lordif: performs Logistic Ordinal Regression Differential Item...
In lordif: Logistic Ordinal Regression Differential Item Functioning using IRT

Description Usage Arguments Details Value Note Author(s) References See Also Examples

performs iterative hybrid ordinal logistic regression/IRT DIF

lordif(resp.data, group, selection = NULL, criterion = c("Chisqr", "R2", "Beta"), 
pseudo.R2 = c("McFadden", "Nagelkerke", "CoxSnell"), alpha = 0.01, beta.change = 0.1, 
R2.change = 0.02, maxIter = 10, minCell = 5, minTheta = -4, maxTheta = 4, inc = 0.1, 
control = list(), model = "GRM", anchor = NULL, MonteCarlo = FALSE, nr = 100, 
weights = NULL, normwt = TRUE)

`resp.data`	data frame or matrix containing item responses
`group`	a vector of group designations
`selection`	a vector specifying a subset of items to be analyzed or NULL for all items
`criterion`	criterion for flagging (i.e., "Chisqr", "R2", or "Beta")
`pseudo.R2`	pseudo R-squared measure (i.e., "McFadden", "Nagelkerke", or "CoxSnell")
`alpha`	significance level for Chi-squared criterion
`beta.change`	proportionate change for Beta criterion
`R2.change`	R-squared change for pseudo R-squared criterion
`maxIter`	maximum number of iterations for purification
`minCell`	minimum cell frequency to avoid collapsing
`minTheta`	minimum for theta grid
`maxTheta`	maximum for theta grid
`inc`	increment for theta grid
`control`	a list of control variables (refer to the `mirt` function in the mirt package)
`model`	IRT model of choice, either "GRM" or "GPCM" (default: "GRM")
`anchor`	a vector specifying items to be used as anchors or NULL to determine anchors through purification
`MonteCarlo`	TRUE to trigger Monte Carlo simulations to determine empirical thresholds
`nr`	number of replications for Monte Carlo simulations
`weights`	an optional vector (same length as nobs) of fractional case weights (refer to the `lrm` function in the rms package which currently generates warning messages)
`normwt`	set to TRUE to scale weights so they sum to nobs

Performs an ordinal (common odds-ratio) logistic regression differential item functioning (DIF) analysis using IRT theta estimates as the conditioning variable. The graded response model (GRM) or the generalized partial credit model (GPCM) is used for IRT trait estimation. Items flagged for DIF are treated as unique items and group-specific item parameters are obtained. Non-DIF items serve as anchor items to the initial single-group calibration. The procedure runs iteratively until the same set of items is flagged over two consecutive iterations, unless anchor items are specified.

Returns an object (list) of class "lordif" with the following components:

`call`	calling expression
`options`	options used for the run
`selection`	all or a subset of items analyzed
`stats`	matrix containing output statistics
`flag`	logical vector of final flags indicating whether each item is displaying DIF or not
`recoded`	data frame containing recoded item responses
`group`	vector of group designation values
`ng`	scalar for the number of groups
`ncat`	vector of the number of response categories for each item after collapsing/recoding
`calib`	vector of theta estimates based on the overall (non-group-specific) item parameters
`calib.sparse`	vector of theta estimates based on the DIF-free and group-specific item parameters
`iteration`	scalar for the number of iterations
`ipar`	data frame of the overall (non-group-specific) item parameter estimates
`ipar.sparse`	data frame of the group-specific item parameter estimates
`stats.raw`	matrix containing output statistics (the same components as stats above but based on raw scores)
`meanraw`	vector containing mean raw scores
`flag.raw`	logical vector of final DIF flags based on raw scores
`DFIT`	place-holder for DFIT analysis output
`anchor`	vector of items used as anchors
`MonteCarlo`	place-holder for Monte Carlo analysis output

requires the mirt and rms packages

Seung W. Choi <choi.phd@gmail.com>

Choi, S. W., Gibbons, L. E., Crane, P. K. (2011). lordif: An R Package for Detecting Differential Item Functioning Using Iterative Hybrid Ordinal Logistic Regression/Item Response Theory and Monte Carlo Simulations. Journal of Statistical Software, 39(8), 1-30. URL http://www.jstatsoft.org/v39/i08/.

Crane, P. K., Gibbons, L. E., Jolley, L., and van Belle, G. (2006). Differential item functioning analysis with ordinal logistic regression techniques: DIF detect and difwithpar. Medical Care, 44(11 Suppl 3), S115-S123.

rundif

  ## Not run: data(Anxiety)
  ## Not run: resp.data <- Anxiety[paste("R",1:29,sep="")]
  ## Not run: age <- Anxiety$age
  ## Not run: age.DIF <- lordif(resp.data,age,model="GPCM",anchor=c(1:5,7,8,10,12:17,19:23,25:29))
  ## Not run: print(age.DIF)

Loading required package: mirt
Loading required package: stats4
Loading required package: lattice
Loading required package: rms
Loading required package: Hmisc
Loading required package: survival
Loading required package: Formula
Loading required package: ggplot2

Attaching package: 'Hmisc'

The following objects are masked from 'package:base':

    format.pval, units

Loading required package: SparseM

Attaching package: 'SparseM'

The following object is masked from 'package:base':

    backsolve