BROWSE

Related Researcher

Author

Jung, Sunho
Division for Technology Management
Research Interests
  • Marketing Engineering

ITEM VIEW & DOWNLOAD

Exploratory factor analysis for small samples

Cited 0 times inthomson ciCited 9 times inthomson ci
Title
Exploratory factor analysis for small samples
Author
Jung, SunhoLee, Soonmook
Keywords
Exploratory factor analysis; Monte Carlo simulations; Near singular covariance matrix; Regularization; Small sample size
Issue Date
201109
Publisher
SPRINGER
Citation
BEHAVIOR RESEARCH METHODS, v.43, no.3, pp.701 - 709
Abstract
Traditionally, two distinct approaches have been employed for exploratory factor analysis: maximum likelihood factor analysis and principal component analysis. A third alternative, called regularized exploratory factor analysis, was introduced recently in the psychometric literature. Small sample size is an important issue that has received considerable discussion in the factor analysis literature. However, little is known about the differential performance of these three approaches to exploratory factor analysis in a small sample size scenario. A simulation study and an empirical example demonstrate that regularized exploratory factor analysis may be recommended over the two traditional approaches, particularly when sample sizes are small (below 50) and the sample covariance matrix is near singular.
URI
Go to Link
DOI
http://dx.doi.org/10.3758/s13428-011-0077-9
ISSN
1554-351X
Appears in Collections:
SBA_Journal Papers

find_unist can give you direct access to the published full text of this article. (UNISTARs only)

Show full item record

qr_code

  • mendeley

    citeulike

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

MENU