260f Education Bldg
1310 S Sixth
Champaign, IL 61820
Measurements in the social and behavioral sciences are often discrete (e.g., highest degree earned, response option selected on a survey or test, career choice). My research lies at the intersection of statistical models for multivariate categorical data and psychometrics. My current focus is on models with latent variable interpretations, including item response theory models, discrete choice models, and their formulations as generalized linear and non-linear (mixed) models (i.e., HLM, and GLMMS). I am currently working on the relationship between formative and reflective measurement models using statistical graphical models.
In recent work, I have shown that two seemingly different mathematically models are in fact the same. One model is the standard formulation of latent variables models and the other is an alternative formulation. The implication of this work is that models where responses to items reflect a latent variables behave the same as model built on the notion that items define the latent variable. A further implication is that estimation of measurement models can be done with much less complicated routines and these alternative appear to be more stable. I have also extended this work to more complex latent structures.
I have also collaborate on research. I am currently and actively collaborating with a diverse team (Edps, ECS, CS and CITL) that studying online learning. In particular, whether an online context has affordances for underrepresented groups in STEM. We have submitted 2 grants, received 1 internal grant, and submitted 2 papers in the 6 months that we have worked together.
I teach statistics courses to students in the social and behavioral sciences, ACES and business. I regularly teach categorical data analysis, multivariate analysis, and multilevel modeling. I am developing a graduate seminar on Bayesian Statistics.
I encourage students to develop their own research ideas and interests. One way is by allowing students to do project in lieu of taking an exam. I mentor students both within my own department and from other departments.
Students in categorical data analysis and multilevel models have the option of doing a project, which are often published or parts of larger research projects. For example, a project spanned both courses, developed into a dissertation, and the student won the Seymour Sudman dissertation award.
I met at least weekly with one student working on her dissertation and another who is developing his proposal for his early research project.
Additional Campus Affiliations
Professor, Educational Psychology
Anderson, C. J., Kateri, M., & Moustaki, I. (2022). Log-linear and log-multiplicative association models for categorial data. In M. Kateri, & I. Moustaki (Eds.), Trends and Challenges in Categorical Data Analysis Springer.
Anderson, C. J. (2021). Pseudo-likelihood estimation of log-multiplicative association models: The pleLMA (0.2.1) package in R. The Comprehensive R Archive Network.
Huang, M., & Anderson, C. (2021). A Bayesian Solution to Non-convergence of Crossed Random Effects Models. In M. Wiberg, D. Molenaar, J. González, U. Böckenholt, & J-S. Kim (Eds.), Quantitative Psychology - The 85th Annual Meeting of the Psychometric Society (pp. 297-307). (Springer Proceedings in Mathematics and Statistics; Vol. 353). Springer. https://doi.org/10.1007/978-3-030-74772-5_27
Anderson, C. J., Embretson, S., Meulman, J., Moustaki, I., von Davier, A. A., Wiberg, M., & Yan, D. (2020). Stories of successful careers in psychometrics and what we can learn from them. In M. Wiberg, D. Molenaar, J. González, U. Böckenholt, & J-S. Kim (Eds.), Quantitative Psychology - 84th Annual Meeting of the Psychometric Society, IMPS 2019 (pp. 1-17). (Springer Proceedings in Mathematics and Statistics; Vol. 322). Springer. https://doi.org/10.1007/978-3-030-43469-4_1
Anderson, C. J., & Timmer, J. (2020). Using projects to teach statistics in the social sciences. In J. Rogers (Ed.), Teaching Statistics in the 21st Century Taylor and Francis.