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Probability sampling


Probability Sampling

    There all universes have equal opportunity to get selected which we cannot see in non-probability sampling. The researcher may use

I.                   Simple random sampling 

This is the simplest form of random sampling. The main objective of the Simple random Sampling is to select sample size units out of Population size. This sampling method give equal opportunity to responds to the researcher. This type of sampling is known as Chance Sampling or Probability sampling where each and every item in the population has an equal chance to inclusion in the sample.

            Steps follow to achieve a Simple Random Samplinga.       Assign number to each population unit
b.      Decide the sample size
c.       Required sampling unit can be selected either by lottery method or using random number table.

II.                Systematic random sampling
This is a method of selecting a sample when a list of the units of the population to be sampled is available. There are the general steps needed to follow in order to achieve a systematic random sampling:


a.       Assign number to each population unit
b.      Decide the sample size
c.       Obtain sample interval
d.      Randomly select first integer between interval size

III.             Stratified random sampling
This technique is applied if the population from which a sample is to be drawn does not constitute a homogenous group. In this techniques population stratified into a number of non-overlapping sub-population or strata and sample items are drawn from each stratum adopting simple random sample of fraction. If the item selected from each stratum is based on simple random sampling the entire procedure, First stratification and then simple random sampling.

IV.             Cluster random sampling
Cluster or area sampling is used when the researcher does not have complete information of population but has information about cluster. It is the grouping of population and then selecting the cluster/groups rather than individual elements/units for inclusion in the sample.


Steps followed in Cluster samplinga.       Divide population into cluster
b.      Randomly select the cluster into non-overlapping area
c.       Measures all units within selected clusters

V.                Multi-Stage Random sampling

This technique is applied for a big inquiries extending to a considerably large geographical area. Under this sampling first researcher select large primary sampling units such as regions, then zones, then districts, then VDCs then Town then individual. By combining different methods researcher is able to achieve a rich variety of probabilistic sampling methods that can be used in a wide range of social research context. 




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