Simple, Powerful Statistics: An Instantiation of a Better ‘Mousetrap’

Mark D. Roberts
{"title":"Simple, Powerful Statistics: An Instantiation of a Better ‘Mousetrap’","authors":"Mark D. Roberts","doi":"10.2458/V2I2.15989","DOIUrl":null,"url":null,"abstract":"R.S. Rodger fully developed, more than three decades ago, probably the most powerful methodology which exists for detecting real differences among population means (μ’s) following an analysis of variance. Since it is a post hoc method, a theoretically infinite number of potential statistical decisions may be considered, but Rodger’s method limits the final number of decisions to a single set which contains exactly J-1 (i.e., v1, the number of means in a study minus one) of them. It also constrains the number of these J-1 decisions that may be declared statistically “significant.” Rodger’s method utilizes a decision-based error rate, and ensures that the expected rate of rejecting null contrasts that should not have been rejected (i.e., the type 1 error rate) will be less than or equal to either five or one percent, regardless of the number of contrasts examined by a researcher prior to finally deciding upon the scientifically optimal set of decisions. The greatest virtue of Rodger's method, though, is not its considerable power, but its explicit specification of the magnitude of the differences that the researcher will claim to exist among the population parameters. The implied true means that this method calculates are the theoretical population μ’s that are logically implied, and mathematically entailed, by the J-1 statistical decisions that the researcher has made. These implied true means can assist other researchers in confirming or disconfirming population parameter claims made by those who use Rodger’s method. A free computer program (SPS) that instantiates Rodger’s method, and thereby makes its use accessible to every researcher who has access to a Windows-based computer, is available from the author. DOI:10.2458/azu_jmmss_v2i2_roberts","PeriodicalId":90602,"journal":{"name":"Journal of methods and measurement in the social sciences","volume":"34 1","pages":"63-79"},"PeriodicalIF":0.0000,"publicationDate":"2011-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"9","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of methods and measurement in the social sciences","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2458/V2I2.15989","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 9

Abstract

R.S. Rodger fully developed, more than three decades ago, probably the most powerful methodology which exists for detecting real differences among population means (μ’s) following an analysis of variance. Since it is a post hoc method, a theoretically infinite number of potential statistical decisions may be considered, but Rodger’s method limits the final number of decisions to a single set which contains exactly J-1 (i.e., v1, the number of means in a study minus one) of them. It also constrains the number of these J-1 decisions that may be declared statistically “significant.” Rodger’s method utilizes a decision-based error rate, and ensures that the expected rate of rejecting null contrasts that should not have been rejected (i.e., the type 1 error rate) will be less than or equal to either five or one percent, regardless of the number of contrasts examined by a researcher prior to finally deciding upon the scientifically optimal set of decisions. The greatest virtue of Rodger's method, though, is not its considerable power, but its explicit specification of the magnitude of the differences that the researcher will claim to exist among the population parameters. The implied true means that this method calculates are the theoretical population μ’s that are logically implied, and mathematically entailed, by the J-1 statistical decisions that the researcher has made. These implied true means can assist other researchers in confirming or disconfirming population parameter claims made by those who use Rodger’s method. A free computer program (SPS) that instantiates Rodger’s method, and thereby makes its use accessible to every researcher who has access to a Windows-based computer, is available from the author. DOI:10.2458/azu_jmmss_v2i2_roberts
简单,强大的统计:一个更好的“捕鼠器”的实例
R.S.罗杰在三十多年前就充分发展了可能是最强大的方法,可以通过方差分析来检测总体均值(μ s)之间的实际差异。由于这是一种事后方法,理论上可以考虑无限数量的潜在统计决策,但Rodger的方法将最终决策数量限制在一个集合中,该集合恰好包含J-1(即v1,研究中的平均值数减去1)。它还限制了这些可能被宣布为统计上“重要”的J-1决定的数量。Rodger的方法利用基于决策的错误率,并确保拒绝不应该被拒绝的零对比的预期率(即类型1错误率)将小于或等于5%或1%,而不管研究人员在最终决定科学上最优的决策集之前检查了多少对比。Rodger的方法最大的优点不是它的强大,而是它明确地说明了研究人员声称的总体参数之间存在的差异的大小。隐含的真值意味着该方法计算的是理论总体的μ值,这些μ值是研究人员所做的J-1统计决策在逻辑上和数学上隐含的。这些隐含的真均值可以帮助其他研究人员确认或否定那些使用罗杰方法的人所提出的总体参数要求。作者提供了一个免费的计算机程序(SPS),该程序实例化了罗杰的方法,从而使每个能够访问基于windows的计算机的研究人员都可以使用它。DOI: 10.2458 / azu_jmmss_v2i2_roberts
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
审稿时长
26 weeks
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信