EFA.dimensions (version 0.1.6)

SALIENT: Salient loadings criterion for determining the number of factors.

Description

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.

Usage

SALIENT(data, salvalue, numsals, corkind, verbose)

Arguments

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', and 'polychoric'. Required only if the entered data is not a correlation matrix.

verbose

Should detailed results be displayed in console? The options are: TRUE (default) or FALSE.

Value

The number of factors according to the salient loadings criterion.

References

Gorsuch, R. L. (1997a). Exploratory factor analysis: Its role in item analysis. Journal of Personality Assessment, 68, 532-560. 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.

Examples

# NOT RUN {
# test on the Harman correlation matrix
SALIENT(data_Harman, salvalue=.4, numsals=3, corkind='pearson', verbose=TRUE)

# }
# NOT RUN {
# test on the Rosenberg Self-Esteem Scale (RSE) using Pearson correlations
SALIENT(data_RSE, salvalue=.4, numsals=3, corkind='pearson', verbose=TRUE)

# test on the Rosenberg Self-Esteem Scale (RSE) using polychoric correlations
SALIENT(data_RSE, salvalue=.4, numsals=3, corkind='polychoric', verbose=TRUE)
# }