Department of Psychology, Arizona State University.
Psychol Methods. 2018 Dec;23(4):654-671. doi: 10.1037/met0000174. Epub 2018 Mar 29.
This article describes benchmark validation, an approach to validating a statistical model. According to benchmark validation, a valid model generates estimates and research conclusions consistent with a known substantive effect. Three types of benchmark validation-(a) benchmark value, (b) benchmark estimate, and (c) benchmark effect-are described and illustrated with examples. Benchmark validation methods are especially useful for statistical models with assumptions that are untestable or very difficult to test. Benchmark effect validation methods were applied to evaluate statistical mediation analysis in eight studies using the established effect that increasing mental imagery improves recall of words. Statistical mediation analysis led to conclusions about mediation that were consistent with established theory that increased imagery leads to increased word recall. Benchmark validation based on established substantive theory is discussed as a general way to investigate characteristics of statistical models and a complement to mathematical proof and statistical simulation. (PsycINFO Database Record (c) 2018 APA, all rights reserved).
本文描述了基准验证，这是一种验证统计模型的方法。根据基准验证，有效的模型生成的估计和研究结论应该与已知的实质性效果一致。描述并举例说明了三种类型的基准验证：（a）基准值，（b）基准估计和（c）基准效应。基准验证方法对于假设不可测试或非常难以测试的统计模型特别有用。基准效应验证方法应用于使用已建立的效果（即增加心理意象可提高单词回忆）评估八项研究中的统计中介分析。统计中介分析得出的关于中介的结论与增加意象会导致增加单词回忆的既定理论一致。基于既定实质性理论的基准验证被讨论为研究统计模型特征的一种通用方法，也是对数学证明和统计模拟的补充。（PsycINFO 数据库记录（c）2018 APA，保留所有权利）。
