ISSN 1671-3710
CN 11-4766/R

Advances in Psychological Science ›› 2018, Vol. 26 ›› Issue (2): 358-367.doi: 10.3724/SP.J.1042.2018.00358

• Research Methods • Previous Articles     Next Articles

 Full-information item bifactor analysis: Model, parameter estimation and application

 MAO Xiuzhen1; XIA Menglian2; XIN Tao3   

  1.  (1 School of Educational Science, Sichuan Normal University; 2 School of Teacher Education and Psychology, Sichuan Normal University, Chengdu 610068, China) (3 Collaborative Innovation center, Beijing Normal University, Beijing 100875, China)
  • Received:2017-06-09 Online:2018-02-15 Published:2017-12-26
  • Contact: XIN Tao, E-mail:
  • Supported by:

Abstract:  Full-information item bifactor analysis is an important statistical method in psychological and educational measurement, which can be seen as a rediscovery of the classical bifactor model and has seen wide applications in the past two decades. The item response model of full-information item bifactor analysis is described upon the introduction of the conception and characterization of full-information item bifactor analysis. Further, we introduced the dimension reduction method used in parameter estimation. Then, examples are provided for applications of the full-information item bifactor model in test measurement structure exploration or confirmation, score interpretation, and computerized adaptive testing. The measurement structure of full-information item bifactor analysis is accordance with most tests in the areas of psychology, education, and medical science. With the advantage in dimension reduction, it is believed that the full-information item bifactor analysis could be valuable and useful in various situations. At last, some future research directions and suggestions are put forward including parameter estimation, test linking, differential item functioning, model fit testing, and application of bifactor item response theory to computerized adaptive testing.

Key words:  bifactor model, full-information item bifactor analysis, bifactor item reponse theory model, dimension reduction

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