Topic for investigation

Geography 3910 · CCEA AS Level · 9 min read · Updated: 1 Sept 2026

Welcome to AS 3: Fieldwork Skills and Techniques in Geography

Fieldwork is the beating heart of geography. Instead of just reading about rivers, sand dunes, or urban zones in a textbook, you get to step outside, collect your own real-world data, and test geographical theories first-hand. In Unit AS 3 (Paper Code: SGG31), you will be examined on your own fieldwork experience as well as broader geographical skills.

The AS 3 exam lasts 1 hour, is out of 60 marks, and is split into two compulsory sections:

• Section A (Question 1): Focuses entirely on your own primary fieldwork investigation.
• Section B (Question 2): Focuses on geographical skills using unfamiliar resources, data, maps, or GIS.

Don't worry if fieldwork analysis feels daunting at first! By breaking the investigation down into clear, logical stages, you will master every skill required to score top marks.

Stage 1: Setting Up the Investigation & Exam Room Requirements

The Two Items You Must Bring Into the Examination Room

When you walk into your AS 3 exam, you are required to bring two vital documents:

1. The Fieldwork Summary Statement: A written statement that must strictly be 100 words or fewer. It outlines your Title, Aim, Location/Context, and Hypotheses.
2. The Table of Data: A neat table containing the raw primary data you gathered first-hand in the field. This is submitted alongside your exam booklet at the end of the 1 hour.

Formulating Titles, Aims, and Hypotheses

Every successful geographical investigation begins with a clear direction:

• Geographical Title: A concise description of your study topic and where it happened (e.g., An investigation into downstream changes in channel characteristics along the River Shimna).
• Aim: The overall goal of what you are trying to find out (e.g., To assess whether river channel variables conform to the Bradshaw Model).
• Hypotheses ($H_1$): Testable, directional predictions stating the relationship between two variables (e.g., Channel width increases significantly with distance downstream).
• Null Hypothesis ($H_0$): A statement of no relationship or no difference (e.g., There is no significant relationship between channel width and distance downstream).

Quick Review: Summary Statement Rules

Rule: Maximum 100 words.
Must Include: Title, Aim, Hypotheses, Location/Context.
Must NOT Include: Analysis, evaluation, methodology descriptions, or conclusions.

Stage 2: Sampling Strategies (Choosing Where and What to Measure)

You cannot measure every single pebble on a beach or every drop of water in a river. You must use a sampling strategy to select a representative sample.

1. Random Sampling

• How it works: Every point or item in the study area has an equal probability of being chosen, often using a random number table or generator to determine coordinates.
• Advantages: Removes researcher bias completely.
• Disadvantages: Points can accidentally cluster together, leaving large parts of the study area unmeasured.

2. Systematic Sampling

• How it works: Data is collected at regular, equal spatial or temporal intervals (e.g., taking a measurement every 50 metres along a river transect, or surveying every 10th pedestrian).
• Advantages: Ensures an even, regular spread of data across the entire study area; straightforward to carry out.
• Disadvantages: Can introduce bias if the sampling interval accidentally coincides with a repeating pattern in the environment.

3. Stratified Sampling

• How it works: The population or area is split into distinct sub-groups (strata) based on known characteristics (e.g., dividing a sand dune system into embryo, fore, and yellow dunes; or dividing people by age brackets), and samples are taken from each sub-group in proportion to its size.
• Advantages: Ensures minority groups or smaller zones are not missed; highly representative.
• Disadvantages: Requires prior knowledge of the study area to divide it into strata accurately.

4. Pragmatic (Opportunistic) Sampling

• How it works: Sites are chosen based on accessibility, safety, or logistical convenience (e.g., only sampling where river banks are safe to enter).
• Limitations: Highly vulnerable to bias and often unrepresentative of the entire landscape.

Memory Aid for Sampling: Remember R-S-S-P — Random (numbers), Systematic (spaces), Stratified (strata/sub-groups), Pragmatic (practical/safe).

Stage 3: Data Presentation Techniques

Once you return from the field, you must present your raw data using clear graphical and cartographic techniques.

