What Can We Learn from “Not Much More than g”?

Murphy, Kevin R.

doi:10.3390/jintelligence5010008

Cited by 13 publications

(14 citation statements)

References 41 publications

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“…Information deeply entrenched in memory allows for rapid processing (Baddeley, ), but the processor is the same as that used by reflective processing. Prior research using the CRT may have found incremental predictive power over g due to specific abilities such as cognitive speededness and quantitative reasoning; previous research demonstrates that specific abilities have utility in providing incremental predictive power over g (e.g., Lievens & Reeve, ; Murphy, ; Park, Lubinski, & Benbow, ). Thus, using pre‐existing cognitive constructs (i.e., mental abilities) to understand causal antecedents of decision making may be the most effective path to explain cognitive bias and decision‐making processes.…”

Section: Discussionmentioning

confidence: 99%

“…previous research demonstrates that specific abilities have utility in providing incremental predictive power over g (e.g., Lievens & Reeve, 2012;Murphy, 2017;Park, Lubinski, & Benbow, 2008). Thus, using pre-existing cognitive constructs (i.e., mental abilities) to understand causal antecedents of decision making may be the most effective path to explain cognitive bias and decision-making processes.…”

Section: Figurementioning

confidence: 99%

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Assessing the validity of inferences from scores on the cognitive reflection test

Blacksmith

Yang

Behrend

et al. 2019

Behavioral Decision Making

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Decision‐making researchers purport that a novel cognitive ability construct, cognitive reflection, explains variance in intuitive thinking processes that traditional mental ability constructs do not. However, researchers have questioned the validity of the primary measure because of poor construct conceptualization and lack of validity studies. Prior studies have not adequately aligned the analytical techniques with the theoretical basis of the construct, dual‐processing theory of reasoning. The present study assessed the validity of inferences drawn from the cognitive reflection test (CRT) scores. We analyzed response processes with an item response tree model, a method that aligns with the dual‐processing theory in order to interpret CRT scores. Findings indicate that the intuitive and reflective factors that the test purportedly measures were indistinguishable. Exploratory, post hoc analyses demonstrate that CRT scores are most likely capturing mental abilities. We suggest that future researchers recognize and distinguish between individual differences in cognitive abilities and cognitive processes.

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Section: Discussionmentioning

confidence: 99%

Section: Figurementioning

confidence: 99%

Assessing the validity of inferences from scores on the cognitive reflection test

Blacksmith

Yang

Behrend

et al. 2019

Behavioral Decision Making

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“…However, there are several plausible explanations for how observed variables could be positively correlated even in the absence of such an underlying, causal general factor [49,50,51,52,53]. Although a general ability construct provides an extremely effective and efficient predictor of performance across a wide variety of domains [15,16,17], it does not appear to have significantly advanced our understanding of the manner in which cognitive ability relates to important practical outcomes (i.e., “ g is poorly defined and poorly understood” [54], p. 3). A set of less parsimonious—but more substantively interpretable—specific abilities could provide the alternative required to develop a better articulated theory of how cognitive ability relates to practical outcomes, and in so doing, further enhance the value of specific abilities as predictors of these same outcomes.…”

Section: Discussionmentioning

confidence: 99%

Aligning Predictor-Criterion Bandwidths: Specific Abilities as Predictors of Specific Performance

Wee

2018

J. Intell.

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The purpose of the current study is to compare the extent to which general and specific abilities predict academic performances that are also varied in breadth (i.e., general performance and specific performance). The general and specific constructs were assumed to vary only in breadth, not order, and two data analytic approaches (i.e., structural equation modeling [SEM] and relative weights analysis) consistent with this theoretical assumption were compared. Conclusions regarding the relative importance of general and specific abilities differed based on data analytic approaches. The SEM approach identified general ability as the strongest and only significant predictor of general academic performance, with neither general nor specific abilities predicting any of the specific subject grade residuals. The relative weights analysis identified verbal reasoning as contributing more than general ability, or other specific abilities, to the explained variance in general academic performance. Verbal reasoning also contributed to most of the explained variance in each of the specific subject grades. These results do not provide support for the utility of predictor-criterion alignment, but they do provide evidence that both general and specific abilities can serve as useful predictors of performance.

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“…Reflecting this shift, Kell and Lang [4] summarize that “recent studies have variously demonstrated the importance of narrower abilities above and beyond g .” (p. 11). However, this debate is far from settled [8].…”

Section: Introductionmentioning

confidence: 99%

Bifactor Models for Predicting Criteria by General and Specific Factors: Problems of Nonidentifiability and Alternative Solutions

et al. 2018

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The bifactor model is a widely applied model to analyze general and specific abilities. Extensions of bifactor models additionally include criterion variables. In such extended bifactor models, the general and specific factors can be correlated with criterion variables. Moreover, the influence of general and specific factors on criterion variables can be scrutinized in latent multiple regression models that are built on bifactor measurement models. This study employs an extended bifactor model to predict mathematics and English grades by three facets of intelligence (number series, verbal analogies, and unfolding). We show that, if the observed variables do not differ in their loadings, extended bifactor models are not identified and not applicable. Moreover, we reveal that standard errors of regression weights in extended bifactor models can be very large and, thus, lead to invalid conclusions. A formal proof of the nonidentification is presented. Subsequently, we suggest alternative approaches for predicting criterion variables by general and specific factors. In particular, we illustrate how (1) composite ability factors can be defined in extended first-order factor models and (2) how bifactor(S-1) models can be applied. The differences between first-order factor models and bifactor(S-1) models for predicting criterion variables are discussed in detail and illustrated with the empirical example.

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What Can We Learn from “Not Much More than g”?

Cited by 13 publications

References 41 publications

Assessing the validity of inferences from scores on the cognitive reflection test

Assessing the validity of inferences from scores on the cognitive reflection test

Aligning Predictor-Criterion Bandwidths: Specific Abilities as Predictors of Specific Performance

Bifactor Models for Predicting Criteria by General and Specific Factors: Problems of Nonidentifiability and Alternative Solutions

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