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Psychological Sciences

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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., Garrido, L. E., & Golino, H. (2023). Unique variable analysis: A network psychometrics method to detect local dependence. Multivariate Behavioral Research, 1-18. https://doi.org/10.1080/00273171.2023.2194606
 
Christensen, A. P., Garrido, L. E., Guerra-Peña, K., & Golino, H. (2023). Comparing community detection algorithms in psychometric networks: A Monte Carlo simulation. Behavior Research Methods, 1-21. https://doi.org/10.3758/s13428-023-02106-4
 
Christensen, A. P., & Kenett, Y. N. (2023). Semantic network analysis (SemNA): A tutorial on preprocessing, estimating, and analyzing semantic networks. Psychological Methods, 28(4), 860–879. https://doi.org/10.1037/met0000463

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. https://doi.org/10.1007/s11336-021-09820-y

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. https://doi.org/10.3390/psych3030032
 
Christensen, A. P., & Golino, H. (2021). Factor or network model? Predictions from neural networks. Journal of Behavioral Data Science, 1(1), 85-126. https://doi.org/10.35566/jbds/v1n1/p5
 
Christensen, A. P., & Golino, H. (2021). On the equivalency of factor and network loadings. Behavior Research Methods, 53(4), 1563-1580. https://doi.org/10.3758/s13428-020-01500-6
 
Personality
 
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. https://doi.org/10.1002/per.2265
 
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. https://doi.org/10.1080/00223891.2018.1467428
 
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. https://doi.org/10.1002/per.2157
 
For more of his work, see Google Scholar

Honors

Association for Psychological Science (APS) 2024 Rising Star

Innovation in Teaching: Generative AI (2024)