genPattern {catR}

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

Description

This command generates item responses patterns for a given matrix of item parameters of any specified dichotomous or polytomous IRT model and a given (set of) ability value(s).

Usage

genPattern(th, it, model = NULL, D = 1, seed = NULL)
 

Arguments

th

numeric: a vector of true underlynig ability values (can be a singlre value too).

it

numeric: a suitable matrix of item parameters. See Details.

model

either NULL (default) for dichotomous models, or any suitable acronym for polytomous models. Possible values are "GRM", "MGRM", "PCM", "GPCM", "RSM" and "NRM". See Details.

D

numeric: the metric constant. Default is D=1 (for logistic metric); D=1.702 yields approximately the normal metric (Haley, 1952). Ignored if model is not NULL.

seed

either the random seed value or NULL (default). See Details.

Details

This function permits to randomly generate item responses for a set of given ability levels, specified by the argument thr, and for a given matrix of item parameters, specified by the argument it. Both dichotomous and polytomous IRT models can be considered and item responses are generated accordingly.

For dichotomous models, item responses are generated from Bernoulli draws, using the rbinom function. For polytomous models they are generated from darws from a multinomial distribution, using the rmultinom function. In both cases, success probabilities are obtained from the Pi function.

Note that for polytomous models, item responses are coded as 0 (for the first response category), 1 (for the second category), ..., until g_j (for the last category), in agreement with the notations used in the help files of, e.g., the genPolyMatrix function.

Dichotomous IRT models are considered whenever model is set to NULL (default value). In this case, it must be a matrix with one row per item and four columns, with the values of the discrimination, the difficulty, the pseudo-guessing and the inattention parameters (in this order). These are the parameters of the four-parameter logistic (4PL) model (Barton and Lord, 1981).

Polytomous IRT models are specified by their respective acronym: "GRM" for Graded Response Model, "MGRM" for Modified Graded Response Model, "PCM" for Partical Credit Model, "GPCM" for Generalized Partial Credit Model, "RSM" for Rating Scale Model and "NRM" for Nominal Response Model. The it still holds one row per item, end the number of columns and their content depends on the model. See genPolyMatrix for further information and illustrative examples of suitable polytomous item banks.

The random pattern generation can be fixed by setting seed to some numeric value. By default, seed is NULL and the random seed is not fixed.

Value

If th holds a single value, output is a vector with the item responses in the order of appearance of the items in the it matrix. If th is a vector of numeric values, output is a response matrix with one row per th value and one column per item.

Author(s)

David Magis
Department of Psychology, University of Liege, Belgium
[e-mail]

References

Barton, M.A., and Lord, F.M. (1981). An upper asymptote for the three-parameter logistic item-response model. Research Bulletin 81-20. Princeton, NJ: Educational Testing Service.

Haley, D.C. (1952). Estimation of the dosage mortality relationship when the dose is subject to error. Technical report no 15. Palo Alto, CA: Applied Mathematics and Statistics Laboratory, Stanford University.

Magis, D. and Barrada, J. R. (2017). Computerized Adaptive Testing with R: Recent Updates of the Package catR. Journal of Statistical Software, Code Snippets, 76(1), 1-18. doi: 10.18637/jss.v076.c01

Magis, D., and Raiche, G. (2012). Random Generation of Response Patterns under Computerized Adaptive Testing with the R Package catR. Journal of Statistical Software, 48 (8), 1-31. doi: 10.18637/jss.v048.i08

See Also

rbinom and rmultinom for random draws; genPolyMatrix, Pi

Examples

## Dichotomous models ##
 
 # Loading the 'tcals' parameters 
 data(tcals)

 # Selecting item parameters only
 tcals <- as.matrix(tcals[,1:4])
 
 # Generation of a response pattern for ability level 0
 genPattern(th = 0, tcals)

 # Generation of 10 response patterns for ability levels randomly drawn from N(0,1)
 genPattern(th = rnorm(10), tcals)

 # Generation of a single response for the first item only
 genPattern(th = 0, tcals[1,])

## Polytomous models ##

 # Generation of an item bank under GRM with 100 items and at most 4 categories
 m.GRM <- genPolyMatrix(100, 4, "GRM")
 m.GRM <- as.matrix(m.GRM)

 # Generation of a response pattern for ability level 0
 genPattern(0, m.GRM, model = "GRM")

 # Generation of 10 response patterns for ability levels randomly drawn from N(0,1)
 genPattern(rnorm(10), m.GRM, model = "GRM")

 # Generation of a single response for the first item only
 genPattern(0, m.GRM[1,], model = "GRM")

 # Loading the cat_pav data
 data(cat_pav)
 cat_pav <- as.matrix(cat_pav)

 # Generation of a response pattern for ability level 0
 genPattern(0, cat_pav, model = "GPCM")

 # Generation of a single response for the first item only
 genPattern(0, cat_pav[1,], model = "GPCM")