Technical Skills

Statistical Software: R and Stata (data management, recoding, and analysis)

Quantitative: OLS Regression Models, Multinomial Logit Models, Multilevel & Panel Data Analysis, MAIHDA Approach, Qualtrics, Machine Learning models (Linear Regression, Logistic Regression, Clustering, Decision Trees, Random Forests)

Relevant Courses: Overview of Sociological Methods, Graduate Statistical Analysis I, Graduate Statistical Analysis II, Machine Learning for Educational Data Science, Multilevel & Panel Data Analysis

General Tools: Github, R Studio

Teaching Undergraduate Statistics

“Quantitative Methods in Sociology” SOC 312, Department of Sociology, University of Oregon

TA (Spring 2024) and Instructor of Record (Winter 2026)

Course Description: This course will teach the fundamentals of analyzing numerical data in a social science context. Students will learn effective ways of presenting informational summaries, the use of statistical inference from samples to populations, the linear model that forms the basis of much social science research, and the ability to think critically about how to consume statistical information. Emphasis will be on an intuitive understanding of statistical results, and their practical application.

Learning Objectives:

  1. Describe the distribution of quantitative and categorical variables using graphical and numerical techniques.
  2. Describe the relationships between variables using two-way tables, mean differences, and regression models.
  3. Conduct and interpret tests of statistical inference.
  4. Interpret OLS regression model results from real research, including dummy variables and interaction terms.
  5. Become familiar with performing analysis using R and Posit Cloud.