The equateMultiple package computes:
Data preparation follows the same steps of the equateIRT package.
Load the package equateMultiple and the data
## Caricamento del pacchetto richiesto: equateIRT
Estimate a two parameter logistic model for 5 data sets with the R package mirt
library("mirt")
m1 <- mirt(data2pl[[1]], SE = TRUE)
m2 <- mirt(data2pl[[2]], SE = TRUE)
m3 <- mirt(data2pl[[3]], SE = TRUE)
m4 <- mirt(data2pl[[4]], SE = TRUE)
m5 <- mirt(data2pl[[5]], SE = TRUE)
Create an object of class modIRT
mlist<- list(m1, m2, m3, m4, m5)
test <- paste("test", 1:5, sep = "")
mods <- modIRT(est.mods = mlist, names = test, display = FALSE)
The linkage plan
## [,1] [,2] [,3] [,4] [,5]
## [1,] 20 10 0 0 10
## [2,] 10 20 10 0 0
## [3,] 0 10 20 10 0
## [4,] 0 0 10 20 10
## [5,] 10 0 0 10 20
Estimation of the equating coefficients using the multiple mean-mean method. Form 1 is the base form.
## Computation of equating coefficients . . . .
## Computation of standard errors . . . .
## Equating coefficients:
## EQ Form Estimate StdErr
## A test1 1.00000 0.000000
## A test2 0.84001 0.018641
## A test3 0.84285 0.021321
## A test4 0.83876 0.020682
## A test5 1.02323 0.021556
## B test1 0.00000 0.000000
## B test2 0.10723 0.022389
## B test3 0.20275 0.023998
## B test4 0.36789 0.024059
## B test5 0.50312 0.023977
Estimation of the equating coefficients using the multiple mean-geometric mean method.
## Computation of equating coefficients . . . .
## Computation of standard errors . . . .
## Equating coefficients:
## EQ Form Estimate StdErr
## A test1 1.00000 0.000000
## A test2 0.83813 0.018688
## A test3 0.83986 0.021370
## A test4 0.83575 0.020736
## A test5 1.02115 0.021623
## B test1 0.00000 0.000000
## B test2 0.10726 0.022373
## B test3 0.20316 0.023898
## B test4 0.36779 0.023992
## B test5 0.50293 0.023952
Estimation of the equating coefficients using the multiple item response function method.
## Computation of equating coefficients . . . .
## Computation of standard errors . . . .
## Equating coefficients:
## EQ Form Estimate StdErr
## A test1 1.00000 0.000000
## A test2 0.83588 0.018346
## A test3 0.83551 0.020907
## A test4 0.82863 0.020163
## A test5 1.01232 0.021216
## B test1 0.00000 0.000000
## B test2 0.10838 0.021732
## B test3 0.20976 0.022989
## B test4 0.37218 0.023038
## B test5 0.49821 0.023505
Estimation of the equating coefficients using the multiple item response function method. The initial values are the estimates obtained with the multiple mean-geometric mean method.
## Computation of equating coefficients . . . .
## Computation of equating coefficients . . . .
## Computation of standard errors . . . .
## Equating coefficients:
## EQ Form Estimate StdErr
## A test1 1.00000 0.000000
## A test2 0.83588 0.018346
## A test3 0.83551 0.020907
## A test4 0.82863 0.020163
## A test5 1.01232 0.021216
## B test1 0.00000 0.000000
## B test2 0.10838 0.021732
## B test3 0.20976 0.022989
## B test4 0.37218 0.023038
## B test5 0.49821 0.023505
Estimation of the equating coefficients using the multiple test response function method.
## Computation of equating coefficients . . . .
## Computation of standard errors . . . .
## Equating coefficients:
## EQ Form Estimate StdErr
## A test1 1.00000 0.000000
## A test2 0.83636 0.018414
## A test3 0.83687 0.021036
## A test4 0.83097 0.020288
## A test5 1.01625 0.021242
## B test1 0.00000 0.000000
## B test2 0.10677 0.021781
## B test3 0.20626 0.023079
## B test4 0.36896 0.023105
## B test5 0.49615 0.023550
Estimation of the equating coefficients using the likelihood-based method.