中文摘要：

随着人们对测验反馈结果精细化的需求逐渐提高,具有认知诊断功能的测量方法逐渐受到人们的关注。在认知诊断模型（CDMs）闪耀着光芒的同时,另一类能够在连续量尺上提供精细反馈的多维IRT模型（MIRTMs）似乎受到些许冷落。为探究MIRTMs潜在的认知诊断功能,本文以补偿模型为视角,聚焦于分别属于MIRTMs的多维两参数logistic模型（M2PLM）和属于CDMs的线性logistic模型（LLM）;之后为使两者具有可比性,可对补偿M2PLM引入验证性矩阵（Q矩阵）来界定题目与维度之间的关系,进而得到验证性的补偿M2PLM（CC-M2PLM）,并通过把潜在特质按切点划分为跨界属性,以期使CC-M2PLM展现出其本应具有的认知诊断功能;预研究表明logistic量尺上的0点可作为相对合理的切点;然后,通过模拟研究对比探究CC-M2PLM和LLM的认知诊断功能,结果表明CC-M2PLM可用于分析诊断测验数据,且认知诊断功能与直接使用LLM的效果相当;最后,以两则实证数据为例来说明CC-M2PLM在实际诊断测验分析中的可行性。

英文摘要：

Traditional testing methods, such as classical testing theory or unidimensional item response theory models（UIRMs）, typically provide a single sum score or overall ability. Advances in psychometrics have focused on measuring multiple dimensions of ability to provide more detailed and refined feedback for students. In recent years, cognitive diagnostic models（CDMs） have received great attention, particularly in the areas of educational and psychological measurement. The outcome of a DCM analysis is a profile of a set of attributes, α, also called a latent class, for each person; this provides cognitive diagnostic information about distinct skills underlying a test that students mastery or non-mastery. During the same period, another kind of models, multidimensional IRT models（MIRTMs）, which also can provide fine-grained information about students＇ strengths and weaknesses in the learning process were neglected. MIRTMs are different from CDMs in that latent variables in MIRTMs are continuous（namely, latent traits; θ） rather than categorical（typically binary）. However, categorical variables in CDMs may be too rough to describe students＇ skills when compared with the continuous latent traits in MIRTMs. Diagnostic measurement is the process of analyzing data from a diagnostic assessment for the purpose of making classification-based decisions. Currently, all testing method that have cognitive diagnostic function require substantive information about the attributes involved in specific items. Especially for CDMs, a confirmatory matrix that indicating which latent variables are required for an item, often referred to as Q matrix, is a essential term to analysis response data. Actually, such confirmatory matrices also exist in some multidimensional IRT models（MIRTMs）, such as the scoring matrix in multidimensional random coefficients multinomial logit model. Therefore, it can be deduced that when MIRTMs are formulated in a confirmatory model defined by Q matrix, may also have diagnostic pote