John C. Flournoy, PhD

Social Scientist, Methods Geek—Currently open to consulting and full-time work

An illustrated portrait of my face, smiling, by Sâmara Lígia

Portrait by Sâmara Lígia

I’m a research scientist who has primarily worked on how psychological and health constructs get measured, how they change over time, and most of all how to model all that well. That work has spanned adolescent development and developmental neuroimaging, outcomes measurement at an outpatient psychiatry clinic, and, at the Developer Success Lab, what predicts how long software work takes. For me, the common thread is well-being: how young people develop and thrive, how people fare under stress, how patients respond in care, and how things get done at work. The unique skills I bring to that are:

  • Bayesian modeling in Stan and brms in R, and the ability to create durable, reproducible tools when they don’t already exist (e.g., containers or packages)
  • A PhD in psychology from the University of Oregon and six years as a developmental neuroscientist at Harvard, working on adolescent development and stress, including building the task-fMRI pipeline for the reward and cognitive control tasks in the Human Connectome Project in Development
  • A critical thoughtfulness that leads me to ask things like what a number actually measures, and what causal story our analysis can actually tell
Flournoy, J. C. (2018). riclpmr [R package]. https://github.com/jflournoy/riclpmrCosme, D., Flournoy, J. C., & Vijayakumar, N. (2018). auto-motion: Automated assessment of motion artifacts in fMRI data [Software]. https://github.com/dsnlab/auto-motionFlournoy, J. C. (2024). hcpd_tfMRI: fMRI analyses for the CARIT and Guessing tasks in the HCP-D data [Software]. https://github.com/andlab-harvard/hcpd_tfMRIFlournoy, J. C. (2024). Causal inference in developer thriving science. Developer Success Lab, 1(1). https://dsl.pubpub.org/pub/causal-inf-rohrer

In practice, that has meant:

  • Statistical modeling across very different kinds of data (clinical assessment batteries and neuroimaging, surveys and experiments, and incidental records like Jira tickets), with appropriate uncertainty carried through from the measurement to the conclusions
  • Measurement work across several projects: Bayesian item response models fit to a clinic’s outcome batteries (and shorter forms built from them), tests of whether a scale means the same thing across age groups and income levels (and an R package for setting the cutoffs those tests use), and the test-retest reliability of task-fMRI in adolescents scanned up to ten times, a month apart
  • Longitudinal data at very different cadences (several surveys a day, monthly scans, multi-year panels), modeled so that change within a person isn’t confused with stable differences between people
  • Construct validity as a starting point in the work rather than a box to be checked, a view shaped a lot by Alexandrova’s “A Philosophy for the Science of Well-Being”
Flournoy, J. C., Lee, C. S., Wu, M., & Hicks, C. M. (2025). No silver bullets: Why understanding software cycle time is messy, not magic. Empirical Software Engineering, 30(6), 174.Thalmayer, A. G., Saucier, G., Srivastava, S., Flournoy, J. C., & Costello, C. K. (2019). Ethics-relevant values in adulthood: Longitudinal findings from the Life and Time Study. Journal of Personality, 87(6), 1119–1135.Ludwig, R. M., Flournoy, J. C., & Berkman, E. T. (2019). Inequality in personality and temporal discounting across socioeconomic status? Assessing the evidence. Journal of Research in Personality, 81, 79–87.Flournoy, J. C. (2018). milav [R package]. https://github.com/jflournoy/milavFlournoy, J. C., Bryce, N. V., Dennison, M. J., Rodman, A. M., McNeilly, E. A., Lurie, L. A., Bitran, D., Reid-Russell, A., Vidal Bustamante, C. M., Madhyastha, T., & McLaughlin, K. A. (2024). A precision neuroscience approach to estimating reliability of neural responses during emotion processing: Implications for task-fMRI. NeuroImage, 285, 120503.Matta, T. H., Flournoy, J. C., & Byrne, M. L. (2018). Making an unknown unknown a known unknown: Missing data in longitudinal neuroimaging studies. Developmental Cognitive Neuroscience, 33, 83–98.Flournoy, J. C. (2024). star_digital-phenotyping: Digital phenotyping pipeline for the STAR study [Software]. https://github.com/stressdev/star_digital-phenotypingVidal Bustamante, C. M., Rodman, A. M., Dennison, M. J., Flournoy, J. C., Mair, P., & McLaughlin, K. A. (2020). Within-person fluctuations in stressful life events, sleep, and anxiety and depression symptoms during adolescence: A multiwave prospective study. Journal of Child Psychology and Psychiatry, 61(10), 1116–1125.Alexandrova, A. (2017). A philosophy for the science of well-being. Oxford university press.

I strive to be effective through this intimate dialogue between our numeric approach, our theory, and our philosophy of the phenomena of interest.

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