Measurement invariance underlies construct validity generalization in psychology and must be demonstrated prior to any cross-population comparison of means and validity correlations. The purpose of this study was to evaluate the measurement invariance of the Wechsler Intelligence Scale for Children-Fifth Edition (WISC-V) across Australia and New Zealand (A&NZ) versus the U.S. normative samples. The WISC-V is the most widely used assessment of intelligence in children. Participants were census matched, nationally representative samples from A&NZ (n = 528) and the United States (n = 2,200) who completed the WISC-V standardization version. Baseline model estimation was conducted to ensure the same model showed acceptable fit in both samples separately. Measurement invariance was then examined across A&NZ and United States. The five-factor scoring model described in the test manual showed excellent fit in both samples. Results showed that the WISC-V demonstrated strict metric measurement invariance across the A&NZ and U.S. samples. Further, the results were consistent with the Cattell-Horn-Carroll (CHC) framework of cognitive abilities, indicating the generalizability of cognitive abilities across cultures. Small but significant differences in visual spatial latent means were found across females, highlighting the importance of local normative data. These findings suggest that the WISC-V scores can be meaningfully compared across A&NZ and United States and that the constructs, which align with CHC theory, and associated construct validity research, generalize across countries.
Public Significance StatementThis study examined the generalizability of the WISC-V, the most widely used intelligence test for children, across nationally representative samples from A&NZ and the United States. The results suggest that WISC-V scores can be meaningfully compared across A&NZ and U.S. children and that the psychological constructs measured by the WISC-V generalize across countries. However, small mean score differences highlight the importance of local normative data.