Dr. Axinn is a Professor of survey research, population studies, sociology, and public policy at the University of Michigan. Axinn is a social demographer studying community, intergenerational, and social psychological influences on marriage, childbearing, reproductive health, mental health, and the natural environment. He is the former director of Michigan’s Survey Research Center, which has an active program of methodological research on longitudinal studies, survey data collection, and mixed-method studies. He is the director of the Chitwan Valley Family Study (CVFS), a 30-year, mixed-method longitudinal study in Nepal. He also currently serves as the principal investigator of the U.S. Panel Study of Income Dynamics Transition into Adulthood Supplement (PSID-TAS) 2025 and a co-investigator on the team leading the American Family Health Study (AFHS).
He is the MPI of an NIMH R01 study (R01MH110872), which utilized diagnostic interviews to assess mental disorders in over 10,700 members of the CVFS and collected their DNA. This study employed several innovative data collection tools that he developed, including mixed-mode, multi-device tools for tracking respondents over time and using Life History Calendars to enhance the measurement of mental disorder symptoms. The measurement of mental health embedded in this whole-family longitudinal study has supported a wide range of analytic advances, including the estimation of age-specific mental health differences in long-term consequences of exposure to armed conflict, discovery of community-level social support programs that reduce the prevalence of major depressive disorder, and long-term intergenerational consequences of parental mental disorders.
Within Data Science and AI, he is excited about developing innovative tools to assist scientists in minimizing the consequences of total survey errors when estimating causal models using longitudinal data. This work will advance the understanding of the discrepancies that result from differences in question-wording, order, response options, mode of interview, and respondents’ characteristics (such as native languages) across multiple surveys of the same or closely related topics. This research has high potential to guide a new generation of data collection methods that support scientists in making causal inferences.
The most significant scientific contribution he would like to make is in helping the next generation of scientists use innovative data collection methods to advance answers to high-priority research questions.