Creating abbreviated measures from lengthy questionnaires is important for reducing respondent burden while improving response quality. Though factor analytic strategies have been used to guide item retention for abbreviated questionnaires, item retention can be conceptualized as a feature selection task amenable to machine learning approaches. The present study tested a machine learning-guided approach to item retention, specifically item-level importance as measured by Shapley values for the prediction of total score, to create abbreviated versions of the Penn State Worry Questionnaire (PSWQ) in a sample of 3,906 secondary school students. Results showed that Shapley values were a useful measure for determining item retention in creating abbreviated versions of the PSWQ, demonstrating concordance with the full PSWQ. As item-level importance varied based on the proportion of the worry distribution predicted (e.g., high versus low PSWQ scores), item retention is dependent on the intended purpose of the abbreviated measure. Illustrative examples are presented.

从冗长的调查问卷中创建简短的衡量标准对于减轻受访者负担并提高答复质量非常重要。尽管因子分析策略已用于指导简短问卷的项目保留，但项目保留可以概念化为适合机器学习方法的特征选择任务。本研究测试了机器学习引导的项目保留方法，特别是通过用于预测总分的 Shapley 值来衡量的项目级别重要性，以在 3,906 名中学样本中创建宾夕法尼亚州立大学忧虑问卷 (PSWQ) 的简化版本学校学生。结果表明，Shapley 值是在创建精简版 PSWQ 时确定项目保留率的有用指标，证明了与完整 PSWQ 的一致性。由于项目级别的重要性根据预测的担忧分布的比例（例如，高与低 PSWQ 分数）而变化，因此项目保留取决于缩写测量的预期目的。给出了说明性例子。