330.636.89
Methods for Handling Missing Data in Psychosocial Research
Location
Internet
Term
Summer Institute
Department
Mental Health
Credit(s)
1
Academic Year
2025 - 2026
Instruction Method
Asynchronous Online with Some Synchronous Online
Auditors Allowed
Yes, with instructor consent
Available to Undergraduate
Yes
Grading Restriction
Letter Grade or Pass/Fail
Course Instructor(s)
Course Instructor(s)
Trang Nguyen
Contact Name
Frequency Schedule
Every Year
Resources
Prerequisite
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
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
- 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
This course is evaluated as follows:
- 10% Participation
- 90% Final Exam
Course attendees are not expected to have extensive background in statistical methods. Students are expected to do prior readings before the start of class. Students are required to have completed the required Introduction to Online Learning module before the class begins.