Item response theory parameters have to be estimated, and because of the estimation process, they do have uncertainty in them. In most large-scale testing programs, the parameters are stored in item banks, and automated test assembly algorithms are applied to assemble operational test forms. These algorithms treat item parameters as fixed values, and uncertainty is not taken into account. As a consequence, resulting tests might be off target or less informative than expected. In this article, the process of parameter estimation is described to provide insight into the causes of uncertainty in the item parameters. The consequences of uncertainty are studied. Besides, an alternative automated test assembly algorithm is presented that is robust against uncertainties in the data. Several numerical examples demonstrate the performance of the robust test assembly algorithm, and illustrate the consequences of not taking this uncertainty into account. Finally, some recommendations about the use of robust test assembly and some directions for further research are given.

Additional Metadata
Keywords automated test assembly, computerized adaptive testing, item parameter estimation, item response theory, robust optimization, robust test assembly, uncertainty
Persistent URL dx.doi.org/10.1177/0146621612469825, hdl.handle.net/1765/70001
Journal Applied Psychological Measurement
Citation
Veldkamp, B.P, Matteucci, D, & de Jong, M.G. (2013). Uncertainties in the Item Parameter Estimates and Robust Automated Test Assembly. Applied Psychological Measurement, 37(2), 123–139. doi:10.1177/0146621612469825