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Stress is a significant contributor to physical and mental health. However, there is considerable variability in response to stress, underscoring the role of stress perception in health outcomes. Perceptions of stress are influenced by socio-demographic and cultural factors. The Perceived Stress Scale (PSS) has been used extensively to assess stress among different populations and for cross-cultural comparisons. Given its wide-ranging application, the rigorous assessment of its psychometric properties is necessary to solidify its standing as a dependable measure. The scale was originally conceptualized as unidimensional but there has been controversy regarding its factor structure, leading to various factor models. Conclusions about the structure of the PSS were largely based on fit indices in confirmatory factor analysis and often did not go beyond fit indices. In addition, classical test theory indices are sample dependent and it is not surprising that instruments can exhibit different properties in different samples. Over reliance on fit indices can lead to overly simplistic interpretations of underlying dimensionality. Hence, the current study extends this research by using item response theory and examining ancillary bifactor indices to determine the amount of variance explained by the total scale and subscales. Participants were students (n = 322) at a South African institution who completed the PSS and a demographic questionnaire. Fit indices in confirmatory factor analysis indicated that a one-factor model, a bifactor model and a correlated-two factor model fit the data to an acceptable degree (GFI > 0.95, CFI > 0.90, RMSEA

The dimensionality is one of the most investigated concepts in the psychological assessment, and there are many ways to determine the dimensionality of a measured construct. The Automated Item Selection Procedure (AISP) and the DETECT are non-parametric methods aiming to determine the factorial structure of a data set. In the current study, dimensionality results provided by the two methods were compared based on the original factorial structure defined by the scale developers. For the comparison of the two methods, the data was obtained by implementing a scale measuring academic dishonesty levels of bachelor students. The scale was conducted on junior students studying at a public and a private university. The dataset was analyzed by using the AISP and DETECT analyses. The "mokken" and "sirt" packages on the R program were utilized for the AISP and DETECT analyses, respectively. The similarities and differences between the findings provided by the methods were analyzed depending on the original factor structure of the scale verified by the scale developers. [ABSTRACT FROM AUTHOR]

Purpose: Mokken scale analysis (MSA) is an attractive scaling procedure for ordinal data. MSA is frequently used in health-related quality of life research. Two of MSA's prime features are the scalability coefficients and the automated item selection procedure (AISP). The AISP partitions a (large) set of items into scales based on the observed item scores; the resulting scales can be used as measurement instruments. There exist two issues in MSA: First, point estimates, standard errors, and test statistics for scalability coefficients are inappropriate for clustered item scores, which are omnipresent in quality of life research data. Second, the AISP insufficiently takes sampling fluctuation of Mokken's scalability coefficients into account. Methods: We solved both issues by providing point estimates and standard errors for the scalability coefficients for clustered data and by implementing a Wald-based significance test in the AISP algorithm, resulting in a test-guided AISP (T-AISP), that is available for both nonclustered and clustered test scores. Results: We integrated the T-AISP into a two-step, test-guided MSA for scale construction, to guide the analysis for nonclustered and clustered data. The first step is performing a T-AISP and select the final scale(s). For clustered data, within-group dependency is investigated on the final scale(s). In the second step, the strength of the scale(s) is determined and further analyses are performed. The procedure was demonstrated on clustered item scores obtained from administering a questionnaire on quality of life in schools to 639 students nested in 30 classrooms. Conclusions: We developed a two-step, test-guided MSA for scale construction that takes into account sample fluctuation of all scalability coefficients and that can be applied to item scores obtained by a nonclustered or clustered sampling design. [ABSTRACT FROM AUTHOR]