The DOI matches its Crossref record; 26 of the 26 references it cites were found in a registry or an index.
Comparative fit indexes in structural models.
Abstract
Normed and nonnormed fit indexes are frequently used as adjuncts to chi-square statistics for evaluating the fit of a structural model. A drawback of existing indexes is that they estimate no known population parameters. A new coefficient is proposed to summarize the relative reduction in the noncentrality parameters of two nested models. Two estimators of the coefficient yield new normed (CFI) and nonnormed (FI) fit indexes. CFI avoids the underestimation of fit often noted in small samples for Bentler and Bonett's (1980) normed fit index (NFI). FI is a linear function of Bentler and Bonett's non-normed fit index (NNFI) that avoids the extreme underestimation and overestimation often found in NNFI. Asymptotically, CFI, FI, NFI, and a new index developed by Bollen are equivalent measures of comparative fit, whereas NNFI measures relative fit by comparing noncentrality per degree of freedom. All of the indexes are generalized to permit use of Wald and Lagrange multiplier statistics. An example illustrates the behavior of these indexes under conditions of correct specification and misspecification. The new fit indexes perform very well at all sample sizes.
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“Model fit in the LGC analyses was evaluated using standard model fit indices: the comparative fit index (CFI; Bentler, 1990), the Tucker–Lewis index (TLI; Tucker & Lewis, 1973), and the root-mean-square error of approximation (RMSEA; Steiger, 1990). Acceptable model fit was evaluated using standard model fit criteria: CFI and TLI values close to .95 or above and RMSEA values close to 0.08 or below”
From Trajectories of change in well-being during cognitive behavioral therapies for anxiety disorders: Quantifying the impact and covariation with improvements in anxiety (2020) · cites this paper as Bentler, 1990
In the citing paper
“Acceptable model fit was determined using accepted cut-off guidelines for RMSEA (<0.08) and SRMR (≤0.08) (75–77).”
In the citing paper
“Both hypothesized models have reasonably strong indices of fit, as indicated in Table 5. Comparative fit indices (CFIs) of 0.97 and 0.963 respectively indicate that the models exhibit good fit according to several general guidelines and recommendations (Bentler, 1990, MacCallum et al., 1996, Hu and Bentler, 1999).Similarly, the models also both have high Tucker-Lewis indices (TLIs) of greater than”
From Discriminatory attitudes between ridesharing passengers (2020) · cites this paper as Bentler, 1990
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Metadata
- Authors
- Bentler, P. M.
- Affiliations
- University of California, Los Angeles (US)
- Corresponding author
- Bentler, P. M.
- ORCID
- Bentler, P. M.
- Venue
- Psychological Bulletin
- Published
- 1990
- Volume
- 107
- Issue
- 2
- Pages
- 238-246
- ISSN
- 0033-2909 (ISSN-L)
- 1939-1455
- OpenAlex
- W2018201949
- Cited by
- 25,266
- References
- 26
- Citations by year
- 2026: 819 · 2025: 1270 · 2024: 1256 · 2023: 1284 · 2022: 1450 · 2021: 1427 · 2020: 1212 · 2019: 1213 · 2018: 1190 · 2017: 1105 · 2016: 1164 · 2015: 1143 · 2014: 1130 · 2013: 1110 · 2012: 998
- FWCI
- 84.24
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Psychological Bulletin (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Institute on Drug Abuse (DA01070, DA-00017, P01DA001070)
- 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
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Distribution Estimation and Applications
- Keywords
- comparative fit index, structural equation modeling, model fit assessment, noncentrality parameter, nested models, Wald statistic, small sample bias, model misspecification
- MeSH terms
- Humans, Personality Tests, Psychometrics, Reference Values, Models, Statistical
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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