Complete SAS Programming Guide - Learn SAS and Become a Data Ninja - Sources, Patterns, and Mechanisms of Missing Data

The video discusses the sources, patterns, and mechanisms of missing data, highlighting outdated techniques like complete case analysis and mean imputation. It emphasizes the importance of understanding missing data to choose the right technique, such as multiple imputation using SAS. The video covers sources like income and medical research, patterns like generalized and structured, and mechanisms like MCAR, MAR, and MNAR, explaining their implications on data analysis.

What outdated technique is commonly used to handle missing data?

• Multiple imputation

• Complete case analysis

• Data augmentation

• Bayesian inference

Which of the following is a common source of missing data in medical research?

• Excessive data collection

• Equipment failures

• High response rates

• Over-reporting of income

What is a characteristic of a generalized missing data pattern?

• It follows a predictable sequence

• It is always related to income data

• It is random with no specific pattern

• It occurs only in experimental studies

In which type of study is a structured missing data pattern often observed?

• Cross-sectional studies

• Medical studies

• Meta-analyses

• Case-control studies

What does MCAR stand for in the context of missing data?

• Missing Categorically at Random

• Missing Consistently at Random

• Missing Completely at Random

• Missing Conditionally at Random

Which mechanism of missing data occurs when other variables predict the missingness?

• MAR

• MCAR

• MNAR

• None of the above

What is a key difference between MAR and MNAR?

• MAR is less common than MNAR

• MNAR is predictable by other variables

• MNAR is completely random

• MAR allows for a full range of data