Probability sampling with application,advantages and disadvantages

Probability Sampling

Probability sampling is based on the concept of random selection where each population elements have non-zero chance to be occurred as sample. Sampling techniques can be divided into two categories: probability sampling and non-probability sampling. Randomization or chance is the core of probability sampling techniques.
For example, if a researcher is dealing with a population of 100 people, each person in the population would have the odds of 1 out of 100 for being chosen. This differs from non-probability sampling, in which each member of the population would not have the same odds of being selected.
Probability sampling.statistical aid

Different types of probability sampling

Application of probability sampling

·    In opinion poll, a relatively small number of persons are interviewed and their opinions on current issues are solicited in order to discover the attitude of the community as a whole.
·    At border stations, customs officers enforce the laws by checking the effects of only a small number of travelers crossing the border.
·    A departmental store wises to examine whether it is losing or gaining customers by drawing a sample from its lists of credit card holders by selecting every tenth name.
·    In a manufacturing company, a quality control officer take one sample from every lot and if any sample is damage then he reject that lot.

Advantages of Probability Sampling

  Creates samples that are highly representative of the population.
  Sampling bias is tens to zero.
  Higher level of reliability of research findings.
  Increased accuracy of sampling error estimation.
  The possibility to make inferences about the population.

Disadvantages of Probability Sampling

  Higher complexity compared to non-probability sampling.
  More time consuming, especially when creating larger sample.
  Usually more expensive than non-probability sampling.

Cluster sampling
Cluster sampling is defined as a sampling method where multiple clusters of people are created from a population where they are indicative of homogenous characteristics and have an equal chance of being a part of the sample. In this sampling method, a simple random sample is created from the different clusters in the population.
Area sampling

·        Consumes less time and cost
·        Convenient access
·        Least loss in accuracy of data
·        Ease of implementation

·        Higher sampling error, which can be expressed is the so-called “design effect”
·        Biased samples
·        Errors

An example of cluster sampling is area sampling or geographical cluster sampling. Each cluster is a zero graphical area because a geographically dispersed population can be achieved by grouping several respondents with in a local area into a cluster.
Cluster sampling is used to estimate high mortalities increase such as wars, famines and natural disasters.

How to analyze data using SPSS (for beginners)-part 1
How to analyze data using SPSS (part-2), how to input data in SPSS
How to analyze data using SPSS (part-3), how to import data in SPSS from excel file
How to analyze data using SPSS (part-4), how to sort data in SPSS
How to analyze data using SPSS ( part-5) how to merge file in SPSS?
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How to analyze data using SPSS (part-7), Finding missing values, Replacing missing values, Coding missing values in SPSS
How to analyze data using SPSS (part-8), variable transformation, Recoding variables in spss

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