Masking misfit in confirmatory factor analysis by increasing unique variances
A cautionary note on the usefulness of cutoff values of fit indices.
Bibliographic Data
| ID | 23330935 |
|---|---|
| Authors | Moritz Heene (0009-0007-4956-2417, University of Graz), Sven Hilbert (0000-0001-5808-8357, Ludwig-Maximilians-Universität München), Clemens Draxler (0000-0001-8692-9897, Ludwig-Maximilians-Universität München), Matthias Ziegler (0000-0003-4994-9519, Humboldt-Universität zu Berlin), Markus Bühner (0000-0002-0597-8708, University of Graz) |
| Year | 2011 |
| Volume | 16 |
| Issue | 3 |
| Pages | 319-336 |
| Publication date | 2011-09-01 |
| Peer Reviewed | Yes |
| Open Access | No |
| Type | ARTICLE |
| Venue | Psychological Methods (JOURNAL) |
| Journal identifiers | ISSN: 1082-989X • E-ISSN: 1939-1463 |
| Publisher | American Psychological Association (APA) (PUBLISHER) |
| DOI | 10.1037/a0024917 |
| PMID | 21843002 |
| OpenAlex | W2152225530 |
| Language | EN |
| Citations received | 106 |
| References cited | 30 |
Fit indices are widely used in order to test the model fit for structural equation models. In a highly influential study, Hu and Bentler (1999) showed that certain cutoff values for these indices could be derived, which, over time, has led to the reification of these suggested thresholds as "golden rules" for establishing the fit or other aspects of structural equation models. The current study shows how differences in unique variances influence the value of the global chi-square model test and the most commonly used fit indices: Root-mean-square error of approximation, standardized root-mean-square residual, and the comparative fit index. Using data simulation, the authors illustrate how the value of the chi-square test, the root-mean-square error of approximation, and the standardized root-mean-square residual are decreased when unique variances are increased although model misspecification is present. For a broader understanding of the phenomenon, the authors used different sample sizes, number of observed variables per factor, and types of misspecification. A theoretical explanation is provided, and implications for the application of structural equation modeling are discussed.
Algorithm · Confirmatory factor analysis · Cutoff · Econometrics · Goodness of fit · Mean squared error · Residual · Root mean square · Sample size determination · Square root · Statistics · Structural equation modeling · Cognitive Abilities and Testing · Mathematics · Personality Traits and Psychology · Psychometric Methodologies and Testing
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| Unique citing works | 106 |
|---|---|
| Citations per year | 7,57 |
| Citation span | 2012 - 2026 (15) |
| Citation velocity | current |
| Highly cited | Yes |
| Citation types | Neutral: 97 |