Balancing efficacy and computational burden: weighted mean, multiple imputation, and inverse probability weighting methods for item non-response in reliable scales.

ABSTRACT:

Importance

Scales often arise from multi-item questionnaires, yet commonly face item non-response. Traditional solutions use weighted mean (WMean) from available responses, but potentially overlook missing data intricacies. Advanced methods like multiple imputation (MI) address broader missing data, but demand increased computational resources. Researchers frequently use survey data in the All of Us Research Program (All of Us), and it is imperative to determine if the increased computational burden of employing MI to handle non-response is justifiable.

Objectives

Using the 5-item Physical Activity Neighborhood Environment Scale (PANES) in All of Us, this study assessed the tradeoff between efficacy and computational demands of WMean, MI, and inverse probability weighting (I

SUBMITTER: Guide A 

PROVIDER: S-EPMC11631082 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature