Teaching Interests

Panel data or longitudinal data consist of multiple measures over time on a sample of individuals. These types of data occur extensively in both observational and experimental studies in social, behavioral, and health sciences. This course will provide an introduction to the principles and methods for the analysis of panel data. Whereas some supporting statistical theory will be given, emphasis will be on data analysis and interpretation of models for longitudinal data. Problems will be motivated by applications primarily in social sciences.

Panel data or longitudinal data consist of multiple measures over time on a sample of individuals. These types of data occur extensively in both observational and experimental studies in social, behavioral, and health sciences. This course will provide an introduction to the principles and methods for the analysis of panel data. Whereas some supporting statistical theory will be given, emphasis will be on data analysis and interpretation of models for longitudinal data. Problems will be motivated by applications primarily in social sciences.

This course teaches statistical methods for analyzing categorical data, with an emphasis on practical applications rather than statistical theories. The goal of this course is to teach sociology students to learn from categorical data. The course stresses the use of various statistical methods to explain the phenomena and test models in order to address social science and policy questions, broadly defined. Familiarity with multivariate linear regression models for continuous dependent variables is assumed. Portions of textbooks and selected articles in the current literature will be assigned as Readings. There will also be a weekly tutorial taught by the teaching assistant.

This is an undergraduate seminar in social stratification. Social stratification is broadly defined as the unequal distribution of scarce resources and of the processes by which these resources are allocated to individuals, groups, and populations. The study of stratification encompasses income and wealth inequality, socioeconomic hierarchies and privileges, poverty and unemployment, social mobility over the life course and across generations, inequality in the educational system, race-ethnic and gender inequality, globalization and the future of work, beliefs, attitudes, and perceptions of inequality and opportunity, neighborhood segregation, and the consequences of inequality and policy interventions. Over this semester, we will investigate such questions as: How likely are individuals to end up in the same social stratum as their parents? Will globalization and automation exacerbate or reduce inequality in workplace? Is there growing inequality in the U.S. and around the globe and, if so, why? In this class, we cover the concepts, theories, facts, and methods of analysis used by sociologists to understand social stratification. This course takes most of its examples from the contemporary U.S., but we will place U.S. in historical and comparative perspectives as well.

Statistical analysis is the process of consolidating and analyzing distinct samples of data to divulge patterns or trends and anticipating future events/situations to make appropriate decisions. This course is an introduction to data description and analysis. The course concentrates on data analysis, and the way one links theory and data. By the end of the course you should have a good idea of how to make sociological sense out of a body of quantitative data. Toward this end, we will cover a variety of techniques, including tabular analysis, regression analysis, regression diagnostics, missing data, and related topics. This is not only a statistics course, but also a course that teaches you procedures to draw substantive conclusions about how the social world works. We will also discuss how to organize your work and present your results. We focus on the quantitative analysis of data from probability samples of well-defined populations. Data collection procedures will be essentially ignored – that is, mentioned only in discussions of data-analytic issues.

This course is an introduction to the concepts and methods of demographic analysis. It is intended to provide students with a general understanding of the processes that shape population size, structure, and dynamics and with the logical bases for the most frequent measures of these processes. The emphasis will be on measurement issues in human population while making clear the broader relevance of demographic analysis to the study of any population or system.

Categorical data analysis is the analysis of data where the response variable has been grouped into a set of mutually exclusive ordered (such as age group) or unordered (such as eye color) categories.

This course is an introduction to data description and analysis. The philosophy behind this course is that there are some lawful, reasonably stable relationships among social phenomena and causes are identifiable in a probabilistic sense. You will learn a coherent scientific language for systematically describing data and drawing logical conclusions. You will also learn how to produce statistical information needed to answer sociological questions. The goal of this course is to develop your ability to increase your level of understanding of the most common statistical concepts and analytical tools and to help you to develop your own research paper. The scientific method, as applied to social sciences, includes a variety of research approaches, tools, and techniques, for collecting and analyzing qualitative or quantitative data. These methods include laboratory experiments, field surveys, case research, ethnographic research, action research, and so forth.