Björn Andersson
Academic interests
Currently I am working with methodological development for latent variable models such as item response theory (IRT) and structural equation models. I am also involved in research on equating/linking in educational assessment and various applications in education, gerontology and psychology. In addition, I am the maintainer of the R package kequate which implements the kernel method of test equating in an open source software package and irtreliability which enables the estimation of IRT reliability and associated confidence intervals.
For a full list of publications see: http://orcid.org/0000-0002-9007-2440
Background
Prior to joining CEMO I was a post-doctoral researcher at the Collaborative Innovation Center of Assessment towards Basic Education Quality at Beijing Normal University where I worked on improving the NAEQ, a large-scale Chinese assessment of basic education quality.
Educational background:
Ph.D. Statistics, Uppsala University, 2014.
M.Sc. Statistics, Umeå University, 2011.
B.Sc. Statistics, Umeå University, 2010.
Publications
- Liu, Yanlou; Xin, Tao; Andersson, Bjørn & Tian, Wei (2019). Information matrix estimation procedures for cognitive diagnostic models. British Journal of Mathematical & Statistical Psychology. ISSN 0007-1102. 72(1), s 18- 37 . doi: 10.1111/bmsp.12134
- Luo, Hao; Andersson, Björn Håkan; Tang, Jennifer & Wong, Gloria (2019). Applying Item Response Theory Analysis to the Montreal Cognitive Assessment in a Low-Education Older Population. Assessment (Odessa, Fla.). ISSN 1073-1911. s 1- 13 . doi: 10.1177/1073191118821733
- Gao, Yizhu; Zhai, Xiaoming; Andersson, Björn Håkan; Zeng, Pingfei & Xin, Tao (2018). Developing a Learning Progression of Buoyancy to Model Conceptual Change: A Latent Class and Rule Space Model Analysis. Research in Science Education. ISSN 0157-244X. s 1- 20 . doi: 10.1007/s11165-018-9736-5
- Liu, Yanlou; Andersson, Björn Håkan; Tao, Xin; Zhang, Haiyan & Wang, Lingling (2018). Improved Wald Statistics for Item-Level Model Comparison in Diagnostic Classification Models. Applied Psychological Measurement. ISSN 0146-6216. . doi: 10.1177/0146621618798664