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Fieldwork Skills » Collecting Fieldwork Data

What you'll learn this session

Study time: 30 minutes

Edexcel spec: 1.4

Three ways to sample: random, systematic and stratified
The difference between quantitative and qualitative data
How primary and secondary data work together in an enquiry
How to judge whether your data is accurate and reliable

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Why do we sample?

You cannot measure every pebble on a beach or ask every person in a town. There is not enough time. So geographers collect data from a smaller part of the whole group. This smaller part is called a sample. A good sample gives you a fair picture of the whole place.

Key terms:

Sample: a smaller number of items or people chosen from the whole group to measure or ask.
Random sampling: every item has an equal chance of being picked.
Systematic sampling: picking items at regular intervals, such as every 10 metres.
Stratified sampling: picking items from each sub-group in proportion to the size of that group.

Three ways to sample

🎲 Random

Use random numbers to choose the spots. For example, number the squares on a grid laid over a beach, then let a calculator pick the squares. It avoids bias (unfairly favouring some spots), because you do not choose your favourite spots. But you may miss part of the area by chance.

📏 Systematic

Measure at regular intervals, such as one reading every 10 metres along a line. It covers the whole area evenly and is quick and easy to do. But it can miss a feature that falls between your points.

👥 Stratified

First split the group into sub-groups. Then sample each one in proportion to its size. It makes sure every part is fairly shown, but you need to know the sizes of the sub-groups first.

Worked example

You want to ask 50 people about hazards in a town. The town is 60% adults and 40% under-18s. A stratified sample is in proportion, so you ask 60% of 50 = 30 adults and 40% of 50 = 20 under-18s. Check: 30 + 20 = 50.

Quantitative and qualitative data

Fieldwork data comes in two types. Your enquiry needs both.

🔢 Quantitative data

Data measured as numbers. Examples: river width in metres, water velocity, the length of pebbles in millimetres, or the number of people who answer "yes" to a question. You can draw graphs and work out averages from it.

🎨 Qualitative data

Data that describes things in words or pictures. Examples: annotated field sketches, photographs, or notes on how a beach feels to visit. It shows what a place is like, but it is harder to compare and cannot be averaged.

Each type helps with different jobs. Numbers let you test a hypothesis. Sketches and photographs show the features you cannot see in a table. Used together, they give a fuller answer.

Primary and secondary data

Remember, primary data is collected by you and secondary data is collected by someone else. Each has strengths and weaknesses.

Primary data is about your exact site and fits your question. But it takes time, and you only have a short time at the site.
Secondary data can cover a longer time or a wider area, for example records from a local weather station or historic maps. But you did not collect it, so you cannot be sure how carefully it was collected, and it may not match your site exactly.

The spec says you must use at least two different secondary data sources. Using both types lets you check one against the other.

Is your data accurate and reliable?

When you evaluate data, two ideas matter. People often mix them up.

Accurate: close to the true value. A badly read ruler or an instrument that is not held still gives inaccurate data.
Reliable: would give similar results if repeated. A bigger sample and repeat measurements make data more reliable.

Ways to improve your data:

Use the same equipment and method at every site.
Take repeat readings and use the mean, rather than one reading.
Collect from a larger sample.
Work at the same time of day and in similar weather, where you can.
Check your results against secondary data.

Worked example

A student measures the depth of a river three times at one spot: 24 cm, 26 cm and 25 cm. The mean is (24 + 26 + 25) ÷ 3 = 75 ÷ 3 = 25 cm. The readings are close together, so the measurement is reliable. One reading of 40 cm would stand out as an anomaly (a reading far from the others) and should be checked.

Common mistakes

Saying qualitative data is "not real data". It is valid. It gives different information from numbers.
Writing "systematic" when you mean "random". Systematic means a regular pattern, random means by chance.
Using "accurate" and "reliable" as if they mean the same thing. Accurate is about being close to the true value. Reliable is about getting similar results again.
Saying a sample is reliable just because it is "good". Say why, for example "the sample was large and repeated".

Exam-style question

A student investigates how the size of pebbles changes along a beach. They collected primary data by measuring pebble length, and secondary data from a local geology map.

(a) Describe how the student could use systematic sampling to choose where to measure pebbles. [2 marks]

(b) Give one example of qualitative primary data they could collect. [1 mark]

Model answer

(a) Measure pebbles at regular intervals along the beach, for example every 10 metres from the start (1). Measure the same number of pebbles at each point (1).

(b) An annotated field sketch (or photographs) of the beach (1).

(c) Measure a larger number of pebbles at each point (1), because this reduces the effect of any unusual pebbles, so repeating the survey would give similar results (1).

Exam tip

In part (a), a regular interval with a figure, such as "every 10 metres", earns the mark. "Spread out" is too vague.

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