Arbeitspapier
On the estimation of standard errors in cognitive diagnosis models
Cognitive diagnosis models (CDMs) are an increasingly popular method to assess mastery or nonmastery of a set of fine-grained abilities in educational or psychological assessments. Several inference techniques are available to quantify the uncertainty of model parameter estimates, to compare different versions of CDMs or to check model assumptions. However, they require a precise estimation of the standard errors (or the entire covariance matrix) of the model parameter estimates. In this article, it is shown analytically that the currently widely used form of calculation leads to underestimated standard errors because it only includes the items parameters, but omits the parameters for the ability distribution. In a simulation study, we demonstrate that including those parameters in the computation of the covariance matrix consistently improves the quality of the standard errors. The practical importance of this finding is discussed and illustrated using a real data example.
- Language
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Englisch
- Bibliographic citation
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Series: Working Papers in Economics and Statistics ; No. 2016-25
- Classification
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Wirtschaft
Multiple or Simultaneous Equation Models; Multiple Variables: General
Model Evaluation, Validation, and Selection
Econometric Software
- Subject
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cognitive diagnosis model
G-DINA
standard errors
information matrix
- Event
-
Geistige Schöpfung
- (who)
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Philipp, Michel
Strobl, Carolin
de la Torre, Jimmy
Zeileis, Achim
- Event
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Veröffentlichung
- (who)
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University of Innsbruck, Research Platform Empirical and Experimental Economics (eeecon)
- (where)
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Innsbruck
- (when)
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2016
- Handle
- Last update
-
10.03.2025, 11:44 AM CET
Data provider
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
Object type
- Arbeitspapier
Associated
- Philipp, Michel
- Strobl, Carolin
- de la Torre, Jimmy
- Zeileis, Achim
- University of Innsbruck, Research Platform Empirical and Experimental Economics (eeecon)
Time of origin
- 2016
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