Dynamic fit index cutoffs for confirmatory factor analysis models.
Abstract
cutoffs such that derivation of cutoffs is adaptively tailored to the specific model and data characteristics being evaluated. Unlike previously proposed simulation-based techniques, our method removes existing barriers to implementation by providing an open-source, Web based Shiny software application that automates the entire process so that users neither need to manually write any software code nor be knowledgeable about foundations of Monte Carlo simulation. Additionally, we extend fit index cutoff derivations to include sets of cutoffs for multiple levels of misspecification. In doing so, fit indices can more closely resemble their originally intended purpose as effect sizes quantifying misfit rather than improperly functioning as ad hoc hypothesis tests. We also provide an approach specifically designed for the nuances of 1-factor models, which have received surprisingly little attention in the literature despite frequent substantive interests in unidimensionality. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
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Meaningful differences between well-fitting models were evaluated based on good fit according to CFI, RMSEA, and SRMR; ΔCFI ≤ 0.01 for nested models; and/or smallest AIC.
“As utilizing fixed cut offs for evaluating global model fit is not recommended (McNeish and Wolf 2023), meaningful differences between well -fitting models were evaluated based upon the following criteria: (a) exhibit good fit according to CFI, RMSEA, and SRMR indices; (b) demonstrate a Δ CFI value ≤ 0.01 for nested models; and/or (c) display the smallest AIC value.”
From Beyond Primary Assessment on the WISC-V: An Investigation of the Structural Validity of the Ancillary Scores (2025) · cites this paper as McNeish and Wolf 2023
The confirmatory factor analysis results showed that the Czech adaptation of the original 23-item model did not fit the data satisfactorily based on the dynamic fit index cut-offs.
“The confirmatory factor analysis results showed that the Czech adaptation of the origi -nal 23-item model did not fit the data satisfactorily based on the dynamic fit index cut-offs (McNeish & Wolf, 2023).”
From ASSESSING PSYCHOLOGICAL FLEXIBILITY: THE PSYCHOMETRIC PROPERTIES OF THE CZECH TRANSLATION OF THE COMPACT QUESTIONNAIRE (2024) · cites this paper as McNeish & Wolf, 2023
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Scherer, R., & Campos, D. G. (2022) · Educational Research Review · doi:10.1016/j.edurev.2022.100479 · Cited by 63 · see citing works
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Examining an Integrated Factor Structure of Schoolwide MTSS Implementation Measures
Santiago, R. T., Hall, G. J., Garbacz, S. A., Gulbrandson, K., & Albers, C. A. (2025) · Journal of Positive Behavior Interventions · doi:10.1177/10983007241249524 · Cited by 5 · see citing works
Department of Psychology, F. O. A., Ptáček, M., & Jelínek, M. (2024) · Ceskoslovenska Psychologie · doi:10.51561/cspsych.68.1.30 · Cited by 3 · see citing works
- Authors
- McNeish, D., & Wolf, M. G.
- Venue
- Psychological Methods
- Published
- 2023-02
- Volume
- 28
- Issue
- 1
- Pages
- 61-88
- ISSN
- 1082-989X (ISSN-L)
- 1939-1463
- OpenAlex
- W3162227554
- Cited by
- 443
- References
- 0
- Citations by year
- 2026: 117 · 2025: 111 · 2024: 87 · 2023: 73 · 2022: 47 · 2021: 7 · 2020: 1
- FWCI
- 32.69
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Psychological Methods (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Social Sciences › Decision Sciences › Management Science and Operations Research › Psychometric Methodologies and Testing
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Mental Health Research Topics
- Keywords
- confirmatory factor analysis, model misspecification, unidimensionality, one-factor model, effect size, misfit, Shiny app, Monte Carlo simulation, hypothesis testing
- MeSH terms
- Computer Simulation, Factor Analysis, Statistical, Humans, Monte Carlo Method, Software, Reproducibility of Results
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