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最新版 Winsteps 3.65

 相關軟體  Winsteps /Facets

Winsteps是基於Windows的軟體，用Rasch-Model 的很多應用幫助，特別是在教育測試，看法調查和等級規模分析的區域內。

Rasch analysis is a method for obtaining objective, fundamental, linear measures (qualified by standard errors and quality-control fit statistics) from stochastic observations of ordered category responses. Georg Rasch, a Danish mathematician, formulated this approach in 1953 to analyze responses to a series of reading tests (Rasch G, Probabilistic Models for Some Intelligence and Attainment Tests, Chicago: MESA Press, 1992, with
instructive Foreword and Afterword by B.D. Wright). Rasch is pronounced like the English word rash in Danish, and like the English sounds raa-sch in German. The German pronunciation, raa-sch, is used to avoid misunderstandings.
The person and item total raw scores are used to estimate linear measures. Under Rasch model conditions, these measures are item-free (item-distribution-free) and person-free (person-distribution-free). So that the measures are statistically equivalent for the items regardless of which persons (from the same population) are analyzed, and for the items regardless of which items (from the same population) are analyzed. Analysis of the data at the response-level indicates to what extent these ideals are realized within any particular data set.

The Rasch models implemented in Winsteps include the Georg Rasch dichotomous, Andrich "rating scale", Masters "partial credit", Bradley-Terry "paired comparison", Glas "success model", Linacre "failure model" and most combinations of these models. Other models such as binomial trials and Poisson can also be analyzed by anchoring (fixing) the response structure to accord with the response model. (If you have a particular need, please let us know as Winsteps is continually being enhanced.)
The estimation method is JMLE, "Joint Maximum Likelihood Estimation", with initial starting values provided by PROX, "Normal Approximation Algorithm".