Data Use, Quality and Cost in Policy Research
Impact of Bias on Data Interpretation
How do preconceived biases influence the way that individuals interpret data? In IEEE Transactions on Visualization and Computer Graphics, psychologist and IPR associate Steven Franconeri and his team investigate this question by conducting two studies. In the first study, 295 adult participants viewed four different scatterplots, two with moderate correlations of 0.4, and two that were highly related with correlations of 0.6. The scatterplots were either labeled with named, related variables, such as number of environmental regulations and air quality, or with “X” and “Y.” After viewing them, participants reported how much they believed the variables for each scatterplot to be related. The second study was conducted similarly, except 300 participants reported how much they believed the named variables from the first study were related before they saw the scatterplots labeled with “X” and “Y.” The results of both studies demonstrate that participants who thought the variables were related overestimated the correlation values, while participants who thought that they were not underestimated the correlation values. The research shows that the participants’ preconceived biases influenced their viewing of the data. These findings are significant for researchers whose data interpretations may be influenced by their hypotheses or preconceived beliefs, as well as for data communicators because no matter how definitive the data are, their audience may misinterpret them.
Developing a Meaningful Measure of Food Policy Progress
Inclusive and accountable food policy processes are the critical structures that enable developing nations to reduce poverty, improve nutritional outcomes, increase employment, and mitigate the effects of climate change. Many developing countries have inadequate policy processes, but existing assessments of policy processes are “mashups” of non-statistical metrics that make meaningful cross-country comparison difficult at best. In World Development, Northwestern University’s James Oehmke and co-authors, including IPR anthropologist Sera Young and Buehler Center Director and IPR associate Lori Post, develop a food and agricultural policy readiness index that measures a country's ability or "readiness" to pursue inclusive policy to end poverty and hunger. Application of this measurement tool can be a game-changer in helping developing countries' governments to really move the needle on poverty reduction and food security, especially in light of growing threats from climate change. The researchers classify 19 developing countries and five regional economic communities into three levels of policy readiness by applying measurement analysis to 338 policy commitments and actions in these countries between 2013 and 2015, as tracked by the U.S. Government’s Feed the Future initiative. Countries in the highest category of policy readiness include Mali and Rwanda, where research has found food policies to be sensible and effective. Kenya had the lowest policy readiness score, partly because observation occurred during the constitutional devolution of the national government. In Mozambique, the researchers used the measurement to inform where donors had contributed to national policy commitments and where donors were impediments to policy change. Countries with low readiness scores would benefit most from donor support to strengthen inclusive policy processes such as giving greater voice to the marginalized. Countries with high readiness scores would benefit most from support for specific policy changes.
Two New Measures of Structural Racism in Schools
Previous research shows that encountering racism from others predicts poor physical and mental health, but scholars are still working to measure the effects of structural racism on health. In SSM – Population Health, IPR anthropologist Thomas McDade, former postdoctoral fellows Jessica Polos and Stephanie Koning, and their colleagues take a biosocial perspective to analyze survey data from the National Longitudinal Study of Adolescent to Adult Health. They create two indices of structural racism in schools, focusing on a representative sample of over 12,000 students aged 11–21 who were surveyed in 1994–95. The researchers’ first measure, a school contextual disadvantage index (CDI), captures resources and opportunities across schools, thus including historic concentration of Black students in more disadvantaged schools. The second, the school structural racism index (SRI), compares Black and White students’ shares of resources and opportunities within schools. As a final step, the researchers relate the indices to depression in adolescents. The findings demonstrate that Black students were more than twice as likely to attend schools with higher CDI levels compared to White students. Increases in CDI were linked to increased depressive symptoms among all students, but the increase was highest among Black girls. Separately, exposure to racism within school, measured by the SDI, is associated with more depressive symptoms in Black boys and girls. This means that Black youth at more advantaged schools, which tend to have lower percentages of Black students, are at a higher risk of depression. McDade is the Carlos Montezuma Professor of Anthropology.