CFA {EGAnet}

R Documentation

CFA Fit of EGA Structure

Description

Verifies the fit of the structure suggested by EGA using confirmatory factor analysis

Usage

CFA(ega.obj, data, estimator, plot.CFA = TRUE, layout = "spring", ...)

Arguments

ega.obj	An EGA object
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 lavOptions for more details
plot.CFA	Logical. Should the CFA structure with its standardized loadings be plot? Defaults to TRUE
layout	Layout of plot (see semPaths). Defaults to "spring"
...	Arguments passed to cfa

Value

Returns a list containing:

fit	Output from cfa
summary	Summary output from lavaan-class
fit.measures	Fit measures: chi-squared, degrees of freedom, p-value, CFI, RMSEA, GFI, and NFI. Additional fit measures can be applied using the fitMeasures function (see examples)

Author(s)

Hudson F. Golino <hfg9s at virginia.edu>

References

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.

See Also

EGA to estimate the number of dimensions of an instrument using EGA and bootEGA to investigate the stability of EGA's estimation via bootstrap.

Examples

# 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)

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