Two-stage methodology for regression, path analysis, and structural equation models with item-level missingness

拟议的研究的目标是开发和评估一种新的方法来处理不完整的数据，该方法适用于个别项目数据缺失的独特情况，但统计模型处于组合(项目总和)的水平。这种情况经常发生在社会科学领域。例如，在心理学中，模型中的变量通常是量表分数。如果变量“自尊”被用于回归，它将被计算为构成自尊量表的10个项目的总分。第二个应用是在结构方程模型(SEM)的背景下--复杂的多变量回归模型，它可能涉及潜在变量，并允许测试复杂的心理学理论。在这种情况下，相对于心理学中常用的典型样本量，模型往往较大。建议通过将项目组合成复合材料来减小模型尺寸。

The proposed research has the goal of developing and evaluating a new methodology to deal with incomplete data, relevant for the unique situation when data are missing on the individual items, but the statistical model is at the level of composites (sums of items). This scenario often occurs in the social sciences. For example, in psychology the variables in the model are often scale scores. If the variable “self-esteem” is to be used in regression, it will be computed as the sum score of 10 items that comprise the self-esteem scale. A second application is in the context of structural equation models (SEMs)--complex multivariate regression models that may involve latent variables and allow for testing of complex psychological theories. In this case, models are often large relative to the typical sample sizes commonly used in psychology. It is recommended to reduce model size by combining items into composites.