{"title":"估算具有相关残差的探索性因子分析模型的高效稳健计算方法。","authors":"Guangjian Zhang, Dayoung Lee","doi":"10.1037/met0000609","DOIUrl":null,"url":null,"abstract":"<p><p>A critical assumption in exploratory factor analysis (EFA) is that manifest variables are no longer correlated after the influences of the common factors are controlled. The assumption may not be valid in some EFA applications; for example, questionnaire items share other characteristics in addition to their relations to common factors. We present a computationally efficient and robust method to estimate EFA with correlated residuals. We provide details on the implementation of the method with both ordinary least squares estimation and maximum likelihood estimation. We demonstrate the method using empirical data and conduct a simulation study to explore its statistical properties. The results are (a) that the new method encountered much fewer convergence problems than the existing method; (b) that the EFA model with correlated residuals produced a more satisfactory model fit than the conventional EFA model; and (c) that the EFA model with correlated residuals and the conventional EFA model produced very similar estimates for factor loadings. (PsycInfo Database Record (c) 2024 APA, all rights reserved).</p>","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":" ","pages":""},"PeriodicalIF":7.6000,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A computationally efficient and robust method to estimate exploratory factor analysis models with correlated residuals.\",\"authors\":\"Guangjian Zhang, Dayoung Lee\",\"doi\":\"10.1037/met0000609\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>A critical assumption in exploratory factor analysis (EFA) is that manifest variables are no longer correlated after the influences of the common factors are controlled. The assumption may not be valid in some EFA applications; for example, questionnaire items share other characteristics in addition to their relations to common factors. We present a computationally efficient and robust method to estimate EFA with correlated residuals. We provide details on the implementation of the method with both ordinary least squares estimation and maximum likelihood estimation. We demonstrate the method using empirical data and conduct a simulation study to explore its statistical properties. The results are (a) that the new method encountered much fewer convergence problems than the existing method; (b) that the EFA model with correlated residuals produced a more satisfactory model fit than the conventional EFA model; and (c) that the EFA model with correlated residuals and the conventional EFA model produced very similar estimates for factor loadings. (PsycInfo Database Record (c) 2024 APA, all rights reserved).</p>\",\"PeriodicalId\":20782,\"journal\":{\"name\":\"Psychological methods\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":7.6000,\"publicationDate\":\"2024-09-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Psychological methods\",\"FirstCategoryId\":\"102\",\"ListUrlMain\":\"https://doi.org/10.1037/met0000609\",\"RegionNum\":1,\"RegionCategory\":\"心理学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"PSYCHOLOGY, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Psychological methods","FirstCategoryId":"102","ListUrlMain":"https://doi.org/10.1037/met0000609","RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"PSYCHOLOGY, MULTIDISCIPLINARY","Score":null,"Total":0}
A computationally efficient and robust method to estimate exploratory factor analysis models with correlated residuals.
A critical assumption in exploratory factor analysis (EFA) is that manifest variables are no longer correlated after the influences of the common factors are controlled. The assumption may not be valid in some EFA applications; for example, questionnaire items share other characteristics in addition to their relations to common factors. We present a computationally efficient and robust method to estimate EFA with correlated residuals. We provide details on the implementation of the method with both ordinary least squares estimation and maximum likelihood estimation. We demonstrate the method using empirical data and conduct a simulation study to explore its statistical properties. The results are (a) that the new method encountered much fewer convergence problems than the existing method; (b) that the EFA model with correlated residuals produced a more satisfactory model fit than the conventional EFA model; and (c) that the EFA model with correlated residuals and the conventional EFA model produced very similar estimates for factor loadings. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
期刊介绍:
Psychological Methods is devoted to the development and dissemination of methods for collecting, analyzing, understanding, and interpreting psychological data. Its purpose is the dissemination of innovations in research design, measurement, methodology, and quantitative and qualitative analysis to the psychological community; its further purpose is to promote effective communication about related substantive and methodological issues. The audience is expected to be diverse and to include those who develop new procedures, those who are responsible for undergraduate and graduate training in design, measurement, and statistics, as well as those who employ those procedures in research.