Under the Guidance of Dr. ADITHYA KUMARI H. Associate Professor DOS in Library and Information Science University of Mysore Mysore By Poornima Research Scholar

Population is the area in which the information is obtained. Sample is a section of your population that you are actually going to survey. It is important to have a sample that will represent the entire population in order to minimize biases. For example: If you want to know how American citizens feel about the war in Iraq. Population represents the United States and the sample is 500 citizens selected randomly from each state. Since the answers all over the US would greatly vary, it is important to have everyone in the population represented in the sample. This is usually done through random sampling, which assumes no biases seeing as the subjects were selected at random.

 Population refers to an entire group or elements with common characteristics  A complete set of elements (persons or objects) that possess some common characteristic defined by the sampling criteria established by the researcher  Sampling is the process whereby a small proportion or subgroup of a population is selected for analysis  Sample refers to the small subgroup which is thought to be representative of the larger population

Sampling Methods:- There are two types: 1.Probability Sampling Method: Sampling when the probability is known - rely on randomness 2.Non-Probability Sampling Method Probability is not known (e.g., purposive sampling, convenience sampling, quota sampling)

1) A simple random sample:- A simple random sample is obtained by choosing elementary units in search a way that each unit in the population has an equal chance of being selected. A simple random sample is free from sampling bias. However, using a random number table to choose the elementary units can be cumbersome. If the sample is to be collected by a person untrained in statistics, then instructions may be misinterpreted and selections may be made improperly. Instead of using a least of random numbers, data collection can be simplified by selecting say every 10th or 100th unit after the first unit has been chosen randomly as discussed below. Such a procedure is called systematic random sampling.

2) A stratified sample :- A stratified sample is obtained by independently selecting a separate simple random sample from each population stratum. A population can be divided into different groups may be based on some characteristic or variable like income of education. Like anybody with ten years of education will be in group A, between 10 and 20 in group B and between 20 and 30 in group C. These groups are referred to as strata. We can then randomly select from each stratum a given number of units which may be based on proportion like if group A has 100 persons while group B has 50, and C has 30 you may decide you will take 10% of each. So you end up with 10 from group A, 5 from group B and 3 from group C.

3 ) A cluster sample:- A cluster sample is obtained by selecting clusters from the population on the basis of simple random sampling. The sample comprises a census of each random cluster selected. For example, a cluster may be something like a village or a school or a state. So we can decide all the elementary schools in New Delhi as clusters. If we want 20 schools selected to be selected, then we can use simple or systematic random sampling to select the schools, and then every school selected becomes a cluster.

1) Convenience Sampling:- Where the researcher questions anyone who is available. This method is quick and cheap. However we do not know how representative the sample is and how reliable the result. 2) Quota Sampling:- Using this method the sample audience is made up of potential purchasers of your product. For example if you feel that your typical customers will be male between 18-23, female between 26-30, then some of the respondents you interview should be made up of this group, i.e. a quota is given. 3) The judgement sample:- A judgement sample is obtained according to the discretion of someone who is familiar with the relevant characteristics of the population.