Pre-experimental designs are called such because they often happen before a true experiment is conducted. In cases where the administration of the stimulus is quite costly or otherwise not possible, a one-shot case study design might be used. In this instance, no pretest is administered, nor is a control group present.

Pre-experimental designs are research schemes in which a subject or a group is observed after a treatment has been applied, in order to test whether the treatment has the potential to cause change.

Two-group post: X O. O. – x = intervention. – o = observation.

One type of pre-experimental design is the one shot case study in which one group is exposed to a treatment or condition and measured afterwards to see if there were any effects. There is no control group for comparison. An example of this would be a teacher using a new instructional method for their class.

Three major types of pre-experimental designs are commonly used, either because the research preplanning is inadequate, causing unanticipated problems, or perhaps the situation makes a pretest or a comparison group impossible.

The true experiment is often thought of as a laboratory study. A true experiment is defined as an experiment conducted where an effort is made to impose control over all other variables except the one under study.

Pre-Experimental Designs for Description

Quasi-experimental designs do not use random assignment. Comparison groups are used in quasi-experiments. Matching is a way of improving the comparability of experimental and comparison groups. Quasi-experimental designs and pre-experimental designs are often used when experimental designs are impractical.

Like a true experiment, a quasi-experimental design aims to establish a cause-and-effect relationship between an independent and dependent variable. However, unlike a true experiment, a quasi-experiment does not rely on random assignment. Instead, subjects are assigned to groups based on non-random criteria.

True experiments, in which all the important factors that might affect the phenomena of interest are completely controlled, are the preferred design. Often, however, it is not possible or practical to control all the key factors, so it becomes necessary to implement a quasi-experimental research design.

The greatest advantages of quasi-experimental studies are that they are less expensive and require fewer resources compared with individual randomized controlled trials (RCTs) or cluster randomized trials.

Internal validity is the extent to which a study establishes a trustworthy cause-and-effect relationship between a treatment and an outcome. The less chance there is for “confounding” in a study, the higher the internal validity and the more confident we can be in the findings.

How to check whether your study has internal validity

1. Your treatment and response variables change together.
2. Your treatment precedes changes in your response variables.
3. No confounding or extraneous factors can explain the results of your study.

It differs from experimental research because either there is no control group, no random selection, no random assignment, and/or no active manipulation.” This type of research is often performed in cases where a control group cannot be created or random selection cannot be performed.

A quasi-experiment is an empirical interventional study used to estimate the causal impact of an intervention on target population without random assignment. Quasi-experiments are subject to concerns regarding internal validity, because the treatment and control groups may not be comparable at baseline.

The three main types of scientific experiments are experimental, quasi-experimental and observational/non-experimental. Of the three, the most detailed experiment is also the one that can show cause and effect. That type is the experimental method, and it is also called a randomized control trial.

Quasi-experimental research involves the manipulation of an independent variable without the random assignment of participants to conditions or orders of conditions. Quasi-experimental research eliminates the directionality problem because it involves the manipulation of the independent variable.

Because the independent variable is manipulated before the dependent variable is measured, quasi-experimental research eliminates the directionality problem associated with non-experimental research.

Quasi experiments resemble quantitative and qualitative experiments, but lack random allocation of groups or proper controls, so firm statistical analysis can be very difficult.