INTERNAL_CONSISTENCY: Internal consistency reliability coefficients

In EFA.dimensions: Exploratory Factor Analysis Functions for Assessing Dimensionality

Internal consistency reliability coefficients

Description

Internal consistency reliability coefficients

Usage

INTERNAL_CONSISTENCY(data, extraction = 'minres', reverse_these = NULL, 
	                        auto_reverse = TRUE, verbose=TRUE, factormodel)

Arguments

data	An all-numeric dataframe where the rows are cases & the columns are the variables.
extraction	(optional) The factor extraction method to be used in the omega computations. The options are: 'ML' for maximum likelihood (the default); and 'PAF' for principal axis / common factor analysis.
reverse_these	(optional) A vector of the names of items that should be reverse-coded
auto_reverse	(optional) Should reverse-coding of items be conducted when warranted? TRUE (default) or FALSE
verbose	(optional) Should detailed results be displayed in console? TRUE (default) or FALSE
factormodel	(Deprecated.) Use 'extraction' instead.

Details

When 'auto_reverse = TRUE', the item loadings on the first principal component are computed and items with negative loadings are reverse-coded.

If error messages are produced, try using 'auto_reverse = FALSE'.

If item names are provided for the 'reverse_these' argument, then auto_reverse is not conducted.

The Omega-total coefficient in the output is McDonald's omega total coefficient computed using three factors (one general + 2 group factors), minres extraction, and oblimin rotation. See the OMEGA or the BIFACTOR functions in this package for alternative omega options.

Run one of the following commands for descriptions of the alpha, omega, and other coefficients produced by this function:

• RShowDoc("Coefficient_descriptions_vignettes", package = "EFA.dimensions")
  

• vignette("Coefficient_descriptions_vignettes")
  

Value

A list with the following elements:

int.consist_scale	A vector with the scale omega, Cronbach's alpha, standardized Cronbach's alpha, the mean of the off-diagonal correlations, the median of the off-diagonal correlations, and the rmsr fit coefficient for a 1-factor model
int.consist_dropped	A matrix of the int.consist_scale values for when each item, in turn, is int.consist_dropped from the analyses
item_stats	The item means, standard deviations, and item-total correlations
resp_opt_freqs	The response option frequencies
resp_opt_props	The response option proportions
new_data	The data that was used for the analyses, including any item reverse-codings

Author(s)

Brian P. O'Connor

References

Flora, D. B. (2020). Your coefficient alpha is probably wrong, but which coefficient omega is right? A tutorial on using R to obtain better reliability estimates. Advances in Methods and Practices in Psychological Science, 3(4), 484501.

McNeish, D. (2018). Thanks coefficient alpha, we'll take it from here. Psychological Methods, 23(3), 412433.

Revelle, W., & Condon, D. M. (2019). Reliability from alpha to omega: A tutorial. Psychological Assessment, 31(12), 13951411.

Examples

# Rosenberg Self-Esteem scale items -- without reverse-coding
INTERNAL_CONSISTENCY(data_RSE_not_recoded, extraction = 'minres', 
                     reverse_these = NULL, auto_reverse = FALSE, verbose=TRUE)

# Rosenberg Self-Esteem scale items -- with auto_reverse-coding
INTERNAL_CONSISTENCY(data_RSE_not_recoded, extraction = 'minres',
                     reverse_these = NULL, auto_reverse = TRUE, verbose=TRUE)

# Rosenberg Self-Esteem scale items -- another way of reverse-coding
INTERNAL_CONSISTENCY(data_RSE_not_recoded, extraction = 'minres',
                     reverse_these = c('Q1','Q2','Q4','Q6','Q7'), verbose=TRUE)

EFA.dimensions documentation built on Sept. 14, 2026, 9:08 a.m.