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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.

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.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.

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, then the experimenter might not be positive what the causes are for the results.

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 can represent the outside world from the experiment. External Validity is very important when it comes to statistical research because you want to make sure that have a correct depiction of the population. When external validity is low, the credibility of your research comes into doubt. Reducing threats to external validity can be done by making sure there is a random sampling of participants and random assignment as well.

Design Types

"Person-by-treatment" designs are the most common type of quasi experiment design. In this design, the experimenter measures at least one independent variable. Along with measuring one variable, the experimenter will also manipulate a different independent variable. Because there is manipulating and measuring of different independent variables, the research is mostly done in laboratories. An important factor in dealing with Person-by-treatment designs are that random assignment will need to be used in order to make sure that the experimenter has complete control over the manipulations that are being done to the study.

An example of this type of design was performed at the University of Notre Dame. The study was conducted to see if being mentored for your job led to increased job satisfaction. The results showed that many people who did have a mentor showed very high job satisfaction. However, the study also showed that those who did not receive the mentor also had a high number of satisfied employees. Seibert concluded that although the workers who had mentors were happy, he could not assume that the reason for it was the mentors themselves because of the numbers of the high number of non-mentored employees that said they were satisfied. This is why prescreening is very important so that you can minimize any flaws in the study before they are seen.

"Natural Experiments" are a different type of quasi experiment design used by researchers. It differs from person-by-treatment in a way that there is not a variable that is being manipulated by the experimenter. Instead of controlling at least one variable like the person-by-treatment design, experimenters do not use random assignment and leave the experimental control up to chance. This is where the name "Natural" Experiment comes from. The manipulations occur naturally, and although this may seem like a inaccurate technique, it has actually proven to be useful in many cases. These are the studies done to people who had something sudden happen to them. This could mean good or bad, traumatic or euphoric. An example of this could be studies done by those who have been in a car accident and those who have not. Car accidents can obviously not be placed on by experimenters, they must occur naturally. These events have proven to be useful in studies regarding post traumatic stress disorder cases.