Data Collection (AQA GCSE Geography)

Revision Note

Bridgette Barrett

Written by: Bridgette Barrett

Reviewed by: Jenna Quinn

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 Barrett

Author: Bridgette Barrett

Expertise: Geography Lead

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.

Jenna Quinn

Author: Jenna Quinn

Expertise: Head of New Subjects

Jenna studied at Cardiff University before training to become a science teacher at the University of Bath specialising in Biology (although she loves teaching all three sciences at GCSE level!). Teaching is her passion, and with 10 years experience teaching across a wide range of specifications – from GCSE and A Level Biology in the UK to IGCSE and IB Biology internationally – she knows what is required to pass those Biology exams.