data.sirt: Some Example Datasets for the `sirt` Package

Description

Some example datasets for the sirt package.

Usage

data(data.si01)
data(data.si02)
data(data.si03)
data(data.si04)
data(data.si05)
data(data.si06)

Arguments

format

The format of the datasetdata.si01is:'data.frame': 1857 obs. of 3 variables: $ idgroup: int 1 1 1 1 1 1 1 1 1 1 ... $ item1 : int NA NA NA NA NA NA NA NA NA NA ... $ item2 : int 4 4 4 4 4 4 4 2 4 4 ...
The datasetdata.si02is the Stouffer-Toby-dataset published in Lindsay, Clogg and Grego (1991; Table 1, p.97, Cross-classification A):List of 2 $ data : num [1:16, 1:4] 1 0 1 0 1 0 1 0 1 0 ... ..- attr(*, "dimnames")=List of 2 .. ..$ : NULL .. ..$ : chr [1:4] "I1" "I2" "I3" "I4" $ weights: num [1:16] 42 1 6 2 6 1 7 2 23 4 ...
The format of the datasetdata.si03(containing item parameters of two studies) is:'data.frame': 27 obs. of 3 variables: $ item : Factor w/ 27 levels "M1","M10","M11",..: 1 12 21 22 ... $ b_study1: num 0.297 1.163 0.151 -0.855 -1.653 ... $ b_study2: num 0.72 1.118 0.351 -0.861 -1.593 ...
The datasetdata.si04is adapted from Bartolucci, Montanari and Pandolfi (2012; Table 4, Table 7). The data contains 4999 persons, 79 items on 5 dimensions.List of 3 $ data : num [1:4999, 1:79] 0 1 1 0 1 1 0 0 1 1 ... ..- attr(*, "dimnames")=List of 2 .. ..$ : NULL .. ..$ : chr [1:79] "A01" "A02" "A03" "A04" ... $ itempars :'data.frame': 79 obs. of 4 variables: ..$ item : Factor w/ 79 levels "A01","A02","A03",..: 1 2 3 4 5 6 7 8 9 10 ... ..$ dim : num [1:79] 1 1 1 1 1 1 1 1 1 1 ... ..$ gamma : num [1:79] 1 1 1 1 1 1 1 1 1 1 ... ..$ gamma.beta: num [1:79] -0.189 0.25 0.758 1.695 1.022 ... $ distribution: num [1:9, 1:7] 1 2 3 4 5 ... ..- attr(*, "dimnames")=List of 2 .. ..$ : NULL .. ..$ : chr [1:7] "class" "A" "B" "C" ...
The datasetdata.si05contains double ratings of two exchangeable raters for three items which are inEx1,Ex2andEx3, respectively.List of 3 $ Ex1:'data.frame': 199 obs. of 2 variables: ..$ C7040: num [1:199] NA 1 0 1 1 0 0 0 1 0 ... ..$ C7041: num [1:199] 1 1 0 0 0 0 0 0 1 0 ... $ Ex2:'data.frame': 2000 obs. of 2 variables: ..$ rater1: num [1:2000] 2 0 3 1 2 2 0 0 0 0 ... ..$ rater2: num [1:2000] 4 1 3 2 1 0 0 0 0 2 ... $ Ex3:'data.frame': 2000 obs. of 2 variables: ..$ rater1: num [1:2000] 5 1 6 2 3 3 0 0 0 0 ... ..$ rater2: num [1:2000] 7 2 6 3 2 1 0 1 0 3 ...
The datasetdata.si06contains multiple choice item responses. The correct alternative is denoted as 0, distractors are indicated by the codes 1, 2 or 3.'data.frame': 4441 obs. of 14 variables: $ WV01: num 0 0 0 0 0 0 0 0 0 3 ... $ WV02: num 0 0 0 3 0 0 0 0 0 1 ... $ WV03: num 0 1 0 0 0 0 0 0 0 0 ... $ WV04: num 0 0 0 0 0 0 0 0 0 1 ... $ WV05: num 3 1 1 1 0 0 1 1 0 2 ... $ WV06: num 0 1 3 0 0 0 2 0 0 1 ... $ WV07: num 0 0 0 0 0 0 0 0 0 0 ... $ WV08: num 0 1 1 0 0 0 0 0 0 0 ... $ WV09: num 0 0 0 0 0 0 0 0 0 2 ... $ WV10: num 1 1 3 0 0 2 0 0 0 0 ... $ WV11: num 0 0 0 0 0 0 0 0 0 0 ... $ WV12: num 0 0 0 2 0 0 2 0 0 0 ... $ WV13: num 3 1 1 3 0 0 3 0 0 0 ... $ WV14: num 3 1 2 3 0 3 1 3 3 0 ...

References

Bartolucci, F., Montanari, G. E., & Pandolfi, S. (2012). Dimensionality of the latent structure and item selection via latent class multidimensional IRT models. Psychometrika, 77, 782-802. Lindsay, B., Clogg, C. C., & Grego, J. (1991). Semiparametric estimation in the Rasch model and related exponential response models, including a simple latent class model for item analysis. Journal of the American Statistical Association, 86, 96-107.

Examples

Run this code

#############################################################################
# EXAMPLE 1: Nested logit model multiple choice dataset data.si06
#############################################################################

data(data.si06)
dat <- data.si06

#** estimate 2PL nested logit model
library(mirt)
mod1 <- mirt::mirt( dat , model=1 , itemtype="2PLNRM" , key=rep(0,ncol(dat) ) ,
            verbose=TRUE  )
summary(mod1)
cmod1 <- mirt.wrapper.coef(mod1)$coef
cmod1[,-1] <- round( cmod1[,-1] , 3)

#** normalize item parameters according Suh and Bolt (2010)
cmod2 <- cmod1

# slope parameters
ind <-  grep("ak",colnames(cmod2))
h1 <- cmod2[ ,ind ]
cmod2[,ind] <- t( apply( h1 , 1 , FUN = function(ll){ ll - mean(ll) } ) )
# item intercepts
ind <-  paste0( "d" , 0:9 )
ind <- which( colnames(cmod2) %in% ind )
h1 <- cmod2[ ,ind ]
cmod2[,ind] <- t( apply( h1 , 1 , FUN = function(ll){ ll - mean(ll) } ) )
cmod2[,-1] <- round( cmod2[,-1] , 3)