autoFC (version 0.2.0.1002)

get_CFA_estimates: Conduct Confirmatory Factor Analysis (CFA) and Obtain Parameter Estimates

Description

Reads in responses to Likert-type scales and a specified factor model, performs CFA, and produces parameter estimates required for producing subsequent simulation data (i.e., use as inputs for get_simulation_matrices()).

This function returns factor loadings, intercepts, residual variances, and covariances among latent variables

Usage

get_CFA_estimates(response_data, fit_model, item_names)

Value

A list containing:

• loadings Item loadings for item_names

  intercepts Item intercepts for item_names

  residuals Item residual variances for item_names

  covariances Covariances between latent variables defined in fit_model

  model_fit Model fit for fit_model on response_data

Arguments

response_data

Likert-type response data. Requires the header including variable names.

fit_model

A pre-specified CFA model written in lavaan syntax.

item_names

Names of the items you wish to obtain loadings, intercepts, and residuals. These variable names should appear in response_data.

Author

Mengtong Li

Details

This function is essentially a wrapper for lavaan::parameterEstimates() to obtain specific set of parameter estimates.

Notice that we assume your CFA model does not have hierarchical factor structure, nor does it have cross loadings or correlated residuals.

References

Ashton, M. C., & Lee, K. (2009). The HEXACO–60: A short measure of the major dimensions of personality. Journal of personality assessment, 91(4), 340-345. https://doi.org/10.1080/00223890902935878

See Also

get_simulation_matrices()

Examples

## We have a small response sample data (N = 100) on 
## the HEXACO-60 (Ashton & Lee, 2009) scale in the package
## Names of the items are SS1-SS60 (Single-statement)
rating_data <- HEXACO_example_data
cfa_model <- paste0("H =~ ", paste0("SS", seq(6,60,6), collapse = " + "), "\n",
                   "E =~ ", paste0("SS", seq(5,60,6), collapse = " + "), "\n",
                   "X =~ ", paste0("SS", seq(4,60,6), collapse = " + "), "\n",
                   "A =~ ", paste0("SS", seq(3,60,6), collapse = " + "), "\n",
                   "C =~ ", paste0("SS", seq(2,60,6), collapse = " + "), "\n",
                   "O =~ ", paste0("SS", seq(1,60,6), collapse = " + "), "\n")
cfa_estimates <- get_CFA_estimates(response_data = rating_data,
                                   fit_model = cfa_model, 
                                   item_names = paste0("SS",c(1:60)))