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Computational Precision of the Power Function for Conditional Tests of Assumptions of the Rasch Model
UMIT—The Health and Life Sciences University, Hall in Tirol, Austria
UMIT—The Health and Life Sciences University, Hall in Tirol, Austria
- 1 UMIT—The Health and Life Sciences University, Hall in Tirol, Austria
- 2 UMIT—The Health and Life Sciences University, Hall in Tirol, Austria
Open Journal of Statistics·Volume 08 (2018)·Pages 873–884·Published 14 November 2018·DOI10.4236/ojs.2018.86058
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Abstract
Draxler and Zessin [1] derived the power function for a class of conditional tests of assumptions of a psychometric model known as the Rasch model and suggested an MCMC approach developed by Verhelst [2] for the numerical approximation of the power of the tests. In this contribution, the precision of the Verhelst approach is investigated and compared with an exact sampling procedure proposed by Miller and Harrison [3] for which the discrete probability distribution to be sampled from is exactly known. Results show no substantial differences between the two numerical procedures and quite accurate power computations. Regarding the question of computing time the Verhelst approach will have to be considered much more efficient.
KeywordsConditional TestsConditional Probability DistributionHypergeometric DistributionPower FunctionRandom SamplingRasch Model
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