Throughout geography you should be provided with many opportunities to identify weaknesses or limitations within data. For example, you may be able to comment on sample size or the way in which data was collected. Additionally, you should be able to identify limitations in graphs such as scatter graphs. For example if you were testing the relationship between the wealth and health of a country, then using just the data of three countries would be insufficient so your data is limited.

Spearman's rank correlation coefficient

Spearman's rank correlation coefficient offers the opportunity to use a statistical test to determine the strength of any relationship (correlation) between two sets of data. This enables us to determine how accurate or reliable the data is. Spearman’s rank is a mathematical equation which can be used when at least ten pairs of data are compared in a scatter graph.

The following equation is needed:


{\Sigma} means the total {d^2}

{n} is the number of sets of paired data

{d} is the difference between pairs of ranked data

Spearman's rank always gives an answer between −1 and +1. The numbers between are like a scale, where −1 is a very strong link, 0 is no link and +1 is also a very strong link.

A value of 1 is perfect positive correlation. A value of -1 is a perfect negative correlation. 0 indicates no correlation.

For example, if Spearman's rank was 0.8, because it is close to +1, it means that the link is strong and it is possible to say that those two sets of data are linked, and increase together. If it was −0.8, it is possible to say it was linked and as one increases, the other decreases. If there is no relationship (correlation), a value close to 0 would be arrived at. 0 indicates no correlation therefore the data has no relationship so the results may be limited in what they show/tell us.

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