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部分指定目标矩阵的迭代:在双因素情况下的应用。

Iteration of Partially Specified Target Matrices: Application to the Bi-Factor Case.

作者信息

Abad Francisco J, Garcia-Garzon Eduardo, Garrido Luis E, Barrada Juan R

机构信息

a Autonomous University of Madrid.

b Universidad Iberoamericana.

出版信息

Multivariate Behav Res. 2017 Jul-Aug;52(4):416-429. doi: 10.1080/00273171.2017.1301244. Epub 2017 Apr 4.

Abstract

The current study proposes a new bi-factor rotation method, Schmid-Leiman with iterative target rotation (SLi), based on the iteration of partially specified target matrices and an initial target constructed from a Schmid-Leiman (SL) orthogonalization. SLi was expected to ameliorate some of the limitations of the previously presented SL bi-factor rotations, SL and SL with target rotation (SLt), when the factor structure either includes cross-loadings, near-zero loadings, or both. A Monte Carlo simulation was carried out to test the performance of SLi, SL, SLt, and the two analytic bi-factor rotations, bi-quartimin and bi-geomin. The results revealed that SLi accurately recovered the bi-factor structures across the majority of the conditions, and generally outperformed the other rotation methods. SLi provided the biggest improvements over SL and SLt when the bi-factor structures contained cross-loadings and pure indicators of the general factor. Additionally, SLi was superior to bi-quartimin and bi-geomin, which performed inconsistently across the types of factor structures evaluated. No method produced a good recovery of the bi-factor structures when small samples (N = 200) were combined with low factor loadings (0.30-0.50) in the specific factors. Thus, it is recommended that larger samples of at least 500 observations be obtained.

摘要

当前研究提出了一种新的双因素旋转方法,即带有迭代目标旋转的施密德 - 莱曼法(SLi),该方法基于部分指定目标矩阵的迭代以及由施密德 - 莱曼(SL)正交化构建的初始目标。当因素结构包含交叉载荷、接近零的载荷或两者皆有时,预计SLi能改善先前提出的SL双因素旋转方法(SL以及带有目标旋转的SL(SLt))的一些局限性。进行了蒙特卡罗模拟以测试SLi、SL、SLt以及两种解析双因素旋转方法(双四次极小法和双地质极小法)的性能。结果表明,SLi在大多数情况下都能准确恢复双因素结构,并且总体上优于其他旋转方法。当双因素结构包含交叉载荷和一般因素的纯指标时,SLi相较于SL和SLt有最大的改进。此外,SLi优于双四次极小法和双地质极小法,后两者在评估的因素结构类型中表现不一致。当小样本(N = 200)与特定因素中的低因素载荷(0.30 - 0.50)相结合时,没有一种方法能很好地恢复双因素结构。因此,建议获取至少500个观测值的更大样本。

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