Estimating standardized linear contrasts of means with desired precision.

2009/01/01 by Douglas G. Bonett · 1 citation
Decision Sciences · Mathematics · #Optimal Experimental Design Methods #Statistical Methods in Clinical Trials #Advanced Statistical Methods and Models #Sample size determination #Confidence interval #Statistics #Inference #Statistical inference #Sample (material) #Mathematics #Computer science #Artificial intelligence

paper · doi:10.1037/a0014270

openalex publication_date 2009/01/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/22

Abstract

L. Wilkinson and the Task Force on Statistical Inference (1999) recommended reporting confidence intervals for measures of effect sizes. If the sample size is too small, the confidence interval may be too wide to provide meaningful information. Recently, K. Kelley and J. R. Rausch (2006) used an iterative approach to computer-generate tables of sample size requirements for a standardized difference between 2 means in between-subjects designs. Sample size formulas are derived here for general standardized linear contrasts of k > or = 2 means for both between-subjects designs and within-subjects designs. Special sample size formulas also are derived for the standardizer proposed by G. V. Glass (1976).

Citations

• Primary, Secondary, and Meta-Analysis of Research

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• Effect size [wikipedia]

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