Higher-order models versus direct hierarchical models: g as superordinate or breadth factor?

By Gilles E. Gignac. 2008

159 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/3d61ac0e30f7ee5e1ab024c1889fd101bad2e019

How researchers see this paper

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

Citations in the corpus

John Carroll’s Views on Intelligence: Bi-Factor vs. Higher-Order Models (2015, background): “In bi-factor models, the major difference between g and F is the breadth of MVs that they influence [50].”
Using Bifactor Exploratory Structural Equation Modeling to Test for a Continuum Structure of Motivation (2018, background): “However, higher-order factor models rely on highly restrictive implicit assumptions that may not hold in practice and may explain why they often fail to meet minimal requirements of adequate fit (Gignac, 2008; Morin et al., 2016; Reise, 2012).”
Exploratory bifactor analysis of the Wechsler Intelligence Scale for Children—Fifth Edition with the 16 primary and secondary subtests (2015, background): “This posits that the general factor directly influences performance on the subtests instead of indirectly influencing subtest performance through full mediation by the first-order factors (Gignac, 2008).”
Wechsler Preschool and Primary Scale of Intelligence—Fourth Edition (2012, background): “Also disappointing was the exclusive presentation of only higher-order measurement models that conceive general intelligence as a superordinate construct (Gignac, 2008) that has influences on subtests fully mediated through the first-order factors.”
The Nature and Dynamics of Relevance and Valence Appraisals: Theoretical Advances and Recent Evidence (2013, background): “However, it often has been observed that different facets load on a general superfactor (Gignac, 2008).”
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 (2015, background): “Bi-factor models have been especially influential in research on human cognitive abilities because they frequently better fit the data than other models [22] and because they instantiate a perspective that differs from that offered by other models [33].”
The higher-order model imposes a proportionality constraint: That is why the bifactor model tends to fit better (2016, methodology): “Thus, only indirectly does the general factor in the higher-order model influence the indicators or subtests (Gignac, 2008; McDonald, 1999).”