You may be more familiar with correlational research than you realize. For example, when the doorbell rings at a particular time of day, you know it’s the mailman dropping off a package. You came to the conclusion that there is a relationship between the doorbell and the mailman at a particular time of day after observing the doorbell and the mailman, two variables, over time. This is essentially correlational research.
Let’s look more closely at what correlational research is and how you can use it to spot patterns and trends.
As we alluded to in our mailman example, correlational research is a non-experimental research method in which two variables are observed in order to establish a statistically corresponding relationship between them. The goal of correlational research is to identify variables that have a relationship in which a change in one creates a change in the other—without influence from any extraneous variable.
Correlational research has, for example, identified a relationship between watching violent television and aggressive behaviors. But we must remember that correlational is not the same as causal. To prove that viewing violent shows on television causes aggression, experimental studies were needed. Correlational research established that there was a relationship, but experimental research was needed to prove the type of relationship.
Correlational research is one of several types of research design. So, what are the key characteristics of correlational research?
There are several benefits to conducting a correlational research study:
There is no need to set up a controlled environment or staged interaction. In correlational research, you simply observe the two variables, their natural relationship, and their effects on each other. Observation takes place in the natural environment of the variables, and neither variable is manipulated.
Correlational research generally involves two or more sets of data. By conducting correlational studies over time, you can observe patterns and trends that establish further relationship attributes. Data can either be collected by observation or archival data, which we will discuss in more detail later in this article.
Target market identification
Used in marketing, your correlational research may help you identify a new potential target market. For example, if you observe shoppers at a local grocery for an entire week, you might conclude that older shoppers tend to visit the store early in the morning. This relationship between time of day and customer age will help you target your advertising appropriately.
Correlation research is conducted through observation only. In cases where experimental research is considered unethical, correlational research may be used to establish whether there is a relationship between two variables.
Correlational research takes less time and capital to conduct than experimental research. This is a particular advantage when working with limited funding.
As with any research method, there are limitations to correlational research:
Limited in scope
Correlational research is limited to providing statistical information from two variables only. It can uncover previously unknown relationships, but it cannot provide a conclusive reason for why the relationship exists.
No causal data
This research method only identifies a relationship between variables, it does not identify which of the variables creates the statistical pattern or which variable has the most influence. There is no evidence for cause and effect, so another research method must be used to determine the causal relationship.
Depends on historical data
Because correlational research depends on the past to determine the relationship between the variables, it cannot be a reliable source as a standard variable for future predictions.
Correlational and experimental research differ in four main ways: methodology, observation, causality, and number of variables.
Let’s take a deeper look at these differences:
Methodology is the main difference between correlational and experimental research. In experimental research, the researcher introduces a catalyst or trigger to evaluate its effect on the variables in the study. In correlational research, the researcher simply observes the variables, watching for a statistical pattern that links them naturally. There is no interaction between the researcher and the variables, and no triggers or catalysts are introduced.
In correlational research, the researcher passively observes and measures the relationship between variables. In experimental research, the researcher actively triggers a change in the behavior of the variables and observes and records the resulting reactions and behaviors.
Correlational research establishes statistical patterns connecting two variables but does not determine cause and effect. Experimental researchers introduce a catalyst and establish its effect on the variables, thereby establishing a causal relationship.
Number of variables
Experimental research can include an unlimited number of variables. Correlational research includes only two variables.
There are three possible outcomes of correlational research, each with its own defining characteristics. Results can be expressed in terms of a correlation coefficient. This is the measure of the strength of the correlation. It can range from -1.00 to +1.00. You’ll hear more about correlation coefficients in the analysis section of this article.
This type of correlational research method involves two statistically corresponding variables where an increase or decrease in one variable creates a like change in the other. A correlation coefficient close to +1.00 indicates a strong positive correlation.
Example: When income increases, spending increases.
Negative correlational research involves two statistically opposite variables where an increase in one variable creates a decrease or alternate effect in the other. A correlation coefficient close to -1.00 indicates a strong negative correlation.
Example: If prices in a store increase, sales then decrease.
No correlation, or zero correlational research involves two variables that may not be statistically connected. A change in one variable may not trigger a corresponding or alternate change in the other. A correlation coefficient of 0 indicates no correlation.
Example: A person’s height has no correlation with the salary they earn.
Correlational research is frequently used in market research. You can gather data quickly and easily with observation and generalize your findings. This is helpful in finding out what areas require further research.
