itempar is both, a class to represent item parameters of item
response models as well as a generic function. The generic function can be
used to extract the item parameters of a given item response model.
For Rasch models and n-parameter logistic models, itempar returns the
estimated item difficulty parameters \(\hat{\beta}_{j}\) under the
restriction specified in argument ref. For rating scale models,
itempar returns computed item location parameters \(\hat{\beta}_{j}\)
under the restriction specified in argument ref. These are computed
from the estimated item-specific parameters \(\hat{\xi}_{j}\) (who mark the
location of the first category of an item on the latent theta axis). For
partial credit models and generalized partial credit models, itempar
returns ‘mean’ absolute item threshold parameters, \(\hat{\beta}_{j}
= \frac{1}{p_{j}} \sum_{k = 1}^{p_{j}}\hat{\delta}_{jk}\), i.e., a single
parameter per item is returned which results as the mean of the absolute item
threshold parameters \(\hat{\delta}_{jk}\) of this item. Based upon these
‘mean’ absolute item threshold parameters \(\hat{\beta}_{j}\), the
restriction specified in argument ref is applied. For all models, the
variance-covariance matrix of the returned item parameters is adjusted
according to the multivariate delta rule.
For objects of class itempar, several methods to standard generic
functions exist: print, coef, vcov. coef and
vcov can be used to extract the estimated calculated item parameters
and their variance-covariance matrix without additional attributes. Based on
this Wald tests or confidence intervals can be easily computed, e.g., via
confint.
Two-sample item-wise Wald tests for DIF in the item parameters can be
carried out using the function anchortest.