Construct Validity of the WISC-IV UK With a Referred Irish Sample: CFA With 15 Subtests

Watkins, Marley W.; Canivez, Gary L.; James, Kate; Good, Rebecca; James, Trevor

doi:10.1037/e511062017-001

Cited by 1 publication

(2 citation statements)

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“…Researchers using Monte Carlo methods cannot reasonably include all empirical conditions so the choice of population parameters must be carefully made. Therefore, to provide representation of a subset of applied conditions involving cognitive abilities, we based the generating population parameters on the standardized solutions from published, applied studies [16,[40][41][42]. That is, we took the the reported parameters directly from these studies.…”

Section: Data Generationmentioning

confidence: 99%

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Are Fit Indices Biased in Favor of Bi-Factor Models in Cognitive Ability Research?: A Comparison of Fit in Correlated Factors, Higher-Order, and Bi-Factor Models via Monte Carlo Simulations

et al. 2015

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Bi-factor confirmatory factor models have been influential in research on cognitive abilities because they often better fit the data than correlated factors and higher-order models. They also instantiate a perspective that differs from that offered by other models. Motivated by previous work that hypothesized an inherent statistical bias of fit indices favoring the bi-factor model, we compared the fit of correlated factors, higher-order, and bi-factor models via Monte Carlo methods. When data were sampled from a true bi-factor structure, each of the approximate fit indices was more likely than not to identify the bi-factor solution as the best fitting. When samples were selected from a true multiple correlated factors structure, approximate fit indices were more likely overall to identify the correlated factors solution as the best fitting. In contrast, when samples were generated from a true higher-order structure, approximate fit indices tended to identify the bi-factor solution as best fitting. There was extensive overlap of fit values across the models regardless of true structure. Although one model may fit a given dataset best relative to the other models, each of the models tended to fit the data well in absolute terms. Given this variability, models must also be judged on substantive and conceptual grounds.

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Section: Data Generationmentioning

confidence: 99%

“…Many CFA analyses have specified multiple correlated factors or higher-order models [15][16][17][18][19][20][21][22][23]. Uniquely, Gustafsson and Balke [24] applied what they termed a nested factor model, which was identical to the bi-factor model of Holzinger and Swineford [1].…”

Section: Introductionmentioning

confidence: 99%