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

可以为调查研究实施计划缺失 (PM)，以减少研究时间和受访者疲劳。基于大五人格数据的大量样本，本研究模拟了包括PM设计（三项形式和随机百分比[RP]），缺失量和样本大小等因素如何影响完整信息最大可能性（FIML）的能力。 ) 估计以处理缺失数据。结果表明，尽管 FIML 处理缺失数据的有效性随着样本量的减少和缺失数据量的增加而降低，但在极端条件下，估计值仅与真实值有很大的偏差。此外，具体的 PM 设计，无论是三形式还是 RP 设计，都没有什么区别，尽管 RP 设计应该更容易用于基于计算机的调查。