lordif (version 0.1-9)

lordif: performs LOgistic Regression Differential Item Functioning using IRT

Description

performs iterative hybrid ordinal logistic regression/IRT DIF

Usage

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, NQ=41)

Arguments

resp.data

data frame or matrix containing item responses

group

a vector of group designations

selection

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

NQ

number of quadrature points for IRT parameter estimation (maximum of 61)

Value

• Returns a 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 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 group-specific item parameters (for DIF items)
• 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 DIF final flags based on raw scores

Details

Performs a ordinal (common odds-ratio) logistic regression DIF analysis using IRT theta estimates as the conditioning variable. The graded response (GR) model is used for IRT trait estimation. Flagged items are treated as separate items and group-specific item parameters are obtained. The procedure runs iteratively until the same set of items is flagged over two iterations.

References

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.

See Also

rundif

Examples

data(Anxiety)
  resp.data <- Anxiety[paste("R",1:29,sep="")] 
  age <- Anxiety$age 
  age.DIF <- lordif(resp.data,age)
  #print(age.DIF)