| rundif {lordif} | R Documentation |
runs ordinal logistic regression DIF
Description
Runs ordinal logistic regression DIF
Usage
rundif(item, resp, theta, gr, criterion, alpha, beta.change, pseudo.R2, R2.change, wt)
Arguments
item |
a selection of items to be analyzed |
resp |
a data frame containing item responses |
theta |
a conditioning (matching) variable |
gr |
a vector of group identifiers |
criterion |
criterion for flagging (i.e., "CHISQR", "R2", or "BETA") |
alpha |
significance level for Chi-squared criterion |
beta.change |
proportional change for Beta criterion |
pseudo.R2 |
pseudo R-squared measure (i.e., "McFadden", "Nagelkerke", or "CoxSnell") |
R2.change |
R-squared change for pseudo R-squared criterion |
wt |
optional sample weights |
Details
The argument item lists the column numbers of the data frame resp to be included in the analysis.
Value
Returns a list of the following components:
stats |
a data frame containing output statistics |
flag |
a logical vector of DIF flags |
Author(s)
Seung W. Choi <choi.phd@gmail.com>
References
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.
See Also
Examples
## Not run: rundif(item,resp,theta,gr)