Brief descriptions of a few of my past and current course offerings:


Education and Inequality in the United States (Undergraduate)

Why do we hear so much about “achievement gaps” between students from different racial/ethnic groups, social class backgrounds, genders, and geographic locations, yet things never seem to change very much? Are problems with the U.S. education system truly causing the nation to fall behind other countries? If so many influential commentators seem to believe that the U.S. schooling system is “broken”, why has no one found a way to fix it?

Before we can begin answering these questions, we must develop a sociological perspective on schooling by critically examining the many functions education serves in a complex society. Viewing education through a sociological lens allows us to consider how schooling reflects, reinforces, and undermines different elements of the social structure.

This course draws on sociological theory and research to examine schooling-related issues including education and social mobility; family background and student achievement; schools and socialization; racial, ethnic, gender, and generational disparities in educational outcomes; and the sociology of education reform.


Intermediate Social Statistics (Undergraduate/Graduate)

This course covers elementary statistical techniques frequently used in social science research. The goal of the class is for students to learn the fundamental tools of quantitative research and to become critical consumers of data and statistics encountered in everyday life.  The first part of the course will introduce descriptive statistics. These measures of central tendency, variation, and distribution allow social scientists to describe social phenomena. The focus will then proceed to inferential statistics, which are used by social scientists to infer the nature of relationships among groups of variables. The concepts and skills learned in this part of the course include calculating measures of association, calculating confidence intervals, and hypothesis testing, including an introduction to basic regression methods.

The emphasis will be on the logic of statistical procedures and their application in the context of social research as opposed to a formal treatment of statistical theory. The mathematics of statistics will be de-emphasized, but not avoided.  Understanding statistical concepts will be facilitated by hands-on data analysis using the statistical software Stata.  This software package enables students to easily conduct exploratory data analysis as well as more complex multivariate modeling.  No prior computing expertise is required, and instruction in the use of computers for data analysis will be provided. The course will be organized around two weekly lectures and one recommended weekly lab session.


Quantitative Analysis in Sociology (Graduate)
This is the second course in the graduate quantitative methods sequence in the Department of Sociology, required of Ph.D. students. Building on the material covered in Intermediate Social Statistics, this course focuses on multivariate regression analysis methods commonly used by sociologists. This course includes two components: a lecture-based theoretical overview of core statistical concepts and a lab-based programming component using Stata software. Compared to a similar course in a statistics department, Quantitative Analysis in Sociology places much greater emphasis on data management/analysis skills, substantive interpretation of parameter estimates, and practical applications than probability/statistical theory or mathematical computation.



Seminar in the Sociology of Education (Graduate)

This 10-week course introduces graduate students to a selection of core theoretical perspectives and research topics in the sociology of education. Course readings offer macro- and micro-sociological perspectives on education’s multiple institutional roles in stratified societies, the United States in particular. This course emphasizes the diversity of theoretical approaches and research methodologies in the sociology of education, and highlights the contributions made by sociological research to our understanding of issues related to socialization and stratification.


Longitudinal Data Analysis (Graduate)

This course introduces graduate students to widely-used methods for analyzing longitudinal survey data in the social sciences. We focus on two general types of inquiry: (1) the analysis of change over time, focusing on multilevel change (aka, growth curve) models and (2) the analysis of event timing via event history analysis. The course concludes with an introduction to causal analysis using longitudinal data. Students apply these methods in analyses of instructor-provided data using the Stata software package over the course of the semester. For their final projects, students conduct an original longitudinal analysis on a topic and dataset of their choice, producing a manuscript suitable for presentation at a professional conference or publication in a peer-reviewed journal.