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330.616.11 Missing Data Procedures for Psychosocial Research


Mental Health
Summer Inst. term
2 credits
Academic Year:
2017 - 2018
East Baltimore
Auditors Allowed:
Yes, with instructor consent
Grading Restriction:
Letter Grade or Pass/Fail
Elizabeth Stuart
Course Instructor:
Frequency Schedule:
Every Other Year

Familiarity with linear and logistic regression models.


Since analyses that use just the individuals for whom data is observed can lead to bias and misleading results, students discuss types of missing data, and its implications on analyses. Covers solutions for dealing with both types of missing data. These solutions include weighting approaches for unit non-response and imputation approaches for item non-response. Emphasizes practical implementation of the proposed strategies, including discussion of software to implement imputation approaches. Focuses on recently developed software to implement multiple imputation, such as IVEware for SAS and ICE for Stata. Examples come from school-based prevention research as well as drug abuse and dependence.

Learning Objectives:

Upon successfully completing this course, students will be able to:

  1. List the types of missing data
  2. Explain the implications of missing data on study conclusions
  3. Implement the primary strategies for dealing with missing data, including weighting and imputation, and their pros and cons
  4. Implement weighting approaches to deal with attrition
  5. Implement multiple imputation approaches to deal with general missing data patterns
  6. Understand which observational studies can be viewed as having sequentially ignorable assignment of treatments, and learn ways to estimate their causal effects
Methods of Assessment:

Take home final

Instructor Consent:

No consent required

Special Comments:

Course attendees are not expected to have extensive background in statistical methods.