. "Appendix E: An In-Depth Look at Study Designs and Methodologies." Bridging the Evidence Gap in Obesity Prevention: A Framework to Inform Decision Making. Washington, DC: The National Academies Press, 2010.
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Bridging the Evidence Gap in Obesity Prevention: A Framework to Inform Decision Making
to smoking did not change in either city after March 2002 (nonequivalent dependent variable). In some time series applications, a design element known as switching replications can be used, which involves locating another, similar city in which the intervention was introduced at a different timepoint. In their study of the introduction of television and its effects on crime rates, for example, Hennigan and colleagues (1982) located 34 medium-sized cities in which television was introduced in 1951 and 34 cities matched for region and size in which television was introduced in 1954 following the lifting of a freeze on new broadcasting licenses by the FCC. They found a similar effect of the introduction of television on crime rates (e.g., an increase in larceny) beginning in 1951 in the prefreeze cities and in 1954 in the postfreeze cities. In both the Khuder et al. and Hennigan et al. studies, the addition of the design element greatly reduced the likelihood that any threat to the level of certainty of the causal inference (internal validity) could account for the results obtained. As with the RD design, moreover, the ITS design can often be implemented with the full population of interest so that it provides direct evidence of population-level effects.
The observational study (also known as the nonequivalent control group design) is a quasi-experimental design that is commonly used in applied research on interventions, likely because of its ease of implementation. In this design, a baseline measure and a final outcome measure are collected on all participants. Following the baseline measurement, one group is given the treatment, while the second, comparison group does not receive the treatment. The groups may be preexisting (e.g., schools, communities), or unrelated participants may self-select into the treatment in some manner. For example, Roos and colleagues (1978) used this design to compare the health outcomes of children who received and did not receive tonsillectomies in a province of Canada. The bases on which the selection into the tonsillectomy treatment occurred were unknown and presumed to be nonrandom, possibly depending on such factors as the child’s medical history, the family, the physician, and the region. The challenge of this design is that several threats associated with possible interactions between selection and other threats to level of certainty (internal validity) might be plausible. These threats must be addressed if strong causal inferences are to be drawn.
To illustrate this design, consider an evaluation of a campaign to increase sales of lottery tickets (Reynolds and West, 1987). State lottery tickets are sold primarily in convenience stores and contribute to general state revenue or revenue for targeted programs (e.g., education) in several states. The stores refused randomization, ruling out an RCT. The Arizona lottery commission wished to evaluate the effectiveness of a sales campaign to increase lottery ticket sales in an 8-week-long lottery game. In the “Ask for the Sale” campaign, store clerks were instructed to ask each adult customer during checkout if he or she wished to purchase a lottery ticket. A nearby sign