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

点击翻译按钮获取中文摘要

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.