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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. 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. == Design == The first part of creating a quasi-experimental design is to identify the variables. The quasi-independent variable will be the x-variable, the variable that is manipulated in order to affect a dependent variable. “X” is generally a grouping variable with different levels. Grouping means two or more groups such as a treatment group and a placebo or control group (placebos are more frequently used in medical or physiological experiments). The predicted outcome is the dependent variable, which is the y-variable. In a time series analysis, the dependent variable is observed over time for any changes that may take place. Once the variables have been identified and defined, a procedure should then be implemented and group differences should be examined. In an experiment with random assignment, study units have the same chance of being assigned to a given treatment condition. As such, random assignment ensures that both the experimental and control groups are equivalent. In a quasi-experimental design, assignment to a given treatment condition is based on something other than random assignment. Depending on the type of quasi-experimental design, the researcher might have control over assignment to the treatment condition but use some criteria other than random assignment (e.g., a cutoff score) to determine which participants receive the treatment, or the researcher may have no control over the treatment condition assignment and the criteria used for assignment may be unknown. Factors such as cost, feasibility, political concerns, or convenience may influence how or if participants are assigned to a given treatment conditions, and as such, quasi-experiments are subject to concerns regarding internal validity (i.e., can the results of the experiment be used to make a causal inference?). 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 are 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. There are several types of quasi-experimental designs, each with different strengths, weaknesses and applications. These designs include (but are not limited to):〔 *Difference in differences (pre-post with-without comparison) *Nonequivalent control groups design * *no-treatment control group designs * *nonequivalent dependent variables designs * *removed treatment group designs * *repeated treatment designs * *reversed treatment nonequivalent control groups designs * *cohort designs * *post-test only designs * *regression continuity designs *Regression discontinuity design *case-control design * *time-series designs * *multiple time series design * *interrupted time series design * *Propensity score matching * *Instrumental variables *Panel analysis Of all of these designs, the regression discontinuity design comes the closest to the experimental design, as the experimenter maintains control of the treatment assignment and it is known to “yield an unbiased estimate of the treatment effects”.〔 It does, however, require large numbers of study participants and precise modeling of the functional form between the assignment and the outcome variable, in order to yield the same power as a traditional experimental design. 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), 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 regression, 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. Moreover, the developing use of propensity score matching to match participants on variables important to the treatment selection process can also improve the accuracy of quasi-experimental results. In sum, quasi-experiments are a valuable tool, especially for the applied researcher. On their own, quasi-experimental designs do not allow one to make definitive causal inferences; however, they provide necessary and valuable information that cannot be obtained by experimental methods alone. Researchers, especially those interested in investigating applied research questions, should move beyond the traditional experimental design and avail themselves of the possibilities inherent in quasi-experimental designs.〔 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「Quasi-experiment」の詳細全文を読む スポンサード リンク
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