Your SlideShare is downloading. ×
Upcoming SlideShare
Loading in...5

Thanks for flagging this SlideShare!

Oops! An error has occurred.

Saving this for later? Get the SlideShare app to save on your phone or tablet. Read anywhere, anytime – even offline.
Text the download link to your phone
Standard text messaging rates apply



Published on

Published in: Technology, Education
  • Be the first to comment

  • Be the first to like this

No Downloads
Total Views
On Slideshare
From Embeds
Number of Embeds
Embeds 0
No embeds

Report content
Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

No notes for slide


  • 1. Collecting Data. Sampling.
  • 2. Why do we usually sample?
    • Is it possible to measure all the pebbles on a beach to work out the average pebble size?
    • Chances are the answer is no- therefore as geographers we can sample a number of the population to gain an insight into the patterns.
    • We can also sample in human geography as well.
  • 3. Why is sampling desirable?
    • It is quicker than measuring every item.
    • It is cheaper
    • Often it is impossible to measure everything.
    • It is unnecessary to measure the whole population because a carefully chosen sample can give you a result which is close to the figure you would obtain even if you measured every pebble.
  • 4. Why is sampling desirable?
    • We may wish to take a snapshot of the population at one moment in time- for example meteorological readings are taken simultaneously at only a limited number of sample sites in the UK and sent to the met office.
    • It is sometimes impossible to gain access to the complete population
      • For example if we asked a questionnaire to everybody in Plymouth about their shopping habits some could be quite in their rights to say no.
  • 5. Why is sampling desirable?
    • We may not know the population size or location and therefore we would be forced to sample.
  • 6. Avoiding Bias
    • Bias can arise for many reasons.
    • The data from which the sample is taken is biased- For example why would selecting the addresses of 100 people from a telephone directory be biased?
    • Insufficient care in the choice of sample may result in an unrepresentative data set- for example asking a spread of young and old people is better than only asking young or only old.
  • 7. Avoiding Bias
    • 4. The time the sample was taken may produce bias- Why would asking a street questionnaire at 10:00 on a weekday be biased?
  • 8. Sampling Methods.
    • Before selecting a sample it is necessary to decide on the sampling method.
    • When selecting samples from an area on the ground or from a map there is one decision to be made
      • Are you looking for points, lines or quadrats- these are known as the three spatial sampling methods
  • 9. Point sampling
    • This involves choosing individual points and sampling those points, such as specific houses down a street or crop types on a land use map.
  • 10.  
  • 11. Line sampling
    • This involves taking measurements along a line- for example to sample vegetation across a series of sand dunes and note the dominant vegetation type along each part of the line.
  • 12.  
  • 13. Quadrat sampling.
    • Also known as area sampling
    • This involves marking a square on the ground and noting the occurrence of the feature you are interested in within the square.
  • 14.  
  • 15. How do we decide what to do?
    • There are three commonly used methods.
    • Random Sampling.
      • Decide how many sample points you want.
      • Obtain random numbers- either from a published sheet or from a calculator which generates random numbers.
      • Overlay a map of the area with grid lines and number them.
      • Use random numbers to read off grid references.
      • Find out the land use at each points chosen by visiting the places on the ground or from a land use map.
  • 16. Random sampling.
    • Can be used for most types of sampling
    • The provide a means by which we can select samples in the knowledge that no human bias is involved.
    • The disadvantage is that if the sample size is small we might obtain an unrepresentative result.
  • 17. 2. Stratified sampling
    • With this type of sampling we start by asking the question Are there subsets of the pattern being measured that must be included within our sample?
    • For example- we might think older people shop differently to young- therefore our sample must contain old and young people.
    • These subsets are known as strata.
  • 18. 2. Stratified sampling
    • In order to sample this way you plot the points on a map as in random sampling but do not allow more than the allotted number of points to fall on each area of study
      • you then ignore an excess points which are chosen by random numbers.
  • 19. 2. Stratified sampling
    • This has the great advantage in that it helps to reduce any bias which might possibly arise if samples were chosen completely at random.
    • The only problem with the method is identifying what the strata should be.
    • The difficulty is knowing for certain that the strata are relevant and how many strata to create within each- how many age division for example.
  • 20. 3. Systematic sampling.
    • In this method the sample is chosen according to some agreed interval- i.e we visit every 5 th house or measure every tenth pebble.
    • The advantages of this method are great
      • It ensures complete coverage of the map.
      • It is simple to do.
    • The danger is that the systematic sample might inadvertently pick up some underlying regularity.
  • 21. Sample size.
    • The size of the sample will usually be dictated by the time available.
    • The larger the sample the better the quality of the results.
    • Also your capacity to handle the data collected influences the size of the sample- for example computers now allow us to collect more data and produce accurate analysis and graphs.
  • 22. Required sample size.
    • There is a way to calculate the best possible sample size- known as the required sample size.
    • We will use measuring pebbles on a beach to calculate this.
  • 23. Required sample size.
    • Collect a pilot sample of 30 pebbles- Measure their lengths.
    • Calculate the standard deviation of the results- this is how far the results are away from the mean- another lesson for this.
    • Decide on a tolerable margin of error- how close you wish to be to the result you would obtain if you measured all the pebbles- e.g 0.5 cm
    • Decide on the level of certainty you wish to achieve- e.g 90% sure that the average length of the pebbles measured lies in the 0.5 cm margin or error.
    • Calculate using this formula
      • zs
      • n= d
    2 N= required sample size. Z= z score (a different table you aquire as a table of numbers) S =standard deviation. d= Tolerable margin of error.