{"title":"在没有数据的情况下进行方差分析","authors":"J. Stauffer, A. Saran","doi":"10.18374/jims-20-1.3","DOIUrl":null,"url":null,"abstract":"We present a simple method for conducting factorial ANOVAs in the absence of data. The method relies on descriptive statistics, namely the mean, variance, and sample size for each cell in the design. We briefly describe how this method can easily generalized to any number of factors, allowing us to analyze n-way factorial ANOVAs with any number of interactions. We then introduce the idea that this method allows us to (a) perform ANOVAs from existing studies that did not themselves perform ANOVAs and (b) combine descriptives from multiple studies in order to cumulate them into a sort of meta-analytic ANOVA.","PeriodicalId":41612,"journal":{"name":"International Journal of Management Studies","volume":"21 1","pages":""},"PeriodicalIF":1.0000,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"CONDUCTING ANOVAS WITHOUT DATA\",\"authors\":\"J. Stauffer, A. Saran\",\"doi\":\"10.18374/jims-20-1.3\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We present a simple method for conducting factorial ANOVAs in the absence of data. The method relies on descriptive statistics, namely the mean, variance, and sample size for each cell in the design. We briefly describe how this method can easily generalized to any number of factors, allowing us to analyze n-way factorial ANOVAs with any number of interactions. We then introduce the idea that this method allows us to (a) perform ANOVAs from existing studies that did not themselves perform ANOVAs and (b) combine descriptives from multiple studies in order to cumulate them into a sort of meta-analytic ANOVA.\",\"PeriodicalId\":41612,\"journal\":{\"name\":\"International Journal of Management Studies\",\"volume\":\"21 1\",\"pages\":\"\"},\"PeriodicalIF\":1.0000,\"publicationDate\":\"2020-03-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Management Studies\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.18374/jims-20-1.3\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"MANAGEMENT\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Management Studies","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.18374/jims-20-1.3","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"MANAGEMENT","Score":null,"Total":0}
We present a simple method for conducting factorial ANOVAs in the absence of data. The method relies on descriptive statistics, namely the mean, variance, and sample size for each cell in the design. We briefly describe how this method can easily generalized to any number of factors, allowing us to analyze n-way factorial ANOVAs with any number of interactions. We then introduce the idea that this method allows us to (a) perform ANOVAs from existing studies that did not themselves perform ANOVAs and (b) combine descriptives from multiple studies in order to cumulate them into a sort of meta-analytic ANOVA.