Statistics Modules
The consultants at the Statistical Consulting and Survey Research Unit have collaboratively compiled a book containing a variety of topics encountered at the help desk. Some modules consist of materials from our workshops. We welcome suggestions and feedback to improve the book.
Resources from past workshops
Materials shared during some of our past workshops:
- R for Beginners: Your Data Adventure Begins (Slides, Data 1, Data 2)
- Statistics Refresher: Revisiting the Essentials (Slides, Data 1, Data 2)
- Unleash the Power of Your Data (Slides, Data)
- Mean Business: A Hands-on Guide to Group Comparison (Slides, Data)
- Unlock the Power of Linear Regression (Slides)
- How-to Guide for Developing Questionnaires (Session 1, Session 2, Session 3, Session 4) (4-Part Recorded Presentation)
- Unlock the First Steps to Binary Data Analysis (Slides)
- Explore Your Count Data (Slides)
- Data Organization Made Easy with dplyr(Slides, Data)
- Turning Numbers into Love Data (Slides, Data)
- Love at First Stat -- Rediscover the Fundamentals (Slides, Data)
- Research Planning Through A Statistician's Eyes (Slides)
Recorded Presentations
GRADNavigate 2025: Overview of our services available for graduate students and pointers for avoiding bot attacks.
SCSRU Roundtable Discussion: Bots & Fraudulent Data in Survey Research: A panel of experts discusses the impact of bots and fraudulent data in survey research, exploring best practices for detecting and preventing bot participation, identifying and cleaning fraudulent data, and addressing the challenges posed by imposter respondents.
Data Analyst Forum: A brief introduction to using Qualtrics and an overview of our services available for staff and faculty
Introduction to Large Language Models with Applications in Text Classification: This workshop offers an introduction to the core concepts underlying Large Language Models (LLMs) like BERT and GPT, as well as the various tools available to users interested in employing these models for practical tasks. As the motivating example, we use LLMs to classify text answers to questions on a real-world survey. (Slides, jupyter notebook file with code)