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是非常重要的有效性测量量表在癌症控制和健康科学的一般。虽然我们有量表来衡量信念（基于健康信念模型），这些信念可以预测癌症筛查行为，但我们不知道文化偏见的程度。因此，我们在心理测量学有效性的评估中缺少了一个关键的组成部分。用于检测项目偏差的统计技术被称为检测项目差异功能（DIF）的程序。在某些情况下，两个最常用的DIF程序，逻辑回归和Mantel-Haenszel，可以不准确地显示膨胀的I型错误。本方法建议的主要目的是通过使用替代匹配评分而不是传统的汇总项目评分来提高logistic回归和Mantel-Haenszel程序的功效和I型错误。使用来自两个NIH资助的大型随机干预试验的现有数据来增加乳房X线摄影筛查（理想的，因为白人和非洲裔美国人的比例相等），我们将解决这些目标：（1）使用基于数据结构的计算机模拟来将结果推广到乳腺癌筛查信念量表，我们将评估逻辑回归和Mantel-Haenszel DIF程序的创新修改控制I类错误膨胀和提高功效的程度，（2）使用最佳改良的logistic回归和Mantel-Haenszel DIF程序（源自目标1），在两个现有数据集中评估DIF项目，该数据集基于种族、年龄、教育和收入，采用通常用于预测癌症筛查的六个量表（障碍、益处、易感性、宿命论、自我效能和恐惧）。提高DIF程序的准确性将提高癌症控制量表的有效性，为推进癌症控制和预防科学提供坚实的测量基础。此外，研究结果将具有广泛的适用性，以检测其他规模的项目偏见。

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.