{"title":"RETR_PWR: an SAS macro for retrospective statistical power analysis.","authors":"Kristine Y Hogarty, Jeffrey D Kromrey","doi":"10.3758/bf03195537","DOIUrl":null,"url":null,"abstract":"<p><p>In contrast to prospective power analysis, retrospective power analysis provides an estimate of the statistical power of a hypothesis test after an investigation has been conducted rather than before. In this article, three approaches to obtaining point estimates of power and an interval estimation algorithm are delineated. Previous research on the bias and sampling error of these estimates is briefly reviewed. Finally, an SAS macro that calculates the point and interval estimates is described. The macro was developed to estimate the power of an F test (obtained from analysis of variance, multiple regression analysis, or any of several multivariate analyses), but it may be easily adapted for use with other statistics, such as chi-square tests or t tests.</p>","PeriodicalId":79800,"journal":{"name":"Behavior research methods, instruments, & computers : a journal of the Psychonomic Society, Inc","volume":"35 4","pages":"585-9"},"PeriodicalIF":0.0000,"publicationDate":"2003-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.3758/bf03195537","citationCount":"12","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Behavior research methods, instruments, & computers : a journal of the Psychonomic Society, Inc","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3758/bf03195537","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 12
Abstract
In contrast to prospective power analysis, retrospective power analysis provides an estimate of the statistical power of a hypothesis test after an investigation has been conducted rather than before. In this article, three approaches to obtaining point estimates of power and an interval estimation algorithm are delineated. Previous research on the bias and sampling error of these estimates is briefly reviewed. Finally, an SAS macro that calculates the point and interval estimates is described. The macro was developed to estimate the power of an F test (obtained from analysis of variance, multiple regression analysis, or any of several multivariate analyses), but it may be easily adapted for use with other statistics, such as chi-square tests or t tests.