across.reps-methods

across.reps

across.reps,classification-method


Summarises classification values across bugs or jags replications


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.

Dr Chris Wheadon

classify

Classification Accuracy and Consistency under IRT models.

across.reps-methods function

<dl> <dt><code>signature(object = "classification")</code></dt><dd> </dd> </dl>

across.reps-methods: Summarises classification values across bugs or jags replications

Description

Arguments

Methods