QI 103 Lesson 3: Learning from Measures
If you’re reading this, you’re probably interested in improving health care. Perhaps you want to make a difference for hundreds, thousands, or even millions of people. But you can’t visit all those people personally to see how they’re doing—or how your changes may affect them. You’ll have to find a faster, more efficient way to gather that information. In this lesson, you’ll learn when and why sampling makes sense—and how to select your sample without skewing your results. You’ll also learn to stratify your data in different ways to find patterns that might otherwise go unnoticed.
Estimated Time of Completion: 20 minutes
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Learning Objectives
After completing this lesson, you will be able to:
1. Explain the basics of sampling: why and how.
2. Summarize the basic mechanisms for stratifying data.
Contributors
Author(s):
Robert Lloyd, PhD, Executive Director Performance Improvement, Institute for Healthcare Improvement View Profile
Sandra Murray, MA, Improvement Advisor, CT Concepts View Profile
Lloyd Provost, MS, Statistician, Associates in Process Improvement View Profile
Editor(s):
Deepa Ranganathan, Content Manager, Institute for Healthcare Improvement View Profile
Jane Roessner, PhD, Writer, Institute for Healthcare Improvement View Profile
Requirements
You must be a registered IHI.org user to take this lesson.
You must achieve a minimum score of 75% to successfully complete this lesson.