Department of Psychology, Utah State University, Logan, UT, USA.
J Pers. 2021 May;89(3):382-401. doi: 10.1111/jopy.12625. Epub 2021 Mar 5.
Multitrait-multimethod (MTMM) data can be analyzed with single-indicator confirmatory factor analysis (CFA-MTMM) models. Most single-indicator CFA-MTMM models imply-but do not allow testing-the restrictive assumption that method biases generalize (correlate) perfectly across different traits for a given method.
To examine the validity of this assumption, we identified and reviewed 20 published applications of multiple-indicator CFA-MTMM models, which allow testing this assumption. Based on simulated data, we demonstrate the consequences of violating the assumption of perfectly general method effects based on the CT-C(M - 1) approach.
We extracted 111 heterotrait-monomethod method factor correlation estimates, which varied between |.01| and |1.0| (mean = .52) with most correlations being substantially smaller than |1|. The results of our review and simulations show that violations of the assumption of perfectly general method effects (a) are very common, (b) are difficult to detect based on model fit statistics, and (c) can lead to considerable bias in estimates of convergent validity, method specificity, reliability, and method factor correlations in single-indicator models.
We recommend that researchers abandon the use of single-indicator CFA-MTMM models and that they use multiple-indicator CFA-MTMM models whenever possible.
多特质-多方法（MTMM）数据可以通过单指标验证性因子分析（CFA-MTMM）模型进行分析。大多数单指标 CFA-MTMM 模型都隐含着但不允许检验一个限制假设，即对于给定的方法，方法偏差在不同特质之间完全概括（相关）。
为了检验这一假设的有效性，我们确定并回顾了 20 个已发表的多指标 CFA-MTMM 模型的应用，这些模型允许检验这一假设。基于模拟数据，我们根据 CT-C（M-1）方法演示了违反完美通用方法效果假设的后果。
我们提取了 111 个异特质同方法方法因子相关估计值，其范围在 |.01| 和 |1.0| 之间（平均值为.52），大多数相关系数明显小于 |1|。我们的综述和模拟结果表明，违反完美通用方法效果假设的情况（a）非常常见，（b）基于模型拟合统计数据难以检测，（c）可能导致单指标模型中收敛有效性、方法特异性、可靠性和方法因子相关性估计值的相当大偏差。
我们建议研究人员放弃使用单指标 CFA-MTMM 模型，并尽可能使用多指标 CFA-MTMM 模型。
