University of Amsterdam.
Psychol Methods. 2016 Sep;21(3):348-368. doi: 10.1037/met0000072. Epub 2016 Feb 1.
Previous research has suggested that the use of item parcels in structural equation modeling can lead to biased structural coefficient estimates and low power to detect model misspecification. The present article describes the population performance of items, parcels, and scales under a range of model misspecifications, examining structural path coefficient accuracy, power, and population fit indices. Results revealed that, under measurement model misspecification, any parceling scheme typically results in more accurate structural parameters, but less power to detect the misspecification. When the structural model is misspecified, parcels do not affect parameter accuracy, but they do substantially elevate power to detect the misspecification. Under particular, known measurement model misspecifications, a parceling scheme can be chosen to produce the most accurate estimates. The root mean square error of approximation and the standardized root mean square residual are more sensitive to measurement model misspecification in parceled models than the likelihood ratio test statistic. (PsycINFO Database Record
先前的研究表明，在结构方程建模中使用项目包裹可能会导致结构系数估计值存在偏差，并且难以检测模型的不恰当指定。本文描述了在一系列模型不恰当指定下，项目、包裹和量表的总体表现，检验了结构路径系数的准确性、功效和总体拟合指数。结果表明，在测量模型不恰当指定的情况下，任何包裹方案通常会导致更准确的结构参数，但检测不恰当指定的功效较低。当结构模型不恰当指定时，包裹不会影响参数的准确性，但会极大地提高检测不恰当指定的功效。在特定的已知测量模型不恰当指定下，可以选择包裹方案来产生最准确的估计值。在包裹模型中，近似均方根误差和标准化均方根残差比似然比检验统计量对测量模型不恰当指定更为敏感。
