Many researchers that carry out several studies have an option of deciding on an appropriate design that suits them. In fact, the option is to pick between correlational methodologies versus experimental approaches or both. Correlation studies enable the researcher to determine the connection between naturally occurring variables, which is data that already exists. On the other hand, experimental studies permit the researcher to manipulate or regulate the variables by introducing a change and assess its impact on the overall outcome (Jackson, 2015; Campbell & Stanley, 2015) . Consequently, in experimental research, the scholar can come up with conclusions concerning one variable that is leading to the changes in the other. It is, therefore, significant to determine the difference between correlational research and experimental research.
Both correlational and experimental research has to deal with various variables that would be determined whether they are related or otherwise. In simple research work, the researcher has two or more variables that he or she is interested to investigate, namely the dependent and independent variable. Whenever one is doing a study, he is guided with the fact that the independent variable influences the dependent one (Campbell & Stanley, 2015) . For instance, an investigation in which one utilized a fertilizer, he determined that this heightened the overall output of wheat being harvested from the farm at the end of the day. Consequently, the fertilizer being utilized is the independent variable that influences the amount of wheat that is harvested, which is the dependent variable (Adams & Lawrence, 2018) . Therefore, in both the research methods, the variables are majorly used to determine whether one method is preferable and not the others.
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To understand the difference, a determination indicating that heavy drinkers get lower grades in school would qualify to be more of correlational research than experimental research. It is correlational because an experiment could not have been ethical. For instance, if an experiment was to be carried out, the researcher would have to regulate the level or rate of drinking of the student, by making some students to take heavy and more drinks in order that the effects of this could be seen on their grades. This is not feasible. It is the reason that we conclude that heavy drinking is linked with lower grades (Campbell & Stanley, 2015) . It cannot be interpreted that the drinking caused the lower grades since other reasonable interpretations have not been excluded. Probably, student drinks heavily because they have just received poor grades. It could also be that drinking and grades are linked only on the basis that they are both determined by the frequency in which the student is focused on his or her schoolwork.
As indicated earlier, in a correctional investigation, the research teams do not get the opportunity to manipulate the variables in the study. The researcher quantifies the data, which he or she collected regarding the phenomenon. This gives him the chance to determine whether the variables are correlated in any way – that is, if the changes that occur in one variable has some connection with changes that occur in the other (Campbell & Stanley, 2015) . Those interested in doing experiments in such research gathers existing information, like economic data or reports, from government, and evaluate the gathered data with the help of statistical tools such as SPSS (Jackson, 2015). In other times, the outcomes of correlational study can motivate hypothesis that can be verified with a more precise experimental type.
On the other hand, in an experimental method, the researcher or research team controls the independent variable as well as some other elements of the experiment to determine the outcomes. This gives the researchers room to come up with a conclusion to determine whether the independent variable certainly influences the dependable ones, compared to the variables varying concurrently (Adams & Lawrence, 2018) . The researcher also has the chance to remove the influences of other variables that may not be needful in the experiment. For instance, the researcher may opt to utilize specific levels of fertilizers to varying regions of the same wheat field, and assess any disparities in the ultimate wheat yield, as the other factors, such as rainfall, sun exposure, and soil texture, remain constant.
The advantage of the experimental method is that it distinguishes the connection between the independent variable and the dependent one. Experimental studies are in most cases very expensive and challenging to perform compared to adopting the correlational method. On the other hand, a correlation study is not free from other influences on the variables so that it may not be easy to establish the strength of the relation between the two as it exists. Correlation studies can determine a link to determine it matches the value compared to the controlled experiment (Campbell & Stanley, 2015) . According to Jackson (2015), correlation method can also encourage the use of a larger group of data that may be achievable in an experiment. Other researchers will utilize both the experimental and correlational studies to investigate a phenomenon, and later perform an experiment and at the same do a correlational analysis on the outcomes of that experiment. It is significant when both the methods are used complementarily to have authentic and reliable results.
The experimental method determines a causal relationship. A causal relationship is whereby one variable determines the other. Experimental methods are intended to spot this causal relationship between two variable and of course by separating one from the other (control) variables. Variables, here, are factors that can change the outcome. Correlation method determines the correlation relationship in that the association is found out. Correlation of two variables does not automatically guarantee causation between them. Correlation designs are most times preliminary (Campbell & Stanley, 2015) . A correlation is first established between variables before a causal connection is determined. A statistical generalization is used in a correlational study to determine whether there is a relationship, which implies a sufficiently large data. Instances of this include establishing whether there is a correlation between smoking and increased levels of heart diseases. Subsequently, it is significant to determine whether a causal relationship, in this case, would be the best description for a specific correlation. Notably, a correlation does not automatically qualify or involve a causal relationship (Adams & Lawrence, 2018) . In an experimental method, a large sample may not be necessary, as a small one may equally be representative of a huge population. To determine if there is a correlation between two or more variables, the sample to be studied is put into two groups. The first group is the supposed causal factor, while the next second group is the control group, which is incorporated majorly in an experimental design.
In conclusion, the researcher should be careful not to treat the correlational study as if it is an experiment. This will wrongly infer the conclusions made between the variables involved in the study. As such, the researcher’s conclusions will end up being flawed. Correlational research is a research study that is intended to unveil the relationships among variables and give room for the forecasting of future events from current knowledge. Experimental research is research method in which initial correspondence among research participants in beyond one group is formed, subsequently by a control of a particular experience for these groups and the extent of the effect of this control.
References
Adams, K. A., & Lawrence, E. K. (2018). Research methods, statistics, and applications . Sage Publications.
Campbell, D. T., & Stanley, J. C. (2015). Experimental and quasi-experimental designs for research. Ravenio Books.
Jackson, S. L. (2015). Research methods and statistics : A critical thinking approach . Cengage Learning.
