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Alexander Christensen

Assistant Professor of Psychology and Human Development
Data Science Institute Affiliate Faculty

Alexander Christensen (he/him/his) is an assistant professor of psychology and human development who uses network and data science to model dynamical systems in psychology. He views psychological phenotypes as dynamic complex systems: dynamic meaning they change across time and complex meaning the interaction between their components and other systems are often difficult to discern. Further, he views people as teleological meaning they can change the expression of their phenotype using goals, motivations, and values.

Broadly, his work aims to develop dynamic network science tools that capture person-specific variation that can be used to make more accurate measurements (e.g., how depression is quantified) and predictions (e.g., whether someone will become depressed) as well as make better generalizations to broader populations to uncover underlying mechanisms that govern human behavior. These tools are accented by data science techniques such as natural language processing to develop more idiosyncratic representations of who people are.

Part of his mission is to advance the application and transparency of quantitative methods. He maintains, authors, and contributes to several packages in R including {EGAnet}, {latentFactoR}, {NetworkToolbox}, and {SemNeT}.

Representative Publications

Network Science

Christensen, A. P. (2022). Unidimensional community detection: A Monte Carlo simulation, grid search, and comparison. PsyArXiv. [doi] [app]
Golino, H., Nesselroade, J., & Christensen, A. P. (2022). Towards a psychology of individuals: The Ergodicity Information Index and a bottom-up approach for finding generalizations. PsyArXiv. [doi] [app]
Christensen, A. P., Garrido, L. E., & Golino, H. (2022). Unique variable analysis: A network psychometrics method to detect local dependence. PsyArXiv.
Golino, H., Christensen, A. P., Moulder, R., Kim, S., & Boker, S. M. (2022). Modeling latent topics in social media using Dynamic Exploratory Graph Analysis: The case of the right-wing and left-wing trolls in the 2016 US elections. Psychometrika, 87(1), 156-187.
Christensen, A. P., & Kenett, Y. N. (2021). Semantic network analysis (SemNA): A tutorial on preprocessing, estimating, and analyzing semantic networks. Psychological Methods.

Christensen, A. P., & Golino, H. (2021). Estimating the stability of psychological dimensions via bootstrap exploratory graph analysis: A Monte Carlo simulation and tutorial. Psych, 3(3), 479-500.
Christensen, A. P., & Golino, H. (2021). Factor or network model? Predictions from neural networks. Journal of Behavioral Data Science, 1(1), 85-126.
Christensen, A. P., & Golino, H. (2021). On the equivalency of factor and network loadings. Behavior Research Methods, 53(4), 1563-1580.
Christensen, A. P., Golino, H., & Silvia, P. J. (2020). A psychometric network perspective on the validity and validation of personality trait questionnaires. European Journal of Personality, 34(6), 1095-1108.
Christensen, A. P., Cotter, K. N., & Silvia, P. J. (2019). Reopening openness to experience: A network analysis of four openness to experience inventories. Journal of Personality Assessment, 101(6), 574-588.
Christensen, A. P., Kenett, Y. N., Cotter, K. N., Beaty, R. E., & Silvia, P. J. (2018). Remotely close associations: Openness to experience and semantic memory structure. European Journal of Personality, 32(4), 480-492.
For more of his work, see Google Scholar