5 Minutes with Sharon Weinberg

Former Vice Provost for Faculty Affairs at NYU talks data science and higher education

Sharon L. Weinberg is a Professor of Applied Statistics and Psychology at the NYU Steinhardt School of Culture, Education and Human Development, a core member of PRIISM, and a Center for Data Science Affiliated Faculty Member. She was formerly the Vice Provost for Faculty Affairs at NYU and the former President and Chair of the Board of the Jewish Foundation for Education of Women. She holds three degrees from Cornell University, including an A.B. in mathematics and a Ph.D. in psychometrics and research design methodology. She has co-authored two statistics textbooks published by Cambridge University Press, one now in its third edition and one in its first edition, and numerous articles in the areas of statistics education, faculty diversity, higher education, applied statistics, and evaluation methods.

1. What impact have you seen quantitative methods to have on issues of equity in higher education?

From my own personal experience, as Vice Provost for Faculty Affairs at NYU, there were many issues of equity that I was able to address given my expertise as an applied statistician.

I initiated a series of regression-based faculty salary equity analyses using data from the schools across the University; I examined the issue of salary compression, which results when universities, to be competitive in the hiring market, offer prospective junior faculty salaries that are equal to or higher than the salaries of more senior faculty at the university.

I also proposed a new method for assessing whether salary compression exists at a university. I also investigated the way in which faculty diversity at a university typically is measured, and based on my analysis, called for a new, more granular approach that is not based on data aggregated at the university level. Instead, the new more granular approach assesses faculty diversity at the department level, and as such, offers an assessment that is better aligned with the benefits to be reaped by students in having a diverse faculty.

Additionally, I studied the impact of the 1993 legislation that uncapped mandatory retirement at institutions of higher education, and showed by applying survival analysis to NYU data as a case study, that since 1993 there has been an upward shift in the probability distribution of the age at which faculty retire. At NYU, the pre-uncapping retirement age, which then was mandatory, was 70. Since 1993 there has been no mandatory retirement age for faculty nationwide. Each of these investigations resulted in a research paper, and each would not have been possible without analyses based on statistical methods applied to a large dataset.

2. As a researcher with The Research Alliance for New York City Schools, you analyzed the racial disparity that exists in test-taking for the NYC Department of Education’s gifted and talented programs and found that attendance in public Pre-K moderated those disparities in test-taking. How does data science enable these types of discoveries, and how can these discoveries influence public policy to improve equity throughout different levels of education?

With the increasing availability of quantitative data, statistical methods become critically more important for uncovering the story behind the numbers and for gaining insight into phenomena of interest, whether it be for descriptive purposes or to impact policy. There are so many well-known examples of research that address issues of equity across the education spectrum, such as class size, school choice, and emotional and cognitive development through publicly available educational opportunities, such as public Pre-K, English as a second language (ESL), gifted and talented programs (G&T), and magnet and charter schools.

Once again, each study would not have been possible without the combination of reliable data and statistical methods for teasing out the important relationships and effects related to the questions of interest. Although anecdotal information may be revealing in specific situations, as someone once said, the plural of anecdote is not data.

3. What drives your passion for studying equity in higher education?

I have been interested in studying equity for as long as I can remember. A particular passion has been gender equity, which explains, in part, my interest in initiating the series of faculty salary equity studies as Vice Provost for Faculty Affairs and being a driving force in creating the Workload Relief Policy at NYU to strengthen NYU’s commitment to work/life balance.

Some of my other research on gender equity has involved the development of a moral orientation scale, based on Carol Gilligan’s care and justice dimensions of moral development; the study of the differential preferences of men and women for intrinsic and extrinsic job characteristics, echoing the call of others for a more flexible work structure to more suitably accommodate work/life balance; and, more recently, a study to understand why college enrollments favor women in the ratio 60:40 using Coleman’s theory of social capital.

Finally, during this academic year I chaired an NYU Tenured/Tenure-Track Faculty Senators Council (T-FSC) Task Force on Tenure Clock Stoppage to examine whether NYU’s current policy was in need of revision to better meet the needs of its tenure-track faculty, show a greater commitment to work/life balance, and strengthen its ability to recruit and retain the best faculty.