ABSTRACT Planned missing data (PMD) designs allow researchers to collect additional data under time constraints and to reduce participant burden, both of which can occur in social, behavioral, and educational research settings. The imposed missing data patterns, however, can hamper the efficiency of statistical models implemented to test hypotheses that are of interest to substantive researchers, including whether a treatment works the same for all students. Typically, PMD designs result in a modest power deficiency; however, this tenet has not been extended to latent interaction models. Such models are of increasing importance as researchers investigate moderated relationships involving continuous latent variables. Monte Carlo simulations were used to assess the efficacy of various latent interaction estimation methods under PMD designs.

中文翻译：

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具有计划缺失数据设计的潜在交互建模

摘要 计划缺失数据 (PMD) 设计允许研究人员在时间限制下收集额外数据并减少参与者负担，这两者都可能发生在社会、行为和教育研究环境中。然而，强加的缺失数据模式可能会妨碍为测试实质性研究人员感兴趣的假设而实施的统计模型的效率，包括对所有学生的处理是否相同。通常，PMD 设计会导致适度的功率不足；然而，这一原则并没有扩展到潜在的交互模型。随着研究人员调查涉及连续潜在变量的调节关系，此类模型变得越来越重要。Monte Carlo 模拟用于评估 PMD 设计下各种潜在相互作用估计方法的功效。