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.

蒙特卡洛模拟用于评估现代缺失数据技术在估计具有潜在变量的结构方程模型时的性能。

• 批准号: 370960346
• 负责人: Professor Dr. Dieter Urban
• 金额: --
• 依托单位: Abteilung für Soziologie und empirische Sozialforschung (SOWI IV)
• 依托单位国家: 德国
• 项目类别: Research Grants
• 财政年份: 2017
• 资助国家: 德国
• 起止时间: 2016-12-31 至 2020-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/370960346?language=en
• 关键词: Monte; Carlo; simulations; evaluating; performance

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.

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变量的结构方程模型。这些技术包括多重插补、期望最大化和Direct-ML估计方法。使用蒙特卡罗模拟，将根据某些内源性检验标准（如估计偏倚、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）检查MDT的性能。与以前的蒙特卡罗模拟研究评估缺失数据技术的优势，我们将集中在不同的多重插补技术的性能。特别是，我们将调查的相关性，选择一个特定的MI技术时，估计结构方程模型与潜变量的各种数据条件。此外，我们的蒙特卡洛模拟的结果将使用真实的经验数据集证明。这将显示在分析经验数据时未充分应用特定MDT（特别是错误类型的多重插补）的后果。该项目的结果将为社会研究实践提供指导，以便研究人员可以选择最适合其经验数据的MDT（包括最佳的多重插补类型）。