An efficient standard error estimator of the DINA model parameters when analysing clustered data
by Jung Yeon Park; Young-Sun Lee; Matthew S. Johnson
International Journal of Quantitative Research in Education (IJQRE), Vol. 4, No. 1/2, 2017

Abstract: Cognitive diagnostic modelling is often used to analyse educational and psychological data, which are typically collected through cluster sampling with unequal selection probabilities. Jackknife is a resampling technique used to account for the sampling design. It typically gives unbiased estimates of the standard errors of the model parameters, but implementation can be vastly time-consuming. This study proposes an accurate and computationally fast approach for the standard errors of the parameters in the DINA model, one that incorporates the Huber-White sandwich estimator approach. Our simulation study suggests that the proposed sandwich estimator performs well when analysing clustered data structures specifically with moderate to large numbers of clusters. We also demonstrate its applicability to TIMSS 2011 mathematics.

Online publication date: Sat, 02-Sep-2017

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Quantitative Research in Education (IJQRE):
Login with your Inderscience username and password:

    Username:        Password:         

Forgotten your password?


Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com