Overall Model Fit Does Not Imply Linearity in Longitudinal Structural Equation Models
Examining Linear Change Over Time Using Latent Variable Modeling
Bibliographic Data
| ID | 21641819 |
|---|---|
| Authors | Tenko Raykov (0000-0002-8911-5116, Michigan State University, corresponding author), Christine Distefano (0000-0001-7504-6554, University of South Carolina), Natalja Menold (0000-0003-1106-474X, Technical University Dresden) |
| Year | 2024 |
| Volume | 31 |
| Issue | 5 |
| Pages | 882-890 |
| Publication date | 2024-09-02 |
| 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 | 10.1080/10705511.2023.2292959 |
| OpenAlex | W4393218605 |
| Language | EN |
| References cited | 18 |
This article is concerned with the assumption of linear temporal development that is often advanced in structural equation modeling-based longitudinal research. The linearity hypothesis is implemented in particular in the popular intercept-and-slope model as well as in more general models containing it as a component, such as longitudinal structural models with covariates, or models for the study of predictors and correlates of change. In empirical research applications, currently behavioral and social scientists typically evaluate only overall goodness of fit for a considered model. However, this omnibus fit assessment may miss violations of the underlying linearity assumption. To respond to this limitation, the present article discusses a testing procedure for examining the hypothesis of linear growth or decline separately from the widely used overall fit evaluation process. The method is readily utilized with popular latent variable modeling software and is illustrated using a numerical example
Econometrics · Latent growth modeling · Latent variable · Latent variable model · Linearity · Mathematical analysis · Physics · Statistics · Structural equation modeling · Advanced Causal Inference Techniques · Mathematics · Statistical Methods and Bayesian Inference · Statistical Methods in Clinical Trials
Longitudinal Structural Equation Modeling
The control of the false discovery rate in multiple testing under dependency
Dynamic but Structural Equation Modeling of Repeated Measures Data
Controlling the False Discovery Rate
Testing Structural Equation Models
Structural Equations with Latent Variables
Satisfying a Simplex Structure is Simpler Than it Should Be
Using covariance structure analysis to detect correlates and predictors of individual change over time
Principles and Practice of Structural Equation Modeling
A growth curve approach to the measurement of change
| Highly cited | No |
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