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Lynne Zummo

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Lynne Zummo

Graduate School of Education
American Democracy Graduate Fellows

About

Lynne Zummo is a 5th year PhD candidate in the Graduate School of Education. Her research examines the relationships between political ideology and learning of climate science for young people. Her current survey experiments test the effects of framing on data analysis and interpretation by young adults from diverse political backgrounds. 

Examining the variable influences of worldview, framing, and data literacy on young adults' scientific reasoning about climate change and COVID-19

While most scientific issues and ideas stay politically neutral in the public domain in the US, a few in recent history have come to bear social meanings that are politically polarizing. Perhaps the most obvious example is climate change. Despite clear agreement among the scientific community that climate change is real, human-caused, and dangerous, the US public remains divided over it. Recently, the emergence of COVID-19 has experienced moments of similar polarization, particularly early on in the pandemic's arrival to the US. This study seeks to understand complex and potentially interacting influences on the scientific reasoning of young adults around these two issues. It examines the potential influences of worldview (as a proxy for political ideology), data literacy, and framing on scientific reasoning.  To do so, this study will use an experimental intervention. Half of the participants will engage in a learning intervention about climate change. Half will engage in a learning intervention about COVID-19. Within each of these two experimental arms, participants will be randomized into one of two framing conditions. Prior to the intervention, participants will complete a brief assessment to establish their worldview, based on Kahan's (2012) scale. They will also complete a brief assessment to asses data literacy and quantitative reasoning, based on a validated scale. In analysis, covariates such as gender, age, and location will be considered. Worldview, data literacy, treatment condition, and all covariates will be used to predict various features participants' scientific reasoning. These features will be identified through qualitative coding of open-ended text responses.