Machine Learning and Psychological Research: The Unexplored Effect of Measurement
Machine Learning and Psychological Research: The Unexplored Effect of Measurement
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DOI：
10.1177/1745691620902467
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发表时间：
2020-04-29
影响因子：
12.6
通讯作者：
Grimm, Kevin J.
中科院分区：
文献类型：
--
作者：
Jacobucci, Ross;Grimm, Kevin J.
机器学习（即，数据挖掘、人工智能、大数据）在心理科学中的应用越来越多。虽然一些研究领域从一套新的统计工具中受益匪浅，最常见的是使用生物或遗传变量，但在更传统的研究领域，这种炒作并没有得到证实。我们认为，这种现象是由于测量误差导致的，这些误差阻止了机器学习算法准确地建模非线性关系，如果它们确实存在的话。通过一组模拟示例展示了这一缺点，表明机器学习算法和回归之间的模型选择取决于测量质量，而与样本大小无关。最后，我们提出了一系列建议，并讨论了如何更好地将机器学习与心理科学中传统的统计学相结合。
Machine learning (i.e., data mining, artificial intelligence, big data) has been increasingly applied in psychological science. Although some areas of research have benefited tremendously from a new set of statistical tools, most often in the use of biological or genetic variables, the hype has not been substantiated in more traditional areas of research. We argue that this phenomenon results from measurement errors that prevent machine-learning algorithms from accurately modeling nonlinear relationships, if indeed they exist. This shortcoming is showcased across a set of simulated examples, demonstrating that model selection between a machine-learning algorithm and regression depends on the measurement quality, regardless of sample size. We conclude with a set of recommendations and a discussion of ways to better integrate machine learning with statistics as traditionally practiced in psychological science.