Adjusting for information inflation due to local dependency in moderately large item clusters

作     者：Ip, EHS 

作者机构：Univ So Calif Marshall Sch Business Informat & Operat Management Dept Los Angeles CA 90089 USA 

出 版 物：《PSYCHOMETRIKA》 (心理测量学)

年 卷 期：2000年第65卷第1期

页      面：73-91页

核心收录：

学科分类：0402[教育学-心理学(可授教育学、理学学位）] 04[教育学] 0701[理学-数学] 

基　　金：National Assessment of Educational Progress, (R999G30002) Office of Educational Research and Improvement U.S. Department of Education, ED 

主　　题：multivariate binary data item response theory NAEP item cluster conditional correlation function 

摘      要：When multiple items are clustered around a reading passage, the local independence assumption in item response theory is often violated. The amount of information contained in an item cluster is usually overestimated if violation of Local independence is ignored and items are treated as locally independent when in fact they are not. In this article we provide a general method that adjusts for the inflation of information associated with a test containing item clusters. A computational scheme was presented for the evaluation of the factor of adjustment for clusters in the restrictive case of two items per cluster, and the general case of more than two items per cluster. The methodology was motivated by a study of the NAEP Reading Assessment. We present a simulated study along with an analysis of a NAEP data set.