###
**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.

Example

Area sampling

**Advantages**

·
Consumes
less time and cost

·
Convenient
access

·
Least
loss in accuracy of data

·
Ease
of implementation

**Disadvantages**

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

·
Biased
samples

·
Errors

**Application**

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?__

How to analyze data using SPSS ( part-6), how to merge file in SPSS

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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