Posted on: 28th Mar 2020 627 Views

Correlational Research

Correlational research design measures the relationship between two different variables. Students pursuing undergraduate or Masters from the University of Ireland have confusion about when and how to use correlational research. In this article, our research paper team is providing complete detail about correlational research.

Correlational Research

Correlational research definition

Correlational research is a kind of non-experimental investigation, in which the investigator measures two different variables. In correlational research researcher mainly focuses on developing understanding and assessing the statistical relationship between two variables.  Correlational research plays an important role in establishing a relationship between two different variables. The main purpose of correlational research is to develop an understanding of the relationship between two different variables.

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Example of co relational research

You can use a correlational coefficient for identification of the co relational between two different variables.  A correlational coefficient can be referred to as a statistical technique that helps in the calculation of strength between two different variables. The value of variables is measured between -1 and +1.  In correlational coefficient +1 represents the positive relationship between two different variables. -1 indicates a negative co-relationship between two different variables. O means there is no relationship between variables.

Example: The researcher has conducted an investigation for identifying the relationship between cancer and marriage. The two variables for example are marriage and cancer. Let’s assume there is a negative relationship between marriage and cancer. The statement means that an individual who is married will be less likely to have cancer. It does not mean that marriage helps in preventing cancer, you can’t believe such a fact.

Different types of correlational research

The different types of correlational research are :

Positive correlational In a positive correlation, two different variables move in the same direction.If tax increases there is also an increase in the prices of goods.
Negative correlational Two variables move in different directions.Increase in tea consumption leads to a decline in tiredness
Zero correlational No relationship exists between two different variables. There is no co-relationship between height and tea consumption.

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When to use a correlational research design?

Correlational research is a kind of descriptive research. There are mainly two cases when you can utilize correlational research these are:

1. If you want to develop an understanding of the relationship between two different variables. Here, you cannot expect to find a causal relationship between two different variables.

Example: If you intend to conduct research for identifying whether vegetarian people do have a high income or not. In case you don’t believe such fact still you should conduct an investigation as it still will help you in developing understanding about factors that affect the dietary choice of people.

2. If you intend to find is there any causal relationship between two different variables. It is totally unethical to execute experimental research which influences one of the variables.

Example: You Hypothesis that passive smoking which causes asthma in children. You can’t perform an experiment for testing hypotheses. It would be unethical for deliberating exposure to some children to passive smoking. You can perform correlational investigation for finding if children whose parents do smoking are highly prone to asthma.

How to do correlational research?

Correlational research is often executed in psychology as a preliminary technique for collecting information about the subject. There are a number of methods that you can utilize for performing correlational research. You need to use statistical analysis quantitative data for testing hypotheses.

Correlational between two different variables can be strong or weak. Surveys, Archival data, and observations are a few basic techniques that you can use for the collection of data in correlational research. In academic research, you can utilize a combination of different techniques. It is very crucial for you to carefully make a choice of different methods. It is the tactics that will help you in ensuring the reliability and validity of correlational results. Let’s have a detail explanation about different methods of use in correlational research.

  • Surveys

It is one of the best ways to study the relationship between two different variables. Researchers can also conduct an online survey and could ask questions related to the topic from participants. After gathering data it is very much essential for you to analyze it thoroughly using statistical technique. A survey is a best and flexible technique for gathering quick replies.

Example: You for conducting an investigation to identify the relationship between income and vegetarianism, you can utilize the questionnaire technique for the collection of information about the diet of selected people having different incomes. After the accumulation of data, you need to statistically analyze information for identifying whether vegetarians have a high income.

  • Natural observation

It is a kind of field investigation by conducting which you can collect information about the behavior of people in a natural setting.  The naturalistic observation technique mainly includes counting, description and categorizing. In Naturalistic observation you can involve both Quantitative and Qualitative elements. You need to concentrate on information that can be analyses quantitatively.

For example: If you are conducting an investigation then you can observe seminars in college in order to analyze whether there is a co-relationship between gender and class participation.  While observing the college seminar you should note frequency and period of duration of the contribution of students and then you can categories them on the basis of gender.  You can do a statistical analysis of data for determining when girls participate more in seminars or boys.

  • Archival data

It means that you can utilize secondary information. You can collect secondary data from different sources such as polls, official records, etc. Archival data enables you to access large volumes of information through observations.

For Example: If you are performing an investigation to find the influence of working hours on the mental health of people you can collect information using health records, national statistics, etc. You can perform statistical analysis for identifying whether nations, where employees work for a few hours, have good mental health results.

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Causation and Correlational

Students often have confusion between correlational and causation.  They perceive that correlational involves causation.  If in case you have been able to identify the correlational between two different variables it does not means that one variable gives rise to others.

Example 1: After completion of the investigation you found that there is a negative correlational ship between mental health and hours of working.  Evidence of the statement is the people earning low wages for average working hours also have good mental health.  But it does not mean that low paid working hours is the best technique for improving mental health. There are few other variables in the study which influence the relationship between different variables.

In simple words, by performing the correlational research you cannot prove causation. In the context of correlational research, it is very much important for you to thoroughly perform the collection and analysis of information.

Example 2: There is a negative correlational ship between smoking and health. It means that smoking has a negative influence on the health of people. It is a robust fact that is accepted by scientists also.

Conclusion:

It has been concluded from the above that correlational research is a research that helps you in understanding the relationship between different variables. Another fact which has been found is that correlational research causation is not included.

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