Addressing Missing Data in Latent Class Analysis When Using a Three-Step Estimation Approach

IF 2.5 2区心理学 Q2 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS

Structural Equation Modeling: A Multidisciplinary Journal Pub Date : 2024-10-29 DOI:10.1080/10705511.2024.2410240

Sarah Depaoli, Fan Jia, Marieke Visser

引用次数: 0

Abstract

This study specifically focuses on addressing the challenges related to employing missing data techniques when estimating a conditional Latent Class Analysis (LCA) model. In the context of a condit...

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使用三步估计法处理潜类分析中的缺失数据

本研究特别关注在估算条件潜类分析（LCA）模型时采用缺失数据技术所面临的挑战。在有条件潜类分析模型的背景下，缺失数据是一个重要的问题。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Structural Equation Modeling: A Multidisciplinary Journal 数学-数学跨学科应用

CiteScore

8.70

自引率

11.70%

发文量

审稿时长

>12 weeks

期刊介绍： Structural Equation Modeling: A Multidisciplinary Journal publishes refereed scholarly work from all academic disciplines interested in structural equation modeling. These disciplines include, but are not limited to, psychology, medicine, sociology, education, political science, economics, management, and business/marketing. Theoretical articles address new developments; applied articles deal with innovative structural equation modeling applications; the Teacher’s Corner provides instructional modules on aspects of structural equation modeling; book and software reviews examine new modeling information and techniques; and advertising alerts readers to new products. Comments on technical or substantive issues addressed in articles or reviews published in the journal are encouraged; comments are reviewed, and authors of the original works are invited to respond.