Sampling Types
-
A Sample is selected in
such a way that each item
or person in the
population being studied
has a known (non zero)
likelihood of being
included in the sample.
-
Not all items or people
have a chance of being
included in the sample.
Results may be biased.
-
Selection without any logic. All items in the population
have an equal chance of being chosen in the sample.
-
For Example: Scientist randomly selecting people for
researchpurpose.Other examples include:
- Randomly selecting auto components coming out of a
plant
- Randomly selecting a telephonic conversation to check
the quality of the conversation
-
When items are selected at the pre-determined
interval when arrangedin an order
-
Example: Scientist selecting every 2
nd people for
researchpurpose.Other examples include:
- Quality Auditor selecting every 8th transaction as per the
processing time
- Selecting every 30th bottle coming out of the assembly
line
-
When the population has different groups (strata). In
stratified random sampling, independent samples are
drawn from each group. The size of each sample is
proportional to the relative size of the group
-
Example: Scientist randomly selecting people from
each group categorized based on their designation for
research purpose.
-
The population is broken down into many different
clusters, and then clusters or subgroups are randomly
selected.
-
Clusters can be of different ages, locations, people
income, designation, etc.
-
For example: Scientist categorized samples into four
subgroups based on their designation and selects
doctors for the researchpurpose.
-
Judgmental sampling is also known
as purposive sampling.
-
Samples are selected based on the purpose or
intention ofthe research.
-
The method is versatile enough to allow for the
inclusion of items in the sample that are particularly
important.
-
For example: Scientist selects associates wearing tie
for researchpurpose as he intends to do it.
-
One of the easiest sampling methods is convenience
sampling.
-
Samples selection is based on availability and
selecting the samples that are convenient to the
researcher.
-
For example: Scientist selects the people from
various subgroups based on their availability for
researchpurpose.
-
It is similar to stratified sampling, we choose items
based on predetermined characteristics of the
population.
-
For example: Scientist selects all the women in a
locality for research purpose. Other examples
includes
- Tax payers in the age range of 28 – 32 years in a region,
etc. This is a way of collecting samples in a fast way but
leaves space for bias.
-
Existing people are asked to nominate further
people known to them so that the sample increases
in size like a rolling snowball.
-
This method of sampling is effective when a
sampling frame is difficult to identify. There is a
significant risk of selection bias in snowball
sampling, as the referenced individuals will share
common traits with the person who recommends
them.
