Investigating the structure of Intelligence using latent variable and psychometric network modeling: A commentary and reanalysis of McFarland (2020)

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.

Christopher J. Schmank

Statistics Consultant and Instructor/Assistant Professor

My research interests include the impact of psychosocial stress and emotional regulation on various cognitive abilities (i.e., processing speed, rationality, and language production). My additional skills include statistical modeling techniques using latent variable and/or psychometric network analyses. I am also experienced in user experience strategy and research including A/B testing, rapid prototyping, and competitive analyses.