Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/156179
Authors: 
Philipp, Michel
Strobl, Carolin
de la Torre, Jimmy
Zeileis, Achim
Year of Publication: 
2016
Series/Report no.: 
Working Papers in Economics and Statistics 2016-25
Abstract: 
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.
Subjects: 
cognitive diagnosis model
G-DINA
standard errors
information matrix
JEL: 
C30
C52
C87
Document Type: 
Working Paper

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