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Conference Paper: Estimating mixture fit index for cognitive diagnosis models
Title | Estimating mixture fit index for cognitive diagnosis models |
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Authors | |
Issue Date | 2016 |
Publisher | The Psychometric Society. |
Citation | 81st International Meeting of the Psychometric Society (IMPS), Asheville, NC, USA, 11-15 July 2016. In Abstract Book: Talks, p. 21-22 How to Cite? |
Abstract | Rudas et al. (1994) proposed the use of a mixture fit index as goodness-of-fit measure in contingency tables. It assumes that observations can be classified into two groups, namely: those that conform to a parametric model and those that do not. The mixture fit index gives the proportion of misfitting observations. This study applies the mixture fit index to detect aberrant response patterns in the cognitive diagnosis model (CDM) framework. Using nonlinear programming and bisection method, the proposed algorithm iteratively estimates the mixture fit index and posterior probabilities are then used to calculate the likelihood that response patterns belong in the aberrant group. Preliminary results show that the proposed procedure can identify aberrant response patterns for short tests. |
Description | Diagnostic Classification Model- DCM 1 - abstract no. DCM 1a |
Persistent Identifier | http://hdl.handle.net/10722/247989 |
DC Field | Value | Language |
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dc.contributor.author | Santos, K | - |
dc.contributor.author | de la Torre, J | - |
dc.contributor.author | von Davier, M | - |
dc.date.accessioned | 2017-10-18T08:35:59Z | - |
dc.date.available | 2017-10-18T08:35:59Z | - |
dc.date.issued | 2016 | - |
dc.identifier.citation | 81st International Meeting of the Psychometric Society (IMPS), Asheville, NC, USA, 11-15 July 2016. In Abstract Book: Talks, p. 21-22 | - |
dc.identifier.uri | http://hdl.handle.net/10722/247989 | - |
dc.description | Diagnostic Classification Model- DCM 1 - abstract no. DCM 1a | - |
dc.description.abstract | Rudas et al. (1994) proposed the use of a mixture fit index as goodness-of-fit measure in contingency tables. It assumes that observations can be classified into two groups, namely: those that conform to a parametric model and those that do not. The mixture fit index gives the proportion of misfitting observations. This study applies the mixture fit index to detect aberrant response patterns in the cognitive diagnosis model (CDM) framework. Using nonlinear programming and bisection method, the proposed algorithm iteratively estimates the mixture fit index and posterior probabilities are then used to calculate the likelihood that response patterns belong in the aberrant group. Preliminary results show that the proposed procedure can identify aberrant response patterns for short tests. | - |
dc.language | eng | - |
dc.publisher | The Psychometric Society. | - |
dc.relation.ispartof | International Meeting of the Psychometric Society | - |
dc.title | Estimating mixture fit index for cognitive diagnosis models | - |
dc.type | Conference_Paper | - |
dc.identifier.email | de la Torre, J: jdltorre@hku.hk | - |
dc.identifier.authority | de la Torre, J=rp02159 | - |
dc.identifier.hkuros | 279644 | - |
dc.identifier.spage | 21 | - |
dc.identifier.epage | 22 | - |