunderland
Natacha
N
Carragher
Nora
N
Wong
Gavin
G
Andrews
D000328:Adult; D003863:Depression / Q000175:diagnosis* / Q000523:psychology; D003865:Major Depressive Disorder / Q000175:diagnosis* / Q000523:psychology; D039721:Diagnostic and Statistical Manual of Mental Disorders; D005163:Factor Analysis, Statistical; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D008960:Models, Psychological