多维项目反应理论的计量模型、参数估计及应用
Multidimensional Item Response Theory: Psychometric Models, Parameter Estimation and Application
DOI: 10.12677/AP.2015.56048, PDF, HTML, XML, 下载: 3,201  浏览: 13,390  国家科技经费支持
作者: 王 鹏*, 朱新立, 王 芳:山东师范大学心理学院,山东 济南
关键词: 多维项目反应理论计量模型参数估计Multidimensional Item Response Theory Psychometric Model Parameter Estimation
摘要: 多维项目反应理论是现代心理测量理论的新发展,文章对多维项目反应理论的计量模型、参数估计及应用进行了综述,认为多维项目反应理论模型开发应与认知结构相结合,马尔科夫链蒙特卡洛方法能较好地实现多维项目反应理论的参数估计,应加强多种题型、多种多维项目反应模型结合的参数估计研究,建议使用基于最大信息量法的多维项目反应理论模型计算测验的总分。
Abstract: Multidimensional Item Response Theory (MIRT) is the new development of modern psychometric theories. The psychometric models, parameter estimation and application of MIRT are overviewed in this paper. It is concluded that the development of MIRT models should be combined with cognitive construct, the method of MCMC should be used to enhance the parameter estimation of MIRT, the research of the mixed MIRT should be strengthened, and the method of maximum information should be used to get the total score of a test.
文章引用:王鹏, 朱新立, 王芳 (2015). 多维项目反应理论的计量模型、参数估计及应用. 心理学进展, 5(6), 365-375. http://dx.doi.org/10.12677/AP.2015.56048

参考文献

[1] 戴海琦(2010). 心理测量学. 北京: 高等教育出版社.
[2] 付志慧(2010). 多维项目反应模型的参数估计. 硕士论文, 吉林大学, 吉林.
[3] 康春花, 辛涛(2010). 测验理论的新发展: 多维项目反应理论. 心理科学进展, 3期, 530-536.
[4] 漆书青(2003). 现代测量理论在考试中的应用. 武汉: 华中师范大学出版社.
[5] 涂冬波, 蔡艳, 戴海琦, 丁树良(2011). 多维项目反应理论: 参数估计及其在心理测验中的应用. 心理学报, 11期, 1329-1340.
[6] 涂冬波, 漆书青, 蔡艳, 戴海琦, 丁树良(2008). IRT模型参数估计的新方法——MCMC算法. 心理科学, 1期, 177-180.
[7] 王权(2006). “马尔科夫链蒙特卡洛”(MCMC)方法在估计IRT模型参数中的应用. 考试研究, 4期, 45-63.
[8] 谢晶, 张厚粲(2009). 测验等值: 从IRT到MIRT. 心理学探新, 5期, 67-71.
[9] 杨向东(2010). 测验项目反应机制与心理测量模型假设的对应性分析. 心理科学进展, 8期, 1349-1358.
[10] 赵琪(2007). MCMC方法研究. 硕士论文, 山东大学, 济南.
[11] Ackerman, T. A. (1994). Using multidimensional item response theory to understand what items and tests are measuring. Applied Measurement in Education, 4, 255-278.
[12] Adams, R. J., Wilson, M., & Wang, W. (1997). The multidimensional random coefficients multinomial logit model. Applied Psychological Measurement, 21, 1-24.
[13] Akerman, T. A. (1992). Assessing construct validity using multidimensional item response theory. Proceedings of the Annual Meeting of the American Educational Research Association, San Francisco, 20-24 April 1992.
[14] Baker, F. B., & Kim, S. (2004). Item response theory parameter estimation techniques (2nd ed.). New York: Marcel Dekker, Inc.
[15] Berger, M. P. F., & Knol, D. L. (1990). On the assessment of dimensionality in multidimensional item response theory models. Research Report from the Division of Educational Measure and Data Analysis, Enschede: University of Twente.
