dFactors: Eigenvalues from classical studies

In nFactors: Parallel Analysis and Other Non Graphical Solutions to the Cattell Scree Test

Eigenvalues from classical studies

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

dFactors

Format

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

Bentler

$eigenvalues and $nsubjects

Buja

$eigenvalues and $nsubjects

Cliff1

$eigenvalues and $nsubjects

Cliff2

$eigenvalues and $nsubjects

Cliff3

$eigenvalues and $nsubjects

Hand

$eigenvalues and $nsubjects

Harman

$eigenvalues and $nsubjects

Lawley

$eigenvalues and $nsubjects

Raiche

$eigenvalues and $nsubjects

Tucker1

$eigenvalues and $nsubjects

Tucker2

$eigenvalues and $nsubjects

Details

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

Source

Lawley and Hand dataset: 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 Buja 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)

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). Etude exploratoire de la dimensionnalite et des facteurs expliques par une traduction francaise de l'Inventaire des approches d'enseignement de Trigwell et Prosser dans trois universite quebecoises. Mesure et Evaluation en Education, 29(2), 41-61.

Raiche, G., Walls, T. A., Magis, D., Riopel, M. and Blais, J.-G. (2013). Non-graphical solutions for Cattell's scree test. Methodology, 9(1), 23-29.

Tucker, L. D., Koopman, R. F. and Linn, R. L. (1969). Evaluation of factor analytic research 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 OF THE Cliff1 DATASET
 plotuScree(dFactors$Cliff1$eigenvalues)

nFactors documentation built on Oct. 10, 2022, 5:07 p.m.