nFactors (version 2.1)

dFactors: EIGENVALUES VECTORS FROM THE LITTERATURE

Description

Classical examples of eigenvalues vectors used to study the number of factors to retain in the litterature. These examples generally give the number of subjects use to obtain these eigenvalues. The number of subjects is used with the parallel analysis.

Usage

data(dFactors)

format

A list of examples. For each example, a list is also used to give the eigenvalues vector and the number of subjects.

source

Lawley and Hand data set: Bartholomew et al. (2002, p. 123, 126) Bentler dataset: Bentler and Yuan (1998, p. 139-140) Buja datasets: Buja and Eyuboglu (1992, p. 516, 519) < Number of subjects not specified by Baju and Eyuboglu > Cliff datasets: Cliff (1970, p. 165) Raiche dataset: Raiche, Langevin, Riopel and Mauffette (2006) Raiche dataset: Raiche, Riopel and Blais (2006, p. 9) Tucker datasets: Tucker et al. (1969, p. 442)

Details

Other datasets will be added in future versions of the package.

References

Bartholomew, D. J., Steele, F., Moustaki, I. and Galbraith, J. I. (2002). The analysis and interpretation of multivariate data for social scientists. Boca Raton, FL: Chapman and Hall. Bentler, P. M. and Yuan, K.-H. (1998). Tests for linear trend in the smallest eigenvalues of the correlation matrix. Psychometrika, 63(2), 131-144. Buja, A. and Eyuboglu, N. (1992). Remarks on parallel analysis. Multivariate Behavioral Research, 27(4), 509-540. Cliff, N. (1970). The relation between sample and population characteristic vectors. Psychometrika, 35(2), 163-178. Hand, D. J., Daly, F., Lunn, A. D., McConway, K. J. and Ostrowski, E. (1994). A handbook of small data sets. Boca Raton, FL: Chapman and Hall. Lawley, D. N. and Maxwell, A. E. (1971). Factor analysis as a statistical method (2nd edition). London: Butterworth. Raiche, G., Langevin, L., Riopel, M. and Mauffette, Y. (2006, accepted). Mesure et Evaluation en Education. Raiche, G., Riopel, M. and Blais, J.-G. (2006). Non graphical solutions for the Cattell's scree test. Paper presented at the International Annual meeting of the Psychometric Society, Montreal. [http://www.er.uqam.ca/nobel/r17165/RECHERCHE/COMMUNICATIONS/] Tucker, L. D., Koopman, R. F. and Linn, R. L. (1969). Evaluation of factor analytic reserach procedures by mean of simulated correlation matrices. Psychometrika, 34(4), 421-459. Zoski, K. and Jurs, S. (1993). Using multiple regression to determine the number of factors to retain in factor analysis. Multiple Linear Regression Viewpoint, 20(1), 5-9.

Examples

## EXAMPLES FROM DATASET
 data(dFactors)

## COMMAND TO VISUALIZE THE CONTENT AND ATTRIBUTES OF THE DATASETS
 names(dFactors)
 attributes(dFactors)
 dFactors$Cliff1$eigenvalues
 dFactors$Cliff1$nsubjects

## SCREE PLOT
 plotuScree(dFactors$Cliff1$eigenvalues)