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The quasi-independent variable will be the x-variable, the variable that is manipulated in order to affect a dependent variable.
A quasi-experiment is an empirical interventional study used to estimate the causal impact of an intervention on target population without random assignment.
Quasi-experimental research shares similarities with the traditional experimental design or randomized controlled trial, but it specifically lacks the element of random assignment to treatment or control.
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.
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.This is particularly true if there are confounding variables that cannot be controlled or accounted for.The first part of creating a quasi-experimental design is to identify the variables.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).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.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 short, naturally occurring variables are measured within quasi experiments.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”.