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The video discusses the sources, patterns, and mechanisms of missing data, highlighting outdated techniques like complete case analysis and mean imputation.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 are the sources of missing data mentioned in the text?

What outdated techniques are commonly used to handle missing data?

How can the patterns of missing data impact the choice of technique used to handle it?

Describe the two main patterns of missing data discussed in the text.

What is the difference between missing completely at random (MCAR) and missing at random (MAR)?

Explain the concept of missing not at random (MNAR) with an example.

What are some reasons why individuals might not report their income?