Croon’s Bias-corrected Estimation for Multilevel Structural Equation Models with Latent Interactions
Bibliographic Data
| ID | 21641710 |
|---|---|
| Authors | Kyle Cox (0000-0002-7173-4701, University of North Carolina at Charlotte, corresponding author), Benjamin Kelcey (0000-0002-5083-1406, University of Cincinnati), Fangxing Bai (0009-0007-7041-2683, University of Cincinnati) |
| Year | 2023 |
| Volume | 30 |
| Issue | 3 |
| Pages | 467-480 |
| Publication date | 2023-05-04 |
| 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 | 10.1080/10705511.2022.2140290 |
| OpenAlex | W4310092713 |
| Language | EN |
| Citations received | 2 |
| References cited | 28 |
Consideration of moderation effects is important in a range of substantive applications that include nested data and latent variables. A critical limitation in multilevel structural equation models with computationally demanding latent interactions is the poor performance (e.g., convergence failure and biased estimates) of common estimators such as maximum likelihood. To address this issue, we developed extensions to Croon’s bias-corrected estimation for multilevel structural equation models with latent interactions. After detailing these new corrections, we conducted several simulation studies to probe the accuracy and efficiency of the estimator. It produced accurate coefficient estimates with a variety of latent interactions while avoiding major convergence failure issues even when sample sizes were limited. The availability of Croon’s estimation for two-level structural equation models with latent interactions provides researchers a viable alternative estimator even when sample sizes are limited
Econometrics · Economics · Estimation · Estimator · Latent variable · Moderation · Multilevel model · Sample size determination · Statistics · Structural equation modeling · Advanced Causal Inference Techniques · Computer Science · Engineering · Mathematics · Psychometric Methodologies and Testing · Social and Intergroup Psychology
Maximum Likelihood Estimation of Latent Interaction Effects with the LMS Method
Multilevel structural equation models for assessing moderation within and across levels of analysis.
Bayesian Versus Frequentist Estimation for Structural Equation Models in Small Sample Contexts
MplusAutomation
Factor Score Regression in Connected Measurement Models Containing Cross-Loadings
Croon’s Bias-Corrected Estimation of Latent Interactions
Multilevel Analysis of Mediation, Moderation, and Nonlinear Effects in Small Samples, Using Expected a Posteriori Estimates of Factor Scores
Bayesian estimation of single and multilevel models with latent variable interactions
Croon’s Bias-Corrected Estimation for Multilevel Structural Equation Models with Non-Normal Indicators and Model Misspecifications
Factor Score Regression in the Presence of Correlated Unique Factors
Hypothesis Testing Using Factor Score Regression
Multilevel Factor Score Regression
Robustness Studies in Covariance Structure Modeling
When Treatment Adherence Matters
Estimating the nonlinear and interactive effects of latent variables
| Citations per year | 1 |
|---|---|
| Citation span | 2024 - 2026 (3) |
| Highly cited | No |