Use of factor mixture modeling to capture Spearman's law of diminishing returns

Abstract

Spearman's law of diminishing returns (SLODR) posits that at higher levels of general cognitive ability the general factor ( g) performs less well in explaining individual differences in cognitive test performance. Research has generally supported SLODR, but previous research has required the a priori division of respondents into separate ability or IQ groups. The present study sought to obviate this limitation through the use of factor mixture modeling to investigate SLODR in the Kaufman Assessment Battery for Children-Second Edition (KABC-II). A second-order confirmatory factor model was modeled as a within-class factor structure. The fit and parameter estimates of several models with varying number of classes and factorial invariance restrictions were compared. Given SLODR, a predictable pattern of findings should emerge when factor mixture modeling is applied. Our results were consistent with these SLODR-based predictions, most notably the g factor variance was less in higher g mean classes. Use of factor mixture modeling was found to provide support for SLODR while improving the model used to investigate SLODR.