Work

A practical framework for selecting and interpreting regression models for binary outcomes in social science and public health studies

Year: 2026

Type: article

Abstract: In social science and public health research, binary outcomes are common (e.g., disease presence, mental health status, health behaviors), as they simplify complex constructs into clear categories, facilitating interpretation, communication, and application in clinical or policy decision-making. Yet methodological missteps persist, particularly the misuse of logistic regression when binary outcomes are common and the use of unclear statistical language. This commentary discusses four frequent modeling and terminological errors, illustrates how they hinder valid inference in the social science and public health discipline, and proposes a practical framework for selecting and reporting models in cross-sectional studies of social and health phenomena.

Source: Discover Social Science and Health

DOI: 10.1007/s44155-026-00357-y

Author: Thinh Toan Vu

Institutions: The Graduate Center, CUNY, York College, City University of New York, CUNY Graduate School of Public Health and Health Policy

Language: English

Topic: Statistical Methods in Epidemiology

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $1,490