Analysing DASS through structural equation modeling

Abstract

Structural Equation Modeling (SEM) is a statistical technique that investigates causal relationships between observed and latent variables. This dissertation provides the theory underlying the SEM framework, with specific attention directed to Muthén (1984)’s categorical variable methodology (CVM) that makes use of the weighted least squares (WLS) estimator to estimate the model parameters of a SEM fitted on observed categorical and ordinal data. SEM is applied to a dataset, obtained from Open-Source Psychometrics Project (2018) which consists of 42-ordinal-items related to the Depression, Anxiety, Stress Scale (DASS) where item value takes values from 1 to 4. The DASS is a clinical assessment tool composed of three self-report scales designed to measure the severity of symptoms common to depression, anxiety, and stress. One of the objectives was to use the DASS data to examine the mental health status in Europe focusing on four countries, namely, Germany, France, Great Britain and Poland. SEM was used to investigate any causal relationship between Depression, Anxiety, and Stress, and to examine the impact of gender and sexual orientation on these latent variables. Initially the study was conducted without considering the different countries present in the data. Subsequently, following testing for measurement invariance, a Multiple Group Analysis SEM was fitted, to determine whether the causal relationships between Depression, Anxiety, Stress, Gender and Sexual Orientation vary across the four countries. Interestingly, in France and Great Britain, LGBTQIA+ affiliation increased the likelihood of experiencing depression, less so in Germany, while in Poland, LGBTQIA+ individuals showed a lower likelihood of experiencing depression.