[Rasch] Q3 index and local dependence
trevor.bond at jcu.edu.au
Tue Mar 27 13:34:28 AEDT 2018
That’s an important note about Winsteps Table 23.1
From: Rasch <rasch-bounces at acer.edu.au<mailto:rasch-bounces at acer.edu.au>> on behalf of Mike Linacre <mike at winsteps.com<mailto:mike at winsteps.com>>
Reply-To: "rasch at acer.edu.au<mailto:rasch at acer.edu.au>" <rasch at acer.edu.au<mailto:rasch at acer.edu.au>>
Date: Tuesday, 27 March 2018 12:14 pm
To: "rasch at acer.edu.au<mailto:rasch at acer.edu.au>" <rasch at acer.edu.au<mailto:rasch at acer.edu.au>>
Subject: Re: [Rasch] Q3 index and local dependence
Q3 is the correlation between the raw residuals of pairs of items. These are listed, for instance, in Winsteps Table 23.99 with PRCOMP=R
Summarizing the Q3's across all items with PCA makes the dependency pattern clearer. See, for instance, Winsteps Table 23.1
Hope this help.
On 3/27/2018 10:38 AM, Bond, Trevor wrote:
It has been claimed that Yen’s Q3 index (1984) can be used to identify local dependence in Rasch analyses.
e.g., Chou, Y.-T., & Wang, W.-C. (2010). Checking dimensionality in item response models with principal component analysis on standardized residuals. Educational and Psychological Measurement, 70(5), 717-731.
Christensen, K. B., Makransky, G., & Horton, M. (2017). Critical values for Yen’s Q3: Identification of local dependence in the Rasch model using residual correlations. Applied Psychological Measurement, 41(3), 178-194.
Have you used Q3 for that purpose? Do you know of papers that have adopted it? Is it helpful/effective? Is it available in the software that you use, or know about?
Looking forward to your advice and guidance.
Mike Linacre, mike at winsteps.com<mailto:mike at winsteps.com> or winsteps1234 at gmail.com<mailto:winsteps1234 at gmail.com>
Winsteps 4.0.1 and Facets 3.80.4 - <http://www.winsteps.com/index.htm> www.winsteps.com<http://www.winsteps.com>
Concerned about aches, pains, youthfulness? Mike suggests Liquid Biocell<https://www.modere.com/liquidbiocell?referralCode=790842>
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