Hierarchical structure of dysfunctional beliefs in obsessive-compulsive disorder

Cogn Behav Ther. 2005;34(4):216-28. doi: 10.1080/16506070510041167.

Abstract

The Obsessive Beliefs Questionnaire was developed as a comprehensive measure of dysfunctional beliefs, which cognitive models consider to be etiologically related to obsessive-compulsive disorder. Obsessive Beliefs Questionnaire subscales tend to be highly correlated, which raises the question of whether obsessive-compulsive-related beliefs are hierarchically structured, consisting of lower-order factors loading on 1 or more higher-order factors. To investigate the nature and relative importance of these factors, a hierarchical factor analysis was conducted (n = 202 obsessive-compulsive disorder patients), using a Schmid-Leiman transformation. Results indicated a higher-order (general factor) and 3 lower-order factors: (i) responsibility and overestimation of threat, (ii) perfectionism and intolerance of uncertainty and (iii) importance and control of thoughts. The high-order factor accounted for more variance in Obsessive Beliefs Questionnaire scores (22%) than did the lower-order factors (6-7%), thereby underscoring the importance of the higher-order factor. Despite the importance of the higher-order factor, the lower-order factors significantly predicted unique variance in measures of obsessive-compulsive symptoms, including severity ratings of compulsions. These finding suggest that cognitive models of obsessive-compulsive disorder should take into consideration the hierarchic structure of obsessive-compulsive-related beliefs.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adult
  • Analysis of Variance
  • Culture*
  • Factor Analysis, Statistical
  • Female
  • Humans
  • Male
  • Middle Aged
  • Obsessive-Compulsive Disorder / diagnosis
  • Obsessive-Compulsive Disorder / psychology*
  • Outcome Assessment, Health Care / statistics & numerical data
  • Personality Inventory / statistics & numerical data*
  • Psychometrics / statistics & numerical data