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QUASI-EXPERIMENT

"Quasi" means as if or almost, so a quasi-experiment means almost a true experiment. Quasi-experiment is a research design having some but not all of the characteristics of a true experiment. The element most frequently missing in quasi-experiment research design is random assignment of subjects to the control and experimental conditions.

Examples of quasi-experiment research design are the natural experiment (where nature has assigned subjects to the two conditions) or trend analysis.

There are many varieties of quasi-experiment research designs, and there is generally little loss of status or prestige in doing a quasi-experiment instead of a true experiment, although you occasionally run into someone who is biased against quasi-experiments.

Common characteristics of quasi-experiment include the following:

  • matching instead of randomized testing is used. Someone studying the effects of a new police strategy in town would try to find a similar town somewhere in the same geographic region. That other town would have citizen demographics that are very similar to the experimental town. That other town is not technically a control group, but a comparison group, and this matching strategy is sometimes called nonequivalent group design. 

  • time series analysis is involved. A time series is perhaps the most common type of longitudinal research found in criminal justice.

  • the unit of analysis is often something different than people. Quasi-experiment is well suited for "fuzzy" or contextual concepts such as sociological quality of life, anomie, disorganization, morale, climate, atmosphere, and the like.

One of the intended purposes for doing quasi-experiment research is to capture longer time periods and a sufficient number of different events to control for various threats to validity and reliability. The hope is that the design will generate stable, reliable findings and tell us something about the effects of time itself. In fact, for a noninterrupted time series, the independent variable is usually time itself, for example, if you were monitoring rises and falls in crime rates and attributing it to changes in society over time. Quasi-experiment is somewhat creative or unusual in what it attributes the cause of something to, and this is the case because we aren't using a true experiment where we manipulate some independent variable in order to assess causality.
In quasi-experiment, the word "trend" is used instead of cause, and we are interested in finding the one true trend. Unfortunately, this kind of research often uncovers several trends, and the major ones are usually developed into "syndromes" or "cycles" while the minor ones are just referred to as normal or abnormal events. Say, for example, during the course of your research a bunch of college students from Florida State on spring break descended upon your town and started partying wildly.
Because quasi-experiment research designs tend to involve many different, but interlocking relationships between variables, it's advisable that the researcher engage in modeling the causal relationships. This allows identification of spurious and intervening variables. Spurious variables should be thrown out; intervening variables require multiplying the effects of two variables; and suppression refers to when part of a variable affects part of another variable even though the bivariate relationship is nonsignificant. Models also permit elaboration and specification. A variety of causal modeling techniques exist, from the fairly simple use of crosstabulation tables to run partial correlation analysis to the more sophisticated, and rarely-seen technique of path analysis which is essentially a regression run of each variable on every other variable. In some undergraduate courses like this, students sometimes analyze crosstabs for almost the whole semester; that's how important some instructors think modeling is as a heuristic device for teaching research methods. Books on Quasi-experiment.