G45: Fieldwork: Analysis, Conclusions and Evaluation
Analysing fieldwork data using statistical techniques, drawing valid conclusions, and evaluating the reliability and limitations of your investigation.
๐ Data Analysis
Key Concept: Data analysis means identifying patterns, trends, and relationships in your data. It involves describing what the data shows, calculating statistics, and explaining the geographical reasons behind the patterns you find.
Describing Patterns and Trends
When analysing data, you should:
- Identify the overall pattern: Does the data increase, decrease, or stay constant? Is there a clear trend?
- Use data to support your description: Quote specific values (e.g. "velocity increased from 0.2 m/s at Site 1 to 1.4 m/s at Site 10")
- Identify anomalies: Any data points that don't fit the pattern should be noted and explained
- Explain the geography: Use geographical understanding to explain why the pattern occurs
Example: Analysing River Data
"River velocity generally increased downstream, from 0.23 m/s at Site 1 (source) to 1.38 m/s at Site 10 (lower course). This follows the Bradshaw Model, which predicts velocity increases downstream as the channel becomes wider and deeper, reducing friction. However, there is an anomaly at Site 7 where velocity decreased to 0.65 m/s from 0.92 m/s at Site 6. This can be explained by a meander at Site 7 where the river widens and deposits sediment on the inside bend, temporarily reducing flow."
๐ Statistical Techniques
Mean, Median, and Mode
Mean = Sum of all values รท Number of values
Median = Middle value when data is arranged in order
Mode = Most frequently occurring value
Range = Highest value โ Lowest value
| Measure | Advantage | Disadvantage |
|---|---|---|
| Mean | Uses all data values; useful for comparison | Distorted by extreme values (outliers) |
| Median | Not affected by extreme values | Doesn't use all data; less useful for further analysis |
| Mode | Shows the most common value; works for categorical data | May not exist or may have multiple modes; ignores other values |
| Range | Shows the spread of data simply | Only uses two values; affected by outliers |
Example: Calculating Statistics
Environmental quality scores from 10 sites: 15, 22, 18, 45, 20, 19, 21, 17, 23, 20
Mean: (15+22+18+45+20+19+21+17+23+20) รท 10 = 220 รท 10 = 22
Median: Arranged: 15, 17, 18, 19, 20, 20, 21, 22, 23, 45 โ Median = (20+20)รท2 = 20
Mode: 20 (appears twice)
Range: 45 โ 15 = 30
Note: The mean (22) is higher than the median (20) because the outlier of 45 pulls the mean upwards. The median gives a better representation of the "typical" score.
Interquartile Range (IQR)
Definition: The IQR measures the spread of the middle 50% of data, eliminating the influence of outliers. It is calculated as Q3 โ Q1, where Q1 is the 25th percentile and Q3 is the 75th percentile.
Calculating IQR:
1. Arrange data in order
2. Find Q1 (median of the lower half)
3. Find Q3 (median of the upper half)
4. IQR = Q3 โ Q1
A small IQR = data clustered closely; large IQR = data widely spread
Spearman's Rank Correlation Coefficient
Definition: Spearman's Rank measures the strength and direction of the relationship between two variables. It produces a value between -1 and +1. This is the most important statistical test for GCSE Geography fieldwork.
Spearman's Rank Formula:
rs = 1 โ (6 ร ฮฃdยฒ) รท (nยณ โ n)
Where: d = difference in rank between each pair; n = number of pairs; ฮฃdยฒ = sum of squared rank differences
Interpreting the result:
+1.0 = Perfect positive correlation
+0.7 to +0.9 = Strong positive correlation
+0.4 to +0.6 = Moderate positive correlation
+0.1 to +0.3 = Weak positive correlation
0 = No correlation
-0.1 to -0.3 = Weak negative correlation
-0.4 to -0.6 = Moderate negative correlation
-0.7 to -0.9 = Strong negative correlation
-1.0 = Perfect negative correlation
Example: Spearman's Rank Calculation
Testing the relationship between distance downstream (m) and river velocity (m/s):
| Site | Distance (m) | Velocity (m/s) | Rank Dist | Rank Vel | d | dยฒ |
|---|---|---|---|---|---|---|
| 1 | 0 | 0.23 | 1 | 1 | 0 | 0 |
| 2 | 500 | 0.31 | 2 | 2 | 0 | 0 |
| 3 | 1000 | 0.45 | 3 | 3 | 0 | 0 |
| 4 | 1500 | 0.58 | 4 | 4 | 0 | 0 |
| 5 | 2000 | 0.72 | 5 | 5 | 0 | 0 |
| 6 | 2500 | 0.89 | 6 | 6 | 0 | 0 |
| 7 | 3000 | 0.65 | 7 | 5 | 2 | 4 |
| 8 | 3500 | 1.05 | 8 | 7 | 1 | 1 |
ฮฃdยฒ = 0+0+0+0+0+0+4+1 = 5; n = 8
rs = 1 โ (6 ร 5) รท (512 โ 8) = 1 โ 30/504 = 1 โ 0.06 = +0.94
Result: +0.94 = strong positive correlation. Distance downstream and river velocity are strongly positively correlated, supporting the Bradshaw Model. The anomaly at Site 7 (a meander) slightly reduces the correlation.
