Data Collection (OCR GCSE Geography B)

Revision Note

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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

Data Types - Strengths and Limitations

 

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 Methods - Advantages and Disadvantages

Sampling type

Advantages

Disadvantages

Random

Least biased of sampling; all 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 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.