DIF: Differential item functioning statistics

Description

This function runs the Wald and likelihood-ratio approaches for testing differential item functioning (DIF). This is primarily a convenience wrapper to the multipleGroup function for performing standard DIF procedures. Independent models can be estimated in parallel by defining a parallel object with mirtCluster, which will help to decrease the runtime. For best results, the baseline model should contain a set of 'anchor' items and have freely estimated hyper-parameters in the focal groups.

Usage

DIF(MGmodel, which.par, scheme = "add",
  items2test = 1:extract.mirt(MGmodel, "nitems"), seq_stat = "SABIC",
  Wald = FALSE, p.adjust = "none", return_models = FALSE,
  max_run = Inf, plotdif = FALSE, type = "trace", simplify = TRUE,
  verbose = TRUE, ...)

Arguments

MGmodel

an object returned from multipleGroup to be used as the reference model

which.par

a character vector containing the parameter names which will be inspected for DIF

scheme

type of DIF analysis to perform, either by adding or dropping constraints across groups. These can be:

'add': parameters in which.par will be constrained each item one at a time for items that are specified in items2test. This is beneficial when examining DIF from a model with parameters freely estimated across groups, and when inspecting differences via the Wald test
'drop': parameters in which.par will be freely estimated for items that are specified in items2test. This is useful when supplying an overly restrictive model and attempting to detect DIF with a slightly less restrictive model
'add_sequential': sequentially loop over the items being tested, and at the end of the loop treat DIF tests that satisfy the seq_stat criteria as invariant. The loop is then re-run on the remaining invariant items to determine if they are now displaying DIF in the less constrained model, and when no new invariant item is found the algorithm stops and returns the items that displayed DIF
'drop_sequential': sequentially loop over the items being tested, and at the end of the loop treat items that violate the seq_stat criteria as demonstrating DIF. The loop is then re-run, leaving the items that previously demonstrated DIF as variable across groups, and the remaining test items that previously showed invariance are re-tested. The algorithm stops when no more items showing DIF are found and returns the items that displayed DIF

items2test

a numeric vector, or character vector containing the item names, indicating which items will be tested for DIF. In models where anchor items are known, omit them from this vector. For example, if items 1 and 2 are anchors in a 10 item test, then items2test = 3:10 would work for testing the remaining items (important to remember when using sequential schemes)

seq_stat

select a statistic to test for in the sequential schemes. Potential values are (in descending order of power) 'AIC', 'AICc', 'SABIC', 'HQ', and 'BIC'. If a numeric value is input that ranges between 0 and 1, the 'p' value will be tested (e.g., seq_stat = .05 will test for the difference of p < .05 in the add scheme, or p > .05 in the drop scheme), along with the specified p.adjust input. For models fitted with prior distributions 'DIC' is also supported, though for these models the p-value approach is not

Wald

logical; perform Wald tests for DIF instead of likelihood ratio test?

p.adjust

string to be passed to the p.adjust function to adjust p-values. Adjustments are located in the adj_pvals element in the returned list

return_models

logical; return estimated model objects for further analysis? Default is FALSE

max_run

a number indicating the maximum number of cycles to perform in sequential searches. The default is to perform search until no further DIF is found

plotdif

logical; create item plots for items that are displaying DIF according to the seq_stat criteria? Only available for 'add' type schemes

type

the type of plot argument passed to plot(). Default is 'trace', though another good option is 'infotrace'. For ease of viewing, the facet_item argument to mirt's plot() function is set to TRUE

simplify

logical; simplify the output by returning a data.frame object with the differences between AIC, BIC, etc, as well as the chi-squared test (X2) and associated df and p-values

verbose

logical print extra information to the console?

...

additional arguments to be passed to multipleGroup and plot

Details

Generally, the precomputed baseline model should have been configured with two estimation properties: 1) a set of 'anchor' items, where the anchor items have various parameters that have been constrained to be equal across the groups, and 2) contain freely estimated latent mean and variance terms in all but one group (the so-called 'reference' group). These two properties help to fix the metric of the groups so that item parameter estimates do not contain latent distribution characteristics.

