Measurement invariance of the Hopkins Symptoms Checklist: A novel multigroup alignment analytic approach to a large epidemiological sample across eight conflict-affected districts from a nation-wide survey in Sri Lanka

Abstract

Background: The alignment method, a novel psychometric approach, represents a more flexible procedure for establishing measurement invariance in geographically, ethnically, or linguistically diverse samples, especially in large epidemiological surveys. Although the Hopkins Symptoms Checklist (HSCL-25) has been used extensively in the field to assess anxiety and depressive symptoms, questions remain about the comparability of findings when the instrument is applied across regions in large-scale national surveys. Methods: The present study is the first in the field to apply the alignment method to test the structure and measurement invariance of the anxiety and depression dimensions of the HSCL-25 amongst Sri Lankan subpopulations (n = 8456) stratified by geographical regions, levels of past exposure to conflict, and ethnic composition. Results: Multigroup CFA analysis yielded non-converging models requiring substantial modifications to the models. As a result, multigroup alignment analysis was applied and the results supported the bifactorial structure and measurement invariance of the HSCL-25 across eight (severe and moderate) conflict-affected districts. The alignment analysis based on a good-fitting configural model yielded a metric non-invariance of 22.22% and scalar non-invariance of 5.88% (both under the established 25% threshold). The bifactorial model outperformed the tripartite and other models. In comparison to the anxiety items, the depressive items showed higher levels of metric non-invariance across districts. Conclusions: Our findings demonstrate the methodological feasibility of applying the alignment method to test the structure and invariance of the HSCL across ethnically diverse populations living in conflict-affected districts in Sri Lanka. Further studies are needed to examine ethnicity and language factors more critically Show more