Testing Gender Invariance of the Hospital Anxiety and Depression Scale Using the Classical Approach and Bayesian Approach

Purpose: Measurement invariance is an important attribute for the Hospital Anxiety and Depression Scale (HADS). Most of the confirmatory factor analysis studies on the HADS adopt the classical maximum likelihood approach. The restrictive assumptions of exact-zero cross-loadings and residual correlations in the classical approach can lead to inadequate model fit and biased parameter estimates. The present study adopted both the classical approach and the alternative Bayesian approach to examine the measurement and structural invariance of the HADS across gender.

Methods: A Chinese sample of 326 males and 427 females was used to examine the two-factor model of the HADS across gender. Configural and scalar invariance of the HADS were evaluated using the classical approach with the robust-weighted least-square estimator and the Bayesian approach with zero-mean, small-variance informative priors to cross-loadings and residual correlations.

Results: Acceptable and excellent model fits were found for the two-factor model under the classical and Bayesian approaches, respectively. The two-factor model displayed scalar invariance across gender using both approaches. In terms of structural invariance, females showed a significantly higher mean in the anxiety factor than males under both approaches.

Conclusion: The HADS demonstrated measurement invariance across gender and appears to be a well-developed instrument for assessment of anxiety and depression. The Bayesian approach is an alternative and flexible tool that could be used in future invariance studies.