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Recognizing missing data mechanisms

In this exercise, you will face six different scenarios in which some data are missing. Try assigning each of them to the most likely missing data mechanism. As a refresher, here are some general guidelines:

  • If the reason for missingness is purely random, it's MCAR.
  • If the reason for missingness can be explained by another variable, it's MAR.
  • If the reason for missingness depends on the missing value itself, it's MNAR.

Diese Übung ist Teil des Kurses

Handling Missing Data with Imputations in R

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Interaktive Übung

In dieser interaktiven Übung kannst du die Theorie in die Praxis umsetzen.

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