The research field is expanding quickly, and with this, it is becoming more structured. Researchers use different kinds of research methods and designs to understand various phenomena. One such research type is quasi-experimental research. While quasi means resemblance, this type of research is similar to a real experiment which is created to understand the cause-and-effect relationship between variables.
Today we will explore the nuances of quasi-experimental research and unveil its types. Later, we will also study some examples of quasi-experimental research and draw a comparison between quasi-experimental and experimental research. Let’s get started!
What is Quasi-Experimental Research?
Let us start with defining quasi-experimental research. It is a type of research methodology that resembles experimental research. It tries to establish a cause-and-effect link between the dependent variable and the independent variable.
The research participants are not randomly selected and assigned to non-randomly conditions. However, since participants are not randomly chosen, there may be additional disparities between conditions in quasi-experimental research. Quasi-experiments fall midway somewhere between correlational research and real experiments in terms of internal consistency.
This type of research technique employs quantitative data collection methods and is one of the most popular quantitative research methodologies. It majorly involves numerical data and statistical analysis to draw conclusions.
Here are a few quasi-experimental research examples for a better understanding of how this methodology is put to action:
- A study that contrasts the performance of two forms of therapy for a particular medical condition. Participants choose which therapy they want to use in this study, and the researcher does not assign them at random.
- Research that looks into how a new teaching approach affects student achievement. Here students are not randomized into groups that receive the new or conventional teaching strategy.
Benefits of Quasi-Experimental Research
Quasi-experimental research offers several benefits in the field of scientific inquiry. Some of the key advantages include:
- Real-World Settings
Due to the fact that they are conducted in actual environments, quasi-experimental studies have a distinct advantage because they enable researchers to watch and study phenomena as they develop naturally. This improves the study’s external validity and greatly increases the applicability of its findings to actual circumstances.
Quasi-experimental studies offer insightful information that fills the gap between theory and practice by accurately capturing the subtleties and complexity of real-world contexts.
- Ethical Considerations
This kind of research becomes a useful alternative when conducting a traditional experimental study with random assignment is not practical or ethically acceptable. It gives researchers a chance to investigate causal relationships without imposing interventions or manipulating data in ways that might be unethical.
By using this strategy, researchers can continue to look into and comprehend causal effects while upholding moral principles and guaranteeing the reliability of the research procedure.
- Increased Generalizability
The scope of participant inclusion is widened by quasi-experimental designs, enabling more generalizable results for larger populations. Investigating intricate social or behavioral phenomena that are challenging to replicate in controlled laboratory settings is made easier.
Quasi-experimental research improves the generalizability and practical applicability of the findings across diverse populations and real-life scenarios by embracing the complexities of real-world contexts.
- Longitudinal Studies
Researchers can conduct longitudinal studies using quasi-experimental designs by keeping track of participants for a long time. This makes it possible to investigate long-term effects and how different variables relate to one another over time or at different stages of development.
This research offers important insights into the phenomena’ dynamic nature and interconnectedness by observing changes and trends over time.
ALSO READ: Longitudinal Study: A Capsule Guide For First-Time ResearchersQuasi-Experimental Research Types
Many types of quasi-experimental research designs can be used to conduct quasi-experiments. The three most popular ones amongst them include non-equivalent groups, time-series design, and natural experiments.
- Non-Equivalent Groups
When using this design, researchers try to account for any possible confounding variables by making appropriate adjustments in their research or choosing groups that are as similar as possible. This type of quasi-experimental design is the most commonly used by researchers.
- Time-Series Design
Multiple dependent variable measurements throughout time are analyzed in the time-series design. After introducing the intervention at a certain period, the researcher examines the pre-intervention and post-intervention measurements. This design is susceptible to historical and maturation risks of internal validity.
- Natural Experiments
In both lab and field testing, researchers often decide the groups where the participants should be placed. In a natural experiment, participants are randomly or sporadically assigned to the control therapy due to an outside occurrence or circumstance (“nature”).
While some assign random participants to the group, natural investigations are observational rather than experimental, which differs from a true experiment.
Characteristics of Quasi-Experimental Method
The major characteristics of the quasi-experimental method are listed below:
- Non-random assignment: The participants chosen for research are divided into groups non-randomly.
- No manipulation of independent variables: The independent variable is not manipulated in this research; however, the dependent variable is manipulated.
- Real settings: Generally, quasi-experimental research is conducted in real-world settings instead of labs.
- Less control over extraneous variables: The researcher does not have complete control over extraneous variables in this research method. These variables may influence the results of the study.
Quasi-Experimental vs Experimental Research
To understand quasi-experimental research in its entirety, it is crucial to draw a comparison between quasi-experimental and experimental research. Both research aims to create a cause-and-effect relationship between different variables.
The major difference is that experimental research has more control over the variables than quasi-experimental research. In experimental studies, the researcher uses a randomization method to divide subjects into groups and then changes some external factors.
However, the researcher gives participants to groups non-randomly in quasi-experimental research.
Instruments Used in Quasi-Experimental Research
While quasi-experimental research leverages quantitative data collection methods, qualitative data comprises numerical and statistical data. Some of the common instruments used in quasi experimental research are the following:
- Surveys
Surveys are commonly used in quasi-experimental research to collect data on participants’ attitudes, beliefs, and behaviors. Surveys are a set of questions framed to gather information related to the topic being studied.
Tools like SurveyPoint help you create beautiful surveys in no time. Simply pick a survey design from their template library and customize it as required.
- Observations
Observations collect data on participants’ behaviors and interactions in real-world settings. It involves observing and examining the phenomenon.
- Secondary Data
Researchers may also use existing data, also known as secondary data, such as medical records or school records, to gather data for their research.
What is Quasi-Experimental Research Design?
In a quasi-experimental design, the participants are not randomly divided into groups, but the researcher alters or monitors an independent variable. Individuals are classified based on characteristics they already share, such as age, gender, or the frequency with which they have been exposed to a given stimulus.
In the quasi-experimental design, it is more difficult than in a true experiment to make conclusions regarding cause and effect. When randomization is not feasible or ethical, quasi-experimental approaches can be very useful. They are also less expensive and require fewer resources when compared to actual experimental research.
A good quasi experimental design example can be drawn like this— Research was carried out to gauge how well a new approach to teaching affected student achievement. The participants were not divided into groups at random. Instead, the study was carried out in a real-world environment.
The experimental group was taught using the novel approach, whereas the control group was instructed using the conventional approach. Student achievement was the dependent variable, and the teaching approach was the independent variable.