Significance Testing
Key Concept: To determine if your Spearman's Rank result is statistically significant (not just due to chance), compare your calculated rs value to the critical value table at the 95% confidence level (p = 0.05). If your calculated value exceeds the critical value, the result is significant and you can reject the null hypothesis.
| Number of pairs (n) | Critical value (p = 0.05) |
|---|---|
| 5 | 1.00 |
| 6 | 0.89 |
| 7 | 0.79 |
| 8 | 0.74 |
| 9 | 0.68 |
| 10 | 0.65 |
| 12 | 0.59 |
In the example above (n=8, rs=+0.94): the critical value at p=0.05 is 0.74. Since 0.94 > 0.74, the result is statistically significant - there is a less than 5% probability the correlation occurred by chance.
โ Drawing Conclusions
Key Skill: A conclusion directly answers the original question or hypothesis, using evidence from your data and analysis. It should state whether the hypothesis is supported or rejected, and explain why with reference to geographical theory.
Structure for a Good Conclusion
- State whether the hypothesis is supported or rejected
- Summarise the key evidence that led to this decision (use data)
- Link to geographical theory - does your finding match what theory predicts?
- Explain any anomalies - results that didn't fit the pattern
- Acknowledge limitations - what might make your conclusion less certain?
Example Conclusion
"The hypothesis that river velocity increases with distance downstream is supported by the data. Velocity increased from 0.23 m/s at Site 1 to 1.38 m/s at Site 10, showing a clear positive trend. Spearman's Rank correlation of +0.94 confirms a strong positive relationship that is statistically significant (exceeds the critical value of 0.74 at n=8). This supports the Bradshaw Model, which predicts that velocity increases downstream as the channel becomes more efficient through hydraulic smoothing and increased discharge from tributaries. The anomaly at Site 7 (velocity decreased to 0.65 m/s) can be explained by a meander causing deposition on the inside bend, temporarily reducing flow. Overall, the fieldwork findings strongly support the hypothesis and the Bradshaw Model for this river."
๐ Evaluating Reliability and Validity
Reliability: Can the results be replicated? If you repeated the investigation, would you get similar results? Validity: Do the results actually measure what you intended to measure? Do they accurately answer the research question?
Factors Affecting Reliability
| Factor | How It Affects Reliability | How to Improve |
|---|---|---|
| Sample size | Too few sites makes results unrepresentative | Collect data at more sites (minimum 8-10 for river studies) |
| Sampling method | Poor sampling may miss important areas | Use systematic or stratified sampling for better coverage |
| Measurement error | Human error in reading instruments or recording data | Take repeat readings; use standardised methods; cross-check data |
| Timing | Results may vary with weather, time of day, or season | Note conditions; collect data at a consistent time; acknowledge limitations |
| Day of visit | Weekend vs weekday results may differ in urban studies | Visit on a typical day; acknowledge the limitation; use secondary data to verify |
Factors Affecting Validity
- Does the data measure what you intended? E.g. does an environmental quality survey truly measure "quality" or just physical appearance?
- Is the hypothesis appropriate? An over-simplistic hypothesis may not capture the complexity of the real world
- Are the methods suitable? E.g. measuring only velocity may miss important channel changes like hydraulic radius
- Are the conclusions justified? Correlation does not prove causation - two variables may be related but one may not cause the other
Evaluating Your Investigation - Key Questions:
1. Were the results reliable? (Could they be replicated?)
2. Were the results valid? (Did they truly answer the question?)
3. What were the main limitations of your methods?
4. How could you improve the investigation if you repeated it?
5. What additional data would strengthen your conclusions?
6. Are there alternative explanations for your findings?
โ Practice Questions
Q1: Explain the difference between the mean and the median, and when each might be more appropriate.
Q2: A Spearman's Rank calculation gives a result of +0.82 with 10 pairs of data. The critical value at p=0.05 for n=10 is 0.65. Explain what this result means.
Q3: Describe three factors that could affect the reliability of fieldwork data.
Q4: Explain how you would write a conclusion for a geographical enquiry.
Q5: What is the difference between reliability and validity in fieldwork? Why are both important?
โ Answers
- The mean is the sum of all values divided by the number of values; the median is the middle value when data is arranged in order. The mean is more appropriate when data has no extreme outliers and you want to use all data values (e.g. average river velocity at a site). The median is more appropriate when there are outliers that would distort the mean (e.g. environmental quality scores where one extremely high or low score would pull the mean but not affect the median). In the example with scores including a 45 outlier, the median (20) better represents the typical score than the mean (22).
- The result of +0.82 indicates a strong positive correlation between the two variables. Since 0.82 exceeds the critical value of 0.65 at the 95% confidence level (p=0.05), the result is statistically significant. This means there is less than a 5% probability that the correlation occurred by chance. Therefore, you can reject the null hypothesis and conclude that there is a genuine positive relationship between the two variables.
