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Carolyn Anderson


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.

Key Professional Appointments

  • Professor Emerita, Educational Psychology, University of Illinois, Urbana-Champaign
Awards, Honors, Associations

APA Fellow, American Psychological Association, 2018

Research & Service

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.


Anderson, C. J., Kateri, M., & Moustaki, I. (2023). Log-Linear and Log-Multiplicative Association Models for Categorical Data. In M. Kateri, & I. Moustaki (Eds.), Trends and Challenges in Categorical Data Analysis: Statistical Modelling and Interpretation (Statistics for Social and Behavioral Sciences). Springer.  link >

Chen, D., & Anderson, C. J. (2022). Categorical data analysis. In International Encyclopedia of Education: Fourth Edition (pp. 575-582). Elsevier.  link >

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.  link >

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.  link >

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.

Jay, V., Henricks, G. M., Anderson, C. J., Angrave, L. C., Bosch, N., Williams-Dobosz, D., Shaik, N., Bhat, S. P., & Perry, M. (2020). Online discussion forum help-seeking behaviors of students underrepresented in STEM. In M. Gresalfi, & I. S. Horn (Eds.), 14th International Conference of the Learning Sciences: The Interdisciplinarity of the Learning Sciences, ICLS 2020 - Conference Proceedings (Vol. 2, pp. 809-810). (Computer-Supported Collaborative Learning Conference, CSCL; Vol. 2). International Society of the Learning Sciences (ISLS).  link >

Ng, S., Payne, B. R., Liu, X., Anderson, C. J., Federmeier, K. D., & Stine-Morrow, E. A. L. (2020). Execution of Lexical and Conceptual Processes in Sentence Comprehension among Adult Readers as a Function of Literacy Skill. Scientific Studies of Reading, 24(4), 338-355.  link >

Poulsen, S., Anderson, C. J., & West, M. (2020). The relationship between course scheduling and student performance. CEUR Workshop Proceedings, 2734.

Bosch, N., Wes Crues, R., Henricks, G. M., Perry, M., Angrave, L. C., Shaik, N., Bhat, S. P., & Anderson, C. J. (2018). Modeling key differences in underrepresented students' interactions with an online STEM course. In Proceedings of the Technology, Mind, and Society Conference, TechMindSociety 2018 Article a6 (ACM International Conference Proceeding Series). Association for Computing Machinery.  link >

Crues, R. W., Henricks, G. M., Perry, M., Bhat, S., Anderson, C. J., Shaik, N., & Angrave, L. (2018). How do gender, learning goals, and forum participation predict persistence in a computer science MOOC? ACM Transactions on Computing Education, 18(4), Article 18.  link >

Wes Crues, R., Bosch, N., Anderson, C. J., Perry, M., Bhat, S., & Shaik, N. (2018). Who they are and what they want: Understanding the reasons for MOOC enrollment. Paper presented at 11th International Conference on Educational Data Mining, EDM 2018, Buffalo, United States.

Anderson, C. J., & Yu, H. T. (2017). Properties of second-order exponential models as multidimensional response models. In W.-C. Wang, M. Wiberg, S. A. Culpepper, J. A. Douglas, & L. A. van der Ark (Eds.), Quantitative Psychology - 81st Annual Meeting of the Psychometric Society, 2016 (pp. 9-19). (Springer Proceedings in Mathematics and Statistics; Vol. 196). Springer.  link >

Paek, Y., & Anderson, C. J. (2017). Pseudo-likelihood estimation of multidimensional response models: Polytomous and dichotomous items. In W.-C. Wang, M. Wiberg, S. A. Culpepper, J. A. Douglas, & L. A. van der Ark (Eds.), Quantitative Psychology - 81st Annual Meeting of the Psychometric Society, 2016 (pp. 21-30). (Springer Proceedings in Mathematics and Statistics; Vol. 196). Springer.  link >

Steen-Baker, A. A., Ng, S., Payne, B. R., Anderson, C. J., Federmeier, K. D., & Stine-Morrow, E. A. L. (2017). The effects of context on processing words during sentence reading among adults varying in age and literacy skill. Psychology and aging, 32(5), 460-472.  link >

Ali, U. S., Chang, H., & Anderson, C. J. (2015). Location Indices for Ordinal Polytomous Items Based on Item Response Theory. ETS Research Report Series, 2015(2), 1-13.  link >

Allen, N. E., Todd, N. R., Anderson, C. J., Davis, S. M., Javdani, S., Bruehler, V., & Dorsey, H. (2013). Council-Based Approaches to Intimate Partner Violence: Evidence for Distal Change in the System Response. American journal of community psychology, 52(1-2), 1-12.  link >

Anderson, C. J., & Douglas, J. A. (Eds.) (2013). Classification in Educational Testing. Journal of Classification, 30(2).

