title: Conditional statistical inference with multistage testing designs.
abstract: In this paper it is demonstrated how statistical inference from multistage test designs can be made based on the conditional likelihood. Special attention is given to parameter estimation, as well as the evaluation of model fit. Two reasons are provided why the fit of simple measurement models is expected to be better in adaptive designs, compared to linear designs: more parameters are available for the same number of observations; and undesirable response behavior, like slipping and guessing, might be avoided owing to a better match between item difficulty and examinee proficiency. The results are illustrated with simulated data, as well as with real data.
authors: Psychometric Research Center, Cito Institute for Educational Measurement, P.O. Box 1034, 6801 MG, Arnhem, The Netherlands, robert.zwitser@cito.nl.
authors: Robert J
authors: RJ
authors: Zwitser
authors: Gunter
authors: G
authors: Maris
mesh_terms: D000465:Algorithms; D003627:Data Interpretation, Statistical; D004521:Educational Measurement / Q000379:methods*; D006801:Humans; D016013:Likelihood Functions; D008432:Mathematical Computing; D012044:Regression Analysis