Abandon All Thumbs Ye Who Model: An Up-to-Date Tutorial on Fitting CFA Models

Confirmatory factor analysis (CFA) is the bread and butter of many psychology and social science researchers. To evaluate how the data aligns with the instruments’ hypothesised structure, researchers often rely on the so-called rules of thumb, model fit cutoffs, such as RMSEA ≤ .05 or CFI ≥ .95, whose validity has been widely criticised. The cutoffs have been derived from experience or based on simulation studies. However, it is impossible to use universal cutoffs for models and data with characteristics that differ from those used in prior experience or simulations. In this paper, we offer an approach to model fit assessment grounded in conceptual understanding and situational character of the fit measures. We hope that this tutorial, together with an R script with illustrative examples and a model-fitting decision tree, may inspire fellow researchers to conduct more in-depth fit assessments and prevent the accumulation of bad-fitting models in the literature.

Authors

Publication Details

Published
2025-09-26
DOI
https://doi.org/10.31219/osf.io/zkp54_v2
Primary Topic
Advanced Statistical Modeling Techniques
Type
article
Field-Weighted Citation Impact
0.00

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article

Abandon All Thumbs Ye Who Model: An Up-to-Date Tutorial on Fitting CFA Models

Edita Chvojka, Anna Literová, Petr Palíšek
Advanced Statistical Modeling Techniques
article

Abandon All Thumbs Ye Who Model: An Up-to-Date Tutorial on Fitting CFA Models

Edita Chvojka, Anna Literová, Petr Palíšek
article en

Abstract

Confirmatory factor analysis (CFA) is the bread and butter of many psychology and social science researchers. To evaluate how the data aligns with the instruments’ hypothesised structure, researchers often rely on the so-called rules of thumb, model fit cutoffs, such as RMSEA ≤ .05 or CFI ≥ .95, whose validity has been widely criticised. The cutoffs have been derived from experience or based on simulation studies. However, it is impossible to use universal cutoffs for models and data with characteristics that differ from those used in prior experience or simulations. In this paper, we offer an approach to model fit assessment grounded in conceptual understanding and situational character of the fit measures. We hope that this tutorial, together with an R script with illustrative examples and a model-fitting decision tree, may inspire fellow researchers to conduct more in-depth fit assessments and prevent the accumulation of bad-fitting models in the literature.

Grantová Agentura České Republiky
Openalex Percentile: Top 70%
Advanced Statistical Modeling Techniques
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