Promoting Knowledge Accumulation About Intervention Effects: Exploring Strategies for Standardizing Statistical Approaches and Effect Size Reporting

Toward the goal of more rapid knowledge accumulation via better meta-analyses, this article explores statistical approaches intended to increase the precision and comparability of effect sizes from education research. The featured estimate of the proposed approach is a standardized mean difference effect size whose numerator is a mean difference that has been adjusted for baseline differences in the outcome measure, at a minimum, and whose denominator is the total variance. The article describes the utility and efficiency of covariate adjustment through baseline measures and the need to standardize effects on a total variance that accounts for variation at multiple levels. As computation of the total variance can be complex in multilevel studies, a shiny application is provided to assist with computation of the total variance and subsequent effect size. Examples are provided for how to interpret and input the required calculator inputs.

为了通过更好的元分析更快速地积累知识这一目标，本文探讨了旨在提高教育研究中效应量的精度和可比性的统计方法。所提出方法的特色估计量是一个标准化均数差效应量，其分子是一个至少针对结果测量中的基线差异进行了调整的均数差，其分母是总方差。本文描述了通过基线测量进行协变量调整的效用和效率，以及在考虑多个层面变异的总方差上对效应进行标准化的必要性。由于在多层研究中总方差的计算可能很复杂，本文提供了一个 shiny应用程序来辅助总方差的计算以及后续效应量的计算。还提供了如何解释和输入所需计算器输入值的示例。