Correlations
Key Terms
Correlations- A mathematical technique in which the researcher investigates an association with 2
variables, called co-variables.
Correlation Coefficient- A number between -1 and +1 that represents the direction and strength of a
relationship between co-variables.
Types of correlation
Correlation illustrates the strength and direction of an association between two or more co-variables
(things that are being measured). Correlations can be plotted on a scattergram.
Positive correlation- as one variable increases, so does the other.
Negative correlation- as one variable decreases, the other increases.
No (zero) correlation- there is no correlation between the two variables.
The difference between correlations and experiments
In an experiment, the researcher controls or manipulates the independent variable in order to
measure the effect on the dependent variable. In a correlation, there is no such manipulation of one
variable and therefore it is not possible to establish cause and effect between one co-variable and
another. Even if there is a strong positive correlation between two variables it does not have to be
the cause of another. Other variables are called intervening variables.
Writing a correlational hypothesis
There is no IV and DV in a correlation.
Co-variables must be operationalised:
● Directional: there is a positive or negative correlation between co-variable and B
● Non-Directional: there is a correlation between co-variable A and B
Correlation Coefficient
Two statistical tests produce correlation coefficients. It is a numerical value between +1 and -1. This
value tells us the strength and direction of the correlation.
+1 represents perfect positive correlation.
-1 represents perfect negative correlation
The stronger the correlation coefficient to +1 or -1, the stronger it is. The closer to zero it is, the
weaker the correlation is.
Even if the correlation coefficient is weak, data can be significant in difference.
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Key Terms
Correlations- A mathematical technique in which the researcher investigates an association with 2
variables, called co-variables.
Correlation Coefficient- A number between -1 and +1 that represents the direction and strength of a
relationship between co-variables.
Types of correlation
Correlation illustrates the strength and direction of an association between two or more co-variables
(things that are being measured). Correlations can be plotted on a scattergram.
Positive correlation- as one variable increases, so does the other.
Negative correlation- as one variable decreases, the other increases.
No (zero) correlation- there is no correlation between the two variables.
The difference between correlations and experiments
In an experiment, the researcher controls or manipulates the independent variable in order to
measure the effect on the dependent variable. In a correlation, there is no such manipulation of one
variable and therefore it is not possible to establish cause and effect between one co-variable and
another. Even if there is a strong positive correlation between two variables it does not have to be
the cause of another. Other variables are called intervening variables.
Writing a correlational hypothesis
There is no IV and DV in a correlation.
Co-variables must be operationalised:
● Directional: there is a positive or negative correlation between co-variable and B
● Non-Directional: there is a correlation between co-variable A and B
Correlation Coefficient
Two statistical tests produce correlation coefficients. It is a numerical value between +1 and -1. This
value tells us the strength and direction of the correlation.
+1 represents perfect positive correlation.
-1 represents perfect negative correlation
The stronger the correlation coefficient to +1 or -1, the stronger it is. The closer to zero it is, the
weaker the correlation is.
Even if the correlation coefficient is weak, data can be significant in difference.
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