ArticleMultivariate behavioral research
Model Implied Instrumental Variables (MIIVs): An Alternative Orientation to Structural Equation Modeling.
Article in Multivariate behavioral research. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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18 citing papers in PubMed.
- Accelerated biological aging and brain structural alterations linking air pollution to dementia risk: a prospective cohort study.The journal of nutrition, health & aging · 2026Article
- Appropriate modeling of endogeneity in cross-lagged models: Efficacy of auxiliary and model-implied instrumental variables.Behavior research methods · 2025Article
- A Model Implied Instrumental Variable Approach to Exploratory Factor Analysis (MIIV-EFA).Psychometrika · 2024Article
- Optimal Instrument Selection using Bayesian Model Averaging for Model Implied Instrumental Variable Two Stage Least Squares Estimators.Structural equation modeling : a multidisciplinary journal · 2024Article
- Testing the expensive-tissue hypothesis' prediction of inter-tissue competition using causal modelling with latent variables.Evolutionary human sciences · 2024Article
- An introduction to model implied instrumental variables using two stage least squares (MIIV-2SLS) in structural equation models (SEMs).Psychological methods · 2022Review
- Fifty years of structural equation modeling: A history of generalization, unification, and diffusion.Social science research · 2022Article
- Two-stage path analysis with definition variables: An alternative framework to account for measurement error.Psychological methods · 2022Article
- Investigation of measurement invariance in longitudinal health-related quality of life in preemptive or previously dialyzed kidney transplant recipients.Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation · 2022Article
- Estimating and Testing Random Intercept Multilevel Structural Equation Models with Model Implied Instrumental Variables.Structural equation modeling : a multidisciplinary journal · 2022Article
- Will COVID-19-related economic worries superimpose health worries, reducing nonpharmaceutical intervention acceptance in Germany? A prospective pre-registered study.International journal of psychology : Journal international de psychologie · 2021Article
- A unified model-implied instrumental variable approach for structural equation modeling with mixed variables.Psychometrika · 2021Article
- Subjective health in adolescence: Comparing the reliability of contemporaneous, retrospective, and proxy reports of overall health.Social science research · 2021Article
- Estimating Structural Equation Models Using James-Stein Type Shrinkage Estimators.Psychometrika · 2021Article
- When Good Loadings Go Bad: Robustness in Factor Analysis.Structural equation modeling : a multidisciplinary journal · 2020Article
- Self-Efficacy Between Previous and Current Mathematics Performance of Undergraduate Students: An Instrumental Variable Approach to Exposing a Causal Relationship.Frontiers in psychology · 2020Article
- Neuroimaging of individual differences: A latent variable modeling perspective.Neuroscience and biobehavioral reviews · 2019Review
- MIIVefa: An R Package for a New Type of Exploratory Factor Anaylysis Using Model-Implied Instrumental Variables.Multivariate behavioral researchArticle
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Abstract
Few dispute that our models are approximations to reality. Yet when it comes to structural equation models (SEMs), we use estimators that assume true models (e.g. maximum likelihood) and that can create biased estimates when the model is inexact. This article presents an overview of the Model Implied Instrumental Variable (MIIV) approach to SEMs from Bollen (1996). The MIIV estimator using Two Stage Least Squares (2SLS), MIIV-2SLS, has greater robustness to structural misspecifications than system wide estimators. In addition, the MIIV-2SLS estimator is asymptotically distribution free. Furthermore, MIIV-2SLS has equation-based overidentification tests that can help pinpoint misspecifications. Beyond these features, the MIIV approach has other desirable qualities. MIIV methods apply to higher order factor analyses, categorical measures, growth curve models, dynamic factor analysis, and nonlinear latent variables. Finally, MIIV-2SLS permits researchers to estimate and test only the latent variable model or any other subset of equations. In addition, other MIIV estimators beyond 2SLS are available. Despite these promising features, research is needed to better understand its performance under a variety of conditions that represent empirical applications. Empirical and simulation examples in the article illustrate the MIIV orientation to SEMs and highlight an R package MIIVsem that implements MIIV-2SLS.
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