【摘要】：本文通過使用BMIRT軟件，設置不同的實驗條件：被試樣本量（1000和3000）×題目量（25和50）×能力相關（0.3和0.7），模擬生成多維三參數(shù)補償數(shù)據(jù)和非補償數(shù)據(jù)，并用多維三參數(shù)補償模型和非補償模型進行參數(shù)估計。通過比較項目參數(shù)和能力參數(shù)的RMSE值，，實現(xiàn)各種實驗條件下的多維補償模型與非補償模型的參數(shù)返真性比較。結果發(fā)現(xiàn)，無論是估計多維補償數(shù)據(jù)還是非補償數(shù)據(jù)，三參數(shù)多維補償模型的參數(shù)返真性都比三參數(shù)多維非補償模型的參數(shù)返真性更好。尤其當估計多維補償數(shù)據(jù)時，三參數(shù)多維補償模型估計的能力參數(shù)RMSE值幾乎是三參數(shù)多維非補償模型的一半，顯著優(yōu)于三參數(shù)非補償模型估計的能力參數(shù)返真性。 本研究還將多維項目反應理論補償模型與非補償模型應用于瑞文高級推理測驗中，發(fā)現(xiàn)多維補償模型比多維非補償模型擬合的更好。本研究使用多維項目反應理論補償模型對高級瑞文推理測驗進行深入分析，探究瑞文高級推理測驗的各題目質量、難度及主要測量的認知成分，結果發(fā)現(xiàn)瑞文高級推理測驗的整體區(qū)分度較好，并且項目難度幾乎隨著題序增大而增大。在五個能力維度上，瑞文高級推理測驗試題的認知成分難度按A/S、CR、PP、D3和D2依次遞增。最后，在多維補償模型與非補償模型對瑞文高級推理測驗的被試能力參數(shù)估計的基礎上，對被試在解決瑞文高級推理測驗項目時能力間的相互作用進行了探索性分析，結果發(fā)現(xiàn)被試在解決瑞文高級推理測驗項目時，CR、PP以及D3能力之間存在相互補償關系，A/S與D2能力之間也存在相互補償關系。 最后，本文指出了該研究的不足，并對未來的研究提出展望。
[Abstract]:In this paper, by using BMIRT software, we set up different experimental conditions: sample size (1000 and 3000) 脳 subject quantity (25 and 50) 脳 ability correlation (0. 3 and 0. 7) to simulate the generation of multi dimensional three parameter compensation data and non compensation data. The multi-dimensional three parameter compensation model and the non-compensation model are used to estimate the parameters. By comparing the RMSE values of the project parameters and the capability parameters, the parameter fidelity comparison between the multi-dimensional compensation model and the non-compensation model under various experimental conditions is realized. The results show that the parametric fidelity of the three-parameter multi-dimensional compensation model is better than that of the three-parameter multi-dimensional non-compensation model, regardless of whether it is the estimation of the multi-dimensional compensation data or the non-compensated data. In particular, when estimating multidimensional compensation data, the capability parameter RMSE estimated by the three-parameter multi-dimensional compensation model is almost half of that of the three-parameter multi-dimensional non-compensation model, which is significantly better than the capability parameter fidelity of the three-parameter non-compensation model estimation. The multi-dimensional item response theory compensation model and the non-compensation model are also applied to the Raven advanced reasoning test. It is found that the multidimensional compensation model fits better than the multidimensional non-compensation model. In this study, the multidimensional item response theory compensation model was used to deeply analyze the advanced Raven reasoning test, and to explore the quality, difficulty and cognitive components of the Raven advanced reasoning test. The results show that the overall classification of Raven advanced reasoning test is good and the project difficulty increases with the increase of item order. In the five ability dimensions, the difficulty of cognitive components in the Raven Advanced reasoning Test was increased by A / S / C / PPD _ 3 and D _ 2 respectively. Finally, on the basis of multi-dimensional compensation model and non-compensation model to estimate the ability parameters of the Raven advanced reasoning test, the interaction between the ability of the participants in solving the Raven advanced reasoning test items is analyzed. The results show that there is a mutual compensation relationship between CR,PP and D3 ability and between A / S and D _ 2 ability in solving Raven advanced reasoning test items. Finally, this paper points out the deficiency of this research and puts forward the prospect of future research.
【學位授予單位】：江西師范大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：B841

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