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Recovery Accuracy of Measurement Model and Structural Coefficients of Extended Bifactor-( S -1) and ( S·I -1) Models

Bibliographic Data

ID21641973
AuthorsJason C Immekus (0000-0002-4692-1687, University of Louisville, corresponding author), Holmes Finch (Ball State University), Brian F French (0000-0002-3896-7888, Washington State University)
Year2023
Volume30
Issue4
Pages633-644
Publication date2023-07-04
Peer ReviewedYes
Open AccessNo
TypeARTICLE
VenueStructural Equation Modeling: A Multidisciplinary Journal (JOURNAL)
Journal identifiersISSN: 1070-5511 • E-ISSN: 1532-8007
PublisherInforma UK Limited (PUBLISHER • GB)
DOI10.1080/10705511.2022.2137806
OpenAlexW4311326593
LanguageEN
Citations received1
References cited26

Bifactor-(S-1) and (S·I-1) models represent general factor measurement models intended to overcome anomalous results associated with bifactor models. Research has centered on model utility using real data, with little consideration of conditions associated with parameter recovery accuracy. Thus, there is a lack of empirical evidence regarding performance of these models under known conditions. This simulation study investigated the parameter recovery of bifactor-(S-1) and (S·I-1) models under varying conditions based on intelligence and psychological test data, including the regression coefficient between the primary dimension to an external criterion. Furthermore, regression coefficient recovery accuracy was compared to results based on a unidimensional model to examine consequences associated with ignoring an instrument’s multidimensional structure

Econometrics · Linear regression · Regression · Regression analysis · Statistics · Cognitive Abilities and Testing · Computer Science · Functional Brain Connectivity Studies · Mathematics · Mental Health Research Topics

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    Open Access•Samuel Green, Yanyun Yang•Educational and Psychological…•2018

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Unique citing works1
Citations per year0,5
Citation span2024 - 2024 (1)
Citation velocityrecent
Highly citedNo
Citation typesNeutral: 1

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