The Coronavirus Anxiety Scale : Cross-National Measurement Invariance and Convergent Validity Evidence
| Authors | |
|---|---|
| Year of publication | 2024 |
| Type | Article in Periodical |
| Magazine / Source | Psychological Assessment |
| MU Faculty or unit | |
| Citation | |
| web | https://psycnet.apa.org/record/2024-28534-001 |
| Doi | http://dx.doi.org/10.1037/pas0001270 |
| Keywords | coronavirus anxiety; measurement invariance; alignment; validity; culture |
| Attached files | |
| Description | Coronavirus Anxiety Scale (CAS) is a widely used measure that captures somatic symptoms of coronavirus-related anxiety. In a large-scale collaboration spanning 60 countries (Ntotal = 21,513), we examined the CAS's measurement invariance and assessed the convergent validity of CAS scores in relation to the fear of COVID-19 (FCV-19S) and the satisfaction with life (SWLS-3) scales. We utilized both conventional exact invariance tests and alignment procedures, with results revealing that the single-factor model fit the data well in almost all countries. Partial scalar invariance was supported in a subset of 56 countries. To ensure the robustness of results, given the unbalanced samples, we employed resampling techniques both with and without replacement and found the results were more stable in larger samples. The alignment procedure demonstrated a high degree of measurement invariance with 9% of the parameters exhibiting noninvariance. We also conducted simulations of alignment using the parameters estimated in the current model. Findings demonstrated reliability of the means but indicated challenges in estimating the latent variances. Strong positive correlations between CAS and FCV-19S estimated with all three different approaches were found in most countries. Correlations of CAS and SWLS-3 were weak and negative but significantly differed from zero in several countries. Overall, the study provided support for the measurement invariance of the CAS and offered evidence of its convergent validity while also highlighting issues with variance estimation. |
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