330.636.89
Methods for Handling Missing Data in Psychosocial Research

Location
Internet
Term
Summer Institute
Department
Mental Health
Credit(s)
1
Academic Year
2023 - 2024
Instruction Method
Asynchronous Online with Some Synchronous Online
Start Date
Wednesday, May 31, 2023
End Date
Friday, June 16, 2023
Auditors Allowed
Yes, with instructor consent
Available to Undergraduate
Yes
Grading Restriction
Letter Grade or Pass/Fail
Course Instructor(s)
Elizabeth Stuart
Course Instructor(s)
Trang Nguyen
Contact Name
Elizabeth Stuart
Frequency Schedule
Every Year
Prerequisite

Familiarity with linear and logistic regression models

Description
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:
  1. List the types of missing data
  2. Explain the implications of missing data on study conclusions
  3. Describe the primary strategies for dealing with missing data, including weighting and imputation, and their pros and cons
  4. Articulate the steps in implementing weighting approaches to deal with attrition
  5. 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
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. Students are required to have completed the required Introduction to Online Learning module before the class begins.