# 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

Middle

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

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.

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