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Books on Quasi-experiment, True 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-experimental 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-experiments include the following:

  • matching instead of randomized testing is used. Example, someone studying the effects of a new police strategy in town would try to find a similar town somewhere in the same geographic region, perhaps in a 5-state area. 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. A time series can be interrupted or noninterrupted. Both types examine changes in the dependent variable over time, with only an interrupted time series involving before and after measurement. Example, someone might use a time series to look at crime rates as a new law is taking effect. This kind of research is sometimes called impact analysis or policy analysis. 

  • the unit of analysis is often something different than people. Of course, any type of research can study anything - people, cars, crime statistics, neighborhood blocks. However, quasi-experiments are well suited for "fuzzy" or contextual concepts such as sociological quality of life, anomie, disorganization, morale, climate, atmosphere, and the like. This kind of research is sometimes called contextual analysis. 

One of the intended purposes for doing quasi-experimental 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. Almost all quasi-experiments are somewhat creative or unusual in what they attribute 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. Instead, at best, we have a statistical baseline and some interventions that have occurred naturally or were created by the researcher. 
In quasi-experiments, 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. You might call this the "Florida State syndrome" or something like that. Say, for example, a series of full moons came close together during the course of your study. You might call this the "full moon cycle." The point is that neither of these would be the true trend, but they might be trends nonetheless. 
Because quasi-experimental 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, as well as a number of other variable relationship types like suppression effects. 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. Elaboration is the process of reclassifying or subclassifying your variables, sometimes even switching around your independent and dependent variables. Specification is the process of making your dependent variable more narrow (only left-handed, lower-class black males) and then multiplying some of your independent variables into a new, more powerful interaction term which has to be interpreted as some new kind of variable, not the additive sum of the original variables. A variety of causal modeling techniques exist (see Asher 1983), 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.