In mdsteiner/EFAdiff: Fast and Flexible Implementations of Exploratory Factor Analysis Tools

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EFAtools

The EFAtools package provides functions to perform exploratory factor analysis (EFA) procedures and compare their solutions. The goal is to provide state-of-the-art factor retention methods and a high degree of flexibility in the EFA procedures. This way, implementations from R psych and SPSS can be compared. Moreover, functions for Schmid-Leiman transformation, and computation of omegas are provided. To speed up the analyses, some of the iterative procedures like principal axis factoring (PAF) are implemented in C++.

Installation

You can install the release version from CRAN with:

install.packages("EFAtools")

You can install the development version from GitHub with:

install.packages("devtools")
devtools::install_github("mdsteiner/EFAtools")

To also build the vignette when installing the development version, use:

install.packages("devtools")
devtools::install_github("mdsteiner/EFAtools", build_vignettes = TRUE)

Example

Here are a few examples on how to perform the analyses with the different types and how to compare the results using the COMPARE function. For more details, see the vignette by running vignette("EFAtools", package = "EFAtools"). The vignette provides a high-level introduction into the functionalities of the package.

# load the package
library(EFAtools)

# Run all possible factor retention methods
N_FACTORS(test_models$baseline$cormat, N = 500, method = "ML")

# A type SPSS EFA to mimick the SPSS implementation with
# promax rotation
EFA_SPSS <- EFA(test_models$baseline$cormat, n_factors = 3, type = "SPSS",
                  rotation = "promax")

# look at solution
EFA_SPSS

# A type psych EFA to mimick the psych::fa() implementation with
# promax rotation
EFA_psych <- EFA(test_models$baseline$cormat, n_factors = 3, type = "psych",
                  rotation = "promax")

# compare the type psych and type SPSS implementations
COMPARE(EFA_SPSS$rot_loadings, EFA_psych$rot_loadings,
        x_labels = c("SPSS", "psych"))

# Average solution across many different EFAs with oblique rotations
EFA_AV <- EFA_AVERAGE(test_models$baseline$cormat, n_factors = 3, N = 500,
                      method = c("PAF", "ML", "ULS"), rotation = "oblique",
                      show_progress = FALSE)

# look at solution
EFA_AV

# Perform a Schmid-Leiman transformation
SL <- SL(EFA_psych)

# Based on a specific salience threshold for the loadings (here: .20):
factor_corres <- SL$sl[, c("F1", "F2", "F3")] >= .2

# Compute omegas from the Schmid-Leiman solution
OMEGA(SL, factor_corres = factor_corres)

Citation

If you use this package in your research, please acknowledge it by citing:

Steiner, M.D., & Grieder, S.G. (2020). EFAtools: An R package with fast and flexible implementations of exploratory factor analysis tools. Journal of Open Source Software, 5(53), 2521. https://doi.org/10.21105/joss.02521

Contribute or Report Bugs

If you want to contribute or report bugs, please open an issue on GitHub or email us at [e-mail] or [e-mail].

mdsteiner/EFAdiff documentation built on Jan. 10, 2023, 8:54 a.m.