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catR (version 3.16)

Generation of IRT Response Patterns under Computerized Adaptive Testing

Description

Provides routines for the generation of response patterns under unidimensional dichotomous and polytomous computerized adaptive testing (CAT) framework. It holds many standard functions to estimate ability, select the first item(s) to administer and optimally select the next item, as well as several stopping rules. Options to control for item exposure and content balancing are also available (Magis and Barrada (2017) ).

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Version

Install

install.packages('catR')

Monthly Downloads

1,432

Version

3.16

License

GPL (>= 2)

Maintainer

David Magis

Last Published

July 28th, 2018

Functions in catR (3.16)

Maximum likelihood weighted information (MLWI) and maximum posterior weighted information (MPWI)

Observed information function (dichotomous and polytomous models)

Item bank generation (dichotomous models)

Random generation of item response patterns under dichotomous and polytomous IRT models

Testing the format of the list for content balancing under dichotomous or polytomous IRT models

Testing the format of the input lists

Standard error of ability estimation (dichotomous and polytomous models)

simulateRespondents

Simulation of multiple examinees of adaptive tests

Item information functions, first and second derivatives (dichotomous and polytomous models)

Function \(J(\theta)\) for weighted likelihood estimation (dichotomous and polytomous IRT models)

Checking whether the stopping rule is satisfied

EAP ability estimation (dichotomous and polytomous IRT models)

Ability estimation (dichotomous and polytomous models)

Standard error of EAP ability estimation (dichotomous and polytomous IRT models)

(Maximum) Expected Information (MEI)

Kullback-Leibler (KL) and posterior Kullback-Leibler (KLP) values for item selection

Item response probabilities, first, second and third derivatives (dichotomous and polytomous models)

Item membership assessment for a-stratified sampling

Breaking the item bank in item parameters and group membership (for content balancing)

Items parameters of the CAT_PAV data set (with item names)

Selection of the next item

Random generation of adaptive tests (dichotomous and polytomous models)

Expected Posterior Variance (EPV)

Full distribution of ability estimator (dichotomous models only)

Selection of the first items

Items parameters of the TCALS 1998 data set and subgroups of items

Global-discrimination index (GDI) and posterior global-discrimination index (GDIP) for item selection

Item bank generation (polytomous models)

Numerical integration by linear interpolation (for catR internal use)