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What is Correlational Research? Definition, Examples, and Methods

Learn all about correlational research with definitions of correlational research, examples of correlational research, and methods of correlational research.

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What is Correlational Research?

By definition, correlational research refers to a non-experimental research method that studies the relationship between two variables with the help of statistical analysis. 

Correlational research design does not study the effects of extraneous variables on the variables under study. 

In terms of market research, a correlational study is generally used to study quantitative data and identify whether any patterns, trends, or insights exist between consumer behavior and market variables such as; advertisements, discounts, as well as discounts on products.

Uses of Correlational Research

Correlational research design is useful for all kinds of quantitative data sets, but it is commonly used within market research. Market researchers find it useful to use correlational design with Customer Effort Score Surveys and their association with sales; Customer Experience (CX) and its relationship with customer loyalty, as well as Net Promoter Score Surveys and its correlation with brand image or management. 

These surveys include many relevant questions that make them ideal to study in correlational research design. In market research, correlational methods help in isolating variables and seeing how they interact with each other.

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Correlational Research Examples

Correlation coefficient is used in correlational design to measure the strength of the relationship between two variables. It is a statistical measure. There are several types of correlation coefficients, the most popular being Pearson’s correlation coefficient. 

A correlation coefficient ranges from -1 to +1. A correlation coefficient of +1 indicates a perfect positive correlation whereas a correlation coefficient of -1 indicates a perfect negative correlation between two variables. A correlation coefficient of 0 indicates that there is no relationship between the variables under study.

Let’s understand this with an example of correlational research:

Consider a hypothetical study on hypertension and marital satisfaction where a researcher is aiming to study the relationship between disease (hypertension) and marital satisfaction. If the researcher finds a negative correlation between these two variables indicating that as marital satisfaction increases, experiences of hypertension decrease. 

However, this does not mean that marital dissatisfaction is causing hypertension, it just highlights an association between them. In correlational research design, none of the variables under study are manipulated or changed. They are just measured and the associations between them are observed or examined.

For instance, you want to understand if there is a correlation between how much you earn and spend. 

You may carry out correlational research to see if any relationship between the two exists. 

If you find out positive correlation it indicates that as the amount of earning increases the spending also increases. 

Let’s look at another example of correlational research. 

A dietician may want to find out if there is any correlation between vegetarianism and a healthy body. The dietician conducts research on a group of people with different diets (vegan, non-vegetarians, and vegetarians). The dietician then statistically analyzes the result to 

determine whether the people with a Vegetarian diet are healthier than others.

What are different types of correlational research outputs?

Typically there are three types of correlational research:

  1. Positive correlation
  2. Negative correlation
  3. Zero correlation
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Positive correlation

A positive correlation indicates that there is a positive relationship between the two variables. In this kind of relation, as one variable increases, the other variable also increases. For instance, the number of cars a person owns is positively correlated with their income. More the income, more the number of cars.

Negative correlation

A negative correlation indicates that there is a negative relationship between the two variables. In this kind of correlation, as one variable increases, the other variable decreases. For example, there is a negative relationship between levels of stress and life satisfaction. This indicates that as stress levels increase, life satisfaction decreases.

Zero correlation

Zero correlation indicates that there is no relationship between the two variables. A change in one variable does not lead to any changes in the other variable. An example of zero correlation is the relationship between intelligence and height. An increase in height does not lead to any changes in the intelligence of an individual.

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What are the different methods of correlational research?

There are two methods in which data is collected in a correlational study:

  1. Naturalistic observation
  2. Archival data

Naturalistic observation

In naturalistic observation, the participants of the study are observed in their natural environments. Naturalistic observation is a kind of field study. The researcher can observe participants in grocery stores, cinemas, playgrounds, schools, etc.

Researchers who use naturalistic observation as a means of data collection observe individuals as unobtrusively as possible. This is because they don’t want the participants to be aware of being observed as it may influence their behavior and they may not be their natural selves. 

For instance, if the researcher is observing consumers in a grocery store and the kind of items they usually buy, it is ethically acceptable as participants know that they are subjected to being observed in public spaces. The data collected in naturalistic observation can be qualitative or quantitative. 

Archival data

Archival data is another way to collect data for correlational research design. This type of data has been collected previously by doing similar studies. Archival data is usually collected through primary research. Archival data tends to be more straightforward as compared to the data collected through naturalistic observation. There is no scope for the observer effect in archival data. 

For instance, assessing the average customer satisfaction with electronic products for a particular brand in America is straightforward.

What are the advantages of correlational research?

Correlational research comes with a distinct set of advantages:

  1. Planning to conduct a correlational study motivates and inspires researchers to ask relevant questions in the survey for assessing the attitudes of customers.
  2. Correlational design helps researchers to identify the variables that have the strongest relationships and make better decisions in the long run.
  3. Correlational methods can also guide future research.
  4. Correlational design helps researchers determine the direction and strength of the relationship between different variables.
  5. Correlational methods are easier to interpret, cost-effective, and more applicable in day-to-day business decision-making.

Now, let’s look at some of the disadvantages.

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What are the disadvantages of correlational research?

While there are various advantages, there are a few disadvantages of correlational research such as

  1. Correlational methods don’t have the scope to imply causation. They only give us information about the association between two variables.
  2. The correlational design does not omit the likelihood of other extraneous variables affecting the main variables under study. For instance, stress is not the only variable that has a relationship with happiness. Other variables such as emotional intelligence, subjective well-being, as well as the quality of social relationships also affect happiness.
  3. Correlational methods are not useful when researchers want to see the isolated effects of one variable on another.

Conclusion

While correlation does not necessarily imply causation, causation does imply correlation. Correlational research is a stepping-stone to the more powerful experimental method and is more useful than it may seem because some of the recently developed complex correlational designs allow for some very limited causal inferences.

Findings from correlational research can be used to determine prevalence and relationships among variables, and to forecast events from current data and knowledge. 

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