Glaciated Landscape Skills (AQA A Level Geography)

• Geographical skills are working skills essential to developing a synoptic approach to answering questions but also observing the 'bigger picture' in geography
• It is important to be confident with a mixture of numerical quantitative skills and qualitative written communication skills 
• Many of the skills are already outlined elsewhere in the revision notes

Understanding data

• In data analysis, variables are the amounts that have been measured - the number of drumlins or the size of erratics
• For each variable, a value is noted against each sample - drumlin a, drumlin b etc
• The data set is the collection of total values and is then analysed further
• The sample size and type of data, influence the choice of statistical test to use 
  • Spearman's rank would be used to test for correlation between two variables etc. 
• Types of data can be grouped into:
  • Nominal
  • Ordinal
  • Interval
  • Ratio
• Nominal
  
  • Also known as categorical data, its main purpose is to classify or group information
  • Data is organised into distinct categories, but the categories have no numerical or quantitative meaning
  • Examples of categories can include things such as dog, cat, blue, male and female etc.
  • Or they can be labelled with numerical codes such as 1 for glacial 2 for periglacial etc.
  • They can be summarised using percentages or frequencies e.g. 40% of periglacial pingos are open system
  • Remember they have no order or mathematical relationship and performing statistical analysis is pointless
• Ordinal
  • Ordinal data is a type of data that can be ordered or ranked into categories
  • Examples include:
    • Primary school, secondary school, sixth form or college, and university
      • The categories show a clear progression/order on levels of possible education 
    • Very satisfied, satisfied, neutral, dissatisfied and very dissatisfied
      • These categories show levels of satisfaction, but intervals between them may not be equal
  • Ordinal data allows ranking and comparing of vales, but doesn't provide information on size of the differences  between the categories 
  • Statistical analysis and interpretation can be used such as calculating median, mode, or Spearman's rank; but ordinal data doesn't allow for mathematical calculations such as adding or subtracting
• Interval
  • Interval data is the same as ordinal data, but the intervals between the categories is constant
    • For example pH values of water; scale of temperature or time on a clock
  • Interval data is therefore, a more precise measurement compared to nominal and ordinal data, but it does not include a true zero point
  • Interval data allows for various statistical operations such as calculating mean, median mode, standard deviations, and conducting tests such as t and u-tests 
• Ratio
  • Unlike interval data, ratio data includes a true zero point, which allows for a more comprehensive analysis of the data
  • The difference between any two consecutive values is the same throughout the entire range of the data
  • The true zero point indicates a total absence of the value at that point
  • This makes ratio data the highest level of measurement in terms of precision and mathematical operations
  • Examples include:
    • Weight: an object is weighed in kilograms and grams
    • If a value of zero is recorded it means there was no weight to the object
    • Distance: the distance travelled by a glacier for one day is recorded in centimetres 
    • A recorded value of zero indicates that the glacier travelled no distance
  • Ratio data allows for a wide range of mathematical operations, including addition, subtraction, multiplication, and division
  • Statistical analysis techniques applicable to ratio data include measures of central tendency (mean, median, mode), measures of dispersion (range, variance, standard deviation), and parametric tests

Percentage and percentage change

• To give the amount A as a percentage of sample B, divide A by B and multiply by 100
  • In 2020, 25 out of 360 homes in Catland were burgled
  • What is the percentage (to the nearest whole number) of homes burgled?

• A percentage change shows by how much something has either increased or decreased

• • In 2021 only 21 houses were burgled. What is the percentage change in Catland?
  • 

• • There has been a decrease of 16% in the rate of burglaries in the Catland area
• Remember that a positive figure shows an increase but a negative is a decrease

Mann-Whitney U test

• Also known as the Wilcoxon rank-sum test, it is a nonparametric test used to compare two independent groups, population or samples to determine if there is a significant difference between their distributions
• It makes no assumptions of the data being normally distributed
• The test works by assigning ranks to the observations from both groups combined and considers all the values as a single pool
• Then, it compares the sums of the ranks for each group
• The test looks at whether the distributions of the two groups differ significantly based on the ranks

• A general outline of how the Mann-Whitney U test works:
  • Combine the data from both groups into a single dataset
  • Rank the combined data, assigning a rank to each observation (identical data are given an average rank)
  • Then calculate the sum of the ranks for each group
  • Use the U statistic (the smaller of the two sums of ranks) to determine the test statistic
  • Compare the test statistic to the critical values in the Mann-Whitney U distribution or use a significance level to determine if the difference between the groups is statistically significant
  • If the p-value is below the chosen significance level (often 0.05), the test concludes that there is a significant difference between the groups
• The Mann-Whitney U test does not make any specific distribution for the data and is effective in comparing ordinal or continuous variables between two independent groups