A Unified Method for Determining the Sample Size Needed for Evaluation of Mean Differences in Hierarchical Research Designs

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  • USAMI SATOSHI
    GRADUATE SCHOOL OF EDUCATION, THE UNIVERSITY OF TOKYO· JAPAN SOCIETY FOR THE PROMOTION OF SCIENCE

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  • 階層的なデータ収集デザインにおける2群の平均値差の検定・推定のためのサンプルサイズ決定法と数表の作成
  • 階層的なデータ収集デザインにおける2群の平均値差の検定・推定のためのサンプルサイズ決定法と数表の作成 : 検定力および効果量の信頼区間の観点から
  • カイソウテキ ナ データ シュウシュウ デザイン ニ オケル 2グン ノ ヘイキンチサ ノ ケンテイ ・ スイテイ ノ タメ ノ サンプルサイズ ケッテイホウ ト スウヒョウ ノ サクセイ : ケンテイリョク オヨビ コウカリョウ ノ シンライ クカン ノ カンテン カラ
  • —検定力および効果量の信頼区間の観点から—

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  Hierarchical data sets arise when data for individuals (e.g., students, clients, or citizens) are nested within various groups (e.g., classes, hospitals, or regions), and often appear in social science research.  For such data, a hierarchical linear model, which considers the dependent structure of individuals’ data within the same groups, is useful. In the present research, we derived formulas in order to evaluate the sample size needed for research designs with hierarchical data, focusing on the mean difference between 2 groups for cases of both multisite randomized trials (MRTs) and cluster randomized trials (CRTs).  These formulas were derived in light of both statistical power and the confidence interval of the effect size.  Additionally, for practical purposes, numerical tables were constructed that could easily be used to determine the sample size needed.  The numerical tables for multisite randomized trials can also be used for paired designs, such as repeated measures designs and randomized block designs.

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