Parameter Estimation of the Reduced RUM Using the EM Algorithm
Diagnostic classification models (DCMs) are psychometric models widely discussed by researchers nowadays because of their promising feature of obtaining detailed information on students’ mastery on specific attributes. Model estimation is essential for further implementation of these models, and est...
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| Published in: | Applied psychological measurement Vol. 38; no. 2; pp. 137 - 150 |
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| Main Authors: | , , |
| Format: | Journal Article |
| Language: | English |
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Los Angeles, CA
SAGE Publications
01.03.2014
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| ISSN: | 0146-6216, 1552-3497 |
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| Abstract | Diagnostic classification models (DCMs) are psychometric models widely discussed by researchers nowadays because of their promising feature of obtaining detailed information on students’ mastery on specific attributes. Model estimation is essential for further implementation of these models, and estimation methods are often developed within some general framework, such as generalized diagnostic model (GDM) of von Davier, the log-linear diagnostic classification model (LDCM), and the generalized deterministic input, noisy-and-gate (G-DINA). Using a maximum likelihood estimation algorithm, this article addresses the estimation issue of a complex compensatory DCM, the reduced reparameterized unified model (rRUM), whose estimation under general frameworks could be lengthy due to the complexity of the model. The proposed estimation method is demonstrated on simulated data as well as a real data set, and is shown to provide accurate item parameter estimates for the rRUM. |
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| AbstractList | Diagnostic classification models (DCMs) are psychometric models widely discussed by researchers nowadays because of their promising feature of obtaining detailed information on students' mastery on specific attributes. Model estimation is essential for further implementation of these models, and estimation methods are often developed within some general framework, such as generalized diagnostic model (GDM) of von Davier, the log-linear diagnostic classification model (LDCM), and the generalized deterministic input, noisy-and-gate (G-DINA). Using a maximum likelihood estimation algorithm, this article addresses the estimation issue of a complex compensatory DCM, the reduced reparameterized unified model (rRUM), whose estimation under general frameworks could be lengthy due to the complexity of the model. The proposed estimation method is demonstrated on simulated data as well as a real data set, and is shown to provide accurate item parameter estimates for the rRUM. |
| Author | Feng, Yuling Habing, Brian T. Huebner, Alan |
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| Cites_doi | 10.1007/s11336-011-9207-7 10.1111/j.2517-6161.1977.tb01600.x 10.1111/j.1745-3984.2008.00069.x 10.1007/s11336-008-9089-5 10.1177/0013164410388832 10.1111/j.1745-3984.1983.tb00212.x 10.1177/01466210122032064 10.1348/000711007X193957 10.3102/1076998607309474 10.1177/0146621609331960 |
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| References | Huebner, Wang 2011; 71 de la Torre 2008; 45 Henson, Templin 2009 Henson, Templin, Willse 2009; 74 de la Torre 2009; 34 Liu, Douglas, Henson 2009; 33 Tatsuoka 1983; 20 von Davier 2008; 61 Junker, Sijtsma 2001; 25 de la Torre 2011; 76 Dempster, Laird, Rubin 1977; 39 bibr19-0146621613502704 bibr14-0146621613502704 bibr12-0146621613502704 Muthén L. K. (bibr15-0146621613502704) 1998 bibr3-0146621613502704 diBello L. (bibr6-0146621613502704) 2007; 26 Kunina-Habenicht O. (bibr13-0146621613502704) 2011 bibr11-0146621613502704 Bartholomew D. (bibr1-0146621613502704) 1999 Rupp A. (bibr17-0146621613502704) 2010 bibr7-0146621613502704 bibr4-0146621613502704 bibr2-0146621613502704 bibr16-0146621613502704 bibr5-0146621613502704 Henson R. A. (bibr9-0146621613502704) 2009 bibr10-0146621613502704 bibr18-0146621613502704 Henson R. A. (bibr8-0146621613502704) 2007 |
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| Title | Parameter Estimation of the Reduced RUM Using the EM Algorithm |
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