Which is data set for multiple imputation in SAS?
The data set hsb_mar.sas7bdat which is based on hsb2.sas7bdat used for this seminar can be downloaded from the link. The SAS code for this seminar is developed u sing SAS 9.4 and SAS/STAT 13.1. So me of the variables have value labels (formats) associated with them.
What do you need to know about multiple imputation?
Multiple imputation and other modern methods such as direct maximum likelihood generally assumes that the data are at least MAR, meaning that this procedure can also be used on data that are missing completely at random.
When to use fully conditional specification for imputation?
The Fully Conditional Specification (FCS) method is widely used for imputation of missing data for large mixed sets of continuous, nominal, ordinal, count and semi-continuous variables.
Why is a covariance matrix used in multiple imputation?
Meaning that a covariance (or correlation) matrix is computed where each element is based on the full set of cases with non-missing values for each pair of variables. This method became popular because the loss of power due to missing information is not as substantial as with complete case analysis.
How can I perform multiple imputation on longitudinal data?
The following example shows how to impute longitudinal data, accommodating the structure of this type of data. The example dataset contains data on student’s reading and math scores at three time points ( read and math respectively), as well as data on the time invariant covariates female, private, and ses.
When to use n not equal across variables in SAS?
The first thing you should see is the note that SAS prints to your log file stating “N not equal across variables in data set. This may not be appropriate. The smallest value will be used.”. One of the main drawbacks of this method is no consistent sample size.