The stages of geographical enquiry; the collection of primary and secondary data using appropriate physical and human methods; sampling strategies and their justification; and the evaluation of data reliability, accuracy and bias.

What this dot point is asking

OCR requires you to understand geographical fieldwork as a structured enquiry: the stages of the process, the collection of primary and secondary data using appropriate physical and human methods, the choice and justification of sampling strategies, and the evaluation of data reliability, accuracy and bias. This underpins the Independent Investigation and is examined as AO3 in the written papers.

The answer

The stages of geographical enquiry

The enquiry process turns curiosity into evidence-based conclusions. It begins by framing a focused, answerable question grounded in geographical theory (a model, a concept, a debate), then designing a method capable of testing it. The later stages, presentation, analysis and especially evaluation, are where marks are concentrated, because they demonstrate the geographer's critical judgement, not just data-gathering. Understanding the whole cycle, and how each stage depends on the previous one, is what the written papers and the coursework both reward.

Primary and secondary data; physical and human methods

Fieldwork uses two data types. Primary data are collected first-hand by the investigator, river velocity and channel measurements, beach sediment analysis, pedestrian and traffic counts, questionnaires and interviews, environmental quality surveys. Secondary data come from existing sources, census data, official statistics, maps, historic photographs, providing context and comparison. Methods are also split between physical geography (measuring discharge with a flow meter, pebble size and shape, infiltration rates, microclimate) and human geography (questionnaires, environmental quality indices, bipolar surveys, land-use mapping, pedestrian counts). Combining primary and secondary, and physical and human where relevant, gives the most robust investigation, the basis of the required minimum four days of fieldwork.

Sampling strategies and their justification

Because it is rarely possible to measure everything, fieldwork samples, and the strategy must be chosen and justified. Random sampling gives every member of the population an equal chance (using random numbers or a grid), which avoids bias but can by chance miss small subgroups. Systematic sampling takes every nth member or samples at fixed intervals (every $10$ m along a transect), giving even, simple coverage but risking a hidden periodicity. Stratified sampling divides the population into subgroups and samples each in proportion, ensuring all groups are represented, ideal where the population is not uniform. The right choice depends on the aim and the structure of the population, and examiners reward a justified selection rather than a default one.

Evaluating reliability, accuracy and bias

A defensible conclusion depends on evaluating the data. Reliability asks whether repeating the method would give consistent results (improved by repeated readings and an adequate sample size). Accuracy asks how close measurements are to the true value (affected by instrument precision and technique). Bias is systematic error or skew, a leading questionnaire, sampling at an unrepresentative time, or a non-representative sample. Recognising these limitations does not invalidate an investigation; it bounds the confidence of its conclusions and motivates improvements (larger or more representative samples, repeated and calibrated measurements, neutral question wording, sampling across times and conditions). This critical evaluation is the highest-order AO3 skill and is heavily weighted in the Independent Investigation.

Examples in context

Example 1. A river fieldwork enquiry. Testing whether velocity and channel width increase downstream (the Bradshaw model) involves primary physical data, flow-meter velocity, tape-measured width and depth, sampled systematically at intervals along the river, plus secondary data (OS maps, discharge records). Analysis with Spearman's rank tests the relationship, and evaluation considers reliability (repeated readings), accuracy (instrument calibration) and bias (a single dry-weather visit). It demonstrates the full enquiry cycle in physical geography and links to the statistics dot point.

Example 2. An urban quality-of-life enquiry. Investigating how environmental quality varies across a city uses human methods, an environmental quality survey (bipolar scoring), questionnaires on residents' perceptions, and land-use mapping, with stratified sampling across contrasting neighbourhoods so each is represented. Secondary census and deprivation data provide context. Evaluation addresses questionnaire bias (wording, sample), reliability and the subjectivity of bipolar scoring. It shows enquiry in human geography and links to the Changing Spaces; Making Places place studies.

Try this

Q1. Distinguish between primary and secondary data, with one example of each. [3 marks]

• Cue. Primary data are collected first-hand (for example river velocity measurements or a questionnaire); secondary data come from existing sources (for example census data or maps).

Q2. Explain why evaluating data reliability matters when drawing fieldwork conclusions. [3 marks]

• Cue. Unreliable data (too small a sample, inconsistent or biased method) can produce misleading conclusions, so evaluating reliability bounds the confidence of the conclusion and indicates how the method could be improved.