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Quiz yourself on Human Geography FieldworkGet your flashcardsBuild my revision planTrack my progress⚖️ Comparing Data Collection Methods
In the exam you may be asked to compare methods or justify your choice. This table gives you the evidence to do that precisely.
| Method | Type of data | Best for testing | Main advantage | Main limitation | How to improve |
|---|---|---|---|---|---|
| Environmental Quality Survey (EQS) | Qualitative judgements converted to numbers (quasi-quantitative) | Hypothesis: EQ changes with distance from CBD | Covers multiple criteria simultaneously; quick to administer; allows direct comparison between sites | Subjective — different observers score the same street differently; results represent perceptions, not objective measurements | Use photographic benchmarks; multiple observers; calculate mean; conduct at same time of day at all sites |
| Pedestrian Count | Quantitative — a pure number, no judgement involved | Hypothesis: pedestrian density decreases with distance from CBD | Objective — no subjectivity; easy to replicate; produces ratio-level data suitable for statistical testing | Highly sensitive to time of day, weather, and special events; a single count is not representative | Repeat 3× at each site; survey all sites at same time; repeat on different days of week |
| Land Use Survey | Categorical (nominal) — type of land use, not a quantity | Hypothesis: land use transitions from commercial to residential with distance from CBD | Directly tests the core prediction of Burgess's model; produces visual evidence (colour-coded maps) | Ground floor may not represent building's dominant use; categorisation can be ambiguous; snapshot in time | Survey upper floors where accessible; use an agreed categorisation scheme with clear definitions before fieldwork |
| Questionnaire | Mixed — quantitative (Likert scales) and qualitative (open questions) | Hypothesis: perceived quality of life is higher in outer suburbs than inner city | Captures perceptions and attitudes that observational methods miss; open questions can reveal unexpected factors | Temporal and social desirability bias; small samples; response rate variable; leading question wording can distort results | Random sampling (every 5th person); larger sample (50+ per site); survey at multiple times; pilot-test question wording |
| Traffic Count | Quantitative | Hypothesis: traffic volume decreases with distance from CBD | Objective and straightforward; vehicle type classification adds depth (car, lorry, bus, bike) | Peak vs off-peak variation is enormous; road closures or incidents distort results; does not capture road width relative to traffic volume | Repeat at different times (morning peak, off-peak, lunchtime); survey all sites simultaneously if enough group members |
Quick Check: Why is the EQS described as "quasi-quantitative" rather than truly quantitative?
The EQS produces numbers, but those numbers are based on personal judgements — they represent what an observer perceives about the environment, not an objective physical measurement like temperature or distance. Because two different observers rating the same street may give different scores (one person's "3 for noise" is another's "2 for noise"), the data contains subjective variation that a truly quantitative measurement would not. The data therefore appears quantitative but actually reflects the observer's perception, which is why reducing subjectivity — through photographic benchmarks and multiple observers — is essential for improving reliability.
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