[Rasch] analysing CTT construct using IRT

Mike Linacre (RMT) rmt at rasch.org
Tue Oct 27 11:25:09 EST 2009


Thank you for telling us about your research. Please help us to understand 
the situation better ....

Are you talking about the Test Characteristic Curve = raw-score to measure 
transformation (higher measures for higher scores)
the Test Information Function = precision of the measures (usually peaks 
near the center of the test) ?

Mike Linacre

At 10/26/2009, you wrote:
>Dear Listers,
>I am very new to the IRT field so please forgive me if my question is too 
>I am using the PISA 2006 data for my doctoral dissertation and so far I 
>have been using CFA methods for analysing the construct properties the 
>Likert scale data in PISA. All constructs in my study have high level of 
>reliability and mostly demonstrate good multigroup invariance properties 
>under CFA/SEM methods. Recently, I was introduced to the IRT methods and 
>start to development new perspectives on measurement issues of the data at 
>In PISA, many of the motivational constructs are measured through Likert 
>scales and were measured through well established constructs developed 
>under the framework of CTT. Using IRT method, I found that the 
>difficulties of items in these constructs are often clustered at middle or 
>high levels of the trait. The Test Characteristic Curves are always peaked 
>at one place rather than spread all over the trait levels. I guess this 
>means that the latent variable is not realiable enough for all the levels. 
>So I was wondering if these constructs are valid enough to use for further 
>analysis or whether I should just stick to my old way of analying this 
>data, i.e. using CFA/SEM and treating the likert data as if they were 

Mike Linacre
Editor, Rasch Measurement Transactions
rmt at rasch.org www.rasch.org/rmt/ Latest RMT:  23:2 Autumn 2009

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