Raykov Tenko, Marcoulides George A
Michigan State University, East Lansing, MI, USA.
University of California, Santa Barbara, CA, USA.
Educ Psychol Meas. 2018 Jun;78(3):504-516. doi: 10.1177/0013164416678650. Epub 2016 Nov 19.
This article outlines a procedure for examining the degree to which a common factor may be dominating additional factors in a multicomponent measuring instrument consisting of binary items. The procedure rests on an application of the latent variable modeling methodology and accounts for the discrete nature of the manifest indicators. The method provides point and interval estimates (a) of the proportion of the variance explained by all factors, which is due to the common (global) factor and (b) of the proportion of the variance explained by all factors, which is due to some or all other (local) factors. The discussed approach can also be readily used as a means of assessing approximate unidimensionality when considering application of unidimensional versus multidimensional item response modeling. The procedure is similarly utilizable in case of highly discrete (e.g., Likert-type) ordinal items, and is illustrated with a numerical example.
本文概述了一种程序,用于检验在由二元项目组成的多分量测量工具中,一个共同因素在多大程度上可能主导其他因素。该程序基于潜变量建模方法的应用,并考虑了显性指标的离散性质。该方法提供了以下方面的点估计和区间估计:(a) 所有因素解释的方差比例中,归因于共同(全局)因素的部分;(b) 所有因素解释的方差比例中,归因于某些或所有其他(局部)因素的部分。当考虑应用单维与多维项目反应建模时,所讨论的方法也可很容易地用作评估近似单维性的一种手段。该程序在高度离散(例如李克特式)有序项目的情况下同样适用,并通过一个数值示例进行说明。