Engaging Undergraduate Students in Disability Statistics: A Project Example: Understanding the Sexual Orientation and Gender Intersection-Related Healthcare Disparities from a Social Context Perspective

In this talk, we will discuss a collaborative project: Understanding the Sexual Orientation and Gender Intersection-Related Healthcare Disparities from a Social Context Perspective. The project was conducted in the mentor's Disability Inclusion Analytics Lab. The mentor has expertise in disability statistics using public population surveys and designed this project from a social context perspective using multilevel modeling. The mentee, an undergraduate junior majoring in Statistical and Data Sciences, had completed a second course in statistics (Multiple Regression) and a Data Science 2 course (Programming for Data Science in R) before joining the project. She brought her programming, statistical analysis, and visualization skills gained from classrooms and textbooks to the project. The mentor always encourages her students to be creative in problem-solving in her lab. Through weekly lab meetings, the mentor and mentee discussed what statistical analysis makes sense in this particular disability inclusion context, what scientific literature helps identify research gaps and form research questions. First, the mentee built and interpreted baseline logistic regression models with an interaction term of sexual orientation and gender using the National Health Interview Survey data. Second, the mentor introduced multilevel models and deepened the mentee's understanding of her multiple regression coursework. Then, the mentor focused on the quantitative intersectional analysis for logistic regression and complex survey design for easier and clearer interpretations of intersectional results and more actionable policy implications for inter-categorical groups.