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examBoard: AQA
examType: GCSE
lessonTitle: Scatter Diagrams
Psychologists often want to find out if there's a relationship between two variables. For example, is there a link between the number of hours spent studying and exam results? Or between the amount of screen time and quality of sleep? This is where correlation comes in!
Key Definitions:
Correlation studies are useful because they allow psychologists to:
It's important to remember that:
A scatter diagram (or scatterplot) is a visual representation of the relationship between two variables. Each dot on the diagram represents one participant or data point, showing their score on both variables.
To create a scatter diagram:
As one variable increases, the other also increases. The dots form an upward pattern from left to right.
Example: Hours of study and exam results (more study = better results)
As one variable increases, the other decreases. The dots form a downward pattern from left to right.
Example: Hours spent on social media and exam results (more social media = worse results)
No clear relationship between the variables. The dots appear randomly scattered with no pattern.
Example: Shoe size and intelligence (no relationship)
Not all correlations are equally strong. The strength of a correlation is shown by how closely the dots cluster around an imaginary line through the scatter diagram.
The dots form a clear pattern and are close to an imaginary line. The correlation coefficient is close to +1 or -1.
Suggests a reliable relationship between variables.
The dots show a pattern but are more spread out. The correlation coefficient is around +0.5 or -0.5.
Suggests a relationship exists but other factors are also involved.
The dots show only a slight pattern. The correlation coefficient is close to 0.
Suggests only a minor relationship between variables.
The correlation coefficient (r) is a precise measure of the strength and direction of a correlation:
Researchers collected data from 100 teenagers about their daily screen time (hours) and their average sleep duration (hours). They plotted this data on a scatter diagram and found a correlation coefficient of r = -0.68.
This indicates a moderate to strong negative correlation, suggesting that as screen time increases, sleep duration tends to decrease. However, this doesn't prove that screen time causes reduced sleep - perhaps teenagers who can't sleep use their devices more, or perhaps another factor (like stress) affects both variables.
This study demonstrates both the usefulness of correlational research (identifying an important relationship) and its limitations (unable to establish causation).
One of the most important things to remember about correlation is that it doesn't prove causation. Just because two variables are related doesn't mean one causes the other.
Sometimes two variables appear related because they're both affected by a third variable.
Example: Ice cream sales and drowning deaths are positively correlated. Does ice cream cause drowning? No! The third variable is hot weather, which increases both ice cream consumption and swimming (leading to more drowning accidents).
Sometimes it's unclear which variable influences the other, or they might influence each other.
Example: Depression and social isolation are correlated. Does depression cause isolation, or does isolation cause depression? It could work both ways in a cycle.
Despite its limitations, correlational research is extremely valuable in psychology:
Researchers found a positive correlation between breakfast consumption and academic performance in school children. Students who regularly ate breakfast tended to perform better in tests.
While this correlation doesn't prove causation, it led to breakfast programmes in many schools. Follow-up experimental studies (where some children were randomly assigned to receive breakfast) confirmed that breakfast does improve concentration and learning.
This shows how correlational research can identify important relationships that can then be investigated further using experimental methods.
When interpreting scatter diagrams, always look at both the direction (positive/negative) and strength (how closely the dots cluster around a line) of the relationship and remember to consider alternative explanations for any correlation you find.
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