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解读不显著结果:基于500个实证研究的量化分析

Interpreting Nonsignificant Results: A Quantitative Investigation Based on 500 Chinese Psychological Research

摘要:不显著结果(如,p > 0.05)在心理学研究中十分常见,容易被误解为接受零假设的证据,并可能导致分组匹配研究的错误推断或者忽视被小样本的不显著结果掩盖的真实效应。但国内目前尚无实证研究对不显著结果的普遍性及其解读进行调查。本研究调查500篇中文心理学实证研究,统计其摘要中出现与不显著结果相关的阴性陈述的频率,判断并统计基于阴性陈述的推断准确性,并使用贝叶斯因子对不显著结果中包含t值的研究进行重新评估。结果表明,36%的摘要提及不显著结果,共包含236个阴性陈述。其中,41%的阴性陈述对不显著结果的解读出现偏差(如,解读为支持了零假设)。对包含t值的研究进行贝叶斯因子分析,结果表明仅有5.1%的不显著结果可以提供强证据支持零假设(BF01 > 10)。与先前对国际心理学期刊的调查结果相比(30%的摘要包含阴性陈述;70%的阴性陈述对不显著结果的解读有误),中文心理学期刊中报告不显著结果的比例以及对不显著结果的解读正确率均更高。但国内研究者仍需进一步加强对不显著结果的认识,推广适于评估不显著结果的统计方法。

英文摘要:P-value is the most widely used statistical index for inference in science. Unfortunately, researchers in psychological science may not be able to interpret p-value correctly, resulting in possible mistakes in statistical inference. Our specific goal was to estimate how nonsignificant results were interpreted in the empirical studies published in Chinese Journals. Frist, We randomly selected 500 empirical research papers published in 2017 and 2018 in five Chinese prominent journals (Acta Psychological Sinica, Psychological Science, Chinese Journal of Clinical Psychology, Psychological Development and Education, Psychological and Behavioral Studies). Secondly, we screened the abstracts of the selected articles and judged whether they contained negative statements. Thirdly, we categorized each negative statement into 4 categories (Correct-frequentist, Incorrect-frequentist: whole population, Incorrect-frequentist: current sample, Difficult to judge). Finally, we calculated Bayes factors based on the t values and sample size associated with the nonsignificant results to investigate whether empirical data provide enough evidence in favor of null hypothesis. Our survey revealed that: (1) 36% of these abstracts (n = 180) mentioned nonsignificant results; (2) there were 236 negative statements in the article that referred to nonsignificant results in abstracts, and 41% negative statements misinterpreted nonsignificant results; (3) 5.1% (n = 2) nonsignificant results can provide strong evidence in favor of null hypothesis (BF01 > 10). The results suggest that Chinese researchers need to enhance their understanding of nonsignificant results and use more appropriate statistical methods to extract information from non-significant results.

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[V2] 2020-10-17 20:40:23 chinaXiv:202003.00056V2 下载全文
[V1] 2020-03-22 20:19:13 chinaXiv:202003.00056v1 查看此版本 下载全文
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