Improving generalizability coefficient estimate accuracy
A way to incorporate auxiliary information
Bibliographic Data
| ID | 4563682 |
|---|---|
| Authors | Zhehan Jiang (0000-0002-1376-9439, University of Alabama, corresponding author), Kevin Walker (0000-0002-9842-9117), Kevin W Walker (0000-0001-9500-487X, University of Alabama), Dexin Shi (0000-0002-4120-6756, University of South Carolina), Jian Cao (0000-0001-9266-1970, Weifang University) |
| Year | 2018 |
| Volume | 11 |
| Issue | 2 |
| Publication date | 2018-05-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Methodological Innovations (JOURNAL) |
| Journal identifiers | ISSN: 2059-7991 • E-ISSN: 2059-7991 |
| Publisher | SAGE Publications Inc (PUBLISHER) |
| DOI | 10.1177/2059799118791397 |
| OpenAlex | W2885395702 |
| Language | EN |
| Citations received | 2 |
| References cited | 41 |
Initially proposed by Marcoulides and further expanded by Raykov and Marcoulides, a structural equation modeling approach can be used in generalizability theory estimation. This article examines the utility of incorporating auxiliary variables into the structural equation modeling approach when missing data is present. In particular, the authors assert that by adapting a saturated correlates model strategy to structural equation modeling generalizability theory models, one can reduce any biased effects caused by missingness. Traditional approaches such as an analysis of variance do not possess such a feature. This article provides detailed instructions for adding auxiliary variables into a structural equation modeling generalizability theory model, demonstrates the corresponding benefits of bias reduction in generalizability coefficient estimate via simulations, and discusses issues relevant to the proposed approach
Data mining · Econometrics · Estimation · Generalizability theory · Machine learning · Missing data · Statistics · Structural equation modeling · Variance (accounting · Bayesian Modeling and Causal Inference · Computer Science · Engineering · Mathematics · Psychometric Methodologies and Testing
The Dependability of behavioral measurements
Analysis of Incomplete Multivariate Data
Generalizability Theory
The prevention and handling of the missing data
Adding Missing-Data-Relevant Variables to Fiml-Based Structural Equation Models
A comparison of inclusive and restrictive strategies in modern missing data procedures.
Core reporting practices in structural equation modeling
Inference and missing data
Missing data
The Effect of Auxiliary Variables and Multiple Imputation on Parameter Estimation in Confirmatory Factor Analysis
Score generalizability of academic writing tasks
Bayesian SEM for Specification Search Problems in Testing Factorial Invariance
A Comparison of Imputation Strategies for Ordinal Missing Data on Likert Scale Variables
Estimating Nonlinear Indirect Effects in Bayesian Semiparametric Structural Equation Model
Coefficient alpha and the internal structure of tests
Three Likelihood-Based Methods for Mean and Covariance Structure Analysis with Nonnormal Missing Data
Principles and Practice of Structural Equation Modeling
Multiple Imputation for Missing Data
| Citations per year | 1 |
|---|---|
| Citation span | 2024 - 2025 (2) |
| Highly cited | No |