{"title":"确定EFA中保留因子的数量:使用SPSS R-Menu v2 0做出更明智的估计","authors":"Matthew Courtney","doi":"10.7275/9CF5-2M72","DOIUrl":null,"url":null,"abstract":"Exploratory factor analysis (EFA) is a common technique utilized in the development of assessment instruments. The key question when performing this procedure is how to best estimate the number of factors to retain. This is especially important as underor over-extraction may lead to erroneous conclusions. Although recent advancements have been made to answer the number of factors question, popular statistical packages do not come standard with these modern techniques. This paper details how to program IBM SPSS Statistics software (SPSS) to conveniently perform five modern techniques designed to estimate the number of factors to retain. By utilizing the five empirically-supported techniques illustrated in this article, researchers will be able to more judiciously model data.","PeriodicalId":20361,"journal":{"name":"Practical Assessment, Research and Evaluation","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2013-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"304","resultStr":"{\"title\":\"Determining the Number of Factors to Retain in EFA: Using the SPSS R-Menu v2 0 to Make More Judicious Estimations\",\"authors\":\"Matthew Courtney\",\"doi\":\"10.7275/9CF5-2M72\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Exploratory factor analysis (EFA) is a common technique utilized in the development of assessment instruments. The key question when performing this procedure is how to best estimate the number of factors to retain. This is especially important as underor over-extraction may lead to erroneous conclusions. Although recent advancements have been made to answer the number of factors question, popular statistical packages do not come standard with these modern techniques. This paper details how to program IBM SPSS Statistics software (SPSS) to conveniently perform five modern techniques designed to estimate the number of factors to retain. By utilizing the five empirically-supported techniques illustrated in this article, researchers will be able to more judiciously model data.\",\"PeriodicalId\":20361,\"journal\":{\"name\":\"Practical Assessment, Research and Evaluation\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-04-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"304\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Practical Assessment, Research and Evaluation\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.7275/9CF5-2M72\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"Social Sciences\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Practical Assessment, Research and Evaluation","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.7275/9CF5-2M72","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"Social Sciences","Score":null,"Total":0}
Determining the Number of Factors to Retain in EFA: Using the SPSS R-Menu v2 0 to Make More Judicious Estimations
Exploratory factor analysis (EFA) is a common technique utilized in the development of assessment instruments. The key question when performing this procedure is how to best estimate the number of factors to retain. This is especially important as underor over-extraction may lead to erroneous conclusions. Although recent advancements have been made to answer the number of factors question, popular statistical packages do not come standard with these modern techniques. This paper details how to program IBM SPSS Statistics software (SPSS) to conveniently perform five modern techniques designed to estimate the number of factors to retain. By utilizing the five empirically-supported techniques illustrated in this article, researchers will be able to more judiciously model data.