I am passionate about understanding how social inequalities are reproduced through people’s relationships, beliefs, and behaviours across the life course. My general focus has been on identifying the causal pathways and social forces driving persistent inequalities between population groups and fields. Within this general interest about analytical mechanisms, my research agenda has three foci: health inequalities, network inequalities, and population dynamics.

Health inequalities. This arm of my agenda focuses on understanding and predicting the complex causes of changes in health inequalities over time and across demographic groups. When I began my dissertation I had planned to bridge social stress research, social psychology, and cultural sociology to improve understanding about beliefs, such as personal control, and mental health. Reading widely I came to realize that extensive interdisciplinary research has shown these beliefs have an important role in causing observed life course inequalities. Yet how and why they cause various health and behavioural outcomes varied according to social demographic characteristics and constellations of personal relationships. For example, gender organizes our interactions and relationships with others in ways that education changes, which in turn shapes who people have to ask for advice or help and their decisions about how much control they have and how they can or will live their life. This leads some groups to have better or worse health, in various ways, from others. To try and make sense of this complexity instead of simply ‘controlling’ for important factors in people’s lives that lead to differences in social arrangements and their consequences, I often take a quantitative intersectional approach. This provides a better perspective on if and how the mechanisms causing substantial differences may (or may not) vary according to gender, education, family background, race-ethnicity, or other important positions in social hierarchies and forms of stratification. It’s not enough to simply assume that differences in the health outcomes of population groups are based on different mechanisms—we must put this to test at different times and in different places. Finally, I have a long-standing interest in health behaviour change. We know that ‘motion is lotion’, but people and social groups routinely engage in health lifestyles that influence how long they will live and general life quality of life. To better understand this, I will continue to investigate the virtuous circle or vicious spiral between how changes in the diverse ways that people define health and illness reflect changes in behaviour, socialization, and the characteristics that people use to draw boundaries with when thinking about groups and populations.

Network inequalities. We cannot escape relationships, and connections with others have pervasive and long-lasting influences on life course development and well-being. This is why this arm of my research identifies whether and to what extent social networks provide or deny opportunities on the basis of social group memberships, and their functions in the reproduction in larger patterns of social inequality. For example, I have a collaborative study of advice networks, showing how homophily and status characteristics influence network closure due to gender dynamics, that will be sent out for peer review during fall 2025. I have also collaboratively published methodological research about the measurement of personal network change. I am particularly interested in opportunities to do more work on social network qualities and change over time, with a keen eye to how this varies according to demographic characteristics and statuses.

Population Dynamics. The final arm of my research agenda focuses on the analytical mechanisms responsible for the unequal distribution of life chances across social groups. Particularly, I am interested in precisely identifying how social demographic characteristics influence life chances as people age, and how this varies across time and between places to reproduce boundaries between population groups or improve social integration. Taking a life course perspective, this arm of my research agenda advances understanding about the mechanisms connecting ascribed or achieved statuses and social inequality, such as opportunity hoarding and equality of opportunity. Collaborative research with Monica Boyd and Lisa Kaida has focused on segregation and integration, and I plan on adapting computational techniques for counterfactually decomposing the DI to other distributional differences in the population, such as the distribution of mortality according to education, gender, race-ethnicity, or generation.

Finally, I have also enjoyed two computational projects on the place of sociology within the larger field of scientific research and publishing. I have a third project planned for early 2026 that draws on the complete Web of Science to examine whether and to what extent we have transcended general linear reality (c.f., Abbott 1988).