Denovan, A ORCID: https://orcid.org/0000-0002-9082-7225 and Dagnall, N
ORCID: https://orcid.org/0000-0003-0657-7604
(2025)
Using exploratory structural equation modelling to examine the psychometric properties of the 10-item Perceived Stress Scale.
Current Psychology.
ISSN 1046-1310
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Abstract
Investigators frequently use the Perceived Stress Scale (PSS-10) to evaluate the extent to which external demands exceed perceived capacity to manage pressure. Analysts utilizing confirmatory factor analysis (CFA) assert that a bifactor model best fits PSS-10 data, though support exists for a two-factor conceptualisation. Since theorists contend that CFA has limitations, this paper assessed whether exploratory structural equation modelling (ESEM) provided a superior factorial solution. Accordingly, this research assessed the adequacy of two-factor vs. bifactor models using CFA and ESEM. Additionally, analyses tested convergent validity, invariance, and predictive validity in relation to well-being outcomes (Life Satisfaction and Somatic Complaints). In Study 1, 1556 (802 males, 754 females) UK-based participants completed the PSS-10 at time points six months apart. In Study 2, 1630 (838 males, 784 females, eight non-binary) UK-based participants completed the PSS-10 alongside measures of Life Satisfaction and Somatic Complaints. Study 1, using latent modelling, found that the two-factor ESEM model (containing Distress and Counter-Stress factors) produced superior fit (vs. CFA and bifactor solutions). In Study 2, structural equation modelling revealed acceptable predictive validity for the two-factor solution; Distress predicted Somatic Complaints and Counter-Stress predicted Life Satisfaction. Gender (Study 1 and 2) and time (Study 1) demonstrated measurement invariance. Latent means across studies indicated that females (vs. males) scored higher on Distress. Overall, ESEM estimated the PSS-10 more accurately. Findings supported the utility of Distress and Counter-Stress factors for predicting well-being indicators. Future research is necessary to consider this distinction in relation to allied health outcomes.
Impact and Reach
Statistics
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