[Rasch] Rasch Digest, Vol 138, Issue 7

Richard Gershon gershon at northwestern.edu
Tue Jan 31 12:28:31 AEDT 2017

It is very typical to run a logarithmic conversion on timing level data prior to analyzing timing data – I believe that this would help address your skew issue as well. No real opinion on using partial-credit model, but I can’t believe that you really want to create 90 categories in any case!
Richard Gershon

From: Rasch <rasch-bounces at acer.edu.au> on behalf of "rasch-request at acer.edu.au" <rasch-request at acer.edu.au>
Reply-To: "rasch at acer.edu.au" <rasch at acer.edu.au>
Date: Monday, January 30, 2017 at 7:00 PM
To: "rasch at acer.edu.au" <rasch at acer.edu.au>
Subject: Rasch Digest, Vol 138, Issue 7

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Today's Topics:

   1. Modeling response time (Stuart Luppescu)


Message: 1
Date: Mon, 30 Jan 2017 13:33:45 -0500
From: Stuart Luppescu <lupp at uchicago.edu<mailto:lupp at uchicago.edu>>
To: rasch <rasch at acer.edu.au<mailto:rasch at acer.edu.au>>
Subject: [Rasch] Modeling response time
Message-ID: <1485801225.29155.19.camel at uchicago.edu<mailto:1485801225.29155.19.camel at uchicago.edu>>
Content-Type: text/plain; charset="UTF-8"

Hello fellow Raschies, I have some item-response-time data that I want
to model, and identify person-item interactions that take an unusually
long period of time, given the expected response time of the person,
and the expected time required by the item. The item-response times
range from 1 second to 90 seconds, and are somewhat positively skewed.?

Can I just apply a Rasch partial-credit model with 90 categories to
these data? If so, recommendations on how to proceed or things to watch
out for?

Stuart Luppescu
Chief Psychometrician (ret.)
UChicago Consortium on School Research

lupp at uchicago.edu<mailto:lupp at uchicago.edu>


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End of Rasch Digest, Vol 138, Issue 7

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