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

用于回归、路径分析和具有项目级缺失的结构方程模型的两阶段方法

• 批准号: RGPIN-2015-05251
• 负责人: Savalei, Victoria
• 金额: $ 1.02万
• 依托单位: University of British Columbia
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=635895
• 关键词: Two; stage; methodology; regression; path

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.

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，相关的独特的情况下，数据缺失的个别项目，但统计模型是在复合材料（项目的总和）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”将用于回归，则将计算为自尊量表的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-复杂的多元回归模型，可能涉及潜在变量，并允许测试复杂的心理理论。在这种情况下，模型通常相对于心理学中常用的典型样本大小来说很大。建议通过将项目合并到组合中来减小模型大小。