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

By Grant B. Morgan, Kari J. Hodge, Kevin E. Wells, Marley W. Watkins. 2015

168 citations recorded by the source corpus.

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.

Subfields: Experimental and Cognitive Psychology, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/21d411454d7aef5f9ae13f50583fdbc61521d48e

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Experimental and Cognitive Psychology

1. Ashley L. Watts
2. I. Waldman
3. Holly E. Poore
4. Gilles E. Gignac
5. Marcin Zajenkowski

In Management Science and Operations Research

1. Ashley L. Watts
2. I. Waldman
3. Gilles E. Gignac
4. Holly E. Poore

Citations in the corpus

• The higher-order model imposes a proportionality constraint: That is why the bifactor model tends to fit better (2016, methodology): “In contrast to previous empirical treatments of the difference between higher-order and bifactor models in relation to model fit (Chen et al., 2006; Morgan et al., 2015; Mulaik & Quartetti, 1997; Murray & Johnson, 2013), the results of this investigation offer, arguably, a very clear explanation…”
• Riskier Tests of the Validity of the Bifactor Model of Psychopathology (2019, background): “Third, numerous simulation studies have demonstrated that bifactor models show preferential fit compared with higher-order and correlated factors models even when the population (“true”) model does not follow a bifactor structure (Greene et al., in press; Maydeu-Olivares & Coffman, 2006; Morgan et ”