心理科学领域内的客观测量——Rasch模型之特点及发展趋势

Objective Measurement in Psychological Science:An Overview of Rasch Model

Rasch模型是在国外学术界受到广泛关注和深入研究的一个潜在特质模型。该模型为解决心理科学领域内测量的客观性问题提供了一个可行性很高的解决方案。而国内关于Rasch模型的理论探讨和应用研究却并不多见。不同于一般项目反应理论,Rasch模型要求所收集的数据必须符合模型的先验要求,而不是使用不同的参数去适应数据的特点。Rasch模型的主要特点(包括个体与题目共用标尺、线性数据、参数分离)确保了客观测量的实现。未来关于Rasch模型的研究方向包括多维度Rasch模型、测验的等值与链接、计算机自适应性考试,大型应用测量系统(比如Lexile系统)等等。

Rasch model is a latent trait model which has drawn international interest among researchers.It provides a promising solution to ensure the objective measurement in psychological science.However,the research and applicatoin of Rasch model are not as popular as expected among domestic scholars.Unlike general IRTs that adopt a ＂the model fits data＂ position and use different parameters to accommodate the idiosyncrasies of the data set,the Rasch model requires that ＂data fit the model＂.Its unique features including the same metric shared by persons and items,data linearity,and parameter separation ensure the achievement of objective measurement.The foci of future development of Rasch model include multidimensional Rasch model,test equating and linking,computer adaptive testing,and Rasch-based measurement system such as Lexile framework.