Reliability and factorial validity of the Core Self-Evaluations Scale: A meta-analytic investigation of wording effects

Abstract

The Core Self-Evaluations Scale (CSES) measures a broad personality trait reflecting individuals’ self-appraisals of their worth, capabilities, and control of their lives. Although the CSES was designed to capture a single trait, factor analytic studies often found more complex measurement structures. These either referred to different content facets or methodological artifacts due to the item wording. The present random-effects meta-analysis summarized correlation matrices from 41 samples including 28,018 respondents. After accounting for acquiescent responding, meta-analytic confirmatory factor analyses revealed a single common factor for all items. The factor was highly reliable (ω = .87) and demonstrated metric measurement invariance across English, German, and Spanish language versions as well as cultural tendencies of individualism and flexibility. However, Chinese translations exhibited substantially lower factor loadings. These results corroborate the use of the CSES as a unidimensional measure, albeit systematic investigations of measurement invariance are recommended before its use in cross-cultural research.

Short-Scale Construction Using Meta-Analytic Ant Colony Optimization

“…Figure 4 shows that the pooled correlations and factor loadings of the acquiescence model were similar, regardless of whether we controlled for the study quality or not. The average difference in factor loadings between the two analyses was small ( M = −0.012; range: −0.029 to 0.002), indicating that differences in the quality of scientific reporting did not affect the statistics that underlie the results of the present meta-analysis (for quite similar results see Gnambs & Schroeders, 2024).…”

“…Therefore, we weighted each correlation matrix by the inverse of the risk of bias score using a Gaussian kernel function (see also Hildebrandt et al, 2016), which means, high-quality studies were included in the recalculations with a proportionally larger sample size. Put differently, we reran the MASEM analyses for a set of hypothetical samples of the highest quality (see the supplement information in Gnambs & Schroeders, 2024, for details on this approach).…”