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Tackling Challenges in Data Pooling: Missing Data Handling in Latent Variable Models with Continuous and Categorical Indicators
Structural Equation Modeling: A Multidisciplinary Journal ( IF 2.5 ) Pub Date : 2024-02-16 , DOI: 10.1080/10705511.2023.2300079 Lihan Chen 1 , Milica Miočević 1 , Carl F. Falk 1
Structural Equation Modeling: A Multidisciplinary Journal ( IF 2.5 ) Pub Date : 2024-02-16 , DOI: 10.1080/10705511.2023.2300079 Lihan Chen 1 , Milica Miočević 1 , Carl F. Falk 1
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Data pooling is a powerful strategy in empirical research. However, combining multiple datasets often results in a large amount of missing data, as variables that are not present in some datasets e...
中文翻译:
应对数据池中的挑战:具有连续和分类指标的潜变量模型中的缺失数据处理
数据池是实证研究中的一种强大策略。然而,组合多个数据集通常会导致大量丢失数据,因为某些数据集中不存在的变量...
更新日期:2024-02-16
中文翻译:
应对数据池中的挑战:具有连续和分类指标的潜变量模型中的缺失数据处理
数据池是实证研究中的一种强大策略。然而,组合多个数据集通常会导致大量丢失数据,因为某些数据集中不存在的变量...