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Conference Paper: Using forward search algorithm for person fit analysis in general cognitive diagnosis models
Title | Using forward search algorithm for person fit analysis in general cognitive diagnosis models |
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Authors | |
Issue Date | 2016 |
Citation | The 22nd International Conference on Computational Statistics (COMPSTAT 2016), Oviedo, Spain, 23-26 August 2016 How to Cite? |
Abstract | Cognitive diagnosis models (CDMs) are psychometric models used to identify the strengths and weaknesses of examinees based on multidimensional attribute patterns. However, these latent attribute profiles may be inaccurately estimated because of an atypical test performance reflected in the response patterns. Person fit assessment is then performed to obtain information regarding the aberrant behavior of the examinees. The aim is to extend the forward search algorithm to general CDMs, particularly the G-DINA (generalized deterministic inputs, noisy and gate) model, to identify aberrant response patterns. Methods to select the initial set, and to progress and monitor the search are explored. Forward plots of goodness-of-fit statistics and Cooks distance are examined to observe drastic changes. A simulation study is conducted to determine the performance of the proposed method on different scenarios. |
Description | Session CG062 Room: Latent variable models: paper no. C0427
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Persistent Identifier | http://hdl.handle.net/10722/247985 |
DC Field | Value | Language |
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dc.contributor.author | Santos, KC | - |
dc.contributor.author | de la Torre, J | - |
dc.contributor.author | Barrios, E | - |
dc.date.accessioned | 2017-10-18T08:35:55Z | - |
dc.date.available | 2017-10-18T08:35:55Z | - |
dc.date.issued | 2016 | - |
dc.identifier.citation | The 22nd International Conference on Computational Statistics (COMPSTAT 2016), Oviedo, Spain, 23-26 August 2016 | - |
dc.identifier.uri | http://hdl.handle.net/10722/247985 | - |
dc.description | Session CG062 Room: Latent variable models: paper no. C0427 | - |
dc.description.abstract | Cognitive diagnosis models (CDMs) are psychometric models used to identify the strengths and weaknesses of examinees based on multidimensional attribute patterns. However, these latent attribute profiles may be inaccurately estimated because of an atypical test performance reflected in the response patterns. Person fit assessment is then performed to obtain information regarding the aberrant behavior of the examinees. The aim is to extend the forward search algorithm to general CDMs, particularly the G-DINA (generalized deterministic inputs, noisy and gate) model, to identify aberrant response patterns. Methods to select the initial set, and to progress and monitor the search are explored. Forward plots of goodness-of-fit statistics and Cooks distance are examined to observe drastic changes. A simulation study is conducted to determine the performance of the proposed method on different scenarios. | - |
dc.language | eng | - |
dc.relation.ispartof | International Conference on Computational Statistics, COMPSTAT 2016 | - |
dc.title | Using forward search algorithm for person fit analysis in general 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 | 279637 | - |