• Join over 1.2 million students every month
  • Accelerate your learning by 29%
  • Unlimited access from just £6.99 per month

Sampling Techniques.

Extracts from this document...

Introduction

Simple Random Sampling

A sampling procedure that assures that each element in the population has an equal chance of being selected is referred to as simple random sampling .Let us assume you had a school with a 1000 students, divided equally into boys and girls, and you wanted to select 100 of them for further study. You might put all their names in a drum and then pull 100 names out. Not only does each person have an equal chance of being selected, we can also easily calculate the probability of a given person being chosen, since we know the sample size (n) and the population (N) and it becomes a simple matter of division:

n/N x 100 or 100/1000 x 100 = 10%

Systematic Sampling

At first sight this is very different. Suppose that the N

...read more.

Middle

 = 5,000, n = 250 therefore k = 5000/250 = 20. Therefore, select every 20th item commencing with (say) 6.

Question : Is it equivalent to simple random sampling? Strictly speaking the answer is No!, unless the list itself is in random order, which it never is (alphabetical, seniority, street number, etc).

Advantages

(i)

easier to draw, without mistakes (cards in file)

(ii)

more precise than simple random sampling as more evenly spread over population

Disadvantages

(i)

if list has periodic arrangement then it can fare very badly

Stratified Sampling

In this random sampling technique, the whole population is first into mutually exclusive subgroups or strata and then units are selected randomly from each stratum. The segments are based on some predetermined criteria such as geographic location, size or demographic characteristic. It is important that the segments be as heterogeneous

...read more.

Conclusion

In this technique you oversample and then weight your data to re-establish the proportions. Let us assume you only have enough budget to survey 300 guests, but you still want at least 100 leisure travellers to have a sufficient number for further analysis. This means that you oversample for leisure travellers at a ratio of 2:1. Therefore, you would need to weight each of your business travellers by 2 (i.e. 2 x 2 = 4) to end up with the proper proportions.

...read more.

This student written piece of work is one of many that can be found in our AS and A Level Probability & Statistics section.

Found what you're looking for?

  • Start learning 29% faster today
  • 150,000+ documents available
  • Just £6.99 a month

Not the one? Search for your essay title...
  • Join over 1.2 million students every month
  • Accelerate your learning by 29%
  • Unlimited access from just £6.99 per month

See related essaysSee related essays

Related AS and A Level Probability & Statistics essays

  1. "The lengths of lines are easier to guess than angles. Also, that year 11's ...

    Neither of the most populated groups contained the correct estimate for the line or the angle, so it is not possible to tell accurately which one was easier, but you can see that the year 11 estimate for the line's most densely populated group was the group next to the

  2. maths coursework sampling

    There are many aspects to consider before understanding why such denominations occur. 1. An error in the input of data or an incorrect method of recording information can result to an extremely large numbers mistake occurring in the processed information (that is the tables), e.g.

  1. Estimating the length of a line and the size of an angle.

    I have turned down quota sampling because it is not very reliable because it depends on the interviewer to choose the sample. Who may choose the sample unfairly or may make up some results, which distorts the data. While the advantage of it is that it is very cheap and you can get exactly what is required.

  2. Case study -Super Savers is wishing to move into the UK Food Retail market.

    their task * Ask panellists questions at each point about the worst possible scenario and how the study could be improved to minimise these contingencies * Know how to analyse and present results for best effect 4.1 HYPOTHESIS: In statistical inference testing a hypothesis is put forward, and the object

  1. Reaction Times

    with the index finger of the student * Standing the students in the same position * Testing all students at the same time of day * Repeating each test 3 times * Using only year 10 students Note: All Reaction's are measured in cm's Methods of sampling Hypothesis 1 Boys

  2. Collect data with a view to estimating population parameters using estimation techniques.

    * The variance of the sample mean is roughly the same as the population variance divided by the sample size * The large the sample size the closer the sample mean and variation are to the population mean and variation.

  1. Factors influencing girls athletic performance throughout secondary school.

    To ensure that fair samples which are representative of the form are chosen, I will take a stratified sample from each form. This means that I will divide them into strata (forms) and then choose a random sample from each category.

  2. Statistics: Survey of Beijing and China during the SARS storm

    Method: �producer: data collected from internet day by day from 1 of March to 31 of April. 1. I have collected the population data from the newspapers, magazines and Internet. 2. I have got the date number and death number.

  • Over 160,000 pieces
    of student written work
  • Annotated by
    experienced teachers
  • Ideas and feedback to
    improve your own work