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Effects of Variable and Subject Sampling on Factor Pattern Recovery

Effects of Variable and Subject Sampling on Factor Pattern Recovery Information about the effects of subject sampling and variable sampling on factor pattern reproduction is critical for both the design of studies and the evaluation of existing studies. This article reports both a review of the available literature and the results of 2 new simulation studies. Conditions investigated include the average number of variables per factor (3:1, 4:1, or 5: 1), the sample size (N= 50, 100, 150, 200, 400, 800), the method of analysis (principal component analysis, image component analysis, maximum likelihood factor analysis), pattern of loadings (equal or unequal), and the size of the average loading (.40, .60, .80). A small but consistent pattern of differences between methods occurred. Subject sample size, variable sample size, and size of the loadings can all strongly affect the degree to which a sample pattern reproduces the population pattern. The frequency of boundary cases in factor analysis is also affected by the same 3 variables. A minimum of 3 variables per factor is critical. Weaknesses in one area can be partially compensated for by strengths in another area. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Psychological Methods American Psychological Association

Effects of Variable and Subject Sampling on Factor Pattern Recovery

Velicer, Wayne F.; Fava, Joseph L.
Psychological Methods , Volume 3 (2): 21 – Jun 1, 1998
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Publisher
American Psychological Association
Copyright
Copyright © 1998 American Psychological Association
ISSN
1082-989x
eISSN
1939-1463
DOI
10.1037/1082-989X.3.2.231
Publisher site
See Article on Publisher Site

Abstract

Information about the effects of subject sampling and variable sampling on factor pattern reproduction is critical for both the design of studies and the evaluation of existing studies. This article reports both a review of the available literature and the results of 2 new simulation studies. Conditions investigated include the average number of variables per factor (3:1, 4:1, or 5: 1), the sample size (N= 50, 100, 150, 200, 400, 800), the method of analysis (principal component analysis, image component analysis, maximum likelihood factor analysis), pattern of loadings (equal or unequal), and the size of the average loading (.40, .60, .80). A small but consistent pattern of differences between methods occurred. Subject sample size, variable sample size, and size of the loadings can all strongly affect the degree to which a sample pattern reproduces the population pattern. The frequency of boundary cases in factor analysis is also affected by the same 3 variables. A minimum of 3 variables per factor is critical. Weaknesses in one area can be partially compensated for by strengths in another area.

Journal

Psychological Methods – American Psychological Association

Published: Jun 1, 1998

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