Quasi experiments are studies that are unable to use randomization to evaluate effectiveness of interventions. This can make it difficult to tease out possible confounders and assess the integrity of any cause and effect claims. Consider this example adapted from a paper in Clinical Infectious Diseases. ^1 A hospital wants to know if providing alcohol-based hand cleaners for staff will reduce the rate at which bacterial infections occur in the patient population. The hospital needs to design a study that will address this question. 1. Suppose a researcher suggests to place these cleaners in randomly chosen patient rooms and not in others. At the end of the study, the infection rates for each group could be compared. Why might this be an impossible design to implement in practice? Give one reason and explain.
2. The hospital is more likely to adopt a quasi-design that collects data for an extended period of time before hand cleaner dispensers are installed, and then compares those findings with data collected after the dispensers are installed. State and explain two possible sources of confounding that might occur with this kind of design.
3. In the absence of random assignment, arguments addressing confounding often have to be made some other way. What are two arguments that one might be able to make to support results obtained from a quasi-design like the one in Question 2?