CFA {EGAnet} | R Documentation |
EGA
StructureVerifies the fit of the structure suggested by EGA
using confirmatory factor analysis
CFA(ega.obj, data, estimator, plot.CFA = TRUE, layout = "spring", ...)
ega.obj |
An |
data |
A dataframe with the variables to be used in the analysis |
estimator |
The estimator used in the confirmatory factor analysis.
'WLSMV' is the estimator of choice for ordinal variables.
'ML' or 'WLS' for interval variables.
See |
plot.CFA |
Logical. Should the CFA structure with its standardized loadings be plot? Defaults to TRUE |
layout |
Layout of plot (see |
... |
Arguments passed to |
Returns a list containing:
fit |
Output from |
summary |
Summary output from |
fit.measures |
Fit measures: chi-squared,
degrees of freedom, p-value, CFI, RMSEA, GFI, and NFI.
Additional fit measures can be applied using the
|
Hudson F. Golino <hfg9s at virginia.edu>
Christensen, A. P., Gross, G. M., Golino, H., Silvia, P. J., & Kwapil, T. R. (2019). Exploratory graph analysis of the Multidimensional Schizotypy Scale. Schizophrenia Research, 206, 43-51.
Golino, H., & Epskamp, S. (2017). Exploratory graph analysis: A new approach for estimating the number of dimensions in psychological research. PLoS ONE, 12, e0174035.
EGA
to estimate the number of dimensions of an instrument using EGA and
bootEGA
to investigate the stability of EGA's estimation via bootstrap.
# Load data
wmt <- wmt2[,7:24]
# Estimate EGA
ega.wmt <- EGA(data = wmt, plot.EGA = FALSE)
# Fit CFA model to EGA results
cfa.wmt <- CFA(ega.obj = ega.wmt, estimator = 'WLSMV', plot.CFA = TRUE, data = wmt)
# Additional fit measures
lavaan::fitMeasures(cfa.wmt$fit, fit.measures = "all")
# Load data
intel <- intelligenceBattery[,8:66]
# Estimate EGA
ega.intel <- EGA(data = intel, plot.EGA = FALSE)
# Fit CFA model to EGA results
cfa.intel <- CFA(ega.obj = ega.intel, estimator = 'WLSMV', plot.CFA = TRUE,
data = intel)