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This package is for someone who is familiar with confirmatory factor
analysis (CFA), but not with item response theory (IRT). Although CFA is
differently developed as opposed to IRT, both methods provide
measurement tools to validate the structure of an inventory in the scale
development. However, CFA is underutilized, mainly because applied
researchers tend not to recognize that CFA and IRT are equivalent with
certain types of indicators, such as graded response. To address this
underutilization, this package can take and provide lavaan
syntax to conduct graded response model under the confirmatory factor
analysis framework.
Simulation and analysis of graded response data with different types of estimator can be done with this package. Also, interactive shiny application is provided with graphics for characteristic and information curves.
Install the latest release from CRAN:
devtools::install_github("sooyongl/GRShiny")The documentation is available at here.
item_pars <- genIRTpar(nitem = 10, ncat = 3, nfac = 1)true_theta <- genTheta(nsample = 500, nfac = 1)grm_dt <- genData(eta = true_theta, ipar = item_pars)lav_syn <- genLavSyn(dat = grm_dt, nfac = 1)runGRM(dat = grm_dt, lav.syntax = lav_syn, estimator = "WL")
runGRM(dat = grm_dt, lav.syntax = lav_syn, estimator = "ML")startGRshiny()These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.