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使用机器学习方法开发和验证瑞文彩色渐进矩阵简版

Development and validation of a short form for the Raven's Coloured Progressive Matrices using a machine learning approach.

作者信息

Yip Charles Chiu Hung, Wong Terry Tin-Yau, Wong Brandon Hoi Dick, Hsu Lucy Shih-Ju

机构信息

Department of Psychology, The University of Hong Kong, Pokfulam, Hong Kong.

出版信息

Br J Dev Psychol. 2025 Jan 23. doi: 10.1111/bjdp.12542.

Abstract

Raven's Coloured Progressive Matrices (CPM) is a widely used assessment tool for measuring general cognitive ability in developmental and educational research, particularly in studies involving young children. However, administering the full set of the 36-item CPM can be burdensome for young participants, hindering its practicality in large-scale studies and reducing research efficiency. In the current study, a short form of the CPM was developed based on a sample of preschoolers (n = 336, mean age = 5.8 years) using penalised regression, a machine learning approach that allows for variable selection. The resulting 12-item CPM short form demonstrated a very strong correlation with the total score of the 36-item full form (r = .94). Further investigations into the short form's item stability, content validity, and concurrent validity collectively supported its psychometric properties as a reliable and valid alternative to the full form. The significance of the CPM short form is also discussed.

摘要

瑞文彩色渐进矩阵测验(CPM)是发展与教育研究中广泛用于测量一般认知能力的评估工具,尤其在涉及幼儿的研究中。然而,对年轻参与者来说,进行全套36项的CPM测验可能很繁琐,这阻碍了它在大规模研究中的实用性,并降低了研究效率。在本研究中,基于一组学龄前儿童样本(n = 336,平均年龄 = 5.8岁),采用惩罚回归(一种允许变量选择的机器学习方法)开发了CPM的简短版本。由此产生的12项CPM简短版本与36项完整版本的总分显示出非常强的相关性(r = 0.94)。对简短版本的项目稳定性、内容效度和同时效度的进一步研究共同支持了其作为完整版本的可靠且有效的替代方案的心理测量特性。还讨论了CPM简短版本的意义。

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