Detection of differential item function (DIF).
difd varlist , ABility(varlist) GRoups(varlist) [CATegorical(varlist) RUnname(str) NUL(#) NUP(#) NUPValue(#) UBeta(#) UBP(#) ULPV(#) UP(#) UPPValue(#) ITemsub(#)]
varlist is the list of variables (items, blocks) to be tested for DIF
id is the subject id variable.
ability is the ability variable(s).
groups is the list of grouping variables. Continuous ‘grouping’ variables are permitted.
categorical is the list of any group variables that are categorical and have more than 2 levels. Note that it is simpler to omit dichotomous variables from this list. Default is none (all continuous or dichotomous).
runname names the log file DIFdRUnname.log. Default is DIFd.log.
nul indicates whether the log-likelihood test will be used as a criterion for non-uniform DIF. Default is yes (1). Nul(0) will omit this criterion.
nup indicates whether the Wald test for the interaction term will be used as a criterion for non-uniform DIF. Default is no (0). Nup(1) will include this criterion.
nupvalue is the p-value for testing non-uniform DIF. Default is 0.05.
ubeta indicates whether the change in the ability coefficient will be used as a criterion for uniform DIF. Default is yes (1). UBeta(0) will omit this criterion.
ubp is percent change in the ability coefficient for determining uniform DIF. Default is .10. A positive change indicates an increase in the relationship between ability and the outcome with a higher value of the grouping variable.
ul indicates whether the log-likelihood test will be used as a criterion for uniform DIF. Default is no (0). UL(1) will include this criterion.
ulpvalue is the p-value for testing uniform DIF with the log-likelhood method. Default is 0.05.
up indicates whether the Wald test for the group term will be used as a criterion for uniform DIF. Default is no (0). UP(1) will include this criterion.
ulpvalue is the p-value for testing uniform DIF with the Wald test. Default is 0.05.
itemsub subtracts the item value from the ability measure. Default is no (0). ITemsub(1) will include this feature.
Sends DIF results to DIFdrunname.log.
DIF results for categorical grouping variables will be in terms of the ordered values of group. For example, if ethnic has 3 levels, 3 sets of DIF results will be reported: ethnic12, ethnic13, ethnic23, where ethnic12 compares the 2 lowest values of ethnic, ethnic13 the lowest and highest, etc.
Generates an output data set, DIFd.dta, which includes individual model results, with Brant test p-values for ordinal items and Hosmer-Lemeshow p-values for binary items. [The relevance of the fit statistics has not been established for DIF.]
Displays warning messages when models do not converge, collinearity problems are observed, models are completely determined, standard errors are large, or Brant tests are not possible.
difd item1-item13, id(id) ru(gender0) ab(theta0) gr(g)
difd apple - item13, id(id) ab(theta0) gr(eth) cat(eth) nupv(0.01) ul(1) ulpv(.01)
Paul Crane, Laura Gibbons, Lance Jolley, and Gerald van Belle. University of Washington, Copyright 2005. Email: firstname.lastname@example.org