Department of Psychology and Waisman Center, University of Wisconsin, Madison, WI, USA.
Departments of Economics, Population Health Sciences and Public Affairs, University of Wisconsin, Madison, WI, USA.
Dev Sci. 2020 Nov;23(6):e12946. doi: 10.1111/desc.12946. Epub 2020 Mar 12.
A variety of new research approaches are providing new ways to better understand the developmental mechanisms through which poverty affects children's development. However, studies of child poverty often characterize samples using different markers of poverty, making it difficult to contrast and reconcile findings across studies. Ideally, scientists can maximize the benefits of multiple disciplinary approaches if data from different kinds of studies can be directly compared and linked. Here, we suggest that individual studies can increase their potential usefulness by including a small set of common key variables to assess socioeconomic status and family income. These common variables can be used to (a) make direct comparisons between studies and (b) better enable diversity of subjects and aggregation of data regarding many facets of poverty that would be difficult within any single study. If kept brief, these items can be easily balanced with the need for investigators to creatively address the research questions in their specific study designs. To advance this goal, we identify a small set of brief, low-burden consensus measures that researchers could include in their studies to increase cross-study data compatibility. These US based measures can be adopted for global contexts.
各种新的研究方法正在为更好地理解贫困影响儿童发展的发展机制提供新的途径。然而，儿童贫困研究通常使用不同的贫困指标来描述样本，这使得很难在研究之间进行对比和协调发现。理想情况下，如果可以直接比较和关联来自不同类型研究的数据，那么科学家可以最大限度地利用多种学科方法的优势。在这里，我们建议个体研究可以通过纳入一小部分共同的关键变量来评估社会经济地位和家庭收入，从而增加其潜在的有用性。这些共同的变量可以用于：(a) 直接比较研究；(b) 更好地实现多样性的主体和关于贫困的许多方面的数据聚合，这在任何单个研究中都很难实现。如果这些项目保持简短，那么它们可以很容易地与研究人员在其特定研究设计中创造性地解决研究问题的需求相平衡。为了实现这一目标，我们确定了一小部分简短、低负担的共识措施，研究人员可以在其研究中纳入这些措施，以提高跨研究数据的兼容性。这些基于美国的措施可以在全球范围内采用。
