Investigating the Structure of Intelligence Using Latent Variable and Psychometric Network Modeling: A Commentary and Reanalysis

Abstract

In a recent publication in the Journal of Intelligence, Dennis McFarland mischaracterized previous research using latent variable and psychometric network modeling to investigate the structure of intelligence. Misconceptions presented by McFarland are identified and discussed. We reiterate and clarify the goal of our previous research on network models, which is to improve compatibility between psychological theories and statistical models of intelligence. WAIS-IV data provided by McFarland were reanalyzed using latent variable and psychometric network modeling. The results are consistent with our previous study and show that a latent variable model and a network model both provide an adequate fit to the WAIS-IV. We therefore argue that model preference should be determined by theory compatibility. Theories of intelligence that posit a general mental ability (general intelligence) are compatible with latent variable models. More recent approaches, such as mutualism and process overlap theory, reject the notion of general mental ability and are therefore more compatible with network models, which depict the structure of intelligence as an interconnected network of cognitive processes sampled by a battery of tests. We emphasize the importance of compatibility between theories and models in scientific research on intelligence.

Exploratory Graph Analysis of the Strengths and Difficulties Questionnaire for Aboriginal and/or Torres Strait Islander Children

“…While (Ribeiro Santiago et al, 2021a) recently compared factorial structures identified from EGA with traditional SDQ factorial structures in Australian children, to the best of our knowledge, this is the first study to directly compare network structures with traditional SDQ factorial structures across any population. While network models have been recently shown to be superior to factor models when evaluating intelligence (Schmank et al, 2021) and even the concentration and empathy of children , to the best of our knowledge, this is also the first study to compare network and factor models to evaluate the SEWB of children. Finally, these results also expand beyond Aboriginal and/or Torres Strait Islander children by suggesting that the construct validity of the five-factor SDQ structure for non-Western cultures is not given, especially for Indigenous groups in which the western concepts of "mental health" are a poor representation of SEWB (Nelson and Wilson, 2017).…”

“…Additionally, the use of network models (instead of factor model) to evaluate behaviours believed to constitute reciprocally reinforcing causal relations instead of factor models (that assumes a latent trait as the common cause of these behaviors) has recently generated debate in several psychological areas such as intelligence (Schmank et al, 2021), loneliness (Chvojková, 2021) or concentration, and empathy of children . For example, in the field of intelligence, Kan et al (2020) and Schmank et al (2021) showed that network models better explained item responses to intelligence tests than factor models and were more aligned with modern theories of intelligence, such as mutualism and process overlap theory (Kovacs and Conway, 2016). In summary, the use of network models and a comparison with latent variable models answer for calls that "matching theoretical and statistical models is necessary to bring data to bear on theories" in psychology (Fried, 2020).…”

Bilingualism caught in a net. A new approach to understanding the complexity of bilingual experience

“…In the network analysis, we followed the guidelines by Kan and colleagues (see also McFarland, 2020;Schmank et al, 2019Schmank et al, , 2021. First, the full partial correlation matrix was derived from the zero-order correlation matrix.…”

Bridging Brain and Cognition: A Multilayer Network Analysis of Brain Structural Covariance and General Intelligence in a Developmental Sample of Struggling Learners

“…Although mutualism is an inherently dynamical theory, therefore requiring longitudinal data to adequately assess, these results are compatible with a cross-sectional interpretation of mutualism’s assumption of (mostly) positive associations among cognitive abilities. Moreover, it must be noted that latent variable ( Kline 2015 ) and network models should not solely be compared using goodness-of-fit indices, but should instead be judged based on “theory compatibility” (see Schmank et al 2021 ) and the proposed “data-generating mechanism” ( van Bork et al 2019 ).…”