Hypothesis Testing, Effect Size, and Fit Indices :

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  • 仮説検定, 効果量, そして適合度指標
  • 仮説検定,効果量,そして適合度指標 : SEMを用いた分散分析の理解
  • カセツ ケンテイ,コウカリョウ,ソシテ テキゴウド シヒョウ : SEM オ モチイタ ブンサン ブンセキ ノ リカイ
  • —SEMを用いた分散分析の理解—
  • Application of Structural Equation Modeling to Analysis of Variance

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Abstract

    In decision making in statistics, the p-value has been exclusively used in statistical hypothesis testing to determine whether the hypothesis is accepted.  However, the p-value has a major drawback : when the sample size is large, the p-value falls below the significance threshold by default, leading to inappropriate conclusions regarding statistical significance.  In recent years, apart from the p-value, effect size is considered important in the process of statistical decision making.  Effect sizes are measures of the strength of a phenomenon (e.g., an experiment or treatment), and they do not depend on the sample size.  As a related issue, in structural equation modeling (SEM), analysts can use many fit indices in model selection.  In addition, the t-test and analysis of variance (ANOVA), frequently used in hypothesis testing, are sub-models of the SEM.  Therefore, analysts can consider many fit indices in the t-test and ANOVA in statistical decision making.  In this study, we provide an example of model selection that references many fit indices by using a one-way within-subjects ANOVA.

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