330.636.89 Methods for Handling Missing Data in Psychosocial Research
- Mental Health
- Summer Inst. term
- 1 credit
- Academic Year:
- 2017 - 2018
- Tue 05/30/2017 - Fri 06/16/2017
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 attrition (non-response) and missingness on individual items. 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. Examples come from school-based prevention research as well as drug abuse and dependence.
- Learning Objectives:
- List the types of missing data
- Explain the implications of missing data on study conclusions
- Describe the primary strategies for dealing with missing data, including weighting and imputation, and their pros and cons
- Articulate the steps in implementing weighting approaches to deal with attrition
- Articulate the steps in implementing multiple imputation approaches to deal with general missing data patterns
- Methods of Assessment:
10% class participation, 90% take home final. The take home final is due on June 30, 2017.
- Instructor Consent:
No consent required
- Special Comments:
Course attendees are not expected to have extensive background in statistical methods. Students are expected to do prior readings before the start of class.