Lance Charles E, Fan Yi
Organizational Research & Development, LLC, Lawrenceville, GA, USA.
University of the Western Cape, Cape Town, South Africa.
Educ Psychol Meas. 2016 Jun;76(3):487-507. doi: 10.1177/0013164415601884. Epub 2015 Aug 19.
We compared six different analytic models for multitrait-multimethod (MTMM) data in terms of convergence, admissibility, and model fit to 258 samples of previously reported data. Two well-known models, the correlated trait-correlated method (CTCM) and the correlated trait-correlated uniqueness (CTCU) models, were fit for reference purposes in comparison to four other under- or unstudied models, including (a) Rindskopf's reparameterization of the CTCM (CTCM-R) model, (b) a correlated trait-constrained uncorrelated method model and two of its more general cases, (c) a correlated trait-constrained correlated method model, and (d) a correlated trait-uncorrelated method model. Results show that (a) the CTCM-R model often solved convergence and admissibility problems with the CTCM model at rates equivalent to the CTCU model and (b) constrained models often provided convergent and admissible solutions but significantly worse model fit, indicating that they are often not plausible when analyzing real data. A follow-up simulation study showed that the CTCM-R model also provided the most accurate estimates of the full range of parameters relevant to a confirmatory factor analytic model of MTMM data.
我们根据收敛性、可接受性以及对258个先前报告数据样本的模型拟合情况，比较了六种用于多特质-多方法（MTMM）数据的不同分析模型。为作比较，拟合了两种著名模型，即相关特质-相关方法（CTCM）模型和相关特质-相关独特性（CTCU）模型，以作为参考，同时还比较了其他四种研究较少或未被研究的模型，包括：（a）Rindskopf对CTCM的重新参数化（CTCM-R）模型；（b）一个相关特质-约束不相关方法模型及其两个更一般的情况；（c）一个相关特质-约束相关方法模型；以及（d）一个相关特质-不相关方法模型。结果表明：（a）CTCM-R模型通常能以与CTCU模型相当的速率解决CTCM模型的收敛和可接受性问题；（b）约束模型通常能提供收敛且可接受的解，但模型拟合明显更差，这表明在分析实际数据时它们通常不太合理。一项后续模拟研究表明，CTCM-R模型还能对与MTMM数据的验证性因素分析模型相关的全范围参数提供最准确的估计。
