MDIFF

Returns a matrix containing the MDIFF values (Reckase, 2009). Only supported for items of class
'dich' and 'graded'.

discrimination

Analysis of discrete response data using
unidimensional and multidimensional item analysis models under the Item
Response Theory paradigm (Chalmers (2012) <doi:10.18637/jss.v048.i06>).
Exploratory and confirmatory item factor analysis models
are estimated with quadrature (EM) or stochastic (MHRM) methods. Confirmatory
bi-factor and two-tier models are available for modeling item testlets using
dimension reduction EM algorithms, while multiple group analyses and
mixed effects designs are included for detecting differential item, bundle,
and test functioning, and for modeling item and person covariates.
Finally, latent class models such as the DINA, DINO, multidimensional latent class,
mixture IRT models, and zero-inflated response models are supported.

Phil Chalmers

mirt

Multidimensional Item Response Theory

Joshua Pritikin

Alexander Robitzsch

Mateusz Zoltak

KwonHyun Kim

Carl F. Falk

Adam Meade

Lennart Schneider

David King

Chen-Wei Liu

Ogreden Oguzhan

MDIFF function

<dl><dt>x</dt>
<dd>an object of class 'SingleGroupClass', or an object of class 'MultipleGroupClass' if a suitable
<code>group</code> input were supplied</dd>
<dt>which.items</dt>
<dd>a vector indicating which items to select. If NULL is used
(the default) then MDISC will be computed for all items</dd>
<dt>group</dt>
<dd>group argument to pass to <code>extract.group</code> function. Required when the input object is
a multiple-group model</dd></dl>

Arguments

Phil Chalmers <a href="/link/rphilip.chalmers%40gmail.com?package=mirt&version=1.42" data-mini-rdoc="mirt::rphilip.chalmers@gmail.com">rphilip.chalmers@gmail.com</a>

Author

Compute multidimensional difficulty index — MDIFF

<dl>

<dt>x</dt>
<dd>an object of class 'SingleGroupClass', or an object of class 'MultipleGroupClass' if a suitable
<code>group</code> input were supplied</dd>


<dt>which.items</dt>
<dd>a vector indicating which items to select. If NULL is used
(the default) then MDISC will be computed for all items</dd>


<dt>group</dt>
<dd>group argument to pass to <code>extract.group</code> function. Required when the input object is
a multiple-group model</dd>

</dl>

Phil Chalmers <a href='mailto:rphilip.chalmers@gmail.com'>rphilip.chalmers@gmail.com</a>

MDIFF: Compute multidimensional difficulty index

Description

Usage

Arguments

Author

References

See Also

Examples