[Rasch] R: item selection based on DIF
Gregory.Stone at UToledo.Edu
Tue Oct 31 01:43:00 AEDT 2017
I would point out a couple of things.
First, using .6-1.4 as a measure for fit is arbitrary. A rule of thumb. Richard Smith has a paper out on a proper calculation for understanding the boundaries appropriate based on sample sizes, number of items, etc. While this range may be great for a a sample of 200, it’s significantly different for a sample of 1000.
Second, I would refer back to a very clear and simple review of DIF by Michael Linacre in a previous SIG newsletter and in the Winsteps instructions. Beyond this we also look at balance and meaning. I would never exclude an item based solely on a DIF statistic. Measurement is a quantitative activity with deep roots in evaluation. We cannot exclude an item just based on fit statistics, but based on our evaluation of why after the fit statistics provide us with a clue. There are and will always be items that evidence dif. For example 2+2=? may be biased in favor of females. Should we immediately delete it? Heavens no. We need to determine why it exhibits DIF. Is it simply random chance? What is inherently biased in this item? What could have made females do better? Better preparation? Finally consider balance. Items balanced for and against females and males is a typical and acceptable event, depending on why.
Statistics are guideposts pointing you in certain directions. They are not the destination. We need, as Luigi has suggested, to explore the meaning before engaging in action.
I would also do not understand “Education Dif”? Level of education?
On Oct 30, 2017, at 5:25 AM, Bond, Trevor <trevor.bond at jcu.edu.au<mailto:trevor.bond at jcu.edu.au>> wrote:
Surely this is the reason we teach? To make items easier for those who learn.
Prof Trevor G BOND
On 30 Oct 2017, at 7:29 pm, prof. Luigi Tesio - AUXOLOGICO <l.tesio at auxologico.it<mailto:l.tesio at auxologico.it>> wrote:
Please do not forget that Rasch analysis aims at measuring an existing (reflective) variable, not at INVENTING (constructive) a latent trait. Add to the discussion outlined below some reasoning concerning the nature of the DIFFING/NON DIFFING items, the reason why they are or not relevant to the hypothesized trait, the potential CAUSES for DIF (not only p values) etc. The more Rasch is taken only as a statistical game, the more you will strive to adapt reality to fit indexes, not the reverse.
Da: Rasch [mailto:rasch-bounces at acer.edu.au] Per conto di Edward Li
Inviato: lunedì 30 ottobre 2017 02:59
A: rasch at acer.edu.au<mailto:rasch at acer.edu.au>
Oggetto: Re: [Rasch] item selection based on DIF
DIF analyses could induce artificial DIF which is an artefact of some items displaying real DIF. For example, when you find some items favouring one group, some other items will inevitably favour the other group. David Andrich and Curt Hagquist have a few papers discussing this topic which could be quite helpful in your case. Please see the reference below.
Andrich, D., & Hagquist, C. (2012). Real and artificial differential item functioning. Journal of Educational and Behavioral Statistics, 37(3).
Andrich, D., & Hagquist, C. (2015). Real and Artificial Differential Item Functioning in Polytomous Items. Educational and Psychological Measurement, 75(2).
From: Rasch <rasch-bounces at acer.edu.au<mailto:rasch-bounces at acer.edu.au>> on behalf of DIMA ALEXANDRA <alexandra.dima at univ-lyon1.fr<mailto:alexandra.dima at univ-lyon1.fr>>
Sent: Monday, 30 October 2017 1:19:17 AM
To: rasch at acer.edu.au<mailto:rasch at acer.edu.au>
Subject: [Rasch] item selection based on DIF
Dear Rasch researchers,
I would like to ask for your advice in a DIF-related issue. We performed a Rasch analysis of a 33-item questionnaire to choose items for a short version, in two steps. First we examined items infit and outfit, and used the thresholds of mean squares outside the 0.6-1.4 range and standardized fit statistics outside the +/-2.0. No items were excluded at this step (all fitted this criterion). Then we examined DIF for gender, age, and education (in this order), and excluded items with DIF > 0.5 logits. We excluded 3 items based on gender DIF, 4 items based on age DIF, and 12 based on education DIF. We thus arrived at a 14-item version, for which we examined again DIF for all 3 variables. We noticed that there appeared DIFs for age > 0.5 logits (e.g. .60, p<.000), even if in the earlier steps of the selection process these items had no problems. This is a noticeable and significant difference according to the winsteps manual (http://www.winsteps.com/winman/table30_1.htm ): « [DIF CONTRAST] should be at least 0.5 logits for DIF to be noticeable. "Prob." shows the probability of observing this amount of contrast by chance, when there is no systematic item bias effect. For statistically significance DIF on an item, Prob. ≤ .05. ». But I do not know how to figure out if this is also a substantive difference and if I should exclude those items as well, in other words continue item selection until all items show DIF <.05 for all variables (age, gender, education). This is particularly puzzling since these items showed acceptable DIF in previous runs. I understand in principle that this can happen, but what does it mean: are this items good enough or not? Should they be kept or excluded? Are there other criteria and tests that I should consider?
Any suggestions or references for further reading would be much appreciated!
Alexandra DIMA PhD, AFBPsS
Marie Curie Research Fellow
EA 7425 HESPER
Health Services and Performance Research
Université Claude Bernard Lyon 1
Domaine Rockefeller- 2eme étage (aile CD)
8 avenue Rockefeller
69373 Lyon 8
W: +33 (0) 4 26 68 82 23
M: +33 (0) 6 32 86 82 37
alexandra.dima at univ-lyon1.fr<mailto:Alexandra.dima at univ-lyon1.fr>
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