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Correlational studies
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A correlation looks for a relationship between two variables, called co-variables. Nothing is manipulated: both are simply measured.
Types of correlation
Positive correlation: as one variable increases, the other increases too. Example: the more hours revised, the higher the test score.
Negative correlation: as one variable increases, the other decreases. Example: the more hours spent awake, the lower the reaction speed.
No correlation: there is no relationship between the two variables. Example: shoe size and IQ.
Correlations are shown on a scatter diagram.
Negative correlation: as one variable increases, the other decreases. Example: the more hours spent awake, the lower the reaction speed.
No correlation: there is no relationship between the two variables. Example: shoe size and IQ.
Correlations are shown on a scatter diagram.
Correlation is not causation
A correlation shows that two variables are linked, but it cannot show cause and effect.
A third variable may cause both. Example: ice cream sales and drowning are positively correlated, but hot weather causes both.
So we cannot say 'revising causes higher scores' from a correlation alone, even if it seems likely.
A third variable may cause both. Example: ice cream sales and drowning are positively correlated, but hot weather causes both.
So we cannot say 'revising causes higher scores' from a correlation alone, even if it seems likely.
Strengths and weaknesses
Strengths: can study variables that would be unethical or impossible to manipulate (such as stress and illness). Uses existing data, so can be quick. Can suggest ideas for later experiments.
Weaknesses: cannot show cause and effect. A third variable may explain the link. Only shows a relationship between the variables measured, not why it exists.
Weaknesses: cannot show cause and effect. A third variable may explain the link. Only shows a relationship between the variables measured, not why it exists.
A study finds that the more time teenagers spend on social media, the lower their self-esteem. What type of correlation is this, and can we say social media causes low self-esteem?
- As one co-variable goes up, the other goes down: a negative correlation.
- Correlation does not show causation: people with low self-esteem might use social media more, or a third variable might explain both.
Answer: A negative correlation; it cannot show that social media causes low self-esteem.
A correlation measures the relationship between two co-variables: positive (both rise), negative (one rises, the other falls) or none. Correlations can study variables that cannot be manipulated, but they cannot show cause and effect, as a third variable may be involved.
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