The Significance Of Correlational Research And What It’s Used For

Correlational research has been a crucial tool in understanding human behavior and health, among many other topics. By using this method to analyze the relationship between the variables, researchers can gain valuable insights into how our environments and interactions influence various aspects of our lives. In this article, we’ll explore what exactly correlation research is, how it differs from other types of studies, and some of its practical implications.

What is correlational research?

Correlational research is a type of non-experimental research that explores the relationships between two or more variables. It examines variables as they naturally occur and does not include any form of manipulation.  

What does it measure?

How to interpret correlation results

The outcome of a correlational research study is usually either positive, negative, or somewhere in between.

Positive correlation vs negative correlation

• A positive correlation means that two variables move in the same direction. When one variable increases, so does the other variable. 
• A negative correlation means that two variables move in opposite directions. When one variable increases, the other decreases. 

Most correlations are somewhere in between these two extremes. For example, imagine that you’re studying the relationship between how much television people watch and how much exercise they get. You might find a slight negative correlation between these two variables—that is, as television watching increases, exercise decreases (and vice versa), but only marginally.

Strength of correlation

The strength of a correlation tells you how closely related two variables actually are. Not all correlations are equal, though. The strength of correlation is measured on a scale of -1 to +1. 

• A strong positive correlation will have a value closer to +1, indicating that as one variable increases, the other tends to increase as well, and the relationship between them is fairly consistent, and they are closely related. 
• A strong negative correlation has a value closer to -1, meaning that as one variable increases, the other decreases, and that the relationship is closely related in the opposite direction. 
• A value close to zero indicates that there is no relationship between the variables, meaning that changes in one do not reliably predict changes in the other. 

Understanding where a correlation falls on this scale can be helpful before drawing any conclusions from the data. Weak correlations generally offer less insight than strong ones, regardless of whether the correlation is positive or negative. 

Why correlation does not prove causation

A key element of correlational research is remembering that just because two variables are correlated does not necessarily mean that one causes the other—that correlation is not the same as causation. Going back to our example above, just because there’s a slight correlation between how much television people watch and how much exercise they get does not mean that watching television causes people to exercise less. There could be other variables—such as free time—that cause both television watching and a lack of exercise, or a wide variety of other factors.

The directionality problem

A directionality problem can arise when there appears to be a correlation between two variables, but it is unclear which variable is influencing the other. 

In other words, just because we know that two variables change together doesn’t mean we know which one is driving the change or that one is impacting the other at all. For example, research may show that not getting enough sleep is correlated to increased stress, but does that mean that stress leads to poor sleep, that poor sleep causes stress, neither, or both? It can be difficult to tell without more in-depth research.

The third variable problem

Another thing to consider is the third variable problem, which occurs when a relationship between two variables appears to exist, but a separate variable is actually responsible for the pattern. While one event may appear to influence another, there can also be a third factor that drives how the variables interact.

What correlation and causation mean in everyday language

When researchers study variables, distinguishing between correlation and causation can be important in research. One challenge is that the two things can appear identical. For example, a numerical value may show a strong relationship between two variables, but that alone does not establish causality. 

What exactly does that mean? To put it simply, correlation says that two things are related, while causation demonstrates that one thing makes another thing happen.

Common ways researchers gather data for correlational studies

How data is collected in field research can be an essential part of ensuring that it is interpreted correctly. Below, we examine various data collection procedures and tools to understand how they affect the ultimate outcomes.

Data collection procedures used in correlational research

The data collection procedure used in research can vary depending on what variables are being studied and the resources available. Common methods may include: 

• Surveys
• Questionnaires
• Interviews
• Observation of behavior in real-world contexts
• Analysis of existing records and data 

Regardless of what type of procedure is used, consistency and standardization when collecting data from all participants can be key to gathering reliable data. Reliable measurement tools can be important, too. Whether researchers are using a questionnaire, standardized test, or checklist, ensuring that the tool reliably measures what it claims to measure helps to ensure consistent results over time and across different users. Poorly designed tools can introduce errors into the study at an early stage and impact the ability to draw accurate conclusions.

Study time frames

The time frame of a study can affect the type of relationships that can be analyzed. For example, a cross-sectional study collects data from a single point in time, showing how variables may relate to one another in that moment, while a longitudinal study follows participants over a certain period of time, allowing researchers to observe how the relationship between variables changes over time. 

Each type of study can have its pros and cons. A longitudinal study can help researchers identify patterns that only appear after a long period of time, but they require more time and resources, while a cross-sectional study can allow researchers to arrive at a conclusion more quickly, but does not allow for the identification of long-term patterns.

Correlational research designs and statistics beyond two variables

When studies involve three or more variables, the analysis can be more complex. Researchers not only have to account for how each individual variable relates to the others but also how those relationships shift when other variables are introduced.

The number of variables being studied can determine whether researchers use either correlation analysis or regression analysis. Correlation analysis is generally used when the researcher wants to understand the direction and the strength of two variables. It identifies a numerical value that shows how closely these variables move together, but does not make predictions about what that relationship means. 

Regression analysis goes further. It is used when a researcher wants to predict the value of one variable in relation to two or more other variables or to understand how each variable contributes to the outcome when other variables are present. 

In other words, correlation analysis is generally used when a researcher wants to know if two variables are related and how strong that relationship is; regression analysis is generally used when researchers want to identify how much one variable predicts or explains another, and is typically more useful.

Real-world examples of correlational research

Looking at real-world examples of correlational research can help understand how correlation analysis can be applied to real life as well as identify its limitations.

Shopping habits example

Correlational research can be used to understand the factors that influence consumer spending. For example, a researcher might look at the relationship between credit card perks and increased spending or time spent on social media and impulse spending. Marketers and brands may use this type of information to make decisions about ad spending, promotions, or customer targeting. That said, although this type of research can identify a correlation between various factors and shopping habits, it may not be able to pinpoint what is causing these changes for individual shoppers.

Store ambiance example

Researchers may look at whether people spend more money if they spend more time in a store or determine if factors like music, lighting, or store layout increase foot traffic or sales in particular departments in a shopping mall. While this type of observational research can determine how consumers may respond to these factors, it can be difficult to determine whether one factor is directly changing consumer behavior or is merely associated with it.

Mental health research example

This type of study can also be applied to mental health research. For example, researchers may examine the relationships between sleep and anxiety levels or how early childhood experiences can impact adult mental health. When studying these variables, it may be considered unethical to directly experiment by manipulating variables like stress or trauma, particularly on people with mental health challenges, which is why observational correlational research can play such an essential role in this field.

Correlational research and therapy

Correlational research has offered significant insights into the efficacy of various therapy treatments. For example, correlational research studies have been performed to analyze the effectiveness of online formats for therapy, which can be more convenient and cost-effective for many individuals. These studies typically involve measuring different outcomes in people who have engaged in this type of therapy over a significant period of time. Researchers may also make comparisons to the same types of results from people who have engaged in traditional in-person therapy. 

Effectiveness of online therapy

A 2021 study, for instance, found that “Clinically, therapy is no less efficacious when delivered via videoconferencing than in-person.” For those who are interested in trying online therapy for themselves, a virtual therapy platform like BetterHelp can be one option to consider. You can use BetterHelp to meet with a licensed provider via phone, video call, and/or online chat to address any mental health challenges you may be facing.