ISSN:
1860-0980
Keywords:
Partial credit model
;
Rasch model
;
item response theory
;
measurement of change
;
assessment of treatment effects
Source:
Springer Online Journal Archives 1860-2000
Topics:
Psychology
Notes:
Abstract The partial credit model is considered under the assumption of a certain linear decomposition of the item × category parametersδ ih into “basic parameters”α j. This model is referred to as the “linear partial credit model”. A conditional maximum likelihood algorithm for estimation of theα j is presented, based on (a) recurrences for the combinatorial functions involved, and (b) using a “quasi-Newton” approach, the so-called Broyden-Fletcher-Goldfarb-Shanno (BFGS) method; (a) guarantees numerically stable results, (b) avoids the direct computation of the Hesse matrix, yet produces a sequence of certain positive definite matricesB k ,k=1, 2, ..., converging to the asymptotic variance-covariance matrix of the $$\hat \alpha _j $$ . The practicality of these numerical methods is demonstrated both by means of simulations and of an empirical application to the measurement of treatment effects in patients with psychosomatic disorders.
Type of Medium:
Electronic Resource
URL:
http://dx.doi.org/10.1007/BF02295182
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