The Struggle Is Real: Challenges and Solutions in Theory Building

Conway, Andrew R. A.; Kovács, Kristóf; Hao, Han; Goring, Sara Anne; Schmank, Christopher J.

doi:10.1080/1047840x.2020.1853468

Cited by 3 publications

(1 citation statement)

References 23 publications

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“…We agree that partial correlations can be used for this purpose; however, this was not the rationale behind the Schmank et al (2019) study. Psychometric network modeling offers an alternative approach to investigations of the underlying structure of individual differences in cognitive abilities (i.e., intelligence), without the assumptions that come with reflective latent variable models (Conway et al 2021; see also Goring et al 2019). According to the view of intelligence endorsed by Schmank et al (2019), process overlap theory (Kovacs and Conway 2016), there is no need to assume that (all or some of) the factors in higher-order factor models represent common causes of individual differences in test performance.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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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.

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