{"title":"乘以 37(或除以 0.027):将效应大小从标准差转换为百分点的惊人精确的经验法则","authors":"Paul von Hippel","doi":"10.3102/01623737241239677","DOIUrl":null,"url":null,"abstract":"Educational researchers often report effect sizes in standard deviation units (SD), but SD effects are hard to interpret. Effects are easier to interpret in percentile points, but converting SDs to percentile points involves a calculation that is not transparent to educational stakeholders. We show that if the outcome variable is normally distributed, we can approximate the percentile-point effect simply by multiplying the SD effect by 37 (or, equivalently, dividing the SD effect by 0.027). For students in the middle three-fifths of a normal distribution, this rule of thumb is always accurate to within 1.6 percentile points for effect sizes of up to 0.8 SD. Two examples show that the rule can be just as accurate for empirical effects from real studies. Applying the rule to empirical benchmarks, we find that the least effective third of educational interventions raise scores by 0 to 2 percentile points; the middle third raise scores by 2 to 7 percentile points; and the most effective third raise scores by more than 7 percentile points.","PeriodicalId":48079,"journal":{"name":"Educational Evaluation and Policy Analysis","volume":null,"pages":null},"PeriodicalIF":2.4000,"publicationDate":"2024-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Multiply by 37 (or Divide by 0.027): A Surprisingly Accurate Rule of Thumb for Converting Effect Sizes From Standard Deviations to Percentile Points\",\"authors\":\"Paul von Hippel\",\"doi\":\"10.3102/01623737241239677\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Educational researchers often report effect sizes in standard deviation units (SD), but SD effects are hard to interpret. Effects are easier to interpret in percentile points, but converting SDs to percentile points involves a calculation that is not transparent to educational stakeholders. We show that if the outcome variable is normally distributed, we can approximate the percentile-point effect simply by multiplying the SD effect by 37 (or, equivalently, dividing the SD effect by 0.027). For students in the middle three-fifths of a normal distribution, this rule of thumb is always accurate to within 1.6 percentile points for effect sizes of up to 0.8 SD. Two examples show that the rule can be just as accurate for empirical effects from real studies. Applying the rule to empirical benchmarks, we find that the least effective third of educational interventions raise scores by 0 to 2 percentile points; the middle third raise scores by 2 to 7 percentile points; and the most effective third raise scores by more than 7 percentile points.\",\"PeriodicalId\":48079,\"journal\":{\"name\":\"Educational Evaluation and Policy Analysis\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.4000,\"publicationDate\":\"2024-04-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Educational Evaluation and Policy Analysis\",\"FirstCategoryId\":\"95\",\"ListUrlMain\":\"https://doi.org/10.3102/01623737241239677\",\"RegionNum\":1,\"RegionCategory\":\"教育学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"EDUCATION & EDUCATIONAL RESEARCH\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Educational Evaluation and Policy Analysis","FirstCategoryId":"95","ListUrlMain":"https://doi.org/10.3102/01623737241239677","RegionNum":1,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"EDUCATION & EDUCATIONAL RESEARCH","Score":null,"Total":0}
Multiply by 37 (or Divide by 0.027): A Surprisingly Accurate Rule of Thumb for Converting Effect Sizes From Standard Deviations to Percentile Points
Educational researchers often report effect sizes in standard deviation units (SD), but SD effects are hard to interpret. Effects are easier to interpret in percentile points, but converting SDs to percentile points involves a calculation that is not transparent to educational stakeholders. We show that if the outcome variable is normally distributed, we can approximate the percentile-point effect simply by multiplying the SD effect by 37 (or, equivalently, dividing the SD effect by 0.027). For students in the middle three-fifths of a normal distribution, this rule of thumb is always accurate to within 1.6 percentile points for effect sizes of up to 0.8 SD. Two examples show that the rule can be just as accurate for empirical effects from real studies. Applying the rule to empirical benchmarks, we find that the least effective third of educational interventions raise scores by 0 to 2 percentile points; the middle third raise scores by 2 to 7 percentile points; and the most effective third raise scores by more than 7 percentile points.
期刊介绍:
Educational Evaluation and Policy Analysis (EEPA) publishes manuscripts of theoretical or practical interest to those engaged in educational evaluation or policy analysis, including economic, demographic, financial, and political analyses of education policies, and significant meta-analyses or syntheses that address issues of current concern. The journal seeks high-quality research on how reforms and interventions affect educational outcomes; research on how multiple educational policy and reform initiatives support or conflict with each other; and research that informs pending changes in educational policy at the federal, state, and local levels, demonstrating an effect on early childhood through early adulthood.