EFAtools: An R package with fast and flexible implementations of exploratory factor analysis tools

In the social sciences, factor analysis is a widely used tool to identify latent constructs underlying task performance or the answers to questionnaire items. Exploratory factor analysis (EFA) is a data-driven approach to factor analysis and is used to extract a smaller number of common factors that represent or explain the common variance of a larger set of manifest variables (see, e.g., Watkins, 2018 for an overview). Several decisions have to be made in advance when performing an EFA, including the number of factors to extract, and the extraction and rotation method to be used. After a factor solution has been found, it is useful to subject the resulting factor solution to an orthogonalization procedure to achieve a hierarchical factor solution with one general and several specific factors. This situation especially applies to data structures in the field of intelligence research where usually high, positive factor intercorrelations occur. From this orthogonalized, hierarchical solution, the variance can then be partitioned to estimate the relative importance of the general versus the specific factors using omega reliability coefficients (e.g., McDonald, 1999). EFAtools is an R package (R Core Team, 2020) that enables fast and flexible analyses in an EFA framework, from tests for suitability of the data for factor analysis and factor retention criteria to hierarchical factor analysis with Schmid-Leiman transformation (Schmid & Leiman, 1957) and McDonald's omegas (e.g., McDonald, 1999). The package's core functionalities are listed in Table 1