Introductory techniques

Questionnaires designed for gathering qualitative opinions are regularly chosen as methods by students at GCSE and A level, but it is not often a technique which is well executed.

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A good starting point is to get students to plan a hypothetical questionnaire themselves, to answer an intentionally broad key question, e.g. Is X High Street of a high quality? Then to ask students within the class to complete the questionnaire and try to evaluate the usefulness of the questions / responses.

Considerations

• Do the questions help to answer the key question, are they valid?
• Is the data collected reliable?
• Are the questions focused, easy to understand, simple to answer, unbiased?
• Is it easy to interpret the data collected?
• How can we ensure our sample is representative?
• Are we able to make links between data, for example do residents have different opinions from visitors to the area?

From this some success criteria can be derived. This success criteria list for good questionnaires could be shared with students, adapted from the Collecting human geographical data KS4 lesson by AQA on Oak National Academy:

• Questionnaires ideally consist of 6-8 questions.
• A range of different question styles are important to gather different responses and data such as open questions, closed questions, scoring or ranking, LIKERT scale, categorical etc.
• Any categories used for people to choose from should be specific, using numbers rather than words e.g. up to X times a month rather than ‘often’ or ‘rarely’.
• Questions should be unbiased and not leading. An example of a leading question: ‘do you agree that McDonalds serves better burgers than Burger King?’
• Questionnaires should survey a range of people of varied demographics.
• Questions and the act of questioning should be ethical, e.g. consider asking people to assign themselves to a broad age range rather than asking their age directly.
• Wherever possible answers should be linked to the people giving them. 

Linking answers to people

This is good practice, as it allows the data to be analysed in context, for example: person A has travelled 3Km to get here, came by car, strongly agrees the town is attractive and would like to see a park and ride in place.

The alternative where we know three people travelled less than 3Km, two people came by car, whilst four people think the town is attractive is less useful as we cannot be sure of who said what. This is achieved either through assigning letters or codes to each participant or utilising a digital form for data collection.

Devising a questionnaire

Students can then devise a class questionnaire based on the success criteria and their pilot study findings using the ArcGIS tool Survey 123. This guide is a helpful tool Guided tour from ArcGIS Survey123, they can then either gather data via their phones, school devices or by asking the public to use a QR code to respond.

How many responses is ‘enough’? Students often fall fowl of small sample sizes, and this is a good lesson to learn before they reach A level and start planning NEAs.

When it comes to questionnaires, the more the better. In an ideal world, to show any real statistical significance, we should be aiming for 5% of the visitors in a given time frame. On a busy high street on a busy day this could be hundreds of people.

Logistically this isn’t feasible alone, but as a class it is more reasonable. Ultimately there is no right or wrong answer, but small sample sizes (less than 20) are hard to draw reliable conclusions from.

Data presentation

Closed questions: Simple bar charts and pie charts are best placed to show this kind of ordinal data.

Scored, ranked or LIKERT data: Data where responses are on a scale in different categories can be represented either as proportional pie charts or radar graphs for easy visual comparison.

Linked data: Where data is linked to other answers a scatter graph is useful to show relationship. Here a simple scatter graph has been sketched in the field, to show individual respondents (shown as different shapes) answers to how long they have travelled to get to town and how much on average they have spent. By identifying the respondent in each case (♥Δ etc) we can students can see patterns linked through the data.