SAMPLING AND SAMPLING DISTRIBUTION

SAMPLING AND SAMPLING DISTRIBUTION

Sampling is the process of selecting a part of the population. Sampling basically employed by most researchers for some or all of the following reasons: a.) reduced cost, b.) for greater speed, for greater for greater efficiency and accuracy.) For greater scope d.) for convenience , e.) for ethical consideration and f.) It is necessity.

To take samples from the population, we may choose any of the following methods. Probability Sampling or Non Probability Sampling.

In non probability sampling the element of the population is taken depending to a large extent on the personal feeling of the researchers or purpose without regard for some chance mechanisms. This method is also known as judgment sampling where the interviews in a household survey are given ‘quotas’ to provide a cross section of the population under study with respect to certain characteristics such as sex, income and residence.

In probability sampling, very element belonging to the population has known non zero probability of being included in the sample. Probability sampling has two major advantages: 1.) the sample data can be evaluated by statistical methods to provide information about the margin of error in the results due to sampling. 2.) Biases are avoided that could enter if judgment were used to select the population elements for the sample.

Methods of Probability Sampling

          Probabilitysampling allows us to calculate sampling error, thus it permits us to judge the goodness of the sample statistics. Too keep tack of this sampling error, different probability sampling methods that would allow us to compute this sampling error may be employed, namely Simple Random Sampling, Stratified, Systematic, Cluster and Sequential sampling.

Simple Random Sampling. A simple random sample is a sample in which each member of the population is equally likely to be selected.

Stratified random Sampling . a stratifiedrandom sampling is a sample in which obtained by grouping first the population into mutually exclusive sets or strata and then drawing simple random samples from each stratum. If equal –sized samples are drawn from each stratum we have equal allocation. If the sample since is drawn are proportional to the subpopulation size then we have proportional allocation

Systematic Random Sample . A systematic sample consists of an elements selected randomly from the first k=N/n elements and every kth subsequent elements.

Cluster Sampling. A cluster sampling is a simple random sample of groups or cluster of elements.

Sequential Sampling. A sequential sampling is a sample drawn in which an initial sample is first drawn and if this initial sample does not lead to a conclusion of acceptable reliability, more units are added- possible one at a time – until a point is reached at which a reliable inference can be made.

I do know that there are more methods of probability sample sampling and formulas are needed for clarity. Hopefully I would give it soon.