This function is used to detect differential item functioning based on the models estimated
in the GDINA
function using the Wald test (Hou, de la Torre, & Nandakumar, 2014) and the likelihood ratio
test (Ma, Terzi, Lee, & de la Torre, 2017). It can only detect DIF for two groups currently.
dif(dat, Q, group, model = "GDINA", method = "wald",
anchor.items = NULL, dif.items = "all",
p.adjust.methods = "bonferroni", approx = FALSE, SE.type = 2, ...)# S3 method for dif
summary(object, ...)
item responses from two groups; missing data need to be coded as NA
Q-matrix specifying the association between items and attributes
a numerical vector with integer 1, 2, ..., # of groups indicating the group each individual belongs to. It must start from 1 and its length must be equal to the number of individuals.
model for each item.
DIF detection method; It can be "wald"
for Hou, de la Torre, and Nandakumar's (2014)
Wald test method, and "LR"
for likelihood ratio test (Ma, Terzi, Lee,& de la Torre, 2017).
which items will be used as anchors? Default is NULL
, which means none of the items are used as anchors.
For LR method, it can also be an integer vector giving the item numbers for anchors or "all"
, which means all items are treated as anchor items.
which items are subject to DIF detection? Default is "all"
. It can also be an integer vector giving the item numbers.
adjusted p-values for multiple hypothesis tests. This is conducted using p.adjust
function in stats,
and therefore all adjustment methods supported by p.adjust
can be used, including "holm"
,
"hochberg"
, "hommel"
, "bonferroni"
, "BH"
and "BY"
. See p.adjust
for more details. "bonferroni"
is the default.
Whether an approximated LR test is implemented? If TRUE, parameters of items except the studied one will not be re-estimated.
Type of standard error estimation methods for the Wald test.
arguments passed to GDINA function for model calibration
dif object for S3 method
A data frame giving the Wald statistics and associated p-values.
summary
: print summary information
Hou, L., de la Torre, J., & Nandakumar, R. (2014). Differential item functioning assessment in cognitive diagnostic modeling: Application of the Wald test to investigate DIF in the DINA model. Journal of Educational Measurement, 51, 98-125.
Ma, W., Terzi, R., Lee, S., & de la Torre, J. (2017, April). Multiple group cognitive diagnosis models and their applications in detecting differential item functioning. Paper presented at the Annual Meeting ofthe American Educational Research Association, San Antonio, TX.
# NOT RUN {
set.seed(123456)
N <- 3000
Q <- sim10GDINA$simQ
gs <- matrix(c(0.1,0.2,
0.1,0.2,
0.1,0.2,
0.1,0.2,
0.1,0.2,
0.1,0.2,
0.1,0.2,
0.1,0.2,
0.1,0.2,
0.1,0.2),ncol = 2, byrow = TRUE)
# By default, individuals are simulated from uniform distribution
# and deltas are simulated randomly
sim1 <- simGDINA(N,Q,gs.parm = gs,model="DINA")
sim2 <- simGDINA(N,Q,gs.parm = gs,model=c(rep("DINA",9),"DINO"))
dat <- rbind(extract(sim1,"dat"),extract(sim2,"dat"))
gr <- c(rep(1,N),rep(2,N))
dif.out <- dif(dat,Q,group=gr)
dif.out2 <- dif(dat,Q,group=gr,method="LR")
# }
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