Exploratory factor analysis revisited: How robust methods support the detection of hidden multivariate data structures in IS research

被引:114
作者
Treiblmaier, Horst [1 ]
Filzmoser, Peter [2 ]
机构
[1] Vienna Univ Econ & Business, Inst Management Informat Syst, A-1090 Vienna, Austria
[2] Vienna Univ Technol, Dept Stat & Probabil Theory, A-1040 Vienna, Austria
关键词
Factor analysis; Exploratory factor analysis; Classical factor analysis; Robust factor analysis; Robust statistics; INFORMATION-SYSTEMS RESEARCH; MODEL; GENERALIZABILITY; USABILITY; SUCCESS; SCALE;
D O I
10.1016/j.im.2010.02.002
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Exploratory factor analysis is commonly used in IS research to detect multivariate data structures. Frequently, the method is blindly applied without checking if the data fulfill the requirements of the method. We investigated the influence of sample size, data transformation, factor extraction method, rotation, and number of factors on the outcome. We compared classical exploratory factor analysis with a robust counterpart which is less influenced by data outliers and data heterogeneities. Our analyses revealed that robust exploratory factor analysis is more stable than the classical method. (C) 2010 Elsevier B.V. All rights reserved.
引用
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页码:197 / 207
页数:11
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