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Overview of missing data types, mean imputation, single (regression) imputation, hot-deck sampling, and multiple imputation.

0:00 Introduction
0:48 Types of Missing Data
5:46 Listwise Deletion
9:18 Mean Imputation
11:46 Regression-Based Imputation
13:44 Hot-or Cold-Deck Sampling
17:10 Stochastic Regression
20:20 Multiple Imputations
23:51 When should you do multiple imputation?
25:42 MCAR, MAR, MNAR data and Multiple Imputation
30:52 How many datasets?
33:42 What if imputed values are outside the normal range of the observed variables? • One option is to restrict values to the observable range
36:27 Should you impute outcomes?
37:36 Should you impute interactions?