Graphical Techniques

• Bar Charts & Compound Bar Charts: Excellent for comparing discrete categories (e.g., land-use types or bedrock geology).
• Scatter Graphs: Ideal for showing the relationship and correlation between two continuous variables (e.g., distance downstream vs. bedload size).
• Histograms: Used for continuous data divided into class intervals (e.g., pebble size distributions).
• Pie Charts: Show proportions of a whole (must sum to $100\%$ or $360^\circ$).
• Kite Diagrams: Show spatial changes in species abundance or percentage cover along a transect line (commonly used in sand dune or vegetation studies).
• Triangular Graphs: Display three percentage variables that always sum to $100\%$ (e.g., soil composition: sand, silt, and clay).
• Dispersion Graphs: Show the spread, range, and clustering of data points around a central value.

Cartographic Techniques

• Choropleth Maps: Areas are shaded in progressively darker tones to represent higher data density or values.
• Dot Distribution Maps: Use dots of identical size, where each dot represents a fixed quantity of a phenomenon.
• Proportional Symbols: Symbols (like circles or squares) scaled in size proportional to the data value at that specific location.
• Isoline Maps: Lines connecting points of equal value (e.g., contour lines for elevation, isobars for pressure, isotherms for temperature).
• Flow-line Maps: Arrows where the width or thickness of the arrow is directly proportional to the volume or volume rate of movement (e.g., traffic or river discharge).

Stage 4: Statistical Analysis (Crunching the Numbers)

CCEA AS 3 requires you to summarize and statistically test your primary data.

1. Measures of Central Tendency

• Mean ($\bar{x}$): The arithmetic average: $\bar{x} = \frac{\sum x}{n}$
• Median: The middle value when all observations are arranged in ascending order.
• Mode: The most frequently occurring value in the dataset.

2. Measures of Dispersion

• Range: The difference between the highest and lowest values: $\text{Range} = \text{Maximum} - \text{Minimum}$
• Interquartile Range (IQR): The spread of the middle $50\%$ of data, less affected by extreme anomalies: $\text{IQR} = Q_3 - Q_1$
• Standard Deviation ($\sigma$): Measures how closely data points cluster around the mean: $\sigma = \sqrt{\frac{\sum (x - \bar{x})^2}{n}}$

3. Inferential Statistics: Spearman’s Rank Correlation Coefficient ($r_s$)

Used to determine the strength and direction of a relationship between two paired variables.

$$$r_s = 1 - \frac{6 \sum d^2}{n(n^2 - 1)}$$$

Where:
• $d$ = difference between the ranks of each pair of data
• $\sum d^2$ = sum of the squared rank differences
• $n$ = number of paired observations

Step-by-Step Method for Spearman’s Rank:

1. List your paired data for Variable 1 and Variable 2.
2. Rank Variable 1 from highest (1) to lowest ($n$). If values are tied, assign them the average of the ranks they would have occupied.
3. Rank Variable 2 independently from highest (1) to lowest ($n$).
4. Calculate the difference in ranks ($d$) for each pair: $d = \text{Rank}_1 - \text{Rank}_2$.
5. Square each difference to get $d^2$.
6. Sum all the squared values to find $\sum d^2$.
7. Substitute $\sum d^2$ and $n$ into the formula and solve.

Interpreting the Calculated $r_s$ Value:

• An $r_s$ value of $+1.0$ indicates a perfect positive correlation.
• An $r_s$ value of $-1.0$ indicates a perfect negative correlation.
• An $r_s$ value of $0$ indicates no correlation.
• Compare your calculated $r_s$ value (ignoring the negative sign if testing negative correlation) against critical values in a significance table at the $p = 0.05$ ($95\%$ confidence level) and $p = 0.01$ ($99\%$ confidence level) for your sample size ($n$).
• If your calculated value exceeds the critical threshold, reject the null hypothesis ($H_0$) and accept your hypothesis ($H_1$).

Stage 5: Geographical Theory and Critical Evaluation

Linking Results to Geographical Models

In Section A, you must explain your findings using established geographical theory:

• Fluvial Studies: Compare downstream changes in depth, width, velocity, and load shape to the Bradshaw Model.
• Coastal Studies: Compare changes in soil pH, organic matter, and vegetation cover across sand dunes to Psammosere succession models.
• Settlement Studies: Compare environmental quality, land value, or pedestrian density to urban models such as the Burgess Concentric Zone Model or Hoyt Sector Model.

