College of Nursing, The Pennsylvania State University, United States; The Ohio State University, College of Social Work, United States; Division of Social Work, California State University, Sacramento, United States.
College of Nursing, The Pennsylvania State University, United States.
Child Abuse Negl. 2020 Apr;102:104397. doi: 10.1016/j.chiabu.2020.104397. Epub 2020 Feb 7.
Black children continue to be found in child welfare outcome measures at rates nearly double those of White children in the United States. Researchers have turned from bias theory to risk theory, arguing that disparity disappears when considering only the subgroup of children in poverty. In this study, we consider whether this phenomenon is an example of Simpson's Paradox, where aggregate findings are confounded by a third factor.
We created a dataset by matching child welfare data to schools in a metropolitan California county.
We consider measures of poverty and racial-ethnic student composition as possible confounders, utilizing compositional data analysis for the latter. Traditional linear and ridge regression models were used to calculate the unadjusted and adjusted effects of each independent variable.
We find only partial evidence of Simpson's Paradox, in that Black to White disparity only disappears in the highest quartile of poverty. Holding poverty constant, only increasing student population non-White composition was significantly associated with reducing Black to White disparity ratios.
In a small, exploratory study, we find that while poverty may serve as an equalizer, diversity racial/ethnic student body composition may serve as a neutralizer. We find that underlying causes of disparity are complex and caution against endorsement of single theories to explain the disproportionate representation of Black children in child welfare. We find utility in analyzing child welfare data with concepts and techniques common in other disciplines and highlight several weaknesses of current child welfare informatics which impact both program evaluation and research.
在美国，黑童在儿童福利结果衡量标准中的比例几乎是白童的两倍。研究人员已从偏见理论转向风险理论，认为当仅考虑贫困儿童亚组时，差异就会消失。在这项研究中，我们考虑这种现象是否是辛普森悖论的一个例子，即总体发现因第三个因素而混淆。
我们通过将儿童福利数据与加利福尼亚州大都市区的学校相匹配，创建了一个数据集。
我们将贫困和种族-族裔学生构成视为可能的混杂因素，后者利用组成数据分析。传统的线性和岭回归模型用于计算每个自变量的未调整和调整效应。
我们仅发现了辛普森悖论的部分证据，即黑童与白童的差异仅在贫困程度最高的四分位数中消失。在保持贫困不变的情况下，只有增加学生群体中非白人的构成，才能显著降低黑童与白童的差异比率。
在一项小型探索性研究中，我们发现，虽然贫困可能起到平衡作用，但多样化的种族/族裔学生群体构成可能起到中和作用。我们发现，差异的根本原因很复杂，因此不应仅通过单一理论来解释黑童在儿童福利中的不成比例代表性。我们发现，用其他学科中常见的概念和技术分析儿童福利数据具有实用性，并强调了当前儿童福利信息学的几个弱点，这些弱点既影响了项目评估，也影响了研究。