Anderson, C. J. (2013). Comment: Marginal Models for Categorical Data and Mokken Scale Analysis. Sociological Methodology, 43(1), 101-104.  link >

Anderson, C. J. (2013). Multidimensional Item Response Theory Models with Collateral Information as Poisson Regression Models. Journal of Classification, 30(2), 276-303.  link >

Anderson, C. J., Kim, J. S., & Keller, B. (2013). Multilevel Modeling of Categorical Response Variables. In L. Rutkowski, M. von Davier, & D. Rutkowski (Eds.), Handbook of International Large-Scale Assessment: Background, Technical Issues, and Methods of Data Analysis (pp. 495-534). Chapman and Hall/CRC.  link >

Kim, J. S., Anderson, C. J., & Keller, B. (2013). Multilevel analysis of assessment data. In Handbook of International Large-Scale Assessment: Background, Technical Issues, and Methods of Data Analysis (pp. 389-424). CRC Press.

Kim, J. S., Anderson, C. J., & Keller, B. (2013). Multilevel Analysis of Assessment Data. In L. Rutkowski, M. von Davier, & D. Rutkowski (Eds.), Handbook of International Large-Scale Assessment: Background, Technical Issues, and Methods of Data Analysis (pp. 403-438). Chapman and Hall/CRC.  link >

Payne, B. R., Gao, X., Noh, S. R., Anderson, C. J., & Stine-Morrow, E. A. L. (2012). The effects of print exposure on sentence processing and memory in older adults: Evidence for efficiency and reserve. Aging, Neuropsychology, and Cognition, 19(1-2), 122-149.  link >

Poteat, V. P., & Anderson, C. J. (2012). Developmental changes in sexual prejudice from early to late adolescence: The effects of gender, race, and ideology on different patterns of change. Developmental psychology, 48(5), 1403-1415.  link >

Tynes, B. M., Umaña-Taylor, A. J., Rose, C. A., Lin, J., & Anderson, C. J. (2012). Online racial discrimination and the protective function of ethnic identity and self-esteem for african american adolescents. Developmental psychology, 48(2), 343-355.  link >

Wilson, T., Perry, M., Anderson, C. J., & Grosshandler, D. (2012). Engaging young students in scientific investigations: Prompting for meaningful reflection. Instructional Science, 40(1), 19-46.  link >

Javdani, S., Allen, N. E., Todd, N. R., & Anderson, C. J. (2011). Examining systems change in the response to domestic violence: Innovative applications of multilevel modeling. Violence Against Women, 17(3), 359-375.  link >

Anderson, C. J. (2010). Central Limit Theorem. In I. B. Weiner, & W. E. Craighead (Eds.), The Corsini Encyclopedia of Psychology John Wiley & Sons, Ltd..  link >

Anderson, C. J., Verkuilen, J., & Peyton, B. L. (2010). Modeling Polytomous Item Responses Using Simultaneously Estimated Multinomial Logistic Regression Models. Journal of Educational and Behavioral Statistics, 35(4), 422-452.  link >

Oh, E., Choi, C. C., Neville, H. A., Anderson, C. J., & Landrum-Brown, J. (2010). Beliefs about affirmative action: A test of the group self-interest and racism beliefs models. Journal of Diversity in Higher Education, 3(3), 163-176.  link >

Rutkowski, L., Vasterling, J. J., Proctor, S. P., & Anderson, C. J. (2010). Posttraumatic Stress Disorder and Standardized Test-Taking Ability. Journal of Educational Psychology, 102(1), 223-233.  link >

Anderson, C. J. (2009). Categorical Data Analysis with a Psychometric Twist. In R. E. Millsap, & A. Maydeu-Olivares (Eds.), The SAGE Handbook of Quantitative Methods in Psychology (pp. 311-336). SAGE Publishing.  link >

Spanierman, L. B., Todd, N. R., & Anderson, C. J. (2009). Psychosocial Costs of Racism to Whites: Understanding Patterns Among University Students. Journal of Counseling Psychology, 56(2), 239-252.  link >

Anderson, C. J., & Rutkowski, L. (2008). Multinomial Logistic Regression. In J. Osborne (Ed.), Best Practices in Quantitative Methods (pp. 390-409). SAGE Publishing.  link >

Shim, S. S., Ryan, A. M., & Anderson, C. J. (2008). Achievement Goals and Achievement During Early Adolescence: Examining Time-Varying Predictor and Outcome Variables in Growth-Curve Analysis. Journal of Educational Psychology, 100(3), 655-671.  link >

Anderson, C. J., Li, Z., & Vermunt, J. K. (2007). Estimation of models in a Rasch family for polytomous items and multiple latent variables. Journal of Statistical Software, 20(6), 1-36.  link >

