mcIRT (version 0.41)

mcIRT-package: IRT models for multiple choice items (mcIRT)

Description

This package provides functions to evaluate multiples choice items or other multicategorial response formats. It is possible to estimate multiple group models and to model interaction effects to examine Differential Item Functioning. Each model has a full accessible design matrix which allows the user to manipulate and set up his own weighting scheme and his own constraints. One application could be modeling and testing item properties by means of customizing the design matrix and estimating an explanatory Nominal Response Model or an explanatory Nested Logit Model (to get an idea what 'explanatory' means, take a look at Boeck & Wilson (2004)).

Details

Package:

mcIRT

Type:

Package

Version:

0.4

Date:

2014-03-14

References

Baker, F. B., & Kim, S.-H. (2004). Item response theory. Parameter estimation techniques (2nd ed.). New York, NY: Marcel-Dekker.

Bock, R. D. (1972). Estimating item parameters and latent ability when responses are scored in two or more nominal categories. Psychometrika, 37, 29-51.

Suh, Y., & Bolt, D. M. (2010). Nested logit models for multiple-choice item response data. Psychometrika, 75, 454-473.

Suh, Y. & Bolt, D. M. (2011). A nested logit approach for investigating distractors as causes of differential item functioning. Journal of Educational Measurement, 48, 188-205.

De Boeck, P. & Wilson, M. (2004). Explanatory Items Response Models. New York, NY: Springer.

Woods, C. M. (2007). Empirical Histograms in Item Response Theory With Ordinal Data. Education and Psychological Measurement, 67:1, 73-87.

Woods, C. M. (2011). DIF Testing With an Empirical-Histogram Approximation of the Latent Density for Each Group. Applied Measurement in Education, 24:3, 256-279.

See Also

reshMG

nrm

nelm

DDF

Examples

## Not run: 
# 
# # Simulating 5 Items within a loop
# ParList <- lapply(1:5,function(x)
# {
#   Item1 <- c(c(-2,-1,1,2),c(-1.2,0.3,0.2,0.7))
#   names(Item1) <- c(paste("zeta",1:4,sep=""),paste("lamb",1:4,sep=""))
#   Item1
# })
# 
# names(ParList) <- paste("item",1:5,sep="")
# 
# # simulate person parameters
# perp1     <- rnorm(1500,0,1)
# 
# # simulate data.frame 
# sim.nrm.1 <- NRM.sim(ParList,perp1)
# 
# # reshape
# reshdat1 <- reshMG(sim.nrm.1,items=1:5,correct=rep(0,5))
# 
# # fit the nrm
# res.nrm <- nrm(reshdat1)
# 
# ## End(Not run)