What are the data types and sampling methods in Advanced Higher Geography data handling?
Handling data types and sampling: distinguishing nominal, ordinal and interval data, and choosing random, regular or stratified sampling, so that the right presentation and statistical test can be selected.
How to handle data types and sampling in SQA Advanced Higher Geography data handling: distinguishing nominal, ordinal and interval data and choosing random, regular or stratified sampling, so the correct graph and statistical test can be selected for the data.
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What this key area is asking
Before you present or test data, you must know what kind of data it is and how it was sampled. The spec distinguishes nominal, ordinal and interval data, and random, regular and stratified sampling. These two ideas control everything downstream: the data type decides which graph and which statistical test are valid, and the sampling decides whether the data is representative enough to trust.
The three data types
Classifying data correctly is the first step in data handling. Get it wrong and you may apply a test the data cannot support.
- Nominal. Categories with no rank (residential, industrial, retail).
- Ordinal. A rank order, gaps unequal or unknown (quality 1 to 5, hierarchy).
- Interval. A numeric scale with equal gaps (degrees, metres per second).
Why the data type matters
The link from data type to method is the practical reason the distinction is examined. Ranked data points to Spearman's rank; equal-interval numeric data points to Pearson's and to means and standard deviations. Categorical data is summarised by counts and the mode, and tested for association with chi-square.
Choosing a sampling method
- Random. Equal chance for every item; removes bias; a general unbiased sample.
- Regular (systematic). Fixed intervals; even coverage; suits transects and grids.
- Stratified. Subgroups sampled in proportion; represents small but important groups.
- Combined. Methods can be mixed, for example stratified then random within strata.
Examples in context
Try this
Q1. Classify each as nominal, ordinal or interval: land use type; a 1 to 5 quality score; temperature in degrees. [3 marks]
- Cue. Land use type is nominal; a 1 to 5 score is ordinal; temperature is interval.
Q2. Which sampling method ensures small but important subgroups are represented? [1 mark]
- Cue. Stratified sampling.
Exam-style practice questions
Practice questions written in the style of SQA exam questions on this dot point, with worked answer explainers. The year tag is the paper they imitate, not the source.
SQA AH data4 marksDistinguish between nominal, ordinal and interval data, giving a geographical example of each.Show worked answer →
Nominal data is categorical with no order (land use types: residential, industrial, retail). Ordinal data has a rank order but unequal or unknown gaps (an environmental quality rating from 1 to 5, or a settlement hierarchy). Interval data is numerical on a scale with equal gaps (temperature in degrees, river velocity in metres per second).
A full answer defines each type, gives a clear geographical example, and notes why the distinction matters: the data type controls which graphs and statistical tests are valid. For example, Spearman's rank needs ordinal (ranked) data, while Pearson's correlation needs interval data, so misclassifying the data leads to the wrong test.
SQA AH data4 marksExplain how random, regular and stratified sampling differ and when each is appropriate.Show worked answer →
Random sampling gives every item an equal chance of selection, removing bias, and suits a general unbiased sample. Regular (systematic) sampling takes items at fixed intervals, giving even coverage, and suits a transect or grid. Stratified sampling divides the population into subgroups and samples each in proportion, ensuring small but important groups are represented.
Strong answers define each method, state when it is appropriate, and link the choice to representativeness and to valid statistics. They may note that sampling can combine methods (for example stratified then random within strata) and that a sound sample is the precondition for trustworthy analysis.
Related dot points
- Graphical presentation of data: bipolar analysis, dispersion diagram, kite diagram, logarithmic graph, polar graph, systems diagrams, scattergraph and triangular graph, and choosing the right graph for the data.
The examinable graphical techniques in SQA Advanced Higher Geography: bipolar analysis, dispersion diagram, kite diagram, logarithmic graph, polar graph, systems diagrams, scattergraph and triangular graph. Covers what each shows and how to choose the right graph for the data.
- Mapping and map-based diagrams: annotated overlay, choropleth map, cross section, dot map, flow line map, isoline map, proportional symbols, sphere of influence map and transect, and choosing the right one for the data.
The examinable mapping and map-based diagram techniques in SQA Advanced Higher Geography: annotated overlay, choropleth, cross section, dot map, flow line, isoline, proportional symbols, sphere of influence and transect. Covers what each shows and how to choose the right one.
- Descriptive statistics: measures of central tendency (mean, median, mode) and measures of dispersion (range, interquartile range, standard deviation, standard error of the mean, coefficient of variation).
The examinable descriptive statistics in SQA Advanced Higher Geography: measures of central tendency (mean, median, mode) and measures of dispersion (range, interquartile range, standard deviation, standard error of the mean, coefficient of variation), and what each reveals about a data set.
- Correlation tests: Spearman's rank correlation for ranked data and Pearson's product moment correlation coefficient for interval data, interpreting the coefficient and its significance.
How to use the two correlation tests in SQA Advanced Higher Geography: Spearman's rank correlation coefficient for ranked data and Pearson's product moment correlation coefficient for interval data, including interpreting the coefficient between minus 1 and plus 1 and judging significance.
- Designing research and fieldwork: setting aims and hypotheses, choosing appropriate primary and secondary techniques, planning a sampling strategy and location, and piloting before collecting data.
How to design a research and fieldwork methodology in SQA Advanced Higher Geography: setting clear aims and hypotheses, selecting appropriate primary and secondary techniques, planning a sampling strategy and a suitable location, and piloting methods before collecting data.
Sources & how we know this
- Advanced Higher Geography Course Specification — SQA (2019)
- Advanced Higher Geography Specimen Question Paper — SQA (2019)