Machine Learning and Prediction in Psychological Assessment

IF 3.2 3区 心理学 Q2 PSYCHOLOGY, APPLIED European Journal of Psychological Assessment Pub Date : 2022-05-01 DOI:10.1027/1015-5759/a000714
M. Fokkema, D. Iliescu, Samuel Greiff, M. Ziegler
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引用次数: 5

Abstract

Abstract. Modern prediction methods from machine learning (ML) and artificial intelligence (AI) are becoming increasingly popular, also in the field of psychological assessment. These methods provide unprecedented flexibility for modeling large numbers of predictor variables and non-linear associations between predictors and responses. In this paper, we aim to look at what these methods may contribute to the assessment of criterion validity and their possible drawbacks. We apply a range of modern statistical prediction methods to a dataset for predicting the university major completed, based on the subscales and items of a scale for vocational preferences. The results indicate that logistic regression combined with regularization performs strikingly well already in terms of predictive accuracy. More sophisticated techniques for incorporating non-linearities can further contribute to predictive accuracy and validity, but often marginally.
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心理评估中的机器学习与预测
摘要来自机器学习(ML)和人工智能(AI)的现代预测方法越来越受欢迎,在心理评估领域也是如此。这些方法为模拟大量预测变量和预测变量与响应之间的非线性关联提供了前所未有的灵活性。在本文中,我们的目的是看看这些方法可能有助于标准效度的评估及其可能的缺点。基于职业偏好量表的子量表和项目,我们将一系列现代统计预测方法应用于预测大学专业完成的数据集。结果表明,逻辑回归与正则化相结合在预测精度方面已经表现得非常好。结合非线性的更复杂的技术可以进一步促进预测的准确性和有效性,但通常是微不足道的。
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来源期刊
CiteScore
6.40
自引率
4.00%
发文量
71
期刊介绍: The main purpose of the EJPA is to present important articles which provide seminal information on both theoretical and applied developments in this field. Articles reporting the construction of new measures or an advancement of an existing measure are given priority. The journal is directed to practitioners as well as to academicians: The conviction of its editors is that the discipline of psychological assessment should, necessarily and firmly, be attached to the roots of psychological science, while going deeply into all the consequences of its applied, practice-oriented development.
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