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Sample size in factor analysis
| Venue: | Psychol. Methods |
| Citations: | 189 - 4 self |
BibTeX
@ARTICLE{Maccallum_samplesize,
author = {Robert C. Maccallum and Keith F. Widaman and Shaobo Zhang and Sehee Hong and Robert C. Maccallum and Shaobo Zhang and Sehee Hong},
title = {Sample size in factor analysis},
journal = {Psychol. Methods},
year = {},
pages = {84--99}
}
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Abstract
The factor analysis literature includes a range of recommendations regarding the minimum sample size necessary to obtain factor solutions that are adequately stable and that correspond closely to population factors. A fundamental misconception about this issue is that the minimum sample size, or the minimum ratio of sample size to the number of variables, is invariant across studies. In fact, necessary sample size is dependent on several aspects of any given study, including the level of communality of the variables and the level of overdetermination of the factors. The authors present a theoretical and mathematical framework that provides a basis for understanding and predicting these effects. The hypothesized effects are verified by a sampling study using artificial data. Results demonstrate the lack of validity of common rules of thumb and provide a basis for establishing guidelines for sample size in factor analysis. In the factor analysis literature, much attention has be;;n given to the issue of sample size. It is widely understood that the use of larger samples in applica-tions of factor analysis tends to provide results such that sample factor loadings are more precise estimates of population loadings and are also more stable, or les s variable, across repeated sampling. Despite gen-eral agreement on this matter, there is considerable di'/ergence of opinion and evidence about the ques-tion of how large a sample is necessary to adequately acnieve these objectives. Recommendations and find-ings about this issue are diverse and often contradic-tory. The objectives of this article are to provide a
Keyphrases
sample size factor analysis factor analysis literature minimum sample size necessary sample size population loading precise estimate minimum ratio common rule several aspect artificial data hypothesized effect considerable di sample factor loading gen-eral agreement sampling study fundamental misconception factor solution mathematical framework population factor much attention