Writing a High-Level Evaluation

Examiners look for mature critique of the methodology rather than superficial excuses. Avoid generic comments like "the weather was bad" or "we ran out of time." Instead, focus on:

• Equipment Limitations: E.g., impeller friction on a mechanical flowmeter at low river velocities, or calliper placement errors when measuring irregular pebble axes.
• Sampling Adequacy: Was your sample size ($n$) large enough to be statistically robust? Did systematic intervals miss key landform variations?
• Anomalous Data: Identify specific outliers in your table of data and provide a geographical or methodological reason for why they occurred (e.g., human dredging, localized bank collapse, footpaths altering dune vegetation).
• Temporal / Seasonal Variations: Acknowledge that data collected on a single day cannot capture seasonal river discharge fluctuations or summer-to-winter coastal profile shifts.

Common Exam Pitfalls to Avoid

• Pitfall 1: Exceeding 100 words in the Summary Statement. Keep it strictly under 100 words. Do not put data, analysis, or conclusions in it.
• Pitfall 2: Giving generic textbook answers. Always quote specific numbers, site names, and units from your own Table of Data when answering Question 1.
• Pitfall 3: Calculation mistakes in Spearman's Rank. Remember to square the differences ($d^2$) before adding them together, and handle tied ranks carefully.
• Pitfall 4: Confusing sampling types. Taking measurements every 10 metres along a transect is systematic sampling, not stratified.

Key Takeaways Summary

• Exam Setup: Bring a 100-word Summary Statement and your Table of Data into the 1-hour AS 3 exam.
• Sampling: Choose between Random, Systematic, Stratified, or Pragmatic depending on the geographical context.
• Presentation: Select the most appropriate graph (e.g., scatter graph for correlation, kite diagram for transects) or cartographic method (e.g., choropleth, isoline).
• Statistics: Use measures of central tendency, dispersion, and Spearman’s Rank ($r_s$) to test hypotheses at the $p = 0.05$ and $p = 0.01$ significance levels.
• Evaluation: Link your results back to geographical theory (Bradshaw, Psammosere, Burgess/Hoyt) and critically assess equipment accuracy, sample size, and anomalies.

Quick check

Can you answer these now?

Open each question to check the key ideas from this chapter.

What two items must candidates bring into the AS 3 examination room?

Candidates must bring a Fieldwork Summary Statement (maximum 100 words) and a Table of Data containing their raw primary data.

What is the difference between an alternative hypothesis ( $H_1$) and a null hypothesis ($H_0$) in geographical investigations?

An alternative hypothesis ($H_1$) predicts a directional relationship between two variables, whereas a null hypothesis ($H_0$) states that no significant relationship or difference exists.

How does systematic sampling operate, and what is its main advantage and disadvantage?

Systematic sampling collects data at regular, equal spatial or temporal intervals; it provides an even spread across the study area but can introduce bias if intervals match environmental patterns.

What is stratified sampling and when should it be used?

Stratified sampling involves dividing a population or study area into distinct sub-groups (strata) based on known characteristics and sampling each in proportion to its size.

Which graphical technique is best suited for illustrating continuous spatial changes in species abundance along an environmental transect?

A kite diagram.

What is the formula and purpose of Spearman’s Rank Correlation Coefficient ($r_s$)?

$r_s = 1 - \frac{6 \sum d^2}{n(n^2 - 1)}$; it is used to measure the strength and direction of a relationship between two ranked continuous variables.

How is statistical significance determined after calculating an $r_s$ value?

By comparing the calculated $r_s$ value against critical values in a significance table for sample size $n$ at the $p = 0.05$ and $p = 0.01$ levels; if it exceeds the critical value, $H_0$ is rejected.

Which theoretical models are typically used to contextualize fluvial, coastal, and settlement fieldwork?

The Bradshaw Model for fluvial studies, Psammosere succession models for sand dune studies, and the Burgess or Hoyt models for urban settlement studies.

What elements should be included in a high-level geographical methodology evaluation?

An effective evaluation focuses on equipment limitations, sampling adequacy, anomalous data identification with geographical reasons, and temporal/seasonal variations.

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