{"title":"应对数据池中的挑战：连续和分类指标潜在变量模型中的缺失数据处理","authors":"Lihan Chen, Milica Miočević, Carl F. Falk","doi":"10.1080/10705511.2023.2300079","DOIUrl":null,"url":null,"abstract":"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...","PeriodicalId":21964,"journal":{"name":"Structural Equation Modeling: A Multidisciplinary Journal","volume":"34 1","pages":""},"PeriodicalIF":2.5000,"publicationDate":"2024-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Tackling Challenges in Data Pooling: Missing Data Handling in Latent Variable Models with Continuous and Categorical Indicators\",\"authors\":\"Lihan Chen, Milica Miočević, Carl F. Falk\",\"doi\":\"10.1080/10705511.2023.2300079\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"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...\",\"PeriodicalId\":21964,\"journal\":{\"name\":\"Structural Equation Modeling: A Multidisciplinary Journal\",\"volume\":\"34 1\",\"pages\":\"\"},\"PeriodicalIF\":2.5000,\"publicationDate\":\"2024-02-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Structural Equation Modeling: A Multidisciplinary Journal\",\"FirstCategoryId\":\"102\",\"ListUrlMain\":\"https://doi.org/10.1080/10705511.2023.2300079\",\"RegionNum\":2,\"RegionCategory\":\"心理学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"MATHEMATICS, INTERDISCIPLINARY APPLICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Structural Equation Modeling: A Multidisciplinary Journal","FirstCategoryId":"102","ListUrlMain":"https://doi.org/10.1080/10705511.2023.2300079","RegionNum":2,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MATHEMATICS, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
Tackling Challenges in Data Pooling: Missing Data Handling in Latent Variable Models with Continuous and Categorical Indicators
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...
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.
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