{"title":"个体具有不同因子负荷时如何改进回归因子评分预测器。","authors":"André Beauducel, Norbert Hilger, Anneke C Weide","doi":"10.1177/00131644251347530","DOIUrl":null,"url":null,"abstract":"<p><p>Previous research has shown that ignoring individual differences of factor loadings in conventional factor models may reduce the determinacy of factor score predictors. Therefore, the aim of the present study is to propose a heterogeneous regression factor score predictor (HRFS) with larger determinacy than the conventional regression factor score predictor (RFS) when individuals have different factor loadings. First, a method for the estimation of individual loadings is proposed. The individual loading estimates are used to compute the HRFS. Then, a binomial test for loading heterogeneity of a factor is proposed to compute the HRFS only when the test is significant. Otherwise, the conventional RFS should be used. A simulation study reveals that the HRFS has larger determinacy than the conventional RFS in populations with substantial loading heterogeneity. An empirical example based on subsamples drawn randomly from a large sample of Big Five Markers indicates that the determinacy can be improved for the factor emotional stability when the HRFS is computed.</p>","PeriodicalId":11502,"journal":{"name":"Educational and Psychological Measurement","volume":" ","pages":"00131644251347530"},"PeriodicalIF":2.3000,"publicationDate":"2025-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12356820/pdf/","citationCount":"0","resultStr":"{\"title\":\"How to Improve the Regression Factor Score Predictor When Individuals Have Different Factor Loadings.\",\"authors\":\"André Beauducel, Norbert Hilger, Anneke C Weide\",\"doi\":\"10.1177/00131644251347530\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Previous research has shown that ignoring individual differences of factor loadings in conventional factor models may reduce the determinacy of factor score predictors. Therefore, the aim of the present study is to propose a heterogeneous regression factor score predictor (HRFS) with larger determinacy than the conventional regression factor score predictor (RFS) when individuals have different factor loadings. First, a method for the estimation of individual loadings is proposed. The individual loading estimates are used to compute the HRFS. Then, a binomial test for loading heterogeneity of a factor is proposed to compute the HRFS only when the test is significant. Otherwise, the conventional RFS should be used. A simulation study reveals that the HRFS has larger determinacy than the conventional RFS in populations with substantial loading heterogeneity. An empirical example based on subsamples drawn randomly from a large sample of Big Five Markers indicates that the determinacy can be improved for the factor emotional stability when the HRFS is computed.</p>\",\"PeriodicalId\":11502,\"journal\":{\"name\":\"Educational and Psychological Measurement\",\"volume\":\" \",\"pages\":\"00131644251347530\"},\"PeriodicalIF\":2.3000,\"publicationDate\":\"2025-08-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12356820/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Educational and Psychological Measurement\",\"FirstCategoryId\":\"102\",\"ListUrlMain\":\"https://doi.org/10.1177/00131644251347530\",\"RegionNum\":3,\"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":"Educational and Psychological Measurement","FirstCategoryId":"102","ListUrlMain":"https://doi.org/10.1177/00131644251347530","RegionNum":3,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MATHEMATICS, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
How to Improve the Regression Factor Score Predictor When Individuals Have Different Factor Loadings.
Previous research has shown that ignoring individual differences of factor loadings in conventional factor models may reduce the determinacy of factor score predictors. Therefore, the aim of the present study is to propose a heterogeneous regression factor score predictor (HRFS) with larger determinacy than the conventional regression factor score predictor (RFS) when individuals have different factor loadings. First, a method for the estimation of individual loadings is proposed. The individual loading estimates are used to compute the HRFS. Then, a binomial test for loading heterogeneity of a factor is proposed to compute the HRFS only when the test is significant. Otherwise, the conventional RFS should be used. A simulation study reveals that the HRFS has larger determinacy than the conventional RFS in populations with substantial loading heterogeneity. An empirical example based on subsamples drawn randomly from a large sample of Big Five Markers indicates that the determinacy can be improved for the factor emotional stability when the HRFS is computed.
期刊介绍：
Educational and Psychological Measurement (EPM) publishes referred scholarly work from all academic disciplines interested in the study of measurement theory, problems, and issues. Theoretical articles address new developments and techniques, and applied articles deal with innovation applications.
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