This checks whether the data is appropriate for Factor Analysis (FA) by running the Bartlett's Test of Sphericity and the Kaiser, Meyer, Olkin (KMO) Measure of Sampling Adequacy (MSA). See details below for more information about the interpretation and meaning of each test.
check_factorstructure(x, n = NULL, ...)check_kmo(x, n = NULL, ...)
check_sphericity_bartlett(x, n = NULL, ...)
A list of lists of indices related to sphericity and KMO.
A data frame or a correlation matrix. If the latter is passed, n
must be provided.
If a correlation matrix was passed, the number of observations must be specified.
Arguments passed to or from other methods.
Bartlett's (1951) test of sphericity tests whether a matrix (of correlations) is significantly different from an identity matrix (filled with 0). It tests whether the correlation coefficients are all 0. The test computes the probability that the correlation matrix has significant correlations among at least some of the variables in a dataset, a prerequisite for factor analysis to work.
While it is often suggested to check whether Bartlett’s test of sphericity is significant before starting with factor analysis, one needs to remember that the test is testing a pretty extreme scenario (that all correlations are non-significant). As the sample size increases, this test tends to be always significant, which makes it not particularly useful or informative in well-powered studies.
(Measure of Sampling Adequacy (MSA) for Factor Analysis.)
Kaiser (1970) introduced a Measure of Sampling Adequacy (MSA), later modified by Kaiser and Rice (1974). The Kaiser-Meyer-Olkin (KMO) statistic, which can vary from 0 to 1, indicates the degree to which each variable in a set is predicted without error by the other variables.
A value of 0 indicates that the sum of partial correlations is large relative to the sum correlations, indicating factor analysis is likely to be inappropriate. A KMO value close to 1 indicates that the sum of partial correlations is not large relative to the sum of correlations and so factor analysis should yield distinct and reliable factors. It means that patterns of correlations are relatively compact, and so factor analysis should yield distinct and reliable factors. Values smaller than 0.5 suggest that you should either collect more data or rethink which variables to include.
Kaiser (1974) suggested that KMO > .9 were marvelous, in the .80s, meritorious, in the .70s, middling, in the .60s, mediocre, in the .50s, miserable, and less than .5, unacceptable. Hair et al. (2006) suggest accepting a value > 0.5. Values between 0.5 and 0.7 are mediocre, and values between 0.7 and 0.8 are good.
Variables with individual KMO values below 0.5 could be considered for exclusion them from the analysis (note that you would need to re-compute the KMO indices as they are dependent on the whole dataset).
This function is a wrapper around the KMO and the cortest.bartlett()
functions in the psych package (Revelle, 2016).
Revelle, W. (2016). How To: Use the psych package for Factor Analysis and data reduction.
Bartlett, M. S. (1951). The effect of standardization on a Chi-square approximation in factor analysis. Biometrika, 38(3/4), 337-344.
Kaiser, H. F. (1970). A second generation little jiffy. Psychometrika, 35(4), 401-415.
Kaiser, H. F., & Rice, J. (1974). Little jiffy, mark IV. Educational and psychological measurement, 34(1), 111-117.
Kaiser, H. F. (1974). An index of factorial simplicity. Psychometrika, 39(1), 31-36.
check_clusterstructure().
library(performance)
check_factorstructure(mtcars)
# One can also pass a correlation matrix
r <- cor(mtcars)
check_factorstructure(r, n = nrow(mtcars))
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