TAM (version 4.2-21)

data.geiser: Dataset from Geiser et al. (2006)

Description

This is a subsample of the dataset used in Geiser et al. (2006) and Geiser and Eid (2010).

Usage

data(data.geiser)

Format

A data frame with 519 observations on the following 24 variables

'data.frame': 519 obs. of 24 variables:
$ mrt1 : num 0 0 0 0 0 0 0 0 0 0 ...
$ mrt2 : num 0 0 0 0 0 0 0 0 0 0 ...
$ mrt3 : num 0 0 0 0 0 0 0 0 1 0 ...
$ mrt4 : num 0 0 0 0 0 1 0 0 0 0 ...
[...]
$ mrt23: num 0 0 0 0 0 0 0 1 0 0 ...
$ mrt24: num 0 0 0 0 0 0 0 0 0 0 ...

References

Geiser, C., & Eid, M. (2010). Item-Response-Theorie. In C. Wolf & H. Best (Hrsg.). Handbuch der sozialwissenschaftlichen Datenanalyse (S. 311-332). VS Verlag fuer Sozialwissenschaften.

Geiser, C., Lehmann, W., & Eid, M. (2006). Separating rotators from nonrotators in the mental rotations test: A multigroup latent class analysis. Multivariate Behavioral Research, 41(3), 261-293. tools:::Rd_expr_doi("10.1207/s15327906mbr4103_2")

Examples

if (FALSE) {
#############################################################################
# EXAMPLE 1: Latent trait and latent class models (Geiser et al., 2006, MBR)
#############################################################################

data(data.geiser)
dat <- data.geiser

#**********************************************
# Model 1: Rasch model
tammodel <- "
  LAVAAN MODEL:
    F=~ 1*mrt1__mrt12
    F ~~ F
  ITEM TYPE:
    ALL(Rasch)
    "
mod1 <- TAM::tamaan( tammodel, dat)
summary(mod1)

#**********************************************
# Model 2: 2PL model
tammodel <- "
  LAVAAN MODEL:
    F=~ mrt1__mrt12
    F ~~ 1*F
    "
mod2 <- TAM::tamaan( tammodel, dat)
summary(mod2)

# model comparison Rasch vs. 2PL
anova(mod1,mod2)

#*********************************************************************
#*** Model 3: Latent class analysis with four classes

tammodel <- "
ANALYSIS:
  TYPE=LCA;
  NCLASSES(4);   # 4 classes
  NSTARTS(10,20); # 10 random starts with 20 iterations
LAVAAN MODEL:
  F=~ mrt1__mrt12
    "
mod3 <- TAM::tamaan( tammodel, resp=dat  )
summary(mod3)

# extract item response functions
imod2 <- IRT.irfprob(mod3)[,2,]
# plot class specific probabilities
matplot( imod2, type="o", pch=1:4, xlab="Item", ylab="Probability" )
legend( 10,1, paste0("Class",1:4), lty=1:4, col=1:4, pch=1:4 )

#*********************************************************************
#*** Model 4: Latent class analysis with five classes

tammodel <- "
ANALYSIS:
  TYPE=LCA;
  NCLASSES(5);
  NSTARTS(10,20);
LAVAAN MODEL:
  F=~ mrt1__mrt12
    "
mod4 <- TAM::tamaan( tammodel, resp=dat )
summary(mod4)

# compare different models
AIC(mod1); AIC(mod2); AIC(mod3); AIC(mod4)
BIC(mod1); BIC(mod2); BIC(mod3); BIC(mod4)
# more condensed form
IRT.compareModels(mod1, mod2, mod3, mod4)

#############################################################################
# EXAMPLE 2: Rasch model and mixture Rasch model (Geiser & Eid, 2010)
#############################################################################

data(data.geiser)
dat <- data.geiser

#*********************************************************************
#*** Model 1: Rasch model
tammodel <- "
LAVAAN MODEL:
  F=~ mrt1__mrt6
  F ~~ F
ITEM TYPE:
  ALL(Rasch);
    "
mod1 <- TAM::tamaan( tammodel, resp=dat  )
summary(mod1)

#*********************************************************************
#*** Model 2: Mixed Rasch model with two classes
tammodel <- "
ANALYSIS:
  TYPE=MIXTURE ;
  NCLASSES(2);
  NSTARTS(20,25);
LAVAAN MODEL:
  F=~ mrt1__mrt6
  F ~~ F
ITEM TYPE:
  ALL(Rasch);
    "
mod2 <- TAM::tamaan( tammodel, resp=dat  )
summary(mod2)

# plot item parameters
ipars <- mod2$itempartable_MIXTURE[ 1:6, ]
plot( 1:6, ipars[,3], type="o", ylim=c(-3,2), pch=16,
        xlab="Item", ylab="Item difficulty")
lines( 1:6, ipars[,4], type="l", col=2, lty=2)
points( 1:6, ipars[,4],  col=2, pch=2)

# extract individual posterior distribution
post2 <- IRT.posterior(mod2)
str(post2)
# num [1:519, 1:30] 0.000105 0.000105 0.000105 0.000105 0.000105 ...
# - attr(*, "theta")=num [1:30, 1:30] 1 0 0 0 0 0 0 0 0 0 ...
# - attr(*, "prob.theta")=num [1:30, 1] 1.21e-05 2.20e-04 2.29e-03 1.37e-02 4.68e-02 ...
# - attr(*, "G")=num 1

# There are 2 classes and 15 theta grid points for each class
# The loadings of the theta grid on items are as follows
mod2$E[1,2,,"mrt1_F_load_Cl1"]
mod2$E[1,2,,"mrt1_F_load_Cl2"]

# compute individual posterior probability for class 1 (first 15 columns)
round( rowSums( post2[, 1:15] ), 3 )
# columns 16 to 30 refer to class 2

#*********************************************************************
#*** Model 3: Mixed Rasch model with three classes
tammodel <- "
ANALYSIS:
  TYPE=MIXTURE ;
  NCLASSES(3);
  NSTARTS(20,25);
LAVAAN MODEL:
  F=~ mrt1__mrt6
  F ~~ F
ITEM TYPE:
  ALL(Rasch);
    "
mod3 <- TAM::tamaan( tammodel, resp=dat  )
summary(mod3)

# plot item parameters
ipars <- mod3$itempartable_MIXTURE[ 1:6, ]
plot( 1:6, ipars[,3], type="o", ylim=c(-3.7,2), pch=16,
        xlab="Item", ylab="Item difficulty")
lines( 1:6, ipars[,4], type="l", col=2, lty=2)
points( 1:6, ipars[,4],  col=2, pch=2)
lines( 1:6, ipars[,5], type="l", col=3, lty=3)
points( 1:6, ipars[,5],  col=3, pch=17)

# model comparison
IRT.compareModels( mod1, mod2, mod3 )
}