A randomly chosen value from an individual in the sample who has similar values on other variables. In other words, find all the sample subjects who are similar on other variables, then randomly choose one of their values on the missing variable.

One advantage is you are constrained to only possible values. In other words, if Age in your study is restricted to being between 5 and 10, you will always get a value between 5 and 10 this way. Another is the random component, which adds in some variability. This is important for accurate standard errors. •Cold deck imputation: A systematically chosen value from an individual who has similar values on other variables. This is similar to Hot Deck in most ways, but removes the random variation. So, for example, you may always choose the third individual in the same experimental condition and block. •Regression imputation: The predicted value obtained by regressing the missing variable on other variables. So instead of just taking the mean, you’re taking the predicted value, based on other variables. This preserves relationships among variables involved in the imputation model, but not variability around predicted values. •Stochastic regression imputation: The predicted value from a regression plus a random residual value. This has all the advantages of regression imputation but adds in the advantages of the random component. Most multiple imputation is based off of some form of stochastic regression imputation. •Interpolation and extrapolation: An estimated value from other observations from the same individual. It usually only works in longitudinal data. Use caution, though. Interpolation, for example, might make more sense for a variable like height in children–one that can’t go back down over time. Extrapolation means you’re estimating beyond the actual range of the data and that requires making more assumptions that you should.

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- Fall '19
- Statistics, Interquartile range, Statistical hypothesis testing