Differences And Similarities Between Quasi-Experimental And Experimental Research Designs

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Experimental Research Designs

Choosing the right experimental design is important to conduct a study effectively. Different cases demand different types of experimental methods to test various theories and offer the basis for scientific knowledge. Both quasi-experimental and experimental research designs are powerful methods to evaluate the cause and effect relationship. However, these methods have some differences, and to select the right method for your study, you need to understand the differences between them.

This article is a perfect guide to understanding the differences and similarities between quasi-experimental and experimental research designs.

Experimental Design:

An experimental research design is a random research design used to evaluate the impact of interventions. In this, participants of the research are divided into two main groups. First is the treatment group, where the participants get the intervention we want to observe. Second is the comparison or control group in which participants do not get any intervention. It is a random study that ensures that each individual in the study gets an equal chance to receive the intervention. Examples of this type of research design are:

  • Pretest-Posttest Control Group Design
  • Matched Pair Design
  • Solomon Four-Groups Design
  • Randomized Blocks Design

Quasi-Experimental Design:

This type of experimental design focuses on establishing a relation of cause and effect between a dependent and independent variable. This research design is used for situations where you cannot use experimental research design for practical or ethical reasons. Examples of Quasi-experimental design are:

  • One-Group Posttest Only Design
  • One-Group Pretest-Posttest Design
  • Static-Group Comparison Design
  • Separate-Sample Pretest-Posttest Design

If you are unable to differentiate the both or unable to use these analyses in your dissertation, get dissertation help online to overcome such issues.

Similarities Between Quasi-experimental and Experimental Research Design:

Objective:

Both experimental design and quasi-experimental design are used for the evaluation of the impact of a treatment or an intervention.

Conditions:

In both research designs, participants of the study are subjected to the same conditions or treatments to measure some outcomes. The researcher then tests whether the differences in the outcomes are related to treatment.

Differences Between Experimental Research Design And Quasi-Experimental Design:

Methods:

In true experiment design, researchers assign subjects randomly to study the treatment. Selecting subjects randomly ensures that each individual gets an equal chance to participate. This way, you can get correct results.

On the other hand, the quasi-experimental design uses non-random methods to assign subjects. Because of the non-random method, you can get more cost-effective and faster responses than true experiment methods.

Control Over The Treatment:

In experimental research design, the researcher designs the treatment. This means he has more control over the research process. In the quasi-experimental design, researchers usually do not control the treatment. In contrast, researchers study the different treatments of previous existing groups.

Using Control Groups:

Control groups in the research are those groups which are separated from the experiment. It means they cannot impact the outcomes, isolating their impact on the experiment. Control groups are used in true experiments. While, in Quasi-experimental design, control groups are not needed. However, using the control groups in a quasi-experimental design can give you better results for the study.

Room For Confounding:

Confounding usually means mixing effects. In this, the impacts of exposure on an outcome are mixed with the impacts of the additional factors. This results in a distortion of the true relationship. There is no room for confounding in experimental research design on a detailed study. In contrast, there is some room for confounding in a quasi-experimental design. But to study the causal relationship in quasi-experiments, you can also use the statistical method.

Level Of Evidence:

In research, there are usually five levels of study. A level of evidence is usually assigned to a study based on methodology and design. A randomized experiment has the highest level of evidence in the hierarchy of evidence. On the other hand, Quasi-experimentation is on the second highest level in the hierarchy of evidence.

Advantages:

True experimentation can help you reduce confounding and bias. It gives you more accurate results as each individual in the study gets an equal chance to receive the intervention. The quasi-experimental design could be used for those studies where you can not do true experiments ethically and practically. It is also very useful for studies with small sample sizes.

Limitations:

The cost of experimental research design is higher as it needs a larger sample size. It also has more ethical limitations and generalizability issues. In some situations, it becomes practically infeasible. The ranking in the hierarchy of evidence is low for quasi-experimentation as the lack of randomization increases confounding and bias.

When To Choose A Quasi-Experimental Design Over A True Experiment?

To study causal relationships, the best approach is randomization. However, the problem with randomness is that it could not be used for all situations. Following are some cases where you should use Quasi-experiment instead of experimental research design:

  • Sometimes being in 1 group can be risky for the study participants, such as randomly choosing people to smoke. In this case, it is best to put participants in control groups.
  • You can also use quasi-experimentations where interventions are used on a population for a particular location. In these cases, using randomization is very difficult. For example, you can use quasi-experiments for an intervention that can decrease air pollution in a particular area. 
  • Randomization is usually suitable for large sample sizes. However, it is best to use quasi-experimentations to work with small sample sizes.

Conclusion:

Both quasi-experimental and experimental research designs are used for a similar purpose, which is to study the cause and effect relationship. However, a quasi-experimental design can be used for cases with some ethical and practical limitations. Moreover, you should also select a quasi-experimental method to study small sample sizes or particular locations. From now on, keep these differences and similarities in your mind before choosing any experimentation method.

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