data.sirt {sirt}R Documentation

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)
data(data.si07)
data(data.si08)
data(data.si09)
data(data.si10)

Format

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(4), 782-802. doi:10.1007/s11336-012-9278-0

DeCarlo, L. T. (2020). An item response model for true-false exams based on signal detection theory. Applied Psychological Measurement, 34(3). 234-248. doi:10.1177/0146621619843823

Fischer, R., & Karl, J. A. (2019). A primer to (cross-cultural) multi-group invariance testing possibilities in R. Frontiers in Psychology | Cultural Psychology, 10:1507. doi:10.3389/fpsyg.2019.01507

Fop, M., & Murphy, T. B. (2018). Variable selection methods for model-based clustering. Statistics Surveys, 12, 18-65. https://doi.org/10.1214/18-SS119

Goodman, L. A. (1970). The multivariate analysis of qualitative data: Interactions among multiple classifications. Journal of the American Statistical Association, 65(329), 226-256. doi:10.1080/01621459.1970.10481076

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(413), 96-107. doi:10.1080/01621459.1991.10475008

Rasch, D., Kubinger, K. D., & Yanagida, T. (2011). Statistics in psychology using R and SPSS. New York: Wiley. doi:10.1002/9781119979630

See Also

Some free datasets can be obtained from
Psychological questionnaires: http://personality-testing.info/_rawdata/
PISA 2012: http://pisa2012.acer.edu.au/downloads.php
PIAAC: http://www.oecd.org/site/piaac/publicdataandanalysis.htm
TIMSS 2011: http://timssandpirls.bc.edu/timss2011/international-database.html
ALLBUS: http://www.gesis.org/allbus/allbus-home/

Examples

## Not run: 
#############################################################################
# EXAMPLE 1: Nested logit model multiple choice dataset data.si06
#############################################################################

data(data.si06, package="sirt")
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 <- sirt::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)

#############################################################################
# EXAMPLE 2: Item response modle based on signal detection theory (IRSDT model)
#############################################################################

data(data.si07, package="sirt")
data <- data.si07

#-- simulate data
set.seed(98)
N <- 2000 # define sample size
# generate membership scores
lambda <- sample(size=N, x=data$trait$x, prob=data$trait$prob, replace=TRUE)
b <- data$pars$b
d <- data$pars$d
items <- data$pars$item
dat <- data$sim_fun(lambda=lambda, b=b, d=d, items=items)

#- estimate IRSDT model as a grade of membership model with two classes
problevels <- seq( 0.025, 0.975, length=20 )
mod1 <- sirt::gom.em( dat, K=2, problevels=problevels )
summary(mod1)

## End(Not run)
[Package sirt version 4.1-15 Index]