sirt (version 0.36-30)

data.big5: Dataset Big 5 from qgraph Package

Description

This is a Big 5 dataset from the qgraph package (Dolen, Oorts, Stoel, Wicherts, 2009). It contains 500 subjects on 240 items.

Usage

data(data.big5)

format

The format is: num [1:500, 1:240] 1 0 0 0 0 1 1 2 0 1 ... - attr(*, "dimnames")=List of 2 ..$ : NULL ..$ : chr [1:240] "N1" "E2" "O3" "A4" ...

source

See big5 in qgraph package.

Details

In the dataset, there exist 48 items for each dimension. The Big 5 dimensions are Neuroticism (N), Extraversion (E), Openness (O), Agreeableness (A) and Conscientiousness (C). Note that the original data was recoded into three categories 0,1 and 2.

References

Dolan, C. V., Oort, F. J., Stoel, R. D., & Wicherts, J. M. (2009). Testing measurement invariance in the target rotates multigroup exploratory factor model. Structural Equation Modeling, 16, 295-314.

Examples

data(data.big5)	
# extract first 10 openness items
items <- which( substring( colnames(data.big5) , 1 , 1 ) == "O"  )[1:10]
dat <- data.big5[ , items ]
I <- ncol(dat)
summary(dat)

# load needed packages
library(TAM); library(eRm); library(CDM); library(mirt);
library(ltm); library(mokken)

#****************
# Model 1: Partial Credit Model
#****************

# M1a: rm.facets (in sirt)
m1a <- rm.facets( dat )
summary(m1a)

# M1b: tam.mml (in TAM)
m1b <- tam.mml( resp=dat )
summary(m1b)

# M1c: gdm (in CDM)
theta.k <- seq(-6,6,len=21)
m1c <- gdm( dat , irtmodel="1PL" ,theta.k=theta.k , skillspace="normal")
summary(m1c)
# compare results with loglinear skillspace
m1c2 <- gdm( dat , irtmodel="1PL" ,theta.k=theta.k , skillspace="loglinear")
summary(m1c2)

# M1d: PCM (in eRm)
m1d <- PCM( dat )
summary(m1d)

# M1e: gpcm (in ltm)
m1e <- gpcm( dat , constraint = "1PL" , control=list(verbose=TRUE))
summary(m1e)

# M1f: mirt (in mirt)
m1f <- mirt( dat , model=1 , itemtype="1PL" , verbose=TRUE)
summary(m1f)
coef(m1f)

#****************
# Model 2: Generalized partial credit model
#****************

# M2a: rm.facets (in sirt)
m2a <- rm.facets( dat , est.a.item=TRUE)
summary(m2a)
# Note that in rm.facets the mean of item discriminations is fixed to 1

# M2b: tam.mml.2pl (in TAM)
m2b <- tam.mml.2pl( resp=dat , irtmodel="GPCM")
summary(m2b)

# M2c: gdm (in CDM)
theta.k <- seq(-6,6,len=21)
m2c <- gdm( dat , irtmodel="2PL" ,theta.k=theta.k ,
            skillspace="normal" , standardized.latent=TRUE)
summary(m2c)

# M2d: gpcm (in ltm)
m2d <- gpcm( dat , control=list(verbose=TRUE))
summary(m2d)

# M2e: mirt (in mirt)
m2e <- mirt( dat , model=1 ,  verbose=TRUE)
summary(m2e)
coef(m2e)

#****************
# Model 3: Nonparametric Item Response Model
#****************

# M3a: ISOP and ADISOP model - isop.poly (in sirt)
m3a <- isop.poly( dat )
summary(m3a)
plot(m3a)

# M3b: Mokken scale analysis (in mokken) 
# Scalability coefficients
coefH(dat)
# Assumption of monotonicity
monotonicity.list <- check.monotonicity(dat)
summary(monotonicity.list)
plot(monotonicity.list)
# Assumption of non-intersecting ISRFs using method restscore
restscore.list <- check.restscore(dat)
summary(restscore.list)
plot(restscore.list)