Teaching
My teaching philosophy centers on fostering critical thinking and active learning. I engage students by asking thoughtful questions that fosters deep reflection and connections between concepts and practice. I also emphasize applying statistical methods to real-world problems to reinforce understanding through relevance and application.
NURSING 911: Introductory Statistics
- Offered in-person at Pearson building in the Fall semester
- This course introduces core statistical concepts and common univariate and bivariate analyses in health and behavioral sciences. Topics include measurement levels, descriptive statistics, sampling distributions, estimation, hypothesis testing, t-tests, one-way ANOVA, nonparametric tests, correlation, simple regression, and power and effect size. Students also develop basic data management skills and gain hands-on experience with statistical software through applied assignments.
- This course is designed for researchers in medical science seeking an introduction to statistics. Email me for a permission number to enroll. See syllabus for details.
NURSING 911L: Statistical Programming I
- Offered in-person at Pearson building in the Fall semester
- The course provides students with fundamental knowledge of the R . The course covers programming language, data management, data analysis, and data visualizations focusing on commonly used procedures for univariate and bivariate analyses in nursing and health sciences.
- Best to enroll together with NURSING 911.
NURSING 966: Quantitative Analysis for Evaluating Health Care Practices
- Offered online in the Spring semester
Independent Study (NURSING 922 Special Reading)
I offer small-group independent studies (2–3 students) that make advanced statistical modeling both accessible and directly relevant to your research. Each group centers on a specific method, with structured, hands-on learning in a supportive peer environment. This format fosters community among doctoral students, helps you apply complex techniques to your dissertation, and provides a clear path to completing projects within a semester. If your research could benefit from one of the statistical models listed below, please reach out for more details.
2020 Spring: Latent Class Growth Analysis
2020 Summer: Factor Analysis
2020 Fall: Latent Class Analysis
2021 Summer: Latent Class Growth Analysis
2022 Summer: Latent Transition Analysis
2023 Spring: Latent Class Growth Analysis
2023 Fall: Mediation Analysis
2024 Spring: Latent Class Analysis
2024 Spring: Survival Analysis
2024 Fall: Mediation Analysis
2025 Spring: Latent Transition Analysis