Teaching

From fitting models to making defensible quantitative decisions

My teaching goal is to help students move from applying statistical procedures to making defensible research decisions across design, measurement, modeling, diagnostics, uncertainty, interpretation, and reproducible communication.

Teaching Philosophy

Effective quantitative work combines technical execution with judgment about what a design and dataset can support. I teach statistical methods within a broader sequence of inquiry: define the research question, understand the data structure, choose a model the data can support, examine assumptions and diagnostics, interpret uncertainty, and communicate conclusions responsibly.

Teaching Areas

CORE AREAS
Quantitative Research Methods
Multilevel Modeling
Bayesian Data Analysis
Psychometrics & Educational Measurement
Structural Equation Modeling
Longitudinal Data Analysis
RELATED AREAS
Introductory & Intermediate Statistics
Item Response Theory
Causal Inference
Survey Methods
Program Evaluation
Applied Data Science

Teaching & Mentoring Experience

UNIVERSITY OF WASHINGTON
Teaching Assistant, EDPSY 576: Multilevel Modeling
Fall 2023

Led weekly R-based laboratories on model specification, diagnostics, interpretation, and reproducible reporting; developed reusable analysis templates; and provided individual methodological consultation to more than 30 graduate students.

UNIVERSITY OF GEORGIA
Invited Guest Lecturer, SOWK 8307: Doctoral Research Practicum II
April 2026

Delivered a doctoral seminar on the quantitative research process, including research questions, study design, measurement, data analysis, interpretation, and responsible reporting.

GRADUATE MENTORING
Research Methods Mentoring

I mentor graduate students and research trainees on study design, measurement, analysis planning, model selection, diagnostics, interpretation, and reproducible documentation, with increasing responsibility as methodological independence develops.

Teaching Approach

01

Authentic Inquiry

I use real research problems to connect statistical technique with substantive reasoning. Code is a tool for quantitative thinking, not an end in itself.

02

Scaffolding & Feedback

I use conceptual explanations, worked examples, annotated code, structured decision prompts, and iterative feedback while maintaining common expectations for methodological reasoning.

03

Reproducible Practice

Students should leave a methods course able to produce an analysis that another researcher can understand, reproduce, and critique, with transparent workflows, diagnostic checks, and clear reporting of uncertainty.