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《Journal of Psychological Science》 2018-06
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Three Methods for the Interval Estimation of AttributeLevel Classification Consistency in Cognitive Diagnostic Assessment

Wang Wenyi;Fang Xiaoting;Ye Baojuan;School of Computer and Information Engineering,Jiangxi Normal University;Center of Mental Health Education and Research,School of Psychology,Jiangxi Normal University;  
As is well known, point estimate contains limited information about a population parameter and cannot give how far it can be from the population parameter. The confidence interval of the parameter can provide more information about the precision of estimated parameters. In evaluating the quality of a test, the confidence interval of reliability has received more and more attention in recent years. However, there is no research on the estimation of the reliability interval of the cognitive diagnostic assessment. The researchers often report the reliability point estimation only, and no one is concerned about the confidence interval in the report. It is far from enough to report the reliability of the cognitive diagnostic assessment. The confidence interval of reliability in cognitive diagnostic assessment should be taken into account. We can estimate error range from the confidence interval.This study investigated and compared three reliability interval estimation methods of the cognitive diagnostic assessment based on DINA model and attribute-level classification consistency(Wang, et al., 2015). There are three approaches to estimate the confidence interval of the cognitive diagnostic assessment: Bootstrap method, parallel test method, and parallel test pairing method. Each of the three approaches produces the standard error, average and confidence interval about attribute-level classification consistency reliability. These factors were considered in the simulation design:(a) The number of test attributes(K=5, and 7);(b) The number of test items(5 attributes is T=15, and 31; 7 attributes is T=21, and 42);(c) The quality of test items [U(0.05,0.25), and U(0.25,0.45)];(d) The number of sample size(n=500, 1000 and 1500);(e) The method for calculating the confidence intervals of attribute-level classification consistency reliability(Bootstrap, parallel test method, and parallel test pairing method). Totally, 72 treatment conditions were generated in terms of the above 5-factor simulation design(i.e., 72=2×3×2×2×3).The simulation results indicated:(1) Whether the tests contained 5 or 7 attributes of the independent attribute hierarchical relationship, the standard error and reliability interval obtained by three interval estimation methods estimated(Bootstrap method, parallel test method and parallel test matching method) were all affected by the quality of test, sample size or test length. With the increase of the number of test items and subjects, the standard error and the length of confidence interval tended to decrease. As test quality degraded, the standard error and the length of confidence interval increased;(2) Whether the tests contained 5 or 7 attributes of the independent attribute hierarchical relationship, the average of the reliability estimated by three interval estimation methods was affected by the quality of test, sample size or test length. With the increase of test length, the average of the reliability showed a larger increase. With the increase of sample size, the change of the average of the reliability was small; as test quality degraded, the average of the reliability showed a larger decline;(3) The parallel test method and the Bootstrap method were close to the standard errors and confidence intervals estimated when test length and sample size was small. However, with the increase of sample size, the estimation accuracy of the Bootstrap method was improved rapidly. When the amount of test items and the sample size was large, the result was basically close to the parallel test matching method. Bootstrap method requires the least time, while parallel test matching method is difficult to achieve in practice. Therefore, Bootstrap method is recommended when estimating the confidence interval of cognitive diagnosis.
【Fund】: 国家自然科学基金项目(31600909 31500909);; 全国教育科学规划教育部重点课题(DHA150285);; 江西省自然科学基金项目(20161BAB212044);; 江西省教育科学2013年度一般课题(13YB032);; 国家社会科学基金项目(16BYY096);; 江西省高校人文社科重点研究基地——心理健康教育研究中心项目(项目编号:JD17078);; 江西省社会科学规划项目(17JY10);; 江西师范大学青年成长基金和江西师范大学博士启动基金的资助
【CateGory Index】: B842.1
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