Using Instrumental Variable Tests to Evaluate Model Specification in Latent Variable Structural Equation Models
Bibliographic Data
| ID | 8019709 |
|---|---|
| Authors | James B Kirby (0000-0001-8491-6072, Agency for Healthcare Research and Quality), K A Bollen (0000-0002-6710-3800, University of North Carolina at Chapel Hill) |
| Year | 2009 |
| Volume | 39 |
| Issue | 1 |
| Pages | 327-355 |
| Publication date | 2009-07-02 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Sociological Methodology (JOURNAL) |
| Journal identifiers | ISSN: 0081-1750 • E-ISSN: 1467-9531 |
| Publisher | SAGE Publishing (PUBLISHER • US) |
| DOI | 10.1111/j.1467-9531.2009.01217.x |
| PMID | 20419054 |
| OpenAlex | W2066903130 |
| Language | EN |
| Citations received | 13 |
| References cited | 23 |
Structural equation modeling (SEM) with latent variables is a powerful tool for social and behavioral scientists, combining many of the strengths of psychometrics and econometrics into a single framework. The most common estimator for SEM is the full-information maximum likelihood (ML) estimator, but there is continuing interest in limited information estimators because of their distributional robustness and their greater resistance to structural specification errors. However, the literature discussing model fit for limited information estimators for latent variable models is sparse compared with that for full-information estimators. We address this shortcoming by providing several specification tests basedon the 2SLS estimator for latent variable structural equation models developed by Bollen (1996). We explain how these tests can be used not only to identify a misspecified model but to help diagnose the source of misspecification within a model. We present and discuss results from a Monte Carlo experiment designed to evaluate the finite sample properties of these tests. Our findings suggest that the 2SLS tests successfully identify most misspecified models, even those with modest misspecification, and that they provide researchers with information that can help diagnose the source of misspecification
Econometrics · Estimator · Instrumental variable · Latent variable · Latent variable model · Machine learning · Monte Carlo method · Robustness (evolution · Specification · Statistics · Structural equation modeling · Variable (mathematics · Behavioral Health and Interventions · Computer Science · Mathematics · Psychometric Methodologies and Testing · Statistical Methods and Bayesian Inference
Eight Myths About Causality and Structural Equation Models
Addressing Endogeneity in International Marketing Applications of Partial Least Squares Structural Equation Modeling
Optimal Instrument Selection Using Bayesian Model Averaging for Model Implied Instrumental Variable Two Stage Least Squares Estimators
An Evaluation of Non-Iterative Estimators in Confirmatory Factor Analysis
On Inconsistency of the Overidentification Test for the Model-implied Instrumental Variable Approach
Model Implied Instrumental Variables (MIIVs)
A Limited Information Estimator for Dynamic Factor Models
MIIVefa
Fifty years of structural equation modeling
Fluffy cuffs
Tension in democratic administration
Welcome to parenthood!? An examination of the far-reaching effects of perceived adoption stigma in the workplace
Instrumental Variables in Sociology and the Social Sciences
Instrumental Variables and GMM
Monte Carlo Experiments
Asymptotically distribution‐free methods for the analysis of covariance structures
An Alternative Two Stage Least Squares (2SLS) Estimator for Latent Variable Equations
The Estimation of Economic Relationships using Instrumental Variables
Specification Tests in Econometrics
Instrumental Variables Regression with Weak Instruments
Estimation and inference in econometrics
Structural Equations with Latent Variables
The Sensitivity of an Empirical Model of Married Women's Hours of Work to Economic and Statistical Assumptions
A Comment on Model Evaluation and Modification
The Noncentral Chi-square Distribution in Misspecified Structural Equation Models
Limited Information Parameter Estimates for Latent or Mixed Manifest and Latent Variable Models
Confirmatory Tetrad Analysis
Latent Variable Models Under Misspecification
Specification searches in covariance structure modeling
An Empirical Evaluation of the Use of Fixed Cutoff Points in RMSEA Test Statistic in Structural Equation Models
Improper Solutions in Structural Equation Models
| Unique citing works | 13 |
|---|---|
| Citations per year | 0,93 |
| Citation span | 2012 - 2025 (14) |
| Citation velocity | recent |
| Highly cited | No |
| Citation types | Neutral: 13 |