3.2 Predicting Missing Data and Dropout

This lecture will be about predicting missing data, which is an important concept to consider for sample size analysis. We will define missing data and discuss the different types of missing data you may encounter. Then, we will talk about how to predict missing data in our designs. Some questions to consider while completing this lesson include:

  • What are the main implications of missing data?
  • Are there different types of missing data?

Learning Objectives

  • Define missing data.
  • Describe the types of missing data.
  • Describe the sources for predicting missing data in a design.

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Lecture Notes: 3.2 Predicting Missing Data and Dropout

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