【摘要】：本文通過使用BMIRT軟件，設(shè)置不同的實(shí)驗(yàn)條件：被試樣本量（1000和3000）×題目量（25和50）×能力相關(guān)（0.3和0.7），模擬生成多維三參數(shù)補(bǔ)償數(shù)據(jù)和非補(bǔ)償數(shù)據(jù)，并用多維三參數(shù)補(bǔ)償模型和非補(bǔ)償模型進(jìn)行參數(shù)估計(jì)。通過比較項(xiàng)目參數(shù)和能力參數(shù)的RMSE值，，實(shí)現(xiàn)各種實(shí)驗(yàn)條件下的多維補(bǔ)償模型與非補(bǔ)償模型的參數(shù)返真性比較。結(jié)果發(fā)現(xiàn)，無論是估計(jì)多維補(bǔ)償數(shù)據(jù)還是非補(bǔ)償數(shù)據(jù)，三參數(shù)多維補(bǔ)償模型的參數(shù)返真性都比三參數(shù)多維非補(bǔ)償模型的參數(shù)返真性更好。尤其當(dāng)估計(jì)多維補(bǔ)償數(shù)據(jù)時(shí)，三參數(shù)多維補(bǔ)償模型估計(jì)的能力參數(shù)RMSE值幾乎是三參數(shù)多維非補(bǔ)償模型的一半，顯著優(yōu)于三參數(shù)非補(bǔ)償模型估計(jì)的能力參數(shù)返真性。 本研究還將多維項(xiàng)目反應(yīng)理論補(bǔ)償模型與非補(bǔ)償模型應(yīng)用于瑞文高級(jí)推理測驗(yàn)中，發(fā)現(xiàn)多維補(bǔ)償模型比多維非補(bǔ)償模型擬合的更好。本研究使用多維項(xiàng)目反應(yīng)理論補(bǔ)償模型對(duì)高級(jí)瑞文推理測驗(yàn)進(jìn)行深入分析，探究瑞文高級(jí)推理測驗(yàn)的各題目質(zhì)量、難度及主要測量的認(rèn)知成分，結(jié)果發(fā)現(xiàn)瑞文高級(jí)推理測驗(yàn)的整體區(qū)分度較好，并且項(xiàng)目難度幾乎隨著題序增大而增大。在五個(gè)能力維度上，瑞文高級(jí)推理測驗(yàn)試題的認(rèn)知成分難度按A/S、CR、PP、D3和D2依次遞增。最后，在多維補(bǔ)償模型與非補(bǔ)償模型對(duì)瑞文高級(jí)推理測驗(yàn)的被試能力參數(shù)估計(jì)的基礎(chǔ)上，對(duì)被試在解決瑞文高級(jí)推理測驗(yàn)項(xiàng)目時(shí)能力間的相互作用進(jìn)行了探索性分析，結(jié)果發(fā)現(xiàn)被試在解決瑞文高級(jí)推理測驗(yàn)項(xiàng)目時(shí)，CR、PP以及D3能力之間存在相互補(bǔ)償關(guān)系，A/S與D2能力之間也存在相互補(bǔ)償關(guān)系。 最后，本文指出了該研究的不足，并對(duì)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(xué)位授予單位】：江西師范大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：B841

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