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

Christopher J Schmank,Sara Anne Goring,Andrew R A Conway,Kristof Kovacs

doi:10.3390/jintelligence9010008

Christopher J Schmank, Sara Anne Goring + Show 2 more

Open Access

https://doi.org/10.3390/jintelligence9010008

Copy DOI

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.

Highlights

IntroductionIn contrast to Kan et al (2019) and Schmank et al (2019), McFarland finds that latent variable models generally outperform network models
McFarland suggests that previous support for network models reported by Kan et al (2019) and Schmank et al (2019) is limited to analyses based on partial, rather than uncorrected, correlation matrices. This finding calls into question recent claims that psychometric network models provide unique support for theories of intelligence like mutualism and process overlap theory
We argue that compatibility between theories and models of intelligence can, and should, guide model selection

Summary

Introduction

In contrast to Kan et al (2019) and Schmank et al (2019), McFarland finds that latent variable models generally outperform network models. McFarland suggests that previous support for network models reported by Kan et al (2019) and Schmank et al (2019) is limited to analyses based on partial, rather than uncorrected, correlation matrices. If correct, this finding calls into question recent claims that psychometric network models provide unique support for theories of intelligence like mutualism and process overlap theory

Objectives

Methods

Results

Conclusion