Professor of Sociology and Department Chair
Jeremy Freese conducts research on various topics that seek to connect biological, psychological, and social processes. He is especially interested in how such connections are altered by large-scale social or technological changes. His work also evaluates different prospective contributions of evolutionary psychological and behavioral genetics to social science. With an interest in policy innovations that emphasize individual informed choice—such as the Medicare prescription drug benefit, Freese studies whether and how such innovations might lead to differences in how much people benefit from them.
Freese is the recipient of several awards and honors, including a two-year fellowship from the Robert Wood Johnson Scholars in Health Policy Program at Harvard University, the American Sociological Association's best disseration award and Clifford C. Clogg Award (methodology section), and a graduate fellowship from the National Science Foundation. He was previously a professor at the University of Wisconsin, Madison.
Innovation, Information, and Inequality. Freese is interested in who stands to benefits most from changes in society, especially from technological or policy innovations. With respect to technology, he and colleagues have studied who is more likely to use the Internet, finding a strong relationship between cognitive ability and Internet use (pdf). This finding connects to another project of his on the recently implemented Medicare prescription drug benefit, as he is interested in whether cognitive differences lead some people to benefit more from this program that emphasizes individual choice in a very complicated decision environment.
Genetics and Social Science. Freese has long been interested in how social science should engage new findings about the biology of behavior, especially those from behavioral genetics and evolutionary psychology. Some of this work has shown that attempts to apply evolutionary reasoning to contemporary social issues is easily misguided, such as toward the question of why parents invest more in some offspring than others (pdf) (see here for an additional paper [pdf]). He argues also that instead of thinking in terms of genetics "versus" social causes, researchers need to understand how social and policy contexts can either accentuate or attenuate the potential relevance of genetic differences (pdf).
Understanding Socioeconomic Health Inequities. Freese is investigating how to better understand and design interventions for health inequities in real-world settings. Freese and Karen Lutfey studied ethnographic data from two diabetes clinics—one catering to higher SES patients, the other to lower. From observations of this complex patient-doctor-clinic environment, they identify many ways in which socioeconomic status (SES) influences the design or successful implementation of a treatment regimen. Expounding on the concept of "fundamental causality (pdf)" which argues that the SES-health relationship cannot be reduced to a simple set of single risk factors, the researchers demonstrate how ethnographic studies can provide important observational data for enriching designs of successful health interventions. (See here for an additional paper [pdf] on this topic.)
Quantitative methods for the social sciences. Freese has written on some of the practical issues that social scientists should consider in analyzing large-scale surveys (pdf) such as the General Social Survey, proposed new standards for replication in sociology, and demonstrated theoretical and measurement problems with the widely used Ryff model (pdf)of psychological well-being. He also co-authored in Regression Models for Categorical Dependent Variables using Stata, 2nd ed. (Stata Press, 2006), one of the first books that explains how social scientists can fit and interpret regression models for categorical data using Stata software.
Freese, J. 2011. Sociology’s contribution to understanding the consequences of medical innovations. Journal of Health and Social Behavior 52(2): 282–84.
Grol-Prokopczyk, H., J. Freese, and R. Hauser. 2011. Using anchoring vignettes to assess group differences in general self-rated health. Journal of Health and Social Behavior 52(2): 246–61.
Freese, J. 2011. Integrating genomic data and social science: Challenges and opportunities. Politics and the Life Sciences 30(2): 88–92.
Freese, J., and K. Lutfey. 2011. Fundamental causality: Challenges of an animating concept for medical sociology. In The Handbook of the Sociology of Health, Illness, and Healing, ed. B. Pescosolido, J. Martin, J. McLeod, and A. Rogers, 67–81. New York: Springer.
Jin, L., F. Elwert, J. Freese, and N. Christakis. 2010. Preliminary evidence regarding the hypothesis that the sex ratio at sexual maturity may affect longevity in men. Demography 47(3): 579–86.
Maynard, D., J. Freese, and N. Schaeffer. 2010. Calling for participation: Requests, blocking moves, and rational (inter)action in survey introductions. American Sociological Review 75(5): 791–814.
Freese, J., and S. Shostak. 2009. Genetics and social inquiry. Annual Review of Sociology 35:107–28.
Shostak, S., J. Freese, B. Link, and J. Phelan. 2009. The politics of the gene: Social status and beliefs about genetics for individual outcomes. Social Psychology Quarterly 72(1): 79–93.
Freese, J. 2008. Genetics and the social science explanation of individual outcomes. American Journal of Sociology 114(S1): S1–S35.
Freese, J., S. Meland, and W. Irwin. 2007. Expressions of positive emotion in photographs, personality, and later-life marital and health outcomes. Journal of Research on Personality 41:488-97.
Lutfey, K., and J. Freese. 2007. Ambiguities of chronic illness management and challenges to the medical error paradigm. Social Science and Medicine 64:314-25.
Flynn, K. E., M. Smith, and J. Freese. 2006. When do older adults turn to the Internet for health information? Findings from the Wisconsin Longitudinal Study. Journal of General Medicine 21:1295-301.
Springer, K. W., R. M. Hauser, and J. Freese. 2006. Bad news indeed for Ryff's six-factor model of well-being. Social Science Research 35:1120-31.
Freese, J., S. Rivas, and E. Hargittai. 2006. Cognitive ability and Internet use among older adults. Poetics 34:236-49.
Freese, J. 2006. The analysis of variance and the social complexities of genetic causation. International Journal of Epidemiology 35:534-36.
Lutfey, K., and J. Freese. 2005. Toward some fundamentals of fundamental causality: Socioeconomic status and health in treatment design for diabetes. American Journal of Sociology 110:1326-72.
Long, J. S., and J. Freese. 2006. Regression Models for Categorical Dependent Variables Using Stata, 2nd ed. College Station, Texas: Stata Press.