AQA · A-Level · Psychology

    Course version: 7182-2025 · Exams from 2027

    Analysis and interpretation of correlation, including correlation…

    Full specification wording

    Data handling and analysis: Analysis and interpretation of correlation, including correlation coefficients.

    A correlation measures how two co-variables move together.

    Checked against the AQA 7182-2025 specification (opens in a new tab) on .

    • Point 6 of 8
    • 4 key terms

    Study notes

    From the checked teaching notes for this specification point.

    The idea

    A correlation measures how two co-variables move together. Neither is manipulated, both are merely measured, which is exactly why a correlation can never on its own establish a cause. The coefficient is one number running from -1 through 0 to +1: its sign gives direction and its size gives strength, so a coefficient of -0.76 describes a relationship precisely as strong as +0.76, simply running the other way. A figure near zero means the two vary independently. Whether a coefficient is significant is decided against a critical value table and depends on sample size, so with a large enough sample even a feeble coefficient reaches significance and means little in practice. Two traps follow: a third variable may be driving both, and a curvilinear relationship yields a coefficient close to zero.

    What earns the marks

    • Correlation defined as an association between two co-variables, both of which are measured rather than manipulated.
    • Direction read from the sign: positive means both rise together, negative means one rises as the other falls, zero means no relationship.
    • Strength read from how close the figure lies to one, independently of its sign.
    • Interpretation set in the stem, stating what the relationship means for the two named variables.
    • Significance judged against a critical value, with sample size affecting whether a given coefficient counts as significant.
    • Evaluation: a correlation cannot demonstrate causation, because an untested third variable may be responsible for both co-variables.
    • Evaluation: a curvilinear relationship, such as arousal and performance, returns a coefficient near zero despite a strong systematic relationship.

    Common mistakes and how to put them right

    • Reading a negative coefficient as a weak one, so that -0.8 is described as weaker than +0.3.

    • Concluding that one variable causes the other, which is the most heavily penalised error on this topic.

    • Calling the two measures independent and dependent variables.

      Put it right: In a correlation they are co-variables.

    • Treating a coefficient of zero as a failed study rather than as a genuine finding of no relationship.

    Exam tips

    • Turn the coefficient into a sentence about the stem's own variables before you evaluate it.
    • If asked why a conclusion is unjustified, name a plausible third variable drawn from the scenario instead of stopping at 'correlation is not causation'.
    • Quote the figure when you evaluate; 'a strong correlation' without the number leaves the interpretation mark unclaimed.

    Try the lesson

    The start of the Data handling and analysis: Analysis and interpretation of correlation, including correlation coefficients. lesson, made from checked teaching notes for this specification point.

    Have a go first

    A researcher calculates a correlation coefficient of -0.85 between the number of hours spent playing video games and scores on a memory test. How should this correlation coefficient be interpreted?

    Listen

    A correlation measures how two co-variables move together. Neither is manipulated, both are merely measured, which is exactly why a correlation can never on its own establish a cause. The coefficient is one number running from -1 through 0 to +1: its sign gives direction and its size gives strength, so a coefficient of -0.76 describes a relationship precisely as strong as +0.76, simply running the other way. A figure near zero means the two vary independently. Whether a coefficient is significant is decided against a critical value table and depends on sample size, so with a large enough sample even a feeble coefficient reaches significance and means little in practice. Two traps follow: a third variable may be driving both, and a curvilinear relationship yields a coefficient close to zero. A strong answer earns marks for these points. Correlation defined as an association between two co-variables, both of which are measured rather than manipulated. Direction read from the sign: positive means both rise together, negative means one rises as the other falls, zero means no relationship. Strength read from how close the figure lies to one, independently of its sign. Interpretation set in the stem, stating what the relationship means for the two named variables. Exam tip: Turn the coefficient into a sentence about the stem's own variables before you evaluate it.

    See it

    A left-to-right flowchart starting from a single node labelled Coefficient, which branches into five paths. The first branch goes to Sign gives direction, which then splits into three: Positive: both rise together, Negative: one rises as the other falls, and Zero: no relationship. The second branch goes to Size gives strength, which leads to Closer to one: stronger, then to Significance judged against a critical value, then to Sample size affects significance. The third branch goes to Cannot demonstrate causation, which leads to Untested third variable may drive both. The fourth branch goes to Curvilinear relationship returns a coefficient near zero.
    It shows how the sign and size of a correlation coefficient are read, how significance is judged, and the two evaluation traps of the third variable and the curvilinear relationship.

    Key words

    Say what each word means before you turn the card.

    Co-variables

    Try answering aloud or in your head before turning the card.

    Correlation coefficient

    Try answering aloud or in your head before turning the card.

    Causation

    Try answering aloud or in your head before turning the card.

    Third variables

    Try answering aloud or in your head before turning the card.

    See every definition
    Co-variables
    The two measures in a correlation, both of which are measured rather than manipulated.
    Correlation coefficient
    One number running from -1 through 0 to +1, whose sign gives direction and whose size gives strength.
    Causation
    A correlation cannot demonstrate this on its own, because an untested third variable may be responsible for both co-variables.
    Third variables
    An untested variable that may be driving both co-variables, so a correlation cannot demonstrate causation.

    Label the diagram

    Some boxes have lost their names. Pick the right name for each number.

    The diagram again, with 3 boxes numbered instead of named. A left-to-right flowchart starting from a single node labelled Coefficient, which branches into five paths. The first branch goes to Sign gives direction, which then splits into three: Positive: both rise together, Negative: one rises as the other falls, and Zero: no relationship. The second branch goes to Size gives strength, which leads to Closer to one: stronger, then to Significance judged against a critical value, then to Sample size affects significance. The third branch goes to Cannot demonstrate causation, which leads to Untested third variable may drive both. The fourth branch goes to Curvilinear relationship returns a coefficient near zero.

    • 1Box 1

    • 2Box 2

    • 3Box 3

    0 of 3 labelled

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