Sampling Techniques

Sampling Techniques

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 units in the

population are numbered 1 to N in some order. To select a systematic

sample of n units, if $k approx N/n$then every k-th unit is selected

commencing with a randomly chosen number between 1 and k. Hence the

selection of the first unit determines the whole sample, e.g., N =

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 as possible.