Salient loadings criterion for the number of factors
Salient loadings criterion for determining the number of factors, as recommended by Gorsuch. Factors are retained when they consist of a specified minimum number (or more) variables that have a specified minimum (or higher) loading value.
SALIENT(data, salvalue, numsals, corkind, Ncases=NULL, verbose)
data |
An all-numeric dataframe where the rows are cases & the columns are the variables, or a correlation matrix with ones on the diagonal. The function internally determines whether the data are a correlation matrix. |
salvalue |
The loading value that is considered salient. Default = .40 |
numsals |
The required number of salient loadings for a factor. Default = 3 |
corkind |
The kind of correlation matrix to be used if data is not a correlation matrix. The options are 'pearson', 'kendall', 'spearman', 'gamma', and 'polychoric'. Required only if the entered data is not a correlation matrix. |
Ncases |
The number of cases. Required only if data is a correlation matrix. |
verbose |
Should detailed results be displayed in console? TRUE (default) or FALSE |
The number of factors according to the salient loadings criterion.
Brian P. O'Connor
Boyd, K. C. (2011). Factor analysis. In M. Stausberg & S. Engler (Eds.),
The Routledge Handbook of Research Methods in the Study of Religion
(pp. 204-216). New York: Routledge.
Gorsuch, R. L. (1997a). Exploratory factor analysis: Its role in item analysis.
Journal of Personality Assessment, 68, 532-560.
# the Harman (1967) correlation matrix SALIENT(data_Harman, salvalue=.4, numsals=3, corkind='pearson', Ncases=305, verbose=TRUE) # Rosenberg Self-Esteem scale items, using Pearson correlations SALIENT(data_RSE, salvalue=.4, numsals=3, corkind='pearson', verbose=TRUE) # Rosenberg Self-Esteem scale items, using polychoric correlations SALIENT(data_RSE, salvalue=.4, numsals=3, corkind='polychoric', verbose=TRUE) # NEO-PI-R scales SALIENT(data_NEOPIR, salvalue=.4, numsals=3, verbose=TRUE)
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