Is Item Imputation Always Better? An Investigation of Wave-Missing Data in Growth Models

• In Event: 75.039 - Structural Equation Modeling Methods Medley: New Approaches for Modeling Growth, Multilevel Mediation, and Invariance Testing

Abstract

Researchers regularly uses questionnaires that are composed by multiple multi-item scales, which are summed during analysis to create a scale score. Questionnaire data presents issues when missing data is present, as a missing item of a multi-item scale would lead to a total missing scale. A researcher applying multiple imputation to a questionnaire can either impute the incomplete items prior to computing scale scores or impute the scale scores directly from other scale scores. The purpose of this study is to examine incomplete item responses in the context of a growth curve analysis. In particular, we explore the recommendation to perform missing data handling at the item level when entire questionnaire batteries are missing.

Authors

• Juan Diego Vera, University of California - Los Angeles

• Craig K. Enders, University of California - Los Angeles