Abstract
The latent growth curve modeling (LGM) and random effects modeling (REM) frameworks are analytically and empirically equivalent for intrinsically linear models and used interchangeably for intrinsically nonlinear models. However, while LGM provides overall model fit indices, REM does not. Overall model fit indices are useful because they evaluate how well a specified model fits data. This paper proposes to translate model fit concepts from LGM to REM to help researchers compute overall model fit indices, including the model chi-square ((Formula presented.)), comparative fit index (CFI), root mean squared error of approximation (RMSEA), and standardized root mean squared residual (SRMR). Three empirical examples were used as illustrations.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 822-830 |
| Number of pages | 9 |
| Journal | Structural Equation Modeling |
| Volume | 30 |
| Issue number | 5 |
| DOIs | |
| State | Published - 2023 |
Bibliographical note
Publisher Copyright:© 2022 Taylor & Francis Group, LLC.
Keywords
- Latent growth curve model
- linear and nonlinear models
- overall model fit indices
- random effects model
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