Personality Research and Assessment in the Era of Machine Learning
Personality Research and Assessment in the Era of Machine Learning
复制标题
机器学习时代的人格研究与评估
DOI：
10.1002/per.2257
复制
发表时间：
2020
影响因子：
5.9
通讯作者：
Bühner, Markus
中科院分区：
文献类型：
--
作者：
Stachl, Clemens;Pargent, Florian;Hilbert, Sven;Harari, Gabriella M.;Schoedel, Ramona;Vaid, Sumer;Gosling, Samuel D.;Bühner, Markus
从移动传感研究中以数字足迹的形式收集的有关人类行为的高维、细粒度数据的可用性不断增加，这将彻底改变人格心理学家进行研究和进行人格评估的方式。这些新类型和数量的数据提出了如何正确分析数据和解释结果的重要问题。机器学习模型非常适合此类数据，使研究人员能够对高度复杂的关系进行建模，并使用重采样方法评估其结果的普遍性和稳健性。机器学习模型的正确使用需要专门的方法培训，考虑此类建模的特定问题。在这里，我们首先简要概述过去在人格心理学中使用机器学习的研究。其次，我们说明了研究人员在构建、解释和验证机器学习模型时面临的主要挑战。第三，我们讨论使用机器学习方法导出的人格量表的评估。第四，我们强调了在建模过程中使用潜在变量所产生的一些关键问题。最后，我们对机器学习模型在人格研究和评估中的未来作用进行了展望。
The increasing availability of high–dimensional, fine–grained data about human behaviour, gathered from mobile sensing studies and in the form of digital footprints, is poised to drastically alter the way personality psychologists perform research and undertake personality assessment. These new kinds and quantities of data raise important questions about how to analyse the data and interpret the results appropriately. Machine learning models are well suited to these kinds of data, allowing researchers to model highly complex relationships and to evaluate the generalizability and robustness of their results using resampling methods. The correct usage of machine learning models requires specialized methodological training that considers issues specific to this type of modelling. Here, we first provide a brief overview of past studies using machine learning in personality psychology. Second, we illustrate the main challenges that researchers face when building, interpreting, and validating machine learning models. Third, we discuss the evaluation of personality scales, derived using machine learning methods. Fourth, we highlight some key issues that arise from the use of latent variables in the modelling process. We conclude with an outlook on the future role of machine learning models in personality research and assessment.