Using correlational research is a good option for these situations:
Investigation of non-causal relationships
Use correlational research when you want to find out if there is a relationship between two variables, but don’t expect to find a causal relationship. In market research, you may discover that people shop more when it’s cold outside. This does not mean that cold weather causes frenzied shopping sprees, but it does show a pattern of behavior.
Exploration of causal relationships between variables
You can use correlational research to discover initial indications or develop theories if you think there may be a causal relationship between two variables, but it is unethical, cost-prohibitive, or impractical to perform experimental research. Continuing the example from above, you observe trends that imply a causal relationship between an increase in shopping and the months of November and December.
Testing new measurement tools
When you develop a new tool for measuring your variables, you can use correlational research to assess whether the tool is consistent and accurate. If you created a new scale to measure customer satisfaction, correlational research would verify its accuracy.
There are three methods of data collection used in correlational research: naturalistic observation, archival data, and surveys.
Each method has its own distinct features:
This correlational research method involves observation of people in their natural environments over a period of time. The researcher must carefully observe the natural behavior patterns of the subjects (variables).
The difficulty inherent to this research method is that the researcher must not let the subjects know they are being observed. If the subjects are aware of the observation, they may deviate from their usual behaviors. They may refrain from participating in certain activities or behave in what they perceive to be a “correct” manner because they are being observed.
The advantage of naturalistic observation is that researchers can observe the subjects in their natural environment, offering an inside look at social behaviors that are unlikely to be observed in a test setting.
This correlational research method uses information that has already been collected about the variables in the research. By using data from earlier studies or historical records (secondary research) of the variables, the researcher can track statistical patterns that have already been determined.
The missing piece with archival data is the reliance upon data that has not been collected by the researcher with their intent and goal in mind. Because it is not primary research, the researcher has no control over the methodology by which it was collected. It may also be difficult to find existing research that aligns with the researcher's current subject.
The most commonly used correlational method is the survey method. Surveys containing questions about the subject of interest are administered to a random sampling of subjects (variables). The most efficient way to conduct correlational research with surveys is to use an online platform.
SurveyMonkey is an agile market research platform that has solutions for all of your research needs. Our solutions include all the tools you need to perform survey research, including data analysis and a customizable dashboard for presenting your results.
Surveys allow researchers to gather a lot of information in a short amount of time. Keep in mind that it is critical to avoid any survey bias that could affect the validity of the results.
Once your data is collected, you can statistically analyze the relationship between the variables with either correlation or regression analysis, or both. The relationship between the variables can also be visualized using a scatter plot.
In correlation analysis, you summarize the relationship between the variables with a correlation coefficient. To determine the number that describes the strength and linear correlation between two variables, you will use the Pearson correlation method or Pearson’s r. The number you arrive at using this method is your correlation coefficient.
In very basic terms, the Pearson correlation uses a scatter plot of your collected data by drawing a line through it to determine if there is a positive, negative, or no relationship between the variables as well as the statistical significance of the correlation.
Once performed, the Pearson correlation method yields a number from -1.00 to +1.00. The value of “r” represents the strength of the relationship between the two variables.
This is the expression for calculation of the correlation coefficient r:
Once the value of r is determined, you must determine whether it is of statistical significance.
Using regression analysis, you can use your collected data to predict how much change in one variable will be associated with a change in the other variable.
Where correlation analysis quantifies the linear relationship between two variables, regression analysis expresses the relationship in the form of an equation. Neither analysis establishes a cause-and-effect relationship.
The following examples will help clarify the uses and analyses of correlational research.
A business notices a decline in the sales of kitchen appliances. They conduct correlational research and find that there is a relationship between recent price increases and declining sales—a negative correlation. They can use survey studies to confirm a causal relationship and take steps to improve sales.
A nutritionist wants to determine if there is a relationship between veganism and general health. She conducts archival correlational research on people with varied diets. Her statistical analysis will show whether those on a vegan diet have better overall health.
You hypothesize that older people are more patient than their younger counterparts. You conduct correlational research by observing people waiting in line at the grocery store. Your analysis shows a strong positive correlation between age and patience.
Correlational research is a critical tool for establishing relationships between two variables. While it cannot determine causality, correlational research can detect a positive, negative, or zero correlation. It is useful in spotting patterns and trends that you may not otherwise have noticed.
When you’re ready to conduct your own correlational research, find your target population quickly and easily with SurveyMonkey Audience. This useful market research solution will help you receive responses from your ideal audience. Find out more about our survey response tool today!
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