QUASI-EXPERIMENT
Sociologyindex, Sociology Books 2012, Books Quasi-experiment, Quasi-Experiment, 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 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.
The word "quasi" means as if or almost, so a quasi-experiment means almost a true experiment. 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. - Quasi-experiments - faculty.ncwc.edu/toconnor/308/308lect06.htm
Common characteristics of quasi-experiments include the following:
matching instead of randomization 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 (over time) 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.
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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 (like the passage of a law) or were created by the researcher
(such as some public relations campaign).
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 (as well as 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 (interaction terms); 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 (e.g., applies
only to 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.
The
Statistical Analysis of Quasi-Experiments
Fundamental
Concepts in the Design of Experiments
"An excellent presentation of the basic concepts of experimental design. It uses many
numerical examples with 'real' data. It is clearly written and at the appropriate level
for my students."--Noel Artiles-Leon, University of Puerto Rico
Product Description
This text is a solid revision and redesign of Charles Hicks' comprehensive fourth edition
of Fundamental Concepts in the Design of Experiments. It covers the essentials of
experimental design used by applied researchers in solving problems in the field. It is
appropriate for a variety of experimental methods courses found in engineering and
statistics departments. Students learn to use applied statistics for planning, running,
and analyzing as an experiment. Students learn to use applied statistics for planning,
running, and analyzing an experiment. The text includes 350+ problems taken from the
author's actual industrial consulting experiences to give students valuable practice with
real data and problem solving. About 60 new problems have been added for this edition. SAS
(Statistical Analysis System) computer programs are incorporated to facilitate analysis.
There is extensive coverage of the analysis of residuals, the concepts of resolution in
fractional replications, the Plackett-Burman designs, and Taguchi techniques. The new
edition will place a greater emphasis on computer use, include additional problems, and
add computer outputs from statistical packages like Minitab, SPSS, and JMP.
The book is written for anyone engaged in experimental work who has a good background in
statistical inference. It will be most profitable reading to those witha background in
statistical methods including analysis of variance. This text is suitable for senior
undergraduate/graduate level students in mathematics, statistics, or engineering. It is
appropriate for a variety of experimental methods courses found in engineering and
statistics deparmtents -- majors in this course are usually in applied statistics;
non-majors, in industrial and electrical engineering, or education and life sciences.
How
to Design and Report Experiments
Text covers step-by-step process of conducting an experiment, from the initial idea stage
to delivering the final lab report. Provides examples and helpful tips to avoid common
pitfalls. Useful for students in psychology or related disciplines with an experimental
focus or content in research methods. Illustrated, with index and references. Softcover,
hardcover available.
Developing
Team Cohesion: A Quasi-Field Experiment