Testing Spearman's hypotheses using a bi-factor model with WAIS-IV/WMS-IV standardization data

Spearman's hypothesis (SH) is a phrase coined by Arthur Jensen, which posits that the size of Black–White mean differences across a group of diverse mental tests is a positive function of each test's loading onto the general intelligence (g) factor. Initially, a correlated vector (CV) approach was used to examine SH, where the results typically confirmed that the magnitude of g loadings were positively correlated with the size of mean group differences in the observed test scores. The CV approach has been heavily criticized by scholars who have argued that a more precise method for examining SH can be better investigated using a multi-group confirmatory factor analysis (MG-CFA). Studies of SH using MG-CFA have been much more equivocal, with results not clearly confirming nor disconfirming SH. In the current study, we argue that a better method for extracting g in both the CV and MG-CFA approaches is to use a bi-factor model. Because non-g factors extracted from a bi-factor approach are independent of g, the bi-factor model allows for a robust examination of the influence of g and non-g factors on group differences on mental test scores. Using co-normed standardization data from the Wechsler Adult Intelligence Scale-Fourth Edition and the Wechsler Memory Scale-Fourth Edition, we examined SH using both CV and MG-CFA procedures. We found support for the weak form of SH in both methods, which suggests that both g and non-g factors were involved in the observed mean score differences between Black and White adults. •Examined Spearman's hypothesis using bi-factor EFA and CFA models•Correlated vectors and multi-group CFA both confirmed Spearman's hypothesis.•Bi-factor CFA models are a robust way to examine Spearman's hypothesis.