Maya Mathur, PhD
Assistant Professor

Maya Mathur is an Assistant Professor at the Stanford University Quantitative Sciences Unit. Her statistical research develops methods for sensitivity analysis and for evidence synthesis, particularly meta-analysis. Current focuses include developing methods for the analysis of multisite replication studies, methods for assessing and correcting for publication bias, and methods for synthesizing replication studies with existing literature. Her substantive research focuses on behavior and health and the experimental cognitive sciences; for example, her most recent empirical direction focuses on behavioral interventions to reduce meat consumption.

Stanford CAP Profile: https://profiles.stanford.edu/maya-mathur

Lab Website: https://www.mayamathur.com/

 

Research Interest:  

Methodological: Evidence synthesis, reproducibility, missing data, causal inference, epidemiology

Substantive: Psychosocial and behavioral correlates of health, evidence-based behavior interventions, human-technology interaction, experimental cognitive sciences

Selected Publications: 

Mathur MB, Reichling DB, Lunardini F, Geminiani A, Antonietti A, Ruijten PAM, et al. Uncanny but not confusing: Multisite study of perceptual category confusion in the Uncanny Valley. Computers in Human Behavior, 103, 21-30.

Mathur MB, VanderWeele TJ. Finding common ground in meta-analysis “wars” on violent video games. Perspectives on psychological science. 2019 Jun.

Mathur MB, VanderWeele TJ. Sensitivity analysis for unmeasured confounding in meta-analyses. Journal of the American Statistical Association. 2019 Apr 26:1-20.

Mathur MB, VanderWeele TJ. New metrics for meta‐analyses of heterogeneous effects. Statistics in medicine. 2019 Apr 15;38(8):1336-42.

Mathur MB, Epel E, Kind S, Desai M, Parks CG, Sandler DP and Khazeni N. 2016. Perceived stress and telomere length: a systematic review, meta-analysis, and methodologic considerations for advancing the field. Brain, behavior, and immunity, 54, pp.158-169.

Mathur MB, Reichling DB. Navigating a social world with robot partners: A quantitative cartography of the Uncanny Valley. Cognition. 2016 Jan 1;146:22-32.

Mathur MB, Bart-Plange DJ, Aczel B, Bernstein MH, Ciunci A, Ebersole CR, et al. Many Labs 5: Registered multisite replication of tempting-fate effects in Risen & Gilovich. Advances in Methods and Practice in Psychological Science. In Press. 

See her website for more details.