Geography Fieldwork

A Level

Data analysis

4. Data analysis

Sophisticated data analysis will help you spot patterns, trends and relationships in your results. Data analysis can be qualitative and/or quantitative, and may include statistical tests. An example of a statistical test is outlined below.

Spearman’s Rank Correlation Test

Spearman’s Rank Correlation is a statistical test to test whether there is a significant relationship between two sets of data.

The Spearman’s Rank Correlation test can only be used if there are at least 10 (ideally at least 15-15) pairs of data.

There are 3 steps to take when using the Spearman’s Rank Correlation Test

Step 1. State the null hypothesis

There is no significant relationship between _______ and _______

Step 2. Calculate the Spearman’s Rank Correlation Coefficient

`r_s = 1-(6∑D^2) / (n(n^2-1))`

  • `r_s` = Spearman's Rank correlation coefficient
  • `D` = differences between ranks
  • `n` = number of pairs of measurements

Step 3. Test the significance of the result

Compare the value of `r_s` that you have calculated against the critical value for `r_s` at a confidence level of 95% / significance value of p = 0.05.

If `r_s` is equal to or above the critical value (p=0.05) the REJECT the null hypothesis. There is a SIGNIFICANT relationship between the 2 variables.

A positive sign for `r_s` indicates a significant positive relationship and a negative sign indicates a significant negative relationship.

If `r_s` (ignoring any sign) is less than the critical value, ACCEPT the null hypothesis. There is NO SIGNIFICANT relationship between the 2 variables.

Worked example

A geographer is interested in whether there is a relationship between the area of a corrie and the height of the corrie's backwall. 

She combines fieldwork measurements with map evidence for 17 corries in an upland area in north Wales. Here are the results.

Corrie backwall height (m) Corrie length (m)
1025 230
1850 180
1780 80
1320 255
440 200
700 170
1600 400
895 180
640 110
625 173
890 160
810 130
1020 255
580 60
885 175
800 200
2110 450

Step 1. State the null hypothesis

There is no significant relationship between the length and the backwall height of a corrie.

Step 2. Calculate the Spearman’s Rank Correlation Coefficient

(a) Rank the measurements

Corrie backwall height (m) Corrie length (m)
Data Rank Data Rank
1850 2 180 8.5
1780 3 80 16
1320 5 255 3.5
440 17 200 6.5
700 13 170 12
1600 4 400 2
895 8 180 8.5
640 14 110 15
625 15 173 11
890 9 160 13
810 11 130 14
1020 7 255 3.5
580 16 60 17
885 10 175 10
800 12 200 6.5
2110 1 450 1

(b) Calculate `D` and `D^2`

Corrie backwall height (m) Corrie length (m) `D` `D^2`
Data Rank Data Rank
1025 6 230 5 -1 1
1850 2 180 8.5 6.5 42.25
1780 3 80 16 13 169
1320 5 255 3.5 -1.5 2.25
440 17 200 6.5 -10.5 110.25
700 13 170 12 -1 1
1600 4 400 2 -2 4
895 8 180 8.5 0.5 0.25
640 14 110 15 1 1
625 15 173 11 -4 16
890 9 160 13 4 16
810 11 130 14 3 9
1020 7 255 3.5 -3.5 12.25
580 16 60 17 1 1
885 10 175 10 0 0
800 12 200 6.5 -5.5 30.25
2110 1 450 1 0 0
         SUM 415.5

(c) Calculate `∑D^2`i.e. the sum of the `D^2`column`= 945.5`

(d) Calculate `r_s`

`r_s = 1-(6∑D^2) / (n(n^2-1))`

`r_s = 1-(6xx415.5) / (17xx(17^2-1))`

`r_s = 0.491`

Step 3. Test the significance of the result

The critical value at `p=0.05` significance level for `17` pairs of measurements is `0.488`

Since our calculated value of `0.491> 0.488` (ignore the minus sign), the null hypothesis can be rejected.

In conclusion, there is a significant positive relationship between the length and backwall height of corries. Although statistically significant, it is not a very strong relationship. Why might this be?