[16] Bock, R. D., & Schilling, S. G. (2003). IRT based item factor analysis. In M. du Toit (Ed.), IRT from SSI: BILOG-MG, MULTILOG, PARSCALE, TESTFACT. Lincolnwood, IL: Scientific Software Internation-al.
[17] Bock, R. D., Gibbons, R. D., & Muraki, E. (1988). Full-information item factor analysis. Applied Psychological Measurement, 12, 261-280.
[18] Bolt, D. M., & Lall, V. F. (2003). Estimation of compensatory and noncompensatory multidimensional item response models using Markov Chain Monte Carlo. Applied Psychological Measurement, 27, 395-414.
[19] DeMars, C. E. (2005). Scoring subscales using multidimensional item response theory models. Proceedings of the Annual Meeting of the American Psychological Association (ED496242), Washington DC, 18-21 August 2005.
[20] Fraser, C., & McDonald, R. P. (1988). NOHARM: Least squares item factor analysis. Multivariate Behavioral Research, 23, 267-269.
[21] Fukuhara, H. (2009). A differential item functioning model for testlet-based items using a bi-factor multidimensional item response theory model: A Bayesian approach. Ph.D. Thesis, Tallahassee, FL: Department of Educational Psychology and Learning Systems, Florida State University.
[22] Gibbons, R. D., & Hedeker, D. (1992). Full information item bi-factor analysis. Psychometrika, 57, 423-436.
[23] Gibbons, R. D., Bock, R. D., Hedeker, D., Weiss, D. J., Segawa, E., Bhaumik, D. K. et al. (2007). Full-information item bifactor analysis of graded response data. Applied Psychological Measurement, 31, 4-19.
[24] Jiang, Y. L. (2005). Estimating parameters for multidimensional item response theory models by MCMC methods. Ph.D. Thesis, East Lansing, MI: Michigan State University.
[25] Kelderman, H., & Rijkes, C. P. M. (1994). Loglinear multidimensional IRT models for polytomously scored items. Psychometrika, 59, 149-176.
[26] Maris, E. (1995). Psychometric latent response models. Psychometrika, 45, 479-494.
[27] Muraki, E. (1999). POLYFACT version 2 (Computer program). Princeton, NJ: Educational Testing Service.
[28] Muraki, E., & Carlson, J. E. (1993). Full-information factor analysis for polytomous item responses. Proceedings of the Annual Meeting of the American Educational Research Association, Atlanta, 12-16 April 1993.
[29] Reckase, M. D. (1985). The difficulty of test items that measure more than one ability. Applied Psychological Measurement, 30, 469-492.
[30] Reckase, M. D. (2009). Multidimensional item response theory. New York: Springer Science + Business Media, LLC.
[31] Reckase, M. D., & McKinley, R. L. (1991). The discriminating power of items that measure more than one dimension. Applied Psychological Measurement, 15, 361-373.
[32] Rotou, O., Elmore, P. B., & Headrick, T. C. (2001). Number correct scoring: Comparison between classical true score theory and multidimensional item response theory. Proceedings of the Annual Meeting of the American Education Research Association, Seattle, 10-14 April 2001.
[33] Sympson, J. B. (1978). A model for testing with multidimensional items. In D. J. Weiss (Ed.), Proceedings of the 1977 Computerized Adaptive Testing Conference. Minneapolis, MN: University of Minnesota.
[34] Whitely, S. E. (1980). Multicomponent latent trait models for ability tests. Psychometrika, 45, 479-494.
[35] Wu, M. L., Adams, R. J., & Wilson, M. R. (1997). ConQuest: Generalized item response modeling software. Victoria: ACER.
[36] Yao, L. (2010). Reporting valid and reliable overall scores and domain scores. Monterey, CA: CTB/McGraw-Hill.
[37] Yao, L., & Schwarz, R. D. (2006). A multidimensional partial credit model with associated item and test statistics: An application to mixed-format tests. Applied Psychological Measurement, 30, 469-492.
[38] Zhang, L. T. (2007). The estimation of multidimensional item response theory models. Ph.D. Thesis, Columbia, SC: University of South Carolina.