While the correlation coefficient is a useful measure, it has its limitations: Correlation coefficients are usually associated with measuring a linear relationship.įor example, if you compare hours worked and income earned for a tradesperson who charges an hourly rate for their work, there is a linear (or straight line) relationship since with each additional hour worked the income will increase by a consistent amount. Where the correlation coefficient is 0 this indicates there is no relationship between the variables (one variable can remain constant while the other increases or decreases).
as one variable decreases the other also decreases, or when one variable increases the other also increases. If the correlation coefficient has a positive value (above 0) it indicates a positive relationship between the variables meaning that both variables move in tandem, i.e. This means that the variables move in opposite directions (ie when one increases the other decreases, or when one decreases the other increases).
If the correlation coefficient has a negative value (below 0) it indicates a negative relationship between the variables. The coefficient's numerical value ranges from +1.0 to –1.0, which provides an indication of the strength and direction of the relationship. In practice, however, it remains difficult to clearly establish cause and effect, compared with establishing correlation.įor two variables, a statistical correlation is measured by the use of a Correlation Coefficient, represented by the symbol (r), which is a single number that describes the degree of relationship between two variables. smoking is correlated with alcoholism, but it does not cause alcoholism). smoking causes an increase in the risk of developing lung cancer), or it can correlate with another (e.g. Theoretically, the difference between the two types of relationships are easy to identify - an action or occurrence can cause another (e.g. This is also referred to as cause and effect. there is a causal relationship between the two events. 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.Ĭausation indicates that one event is the result of the occurrence of the other event i.e. If we consider the two variables "price" and "purchasing power", as the price of goods increases a person's ability to buy these goods decreases (assuming a constant income).Ĭorrelation is a statistical measure (expressed as a number) that describes the size and direction of a relationship between two or more variables. Two or more variables considered to be related, in a statistical context, if their values change so that as the value of one variable increases or decreases so does the value of the other variable (although it may be in the opposite direction).įor example, for the two variables "hours worked" and "income earned" there is a relationship between the two if the increase in hours worked is associated with an increase in income earned.
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