GDINA: dif – R documentation

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dif

Differential item functioning for cognitive diagnosis models

Description

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.

Usage

dif(
  dat,
  Q,
  group,
  model = "GDINA",
  method = "wald",
  anchor.items = NULL,
  dif.items = "all",
  p.adjust.methods = "holm",
  approx = FALSE,
  SE.type = 2,
  ...
)

## S3 method for class 'dif'
summary(object, ...)

Arguments

`dat`	item responses from two groups; missing data need to be coded as `NA`
`Q`	Q-matrix specifying the association between items and attributes
`group`	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`	model for each item.
`method`	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).
`anchor.items`	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.
`dif.items`	which items are subject to DIF detection? Default is `"all"`. It can also be an integer vector giving the item numbers.
`p.adjust.methods`	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. `"holm"` is the default.
`approx`	Whether an approximated LR test is implemented? If TRUE, parameters of items except the studied one will not be re-estimated.
`SE.type`	Type of standard error estimation methods for the Wald test.
`...`	arguments passed to GDINA function for model calibration
`object`	dif object for S3 method

Value

A data frame giving the Wald statistics and associated p-values.

Methods (by generic)

summary: print summary information

Author(s)

Wenchao Ma, The University of Alabama, wenchao.ma@ua.edu
Jimmy de la Torre, The University of Hong Kong

References

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.

Examples

## 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")

## End(Not run)

GDINA

The Generalized DINA Model Framework

v2.8.0

GPL-3

Authors

Wenchao Ma [aut, cre, cph], Jimmy de la Torre [aut, cph], Miguel Sorrel [ctb], Zhehan Jiang [ctb]

Initial release

2020-05-23

dif