Complete SAS Programming Guide - Learn SAS and Become a Data Ninja - Evaluating Missing Data Patterns with SAS

The video tutorial explains how to use SAS code to evaluate missing data patterns without performing imputation. It introduces the PROC MI procedure, setting it up to analyze missing data patterns. The tutorial details how to interpret the results, identifying complete data and various missing data patterns. It also discusses how to distinguish between arbitrary and monotone data patterns by rearranging variables.

What is the purpose of setting the 'NIMPUTE' option to 0 in the 'proc MI' procedure?

• To view missing data patterns without imputation

• To perform data imputation

• To sort the dataset

• To delete missing data

What percentage of the data is complete according to the SAS output?

• 0.33%

• 78.18%

• 1.63%

• 4.07%

In the SAS output, which variable is missing in Group 2?

• Education

• Gender

• Self-employed

• Married

How can you determine if a missing data pattern is arbitrary?

• By rearranging variables to see if the pattern changes

• By checking if there is a clear pattern to the missing data

• By counting the number of missing values

• By ensuring all variables are missing

What is a characteristic of a monotone missing data pattern?

• It is always at the beginning of the dataset

• It has no discernible pattern

• It only affects one variable

• It shows a clear sequence of missing data