Abstract
The Rasch Poisson counts model (RPCM) allows for the analysis of mental speed which represents a basic component of human intelligence. An extended version of the RPCM, which incorporates covariates in order to explain the difficulty, provides a means for modern rule-based item generation. After a short introduction to the extended RPCM we develop locally D-optimal calibration designs for this model. To this end the RPCM is embedded in a particular generalized linear model. Finally, the robustness of the derived designs is investigated.
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Acknowledgements
This research was supported by the Deutsche Forschungsgemeinschaft (DFG) under grant HO 1286/6.
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Graßhoff, U., Holling, H., Schwabe, R. (2013). Optimal Design for Count Data with Binary Predictors in Item Response Theory. In: Ucinski, D., Atkinson, A., Patan, M. (eds) mODa 10 – Advances in Model-Oriented Design and Analysis. Contributions to Statistics. Springer, Heidelberg. https://doi.org/10.1007/978-3-319-00218-7_14
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DOI: https://doi.org/10.1007/978-3-319-00218-7_14
Publisher Name: Springer, Heidelberg
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