Nonparametric goodness-of-fit tests for the Rasch model. A Monte Carlo algorithm realizing a family of nonparametric tests for the Rasch model is introduced which are conditional on the item and subject marginals. The algorithm is based on random changes of elements of data matrices without changing the marginals; most powerful tests against all alternative hypotheses are given for which a monotone characteristic may be computed from the data matrix; alternatives may also be composed. Computation times are long, but exact $p$-values are approximated with the quality of approximation only depending on calculation time, but not on the number of persons. The power and the flexibility of the procedure is demonstrated by means of an empirical example where, among others, indicators for increased item similarities, the existence of subscales, violations of sufficiency of the raw score as well as learning processes were found. Many of the features described are implemented in the program T-Rasch 1.0 by the first author and E. Ponocny-Seliger from 1999.
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References in zbMATH (referenced in 9 articles )
Showing results 1 to 9 of 9.
- Draxler, Clemens: Bayesian conditional inference for Rasch models (2018)
- Bechger, Timo M.; Maris, Gunter: A statistical test for differential item pair functioning (2015)
- Draxler, Clemens; Zessin, Johannes: The power function of conditional tests of the Rasch model (2015)
- Draxler, Clemens: Sample size determination for Rasch model tests (2010)
- Verhelst, Norman D.: An efficient MCMC algorithm to sample binary matrices with fixed marginals (2008)
- Chen, Yuguo; Small, Dylan: Exact tests for the Rasch model via sequential importance sampling (2005)
- Ponocny, Ivo: Addendum to: Nonparametric goodness-of-fit tests for the Rasch model (2002)
- Ponocny, Ivo: Nonparametric goodness-of-fit tests for the Rasch model (2001)
- Poncny, Ivo: Exact person fit indexes for the Rasch model for arbitrary alternatives (2000)