References

Chalmers, R., P. (2012). mirt: A Multidimensional Item Response Theory Package for the R Environment. Journal of Statistical Software, 48(6), 1-29. 10.18637/jss.v048.i06

Chalmers, R. P., Counsell, A., and Flora, D. B. (2016). It might not make a big DIF: Improved Differential Test Functioning statistics that account for sampling variability. Educational and Psychological Measurement, 76, 114-140. 10.1177/0013164415584576

Examples

Run this code

# NOT RUN {
#simulate data where group 2 has a smaller slopes and more extreme intercepts
set.seed(12345)
a1 <- a2 <- matrix(abs(rnorm(15,1,.3)), ncol=1)
d1 <- d2 <- matrix(rnorm(15,0,.7),ncol=1)
a2[1:2, ] <- a1[1:2, ]/3
d1[c(1,3), ] <- d2[c(1,3), ]/4
head(data.frame(a.group1 = a1, a.group2 = a2, d.group1 = d1, d.group2 = d2))
itemtype <- rep('2PL', nrow(a1))
N <- 1000
dataset1 <- simdata(a1, d1, N, itemtype)
dataset2 <- simdata(a2, d2, N, itemtype, mu = .1, sigma = matrix(1.5))
dat <- rbind(dataset1, dataset2)
group <- c(rep('D1', N), rep('D2', N))

#### no anchors, all items tested for DIF by adding item constrains one item at a time.
# define a parallel cluster (optional) to help speed up internal functions
mirtCluster()

# Information matrix with Oakes' identity (not controlling for latent group differences)
# NOTE: Without properly equating the groups the following example code is not testing for DIF,
     # but instead reflects a combination of DIF + latent-trait distribution effects
model <- multipleGroup(dat, 1, group, SE = TRUE)

# Likelihood-ratio test for DIF (as well as model information)
DIF(model, c('a1', 'd'))
DIF(model, c('a1', 'd'), simplify=FALSE) # return list output

#same as above, but using Wald tests with Benjamini & Hochberg adjustment
DIF(model, c('a1', 'd'), Wald = TRUE, p.adjust = 'fdr')

# equate the groups by assuming the first 10 items have no DIF
itemnames <- colnames(dat)
model <- multipleGroup(dat, 1, group, SE = TRUE,
   invariance = c(itemnames[1:10], 'free_means', 'free_var'))

#test whether adding slopes and intercepts constraints results in DIF. Plot items showing DIF
resulta1d <- DIF(model, c('a1', 'd'), plotdif = TRUE)
resulta1d

#test whether adding only slope constraints results in DIF for all items
DIF(model, 'a1')

#Determine whether it's a1 or d parameter causing DIF (could be joint, however)
(a1s <- DIF(model, 'a1', items2test = 11:13))
(ds <- DIF(model, 'd', items2test = 11:13))

####
# using items 4 to 15 as anchors to test for DIF after adjusting for latent-trait differences
itemnames <- colnames(dat)
model_anchor <- multipleGroup(dat, model = 1, group = group,
  invariance = c(itemnames[4:15], 'free_means', 'free_var'))
anchor <- DIF(model_anchor, c('a1', 'd'), items2test = 1:3)
anchor

### drop down approach (freely estimating parameters across groups) when
### specifying a highly constrained model with estimated latent parameters
model_constrained <- multipleGroup(dat, 1, group,
  invariance = c(colnames(dat), 'free_means', 'free_var'))
dropdown <- DIF(model_constrained, 'd', scheme = 'drop')
dropdown

### sequential searches using SABIC as the selection criteria
# starting from completely different models
model <- multipleGroup(dat, 1, group)
stepup <- DIF(model, c('a1', 'd'), scheme = 'add_sequential')
stepup

#step down procedure for highly constrained model
model <- multipleGroup(dat, 1, group,
  invariance = c(itemnames, 'free_means', 'free_var'))
stepdown <- DIF(model, c('a1', 'd'), scheme = 'drop_sequential')
stepdown
# }