Dynamic fit index cutoffs for confirmatory factor analysis models.
Bibliographic Data
| ID | 23317002 |
|---|---|
| Authors | Daniel McNeish (0000-0003-1643-9408), Melissa Gordon Wolf (0000-0001-7677-0659) |
| Year | 2023 |
| Volume | 28 |
| Issue | 1 |
| Pages | 61-88 |
| Publication date | 2023-02-01 |
| 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/met0000425 |
| PMID | 34694832 |
| OpenAlex | W3162227554 |
| Language | EN |
| Citations received | 112 |
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).
Confirmatory factor analysis · Data mining · Econometrics · Generalizability theory · Goodness of fit · Index (typography) · Machine learning · Reliability (semiconductor) · Statistics · Structural equation modeling · Computer Science · Mathematics · Mental Health Research Topics · Psychology
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| Unique citing works | 112 |
|---|---|
| Citations per year | 22,4 |
| Citation span | 2021 - 2026 (6) |
| Citation velocity | current |
| Highly cited | Yes |
| Citation types | Neutral: 97 |