Effective immediately in response to COVID-19, most Technology Training classes will be delivered online until further notice.
In advance of each session, Tech Training will provide you with a Zoom link to your class, along with any required class materials.
This course covers best practices for survey results analysis and reporting - dealing with missing values, making estimates, combining data from different sources, and selecting the right reporting method to share insights gained through surveys.
By the end of this course, you will become familiar with established statistical methods for converting survey responses to insights that can support marketing decisions. Techniques discussed include factor analytics, cluster analysis, discriminant analysis and multi-dimensional scaling.
Learning Objectives:
During this course, you will have the opportunity to develop the following skills:
- Data quality
- Missing data
- Cluster sampling
- Survey data visualization
- Basic statistics -- sample mean and covariance
Topic Outline:
Building a strong hypothesis
- Why hypothesis?
- Visioning the final stories in the beginning
Framework of Survey Results Analysis
- What a successful data collection looks like?
- Questions to ask before analyzing
- Quantifying potential error (metrics to test collected data)
- Describing the quality of data sources
- Predicting common pitfalls
Quantitative research
- Statistical models for data-driven decisions
- Predicting “most-likely” outcomes
Sharing insights
- The 6 Ws
- Static vs. Interactive
- How to present results from open-ended questions
- How to present results from close-ended questions
- When to sharing raw data
- Sharing next steps and closing the loop
Best practices library
- Statistical relevance - Survey data
- Survey data visualization
University IT Technology Training classes are only available to Stanford University staff, faculty, students and Stanford Hospitals & Clinics employees. A valid SUNet ID is needed in order to enroll in a class.
