Evaluating Imputation Techniques for Longitudinal Study of Effectiveness of an Anti-Smoking Campaign

• Activity Number: 462 - SPEED: Survey Research Methods
• Type: Contributed
• Date/Time: Wednesday, August 2, 2017 : 8:30 AM to 10:20 AM
• Sponsor: Survey Research Methods Section
• Author(s): Qiao Ma* and Josiane Bechara and Edward Mulrow and Zachary Haskell Seeskin and Morgane Bennett and Jennifer Cantrell and Elizabeth Hair and Donna Vallone
• Companies: NORC at the University of Chicago and NORC at the University of Chicago and NORC and NORC at the University of Chicago and Truth Initiative and Truth Initiative and Truth Initiative and Truth Initiative
• Keywords: Missing Data ; Truth Initiative ; Hot Deck Imputation ; Multiple Imputation ; Evaluation Nonresponse Bias

Abstract

The Truth Initiative Longitudinal Cohort Study is designed to evaluate the impact of a television and digital campaign on youths' smoking-related knowledge, attitudes and beliefs, perceived social norms, and behaviors over time. The study administers surveys to participants over six waves between 2014 and 2017 and uses multivariate statistical models to evaluate the effectiveness of the media campaign. The survey is subject to nonresponse, which can bias estimates for the evaluation. We evaluate different methods of imputing missing data in the context of a longitudinal study. Hot deck and model-based approaches are compared for both their performance and practicality, and multiple imputation is used to account for the uncertainty in estimates due to missing data. To evaluate different imputation approaches, we use the framework of He and Zaslavsky (2012), which involves duplicating the dataset and comparing the distributions of observed and imputed data.