Exploratory Factor Analysis Revisited: How Robust Methods Support the Detection of Hidden Multivariate Data Structures in IS Research

Horst Treiblmaier, Peter Filzmoser

Research output: Contribution to journalArticlepeer-review

80 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)197-207
Number of pages11
JournalINFORMATION & MANAGEMENT
Volume47
Issue number4
DOIs
Publication statusPublished - May 2010

Keywords

  • Classical factor analysis
  • Exploratory factor analysis
  • Factor analysis
  • Robust factor analysis
  • Robust statistics

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