Rasch model estimation: further topics.
Building on Wright and Masters (1982), several Rasch estimation methods are briefly described, including Marginal Maximum Likelihood Estimation (MMLE) and minimum chi-square methods. General attributes of Rasch estimation algorithms are discussed, including the handling of missing data, precision and accuracy, estimate consistency, bias and symmetry. Reasons for, and the implications of, measure misestimation are explained, including the effect of loose convergence criteria, and failure of Newton-Raphson iteration to converge. Alternative parameterizations of rating scales broaden the scope of Rasch measurement methodology.
Linacre
John M
JM
University of the Sunshine Coast, Australia. ML@winsteps.com
D000465:Algorithms; D003627:Data Interpretation, Statistical; D006801:Humans; D015233:Models, Statistical; D011594:Psychometrics; D012016:Reference Values