[Rasch] OPLM - imputable discrimination

Anthony James luckyantonio2003 at yahoo.com
Thu Jul 6 17:32:23 EST 2006

Hi All,
  Are Rasch fit statistics sensitive to items which have descrimination indices that deviate too much from one?
  I mean do such items appear as misfitting items in Rasch analysis?
  If this is not the case, then apart from checking fit statistics, item discriminations should also be checked and items with deviating discriminations should be deleted, to make sure that the Rasch model holds. 

"Mike Linacre (RMT)" <rmt at rasch.org> wrote:

Has anyone found that imputing item discrimination (so giving the 'flexibility' of the 2PL model) is useful?  Winsteps already reports an item discrimination index. It would be straightforward to make item discrimination imputable. Of course, if you estimate => impute => estimate => impute => .... then the process diverges.

MIke Linacre

At 7/5/2006, you wrote:
  Rama, the OPLM is formally the same as the 2PL model. However, item discrimination in the OPLM is treated as an index rather than a parameter. The index is imputed rather than being estimated. So the 'one' in OPLM implies there is still only one item parameter. The weighted score in the 2PL model is sufficient for the person parameter but the problem is that the values of discrimination parameters are unknown, meaning the weighted raw score "is not a mere statistic, and hence it is impossible to use CML as an estimation method" (Verhelst & Glas, 1995, p. 217). The approach in the OPLM is to impute the values rather than estimating them, though estimates are still used as starting point. The index is restricted to integer values between 1 and about 15. The OPLM is intended to retain the properties of the Rasch model while giving the 'flexibility' of the 2PL model (stated at the beginning of the manual).
  Mike Linacre
Editor, Rasch Measurement Transactions
rmt at rasch.org www.rasch.org/rmt/ Latest RMT: 19:4 Spring

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