Improving Item Bias Detection in Cancer Control Scales

描述(由申请人提供)： 评估项目是否对少数群体有偏见，对于测量量表在癌症控制和一般健康科学中的有效性非常重要。虽然我们有量表来衡量可以预测癌症筛查行为的信念(基于健康信念模型)，但我们不知道文化偏见的程度。因此，我们缺少了心理测量学有效性评估中的一个关键部分。用于检测项目偏差的统计技术称为检测差异项目功能的程序(DIF)。在某些情况下，两种最常用的DIF程序，Logistic回归和Mantel-Haenszel，可能会不准确地显示膨胀的类型I错误。本方法论建议的主要目的是通过使用传统总和评分以外的其他匹配分数来改善Logistic回归和Mantel-Haenszel程序的能力和I类错误。利用NIH资助的两个大型随机干预试验的现有数据来增加乳房X光检查(由于高加索人和非裔美国人的比例相等，这是理想的)，我们将解决这些目标：(1)使用基于数据结构的计算机模拟，将结果推广到乳腺癌筛查信念量表，我们将评估Logistic回归和Mantel-Haenszel DIF程序的创新修改在多大程度上控制了I型错误膨胀和提高能力，以及(2)使用最佳修改的Logistic回归和Mantel-Haenszel DIF程序(源自Aim 1)，基于种族、年龄、教育程度和收入通常用于预测癌症筛查的六个量表(障碍、好处、易感性、宿命论、自我效能和恐惧)。提高DIF程序的准确性将增加癌症控制量表的有效性，为推进癌症控制和预防的科学提供坚实的测量基础。此外，研究结果还将对其他量表的项目偏差检测具有广泛的适用性。

DESCRIPTION (provided by applicant): Assessing whether items are biased against minority groups is very important for the validity of measurement scales in cancer control and in health sciences in general. Although we have scales to measure beliefs (based on the Health Belief Model) that are predictive of cancer screening behavior, we do not know the extent of cultural bias. Thus, we are missing a critical component in the assessment of psychometric validity. The statistical techniques for detecting item bias are called procedures for detecting differential item functioning (DIF). Under certain conditions, two of the most commonly-used DIF procedures, logistic regression and Mantel-Haenszel, can inaccurately display inflated Type I error. The primary purpose of the present methodological proposal is to improve power and Type I error of both the logistic regression and Mantel-Haenszel procedures by using alternative matching scores other than traditional summed-item score. Using existing data from two large NIH-funded randomized intervention trials to increase mammography screening (ideal because of equal percentages of Caucasians and African-Americans), we will address these aims: (1) Using computer simulations based on the data structure for generalizing results to breast cancer screening belief scales, we will evaluate the extent to which innovative modifications of logistic regression and Mantel-Haenszel DIF procedures control Type I error inflation and improve power, and (2) Using the best modified logistic regression and Mantel-Haenszel DIF procedures (derived from Aim 1), assess items for DIF in the two existing data sets based upon ethnicity, age, education and income in six scales commonly used to predict cancer screening (barriers, benefits, susceptibility, fatalism, self-efficacy, and fear). Improved accuracy of DIF procedures will increase the validity of cancer control scales, providing a firm measurement foundation for advancing the science of cancer control and prevention. Additionally, the findings will have broad applicability to detecting item bias in other scales.