FREE Webinar: Assumptions Matter: Diagnosing Problems Before Running the Model

Statistical challenges in dissertations are often blamed on complex methods; but more often, the real issue lies in misaligned models, unchecked assumptions, and avoidable early-stage decisions.

In this practical and insight-driven webinar, Dr. Amelia Andrews will show you how to identify and address potential problems before running your analysis. By understanding the assumptions behind commonly used statistical models and how they connect to your research questions and data structure, you can prevent errors that weaken your results and delay your progress.

This session focuses on practical diagnostic strategies you can apply immediately. You’ll learn how to check assumptions early, recognize warning signs in your data, and adjust your analytic approach to ensure your work is both rigorous and defensible.

What You’ll Learn:

• The core assumptions behind commonly used statistical models
• How to align your model with your research questions and data structure
• How assumption violations typically appear in dissertation research
• Practical methods for checking assumptions before running analyses
• How to interpret warning signs and refine your analytic approach

Who Should Attend: 

• Graduate students working on course papers, theses, or dissertations
• Doctoral candidates preparing for data analysis
• Early-career researchers seeking to strengthen methodological rigor

Date & Time
🗓️ Thursday, April 16th
⏰ 1:00–2:00 PM EST (New York Time)

Taught by: Dr. Amelia Andrews