data.si: Some Example Datasets for the 'sirt' Package

Description Usage Format References See Also Examples

Description

Some example datasets for the sirt package.

Usage

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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. (XXXX). An item response model for true-false exams based on signal detection theory. Applied Psychological Measurement, xx(xx). xxx-xxx. 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. doi: 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

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## 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)

alexanderrobitzsch/sirt documentation built on June 27, 2021, 12:03 a.m.