Factor Structure and Measurement Invariance of the Irrational Beliefs Inventory for University Students in the United States and Iceland

We used multigroup confirmatory factor analysis to evaluate the five-factor measurement model underlying the 50-item Irrational Beliefs Inventory (IBI) in samples of university students in the United States ( n =827) and Iceland ( n =720). Global model fit was marginally acceptable in each sample. F...

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Bibliographic Details
Main Authors: Gudmundur T. Heimisson, Dedrick, Robert F.
Format: Article in Journal/Newspaper
Language:unknown
Published: SAGE Journals 2020
Subjects:
Online Access:https://dx.doi.org/10.25384/sage.c.5214743.v1
https://sage.figshare.com/collections/Factor_Structure_and_Measurement_Invariance_of_the_Irrational_Beliefs_Inventory_for_University_Students_in_the_United_States_and_Iceland/5214743/1
Description
Summary:We used multigroup confirmatory factor analysis to evaluate the five-factor measurement model underlying the 50-item Irrational Beliefs Inventory (IBI) in samples of university students in the United States ( n =827) and Iceland ( n =720). Global model fit was marginally acceptable in each sample. Further analyses identified several sources of model misfit that included weak factor loadings, several item pairs with correlated errors, and items with loadings on more than one factor. Cronbach’s alpha reliability estimates for the five factors were similar for the U.S. and Icelandic samples, and comparable to those reported by the developers of the IBI. Measurement invariance testing supported configural (same form) and metric invariance (equal loadings), but identified only 20 items that had invariant item intercepts across the U.S. and Icelandic groups. Given the finding of partial measurement invariance, we offer caution when using the IBI to make group comparisons for U.S. and Icelandic samples. Recommendations are proposed for ongoing psychometric evaluations of the IBI that would identify strengths of the IBI and items that, if revised or deleted, may improve the quality of the measure for research and clinical purposes.