Emil Coman

Background

About Emil Coman

Complex analytical methods and simplifying them all the way down to undergraduate intro levels:*1) How do we know A causes B?*2) How can we tell what would have been invidividual patients' benefits under alternative scenarios (unrealized)?*3) How to find 1 health disparity, and its causes? a) Latent variable modeling, particularly models of change with Latent Change Scores; b) Scale and measurement testing: measure development, measurement invariance; c) Estimation of treatment effects with alternative modeling (e.g. complier average treatment effect CACE); d) Power analysis for complex designs, including clustered/multi-level over-time mixed methods; e) Multi-level modeling; propensity score matching; f) Mediation expertise extended to cover complications (moderated-mediation and mediated-moderation); g) Longitudinal modeling with added complexity (growth curve modeling); h) Complications that cover almost any ‘joint’ mixture…: dynamic SEM; crosslag panels, SEM mixture modeling [unobserved groupings], MIMIC-multiple ítem multiple cause SEMs, GMM-Growth Mixture Models, modeling of propensity scores, etc; i) Expert user of MPlus, AMOS, & SPSS and user of Stata, some SAS,among others. Specialties: Evaluation of program/intervention effects. Analysis, interpretation, and reporting targeted to specific audiences. See recent publications: http://link.springer.com/article/10.1007%2Fs10461-012-0381-0