TY - JOUR
T1 - Exploratory Factor Analysis Revisited: How Robust Methods Support the Detection of Hidden Multivariate Data Structures in IS Research
AU - Treiblmaier, Horst
AU - Filzmoser, Peter
PY - 2010/5
Y1 - 2010/5
N2 - 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.
AB - 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.
KW - Classical factor analysis
KW - Exploratory factor analysis
KW - Factor analysis
KW - Robust factor analysis
KW - Robust statistics
UR - http://www.scopus.com/inward/record.url?scp=77955229148&partnerID=8YFLogxK
U2 - 10.1016/j.im.2010.02.002
DO - 10.1016/j.im.2010.02.002
M3 - Article
SN - 0378-7206
VL - 47
SP - 197
EP - 207
JO - INFORMATION & MANAGEMENT
JF - INFORMATION & MANAGEMENT
IS - 4
ER -