心理学报
心理學報
심이학보
Acta Psychologica Sinica
2012年
10期
1402~1407
,共null页
多维项目反应理论 多维等级反应模型 项目信息函数 参数估计
多維項目反應理論 多維等級反應模型 項目信息函數 參數估計
다유항목반응이론 다유등급반응모형 항목신식함수 삼수고계
MIRT; MGRM; item information function; parameter estimate
基于因子分析和单维项目反应理论的多维项目反应理论是测量理论的新发展方向之一。但是,多维项目反应理论仍处于不成熟的发展阶段,多数研究也只是以二级评分为主。本文首先介绍了逻辑斯蒂形式的多维等级反应模型,并以二维等级反应模型为例,分析了模型的数学函数图像及其性质。然后,推导出了多维等级反应模型的项目信息函数,并结合实例进行了讨论。进一步地,本文阐述了使用联合极大似然估计和马尔科夫链蒙特卡洛方法估计多维等级反应模型参数的思想。最后,指出了一些有待研究的问题。
基于因子分析和單維項目反應理論的多維項目反應理論是測量理論的新髮展方嚮之一。但是,多維項目反應理論仍處于不成熟的髮展階段,多數研究也隻是以二級評分為主。本文首先介紹瞭邏輯斯蒂形式的多維等級反應模型,併以二維等級反應模型為例,分析瞭模型的數學函數圖像及其性質。然後,推導齣瞭多維等級反應模型的項目信息函數,併結閤實例進行瞭討論。進一步地,本文闡述瞭使用聯閤極大似然估計和馬爾科伕鏈矇特卡洛方法估計多維等級反應模型參數的思想。最後,指齣瞭一些有待研究的問題。
기우인자분석화단유항목반응이론적다유항목반응이론시측량이론적신발전방향지일。단시,다유항목반응이론잉처우불성숙적발전계단,다수연구야지시이이급평분위주。본문수선개소료라집사체형식적다유등급반응모형,병이이유등급반응모형위례,분석료모형적수학함수도상급기성질。연후,추도출료다유등급반응모형적항목신식함수,병결합실례진행료토론。진일보지,본문천술료사용연합겁대사연고계화마이과부련몽특잡락방법고계다유등급반응모형삼수적사상。최후,지출료일사유대연구적문제。
Multidimensional Item Response Theory (MIRT), which is based on factor analysis and unidimensional Item Response Theory (IRT), is one of a new development trend of IRT. It's a fact that MIRT is in an early-developing stage and most studies are mainly concentrated on MIRT models for items with two score categories. With respect to polytomous MIRT models, it's until 1993 that Muraki and Carlson produced a generalization of unidimensional Grade Response Model (GRM) and it uses response functions that have the normal ogive form. Some other models such as multidimensional Generalized Partial Credit Model (MGPCM) and Continuous Response Model (MCRM) are even developed in recent years (Yao & Schwarz, 2006; Ferrando, 2009). In the paper, a form of logistic Multidimensional Graded Response Model (MGRM) is firstly presented. The graphics, which are plotted by Matlab 2007, and properties for a special case of two-dimensional GRM are demonstrated. Then, base on the definition of item information for a dichotomous MIRT models, the item information function for MGRM is derived and item information for a case of two-dimensional GRM discussed. Moreover, the main ideas of Joint Maximum Likelihood Estimation (JML) and Markov Chain Monte Carlo (MCMC) methods to estimate MGRM parameters are stated. Finally, some significant further researches, which include research of item and test information, developing parameter estimate program for MGRM, are illustrated in the paper.