Data Collection (AQA GCSE Geography)

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Bridgette

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Bridgette

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

  • 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 Limitations
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
  • The meaning behind the results is not clear
  • Human error or equipment error can lead to mistakes in measurement
Qualitative Data
  • More in-depth than quantitative data
  • More valid than quantitative 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
  • All sampling methods have advantages and disadvantages
Sampling type Advantages Disadvantages
Random
  • 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
  • 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
  • It is easy and quick making it more straightforward than random sampling
  • It covers the whole study area equally

  • 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
  • It can be used alongside systematic and random sampling
  • Comparisons can be made between sub-sets
  • The proportions of sub-sets need to be known and be accurate

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Bridgette

Author: Bridgette

After graduating with a degree in Geography, Bridgette completed a PGCE over 25 years ago. She later gained an MA Learning, Technology and Education from the University of Nottingham focussing on online learning. At a time when the study of geography has never been more important, Bridgette is passionate about creating content which supports students in achieving their potential in geography and builds their confidence.