Point and interval estimates for a standardized mean difference in paired-samples designs using a pooled standard deviation.

IF 1.3
D. A. Fitts
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引用次数: 1

Abstract

A standardized mean difference using a pooled standard deviation with paired samples ( d p ; paired-pooled design) can be compared directly to a d p from an independent samples design, but the unbiased point estimate g p and confidence interval (CI) for d p cannot unless the population correlation ρ between the scores is known in the paired-pooled design, which it rarely is. The ρ is required to calculate the degrees of freedom ν for the design, and ν is necessary to calculate the g p and CI. If a variable sample correlation is substituted for ρ the ν is only approximate and the sampling distribution for d p is unknown. This article uses simulations to compare the characteristics of the unknown distribution to the noncentral t distribution as an approximation and provides empirically-derived regression equations to compensate for the bias in the approximated CI computed using the noncentral t distribution. The result is an approximate but much more accurate coverage of the CI than previously available. Tables are supplied to assist sample size planning and computer programs are provided for computations. These results are experimental and tentative until the actual distribution can be discovered. The regularity of the deviation in coverage that allows the compensation to work encourages that search.
使用合并标准差的配对样本设计中标准化平均差的点和区间估计。
使用配对样本的合并标准差的标准化平均差(d p;配对合并设计)可以直接与独立样本设计的d p进行比较,但除非在配对合并设计中已知得分之间的总体相关性ρ,否则无法使用无偏点估计g p和d p的置信区间(CI),而这很少是已知的。ρ是计算设计自由度的必要条件,而Γ是计算g p和CI的必要条件。如果用可变样本相关性代替ρ,则Γ只是近似的,d p的采样分布是未知的。本文使用模拟来比较未知分布和非中心t分布的特性作为近似值,并提供经验推导的回归方程来补偿使用非中心t分配计算的近似CI中的偏差。结果是对CI的近似但比以前可用的要准确得多的覆盖。提供表格以协助样本量规划,并提供计算机程序进行计算。在发现实际分布之前,这些结果都是实验性的和试探性的。覆盖范围偏差的规律性允许补偿发挥作用,这鼓励了这种搜索。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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