Student Resources What Is a Correlation? By Kendra Cherry, MSEd Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book." Learn about our editorial process Updated on November 30, 2023 Fact checked Verywell Mind content is rigorously reviewed by a team of qualified and experienced fact checkers. Fact checkers review articles for factual accuracy, relevance, and timeliness. We rely on the most current and reputable sources, which are cited in the text and listed at the bottom of each article. Content is fact checked after it has been edited and before publication. Learn more. by James Lacy Fact checked by James Lacy James Lacy, MLS, is a fact-checker and researcher. Learn about our editorial process Print Correlations indicate relationships between variables. Kiatdd / Wikimedia Commons Table of Contents View All Table of Contents What Is a Correlation Coefficient? Types Scatter Plots and Correlation Strong vs. Weak Correlations Correlation Does Not Equal Causation Illusory Correlations Frequently Asked Questions Trending Videos Close this video player A correlation means that there is a relationship between two or more variables. This does not imply, however, that there is necessarily a cause or effect relationship between them. Instead, it simply means that there is some type of relationship, meaning they change together at a constant rate. A correlation coefficient is a number that expresses the strength of the relationship between the two variables. At a Glance Correlation can help researchers understand if there is an association between two variables of interest. Such relationships can be positive, meaning they move in the same direction together, or negative, meaning that as one goes up, the other goes down. Correlations can be visualized using scatter plots to show how measurements of a variable change along an x- and y-axis.It is important to remember that while correlations can help show a relationship, correlation does not indicate causation. What Is a Correlation Coefficient? A correlation coefficient, often expressed as r, indicates a measure of the direction and strength of a relationship between two variables. When the r value is closer to +1 or -1, it indicates that there is a stronger linear relationship between the two variables. Correlational studies are quite common in psychology, particularly because some things are impossible to recreate or research in a lab setting. Instead of performing an experiment, researchers may collect data to look at possible relationships between variables. From the data they collect and its analysis, researchers then make inferences and predictions about the nature of the relationships between variables. Helpful Hint A correlation is a statistical measurement of the relationship between two variables. Remember this handy rule: The closer the correlation is to 0, the weaker it is. The closer it is to +/-1, the stronger it is. Types of Correlation Correlation strength ranges from -1 to +1. Positive Correlation A correlation of +1 indicates a perfect positive correlation, meaning that both variables move in the same direction together. In other words, +1 is the strong positive correlation you can find. Negative Correlation A correlation of –1 indicates a perfect negative correlation, meaning that as one variable goes up, the other goes down. Zero Correlation A zero correlation suggests that the correlation statistic does not indicate a relationship between the two variables. This does not mean that there is no relationship at all; it simply means that there is not a linear relationship. A zero correlation is often indicated using the abbreviation r = 0. How Are Variables Used in Psychology Research? Scatter Plots and Correlation Scatter plots (also called scatter charts, scattergrams, and scatter diagrams) are used to plot variables on a chart to observe the associations or relationships between them. The horizontal axis represents one variable, and the vertical axis represents the other. Scatter Plot diagram. Investopedia Each point on the plot is a different measurement. From those measurements, a trend line can be calculated. The correlation coefficient is the slope of that line. When the correlation is weak (r is close to zero), the line is hard to distinguish. When the correlation is strong (r is close to 1), the line will be more apparent. Strong vs. Weak Correlations Correlations can be confusing, and many people equate positive with strong and negative with weak. A relationship between two variables can be negative, but that doesn't mean that the relationship isn't strong. A weak positive correlation indicates that, although both variables tend to go up in response to one another, the relationship is not very strong. A strong negative correlation, on the other hand, indicates a strong connection between the two variables, but that one goes up whenever the other one goes down. For example, a correlation of -0.97 is a strong negative correlation, whereas a correlation of 0.10 indicates a weak positive correlation. A correlation of +0.10 is weaker than -0.74, and a correlation of -0.98 is stronger than +0.79. Correlation Does Not Equal Causation Correlation does not equal causation. Just because two variables have a relationship does not mean that changes in one variable cause changes in the other. Correlations tell us that there is a relationship between variables, but this does not necessarily mean that one variable causes the other to change. An oft-cited example is the correlation between ice cream consumption and homicide rates. Studies have found a correlation between increased ice cream sales and spikes in homicides. However, eating ice cream does not cause you to commit murder. Instead, there is a third variable: heat. Both variables increase during summertime. Illusory Correlations An illusory correlation is the perception of a relationship between two variables when only a minor relationship—or none at all—actually exists. An illusory correlation does not always mean inferring causation; it can also mean inferring a relationship between two variables when one does not exist. For example, people sometimes assume that, because two events occurred together at one point in the past, one event must be the cause of the other. These illusory correlations can occur both in scientific investigations and in real-world situations. Stereotypes are a good example of illusory correlations. Research has shown that people tend to assume that certain groups and traits occur together and frequently overestimate the strength of the association between the two variables. For example, suppose someone holds the mistaken belief that all people from small towns are extremely kind. When they meet a very kind person, their immediate assumption might be that the person is from a small town, despite the fact that kindness is not related to city population. What This Means For You Psychology research frequently uses correlations, but it's essential to understand that correlation is not the same as causation. Confusing correlation with causation assumes a cause-effect relationship that might not exist. While correlation can help you see that there is a relationship (and tell you how strong that relationship is), only experimental research can reveal a causal connection. How Confirmation Bias Works Frequently Asked Questions How do you find the correlation coefficient? You can calculate the correlation coefficient in a few different ways, with the same result. The general formula is rXY=COVXY/(SX SY), which is the covariance between the two variables, divided by the product of their standard deviations: How do you calculate a correlation coefficient in Excel? In the cell in which you want the correlation coefficient to appear, enter =CORREL(A2:A7,B2:B7), where A2:A7 and B2:B7 are the variable lists to compare. Press Enter. How do you find a linear correlation coefficient? Finding the linear correlation coefficient requires a long, difficult calculation, so most people use a calculator or software such as Excel or a statistics program. How do you interpret a correlation coefficient? Correlations range from -1.00 to +1.00. The correlation coefficient (expressed as r ) shows the direction and strength of a relationship between two variables. The closer the r value is to +1 or -1, the stronger the linear relationship between the two variables is. What is the difference between correlation and causation? Correlations indicate a relationship between two variables, but one doesn't necessarily cause the other to change. 4 Sources Verywell Mind uses only high-quality sources, including peer-reviewed studies, to support the facts within our articles. Read our editorial process to learn more about how we fact-check and keep our content accurate, reliable, and trustworthy. Mukaka M. A guide to appropriate use of correlation coefficient in medical research. Malawi Med J. 2012;24(3):69-71. Heath W. Psychology Research Methods: Connecting Research to Students’ Lives. Cambridge University Press. Chen DT. When correlation does not imply causation: Why your gut microbes may not (yet) be a silver bullet to all your problems. Harvard University. Association for Psychological Science. Research states that prejudice comes from a basic human need and way of thinking. Additional Reading Correlation and regression. In: Swinscow TDV. Statistics at Square One. The BMJ. By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book." See Our Editorial Process Meet Our Review Board Share Feedback Was this page helpful? Thanks for your feedback! What is your feedback? Helpful Report an Error Other Submit