METHODS: We conducted a differential item functioning (DIF) analysis comparing IRT-based person-reported cumulative social risk scores by income and education in a population-based sample (n = 2122). Six social risk items were analyzed using the two-parameter logistic model and graded response model.
RESULTS: Analyses showed no DIF on an IRT-based cumulative social risk score by education level for the six items examined. Statistically significant DIF was found on three items by income level but the ultimate effect on the scores was negligible.
CONCLUSIONS: Results suggest an IRT-based cumulative social risk score is not biased by education and income level and can be used for comparisons between groups. An IRT-based cumulative social risk score will be useful for combining datasets to examine policy factors affecting social risk and for more efficient screening of patients for social risk using computerized adaptive testing.
方法:我们进行了差异项目功能(DIF)分析,比较了以人群为基础的样本(n=2122)中基于IRT的个人报告的按收入和教育程度的累积社会风险评分。采用双参数Logistic模型和分级响应模型对6个社会风险项目分析。
结果:分析显示,对于所检查的六个项目,基于教育水平的IRT累积社会风险评分没有DIF。按收入水平在三个项目上发现了具有统计学意义的DIF,但对分数的最终影响可以忽略不计。
结论:结果表明,基于IRT的累积社会风险评分不受教育程度和收入水平的影响,可用于组间比较。基于IRT的累积社会风险评分可用于组合数据集,以检查影响社会风险的政策因素,并使用计算机自适应测试更有效地筛查患者的社会风险。