The probabilistic-inputs, noisy conjunctive model for cognitive diagnosis

Pei-Da ZHAN, Wen Chung WANG, Xiaomin LI, Yufang BIAN

Research output: Contribution to conferencePapers


To measure individual difference in latent attributes more precisely, this study proposed a new cognitive diagnosis model (CDM), which is referred as the probabilistic-inputs, noisy conjunctive (PINC) model, by treating the deterministic binary latent attributes as probabilistic, and directly estimating the probability in the model. Simulation studies were conducted to evaluate parameter recovery of the new model, and the results revealed that the parameters can be recovered well with WinBUGS. An empirical example of the Examination for the Certificate of Proficiency in English was provided to demonstrate applications of the new model.


Conference2016 Annual Meeting of American Educational Research Association: "Public Scholarship to Educate Diverse Democracies"
Abbreviated titleAERA 2016
Country/TerritoryUnited States
CityWashington, D.C.
Internet address


Zhan, P., Wang, W.-C., Li, X., & Bian, Y. (2016, April). The probabilistic-inputs, noisy conjunctive model for cognitive diagnosis. Paper presented at the 2016 AERA Annual Meeting: Public scholarship to educate diverse democracies, The Walter E. Washington Convention Centre, Washington, DC.


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