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Discussion question: What are the features of quasi-experimental variables and designs? Under what conditions would this type of approach be appropriate? Provide at least one example in your response. Classmate response to discussion question: Studies that try to assess therapies but do not apply randomization are known as quasi-experiments. Quasi-experiments, like randomized trials, try to prove association between an intervention and a result. Pre- and post-intervention assessments, as well as nonrandomly selected control groups, can all be used in quasi-experimental investigations. Quasi-experiments are studies that attempt to evaluate medicines but do not use randomization. Quasi-experiments, like randomized trials, seek to establish a link between an intervention and a certain outcome. In quasi-experimental studies, pre- and post-intervention evaluations, as well as nonrandomly selected control groups, can all be employed (Harris, 2006). An example given by “The use and interpretation of quasi-experimental studies in medical informatics” (2006) was, hospital informatics staff may be prompted to create and update pharmaceutical shipment systems as pharmacy expenses and adverse occurrences rise. If the increase in expenses or side effects is just an exception within the typical range of the hospital’s pharmaceutical charges, the statistical theory of regression predicts that these high rates will tend to drop even if no action is taken. However, many informatics workers and hospital managers are unable to sit back and wait for this drop to occur. As a result, hospital staff frequently conduct one or more treatments, and if the rate falls, they may incorrectly assume that the drop is due to the intervention. The regression is meant to be an alternative interpretation of the outcome (Harris, 2006). Reference Harris, A. D., McGregor, J. C., Perencevich, E. N., Furuno, J. P., Zhu, J., Peterson, D. E., & Finkelstein, J. (2006). The use and interpretation of quasi-experimental studies in medical informatics. Journal of the American Medical Informatics Association : JAMIA, 13(1), 16–23. https://doi.org/10.1197/jamia.M1749