"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
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
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