{"title":"PLS路径建模中复合材料预测性能的评价","authors":"N. Danks, Soumya Ray, G. Shmueli","doi":"10.2139/ssrn.3055222","DOIUrl":null,"url":null,"abstract":"Efforts to evaluate predictive performance in Partial Least Squares (PLS) path modeling are making major headway, but have largely focused on the prediction of measurement items. There is still a need to clarify what prediction of constructs might entail. We examine the challenges of measuring predictive power and validity at the construct level. We then propose a technique for overcoming these challenges and provide suitable predictive metrics.","PeriodicalId":106740,"journal":{"name":"ERN: Other Econometrics: Econometric Model Construction","volume":"100 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":"{\"title\":\"Evaluating the Predictive Performance of Composites in PLS Path Modeling\",\"authors\":\"N. Danks, Soumya Ray, G. Shmueli\",\"doi\":\"10.2139/ssrn.3055222\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Efforts to evaluate predictive performance in Partial Least Squares (PLS) path modeling are making major headway, but have largely focused on the prediction of measurement items. There is still a need to clarify what prediction of constructs might entail. We examine the challenges of measuring predictive power and validity at the construct level. We then propose a technique for overcoming these challenges and provide suitable predictive metrics.\",\"PeriodicalId\":106740,\"journal\":{\"name\":\"ERN: Other Econometrics: Econometric Model Construction\",\"volume\":\"100 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-10-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"7\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ERN: Other Econometrics: Econometric Model Construction\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.2139/ssrn.3055222\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ERN: Other Econometrics: Econometric Model Construction","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2139/ssrn.3055222","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Evaluating the Predictive Performance of Composites in PLS Path Modeling
Efforts to evaluate predictive performance in Partial Least Squares (PLS) path modeling are making major headway, but have largely focused on the prediction of measurement items. There is still a need to clarify what prediction of constructs might entail. We examine the challenges of measuring predictive power and validity at the construct level. We then propose a technique for overcoming these challenges and provide suitable predictive metrics.