| DIMTESTS {EFA.dimensions} | R Documentation |
Tests for the number of factors
Description
Conducts multiple tests for the number of factors
Usage
DIMTESTS(data, tests, corkind, Ncases, HULL_method, HULL_gof, HULL_cor_method,
CD_cor_method, display)Arguments
data |
An all-numeric dataframe where the rows are cases & the columns are the variables, or a correlation matrix with ones on the diagonal. The function internally determines whether the data are a correlation matrix. |
tests |
A vector of the names of the tests for the number of factors that should be conducted. The possibilities are CD, EMPKC, HULL, MAP, NEVALSGT1, RAWPAR, SALIENT, SESCREE, SMT. If tests is not specified, then tests = c('EMPKC', 'HULL', 'RAWPAR') is used as the default. |
corkind |
The kind of correlation matrix to be used if data is not a correlation matrix. The options are 'pearson', 'kendall', 'spearman', 'gamma', and 'polychoric'. Required only if the entered data is not a correlation matrix. |
Ncases |
The number of cases. Required only if data is a correlation matrix. |
HULL_method |
From EFAtools: The estimation method to use. One of "PAF" (default), "ULS", or "ML", for principal axis factoring, unweighted least squares, and maximum likelihood |
HULL_gof |
From EFAtools: The goodness of fit index to use. Either "CAF" (default), "CFI", or "RMSEA", or any combination of them. If method = "PAF" is used, only the CAF can be used as goodness of fit index. For details on the CAF, see Lorenzo-Seva, Timmerman, and Kiers (2011). |
HULL_cor_method |
From EFAtools: The kind of correlation matrix to be used for the Hull method analyses. The options are 'pearson', 'kendall', and 'spearman' |
CD_cor_method |
From EFAtools: The kind of correlation matrix to be used for the CD method analyses. The options are 'pearson', 'kendall', and 'spearman' |
display |
The results to be displayed in the console: 0 = nothing; 1 = only the # of factors for each test; 2 (default) = detailed output for each test |
Details
This is a convenience function for tests for the number of factors.
The HULL method option uses the HULL function (and its defaults) in the EFAtools package.
From Auerswald & Moshagen (2019):
"The Hull method (Lorenzo-Seva et al., 2011) is an approach based on the Hull heuristic used in other areas of model selection (e.g., Ceulemans & Kiers, 2006). Similar to nongraphical variants of Cattell's scree plot, the Hull method attempts to find an elbow as justification for the number of common factors. However, instead of using the eigenvalues relative to the number of factors, the Hull method relies on goodness-of-fit indices relative to the model degrees of freedom of the proposed model."
The CD (comparison data) method option uses the CD function (and its defaults) in the EFAtools package. The CD method can only be conducted on raw data and not on correlation matrices.
From Auerswald & Moshagen (2019):
"Ruscio and Roche (2012) suggested an approach that finds the number of factors by determining the solution that reproduces the pattern of eigenvalues best (comparison data, CD). CD takes previous factors into account by generating comparison data of a known factorial structure in an iterative procedure. Initially, CD compares whether the simulated comparison data with one underlying factor (j = 1) reproduce the pattern of empirical eigenvalues significantly worse compared with a two-factor solution (j + 1). If this is the case, CD increases j until further improvements are nonsignificant or a preset maximum of factors is reached."
"No single extraction criterion performed best for every factor model. In unidimensional and orthogonal models, traditional PA, EKC, and Hull consistently displayed high hit rates even in small samples. Models with correlated factors were more challenging, where CD and SMT outperformed other methods, especially for shorter scales. Whereas the presence of cross-loadings generally increased accuracy, non-normality had virtually no effect on most criteria. We suggest researchers use a combination of SMT and either Hull, the EKC, or traditional PA, because the number of factors was almost always correctly retrieved if those methods converged. When the results of this combination rule are inconclusive, traditional PA, CD, and the EKC performed comparatively well. However, disagreement also suggests that factors will be harder to detect, increasing sample size requirements to N >= 500."
The recommended tests for the number of factors are: EMPKC, HULL, and RAWPAR. The MAP test is also recommended for principal components analyses. Other possible methods (e.g., NEVALSGT1, SALIENT, SESCREE) are less well-validated and are included for research purposes.
Value
A list with the following elements:
dimtests |
A matrix with the DIMTESTS results |
NfactorsDIMTESTS |
The number of factors according to the first test method specified in the "tests" vector |
Author(s)
Brian P. O'Connor
References
Auerswald, M., & Moshagen, M. (2019). How to determine the number of factors to
retain in exploratory factor analysis: A comparison of extraction methods under
realistic conditions. Psychological Methods, 24(4), 468-491.
Lorenzo-Seva, U., Timmerman, M. E., & Kiers, H. A. (2011). The Hull method
for selecting the number of common factors. Multivariate Behavioral
Research, 46(2), 340-364.
O'Connor, B. P. (2000). SPSS and SAS programs for determining
the number of components using parallel analysis and Velicer's
MAP test. Behavior Research Methods, Instrumentation, and
Computers, 32, 396-402.
Ruscio, J., & Roche, B. (2012). Determining the number of factors to retain
in an exploratory factor analysis using comparison data of known factorial
structure. Psychological Assessment, 24, 282292. doi: 10.1037/a0025697
Zwick, W. R., & Velicer, W. F. (1986). Comparison of five rules for determining
the number of components to retain. Psychological Bulletin, 99, 432-442.
Examples
# the Harman (1967) correlation matrix
DIMTESTS(data_Harman, tests = c('EMPKC','HULL','RAWPAR'), corkind='pearson',
Ncases = 305, display=2)
# Rosenberg Self-Esteem scale items, all possible DIMTESTS
DIMTESTS(data_RSE,
tests = c('CD','EMPKC','HULL','MAP','NEVALSGT1','RAWPAR','SALIENT','SESCREE','SMT'),
corkind='pearson', display=2)
# Rosenberg Self-Esteem scale items, using polychoric correlations
DIMTESTS(data_RSE, corkind='polychoric', display=2)
# NEO-PI-R scales
DIMTESTS(data_NEOPIR, tests = c('EMPKC','HULL','RAWPAR','NEVALSGT1'), display=2)