Monte Carlo simulations for evaluating the performance of modern missing data techniques when estimating structural equation models with latent variables. A systematic analysis of different types of multiple imputation.

在本研究项目中，将测试各种现代缺失数据技术（MDT），这些技术可用于估计具有潜在变量的结构方程模型。这些技术包括多重插值、期望最大化和直接机器学习估计。使用蒙特卡罗模拟，mdt的性能将根据某些内源性测试标准（如估计偏差、收敛性和模型拟合）和不同的外源性模型参数（如不同的值分布、不同的测量尺度、不同的缺失值机制、不同的缺失值份额和不同的样本量等）进行检查。不像以前的蒙特卡罗模拟研究评估缺失数据技术的优势，我们将集中在多重插值的不同技术的性能。特别是，我们将研究在估计具有潜在变量的结构方程模型时选择特定mi技术的各种数据条件的相关性。此外，我们的蒙特卡罗模拟结果将使用真实的经验数据集进行演示。这将显示在分析经验数据时没有充分应用特定MDT（特别是错误类型的多重imputation）的后果。本项目的研究结果将为社会研究实践提供指导，使研究人员能够选择最适合其实证数据的MDT（包括最优多重imputation类型）。

In this research project, various modern missing data techniques (MDT) will be tested that can be used when estimating structural equation models with latent variables. These techniques include methods of multiple imputation, expectation maximization and Direct-ML estimation. Using Monte Carlo simulations the performance of MDTs will be checked in respect to certain endogenous test criteria (such as estimation bias, convergence and model fit) and under varying exogenous model parameters (such as different value distributions, varying measuring scales, different missing value mechanisms, different shares of missing values and various sample sizes etc.). Unlike former Monte Carlo-simulation studies evaluating the advantages of missing data techniques, we will concentrate on the performance of different techniques of multiple imputation. Especially, we will investigate the relevance of various data conditions for selecting a particular MI-technique when estimating structural equation models with latent variables. In addition, the results of our Monte Carlo-simulations will be demonstrated using real empirical data sets. This will show the consequences of not adequately applying a particular MDT (especially a wrong type of multiple imputation) when analyzing empirical data. The results of this project will generate guidelines for social research practice, so that researches can select the MDT (including the optimal type of multiple imputation) that best fits to their empirical data.