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Improving inferential analyses predata and postdata.
Psychological Methods ( IF 7.6 ) Pub Date : 2024-09-09 , DOI: 10.1037/met0000697
David Trafimow 1 , Tingting Tong 2 , Tonghui Wang 2 , S T Boris Choy 3 , Liqun Hu 2 , Xiangfei Chen 2 , Cong Wang 4 , Ziyuan Wang 5
Affiliation  

The standard statistical procedure for researchers comprises a two-step process. Before data collection, researchers perform power analyses, and after data collection, they perform significance tests. Many have proffered arguments that significance tests are unsound, but that issue will not be rehashed here. It is sufficient that even for aficionados, there is the usual disclaimer that null hypothesis significance tests provide extremely limited information, thereby rendering them vulnerable to misuse. There is a much better postdata option that provides a higher grade of useful information. Based on work by Trafimow and his colleagues (for a review, see Trafimow, 2023a), it is possible to estimate probabilities of being better off or worse off, by varying degrees, depending on whether one gets the treatment or not. In turn, if the postdata goal switches from significance testing to a concern with probabilistic advantages or disadvantages, an implication is that the predata goal ought to switch accordingly. The a priori procedure, with its focus on parameter estimation, should replace conventional power analysis as a predata procedure. Therefore, the new two-step procedure should be the a priori procedure predata and estimations of probabilities of being better off, or worse off, to varying degrees, postdata. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

中文翻译:


改进数据前和数据后的推理分析。



研究人员的标准统计程序包括两个步骤。在数据收集之前,研究人员进行功效分析,在数据收集之后,他们进行显着性检验。许多人提出了显着性检验不健全的论点,但这里不会重复这个问题。即使对于狂热爱好者来说,也有通常的免责声明,即零假设显着性检验提供的信息极其有限,从而使它们容易被滥用。有一个更好的后数据选项,可以提供更高级别的有用信息。根据 Trafimow 和他的同事的工作(综述,请参见 Trafimow,2023a),可以根据一个人是否接受治疗,不同程度地估计情况变得更好或更糟的概率。反过来,如果数据后目标从显着性检验转向关注概率优势或劣势,则意味着数据前目标应该相应地转变。先验过程侧重于参数估计,应取代传统的功率分析作为预数据过程。因此,新的两步程序应该是先验程序预数据和不同程度的事后数据变得更好或更差的概率估计。 (PsycInfo 数据库记录 (c) 2024 APA,保留所有权利)。
更新日期:2024-09-09
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