Correlational study

Correlational study is study variation which goal is to reveal any relationship existing between two variables without explaining it's existence or showing cause of chain effect (Gravetter, Forzano, 2018). This study is not based on any controls, manipulations, or interfering with any variables. Only one thing that this study is based on is observation, and "connecting the dots". It takes into consideration two variables of one individual, or of group of an individuals, and measures them and wants to find out if they are related. It also can be done by estimating more than two variables, then we are dealing with other forms of correlational study (Gravetter, Forzano, 2018).

Use cases for correlational studies

There are specific reasons that lead scientists to use correlational studies as their method of research. The first reason to use correlational study is when researchers suspect that there is no causal relationship between variables. Using correlation takes less effort and is cheaper than designing an experiment, so in this case, choosing correlation as a method is a better option. The second reason scientists conduct correlational studies is when they might suspect the existence of causal relation between the variables, but are unable to use experimental design. The reasons may be, for example: no physical possibility of carrying out the experiment, using correlation being the most practical option, carrying out the experiment would be unethical (Coolican, 2017).

Correlational study types

There are a few basic types of correlation being observed in research results (Blalock, 2018):

• Positive correlation - when the value of one variable rises up, the value of the second variable rises up as well.
• Negative correlation - when the value of one variable rises up, the value of the second variable falls down.
• No correlation - the values of the two variables are totally independent of each other.

The methodology of correlational study

One of the main instruments used in correlational research, for example in psychology, are questionnaires. Questionnaire is a set of questions specifically designed to measure chosen variables. Those variables may include personality traits, behavioral patterns, beliefs and other. The researchers may choose to measure correlation between those variables and the results of another questionnaire or other variable, e.g. demographic data.

Examples of Correlational study

• A study done to ascertain if there is a relationship between intelligence level and amount of time students spend studying.
• A study measuring the relationship between the amount of hours an employee works and their productivity.
• A study examining the correlation between the number of hours of sleep and academic performance.
• A study examining the relationship between the number of hours of exercise and overall health.
• A study examining the correlation between diet and cardiovascular health.

Advantages of Correlational study

A correlational study is a useful research tool that can help researchers better understand the relationships between variables. It has many advantages, including:

• It is relatively easy to conduct, with minimal time and cost. As opposed to other types of research, no manipulation or interference with the variables is required, allowing for quick and easy data collection.
• It is non-invasive and does not require the use of any tests or surveys. This makes it ideal for studies that involve participants who may not be willing or able to participate in more involved research methods.
• It can provide a more accurate picture of how two or more variables are related, as it takes into account multiple factors such as age, gender, and socioeconomic status. This makes it a valuable tool for understanding the nuances of relationships between variables.
• Correlational studies can help researchers identify new research questions, hypotheses, and theories that can be explored in more detail. By identifying potential correlations between variables, researchers can gain insight into the relationships between them, which can lead to further investigation.

Limitations of Correlational study

One of the limitations of Correlational study is that it cannot provide a cause-effect relationship or conclusion about the relationship between variables. This means that it cannot prove that one variable causes another. The results of a correlational study can only show a relationship between two variables, not a cause and effect relationship.

• The correlational study cannot indicate whether one variable is the cause of the other, and so a cause-effect relationship cannot be determined.
• The results of correlational study are also subject to interpretation and can be seen differently by different people.
• The correlational study does not take into account any other variables that can affect the relationship between two variables.
• Correlation does not always indicate causation or a causal relationship. For example, if two variables are strongly correlated, it does not necessarily mean that one causes the other.
• The results of correlational studies are usually not generalizable to the entire population, as the sample size is usually small.
• Since the correlational study does not include any interference, it is difficult to assess how the variables interact with each other and how they are affected by external factors.

Other approaches related to Correlational study

One approach related to Correlational study is Regression Analysis. This method allows researchers to estimate the relationship between two variables. It also enables them to predict the value of one variable based on the value of the other variable. Moreover, with the help of regression analysis, researchers can understand the strength of the relationship between two variables, as well as the direction of the relationship.

Another approach related to Correlational study is Factor Analysis. This method is used to identify the underlying factors that explain the relationship between two or more variables. It helps to reduce the number of variables and to identify the factors that explain the relationships between the variables.

Finally, another approach related to Correlational study is Path Analysis. This method is used to study the causal relationships between variables. It also helps to identify the direct or indirect effects of one variable on another.

In summary, Correlational study is a type of research method used to study the relationship between two or more variables. Other approaches related to Correlational study are Regression Analysis, Factor Analysis, and Path Analysis. Each of these methods are used for different purposes and help researchers to understand the relationships between variables.

Correlational study — recommended articles

Three-Way ANOVA — Parametric analysis — Statistical power — Lurking variable — Experimental error — Exploratory factor analysis — Analysis of covariance — Descriptive model — Quantitative research

References

• Armstrong, J. S. (Ed.). (2001). Principles of forecasting: a handbook for researchers and practitioners, Springer Science & Business Media, (30).
• Blalock Jr, H. M. (2018). Causal inferences in nonexperimental research, UNC Press Books.
• Coolican, H. (2017). Research methods and statistics in psychology, Psychology Press.
• Gauch Jr, H. G., Gauch, H. G., & Gauch Jr, H. G. (2003). Scientific method in practice, Cambridge University Press.
• Gravetter, F. J., & Forzano, L. A. B. (2018). Research methods for the behavioral sciences. Cengage Learning.
• Gray, C. W. (1976). The concept of dichotomous correlation. Scandinavian Journal of Psychology, 17(1), 153-159.

Author: Mateusz Fudala