[Rasch] Rasch Digest, Vol 66, Issue 1

Dr Juho Looveer juho.looveer at gmail.com
Tue Jan 4 15:34:34 EST 2011


Greetings and Best Wishes to all for the New Year; hopefully many of us can
meet up at PROMS and The Psychometric Society meeting (IMPS) this year.

I agree with Mark's comments: "So far as the Rasch analysis goes, I would
leave the data missing, even
though zeros are a reasonable imputation in this case.  However, even if
they are reasonable they could distort the item difficulties in odd ways, so
I would leave them missing."

However, I hate imputations.  There are many assumptions made when imputing
data (and "when you assume, you make an ass out of U and me").  I would
prefer to see descriptive statistics based on what you actually have, noting
the actual sample size for each item.
By imputing, you skew and distort the descriptives; that is akin to having a
sample of 20, and imputing that if you had a sample of 1000, they would fit
the same pattern, and hence derive false statistics that make inferences
appear highly reliable due to the "new" sample size.

Humble Regards

Juho

Dr  Juho Looveer
Psychometrician / Analyst 
"Improving the Assessment of Literacy and Numeracy Across the Pacific"
Secretariat of the Pacific Board for Educational Assessment (SPBEA)
26 McGregor Road 
PO Box 2083, Government Buildings,  Suva, Fiji 
Phone: +679-368 0051
Mobile: (00679) 945 6055
Fax: +679-3302898
Email: jlooveer at spbea.org.fj <mailto:mshafraz at spbea.org.fj>  
Web Site: www.spbea.org.fj <http://www.spbea.org.fj/> 


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Today's Topics:

   1. missing data advice needed (Fidelman, Carolyn)
   2. Re: missing data advice needed (Mark Moulton)


----------------------------------------------------------------------

Message: 1
Date: Mon, 3 Jan 2011 16:50:44 -0600
From: "Fidelman, Carolyn" <Carolyn.Fidelman at ed.gov>
Subject: [Rasch] missing data advice needed
To: "rasch at acer.edu.au" <rasch at acer.edu.au>
Message-ID:
	<92E646BC24CBD742A4F9858873A97E0C78032767BB at EDUPTCEXMB01.ed.gov>
Content-Type: text/plain; charset="iso-8859-1"

Hi All,

I have a burning question about treatment of item-level missing data.
Imagine a short reading or math screener given to 5 year olds (individual
administration format, of course) in order to place them on some quick
proficiency scale.  The 20 items are designed to go from easy to hard.
Because young children can become easily frustrated and burdened once they
reach their level, a stop rule is in place in which the administrator ceases
testing once the child has missed 5 items in a row.

My question: is it legit to impute 0s to the missing values (9s) of a record
such as

1110100110000099999

thus making it

1110100110000000000

where the assumption is that the child would not have correctly answered
those last few items had they been administered anyway?

I know that for WINSTEPS and Rasch analysis, missing values are not an
issue.  I'm really thinking in terms of the descriptive statistics that
precede them in a report. It seems more reasonable for purposes of
comparison to report for a set of 1000 examinees  that the p value of, say,
Item 18 is  .09 where n=1000, than to say it is .25 for the 200 examinees
who were actually presented with the item.

Or should I just have two different datasets, one for the Rasch analysis
(leaving the 9s) and another for the descriptives (imputing 0s for 9s)?

Your wisdom much appreciated!  (and Happy New Year!)

Carolyn


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Message: 2
Date: Mon, 3 Jan 2011 16:28:54 -0800
From: Mark Moulton <markhmoulton at gmail.com>
Subject: Re: [Rasch] missing data advice needed
To: "Fidelman, Carolyn" <Carolyn.Fidelman at ed.gov>
Cc: "rasch at acer.edu.au" <rasch at acer.edu.au>
Message-ID:
	<AANLkTi=kMs5bB2RJogRB-qcA7KMN+HMjyTCnZDRcmSjT at mail.gmail.com>
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Dear Carolyn,

So far as the Rasch analysis goes, I would leave the data missing, even
though zeros are a reasonable imputation in this case.  However, even if
they are reasonable they could distort the item difficulties in odd ways, so
I would leave them missing.

"Descriptive" statistics always assume complete data, so it is problematic
to leave the missing data in for that.  But it is also a little problematic
to leave the zeros, though less so.  What I would do myself is calculate the
expected values for all the missing cells, round them to zero or one, and
build your descriptive statistics using the resulting complete data matrix.
 The resulting descriptives will be about the best you could manage.

Winsteps reports cell level expected values (though maybe not for missing
cells -- I can't remember).  Or you could import the data into Excel, along
with person and item measures, and calculate the expected values using
something like:

Eni = if(Xni <> 9,Xni,round(exp(Bn - Di) / (1 + exp(Bn - Di)),0))

(Translation:  If the observed value is not missing, use that, otherwise
round the expected value (same as probability in this case) calculated using
person logit ability Bn and item logit difficulty Di.)

Mark Moulton


On Mon, Jan 3, 2011 at 2:50 PM, Fidelman, Carolyn
<Carolyn.Fidelman at ed.gov>wrote:

>   Hi All,
>
> I have a burning question about treatment of item-level missing data.
> Imagine a short reading or math screener given to 5 year olds (individual
> administration format, of course) in order to place them on some quick
> proficiency scale.  The 20 items are designed to go from easy to
> hard. Because young children can become easily frustrated and burdened
once
> they reach their level, a stop rule is in place in which the administrator
> ceases testing once the child has missed 5 items in a row.
>
> My question: is it legit to impute 0s to the missing values (9s) of a
> record such as
>
> 1110100110000099999
>
> thus making it
>
>  1110100110000000000
>
> where the assumption is that the child would not have correctly answered
> those last few items had they been administered anyway?
>
> I know that for WINSTEPS and Rasch analysis, missing values are not an
> issue.  I'm really thinking in terms of the descriptive statistics that
> precede them in a report. It seems more reasonable for purposes of
> comparison to report for a set of 1000 examinees  that the p value of,
say,
> Item 18 is  .09 where n=1000, than to say it is .25 for the 200 examinees
> who were actually presented with the item.
>
> Or should I just have two different datasets, one for the Rasch analysis
> (leaving the 9s) and another for the descriptives (imputing 0s for 9s)?
>
> Your wisdom much appreciated!  (and Happy New Year!)
>
> Carolyn
>
>
>
> _______________________________________________
> Rasch mailing list
> Rasch at acer.edu.au
> Unsubscribe:
> https://mailinglist.acer.edu.au/mailman/options/rasch/markm%40eddata.com
>
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