Primary data collection methods and sampling

Introduction to Primary Data Collection

Welcome! In this chapter, we are going to look at how you become a "geographical detective." For your AS Level Geography, you are required to complete two days of fieldwork—one focusing on physical geography (like coasts or glaciers) and one on human geography (like regeneration or diverse places). Primary data collection is the heart of this process. It is the information you collect yourself, first-hand, out in the field. Understanding how and why we collect this data is essential for answering those tricky fieldwork questions in Paper 1 and Paper 2.

What is Primary Data?

Primary data is original data that has not been processed or altered by anyone else before you get to it. Think of it as "raw" information.
• Example (Physical): Measuring the size of pebbles on a beach (Topic 2B) or the height of a cirque (Topic 2A).
• Example (Human): Conducting interviews with local residents about their "lived experience" of a town (Topic 4A) or mapping the different cultures in a neighborhood (Topic 4B).
Why do we use it? Because it allows you to investigate a specific question that interests you, ensuring the data is up-to-date and relevant to your study area.

The Golden Rule: Sampling

Imagine you wanted to know the average size of every pebble on a 2km beach. It would take weeks to measure them all! This is where sampling comes in. Sampling is the process of selecting a small part of a larger group to represent the whole. To make sure your data is reliable and valid, you need a strategy.

1. Random Sampling

In random sampling, every person or pebble has an equal chance of being selected. You might use a random number generator to pick coordinates on a map.
• Pros: It removes bias (you aren't just picking the "interesting" looking pebbles).
• Cons: You might accidentally miss a whole section of the beach or town by "luck of the draw."

2. Systematic Sampling

This involves picking samples at regular intervals. For example, you might measure a pebble every \(10\) meters along a beach profile, or interview every \(5\)th person you pass in a shopping center.
• Pros: It ensures good coverage of the entire study area.
• Cons: It might miss specific "hidden" patterns between the intervals.

3. Stratified Sampling

This is used when your study area has distinct groups (strata). If a town is \(60\%\) elderly and \(40\%\) young people, your stratified sample of \(100\) people should include exactly \(60\) elderly and \(40\) young people to be fair.
• Pros: It is highly representative of the actual population.
• Cons: You need prior knowledge (secondary data) of the groups before you start.

Quick Review: Sampling is about saving time while keeping your data "honest." If your sample size \(n\) is too small, your results might be due to chance rather than a real geographical process.

Quantitative vs. Qualitative Methods

The Edexcel specification requires a balance of both quantitative (numbers) and qualitative (meanings and descriptions) methods.

Quantitative Methods (The "Hard" Numbers)

These methods produce numerical data that can be graphed and statistically tested (like using Spearman's Rank or T-tests).
• Beach Profiles (Topic 2B): Using ranging poles and clinometers to measure the slope angle of a coast.
• Sediment Analysis: Measuring the long axis of a pebble in millimeters \( (mm) \). You might investigate outwash sediment size in glacial environments (Topic 2A).
• Environmental Quality Surveys: Giving a numerical score (e.g., \(-3\) to \(+3\)) to factors like litter or noise in a regenerating area (Topic 4A).

Qualitative Methods (The "Human" Side)

These methods help you understand the identity of a place or the attitudes (A) of the people living there. This is vital for Topic 4A and 4B.
• Interviews: Having open-ended conversations with "players" (P) like local business owners or residents.
• Field Sketches and Photographs: Capturing the visual "feel" of a landscape or a new regeneration project.
• Coding: This is a special skill where you take text (like an interview transcript) and look for recurring themes. For example, if 10 people mention "feeling safe," you "code" that as a positive perception of the area.

Did you know? Even social media posts and creative descriptions of a place are considered qualitative data. They help geographers understand how people perceive their environment.

Ethics and Socio-political Implications

When collecting data—especially in "Diverse Places" (Topic 4B)—you must be ethical. This means:
• Informed Consent: People should know why you are asking them questions.
• Anonymity: Not recording names or private addresses.
• Respect: Being aware that some communities may feel "over-researched" or sensitive about issues like inequality or segregation.

Accuracy, Reliability, and Errors

Don't worry if your data isn't perfect! In your exam, you often get marks for identifying sources of error.
• Measurement Error: Using a blunt pebble calliper or a clinometer that is slightly off-level.
• Operator Bias: Two different students might judge "beach roundness" differently.
• Sampling Bias: Only doing your traffic count at 10:00 AM when the "rush hour" is already over.

Key Takeaway: To improve reliability, you should repeat your measurements. To improve validity, you should ensure your equipment is calibrated and your sampling method is appropriate for the geographical question you are asking.

Summary Box

• Primary Data: Collected by you, for your specific investigation.
• Random, Systematic, Stratified: The three main ways to choose where and what to measure.
• Quantitative: Numbers, stats, and measurements (objective).
• Qualitative: Interviews, perceptions, and coding (subjective).
• Ethics: Always be respectful of people and the environment during data collection.

Note: For more information on what to do with your data once you have collected it, see the chapter on "Analysis and presentation of fieldwork data."