Appendix 5, Appendix 6, Appendix 7 - Planning enquiries and evaluating evidence

An answerable geographical question

An enquiry begins with something that can be investigated in a particular environment. “Is this town good?” is vague. “How does environmental quality vary between the regenerated square and adjacent streets?” identifies a measurable comparison. Choose a question that is realistic for the time, equipment, permissions and safe locations available.

Appendix 5 separates seven stages: 1 identify the question; 2 contextualise the fieldwork; 3 design where/how many; 4 choose equipment and record/collect data; 5 process and present; 6 analyse and conclude; 7 evaluate process and results. Later stages may reveal a reason to revisit an earlier decision, but recording the sequence helps explain why the investigation took its form.

Contextualising means researching the place and relevant geographical ideas before collecting evidence. A hypothesis is a testable prediction, such as “mean litter scores improve with distance from the station”; key questions can organise an enquiry without predicting one direction. State how scores are defined: a higher “litter” score could mean cleaner or dirtier unless the scale says so.

Use at least two different secondary sources for each investigation, alongside quantitative and qualitative primary evidence. A local report and a dated census table may serve different purposes. Two screenshots of the same dataset are not two distinct sources. Record who produced each source, when, how and for what purpose. Online census/GIS research is useful, but a source must address the enquiry rather than merely locate the town.

Sampling and fair comparisons

A sample represents a larger population of places, times, objects or people. A random sample gives each eligible member of a defined sampling frame a known equal chance, for example numbered sites selected with random numbers. Asking whoever is easiest to approach is an opportunity sample, even if the researcher calls the choice “random”. A systematic sample uses regular intervals, such as every fifth person or every 100 m. A stratified sample includes meaningful groups, such as upper/middle/lower river reaches or resident age groups, often in proportions matching the population.

Choose by purpose and practical conditions. Systematic sites reveal change along a transect but may repeatedly coincide with a particular land use. Stratification can prevent a small but relevant group being omitted, but inaccurate group proportions create bias. A bigger sample usually reveals more variation and reduces the influence of a single unusual reading; it does not cure a sampling frame that excludes half the community. Repeated times are needed for claims about temporal change.

Use a control or comparison where it helps separate influences: a similar street without a recent intervention may help assess regeneration, but streets can differ in other ways. Keep relevant conditions consistent, such as survey time and scoring method, and record remaining differences. Choose sample size with both variation and safe practical limits in mind.

Measurement, recording and safe work

Primary data are collected first hand for the investigation; secondary data were collected by someone else or for another purpose. Quantitative evidence records numbers, such as temperature or closed-response counts. Qualitative evidence records meaning or character, such as interview explanations and annotated photographs. A scored EQS turns judgement into ordinal numerical data; the numbers do not remove the judgement involved.

Choose equipment and recording sheets together. A usable sheet includes site identifier/coordinates, date/time, variable and unit, repeat readings, method and unusual conditions. Pilot it before collection. Accuracy concerns closeness to the true value; reliability concerns consistent/repeatable results under comparable conditions. Calibration can improve accuracy, repeated measurements reveal random variation, and consistent observers/methods improve comparability. Repeating the same incorrectly calibrated instrument can give reliable but inaccurate values.

For slippery riverbank rocks, a fall is the possible harm. A centre may use a likelihood score of 3 and severity of 3 on five-point scales, producing a rating of 9 out of a possible 25. This is a prioritisation tool, not a measured 36% injury probability. Controls might avoid the bank, relocate the site or require suitable supervised procedures; reassess residual risk. Teachers/centres decide access and follow local rules. A low score never authorises learners to enter dangerous water, storms, traffic or private land.

For surveys, explain the purpose, obtain appropriate consent, allow refusal and avoid collecting names or identifying details that are unnecessary. Photos should avoid identifiable people unless permissions and the enquiry justify them. Responsible fieldwork considers others' safety and dignity as well as the researcher.

From evidence to a conclusion

Process both primary and secondary data: use tallies, means and percentages where appropriate, locate evidence in simple GIS, and choose graphs suited to the variable. Include simple and more complex displays across the investigation, such as pie/bar/line and compound/radial/scatter. Present qualitative evidence in accurate annotations, quotations or transparent themes. A clever graph is useful only if its scale and meaning help answer the question.

Analysis makes evidence-based connections. First identify a pattern with a value or located observation, then use geographical reasoning to explain it. Compare primary and secondary findings rather than treating them as separate projects. Explore contradictions: a clean-looking street with low resident satisfaction may reveal a different dimension of environmental quality, not a failed survey.

Revisit the hypothesis and relevant theory. “Supported at four sites, with an exception beside the school” is more precise than “proved correct”. Use anomalous results and consider further implications for people and the environment. Different stakeholders may value noise, access, cost and habitat differently. Distinguish a supported disagreement about priorities from an unsupported factual assertion.

Command words change the reasoning required. Describe sets out characteristics; compare relates both cases; explain/suggest links reasons and outcomes; analyse/examine breaks down evidence and connects components; assess weighs significance; evaluate judges value or success using evidence, limitations and alternatives; discuss explores reasoned sides. A conclusion should answer the actual question, not repeat every measurement.

Validity, improvements and honest fieldwork

Validity concerns whether the methods/evidence support the intended question and conclusion. A perfectly measured traffic count has limited validity for a claim about residents' health if no health evidence is collected. Evaluate data accuracy, method reliability, sample representativeness and source relevance separately. Link a limitation to its consequence, then propose an improvement that directly addresses it: “repeat at morning, afternoon and evening to test whether a lunchtime pattern persists” is stronger than “collect more data”.

Ask how far the conclusion travels. The same technique can transfer to another location, but categories, equipment exposure, sampling and risks may need adjustment. Keep a reflective record of decisions and seek constructive feedback; a peer can check whether a graph or claim is understandable without inventing new evidence. Independent initiative means finding relevant sources and checking their provenance, not working without supervision in unsafe places.

Familiar fieldwork questions ask about the investigation you actually undertook; keep a clear record of its question, location, methods, evidence and limitations. Unfamiliar questions supply a new investigation in the studied environment and test transfer of method/evidence reasoning. Worked data in these lessons are labelled simulated: they practise planning and analysis and do not certify personal fieldwork. If first-hand collection is constrained by exceptional circumstances, a centre must justify the arrangement to Pearson; substitute data still require the other enquiry stages. NovaLearn cannot make that decision for the centre.