Parameter Estimation of Item Response Theory with Estimation of Latent Distribution
Ability parameter estimation with fixed item parameters
Model comparison
Selecting the best model
A recommendation for category collapsing of items based on item parame...
Extract Standard Errors of Model Coefficients
Extract Model Coefficients
Generating an artificial item response dataset
Re-parameterized two-component normal mixture distribution
Estimated factor scores
Item information function
Test information function
Item and ability parameters estimation for continuous response items
Item and ability parameters estimation for dichotomous items
Item and ability parameters estimation for a mixed-format item respons...
Item and ability parameters estimation for polytomous items
Item fit diagnostics
Latent density function
Extract Log-Likelihood
Recovering original parameters of two-component Gaussian mixture distr...
Plot of item response functions
Plot of the estimated latent distribution
Printing the summary
Printing the result
Recategorization of data using a new categorization scheme
Marginal reliability coefficient of IRT
Summary of the results
Item response theory (IRT) parameter estimation using marginal maximum likelihood and expectation-maximization algorithm (Bock & Aitkin, 1981 <doi:10.1007/BF02293801>). Within parameter estimation algorithm, several methods for latent distribution estimation are available. Reflecting some features of the true latent distribution, these latent distribution estimation methods can possibly enhance the estimation accuracy and free the normality assumption on the latent distribution.