Tag: Missing Data

Analytics
Rick Wicklin 0
3 problems with mean imputation

In a previous article, I showed how to use SAS to perform mean imputation. However, there are three problems with using mean-imputed variables in statistical analyses: Mean imputation reduces the variance of the imputed variables. Mean imputation shrinks standard errors, which invalidates most hypothesis tests and the calculation of confidence

Programming Tips
Rick Wicklin 0
Mean imputation in SAS

Imputing missing data is the act of replacing missing data by nonmissing values. Mean imputation replaces missing data in a numerical variable by the mean value of the nonmissing values. This article shows how to perform mean imputation in SAS. It also presents three statistical drawbacks of mean imputation. How

Rick Wicklin 0
Create patterns of missing data

When simulating data or testing algorithms, it is useful to be able to generate patterns of missing data. This article shows how to generate random and systematic patterns of missing values. In other words, this article shows how to replace nonmissing data with missing data. Generate a random pattern of

Rick Wicklin 0
Visualize missing data in SAS

You can visualize missing data. It sounds like an oxymoron, but it is true. How can you draw graphs of something that is missing? In a previous article, I showed how you can use PROC MI in SAS/STAT software to create a table that shows patterns of missing data in

Rick Wicklin 0
Examine patterns of missing data in SAS

Missing data can be informative. Sometimes missing values in one variable are related to missing values in another variable. Other times missing values in one variable are independent of missing values in other variables. As part of the exploratory phase of data analysis, you should investigate whether there are patterns