An Introduction to Model Implied Instrumental Variables Using Two Stage Least Squares (MIIV-2SLS) in Structural Equation Models (SEMs)

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Citation

Bollen, Kenneth A.; Fisher, Zachary F.; Giordano, Michael L.; Lilly, Adam G.; Luo, Lan; & Ye, Ai (2022). An Introduction to Model Implied Instrumental Variables Using Two Stage Least Squares (MIIV-2SLS) in Structural Equation Models (SEMs). Psychological Methods, 27(5), 752-772. PMCID: PMC8799757

Abstract

Structural equation models (SEMs) are widely used to handle multiequation systems that involve latent variables, multiple indicators, and measurement error. Maximum likelihood (ML) and Diagonally Weighted Least Squares (DWLS) dominate the estimation of SEMs with continuous or categorical endogenous variables, respectively. When a model is correctly specified, ML and DWLS function well. But, in the face of incorrect structures or nonconvergence, their performance can seriously deteriorate. Model Implied Instrumental Variable, Two Stage Least Squares (MIIV-2SLS) estimates and tests individual equations, is more robust to misspecifications, and is noniterative, thus avoiding nonconvergence. This paper is an overview and tutorial on MIIV-2SLS. It reviews the six major steps in using MIIV-2SLS: 1) model specification, 2) model identification, 3) latent to observed (L2O) variable transformation, 4) finding MIIVs, 5) using 2SLS, and 6) tests of overidentified equations. Each step is illustrated using a running empirical example from Reisenzein’s (1986) randomized experiment on helping behavior. We also explain and illustrate the analytic conditions under which an equation estimated with MIIV-2SLS is robust to structural misspecifications. We include additional sections on MIIV approaches using a covariance matrix and mean vector as data input, conducting multilevel SEM, analyzing categorical endogenous variables, causal inference, and extensions and applications. Supplemental online material illustrates input code for all examples and simulations using the R package MIIVsem.

URL

http://dx.doi.org/10.1037/met0000297

Reference Type

Journal Article

Year Published

2022

Journal Title

Psychological Methods

Author(s)

Bollen, Kenneth A.
Fisher, Zachary F.
Giordano, Michael L.
Lilly, Adam G.
Luo, Lan
Ye, Ai

Article Type

Regular

PMCID

PMC8799757

Continent/Country

Nonspecific

ORCiD

Bollen - 0000-0002-6710-3800
Lilly - 0000-0002-3740-1540
Fisher, Z. - 0000-0003-2744-5141