Factor invariance between genders on the Wechsler Intelligence Scale for Children–Fifth Edition

Construct validity of the Wechsler Intelligence Scale For Children – FifthUKEdition: Exploratory and confirmatory factor analyses of the 16 primary and secondary subtests

“…Also, the present analyses were of the standardization sample and thus may not generalize to other populations such as clinical groups or independent samples of non‐clinical groups, participants of different races/ethnicities, or language minorities. While structural invariance across gender has been reported for the US WISC‐V (H. Chen et al ., ), bifactor models and models with fewer group factors were not examined so invariance of alternative models should also be examined across gender.…”

“…However, one study (H. Chen, Zhang, Raiford, Zhu, & Weiss, ) reported factorial invariance of the final publisher preferred WISC‐V higher‐order model with five‐group factors across gender, although it did not examine invariance for rival higher‐order or bifactor models. Likewise, Reynolds and Keith () reported WISC‐V invariance across age groups, but the model they examined for invariance was an oblique five‐factor model, which ignores general intelligence altogether.…”

The importance of measurement invariance in neurocognitive ability testing

“…If one's goal is to measure VC, females are disadvantaged because of this uniform bias on the Information subtest. In a recent study, the authors did conclude that the WISC-IV showed invariance with respect to gender (Chen et al, 2015), highlighting that one should not overgeneralize (non) invariance but rather study it for all relevant versions and contexts.…”

“…With age-based norms, this might mean that two differently aged persons with the same level of cognitive functioning would obtain different IQ scores, raising the somewhat unorthodox question whether it always is sensible to actually use age-based norms. Wais-iV educational levels in spain partial Chen, Zhang, raiford, Zhu, and Weiss (2015) WisC-iV Males and females in the u.s. yes Chen and Zhu (2012) WiC-iV normative vs. clinical samples u.s. partial Dolan (2000) WisC-r Blacks and Whites in the u.s. yes Dolan, roorda, and Wicherts (2004) JaT south african ethnic groups no Dolan et al (2004) gaTB ethnic groups in the netherlands no Dolan et al (2006) Wais-iii Males vs. females in spain partial niileksela, reynolds, and Kaufman (2013) Wais-iV age groups in the u.s. partial Tommasi et al (2015) Wais-r educational levels in italy yes reynolds, ingram, seeley, and newby (2013) Wais-iV normal vs. intellectual disabilities u.s. partial raKiT ethnic groups in the netherlands no Wicherts et al (2004) Wais Cohorts in the netherlands no…”

"Complementary exploratory and confirmatory factor analyses of the French WISC-V: Analyses based on the standardization sample": Correction to Lecerf and Canivez (2018).

“…Third, the publisher selected the best fitting model based solely on the 2 difference for all models with four and five first-order factors (Wechsler, 2016b, Table 5.3, p. 69). However, according to Cheung and Rensvold (2002) and Chen (2007), ⌬CFI Ͼ .01 and ⌬RMSEA Ͼ .015 indicate meaningful differences between models. Nor did the publisher use Akaike's information criterion (AIC) as suggested by Kline (2016).…”