Introduction
There are two main types of psychology research: experiment vs correlational study. While they have some similarities, these types of research are very different. You need to know the difference between them, otherwise you might misunderstand the research design and methods. This paper will provide clear definitions and comparisons of correlational and experimental types research, as well as highlight the differences between experimental and correlational studies.
Correlational Research
Not only is correlational research a popular field in psychology, but it's also a common one in other sciences. It allows you to measure the relationship between variables and not interfere with the process. It is often referred to as observational research. Scientists take into account all variables to determine if they have a connection. In many cases, correlational research helps to identify all possible variables before any other work can be done.
Positive and negative correlations are possible. Positive correlations are positive associations between variables. If one variable is increasing, all other variables will also increase (McLeod "Correlation"). Negative correlations work in a different way. Negative correlations, on the other hand, work differently. If one variable is decreasing while the other increases it will reveal a negative relationship. A case of correlational research is the discovery of low levels in neurotransmitters such as norepinephrine and serotonin in patients with clinical depression. Although this research was able to measure and identify a variable, it didn't necessarily establish a cause and effect relationship between clinical depression, low levels of serotonin, and norepinephrine. (McLeod - "Correlation")
Correlational experiments can be used to predict, test the reliability and validity of the results, as well as theory verification (predictive validity). These experiments are useful for testing naturally occurring variables. Correlations are usually easy to understand and illustrated graphically (McLeod - "Correlation").
Experimental Research
This type of research is perhaps the most well-known in medicine. The results of randomized control studies (which are also experiments) are highly regarded by medical professionals. Evidence derived from an experiment is considered to be top-tier, provided the experiment's premise and execution are flawless. Psychology is a medical field, so experimental research is very popular.
Experimental research is the study of variables that are isolated and manipulated in order to determine how they affect other variables (McLeod's "Experimental Method") Experimental research is used to determine cause-and effect connections. This is why experiments are so useful in testing different hypotheses. The environment of an experiment is controlled to remove the effects from other variables. This would cause the results to be blurred. Milgram Experiment is an example of an experimental method. It tested authority and obedience (McLeod "Experimental Method")
There are many types of experiments. The most popular ones include laboratory, field, and natural. High accuracy is often the strength of any experimental method. The results of experiments cannot be considered 100% accurate because laboratory experiments can distort naturally-occurring variables and processes, while field and natural experiments may have variables that were not accounted for. This could lead to bias in the data and distortion of the conclusions (McLeod "Experimental Method")
The main difference between experimental and correlational research
Apart from the obvious differences between experimental and correlational researchers as well as small deviations from one another, the major difference between the two types of research lies in the fact correlation does not and cannot necessarily imply causality ("Difference")
Although there may be strong evidence that certain variables are connected with others, correlational research does not allow us to draw such a conclusion. Correlational research is able to describe the relationship between two variables, but an experiment is the type of research that determines cause-and effect. This cannot be taken to mean that one variable is responsible for the other. The variables in question are interrelated, so it's possible that the cause-and effect could be caused by another variable.
Sometimes, a correlational study may be all that is needed to understand a naturally occurring phenomenon. However, it might not be possible or ethical to conduct an experiment under certain conditions. It would be unprofessional to ask participants to quit smoking cigarettes to test for lung cancer ("Difference")
Conclusions
Correlational studies are about studying variables in a natural setting and identifying them. These relationships do not necessarily indicate that there is a cause and effect connection between these variables. Experiments isolate certain variables and manipulate them to determine cause and effect between dependent variables. This is the key difference between experimental and correlational studies. Each one has its own uses depending on the context and scope of each individual research.
Works Cited
“Difference between correlational and experimental research.” Difference Between. 2012. Web.
McLeod, Saul. “Correlation.” Simply Psychology, 2008. Web.
—. “Experimental method.” Simply Psychology, 2012. Web.
Cite this page
Correlational and Experimental Research Comparative Essay. (2022, Oct 11). Retrieved from https://proessays.net/essays/correlational-and-experimental-research-comparative-essay
If you are the original author of this essay and no longer wish to have it published on the ProEssays website, please click below to request its removal:
- Business Plan Analysis Essay
- Scholarly Sources and Research Paper Example
- Paper Example on Qualitative Data Analysis Software
- Domino's Pizza Case Study Report
- SWOT Analysis of Apple Inc.
- Paper Example on Contingency Theory: Criticisms and Challenges
- Trustworthiness in Qualitative Research: Vital Component for Validity & Reliability