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  • 标题:Commentary on "A review of effect sizes and their confidence intervals, Part I: The Cohen's d family": The degrees of freedom for paired samples designs.
  • 其他标题:Commentary on "A review of effect sizes and their confidence intervals, Part I: The Cohen's d family": The degrees of freedom for paired samples designs.
  • 本地全文:下载
  • 作者:Fitts, Douglas A.
  • 期刊名称:Tutorials in Quantitative Methods for Psychology
  • 电子版ISSN:1913-4126
  • 出版年度:2020
  • 卷号:16
  • 期号:4
  • 页码:281-294
  • DOI:10.20982/tqmp.16.4.p281
  • 出版社:Université de Montréal
  • 摘要:In their review of effect sizes of the Cohen's d family, Goulet-Pelletier and Cousineau (2018) proposed several methods for generating confidence intervals for the unbiased standardized mean difference, g. Among them they proposed using degrees of freedom u = 2(n - 1) instead of u = (n - 1) for all paired samples designs that use a pooled standard deviation to standardize the mean difference (pooled paired samples) when calculating g and its confidence limits from a noncentral t distribution. Simulations demonstrate that the exact u for a pooled paired samples design vary as a function of the population correlation ho between 2(n - 1) at ho = .0 and (n - 1) at ho = 1.0. This affects the calculation of g and the selection of the appropriate noncentral t distribution for calculating the confidence limits. Using a sample r to estimate the unknown ho causes a further deviation from the presumed noncentral t distribution even when the u are known. These facts adversely affect the coverage of the confidence intervals computed as recommended by the authors. These methods for calculating noncentral t confidence intervals should not be used as described with pooled paired samples designs. Noncentral confidence intervals for either a two sample design or a paired samples design where the mean difference is standardized by the standard deviation of the difference scores are unaffected by this problem. An R script and C source code are provided.
  • 关键词:noncentral t distributions; parameter estimation; simulation
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