The Factor Structure of Cognitive Functioning in Cognitively Healthy Participants: a Meta-Analysis and Meta-Analysis of Individual Participant Data

Abstract

Many neuropsychologists are of the opinion that the multitude of cognitive tests may be grouped into a much smaller number of cognitive domains. However, there is little consensus on how many domains exist, what these domains are, nor on which cognitive tests belong to which domain. This incertitude can be solved by factor analysis, provided that the analysis includes a broad range of cognitive tests that have been administered to a very large number of people. In this article, two such factor analyses were performed, each combining multiple studies. However, because it was not possible to obtain complete multivariate data on more than the most common test variables in the field, not all possible domains were examined here. The first analysis was a factor meta-analysis of correlation matrices combining data of 60,398 healthy participants from 52 studies. Several models from the literature were fitted, of which a version based on the Cattell-Horn-Carroll (CHC) model was found to describe the correlations better than the others. The second analysis was a factor analysis of the Advanced Neuropsychological Diagnostics Infrastructure (ANDI) database, combining scores of 11,881 participants from 54 Dutch and Belgian studies not included in the first meta-analysis. Again, the model fit was better for the CHC model than for other models. Therefore, we conclude that the CHC model best characterizes both cognitive domains and which test belongs to each domain. Therefore, although originally developed in the intelligence literature, the CHC model deserves more attention in neuropsychology. Keywords Factor analysis. Meta-analysis of individual participant data. Meta-analytic SEM. Clinical neuropsychology. Cognitive functioning. Neuropsychological tests. Cattell-horn-Carroll model Electronic supplementary material The online version of this article (

Evidence of Validity for a Newly Developed Digital Cognitive Test Battery

“…In a recent study classifying fluency tests in the CHC framework, Jewsbury and Bowden (2016) found that although strongly related to processing speed and acquired knowledge, performance on tests such as the COWAT and CFT was best described through a separate fluency factor. This factor structure was confirmed by Agelink van Rentergem et al (2020). Following the factor structures proposed by these two studies, we specified five cognitive domains (at the level of "broad abilities" in the CHC vocabulary): processing speed, retrieval fluency, working memory, learning efficiency, and visual-spatial processing.…”

“…Most outcome measures of neuropsychological tests are influenced by demographic factors such as age and education level, which can significantly bias the factor structure if they are unaccounted for Agelink van Rentergem et al (2020). Therefore, the influences of age, highest completed education, and sex on the raw test scores were assessed through separate linear regression models and, if significant at an alpha-level of 0.10, were partialed out using regression-based norming techniques (Testa et al, 2009).…”

“…A competing model that has gained increasing attention in recent years in the neuropsychological field is the Cattell-Horn-Carroll (CHC) model (e.g., Jewsbury et al, 2017;Agelink van Rentergem et al, 2020). The CHC model was originally developed in the intelligence literature as a synthesis of Carroll's three-stratum model and the Cattell-Horn model and has a hierarchical structure (for an extensive review of the history of the CHC model, see McGrew, 2009).…”

“…The most pronounced difference between the CHC model and more traditional neuropsychological models is the absence of a distinct executive functioning factor in the former (Jewsbury et al, 2017). Instead, tests that are traditionally linked to executive functioning are distributed across multiple domains in the CHC model, such as processing speed, fluid reasoning, and retrieval fluency (Jewsbury et al, 2017;Agelink van Rentergem et al, 2020). Floyd et al (2010) argued that executive functioning measures are contaminated by general intelligence to the point that executive functioning as a distinct concept no longer has relevant explanatory power.…”

“…Validity evidence based on the internal structure of the DCTB (American Educational Research Association, 2014), was tested by fitting the test scores to a neuropsychological consensus model and to the CHC model. Based on recent neuropsychological literature (Jewsbury et al, 2017;Agelink van Rentergem et al, 2020), we expected that the CHC model would perform better in terms of model fit than the neuropsychological consensus model.…”