ISSN 1671-3710
CN 11-4766/R

Advances in Psychological Science ›› 2014, Vol. 22 ›› Issue (6): 1036-1046.doi: 10.3724/SP.J.1042.2014.01036

• Research Methods • Previous Articles    

The Data-augmentation Techniques in Item Response Modeling: Current Approaches and New Developments

TIAN Wei;XIN Tao;KANG Chunhua   

  1. (1 Faculty of Education, Beijing Normal University, Beijing 100875, China) (2 Institute of Developmental Psychology, Beijing Normal University, Beijing 100875, China) (3 College of Teacher Education, Zhejiang Normal University, Jinhua 321004, China)
  • Received:2012-11-13 Online:2014-06-15 Published:2014-06-15
  • Contact: XIN Tao


The parameter estimation techniques in item response theory modeling are indispensable to theoretical researches and real applications. This paper focused on its data augmentation techniques and described its historical development from the Bock and Aitkin’s (1981) deterministic EM algorithm to the Cai’s (2010) Metropolis-Hastings Robbins-Monro (MH-RM) algorithm (the integration of Markov Chain Monte Carlo and maximum marginal likelihood estimation, known as the stochastic data augmentation). Currently, the statistical computing still needs to be developed in new applications.

Key words: item response theory, latent trait, data augmentation, maximum marginal likelihood estimation, EM algorithm, MH-RM algorithm