a. Definition:

Simple random sampling is the basic sampling technique where we select a group of subjects (a sample) for study from a larger group (a population). Each individual is chosen entirely by chance and each member of the population has an equal chance of being included in the sample. Every possible sample of a given size has the same chance of selection; i.e. each member of the population is equally likely to be chosen at any stage in the sampling process. b. Advantage:

There are some advantages of using single random sampling :

Firstly, collecting the sample easily since every member is given equal opportunities of being chosen. Another it requires minimum advance knowledge of the population. And the key factor of simple random sampling is its representativeness of the population. c. Disavantage :

However, sometimes random sampling method application impossible in practical terms.

First, it is difficult to be able to have a complete list of all the objects in the target population. For example, when you want prospective study on the injuries caused by traffic accidents, we can not know how many emergency patients will be there day or during the time we gather data.

Second, even with the full list of subjects it is sometimes difficult to randomly select a object. Suppose if we want to measure the satisfaction level of professional nurses in a hospital with 300 nurses that are working and the sample size is 120. It is unreasonable to draw or randomly selected one by one until the object is 120

Thirdly, we can not ensure the complete objectivity of the method. In general, here are two concepts that we should consider : the objects must be selected independently, and all subjects have equal chance of being selected. d.Practical example :

. Imagine you want to carry out a survey of 100 voters in a small town with a population of 1,000 eligible voters. With a town this size, there are "old-fashioned" ways to draw a sample. For example, we could write the names of all voters on a piece of paper, put all pieces of paper into a box and draw 100 tickets at random. You shake the box, draw a piece of paper and set it aside, shake again, draw another, set it aside, etc. until we had 100 slips of paper. These 100 form our sample. And this sample would be drawn through a simple random sampling procedure - at each draw, every name in the box had the same probability of being chosen.

2. Stratified sampling :

a. Definition:

It is a sampling method in which the population is split into several categories that share common characteristics. Items are collected at random from each category, in proportion to the size of the category relative to the population. Stratified sampling may give more reliable results than pure random sampling because it ensures that all categories are fairly represented. b.Advantage :

Ensures units from each main group are included and may therefore be more reliably representative. Should reduce the error due to sampling.

c.Disadvantage :

Selecting the sample is more complex and requires good population information.

The estimates involve complex calculations.

d.Practical example :

In general the size of the sample in each stratum is taken in proportion to the size of the stratum. This is called proportional allocation. Suppose that in a company there are the following staff: * male, full time: 90

* male, part time: 18

* female, full time: 9

* female, part time: 63

* Total: 180

and we are asked to take a sample of 40 staff, stratified according to the above categories. The first step is to find the total number of staff (180) and calculate the percentage in each group. * % male, full time = 90 / 180 = 50%

* % male, part time = 18 / 180 = 10%

* % female, full time = 9 / 180 = 5%

* % female, part time = 63 / 180 = 35%

This tells us that of our sample of 40,

* 50% should be male,...