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<p>Machine learning algorithms have been employed to simplify multidimensional self-report scales for efficiently assessing psychological state. This study first conducted a comprehensive evaluation of six feature selection methods to identify optimal strategies for psychological scale reduction and proposed a novel variation of Cronbach’s alpha for shortened scales. This comparison used 19,118 questionnaire responses from offenders in western China. Greedy selection (GREEDY) was first incorporated into the comparison and provided greater computational efficiency and competitive mean absolute error (MAE) in high-item dimensions. Meanwhile, COMBS yielded the lowest mean absolute error (MAE) in low-item dimensions. Moreover, the variation of Cronbach’s alpha (<InlineEquation ID="IEq1"> <EquationSource Format="TEX">\({\alpha }_{2}\)</EquationSource> <EquationSource Format="MATHML"><math> <msub> <mi>α</mi> <mn>2</mn> </msub> </math></EquationSource> </InlineEquation>) outperformed the standard Cronbach’s alpha (<InlineEquation ID="IEq2"> <EquationSource Format="TEX">\({\alpha }_{1}\)</EquationSource> <EquationSource Format="MATHML"><math> <msub> <mi>α</mi> <mn>1</mn> </msub> </math></EquationSource> </InlineEquation>) in 72.7% of dimensions. Using the optimal models, scale length was reduced by 47% on average. In conclusion, we provided a machine learning framework and a novel variation of Cronbach’s alpha for developing offender-specific short forms to achieve efficient psychological screening.</p>

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A Machine Learning Framework for Offender-Specific Psychological Scale Reduction

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年4月14日

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  • Jia Yao,
  • Nan Jiang,
  • Tianheng Guan,
  • Juan Zhu,
  • Jixing Yin,
  • Xingzhi He,
  • Feng Wen,
  • Zhilan Pi,
  • Wei Zhang

摘要

Machine learning algorithms have been employed to simplify multidimensional self-report scales for efficiently assessing psychological state. This study first conducted a comprehensive evaluation of six feature selection methods to identify optimal strategies for psychological scale reduction and proposed a novel variation of Cronbach’s alpha for shortened scales. This comparison used 19,118 questionnaire responses from offenders in western China. Greedy selection (GREEDY) was first incorporated into the comparison and provided greater computational efficiency and competitive mean absolute error (MAE) in high-item dimensions. Meanwhile, COMBS yielded the lowest mean absolute error (MAE) in low-item dimensions. Moreover, the variation of Cronbach’s alpha ( \({\alpha }_{2}\) α 2 ) outperformed the standard Cronbach’s alpha ( \({\alpha }_{1}\) α 1 ) in 72.7% of dimensions. Using the optimal models, scale length was reduced by 47% on average. In conclusion, we provided a machine learning framework and a novel variation of Cronbach’s alpha for developing offender-specific short forms to achieve efficient psychological screening.