Correlation Definitions, Examples & InterpretationBy Dr. Saul McLeod, updated 2020 Show
Correlation means association - more precisely it is a measure of the extent to which two variables are related. There are three possible results of a correlational study: a positive correlation, a negative correlation, and no correlation.
ScattergramsA correlation can be expressed visually. This is done by drawing a scattergram (also known as a scatterplot, scatter graph, scatter chart, or scatter diagram). A scattergram is a graphical display that shows the relationships or associations between two numerical variables (or co-variables), which are represented as points (or dots) for each pair of score. A scattergraph indicates the strength and direction of the correlation between the co-variables. When you draw a scattergram it doesn't matter which variable goes on the x-axis and which goes on the y-axis. Remember, in correlations we are always dealing with paired scores, so the values of the 2 variables taken together will be used to make the diagram. Decide which variable goes on each axis and then simply put a cross at
the point where the 2 values coincide. Some uses of CorrelationsSome uses of Correlations
Correlation Coefficients: Determining Correlation StrengthInstead of drawing a scattergram a correlation can be expressed numerically as a coefficient, ranging from -1 to +1. When working with continuous variables, the correlation coefficient to use is Pearson’s r. The correlation coefficient (r) indicates the extent to which the pairs of numbers for these two variables lie on a straight line. Values over zero indicate a positive correlation, while values under zero indicate a negative correlation. A correlation of –1 indicates a perfect negative correlation, meaning that as one variable goes up, the other goes down. A correlation of +1 indicates a perfect positive correlation, meaning that as one variable goes up, the other goes up. There is no rule for determining what size of correlation is considered strong, moderate or weak. The interpretation of the coefficient depends on the topic of study. When studying things that are difficult to measure, we should expect the correlation coefficients to be lower (e.g. above 0.4 to be relatively strong). When we are studying things that are more easier to measure, such as socioeconomic status, we expect higher correlations (e.g. above 0.75 to be relatively strong).) In these kinds of studies, we rarely see correlations above 0.6. For this kind of data, we generally consider correlations above 0.4 to be relatively strong; correlations between 0.2 and 0.4 are moderate, and those below 0.2 are considered weak. When we are studying things that are more easily countable, we expect higher correlations. For example, with demographic data, we we generally consider correlations above 0.75 to be relatively strong; correlations between 0.45 and 0.75 are moderate, and those below 0.45 are considered weak. Correlation vs CausationCausation means that one variable (often called the predictor variable or independent variable) causes the other (often called the outcome variable or dependent variable). Experiments can be conducted to establish causation. An experiment isolates and manipulates the independent variable to observe its effect on the dependent variable, and controls the environment in order that extraneous variables may be eliminated. A correlation between variables, however, does not automatically mean that the change in one variable is the cause of the change in the values of the other variable. A correlation only shows if there is a relationship between variables. While variables are sometimes correlated because one does cause the other, it could also be that some other factor, a confounding variable, is actually causing the systematic movement in our variables of interest. Correlation does not always prove causation as a third variable may be involved. For example, being a patient in hospital is correlated with dying, but this does not mean that one event causes the other, as another third variable might be involved (such as diet, level of exercise). Summary
Strengths of Correlations1. Correlation allows the researcher to investigate naturally occurring variables that maybe unethical or impractical to test experimentally. For example, it would be unethical to conduct an experiment on whether smoking causes lung cancer. 2. Correlation allows the researcher to clearly and easily see if there is a
relationship between variables. This can then be displayed in a graphical form. Limitations of Correlations1. Correlation is not and cannot be taken to imply causation. Even if there is a very strong association between two variables we cannot assume that one causes the other. For example suppose we found a positive correlation between watching violence on T.V. and violent behavior in adolescence. It could be that the cause of both these is a
third (extraneous) variable - say for example, growing up in a violent home - and that both the watching of T.V. and the violent behavior are the outcome of this. 2. Correlation does not allow us to go beyond the data that is given. For example suppose it was found that there was an association between time spent on homework (1/2 hour to 3 hours) and number of G.C.S.E. passes (1 to 6). It would not be legitimate to infer from this that spending 6 hours on homework
would be likely to generate 12 G.C.S.E. passes. How to reference this article:How to reference this article:McLeod, S. A. (2018, January 14). Correlation definitions, examples & interpretation. Simply Psychology. www.simplypsychology.org/correlation.html Home | About Us | Privacy Policy | Advertise | Contact Us Simply Psychology's content is for informational and educational purposes only. Our website is not intended to be a substitute for professional medical advice, diagnosis, or treatment. © Simply Scholar Ltd - All rights reserved Is a relationship between two variables where if one variable increases the other one also increases?A positive correlation is a relationship between two variables that move in tandem—that is, in the same direction. A positive correlation exists when one variable decreases as the other variable decreases, or one variable increases while the other increases.
What is a relationship between two variables where if one variable increases the other one decreases?If the correlation coefficient has a negative value (below 0) it indicates a negative relationship between the variables. This means that the variables move in opposite directions (ie when one increases the other decreases, or when one decreases the other increases).
What is the relationship called when both variables increase?Positive Correlation: What It Is, How to Measure It, Examples. Positive correlation is a relationship between two variables in which both variables move in tandem.
What is the relationship between the 2 variables?A correlation is a relationship between two variables. The amount of correlation, or relationship, can be explained in a numerical form called a correlation coefficient - defined as a numerical representation of the strength and direction of the relationship.
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