A Model-Based Shrinkage Target to Avoid Non-convergence in Small Sample SEM
Bibliographic Data
| ID | 21641959 |
|---|---|
| Authors | Julie De Jonckere (0000-0002-9977-5548, Ghent University, corresponding author), Yves Rosseel (0000-0002-4129-4477, Ghent University) |
| Year | 2023 |
| Volume | 30 |
| Issue | 6 |
| Pages | 941-955 |
| Publication date | 2023-11-02 |
| 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 | 10.1080/10705511.2023.2171420 |
| OpenAlex | W4323823140 |
| Language | EN |
| Citations received | 1 |
| References cited | 24 |
Structural equation modeling is prone to a variety of problems when the sample size is small. One solution that attempts to solve the (non-convergence) problem of small sample SEM is found in shrinkage estimation, where a weighted average between the sample variance-covariance matrix (S) and a highly structured shrinkage target (T) is calculated to construct an adjusted sample variance-covariance matrix (Sa), which is then used as input for the analysis. Different target candidates have already been put forward in the literature, but in this paper, we propose using a model-based target matrix specifically tailored for structural equation models. Two simulation studies demonstrate the benefit of using a model-based target over other candidate targets in terms of convergence rate, bias, and MSE, but also emphasize the importance of constructing an optimal shrinkage intensity
Algorithm · Covariance · Covariance matrix · Mathematical optimization · Mean squared error · Minimum-variance unbiased estimator · Rate of convergence · Sample size determination · Shrinkage · Shrinkage estimator · Statistics · Structural equation modeling · Computer Science · Mathematics · Psychometric Methodologies and Testing · Statistical Methods and Bayesian Inference · Statistical Methods and Inference · Applied Mathematics
A systematic review of Bayesian articles in psychology
Bayesian structural equation modeling
Using Bounded Estimation to Avoid Nonconvergence in Small Sample Structural Equation Modeling
Structural Equation Modeling with Small Samples
Evaluating Small Sample Approaches for Model Test Statistics in Structural Equation Modeling
Evaluation of the Bayesian and Maximum Likelihood Approaches in Analyzing Structural Equation Models with Small Sample Sizes
| Citations per year | 0.5 |
|---|---|
| Citation span | 2024 - 2024 (1) |
| Highly cited | No |