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

Questionnaire data present challenges, as a missing item of a multi-item scale would lead to a total missing scale. A researcher applying multiple imputation to an incomplete multi-item questionnaire can impute the incomplete items prior to computing scale scores or impute the scale score entirely. Methodologist have favored item-level imputation because it greatly enhances precision in comparison to scale-level imputation; however, this benefit in precision might not translate into longitudinal data studies where entire questionnaire batteries are missing. We investigated the performance of item- and scale-level imputation model and found that item-level imputation did not produce a precision advantage in estimating any of the growth model parameters and scale-level showed better precision in estimating the slope variance parameter than item-level imputation.