ABSTRACT

Studies using structural equation modeling (SEM) to evaluate theories against observed data rely on multiple sources of evidence to support a proposed model, such as fit indices, variance explained, and comparison of alternative models. Additional evidence can be obtained by evaluating the model results’ sensitivity to an omitted confounder. The phantom variable approach to SEM sensitivity analysis requires manual specification of sensitivity parameters. This study improves on the phantom variable approach by employing the ant colony optimization algorithm to automatically search for sensitivity parameters, if any, that would lead to a change in the study’s conclusions. The proposed method is implemented in the package SEMsens for the R statistical software, and demonstrated with a sensitivity analysis of a model of the complex relation between working memory and writing.

摘要

使用结构方程模型 (SEM) 针对观察数据评估理论的研究依赖于多个证据来源来支持所提出的模型，例如拟合指数、方差解释和替代模型的比较。通过评估模型结果对忽略的混杂因素的敏感性，可以获得额外的证据。SEM 灵敏度分析的幻象变量方法需要手动指定灵敏度参数。本研究通过使用蚁群优化算法自动搜索灵敏度参数（如果有的话）会导致研究结论发生变化，从而改进了幻影变量方法。所提出的方法在 R 统计软件的 SEMsens 包中实现，