A comparative analysis of structural equation modeling and traditional regression analysis in investigating factors affecting health-related quality of life in general Thai population

Previous studies identified individual characteristics as factors affecting health-related quality of life (HRQoL) scores. Nevertheless, most studies employed univariate or multiple regression analyses, which have limitations in handling confounding variables and measurement errors across multiple dependent variables. This study therefore aimed to assess psychometric properties, investigate factors affecting HRQoL using the World Health Organization Quality of Life — Brief Version (WHOQOL-BREF) and the EuroQol five-dimension, five-level questionnaire (EQ-5D-5L) among general Thai population using structural equation modelling (SEM) and to compare the results with the traditional regression analyses. The study utilized secondary data from the 2023 Thai population norms survey. Face-to-face interviews were conducted with 2,000 adults. However, SEM analysis was performed on data from 1,927 participants, after excluding 73 individuals to meet the assumption of normality. The study adapted Ferran’s model to fit the available data and evaluated measurement properties (i.e., internal consistency and convergent and discriminant validity). It also employed partial least squares SEM (PLS-SEM) to examine structural relationships and model properties through predictive relevance (Q2), effect size (f2), and goodness of fitness (GOF). Age significantly impacted WHOQOL-BREF scores (β = $$\:-$$ 0.264, f2 = 0.037). For EQ-5D-5L, age (β = $$\:-$$ 0.304, f2 = 0.071), occupation (β = $$\:-$$ 0.422, f2 = 0.036), and drug (β = $$\:-$$ 0.288, f2 = 0.052) were key factors. However, traditional regression yielded different results for drug and occupation factors on WHOQOL-BREF and MCS scores. General health perception was the strongest predictor of HRQoL for both models exhibiting acceptable reliability and validity. For most dependent variables explained by the model, predictive power was medium to large, except for the mental component summary, which displayed a small predictive value (Q² = 0.115). Both models demonstrated a high fit (GOF: 0.539 and 0.521 for WHOQOL-BREF and EQ-5D-5L, respectively). Different statistical approaches yield varying results as SEM indicates that age, occupation, and drug significantly influence HRQoL scores, whereas traditional regression finds no significant effects for drug and occupation. Nevertheless, these findings can guide policymakers in allocating resources to targeting population groups with low levels of HRQoL.