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  • 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.