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Mathematical Requirements (Appendix C)

Probability, Sampling, Statistical Tests and Uncertainty: Correlation Coefficient

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Overview

Recall questions on Correlation Coefficient.

What you need to know

Answer each one before you reveal it.

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Key term

Distinguish between the product-moment correlation coefficient and the rank correlation coefficient.

Key term

Why would you select a correlation coefficient rather than a t-test?

Key term

Why can a coefficient close to +1 still fail to be significant?

Questions from this subtopic

Each one with the model answer underneath.

  1. 1

    What does a correlation coefficient measure?

    Model answer

    It measures the strength and the direction of the linear relationship between two variables measured on the same individuals.

  2. 2

    What range of values can a correlation coefficient take, and what do the extremes mean?

    Model answer

    It runs from −1 to +1, where +1 is a perfect positive linear relationship, −1 is a perfect negative one and 0 indicates no linear relationship at all.

  3. 3

    A study of two variables gives a correlation coefficient of −0.87. Interpret this value.

    Model answer

    There is a strong negative correlation, so as one variable increases the other tends to decrease in a close to linear way.

  4. 4

    Why would you select a correlation coefficient rather than a t-test?

    Model answer

    A t-test asks whether two groups differ in their means, whereas a correlation coefficient asks whether two variables measured on the same individuals vary together.

  5. 5

    Distinguish between the product-moment correlation coefficient and the rank correlation coefficient.

    Model answer

    The product-moment coefficient requires continuous data that are approximately normally distributed and linearly related, whereas the rank correlation coefficient is used when data are ranked or are not normally distributed.

  6. 6

    Why should a scatter diagram be examined before a correlation coefficient is calculated?

    Model answer

    It shows whether the relationship is roughly linear and reveals outliers, either of which would make the calculated coefficient misleading.

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