Liu Xiaofeng Steven
Department of Educational Studies, University of South Carolina, Columbia, SC, USA.
J Gen Psychol. 2024 Jan-Mar;151(1):54-62. doi: 10.1080/00221309.2023.2172545. Epub 2023 Feb 15.
Cohen's - a common effect size - contains a positive bias. The traditional bias correction, based on strict distribution assumption, does not always work for a small study with limited data. The non-parametric bootstrapping is not limited by distribution assumption and can be used to remove the bias in Cohen's . A real example is included to illustrate the implementation of bootstrap bias estimation and the removal of sizable bias in Cohen's .
科恩d值——一种常见的效应量——存在正向偏差。基于严格分布假设的传统偏差校正,对于数据有限的小型研究并不总是有效。非参数自抽样不受分布假设的限制,可用于消除科恩d值中的偏差。文中包含一个实际例子,以说明自抽样偏差估计的实施以及科恩d值中显著偏差的消除。