Conducts exploratory factor analysis

This function is intended for use on independent samples rather than integrated with k-fold cross-validation.

Usage

run_efa(
  data,
  variables = names(data),
  m = floor(ncol(data)/4),
  rotation = "oblimin",
  simple = TRUE,
  min.loading = NA,
  single.item = c("keep", "drop", "none"),
  identified = TRUE,
  constrain0 = FALSE,
  ordered = FALSE,
  estimator = NULL,
  missing = "listwise",
  ...
)

Arguments

data: a data.frame containing the variables (i.e., items) to factor analyze
variables: character vector of column names in data indicating the variables to factor analyze. Default is to use all columns.
m: integer; maximum number of factors to extract. Default is 4 items per factor.
rotation: character (case-sensitive); any rotation method listed in rotations in the GPArotation package. Default is "oblimin".
simple: logical; Should the perfect simple structure be returned (default) when converting EFA results to CFA syntax? If FALSE, items can cross-load on multiple factors.
min.loading: numeric between 0 and 1 indicating the minimum (absolute) value of the loading for a variable on a factor when converting EFA results to CFA syntax. Must be specified when simple = FALSE.
single.item: character indicating how single-item factors should be treated. Use "keep" (default) to keep them in the model when generating the CFA syntax, "drop" to remove them, or "none" indicating the CFA syntax should not be generated for this model and "" is returned.
identified: logical; Should identification check for rotational uniqueness a la Millsap (2001) be performed? If the model is not identified "" is returned.
constrain0: logical; Should variable(s) with all loadings below min.loading still be included in model syntax? If TRUE, variable(s) will load onto first factor with the loading constrained to 0.
ordered: logical; Should items be treated as ordinal and the polychoric correlations used in the factor analysis? When FALSE (default) the Pearson correlation matrix is used. A character vector of item names is also accepted to prompt estimation of the polychoric correlation matrix.
estimator: if ordered = FALSE, the default is "MLMVS". If ordered = TRUE, the default is "WLSMV". See lavOptions for other options.
missing: default is "listwise". See lavOptions for other options.
...: other arguments passed to lavaan functions. See lavOptions.

Value

A three-element list:

efas lavaan object for each m model
loadings (rotated) factor loading matrix for each m model
cfa.syntax CFA syntax generated from loadings

Details

When converting EFA results to CFA syntax (via efa_cfa_syntax), the simple structure is defined as each variable loading onto a single factor. This is determined using the largest factor loading for each variable. When simple = FALSE, variables are allowed to cross-load on multiple factors. In this case, all pathways with loadings above the min.loading are retained. However, allowing cross-loading variables can result in model under-identification. An identification check is run by default, but can be turned off by setting identified = FALSE.

References

Millsap, R. E. (2001). When trivial constraints are not trivial: The choice of uniqueness constraints in confirmatory factor analysis. Structural Equation Modeling, 8(1), 1-17. doi:10.1207/S15328007SEM0801_1

Examples


# simulate data based on a 3-factor model with standardized loadings
sim.mod <- "f1 =~ .7*x1 + .8*x2 + .3*x3 + .7*x4 + .6*x5 + .8*x6 + .4*x7
                f2 =~ .8*x8 + .7*x9 + .6*x10 + .5*x11 + .5*x12 + .7*x13 + .6*x14
                f3 =~ .6*x15 + .5*x16 + .9*x17 + .4*x18 + .7*x19 + .5*x20
                f1 ~~ .2*f2
                f2 ~~ .2*f3
                f1 ~~ .2*f3
                x9 ~~ .2*x10"
set.seed(1161)
sim.data <- simstandard::sim_standardized(sim.mod, n = 900,
                                          latent = FALSE,
                                          errors = FALSE)[c(2:9,1,10:20)]
# Run 1-, 2-, and 3-factor models
efas <- run_efa(sim.data, m = 3)