Teaching

  • PSYC 109 - Introductory Statistics

    Catalog Description: Introduces application of statistical principles and techniques to psychological research. Topics include descriptive and inferential statistics, significance testing, t-tests, analysis of variance, correlation and regression, and non-parametric tests. Required course for PSYC majors.

    I know, statistics can be scary. I promise that I will do my best to make it fun and meaningful! This will be a hands-on, project-based class where you will practice running your own analyses in the R programming language. Imagine being able to answer questions such as “Which group has significantly stronger satisfaction scores?” or “Does year in school, age, and residence hall predict academic success?”

    Fall 2026 Schedule: Tues/Thurs, 2:45pm-4:00pm

  • PSYC 139 - The Science of Careers

    This upper-level seminar introduces students to the major scientific theories, research methods, and real-world applications that shape career development and vocational psychology. Building on classic and contemporary literature, the course explores how individuals construct career identities, achieve success, and navigate work-life challenges across diverse contexts. Students will critically examine foundational models (e.g., person-environment fit, protean and boundaryless careers) and emerging perspectives that integrate social-justice lenses, emphasizing how society, culture, and equity influence career trajectories. The seminar combines scholarly inquiry with applied learning: students will read and discuss leading journal articles, write reflective essays applying course concepts to their own lives, interview working adults to inform their own career development, and develop novel research questions.

    Prereq: PSYC 37 or permission from instructor. Counts as an elective for the PSYC major and for the Leadership Sequence (Breadth category).

    This is a NEW course, so register early to secure your spot! For a preview of the syllabus (tentative draft; does not yet include course assignment details), click here.

    Fall 2026 Schedule: Wed, 2:45pm-5:00pm

Other classes I have taught at Claremont and beyond include:

  • PSYC 037 Organizational Psychology (at CMC) — an introductory general education course covering the psychology of why people think and behave in the ways that they do at work. We cover an overview of topics ranging from personality to hiring to job attitudes to leadership and teams. My version of this course focuses on application to real-world experiences, debates over “hot topics” in org psych today, and hands-on group projects and activities.

  • PSYC 137 Advanced Statistics - Psychometrics & Multivariate Methods (at CMC) — an advanced seminar on statistical methods commonly used in social science research and practice, such as factor analysis, test bias, structural equation modeling, and multilevel modeling. Students are expected to be proficient in R and have had strong performance in an introductory psychological statistics course (e.g., PSYC 109 at CMC). The emphasis in the class is hands-on experience analyzing data from one’s own research, lab work, or personal interests.

  • MGT 772 Seminar in Organizational Behavior (at CGU) — an executive PhD seminar covering an overview of topics in organizational behavior and organizational psychology. Students must be enrolled in the CGU Drucker executive PhD program and have previously taken a Master’s-level organizational behavior course. Emphasis in this class is on discussing how course concepts apply to real-world executive experiences, reading peer-reviewed journal articles at the doctoral level, and writing your own applied research proposal.

  • PSYC 601 Applied Data Analytics I (at GMU) — an online course in the Master’s in Professional Studies for I-O Psychology program that I developed and serve as the course champion for. The course covers an introduction to statistical methods for I-O psychology, starting with the very basics (descriptive statistics) and reaching advanced regression methods (e.g., introducing logistic regression). Prior experience with undergraduate-level statistics is desired but not required. The course is taught asynchronously online using R programming language.

For general teaching resources such as video lectures in R and Tableau, visit the Teaching Resources page.