QUASI-EXPERIMENT


QUASI-EXPERIMENT
A quasi-experiment is an empirical study used to estimate the causal impact of an intervention on its target population. Quasi-experimental research shares similarities with the traditional experimental design or randomized controlled trial, but they specifically lack the element of random assignment to treatment or control. Instead, quasi-experimental designs typically allow the researcher to control the assignment to the treatment condition, but using some criterion other than random assignment (e.g., an eligibility cutoff mark). In some cases, the researcher may have control over assignment to treatment condition.
Quasi-experiments are subject to concerns regarding internal validity, because the treatment and control groups may not be comparable at baseline. With random assignment, study participants have the same chance of being assigned to the intervention group or the comparison group. As a result, differences between groups on both observed and unobserved characteristics would be due to chance, rather than to a systematic factor related to treatment (e.g., illness severity). Randomization itself does not guarantee that groups will be equivalent at baseline. Any change in characteristics post-intervention is likely attributable to the intervention. With quasi-experimental studies, it may not be possible to convincingly demonstrate a causal link between the treatment condition and observed outcomes. This is particularly true if there are confounding variables that cannot be controlled or accounted for.
GOALS
An experiment is a study in which the researcher manipulates the level of some independent variable and then measures the outcome. Experiments are powerful techniques for evaluating cause-and-effect relationships. Many researchers consider experiments the “gold standard” against which all other research designs should be judged. Experiments are conducted both in the laboratory and in real life situations.
TYPES
There are several types of quasi-experimental research, including:
time series research
equivalent time series samples
equivalent samples materials research
non-equivalent control group
counterbalanced research

IT’S IMPORTANCE
A quasi-experimental study is a type of evaluation which aims to determine whether a program or intervention has the intended effect on a study’s participants. Quasi-experimental studies take on many forms, but may best be defined as lacking key components of a true experiment. While a true experiment includes (1) pre-post test design, (2) a treatment group and a control group, and (3) random assignment of study participants, quasi-experimental studies lack one or more of these design elements.
Since the most common form of a quasi-experimental study includes a pre-post test design with both a treatment group and a control group, quasi-experimental studies are often an impact evaluation that assigns members to the treatment group and control group by a method other than random assignment.
ADVANTAGES AND DISADVANTAGES.
Advantages
Since quasi-experimental designs are used when randomization is impractical and/or unethical, they are typically easier to set up than true experimental designs, which require random assignment of subjects. Additionally, utilizing quasi-experimental designs minimizes threats to external validity as natural environments do not suffer the same problems of artificiality as compared to a well-controlled laboratory setting. Since quasi-experiments are natural experiments, findings in one may be applied to other subjects and settings, allowing for some generalizations to be made about population. Also, this experimentation method is efficient in longitudinal research that involves longer time periods which can be followed up in different environments.
Other advantages of quasi experiments include the idea of having any manipulations the experimenter so chooses. In Natural experiments, the researchers have to let manipulations occur on their own and have no control over them whatsoever. Also, using self selected groups in quasi experiments also takes away to chance of ethical, conditional, etc. concerns while conducting the study.
Disadvantages
Quasi-experimental estimates of impact are subject to contamination by confounding variables.[1] In the example above, a variation in the children’s response to spanking is plausibly influenced by factors that cannot be easily measured and controlled, for example the child’s intrinsic wildness or the parent’s irritability. The lack of random assignment in the quasi-experimental design method may allow studies to be more feasible, but this also poses many challenges for the investigator in terms of internal validity. This deficiency in randomization makes it harder to rule out confounding variables and introduces new threats to internal validity. Because randomization is absent, some knowledge about the data can be approximated, but conclusions of causal relationships are difficult to determine due to a variety of extraneous and confounding variables that exist in a social environment. Moreover, even if these threats to internal validity are assessed, causation still cannot be fully established because the experimenter does not have total control over extraneous variables.
Disadvantages also include the study groups may provide weaker evidence because of the lack of randomness. Randomness brings a lot of useful information to a study because it broadens results and therefore gives a better representation of the population as a whole. Using unequal groups can also be a threat to internal validity. If groups are not equal, which is not always the case in quasi experiments, and then the experimenter might not be positive what the causes are for the results.
SUMMARY
Quasi Experiments are also effective because they use the “pre-post testing”. This means that there are tests done before any data is collected to see if there is any person confounds or if any participants have certain tendencies. Then the actual experiment is done with post test results recorded. This data can be compared as part of the study or the pre-test data can be included in an explanation for the actual experimental data. Quasi experiments have independent variables that already exist such as age, gender, eye color. These variables can either be continuous (age) or they can be categorical (gender). In short, naturally occurring variables are measured within quasi experiments.

CONCLUSION
Though quasi-experiments are sometimes shunned by those who consider themselves to be experimental purists (leading Donald T. Campbell to coin the term “queasy experiments” for them),[6] they are exceptionally useful in areas where it is not feasible or desirable to conduct an experiment or randomized control trial. Such instances include evaluating the impact of public policy changes, educational interventions or large scale health interventions. The primary drawback of quasi-experimental designs is that they cannot eliminate the possibility of confounding bias, which can hinder one’s ability to draw causal inferences. This drawback is often used to discount quasi-experimental results. However, such bias can be controlled for using various statistical techniques such as multiple regressions, if one can identify and measure the confounding variable(s). Such techniques can be used to model and partial out the effects of confounding variables techniques, thereby improving the accuracy of the results obtained from quasi-experiments.

REFERENCES
1. Dinardo, J. (2008). “natural experiments and quasi-natural experiments”. The New Palgrave Dictionary of Economics. pp. 856–859. doi:10.1057/9780230226203.1162. ISBN 978-0-333-78676-5. edit
2. Rossi, Peter Henry; Mark W. Lipsey; Howard E. Freeman (2004). Evaluation: A Systematic Approach (7th ed.). SAGE. p. 237. ISBN 978-0-7619-0894-4.
3. Gribbons, Barry; Herman, Joan (1997). “True and quasi-experimental designs”. Practical Assessment, Research & Evaluation 5 (14).
4. Morgan, G. A. (2000). “Quasi-Experimental Designs”. Journal of the American Academy of Child & Adolescent Psychiatry 39 (6). pp. 794–796. doi:10.1097/00004583-200006000-00020.
5. Shadish; Cook; Cambell (2002). Experimental and Quasi-Experimental Designs for Generalized Causal Inference. Boston: Houghton Mifflin. ISBN 0-395-61556-9

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