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Maximizing research on the adverse effects of child poverty through consensus measures
Author(s) -
Pollak Seth D.,
Wolfe Barbara L.
Publication year - 2020
Publication title -
developmental science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.801
H-Index - 127
eISSN - 1467-7687
pISSN - 1363-755X
DOI - 10.1111/desc.12946
Subject(s) - poverty , socioeconomic status , psychology , set (abstract data type) , variety (cybernetics) , diversity (politics) , data science , public economics , computer science , political science , environmental health , economic growth , economics , medicine , artificial intelligence , population , law , programming language
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.