- Factor 1: Sample size - too few sites makes results unrepresentative and more affected by anomalies. Collecting data at only 3 river sites, for example, would not reliably show downstream trends. Factor 2: Measurement error - human errors in reading instruments (e.g. misreading a flow meter) or recording data (e.g. writing the wrong number) reduce reliability. This can be improved by taking repeat readings. Factor 3: Timing - results vary with conditions; river discharge after heavy rain would be much higher than during a dry period, making comparisons unreliable.
- A conclusion should: (1) State whether the hypothesis is supported or rejected; (2) Summarise the key evidence using specific data values; (3) Link findings to geographical theory (e.g. the Bradshaw Model); (4) Explain any anomalies that didn't fit the pattern; (5) Acknowledge limitations that reduce certainty. The conclusion must directly answer the original question and be fully supported by evidence from the data and analysis.
- Reliability means the results can be replicated - if you repeated the investigation under the same conditions, you would get similar results. It depends on consistent methods, adequate sample size, and accurate measurements. Validity means the results actually measure what was intended and genuinely answer the research question. Both are important because: unreliable results cannot be trusted (they may be random); invalid results may be reliable but measure the wrong thing (e.g. a well-conducted study that measures footfall instead of environmental quality). An investigation must be both reliable AND valid to produce meaningful conclusions.
๐ฏ Exam Tips
- Know how to calculate and interpret Spearman's Rank - it appears frequently on exams
- Always compare your calculated rs to the critical value to test significance
- When describing data, quote specific figures - don't just say "it increased"
- In conclusions, explicitly state "the hypothesis is supported/rejected"
- Evaluation questions are common - prepare by identifying specific limitations of your methods
- Higher mark questions: suggest specific improvements and explain how they would increase reliability or validity
- Remember: correlation does not equal causation
- Use statistical language: significant, correlation, anomaly, confidence level
๐ Exam Technique
Geography Exam Tips โ Fieldwork: Analysis, Conclusions and Evaluation:
1. For Fieldwork: Analysis, Conclusions and Evaluation questions, always name specific case studies with factual detail
2. Use geographical terminology precisely (e.g. specific processes, not vague descriptions)
3. Consider social, economic and environmental perspectives in your evaluations
4. Support your points about Fieldwork: Analysis, Conclusions and Evaluation with data, statistics or named examples
5. For 'assess' or 'evaluate' questions, reach a clear judgement supported by evidence
1. For Fieldwork: Analysis, Conclusions and Evaluation questions, always name specific case studies with factual detail
2. Use geographical terminology precisely (e.g. specific processes, not vague descriptions)
3. Consider social, economic and environmental perspectives in your evaluations
4. Support your points about Fieldwork: Analysis, Conclusions and Evaluation with data, statistics or named examples
5. For 'assess' or 'evaluate' questions, reach a clear judgement supported by evidence
โ ๏ธ Common Errors
Watch Out!
Students often write vague answers without specific geographical evidence. Wrong: Writing generalised statements like 'it causes problems' Correct: Using specific data and named examples, e.g. 'the 2010 Haiti earthquake killed over 200,000 people due to poor building quality'
Students often confuse causes and effects. Wrong: Mixing up what caused the event with what resulted from it Correct: Clearly separate causes (why it happened) from effects (what happened as a result)
Students often describe rather than evaluate. Wrong: Listing strategies without assessing their effectiveness Correct: Weighing up strengths and weaknesses of each approach and reaching a supported judgement
โ๏ธ Model Answer
Full-Mark Response
6 marks: Explain the key factors affecting fieldwork: analysis, conclusions and evaluation.
Fieldwork: Analysis, Conclusions and Evaluation involves multiple interconnected factors that geographers must understand. The key concepts include the processes that create and change fieldwork: analysis, conclusions and evaluation, the impacts on both people and environment, and the strategies used to manage associated challenges. For a comprehensive answer, specific case study evidence should be used throughout, with named examples and data to support each point. Geographical terminology should be used precisely, and the interrelationship between physical and human factors should be demonstrated. Top-level responses evaluate the relative importance of different factors and consider how the situation varies between locations.
Mark scheme: 2 marks for identifying key factors, 2 marks for explaining processes with detail, 2 marks for using specific evidence
๐ AO Deep Dive
Assessment Objective Analysis
AO1 requires knowledge of the key facts and processes related to fieldwork: analysis, conclusions and evaluation. AO2 demands understanding of how and why these processes operate, and their implications. AO3 asks you to analyse, evaluate and make judgements โ this is where grade 9 answers stand out by weighing up competing perspectives and reaching supported conclusions. AO4 may involve interpreting maps, graphs or data related to this topic. To move from grade 5 to grade 9: use precise geographical terminology, support every point with specific case study evidence, and always evaluate rather than just describe.
๐ Exam Questions by Topic
๐ฌ Video Resources
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