There are a number of reasons why a researcher might choose to conduct a quasi-evaluation instead of a true experiment. One reason is that it may not be possible to randomly assign participants to an intervention group and a control group. This could be due to ethical concerns, practical constraints, or other factors. Another reason is that the intervention may be implemented on a large scale, making it difficult or impossible to create a control group.
In order to make a quasi-evaluation as rigorous as possible, researchers can use a variety of methods to control for confounding variables. These methods include:
* Matching: The researcher matches participants in the intervention group to participants in the control group based on a number of characteristics, such as age, gender, race, and socioeconomic status.
* Propensity score matching: This is a statistical method that uses a mathematical model to predict the probability that a participant would have received the intervention. Participants are then matched based on their propensity scores.
* Regression discontinuity design: This is a research design that compares the outcomes of participants who just barely met the criteria for receiving the intervention to the outcomes of participants who just barely missed the criteria.
Quasi-evaluations can provide valuable information about the effectiveness of an intervention. However, it is important to remember that they are not as rigorous as true experiments. When interpreting the results of a quasi-evaluation, it is important to consider the potential for bias and confounding variables.
