Designing Effective Survey Seminar

Seminar recording and materials:

PowerPoint Presentation

• The presentation is available at this link. 
• "LEARNING SAS® HAS NEVER BEEN EASIER" is available from this link.

Seminar description

Data Cleaning

Are you interested in learning how to:

• Write effective survey questions
• Apply methods to reduce bias
• Maximize response rates
• Determine optimal survey length
• Reduce respondent fatigue
• Create effective visual layouts

In this seminar, you will learn how to design effective web-based surveys and work with online respondents. While web-based surveys have many benefits, they also come with their own set of challenges. This seminar will provide participants with practical, easy-to-apply strategies for navigating these issues. We will address the fundamentals of writing effective survey questions and explore how the choices made at the design stage affect response rates and data quality. We will also discuss the advantages and disadvantages of using online samples and evaluate design-based and post-hoc approaches to addressing some of these challenges.

Presenter

Nancy Rausch is a Senior Software Manager and Data Scientist in Research and Development at SAS Institute. She leads a team of engineers that develop SAS’ Data Governance and Data Quality products, with a focus on leveraging AI and Machine Learning methods for data lifecycle management. She is also the Chairperson of the Linux Foundation AI & Data Technology Advisory Council, working to help expand the adoption of open-source technologies at the intersection of AI and Data.  Nancy is a researcher in SAS’ energy technology sector, applying machine learning methods to energy forecasting in support of the smart grid, and an Advisory board member for the NSF funded non-profit LASER Institute promoting Learning Analytics in STEM Education Research.  She is a mentor in SAS’ industry/education partnership program, supporting researchers at North Carolina State University’s Department of Computer Science. She has authored over 20 research papers and publications and presented at numerous conferences on a wide variety of topics related to the intersection of AI and Data Management. She holds a Master of Science degree from Capella University in Data Analytics, a Master of Science degree from Duke University in Computer Engineering, and a Bachelor of Science degree from Michigan Technological University in Electrical Engineering.