An Evaluation of Non-Iterative Estimators in the Structural after Measurement (SAM) Approach to Structural Equation Modeling (SEM)
Bibliographic Data
| ID | 21641855 |
|---|---|
| Authors | Sara Dhaene (0000-0003-0347-831X, Ghent University, corresponding author), Yves Rosseel (0000-0002-4129-4477, Ghent University) |
| Year | 2023 |
| Volume | 30 |
| Issue | 6 |
| Pages | 926-940 |
| Publication date | 2023-11-02 |
| Open Access | Yes |
| 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 | 10.1080/10705511.2023.2220135 |
| OpenAlex | W4384282339 |
| Language | EN |
| Citations received | 4 |
| References cited | 38 |
In Structural Equation Modeling (SEM), the measurement part and the structural part are typically estimated simultaneously via an iterative Maximum Likelihood (ML) procedure. In this study, we compare performance of the standard procedure to the Structural After Measurement (SAM) approach, where the structural part is separated from the measurement part. One appealing feature of the latter multi-step procedure is that it extends the scope of possible estimators, as now also non-iterative methods from factor-analytic literature can be used to estimate the measurement models. In our simulations, the SAM approach outperformed vanilla SEM in small to moderate samples (i.e., no convergence issues, no inadmissible solutions, smaller MSE values). Notably, this held regardless of the estimator used for the measurement part, with negligible differences between iterative and non-iterative estimators. This may call into question the added value of advanced iterative algorithms over closed-form expressions (which generally require less computational time and resources)
Algorithm · Estimator · Iterative method · Mathematical optimization · Statistics · Structural equation modeling · Advanced Statistical Modeling Techniques · Computer Science · Mathematics · Mental Health Research Topics · Psychometric Methodologies and Testing · Applied Mathematics
An introduction to multivariate statistical analysis
Modern factor analysis
The Revised NEO Personality Inventory (NEO-PI-R)
Nonconvergence, Improper Solutions, and Starting Values in Lisrel Maximum Likelihood Estimation
The Effect of Sampling Error on Convergence, Improper Solutions, and Goodness-of-Fit Indices for Maximum Likelihood Confirmatory Factor Analysis
Qgraph
Structural Equations with Latent Variables
Using Bounded Estimation to Avoid Nonconvergence in Small Sample Structural Equation Modeling
An Evaluation of Non-Iterative Estimators in Confirmatory Factor Analysis
Precluding Interpretational Confounding in Factor Analysis with a Covariate or Outcome via Measurement and Uncertainty Preserving Parametric Modeling
New Developments in Factor Score Regression
Model Implied Instrumental Variables (MIIVs)
The Impact of Specification Error on the Estimation, Testing, and Improvement of Structural Equation Models
Limited Information Parameter Estimates for Latent or Mixed Manifest and Latent Variable Models
Latent Variable Models Under Misspecification
Interpretational Confounding of Unobserved Variables in Structural Equation Models
Evaluating replicability of factors in the Revised NEO Personality Inventory
Confirmatory Factor-Analytic Structures and the Theory Construction Process
| Citations per year | 2 |
|---|---|
| Citation span | 2024 - 2026 (3) |
| Highly cited | No |