SALIENT {EFA.dimensions}R Documentation

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)



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.


The loading value that is considered salient. Default = .40


The required number of salient loadings for a factor. Default = 3


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.


The number of cases. Required only if data is a correlation matrix.


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)

[Package EFA.dimensions version Index]