efa_kmo: Kaiser-Meyer-Olkin criterion

Description

This function computes the Kaiser-Meyer-Olkin (KMO) criterion overall and for each variable in a correlation matrix. The KMO represents the degree to which each observed variable is predicted by the other variables in the dataset and with this indicates the suitability for factor analysis.

Usage

efa_kmo(
  x,
  use = c("pairwise.complete.obs", "all.obs", "complete.obs", "everything",
    "na.or.complete"),
  cor_method = c("pearson", "spearman", "kendall", "poly", "tetra")
)

Value

A list containing

KMO: Overall KMO.
KMO_i: KMO for each variable.
settings: A list of the settings used.

Arguments

x: data.frame or matrix. Dataframe or matrix of raw data or matrix with correlations.
use: character. The missing-data policy for raw data. Passed to stats::cor() for "pearson", "spearman", and "kendall"; for "poly" / "tetra" the same policies are applied to the raw data before the polychoric estimation, where "all.obs" and "everything" abort on a missing value instead of returning NA correlations. Default is "pairwise.complete.obs".
cor_method: character. Correlation computed from raw data: "pearson", "spearman", or "kendall" (passed to stats::cor()), or "poly" / "tetra" for polychoric / tetrachoric correlations of ordinal / binary data (a two-step estimator). Default is "pearson".

Details

Kaiser (1970) proposed this index, originally called measure of sampling adequacy (MSA), that indicates how near the inverted correlation matrix $R^{-1}$ is to a diagonal matrix to determine a given correlation matrix's ($R$) suitability for factor analysis. The index is $$KMO = \frac{\sum_{i \neq j} r_{ij}^2}{\sum_{i \neq j} r_{ij}^2 + \sum_{i \neq j} q_{ij}^2}$$ with $Q = SR^{-1}S$ and S = $(diag R^{-1})^{-1/2}$ where $\sum_{i \neq j} r_{ij}^2$ is the sum of squares of the off-diagonal elements of $R$ and $\sum_{i \neq j} q_{ij}^2$ is the sum of squares of the off-diagonal elements of $Q$ (see also Cureton & D'Agostino, 1983).

So KMO varies between 0 and 1, with larger values indicating higher suitability for factor analysis. Kaiser and Rice (1974) suggest that KMO should at least exceed .50 for a correlation matrix to be suitable for factor analysis.

This function was heavily influenced by the psych::KMO() function.

See also efa_bartlett() for another test of suitability for factor analysis.

The efa_kmo function can also be called together with the efa_bartlett() function and with factor retention criteria in the efa_retain() function.

Examples

Run this code

efa_kmo(test_models$baseline$cormat)