Work

Data analysis for behavioral medicine: an introduction to the special issue

Year: 2026

Type: editorial

Abstract: Methods and theory are deeply intertwined in behavioral research: theoretical developments lead to the need for new methods, and methodological innovations, in turn, open the door to new theoretical insights. To continue advancing research in behavior medicine, new methods need to be developed and implemented. At the same time, data collected in this field are becoming increasingly complex, requiring quantitative approaches that are both rigorous and accessible to analyze them effectively. In this Special Issue we have included eighteen papers that highlight developments and applications of quantitative methods for behavioral medicine. Across the papers we identified six main themes: mixture modeling, longitudinal modeling, location-scale models, methods for randomized trials and causal inference, methods using Bayesian inference, and estimating complex, nonlinear relationships.

Source: Journal of Behavioral Medicine

DOI: 10.1007/s10865-026-00661-7

Authors: Mauricio Garnier-Villarreal, Alexander Schoemann, Manshu Yang

Institutions: Vrije Universiteit Amsterdam, East Carolina University, University of Rhode Island

Language: English

Topic: Statistical Methods and Bayesian Inference

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $4,790

Funder: East Carolina University