data.numeracy.RdDataset numeracy with unscored (raw) and scored (scored)
item responses of 876 persons and 15 items.
data(data.numeracy)The format is a list a two entries:
List of 2 $ raw :'data.frame': ..$ I1 : int [1:876] 1 0 1 0 0 0 0 0 1 1 ... ..$ I2 : int [1:876] 0 1 0 0 1 1 1 1 1 0 ... ..$ I3 : int [1:876] 4 4 1 3 4 4 4 4 4 4 ... ..$ I4 : int [1:876] 4 1 2 2 1 1 1 1 1 1 ... [...] ..$ I15: int [1:876] 1 1 1 1 0 1 1 1 1 1 ... $ scored:'data.frame': ..$ I1 : int [1:876] 1 0 1 0 0 0 0 0 1 1 ... ..$ I2 : int [1:876] 0 1 0 0 1 1 1 1 1 0 ... ..$ I3 : int [1:876] 1 1 0 0 1 1 1 1 1 1 ... ..$ I4 : int [1:876] 0 1 0 0 1 1 1 1 1 1 ... [...] ..$ I15: int [1:876] 1 1 1 1 0 1 1 1 1 1 ...
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# (1) Scored numeracy data
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data(data.numeracy)
dat <- data.numeracy$scored
#Run IRT analysis: Rasch model
mod1 <- TAM::tam.mml(dat)
#Item difficulties
mod1$xsi
ItemDiff <- mod1$xsi$xsi
ItemDiff
#Ability estimate - Weighted Likelihood Estimate
Abil <- TAM::tam.wle(mod1)
Abil
PersonAbility <- Abil$theta
PersonAbility
#Descriptive statistics of item and person parameters
hist(ItemDiff)
hist(PersonAbility)
mean(ItemDiff)
mean(PersonAbility)
stats::sd(ItemDiff)
stats::sd(PersonAbility)
if (FALSE) {
#Extension
#plot histograms of ability and item parameters in the same graph
oldpar <- par(no.readonly=TRUE) # save writable default graphic settings
windows(width=4.45, height=4.45, pointsize=12)
layout(matrix(c(1,1,2),3,byrow=TRUE))
layout.show(2)
hist(PersonAbility,xlim=c(-3,3),breaks=20)
hist(ItemDiff,xlim=c(-3,3),breaks=20)
par( oldpar ) # restore default graphic settings
hist(PersonAbility,xlim=c(-3,3),breaks=20)
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# (2) Raw numeracy data
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raw_resp <- data.numeracy$raw
#score responses
key <- c(1, 1, 4, 1, 1, 1, 1, 1, 1, 1, 3, 1, 1, 1, 1)
scored <- sapply( seq(1,length(key)),
FUN=function(ii){ 1*(raw_resp[,ii]==key[ii]) } )
#run IRT analysis
mod1 <- TAM::tam.mml(scored)
#Ability estimate - Weighted Likelihood Estimate
Abil <- TAM::tam.wle(mod1)
#CTT statistics
ctt1 <- TAM::tam.ctt(raw_resp, Abil$theta)
write.csv(ctt1,"D1_ctt1.csv") # write statistics into a file
# use maybe write.csv2 if ';' should be the column separator
#Fit statistics
Fit <- TAM::tam.fit(mod1)
Fit
# plot expected response curves
plot( mod1, ask=TRUE )
}