[Rasch] Why isn't the person separation better?

Agustin Tristan ici_kalt at yahoo.com
Sun Sep 12 02:20:58 EST 2010

Hello, I can calculate the theoretical expected value for separation of your test, but I need table 3.1 and table 10.1 of Winsteps. With this theoretical value you may decide if it could be better or not.
If it can be of some help please send those tables to me.
Agustin Tristan

From: Imogene Rothnie <irothnie at usyd.edu.au>
Subject: Re: [Rasch] Why isn't the person separation better?
To: "Rasch Listserve" <rasch at acer.edu.au>
Date: Thursday, September 9, 2010, 4:10 AM

Dear Trudy, 
What person separation would you regard as good in your context? In the
context of a student examination, for example, I would consider 0.82
very good separation, and would be surprised to find significant
improvement even with more items.

-----Original Message-----
From: rasch-bounces at acer.edu.au [mailto:rasch-bounces at acer.edu.au] On
Behalf Of Stephen Humphry
Sent: Wednesday, 1 September 2010 10:13 PM
To: Tom Bramley; Rasch Listserve
Subject: Re: [Rasch] Why isn't the person separation better?

Trudy, I may be missing something in your post, but good for a given
separation in no way implies the separation should be high. The
separtion depends on targeting, actual separation (as Tom said), and the
discrimination at thresholds.

If you want to improve the separation index, systematically remove items
with low threshold discrimination, which will tend to have higher
Outfit. Items that previously had a higher discrimination may then have
an acceptable discrimination, and the separation will tend to increase.
This, in my view, is what should be done routinely rather than treating
the average (roughly speaking) discrimination of the initial item set as
the reference and omitting items with high (as well as low)
discrimination. To fit the Rasch model, the criterion is uniform
discrimination, not uniform discrimination that is necessarily equal to
the initial 'average' discrimination.

Having said that, you don't necessarily want the separation too high--it
can indicate unwanted dependencies from response set, halo effect or
other such response tendencies.



From: rasch-bounces at acer.edu.au [rasch-bounces at acer.edu.au] On Behalf Of
Tom Bramley [Bramley.T at cambridgeassessment.org.uk]
Sent: Wednesday, 1 September 2010 3:58 PM
To: Rasch Listserve
Subject: Re: [Rasch] Why isn't the person separation better?

Dear Trudy,
Perhaps the problem is simply that there is not enough 'true' variance
in your sample of participants.  What is the range and standard
deviation of the raw scores (which I assume are on a scale of 0-64)?

Tom Bramley
Assistant Director, Research Division
Assessment Research & Development

Cambridge Assessment
1 Regent Street, Cambridge, CB2 1GG
Direct Dial: 01223 553985

Cambridge Assessment is the brand name of the University of Cambridge
Local Examinations Syndicate, a department of the University of
Cambridge. Cambridge Assessment is a not-for-profit organisation.

From: rasch-bounces at acer.edu.au [mailto:rasch-bounces at acer.edu.au] On
Behalf Of Stephanou, Andrew
Sent: 31 August 2010 23:56
To: Rasch at acer.edu.au
Subject: [Rasch] FW: Why isn't the person separation better?

Forwarded to the Rasch listserv on behalf of Trudy Mallinson,
trudy.mallinson at usc.edu

-----Original Message-----
From: mallinso at usc.edu [mailto:mallinso at usc.edu] On Behalf Of Trudy
Sent: Wednesday, 1 September 2010 3:00 AM
To: rasch
Subject: Why isn't the person separation better?

Dear Listserv,

I am analyzing a small data set (about 200 participants, both admission
and discharge currently in the dataset) and 32 items.  Everything about
the instrument looks good - the items mostly fit, the rating scale (3
points) mostly works. The range of persons seems to form a fairly normal
distribution, items are targeted on the people (mean -0.28 logits) with
good range (min -4.07 logits, max 3.55 logits).  But the person
separation is less than stellar at .84.  There are a few items where the
middle category of the rating scale is "submerged" but not disordered.
When I combine it with one of the other categories, the SD increases a
little but so does the error so it's a wash in terms of the separation.
I tried removing the misfitting items (not very misfitting at 1.3 - 1.4
infit MNSQ) same thing, increased SD, increased error, no change in
separation.  I wondered if it could be a dimensionality problem - the
PCA suggests it's unidimensional though.  I separated out the items 
into two groups.  Item psychometrics continue to look OK.  Separation
for the vision items .82, for tactile items .82.  I created an xy plot
of the person measures from the two set so items.  Scores clearly track
together although the shape of the coordinates is basically a

Can any one suggest why, when everything about the items looks good, the
separation is not great, and all the things I've tried don't seem to
improve it in any meaningful way?

Thanks for your thoughts,


Trudy Mallinson, PhD, OTR/L, NZROT

Assistant Professor

University of Southern California

Division of Occupational Science and Occupational Therapy

1540 Alcazar Street, CHP 133, Room 101F

Los Angeles, CA 90089-9003

PH: (323) 442-2950

trudy.mallinson at usc.edu

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