Data Collection (AQA GCSE Geography): Revision Note

Data collection

• Data which records quantities is quantitative data

• Examples of quantitative data are:

  • Numerical data collected in questionnaires

  • Traffic counts

  • Environmental quality surveys

  • River data: velocity, discharge

  • Weather data

• Data which records descriptive information is qualitative data

• Examples of qualitative data:

  • Field sketches and photographs

  • Non-numeric questionnaire data

  • Interview answers

Questionnaires and interviews

• When collecting data via questionnaires or interviews, a number of questioning types can be used:

  • Closed questions where answers are limited to single words, numbers or a list of options

  • Statements use a scale to gauge people's views. For example, strongly agree/agree 

  • Open questions are where the respondent can give any answer

• Questionnaires can be used to gather a large sample of data

• Interviews are more in-depth and tend to be used to gather a smaller data sample

Environmental quality surveys (EQS)

• These are used to collect data about the environmental quality of different sites

• They use the judgement of the person conducting the survey to assess environmental quality against a range of indicators

  • Using a sliding scale (1 - 5) or bipolar scale (-3 to 3)

  • Usually, the lower the score, the more negative the assessment of the environmental quality

• They are subjective because they are based on the opinion of the person completing them

• This can be reduced by:

  • Completing in small groups to reach a consensus regarding the score

  • Using the mode of EQS completed by a number of students

• They produce quantitative data

Strengths of quantitative data

• Possible to have a larger sample size

• Information can often be collected quickly

• Data collection can be duplicated 

• More objective than qualitative data

• More reliable than qualitative data

Limitations of quantitative data

• The meaning behind the results is not clear

• Human error or equipment error can lead to mistakes in measurement

Strengths of qualitative data

• More in-depth than quantitative data

• More valid than quantitative data

Limitations of qualitative data

• Often a small sample size

• Enquiries are not easy to duplicate

• Difficult to make comparisons

• Low reliability

• Time-consuming

Sampling methods

Purpose of sampling

• It gives an overview of the whole feature/population to be sampled

• There is not enough time/equipment/access to measure the whole area being examined

• Sampling provides a representative and statistically valid sample of the whole

Types of sampling

• There are three types of sampling to consider

  • Random

  • Systematic

  • Stratified

Random sampling

• A grid is drawn/placed over the area to be studied

• The squares which include part of the study area are numbered

• The numbers are entered into a random number generator 

• The samples should be collected as near as possible to the points given

Systematic sampling

• The samples are selected at regular intervals for example, every 500 meters or every tenth person

Stratified sampling

• Used when the study area includes significantly different parts known as subsets 

• Is based on the idea that the sample represents the whole population 

• If a questionnaire is being used to collect data and the population of the study area has 10% of people over 65, then the sample should include 10% of people over 65

Advantages and disadvantages of sampling

• All sampling methods have advantages and disadvantages

Random

Advantages

• Least biased of all, sampling all possible sample sites have an equal chance of being selected

• Can be used with a large sample area/population

Disadvantages

• Representation of the overall population may be poor if the random sites miss large areas 

• Some sites selected may not be accessible or safe

Systematic

Advantages

• It is easy and quick, making it more straightforward than random sampling

• It covers the whole study area equally

Disadvantages

• Not all sites have an equal chance of being selected, which increases the bias

• There may be over- or under-representation of a particular feature

Stratified

Advantages

• It can be used alongside systematic and random sampling

• Comparisons can be made between sub-sets

Disadvantages

• The proportions of sub-sets need to be known and be accurate