Charles Driver

© MPI for Human Development
Wissenschaftlicher Mitarbeiter
+49 30 82406-367
driver [at] mpib-berlin [dot] mpg [dot] de

Akademischer Steckbrief: 

I'm trying to change how we think about and model change. I believe that many of the fascinating questions are not amenable to the wonderful lever of experimental control, but also that drawing sensible conclusions about the relationship between changes in observed variables can be somewhat troublesome. To this end, I have been working on modelling approaches that characterise and predict change within an individual based on their current and past states, while also using information about a) the time between measurements, and b) other individuals, to improve our estimates. Such hierarchical continuous time dynamic models can be fit to data using ctsem, the open source R package that myself and colleagues Manuel Voelkle and Han Oud have developed.

I began my studies in psychological science at the University of Queensland in Australia, where a wonderful professor fascinated me with the things we could learn about people and society using a few numbers. A short stint in the methods department at the University of Amsterdam deepened my interest in thinking about what and how we can learn from data. I then completed a Phd at the Max Planck for Human Development in Berlin, while participating in the LIFE graduate school. I'm interested in many substantive topics where my work can have useful input, am very supportive of the open science movement, and am interested in general to work to improve the quality of inference in the social sciences. I sometimes use Researchgate for posting pre-prints, these can be found at: http://www.researchgate.net/profile/Charles_Driver , while the ctsem software and related documentation (vignettes) can be found at http://cran.r-project.org/web/packages/ctsem/index.html

Alle aufklappen Curriculum Vitae

zuklappen Publikationen

Fandakova, Y., Leckey, S., Driver, C. C., Bunge, S. A., & Ghetti, S. (2019). Neural specificity of scene representations is related to memory performance in childhood. NeuroImage, 199, 105-113. doi:10.1016/j.neuroimage.2019.05.050
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Hecht, M., Hardt, K., Driver, C. C., & Voelkle, M. C. (2019). Bayesian continuous-time Rasch models. Psychological Methods. Advance online publication. doi:10.1037/met0000205
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Redhead, D., Cheng, J. T., Driver, C., Foulsham, T., & O'Gorman, R. (2019). On the dynamics of social hierarchy: A longitudinal investigation of the rise and fall of prestige, dominance, and social rank in naturalistic task groups. Evolution and Human Behavior, 40, 222-234. doi:10.1016/j.evolhumbehav.2018.12.001
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Brandmaier, A. M., Driver, C. C., & Voelkle, M. C. (2018). Recursive partitioning in continuous time analysis. In K. van Montfort, J. H. L. Oud, & M. C. Voelkle (Eds.), Continuous time modeling in the behavioral and related sciences (pp. 259-282). Cham: Springer.

Driver, C. C., & Voelkle, M. C. (2018). Hierarchical Bayesian continuous time dynamic modeling. Psychological Methods, 23, 774-799. doi:10.1037/met0000168
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Driver, C. C., & Voelkle, M. C. (2018). Understanding the time course of interventions with continuous time dynamic models. In K. van Montfort, J. H. L. Oud, & M. C. Voelkle (Eds.), Continuous time modeling in the behavioral and related sciences (pp. 79-109). Cham: Springer.

Oud, J. H. L., Voelkle, M. C., & Driver, C. C. (2018). First- and higher-order continuous time models for arbitrary N using SEM. In K. van Montfort, J. H. L. Oud, & M. C. Voelkle (Eds.), Continuous time modeling in the behavioral and related sciences (pp. 1-26). Cham: Springer.

Oud, J. H. L., Voelkle, M. C., & Driver, C. C. (2018). SEM-based CARMA time series models for arbitrary N. Multivariate Behavioral Research, 53, 36-56. doi:10.1080/00273171.2017.1383224
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Voelkle, M. C., Gische, C., Driver, C. C., & Lindenberger, U. (2018). The role of time in the quest for understanding psychological mechanisms. Multivariate Behavioral Research, 53, 782-805. doi:10.1080/00273171.2018.1496813
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Driver, C. C. (2017). Hierarchical continuous time dynamic modelling for psychology and the social sciences. Doctoral dissertation, Humboldt-Universität zu Berlin, Germany. doi:10.18452/18927
(published online 2018: https://dx.doi.org/10.18452/18927)
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Driver, C. C., Oud, J. H. L., & Voelkle, M. C. (2017). Continuous time structural equation modeling with R Package ctsem. Journal of Statistical Software, 77. doi:10.18637/jss.v077.i05
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