Planned Missingness: How to and How Much?

Abstract

Planned missingness (PM) can be implemented for survey studies to reduce study length and respondent fatigue. Based on a large sample of Big Five personality data, the present study simulates how factors including PM design (three-form and random percentage [RP]), amount of missingness, and sample size affect the ability of full-information maximum likelihood (FIML) estimation to treat missing data. Results show that although the effectiveness of FIML for treating missing data decreases as sample size decreases and amount of missing data increases, estimates only deviate substantially from truth in extreme conditions. Furthermore, the specific PM design, whether it be a three-form or RP design, makes little difference although the RP design should be easier to implement for computer-based surveys. The examination of specific boundary conditions for the application of PM as paired with FIML techniques has important implications for both the research methods literature and practitioners regularly conducting survey research