Melanie M. Wall, Ph.D
Department of Biostatistics, Mailman School of Public Health
Director of Mental Health Data Science in the
Department of Psychiatry, Columbia University Medical Center
and the New York State Psychiatric Institute
“Towards empirical methods for examining intersectionality in research”
Intersectionality posits that social categories (e.g. race, gender, sexual orientation) and the forms of social stratification that maintain them (e.g. racism, sexism, homophobia) are interlocking, not discrete. An intersectionality framework considers harms and oppression and also privileges and unearned advantages. By focusing on intersectionality in health research, we can examine axes of social power that underlie our overall health and the systems that support it with the goal of identifying levers for change. As data scientists working within academic research institutions, we have a key role in helping to formulate research questions with our colleagues by offering data analytic ways to operationalize and address them. It is imperative that we elevate the lens of intersectionality into research questions and are prepared to test them with appropriate data analytic tools. In this talk, I will introduce the importance of incorporating intersectionality theory for doing antiracist health science research. I will also describe some common empirical challenges and offer some example data analytic solutions for addressing questions incorporating intersectionality.
Dr. Wall is Director of Mental Health Data Science in the New York State Psychiatric Institute (NYSPI) and Columbia University psychiatry department. She oversees a team of 14 biostatisticians collaborating on predominately NIH funded research projects related to psychiatry. She has worked extensively with modeling complex multilevel and multimodal data on a wide array of psychosocial public health and psychiatric research questions in both clinical studies and large epidemiologic studies (over 300 total journal publications). Her biostatistical expertise includes latent variable modeling (e.g. factor analysis, item response theory, latent class models, structural equation modeling), spatial data modeling (e.g. disease mapping), and longitudinal data analysis including the class of longitudinal models commonly called growth curve mixture models. She received a Ph.D. (1998) from the Department of Statistics at Iowa State University, and a B.S. (1993) in mathematics from Truman State University. Before moving to Columbia University in 2010, she was on faculty in Biostatistics in the School of Public Health at the University of Minnesota.