gpcm.rc

Item threshold parameters. These should be a matrix, with rows for items and columns for categories. Following Muraki, the first column should be zero.

beta

theta

Vector of item categories. A dichotomous item is specified as two categories.

cats

Discrimination parameters. If none are specified the model will default to the Partial Credit Model.

alpha

Obtains the predicted number-correct score distribution from parameters estimated under the Generalised Partial Credit Model.

IRT classification uses the probability that candidates of
a given ability, will answer correctly questions of a specified
difficulty to calculate the probability of their achieving
every possible score in a test. Due to the IRT assumption of
conditional independence (that is every answer given is assumed
to depend only on the latent trait being measured) the
probability of candidates achieving these potential scores can
be expressed by multiplication of probabilities for item
responses for a given ability. Once the true score and the
probabilities of achieving all other scores have been
determined for a candidate the probability of their score lying
in the same category as that of their true score
(classification accuracy), or the probability of consistent
classification in a category over administrations
(classification consistency), can be calculated.

gpcm.rc: IRT Derived Predicted Conditional Number Correct Score Distribution

Description

Usage

Arguments

Value

Details

Examples