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 article is an overview and tutorial on MIIV-2SLS. It reviews the six major steps in using MIIV-2SLS: (a) model specification; (b) model identification; (c) latent to observed (L2O) variable transformation; (d) finding MIIVs; (e) using 2SLS; and (f) 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. Online supplemental material illustrates input code for all examples and simulations using the R package MIIVsem. (PsycInfo Database Record (c) 2021 APA, all rights reserved)

中文翻译：

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介绍在结构方程模型 (SEM) 中使用两阶段最小二乘法 (MIIV-2SLS) 对隐含工具变量进行建模。

结构方程模型（SEM）广泛用于处理涉及潜变量、多个指标和测量误差的多重方程系统。最大似然 (ML) 和对角加权最小二乘 (DWLS) 分别主导着具有连续或分类内生变量的 SEM 估计。当正确指定模型时，ML 和 DWLS 可以正常运行。但是，面对不正确的结构或不收敛，它们的性能可能会严重恶化。模型隐含工具变量、两阶段最小二乘法 (MIIV-2SLS) 估计和测试各个方程，对错误指定更稳健，并且是非迭代的，从而避免不收敛。本文是 MIIV-2SLS 的概述和教程。它回顾了使用 MIIV-2SLS 的六个主要步骤：（a）模型规范；(b) 型号识别；(c) 潜在变量到观测变量的转换；(d) 寻找 MIIV；(e) 使用 2SLS；(f) 过度辨识方程的检验。每个步骤都使用来自 Reisenzein (1986) 的助人行为随机实验的运行经验示例进行说明。我们还解释并说明了使用 MIIV-2SLS 估计的方程对于结构错误指定具有鲁棒性的分析条件。我们还包括有关 MIIV 方法的其他部分，其中使用协方差矩阵和均值向量作为数据输入、进行多级 SEM、分析分类内生变量、因果推理以及扩展和应用。在线补充材料说明了使用 R 包 MIIVsem 的所有示例和模拟的输入代码。（PsycInfo 数据库记录 (c) 2021 APA，保留所有权利）