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Department: Mental Health
Term: Summer Inst. term
Credits: 2 credits
Contact: Elizabeth Stuart
Academic Year: 2013 - 2014
Course Instructor:
  • Elizabeth Stuart

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 Objective(s):
Upon successfully completing this course, students will be able to:
List the types of missing data
Explain the implications of missing data on study conclusions
Implement the primary strategies for dealing with missing data, including weighting and imputation, and their pros and cons
Implement weighting approaches to deal with attrition
Implement multiple imputation approaches to deal with general missing data patterns

Methods of Assessment: Take home final
Location: East Baltimore
Class Times:
  • Mon 06/10/2013 - Tue 06/11/2013
  • Monday 8:30 - 4:30
  • Tuesday 8:30 - 4:30
Enrollment Minimum: 5
Enrollment Maximum: 25
Instructor Consent: No consent required

Familiarity with linear and logistic regression models.

Auditors Allowed: Yes, with instructor consent
Grading Restriction: Letter Grade or Pass/Fail
Special Comments: Course attendees are not expected to have extensive background in statistical methods.
Frequency Schedule: Every Other Year
Next Offered: 2015-2016