Byron J Gajewski, Yu Jiang, Hung-Wen Yeh, Kimberly Engelman, Cynthia Teel, Won S Choi, K Allen Greiner, Christine Makosky Daley
{"title":"对非统计人员进行验证性因子分析教学:心理测量工具复合信度评估的个案研究。","authors":"Byron J Gajewski, Yu Jiang, Hung-Wen Yeh, Kimberly Engelman, Cynthia Teel, Won S Choi, K Allen Greiner, Christine Makosky Daley","doi":"","DOIUrl":null,"url":null,"abstract":"<p><p>Texts and software that we are currently using for teaching multivariate analysis to non-statisticians lack in the delivery of <i>confirmatory</i> factor analysis (CFA). The purpose of this paper is to provide educators with a complement to these resources that includes CFA <i>and</i> its computation. We focus on how to use CFA to estimate a \"composite reliability\" of a psychometric instrument. This paper provides guidance for introducing, via a case-study, the non-statistician to CFA. As a complement to our instruction about the more traditional SPSS, we successfully piloted the software R for estimating CFA on nine non-statisticians. This approach can be used with healthcare graduate students taking a multivariate course, as well as modified for community stakeholders of our Center for American Indian Community Health (e.g. community advisory boards, summer interns, & research team members). The placement of CFA at the end of the class is strategic and gives us an opportunity to do some innovative teaching: (1) build ideas for understanding the case study using previous course work (such as ANOVA); (2) incorporate multi-dimensional scaling (that students already learned) into the selection of a factor structure (new concept); (3) use interactive data from the students (active learning); (4) review matrix algebra and its importance to psychometric evaluation; (5) show students how to do the calculation on their own; and (6) give students access to an actual recent research project.</p>","PeriodicalId":30080,"journal":{"name":"Case Studies in Business Industry and Government Statistics","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2014-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3996839/pdf/nihms419622.pdf","citationCount":"0","resultStr":"{\"title\":\"Teaching Confirmatory Factor Analysis to Non-Statisticians: A Case Study for Estimating Composite Reliability of Psychometric Instruments.\",\"authors\":\"Byron J Gajewski, Yu Jiang, Hung-Wen Yeh, Kimberly Engelman, Cynthia Teel, Won S Choi, K Allen Greiner, Christine Makosky Daley\",\"doi\":\"\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Texts and software that we are currently using for teaching multivariate analysis to non-statisticians lack in the delivery of <i>confirmatory</i> factor analysis (CFA). The purpose of this paper is to provide educators with a complement to these resources that includes CFA <i>and</i> its computation. We focus on how to use CFA to estimate a \\\"composite reliability\\\" of a psychometric instrument. This paper provides guidance for introducing, via a case-study, the non-statistician to CFA. As a complement to our instruction about the more traditional SPSS, we successfully piloted the software R for estimating CFA on nine non-statisticians. This approach can be used with healthcare graduate students taking a multivariate course, as well as modified for community stakeholders of our Center for American Indian Community Health (e.g. community advisory boards, summer interns, & research team members). The placement of CFA at the end of the class is strategic and gives us an opportunity to do some innovative teaching: (1) build ideas for understanding the case study using previous course work (such as ANOVA); (2) incorporate multi-dimensional scaling (that students already learned) into the selection of a factor structure (new concept); (3) use interactive data from the students (active learning); (4) review matrix algebra and its importance to psychometric evaluation; (5) show students how to do the calculation on their own; and (6) give students access to an actual recent research project.</p>\",\"PeriodicalId\":30080,\"journal\":{\"name\":\"Case Studies in Business Industry and Government Statistics\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3996839/pdf/nihms419622.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Case Studies in Business Industry and Government Statistics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Case Studies in Business Industry and Government Statistics","FirstCategoryId":"1085","ListUrlMain":"","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Teaching Confirmatory Factor Analysis to Non-Statisticians: A Case Study for Estimating Composite Reliability of Psychometric Instruments.
Texts and software that we are currently using for teaching multivariate analysis to non-statisticians lack in the delivery of confirmatory factor analysis (CFA). The purpose of this paper is to provide educators with a complement to these resources that includes CFA and its computation. We focus on how to use CFA to estimate a "composite reliability" of a psychometric instrument. This paper provides guidance for introducing, via a case-study, the non-statistician to CFA. As a complement to our instruction about the more traditional SPSS, we successfully piloted the software R for estimating CFA on nine non-statisticians. This approach can be used with healthcare graduate students taking a multivariate course, as well as modified for community stakeholders of our Center for American Indian Community Health (e.g. community advisory boards, summer interns, & research team members). The placement of CFA at the end of the class is strategic and gives us an opportunity to do some innovative teaching: (1) build ideas for understanding the case study using previous course work (such as ANOVA); (2) incorporate multi-dimensional scaling (that students already learned) into the selection of a factor structure (new concept); (3) use interactive data from the students (active learning); (4) review matrix algebra and its importance to psychometric evaluation; (5) show students how to do the calculation on their own; and (6) give students access to an actual recent research project.