Literature DB >> 23261139

Factor mixture model of anxiety sensitivity and anxiety psychopathology vulnerability.

Amit Bernstein1, Timothy R Stickle, Norman B Schmidt.   

Abstract

BACKGROUND: The purpose of the present study was to shed light on the latent structure and nature of individual differences in anxiety sensitivity (AS) and related risk for psychopathology.
METHODS: The present study evaluated the latent structure of AS using factor mixture modeling (FMM; Lubke and Muthén, 2005) and tested the relations between the observed FMM-based model of AS and psychopathology in a large, diverse adult clinical research sample (N=481; 57.6% women; M(SD)(age)=36.6(15.0) years).
RESULTS: Findings showed that a two-class three-factor partially invariant model of AS demonstrated significantly better fit than a one-class dimensional model and more complex multi-class models. As predicted, risk conferred by AS taxonicity was specific to anxiety psychopathology, and not to other forms of psychopathology. LIMITATIONS: The sample was not epidemiologic, self-report and psychiatric interview data were used to index AS and psychopathology, and a cross-sectional design limited inference regarding the directionality of observed relations between AS and anxiety psychopathology.
CONCLUSIONS: Findings are discussed with respect to the nature of AS and related anxiety psychopathology vulnerability specifically, as well as the implications of factor mixture modeling for advancing taxonomy of vulnerability and psychopathology more broadly.
Copyright © 2013. Published by Elsevier B.V.

Entities:  

Mesh:

Year:  2012        PMID: 23261139     DOI: 10.1016/j.jad.2012.11.024

Source DB:  PubMed          Journal:  J Affect Disord        ISSN: 0165-0327            Impact factor:   4.839


  6 in total

1.  Identification of anxiety sensitivity classes and clinical cut-scores in a sample of adult smokers: results from a factor mixture model.

Authors:  Nicholas P Allan; Amanda M Raines; Daniel W Capron; Aaron M Norr; Michael J Zvolensky; Norman B Schmidt
Journal:  J Anxiety Disord       Date:  2014-07-19

2.  Covariate inclusion in factor mixture modeling: Evaluating one-step and three-step approaches under model misspecification and overfitting.

Authors:  Yan Wang; Chunhua Cao; Eunsook Kim
Journal:  Behav Res Methods       Date:  2022-09-12

3.  Mechanisms of change in cognitive behavioral therapy for panic disorder: the unique effects of self-efficacy and anxiety sensitivity.

Authors:  Matthew W Gallagher; Laura A Payne; Kamila S White; Katherine M Shear; Scott W Woods; Jack M Gorman; David H Barlow
Journal:  Behav Res Ther       Date:  2013-09-12

4.  Examining the latent structure of anxiety sensitivity in adolescents using factor mixture modeling.

Authors:  Nicholas P Allan; Laura MacPherson; Kevin C Young; Carl W Lejuez; Norman B Schmidt
Journal:  Psychol Assess       Date:  2014-04-21

5.  Testing Measurement Invariance Across Unobserved Groups: The Role of Covariates in Factor Mixture Modeling.

Authors:  Yan Wang; Eunsook Kim; John M Ferron; Robert F Dedrick; Tony X Tan; Stephen Stark
Journal:  Educ Psychol Meas       Date:  2020-05-28       Impact factor: 2.821

6.  A non-linear dynamical approach to belief revision in cognitive behavioral therapy.

Authors:  David Kronemyer; Alexander Bystritsky
Journal:  Front Comput Neurosci       Date:  2014-05-15       Impact factor: 2.380

  6 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.