Distributional Assumptions of Growth Mixture Models
Implications for Overextraction of Latent Trajectory Classes.
Bibliographic Data
| ID | 23324481 |
|---|---|
| Authors | Daniel J Bauer (0000-0001-6425-3769, North Carolina State University), Patrick J Curran (0000-0002-5772-5120, University of North Carolina at Chapel Hill) |
| Year | 2003 |
| Volume | 8 |
| Issue | 3 |
| Pages | 338-363 |
| Publication date | 2003-01-01 |
| 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 | 10.1037/1082-989x.8.3.338 |
| PMID | 14596495 |
| OpenAlex | W2121297553 |
| Language | EN |
| Citations received | 267 |
| References cited | 37 |
Growth mixture models are often used to determine if subgroups exist within the population that follow qualitatively distinct developmental trajectories. However, statistical theory developed for finite normal mixture models suggests that latent trajectory classes can be estimated even in the absence of population heterogeneity if the distribution of the repeated measures is nonnormal. By drawing on this theory, this article demonstrates that multiple trajectory classes can be estimated and appear optimal for nonnormal data even when only 1 group exists in the population. Further, the within-class parameter estimates obtained from these models are largely uninterpretable. Significant predictive relationships may be obscured or spurious relationships identified. The implications of these results for applied research are highlighted, and future directions for quantitative developments are suggested.
Class (philosophy) · Econometrics · Latent class model · Mixture model · Population · Spurious relationship · Statistical model · Statistics · Trajectory · Advanced Statistical Modeling Techniques · Artificial Intelligence · Computer Science · Demography · Genetic and phenotypic traits in livestock · Mathematics · Statistical Methods and Bayesian Inference
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| Unique citing works | 267 |
|---|---|
| Citations per year | 11,61 |
| Citation span | 2003 - 2026 (24) |
| Citation velocity | current |
| Highly cited | Yes |
| Citation types | Neutral: 231 |