This package provides a framework for conducting a variety of cognitively diagnostic analyses for dichotomous and polytomous responses.
For more details, see here
For an example of how to use the package, see here
Visit here for an online reference
Below is an illustration showing some analyses that you could do using the package. You can copy the code, paste and run it in R console. For a more comprehensive example, see this tutorial.
library(GDINA) dat <- sim10GDINA$simdat Q <- matrix(c(1,0,0, 0,1,0, 0,0,1, 1,0,1, 0,1,1, 1,1,0, 0,0,1, 1,0,0, 1,1,1, 1,0,1),byrow = T,ncol = 3)
est <- GDINA(dat = dat, Q = Q, model = "GDINA")
Qv <- Qval(est) Qv
To avoid using fixed cutoffs and also take uncertainty in item parameter estimation into account, you may consider the stepwise method:
Qv2 <- Qval(est,method = "Wald") Qv2
To further examine the q-vectors that are suggested to be modified, you can draw mesa plots:
plot(Qv, item = 9)
mc <- modelcomp(est) mc
# test level absolute fit mft <- modelfit(est) mft # item level absolute fit ift <- itemfit(est) ift summary(ift) plot(ift)
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