genDichoMatrix {mstR}

Item bank generation (dichotomous models)

Description

This command generates an item bank from prespecified parent distributions for use with dichotomous IRT models. Subgroups of items can also be specified for content balancing purposes.

Usage

genDichoMatrix(items = 100, model = "4PL", aPrior = c("norm", 1, 0.2), 
  bPrior = c("norm", 0, 1), cPrior = c("unif", 0, 0.25), 
  dPrior = c("unif", 0.75, 1), seed = 1)
 

Arguments

items

integer: the number of items to include in the generated item bank.

model

character: the name of the logistic IRT model, with possible values "1PL", "2PL", "3PL" or "4PL" (default).

aPrior

vector of three components, specifying the prior distribution and item parameters for generating the item discrimination levels. See Details.

bPrior

vector of three components, specifying the prior distribution and item parameters for generating the item difficulty levels. See Details.

cPrior

vector of three components, specifying the prior distribution and item parameters for generating the item lower asymptote levels. See Details.

dPrior

vector of three components, specifying the prior distribution and item parameters for generating the item upper asymptote levels. See Details.

seed

numeric: the random seed number for the generation of item parameters (default is 1). See set.seed for further details.

Details

This function permits to generate an item bank under dichotomous IRT models that is compatible for use in MST simulations.

The number of items to be included in the bank is specified by the items argument. Corresponding item parameters are drawn from distributions to be specified by arguments aPrior, bPrior, cPrior and dPrior for respective parameters a_i, b_i, c_i and d_i (Barton and Lord, 1981). Each of these arguments is of length 3, the first component containing the name of the distribution and the last two components coding the distribution parameters.

Possible distributions are:

• the normal distribution N(\mu, \sigma^2), available for parameters a_i and b_i. It is specified by "norm" as first argument while the latter two arguments contain the values of \mu and \sigma respectively.

• the log-normal distribution \log N(\mu, \sigma^2), available for parameter a_i only. It is specified by "lnorm" as first argument while the latter two arguments contain the values of \mu and \sigma respectively.

• the uniform distribution U([a,b]), available for all parameters. It is specified by "unif" as first argument while the latter two arguments contain the values of a and b respectively. Note that taking a and b equal to a common value, say t, makes all parameters to be equal to t.

• the Beta distribution Beta(\alpha, \beta), available for parameters c_i and d_i. It is specified by "beta" as first argument while the latter two arguments contain the values of \alpha and \beta respectively.

Inattention parameters d_i are fixed to 1 if model is not "4PL"; pseudo-guessing parameters c_i are fixed to zero if model is either "1PL" or "2PL"; and discrimination parameters a_i are fixed to 1 if model="1PL". The random generation of item parameters can be controlled by the seed argument.

The random generation of item parameters si being controled by the seed argument.

The output is a data frame with four arguments, with names a, b, c and d for respectively the discrimination a_i, the difficulty b_i, the lower asymptote c_i and the upper asymptote d_i parameters.

Value

A data frame with four arguments:

a

the generated item discrimination parameters.

b

the generated item difficulty parameters.

c

the generated item lower asymptote parameters.

d

the generated item upper asymptote parameters.

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.

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. URL http://www.jstatsoft.org/v48/i08/

Examples

 # Item bank generation with 500 items
 genDichoMatrix(items = 500)

 # Item bank generation with 100 items, 2PL model and log-normal distribution with 
 # parameters (0, 0.1225) for discriminations
 genDichoMatrix(items = 100, model = "2PL", aPrior = c("lnorm", 0, 0.1225))

 # A completely identical method as for previous example
 genDichoMatrix(items = 100, aPrior = c("lnorm", 0, 0.1225), 
  cPrior = c("unif", 0, 0), dPrior = c("unif", 1, 1))