help for pcmdif

Diagnosing and considering Differential Item Functioning when analysing Patient Reported Outcomes using Partial Credit Models

pcmdif (varlist) ,
cov(variable)
[iterate(#)
adapt
robust
bic
homo
all]

Description

pcmdif allows studying the effect of a categorical covariate
on a latent trait. Several effects are simultaneously considered:

- An direct effect of the covariate on the latent trait (corresponding
to a variation of the average latent trait value depending on the status defined
by the considered covariate) This effect is studied by including a group covariate
associated with the latent trait in a partial credit model (PCM). It is referred to
"covariate effect".

- An effect of the covariate on the interpretation of items
(corresponding to a differential item functioning: DIF). This effect is studied by
including interactions between the items difficulties parameters and the group
covariate in the PCM. It is referred to "DIF effect"

The identification of items with DIF ("DIF effect") and the
"covariate effect" are performed using an iterative ascending process
by minimizing the Akaike criterion or the Bayesian Information Criterion,
depending on the selected option.

At each step of the iterative ascending process, models summaries are
produced containing the list of the included effects (a "covariate effect" and/or
each of the possible "DIF effects") and the AIC and BIC criteria values. At the end of the
iterative process, the estimated parameters of the most relevant model are activated.
To make active the associated estimate another model, use the command
estimate replay.

Options

cov identifies the covariate possibly responsible for a
"covariate" or a "DIF" effect.

iterate specifies the (maximum) number of iterations. With the
adapt option, use of the iterate(#) option will cause pcmodel to skip the
"Newton Raphson" iterations usually performed at the end without updating
the quadrature locations.

adapt causes adaptive quadrature to be used instead of
ordinary quadrature.

robust specifies that the Huber/White/sandwich estimator of the
covariance matrix of the parameter estimates is to be used.

bic selection of the model by minimizing the Bayesian
Information Criterion (BIC) rather that the AIC criterion (by default,
minimization of AIC)

homo considers a homogeneous rather than nonhomogeneous DIF
(ie: for a given item, the difference between the difficulty of a given
response category depending on the considered covariate is the same for
all the item response categories. This assumption can refer to the case
that the DIF is related to the understanding of the item utterance rather
than the response categories utterance).

all allows to perform all the possible models rather than thoses
selected by an iterative ascending process for selecting the best model (and
thus identifying items presenting a potential DIF phenomenum).

Example

Simulating the data (using simirt). 5 items are simulated, the last presenting a DIF phenomenum:

. simirt, dim(6) group(0.5) deltagroup(0.5) clear(1)
. replace item5=item6 if group==1(2)
. drop item6(3)
. rename item5 item5dif(4)

Analysing the data using pcmdif:

. pcmdif item1 item2 item3 item4 item5dif, cov(group) aic (5)

Displaying the results of the selected model:

. estimates replay (6)

Displaying the results of the first performed model:

. estimates replay m_1 (7)

Author

Jean-François Hamel

Email:
jeanfrancois.hamel@chu-angers.fr

Also see

Online: help for pcmodel, pcmtest, gllamm, simirt,
raschtest.