January 2024 Online Short Course: 

Introduction to R and Data Analysis

Introduction to R and Data Analysis

Dr. Scott Tonidandel


Wednesday, January 3 – Friday, January 5

Taught daily from 10:00 AM ET – 4:00 PM ET

Instructor Biography

Dr. Scott Tonidandel is a Professor of Management in the Belk College of Business at the University of North Carolina – Charlotte and is a faculty member of the Organizational Science Ph.D. program (http://orgscience.charlotte.edu/). Dr. Tonidandel received his M.A. & Ph.D. in industrial organizational psychology from Rice University and his B.A. from Davidson College. His research interests include issues related to leadership effectiveness, the impact of diversity in organizations, and research methods and statistics. His recent work focuses on people analytics and the interface of big data and the organizational sciences. He co-edited the SIOP Frontiers series volume titled Big Data at Work: The Data Science Revolution and Organizational Psychology and recently completed work on a NSF funded project that uses sensors to understand team interactions and the impact of diversity. Dr. Scott Tonidandel serves as an associate editor for the Journal of Business and Psychology, is a former associate editor for Organizational Research Methods, and is a fellow of the Association for Psychological Science, the American Psychological Association, and the Society for Industrial and Organizational Psychology.

Course Description

This course will provide a gentle introduction to the R computing platform and the R-Studio interface. We will cover the basics of R such as importing and exporting data, understanding R data structures, and R packages. You will also learn strategies for data manipulation within R (compute, recode, selecting cases, etc.) and best practices for data management. We will work through examples of how to conduct basic statistical analyses in R (descriptive, correlation, regression, T-test, ANOVA) and graph those results. Finally, we will explore user-defined functions in R and lay the groundwork for understanding how to perform more complex analyses presented in other CARMA short courses.

Short Course Introduction