Anderson, C. J., & Yu, H. T. (2007). Log-multiplicative association models as item response models. Psychometrika, 72(1), 5-23.  link >

Buki, L. P., Jamison, J., Anderson, C. J., & Cuadra, A. M. (2007). Differences in predictors of cervical and breast cancer screening by screening need in uninsured Latina women. Cancer, 110(7), 1578-1585.  link >

de Rooij, M., & Anderson, C. J. (2007). Visualizing summarizing and comparing odds ratio structures. Methodology, 3(4), 139-148.  link >

Lee, H. K., & Anderson, C. (2007). Validity and topic generality of a writing performance test. Language Testing, 24(3), 307-330.  link >

Tettegah, S., & Anderson, C. J. (2007). Pre-service teachers' empathy and cognitions: Statistical analysis of text data by graphical models. Contemporary Educational Psychology, 32(1), 48-82.  link >

Anderson, C. J. (2006). Additive and Multiplicative Models for Three-Way Contingency Tables: Darroch (1974) Revisited. In M. Greenacre, & J. Blasius (Eds.), Multiple Correspondence Analysis and Related Methods Chapman and Hall/CRC.  link >

Anderson, C. J. (2005). Review: J. D. Singer and J. B. Willett's Applied Longitudinal Data Analysis: Modeling Change and Event Occurrence. Journal of the American Statistical Association, 100(469), 352-353.  link >

Vermunt, J. K., & Anderson, C. J. (2005). Joint correspondence analysis (JCA) by maximum likelihood. Methodology, 1(1), 18-26.  link >

Anderson, C. J. (2002). Analysis of multivariate frequency data by graphical models and generalizations of the multidimensional row-column association model. Psychological Methods, 7(4), 446-467.  link >

Anderson, C. J., & Böckenholt, U. (2000). Graphical regression models for polytomous variables. Psychometrika, 65(4), 497-509.  link >

Anderson, C. J., & Vermunt, J. K. (2000). Log-multiplicative association models as latent variable models for nominal and/or ordinal data. Sociological Methodology, 30(1), 81-121.  link >

Anderson, C. J., Wasserman, S., & Crouch, B. (1999). A p* primer: Logit models for social networks. Social Networks, 21(1), 37-66.  link >

Farmer, H., Rotella, S., Anderson, C., & Wardrop, J. (1998). Gender Differences in Science, Math, and Technology Careers: Prestige Level and Holland Interest Type. Journal of Vocational Behavior, 53(1), 73-96.  link >

Anderson, C. J. (1996). The analysis of three-way contingency tables by three-mode association models. Psychometrika, 61(3), 465-483.  link >

Anderson, C. J., & Wasserman, S. (1995). Log-Multiplicative Models for Valued Social Relations. Sociological Methods & Research, 24(1), 96-127.  link >

Anderson, C. J., & Wasserman, S. (1993). Recent Developments in the Analysis of Categorical Data. Journal of Mathematical Psychology, 37(2), 299-310.  link >

Anderson, C. J., Wasserman, S., & Faust, K. (1992). Building stochastic blockmodels. Social Networks, 14(1-2), 137-161.  link >

Weber, E. U., Anderson, C. J., & Birnbaum, M. H. (1992). A theory of perceived risk and attractiveness. Organizational Behavior and Human Decision Processes, 52(3), 492-523.  link >

Birnbaum, M. H., Anderson, C. J., & Hynan, L. G. (1990). Theories of bias in probability judgment. Advances in Psychology, 68(C), 477-498.  link >

Birnbaum, M. H., Anderson, C. J., & Hynan, L. G. (1989). Two Operations for "Ratios" and "Differences" of Distances on the Mental Map. Journal of Experimental Psychology: Human Perception and Performance, 15(4), 785-796.  link >

Wasserman, S., & Anderson, C. (1987). Stochastic a posteriori blockmodels: Construction and assessment. Social Networks, 9(1), 1-36.  link >


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.


EPSY 587: Hierarchical Linear Models (EPSY 587) This course provides an overview of the use of multilevel models. Students will learn the techniques and theory of hierarchical linear models and apply the methods to data from studies in education, psychology and social sciences. Topics covered include multilevel analyses, random intercept and slope models, 2- and 3-level models, hypothesis testing, model assessment, longitudinal (repeated measures) data, and generalized hierarchical models for categorical variables.

EPSY 589: Categorical Data Analysis in Educational Psychology (EPSY 589) Concepts and methods for analyzing categorical data with an emphasis placed on building and applying models in education, sociology and psychology. Generalized linear models covered including logistic and Poisson regression models, loglinear, logit, and probit models, and models for ordinal data.

EPSY 590: Advanced Seminar in Educational Psychology (EPSY 590) Seminar in educational psychology; topics relate to the areas of specialization represented by the various divisions within the department.