Universitat Rovira i Virgili, Tarragona, Spain.
Behav Res Methods. 2020 Feb;52(1):116-130. doi: 10.3758/s13428-019-01209-1.
A common difficulty in the factor analysis of items designed to measure psychological constructs is that the factor structures obtained using exploratory factor analysis tend to be rejected if they are tested statistically with a confirmatory factor model. An alternative to confirmatory factor analysis is unrestricted factor analysis based on Procrustes rotation, which minimizes the distance from a target matrix proposed by the researcher. In the present article, we focus on the situation in which researchers propose a partially specified target matrix but are prepared to allow their initial target to be refined. Here we discuss RETAM as a new procedure for objectively refining target matrices. To date, it has been recommended that this kind of refinement be guided by human judgment. However, our approach is objective, because the threshold value is computed automatically (not decided on by the researcher) and there is no need to manually compute a number of factor rotations every time. The new procedure was tested in an extensive simulation study, and the results suggest that it may be a useful procedure in factor analysis applications based on incomplete measurement theory. Its feasibility in practice is illustrated with an empirical example from the personality domain. Finally, RETAM is implemented in a well-known noncommercial program for performing unrestricted factor analysis.
在旨在测量心理结构的项目的因子分析中,一个常见的困难是,如果使用验证性因子模型对探索性因子分析获得的因子结构进行统计检验,它们往往会被拒绝。验证性因子分析的替代方法是基于普罗克鲁斯旋转的无约束因子分析,该方法最小化了研究人员提出的目标矩阵与实际矩阵之间的距离。在本文中,我们关注的是研究人员提出部分指定目标矩阵但准备允许其初始目标进行细化的情况。在这里,我们将 RETAM 作为一种客观细化目标矩阵的新方法进行讨论。到目前为止,人们建议这种细化应基于人为判断。然而,我们的方法是客观的,因为阈值是自动计算的(不由研究人员决定),并且不需要每次手动计算多个因子旋转。新方法在广泛的模拟研究中进行了测试,结果表明,它可能是基于不完全测量理论的因子分析应用中的一种有用方法。它在人格领域的实证示例中说明了其实用性。最后,RETAM 在一个著名的非商业无约束因子分析程序中实现。