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Treatment of Not-Administered Items on Individually Administered Intelligence Tests

机译:个体管理的智力测验中非管理项目的处理

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In administration of individually administered intelligence tests, items are commonly presented in a sequence of increasing difficulty, and test administration is terminated after a predetermined number of incorrect answers. This practice produces stochastically censored data, a form of nonignorable missing data. By manipulating four factors (i.e., treatment of nonresponses, ability estimation method, test length, and stopping rule), this study investigated how accurately ability parameters are recovered under these conditions. The results suggest that there might be a complex interaction among these manipulated factors with respect to parameter estimation bias. The worst estimates occur when nonadministered items are treated as incorrect. Treating nonadministered items as not presented or fractionally correct appears to produce more accurate ability estimates. Among the ability estimation methods examined in this study, the Expected A Posterior and Maximum A posteriori methods tend to produce less estimation error in the presence of censored data. The differences in missing data treatment methods tend to have greater impact on ability estimate under maximum likelihood estimation than Bayesian methods.
机译:在进行单独进行的智力测验的管理中,通常以增加难度的顺序显示项目,并且在预定数量的错误答案后终止测验管理。这种做法会产生随机检查的数据,这是一种不可忽略的缺失数据。通过操纵四个因素(即无反应的处理,能力估计方法,测试时间和停止规则),本研究调查了在这些条件下如何准确地恢复能力参数。结果表明,就参数估计偏差而言,这些操纵因素之间可能存在复杂的相互作用。如果将非管理项目视为不正确,则会发生最差的估计。将未管理的项目视为未显示或部分正确的处理似乎可以产生更准确的能力估计。在这项研究中研究的能力估计方法中,在存在删失数据的情况下,期望后验和最大后验方法倾向于产生较少的估计误差。在最大似然估计下,缺失数据处理方法的差异往往比贝叶斯方法对能力估计有更大的影响。

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