In a recent article, Castro-Schilo, Widaman, and Grimm (2013) compared different approaches for relating multitrait–multimethod (MTMM) data to external variables. Castro-Schilo et al. reported that estimated associations with external variables were in part biased when either the correlated traits–correlated uniqueness (CT-CU) or correlated traits–correlated (methods–1) [CT-C(M–1)] models were fit to data generated from the correlated traits–correlated methods (CT-CM) model, whereas the data-generating CT-CM model accurately reproduced these associations. Castro-Schilo et al. argued that the CT-CM model adequately represents the data-generating mechanism in MTMM studies, whereas the CT-CU and CT-C(M–1) models do not fully represent the MTMM structure. In this comment, we question whether the CT-CM model is more plausible as a data-generating model for MTMM data than the CT-C(M–1) model. We show that the CT-C(M–1) model can be formulated as a reparameterization of a basic MTMM true score model that leads to a meaningful and parsimonious representation of MTMM data. We advocate the use confirmatory factor analysis MTMM models in which latent trait, method, and error variables are explicitly and constructively defined based on psychometric theory.

中文翻译：

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多特征多方法分析中的数据生成机制与构造性定义的潜在变量：对 Castro-Schilo、Widaman 和 Grimm（2013 年）的评论

在最近的一篇文章中，Castro-Schilo、Widaman 和 Grimm（2013 年）比较了将多特征多方法 (MTMM) 数据与外部变量相关联的不同方法。Castro-Schilo 等人。据报道，当相关性状相关唯一性（CT-CU）或相关性状相关（方法-1）[CT-C（M-1）]模型适合生成的数据时，与外部变量的估计关联部分存在偏差来自相关性状-相关方法（CT-CM）模型，而生成数据的 CT-CM 模型准确地再现了这些关联。Castro-Schilo 等人。认为 CT-CM 模型充分代表了 MTMM 研究中的数据生成机制，而 CT-CU 和 CT-C(M-1) 模型并不能完全代表 MTMM 结构。在这篇评论中，我们质疑 CT-CM 模型作为 MTMM 数据的数据生成模型是否比 CT-C(M-1) 模型更合理。我们表明，CT-C(M-1) 模型可以表述为基本 MTMM 真实评分模型的重新参数化，从而对 MTMM 数据进行有意义且简洁的表示。我们提倡使用验证性因素分析 MTMM 模型，其中基于心理测量理论明确和建设性地定义潜在特征、方法和错误变量。