help for difdetect April 30, 2015)

Description

Detection of and adjustment for differential item functioning (DIF):

Identifies differential item functioning, creates dummy/virtual items to be used to adjust ability (trait) estimates,
and calculates the ability estimates and standard errors.

difdetect varlist ,
RUnname(str)
ABility(var) GRoup(var)
[NUL(#) NUW(#)
NUPValue(#)	UBeta(#)
UBCH(#) UL(#)
ULPValue(#)	MULtinomial(var)
minsize(#) CYcles(#) NQpt(#)

where:

varlist is the list of variables (items, blocks) to be tested for DIF

runname is the name used in the resulting variables and files (see Remarks).

ability is an ability or trait variable.

group is a binary grouping variable.

Options

multinomial is a multinomial grouping variable.

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.

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 no (0). UB(1) will include this criterion

ubch 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 yes (1). UL(0) will omit this criterion.

ulpvalue is the p-value for testing uniform DIF with the log-likelhood method. Default is 0.05.

minsize is the minimum number of observations/category (default is 20)

nqpt - changes the number of quadrature points from a default of 20.

cycles - changes the maximun number of iterations in the ability estimation.

Remarks

Sends DIF results to DIFdrunname.log.

Generates an output data set, DIFdetect_runname.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.]

Creates two variables, theta_runname, the revised ability estimate that accounts for DIF, and its standard error, se_runname

Creates dummy/virtual items to be used to adjust ability scores for DIF. These items will be of the form itemgroupx, where x = 1 represents the lower value of group, and x = 2 the higher.
For example, if item item1 had DIF by ethnic, the virtual items will be item1ethnic1 and item1ethnic2.

Displays warning messages when models do not converge, collinearity problems are observed, models are completely determined, standard errors are large, Brant tests are not possible, or items have > 15 levels (PARSCALE will reject).

Collapses categories on variables for which the number of observations is below a specified threshold (default is minszie(20)). If you do not want any categories combined, specify minsize(1).

Drops any variable that does not have enough observations for at least 2 categories, and displays a warning message.

Allows missing values.

Note that to fully account for DIF you should run the program again with theta_runname as the new ability score and a new runname. Repeat until the same items are identified with DIF.

Please make sure that Stata's current working directory is the same as your data's directory (help cd).

If you will be adjusting for DIF on multiple categories (groups), subdividing can lead to dummy/virtual item names longer than the 32 character limit.
Our tip is to generate a new grouping variable with a really short name. For example, .gen g=gender

Otherwise you may get an error message about a variable already being defined or being too long, and end up having to rename long variables within Stata.

Written for Stata 13.0.

Examples

difdetect item1-item13, ru(gender0) ab(theta0) gr(g)

difdetect apple - item11, ru(ethnic0) ab(itemtot) gr(eth) nupv(0.01) ul(1) ulpv(.01) minsize(35)

Authors

Laura Gibbons, Paul Crane, Lance Jolley, and Gerald van Belle.

University of Washington, Copyright 2014.

Email: gibbonsl@u.washington.edu

We appreciate the assistance of Tom Koepsell and Rich Jones.