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Understanding Spearman’s Rank Correlation Coefficient

Learn the statistical measure assessing relationship strength between variables. Interpret r.s, formula, significance levels, and more.

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Understanding Spearman’s Rank Correlation Coefficient

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  1. Spearman’s Rank Correlation Coefficient Pages 28 to34

  2. Either write a definition or draw a diagram to illustrate the terms below (see page 28 & 29). Correlation Scatter graphs Independent variable Line of best fit Dependent variable Residual value Positive linear relationship Negative linear relationship Spearman’s Rank CorrelationCoefficient A statistical measure which shows the strength of a relationship between two variables. It also outlines the type of relationship whether positive or negative. Minimum of 10 sets of paired values needed for this test to be statistically reliable. A strong correlation indicates that there is a link between the two factors but not that one causes the other. For example there may be a strong correlation between the incidence of sunburn and the sale of ice cream, but this does not imply that ice cream causes sunburn. This test simply indicates that there may be a causative link which can be explored with further investigation. Key Terms associated with Correlation

  3. Write out the formula for the calculation of r s Interpretation of r s Increasing Increasing Significance Significance -1 0 +1 Perfect No Perfect Negative Correlation Positive Linear Linear Relationship Relationship Spearman’s Rank How do we know if an answer is close enough to 1 to be considered significant?

  4. Questions - Page 32 - 34 • Explain why 95% is a cut off point for significance. • How do you calculate degrees of freedom? • What do significance levels of 95%, 99% and 99.9% mean? • Carefully study the worked example on page 30 & 31.

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