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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 designs share many 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 (eg. an eligibility cutoff mark) .[1] In some cases, the researcher may have no 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, the treatment group will be statistically identical to the control group, on both observed and unobserved characteristics, at baseline (provided that the study has adequate sample size). Any change in characteristics post-intervention is due, therefore, to the intervention alone. 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 aplacebo 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.[3] 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 occuring 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)[3]: Difference in differences (pre-post with-without comparison) Regression discontinuity design case-control 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”.[4] 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. Contents [show]

Ethics

A true experiment would randomly assign children to a scholarship, in order to control for all other variables. Quasi-experiments are commonly used in social sciences, public health, education, and policy analysis, especially when it is not practical or reasonable to randomize study participants to the treatment condition. As an example, suppose we divide households into two categories: Households in which the parents spank their children, and households in which the parents do not spank their children. We can run a linear regression to determine if there is a positive correlation between parents' spanking and their children's aggressive behavior. However, to simply randomize parents to spank or to not spank their children may not be practical or ethical, because some parents may believe it is morally wrong to spank their children and refuse to participate. Some authors distinguish between a natural experiment and a "quasi-experiment".[1][5] The difference is that in a quasi-experiment the criterion for assignment is selected by the researcher, while in a natural experiment the assignment occurs 'naturally,' without the researcher's intervention.

Quasi experiments have outcome measures, treatments, and experimental units, but do no use random assignment. Quasi-experiments are often the design that most people choose over true experiments. The main reason is that they can can usually be conducted while true experiments can not always be. Quasi-experiments are interesting because they bring in features from both experimental and non experimental designs. Measured variables can be brought in, as well as manipulated variables. Usually Quasi-experiments are chosen by experimenters because they maximize internal and external validity.

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 [9] 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.[10] 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.

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.Using a sampling method other than random sampling increases the potential for constructing non-equivalent groups. Ideally, researchers endeavor to obtain experimental and control groups that are alike. This is most effectively achieved and most likely to occur through random selection. Quasi-experimental designs do not use random sampling in constructing experimental and control groups. Another disadvantage lies in the beginning of the research with non-equivalent groups presents a threat to internal validity. Internal validity refers to the degree to which a researcher can be sure that the treatment was responsible for the change in the experimental group. If the researcher does not start with equivalent groups, then the researcher cannot be sure that the treatment was the sole factor causing change. Other confounding factors may have contributed to the change. Therefore, not using random sampling methods to construct the experimental and control groups, increases the potential for low internal validity.

Internal Validity

Internal Validity is the approximate truth about inferences regarding cause-effect or causal relationships. This is why validity is important for quasi experiments because they are all about casual relationships. It occurs when the experimenter tries to control all variables that could effect the results in the experiment. Of course there are always going to be threats to internal validity that could potential. Statistical regression, history and the participants are all possible threats to internal validity. The question you would want to ask while trying to keep internal validity high, is "Is there any other possible reasons for the outcome besides the reason I want it to be?" If so, then internal validity might not be as strong.

External Validity

External Validity is a generalization with results obtained from a smaller sample size and thought to be extended to the rest of the population. When External Validity is high, the generalization is accurate and

Quasi Experiment Types

Person-by-Treatment is the most common type of quasi experiment. These are designed to measure at least one independent variable and manipulate another independent variable. Person-by-treatment are almost always done in a laboratory and will usually use random assignment as well. A classic example of Person-by-treatment quasi experiment was conducted at Northwestern University. The basis of the study was to see if "low and middle class" students felt inferior to the "upper class" students. They asked participants to perform a manipulated measure, as well as a control measure.This is a basic set up of a Person-by-treatment experiment.

Natural Experiments are different from Person-by-Treatment because they do not use random assignment. This in reality, takes away all sorts of control in Nautral Experiments. Natural experiments gets it's name because they have manipulations which occur "naturally". This means that there are no intentional manipulations done.