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Conference Paper: Attribute Classification Accuracy Improvement: Monotonicity Constraints on the G-DINA Model
Title | Attribute Classification Accuracy Improvement: Monotonicity Constraints on the G-DINA Model |
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Authors | |
Issue Date | 2017 |
Citation | The International Meeting of the Psychometric Society, Zurich, Switzerland, 18 - 21 July 2017 How to Cite? |
Abstract | Cognitive Diagnosis Models (CDMs) are restricted latent class models developed to identify students’
mastery and nonmastery of multiple attributes. A common indicator of reliability in CDM is Attribute
Classification Accuracy (ACA). In this work, we explore the consequences of assuming an inappropriate
model, and propose a new version of a general CDM, G-DINA, where a monotonic constraint is included.
A simulation study is conducted to investigate how the ACA of monotonic G-DINA compares with those
of G-DINA and other reduced CDMs. The comparison involves both calibration and validation samples.
We also introduce the use of the Likelihood Ratio (LR) test to evaluate the appropriateness of imposing
this nonlinear constraint. LR Type I error and power in this context is evaluated. For comparison
purposes, the performance of AIC and BIC is also documented. Results show that the ACA of the
monotonic G-DINA model is always better than that of the G-DINA model, and approaches that of the
generating reduced CDMs. These differences were more pronounced in the validation sample indicating
that the lack of parsimony of the G-DINA model affects the generalizability and suitability of the item
parameter estimates across samples. The results also show that the LR test can be used to determine
whether or not monotonicity can be assumed. Overall, this study finds that the appropriateness of the
constrained version of the G-DINA model can be tested empirically, and its proper use (i.e., in situations
where the true CDMs cannot be assumed) leads to improved ACA. |
Persistent Identifier | http://hdl.handle.net/10722/248680 |
DC Field | Value | Language |
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dc.contributor.author | de la Torre, J | - |
dc.contributor.author | Sorrel, MA | - |
dc.date.accessioned | 2017-10-18T08:46:56Z | - |
dc.date.available | 2017-10-18T08:46:56Z | - |
dc.date.issued | 2017 | - |
dc.identifier.citation | The International Meeting of the Psychometric Society, Zurich, Switzerland, 18 - 21 July 2017 | - |
dc.identifier.uri | http://hdl.handle.net/10722/248680 | - |
dc.description.abstract | Cognitive Diagnosis Models (CDMs) are restricted latent class models developed to identify students’ mastery and nonmastery of multiple attributes. A common indicator of reliability in CDM is Attribute Classification Accuracy (ACA). In this work, we explore the consequences of assuming an inappropriate model, and propose a new version of a general CDM, G-DINA, where a monotonic constraint is included. A simulation study is conducted to investigate how the ACA of monotonic G-DINA compares with those of G-DINA and other reduced CDMs. The comparison involves both calibration and validation samples. We also introduce the use of the Likelihood Ratio (LR) test to evaluate the appropriateness of imposing this nonlinear constraint. LR Type I error and power in this context is evaluated. For comparison purposes, the performance of AIC and BIC is also documented. Results show that the ACA of the monotonic G-DINA model is always better than that of the G-DINA model, and approaches that of the generating reduced CDMs. These differences were more pronounced in the validation sample indicating that the lack of parsimony of the G-DINA model affects the generalizability and suitability of the item parameter estimates across samples. The results also show that the LR test can be used to determine whether or not monotonicity can be assumed. Overall, this study finds that the appropriateness of the constrained version of the G-DINA model can be tested empirically, and its proper use (i.e., in situations where the true CDMs cannot be assumed) leads to improved ACA. | - |
dc.language | eng | - |
dc.relation.ispartof | The International Meeting of the Psychometric Society | - |
dc.title | Attribute Classification Accuracy Improvement: Monotonicity Constraints on the G-DINA Model | - |
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 | 279607 | - |
dc.publisher.place | Zurich, Switzerland | - |