Race Variable : How Statistical Practices Reinforce Inequality

From social science and biomedical research to government and media reporting, statistics on racial and ethnic disparities are everywhere. The numbers we typically encounter, however, are not straightforward comparisons. Researchers analyze data using adjustments such as regression models that are intended to address bias and confounding factors. Yet many common statistical practices produce misleading results, and some have flawed assumptions that inadvertently misrepresent the inequalities between groups.

Jay S. Kaufman offers a clear and accessible guide to understanding the use and abuse of statistics on racial and ethnic disparities. Examining dozens of real-world examples spanning medicine, economics, education, and criminal justice, he shows how typical statistical practices―no matter how well-intentioned―have obscured the realities of injustice, with significant consequences for public policy. Kaufman considers how to select and apply statistical adjustments responsibly and systematically, and he proposes ways to improve the explanation and analysis of racial and ethnic inequalities.

Written for readers without a background in statistics, this book provides an essential introduction to quantitative reasoning in terms of social justice. The Race Variable is appropriate for undergraduate and graduate courses across the medical and social sciences―including sociology, demography, public health, epidemiology, medicine, and public policy―that focus on racial and ethnic disparities, and for all readers interested in the statistical foundations of our understanding of inequality.

Jay S. Kaufman is a professor in the Department of Epidemiology, Biostatistics, and Occupational Health at McGill University. A former president of the Society for Epidemiological Research, he is an editor of the journal Epidemiology and coeditor of the textbook Methods in Social Epidemiology (second edition, 2017).