sirt (version 1.0-3)

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)

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 ...

Example Index

Dataset data.si01 mle.pcm.group (Example 1)Dataset data.si02 rasch.mirtlc (Example 3)Dataset data.si03 linking.robust (Example 1)Dataset data.si04 rasch.mirtlc (Example 5)Dataset data.si05 lc.2raters (Example 1)Dataset data.si06 data.sirt (Example 1), nedelsky.irf (Example 2)

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

#############################################################################
# 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)