Teaching
Statistics for Sociologists I (UW-Madison)
Role: Instructor
Term: Summer 2022
Course description:
This course introduces methods of quantitative social research, and how they are used to describe and draw inference from social data. The first part of the course focuses on descriptive statistics. We will cover strategies for exploring and interpreting data and for examining relationships between variables. Topics covered include: describing data with bar charts, boxplots, and histograms; summary statistics; the normal distribution; scatterplots and correlation; regression; and two-way tables. We will also discuss the strengths and weaknesses of various methods of data production.
The second part of the course focuses on statistical inference. In this part of the course, we will discuss the logic and methods of making inferences about populations from sample data. In so doing, you will learn how to test hypotheses with a variety of statistical tests. Topics in this section include: the meaning of statistical significance, how to calculate confidence intervals, and how to conduct statistical tests for means, count data, and regressions. Throughout the course, you will analyze small bodies of data and interpret your findings.
Link to the course syllabus: Link
Statistics for Sociologists II (UW-Madison)
Role: Teaching Assistant
Term: Fall 2021
Course description:
This class is the second of three required courses in quantitative methods for doctoral candidates in sociology. At the time you enroll in this course, I expect you to have a high degree of competence with basic statistics, including measures of central tendency and dispersion, basic analysis of variance and familiarity with bivariate regression. If you chose not to take 360 and are sketchy on any of this material, you should reconsider taking this class.
The primary objective of this class is to help you achieve a deep understanding of ordinary least squares regression (OLS). This understanding will include the mechanics of OLS, assumptions of OLS and consequences of violating those assumptions, and facility with the quantitative and graphical diagnostic tools to evaluate a model’s conformity to those assumptions. Just as important, I will help you become expert at presenting quantitative results from OLS models in a substantively meaningful and accessible way. Finally, we will read several published papers using OLS regression in order to understand and critique the ways in which this tool is used in the field. Engaging in discussions around both substance and method will hopefully demystify the tools we employ and help all of you be smarter consumers and producers of social science.