Dealing with missing data: Key assumptions and methods …
with 1,000 people and 20 variables. Each of the variables has missing data on 5% of the cases, then, you could expect to have complete data for only about 360 individuals, discarding the other 640. It works well when the data are missing completely at random (MCAR), which rarely happens in reality (Nakai & Weiming, 2011). 4.1.2.
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