Welcome Announcement

Welcome to UX Research at Scale: Surveys, Analytics, Online Testing

Google, Weibo, Whatsapp – what these all have in common is that hundreds of millions of people around the world interact with digital products created by teams at these companies every day.

How are they created?

Great experiences come from a deep and nuanced understanding of the people who will be using them – not just averages and statistics, but also, the recognition that there are needs, behaviors, desires, expectations, context that inform what will work. This understanding is built on surveys and research.

Surveys are one of the most common tools people use to collect data. Survey data is used to inform policy, shape marketing campaigns, understand interpersonal relationships and so much more. Of course, “more” includes collecting data to answer UX research questions. Surveys are so common, that many of us have experienced both good and bad surveys. Week One to Week Four will help you understand when to use surveys, and when you do use them to make sure you do it well. This is a general overview of survey methods, intended to give you some practical advice, but also to help you identify areas where you might need more help. With a little care, surveys can be a wonderful method to collect data and help you understand people better.

Today’s ever-expanding research toolkit enables researchers to collect both quantitative and qualitative insights across time and the product design lifecycle, around the world, and in a culturally-sensitive way so that designers, developers, and product managers know what problems to solve, how, and have confidence that their solutions will work. The methods that user experience research practitioners use when supporting products that serve large populations balance digging into those large numbers and storytelling. Weeks five to eight are focused on helping you to understand how to accomplish both of those things.

In this course, you will:

Learn when to use a survey versus other methods. Discover the pros and cons of different types of sample. Learn all of the ways you can add error to your results through survey design. Understand how to get people to respond to your requests to participate in a survey. Practice creating survey questions that are valid and reliable. How to identify effective research questions and approaches that address the needs of your team using a multi-method approach. How to design effective remote unmoderated studies. How to define, prioritize, and track the analytics and key performance metrics that matter to your organization. How to execute effective A/B testing. We’ll be going over practical tips in the video lectures and doing some quizzes and peer review assignments. However, an important aspect of this course is what you can learn from each other. The intent of these discussions is to promote conversations with other learners and help the course team to get to know you better. This is an opportunity to share information about your experiences with survey and research with other learners. If you have questions or observations you feel might be interesting or relevant to others, be sure to share them and return often to participate in follow-up discussions.

If you have any questions or comments throughout the course, we encourage you to view or post in the Discussion. The instructional team will do what we can to answer all relevant questions.

Getting started

This course is self-paced, so you can go through the content at your own convenience. You may navigate the material chronologically using the arrows at the top and bottom of the page. Use the Discussions Forums to start a conversation with your fellow learners and to ask questions about the course content. To get help with a technical problem, please check out the Coursera Support. All the best,

Cliff Lampe (Instructor, Weeks 1 – 4), Associate Professor , School of Information

Lija Hogan (Instructor, Weeks 5 – 8), Director, Solutions Consulting, UserTesting, Lecturer, School of Information

University of Michigan

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