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Stephen G. West, PhD, Naihua Duan, PhD, Willo Pequegnat, PhD, Paul Gaist, PhD, MPH, Don C. Des Jarlais, PhD, David Holtgrave, PhD, José Szapocznik, PhD, Martin Fishbein, PhD, Bruce Rapkin, PhD, Michael Clatts, PhD and Patricia Dolan Mullen, DrPH

Stephen G. West is with Arizona State University, Tempe. Naihua Duan is with Columbia University, New York, NY, and New York State Psychiatric Institute, New York. Willo Pequegnat is with the National Institute of Mental Health, Bethesda, MD. Paul Gaist is with the National Institutes of Health, Bethesda. Don C. Des Jarlais is with Beth Israel Medical Center, New York. David Holtgrave is with the Johns Hopkins University, Bloomberg School of Public Health, Baltimore, MD. José Szapocznik is with the University of Miami School of Medicine, Miami, FL. Martin Fishbein is with the Annenberg School for Communication, University of Pennsylvania, Philadelphia. Bruce Rapkin is with Memorial Sloan-Kettering Cancer Center, New York. Michael Clatts is with the National Development and Research Institutes, Inc, New York. Patricia Mullen is with the University of Texas School of Public Health, Houston.

Correspondence: Requests for reprints should be sent to Stephen G. West, Psychology Department, Arizona State University, Tempe, AZ 85287-1104 (e-mail: sgwest{at}asu.edu).

ABSTRACT

Public health researchers are addressing new research questions (e.g., effects of environmental tobacco smoke, Hurricane Katrina) for which the randomized controlled trial (RCT) may not be a feasible option.

Drawing on the potential outcomes framework (Rubin Causal Model) and Campbellian perspectives, we consider alternative research designs that permit relatively strong causal inferences. In randomized encouragement designs, participants are randomly invited to participate in one of the treatment conditions, but are allowed to decide whether to receive treatment.

In quantitative assignment designs, treatment is assigned on the basis of a quantitative measure (e.g., need, merit, risk). In observational studies, treatment assignment is unknown and presumed to be nonrandom. Major threats to the validity of each design and statistical strategies for mitigating those threats are presented.