Detecting Model Misspecification in Bayesian Piecewise Growth Models
Bibliographic Data
| ID | 21641771 |
|---|---|
| Authors | Sarah Depaoli (0000-0002-1277-0462, University of California, corresponding author), Fan Jia (0000-0003-3855-532X, University of California), Ihnwhi Heo (0000-0002-6123-3639, University of California) |
| Year | 2023 |
| Volume | 30 |
| Issue | 4 |
| Pages | 574-591 |
| Publication date | 2023-07-04 |
| Peer Reviewed | Yes |
| Open Access | No |
| Type | ARTICLE |
| Venue | Structural Equation Modeling: A Multidisciplinary Journal (JOURNAL) |
| Journal identifiers | ISSN: 1070-5511 • E-ISSN: 1532-8007 |
| Publisher | Informa UK Limited (PUBLISHER • GB) |
| DOI | 10.1080/10705511.2022.2144865 |
| OpenAlex | W4311326436 |
| Language | EN |
| Citations received | 6 |
| References cited | 52 |
Bayesian estimation has become increasingly more popular with piecewise growth models because it can aid in accurately modeling nonlinear change over time. Recently, new Bayesian approximate fit indices (BRMSEA, BCFI, and BTLI) have been introduced as tools for detecting model (mis)fit. We compare these indices to the posterior predictive p-value (PPP), and also examine the Bayesian information criterion (BIC) and the deviance information criterion (DIC), to identify optimal methods for detecting model misspecification in piecewise growth models. Findings indicated that the Bayesian approximate fit indices are not as reliable as the PPP for detecting misspecification. However, these indices appear to be viable model selection tools rather than measures of fit. We conclude with recommendations regarding when researchers should be using each of the indices in practice
Bayesian inference · Bayesian information criterion · Bayesian probability · Deviance information criterion · Econometrics · Information Criteria · Model selection · Piecewise · Statistics · Computer Science · Decision-Making and Behavioral Economics · Economic and Environmental Valuation · Mathematics · Statistical Methods and Bayesian Inference
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| Unique citing works | 6 |
|---|---|
| Citations per year | 3 |
| Citation span | 2024 - 2026 (3) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 6 |