[Rasch] Misfitting persons

Commons Commons at tiac.net
Mon Dec 4 02:02:03 AEDT 2017


Sent from my Verizon, Samsung Galaxy smartphone

-------- Original message --------
From: "Bond, Trevor" <trevor.bond at jcu.edu.au> 
Date: 12/3/17  5:38 AM  (GMT-05:00) 
To: rasch at acer.edu.au 
Subject: Re: [Rasch] Misfitting persons 

Dear Nick,
Pls go to Rasch.org and search Ben W’s advice on negative items.
Why choose the PCM? The RSM is more parsimonious if appropriate.
Pls checked category functioning.
There’s a start.

Sent from 007's iPhone 6s 😎

On 3 Dec 2017, at 8:03 PM, Nicholas Reynolds <nicholasaustinreynolds at gmail.com> wrote:

Hi members 

I want advice about a scale with several misfitting items. I am not sure how to modify the data and report on the findings. Could someone point me in the right direction?

The scale is a 
self-report questionnaire of addiction

there are 13 items

there are four response options (strongly disagree to agree)

I have chosen partial credit paramaterization in RUMM2030

My sample size is 560; there is no sign of mistargeting and the PSI is .85


The data are misfitting

The scale has four reverse worded items, all of which are misfitting

The items function well-enough for a majority of the sample. But a substantial minority of people (5-10%) responded unexpectedly to them

Standard residuals for these people are generally > 3 and sometimes > 4.

The unexpected response behaviour is concentrated at the extreme thresholds of these items. This is consistent with the idea that reverse wording has caused participants to respond unexpectedly
 to them  

I am reluctant to remove the misfitting items because they measure important aspects of the latent attribute. Instead, I deleted about 40 participants (7% of the sample), which resolved the item misfit. 

Having deleted the participants, I don't know whether to interpret the item order without them, or to delete the items so that data fit the model, or if there is an alternative approach I might take? 

How would you recommend modifying the data and reporting on these findings? I'd appreciate any advice or reading material.  

Of course, when reporting the findings, I'll suggest that the problematic items re-worded in future, given that I am reasonably sure about the cause of misfit in this context.   




Rasch mailing list

email: Rasch at acer.edu.au


-------------- next part --------------
An HTML attachment was scrubbed...
URL: <https://mailinglist.acer.edu.au/pipermail/rasch/attachments/20171203/833a2521/attachment-0001.html>

More information about the Rasch mailing list