[Rasch] Negative pt-bis and fit of 1.0? How can this be?

Agustin Tristan ici_kalt at yahoo.com
Wed Mar 7 06:18:41 EST 2012


Can you please indicate: (1) How many students are you including in your analysis? (2) What are the values of ZSTD infit?  
Or even better: May you please send a table of your items showing measure, MNSQ Fit, ZSTD fit and the ptbis?
 We can discuss about that.
One of the main problems is to interpretate ptbis as a measure of fit or as a measure of discrimination or whatever. We can discuss about that too.
Regards
Agustin

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________________________________
 From: "Stone, Gregory" <Gregory.Stone at UToledo.Edu>
To: "<rasch at acer.edu.au>" <rasch at acer.edu.au> 
Sent: Tuesday, March 6, 2012 12:54 PM
Subject: Re: [Rasch] Negative pt-bis and fit of 1.0? How can this be?
  

Sending this to you and not the listserv.  I have come to the conclusion that apart from using it as one consideration for dimensionality, fit is a useless statistic when it comes to assessing reasonability of item performance.  While I have no empirical evidence of this, gathered deliberately, it has appeared to me that time after time, decisions made with point biserials are consistently more reasonable than any made with fit, regardless of whether we accept the largely arbitrary range of fit so popularized in books like Bond and Fox, or the more rational fit calculated by folks like Smith. This appears to hold true for small datasets, where many items demonstrate fit concerns, and large datasets where everything, naturally, fits perfectly. Thoughts? 
 
Gregory

 


On Mar 6, 2012, at 1:46 PM, Mark Moulton wrote: 

Stuart, 
>
> 
>Belay my previous message (coffee hadn't kicked in yet). 
>
> 
>You're right.  Variances -- p * (1 - p) at the extreme of p (1 or 0) will be smaller, which should make the misfits bigger. 
>
> 
>Could those cell probabilities p[ni] actually be close to 0.50?  This would happen if the person measures tended to collapse to the center of the scale, an artifact of randomness. 
>
> 
>Mark
>
>
>
>
>On Tue, Mar 6, 2012 at 7:58 AM, Stuart Luppescu <slu at ccsr.uchicago.edu> wrote:
>
>Hello, I'm analyzing items suggested for a course final exam. The
>>problem is that the students the items are tested on have not taken the
>>course yet. This results in very poor performance. The average person
>>measure is -0.78, and the average item p-value is 0.33 (for 4-choice
>>multiple choice items).
>>
>>What is confusing me is that all the items have mean-square fit
>>statistics (infit and outfit) near 1.0, while many of them have negative
>>point-biserial correlations. According to my understanding, the fit
>>statistics are calculated from an aggregation of the squared
>>standardized residuals, which are calculated from the raw residual
>>divided by the score variance. In this case, the expectation of an
>>individual response would be low, so raw residuals would be large. And
>>the score variance at the extremes are lower than in the middle, so you
>>divide a large raw residual by a small score variance and you get a very
>>large standardized residual, right? So, how come the fits are close to
>>expectation? Especially since the pt-bis are low or negative? I don't
>>get it. Can someone explain this to me?
>>
>>Thanks.
>>--
>>Stuart Luppescu -=- slu .at. ccsr.uchicago.edu
>>University of Chicago -=- CCSR
>>才文と智奈美の父 -=-    Kernel 3.2.1-gentoo-r2
>>What we have is nice, but we need something very
>> different.    -- Robert Gentleman
>> Statistical Computing 2003, Reisensburg (June
>> 2003)
>>
>>
>>
>>
>>
>>
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