Research Methodology
Lecture No :15
(Sampling Design / Probability vs Non probility)
Recap
• Sampling is the process of selecting the right
individuals
• Sample is used to represent the whole data or
population
• Sampling process include defining population,
sample frame, sampling design, sample size
and sampling process
Lecture Objectives
• Differentiate between probability and non
probability sampling
• Learn about the types of probability sampling, its
advantages and disadvantages
• Learn about the types of non probability
sampling, its advantages and disadvantages
• Issues relevant to sample design and collection
Probability Sampling
Unrestricted or simple random sampling
• Technique which ensures that each element in
the population has an equal chance of being
selected for the sample.
• The simple random sampling is the least bias
and offer the most generalizability.
Probability Sampling
• The major advantage
sampling is its simplicity.
of
• The
sampling
process
cumbersome and expensive.
simple
random
could
become
Example: Choosing raffle tickets from a drum,
computer-generated
selections,
random-digit
telephone dialing.
Simple random sampling
Probability Sampling
Restricted or complex probability sampling:
• It is an alternate to simple random sampling
design, several complex probability sampling
designs can be used.
• Efficiency is improved in that more information
can be obtained for a given sample size using
the complex probability sampling procedures.
Probability Sampling
The most common complex probability sampling
design
1. Systematic sampling
2. Stratified sampling
3. Cluster sampling
1.
Area sampling
4. Double sampling
Probability Sampling
Systematic Sampling:
• Technique in which an initial starting point is
selected by a random process, after which every
nth number on the list is selected to constitute
part of the sample.
• Sampling interval (SI) = population list size (N)
divided by a pre-determined sample size (n)
• How to draw:
• 1) calculate SI, say (200/20)=10
• 2) select a number between 1 and SI randomly, i.e. 1-10
• 3) go to this number as the starting point and the item on the list
here is the first in the sample, e.g 3
• 4) add SI to the position number of this item and the new position
will be the second sampled item, e.g 3+10=13
• 5) continue this process until desired sample size is reached.
• For systematic sampling to work best, the list
should be random in nature and not have some
underlying systematic pattern.
• E.g: Office directory with the Senior Manager,
Middle manager ….names are listed in each
department. This can create as systematic
problem
Probability Sampling
Stratified Sampling:
• Technique in which simple random subsamples
are drawn from within different strata that share
some common characteristic. Within the group
they are homogenous and among the group
they are heterogeneous.
Probability Sampling
Stratified Sampling
Example: The student body of CIIT is divided into
two groups (management science, engineering)
and from each group, students are selected for a
sample using simple random sampling in each of
the two groups, whereby the size of the sample for
each group is determined by that group’s overall
strength.
Probability Sampling
Cluster Sampling
• Technique in which the target population is first
divided into clusters. Then, a random sample of
clusters is drawn and for each selected cluster
either all the elements or a sample of elements
are included in the sample.
• Cluster samples offer more heterogeneity within
groups and more homogeneity among groups
Probability Sampling
Area sampling
Specific type of cluster sampling in which clusters
consist of geographic areas such as counties, city
blocks, or particular boundaries within a locality.
• Area sampling is less expensive than most other
sampling designs and it is not dependent on
sampling frame.
• Key motivation in cluster sampling is cost
reduction.
Probability Sampling
Area sampling
Example: A city map showing the blocks of the city
is adequate information to allow the researcher to
take a sample of the blocks and obtain data from
the resident therein.
Example: If you wanted to survey the residents of
the city, you would get a city map, take a sample of
city blocks and select respondents within each city
block.
Probability Sampling
Single stage and multistage cluster sampling
• Single stage cluster sampling involves the
division of population into convenient clusters,
randomly choosing the required number of
clusters as sample subjects, and investigating all
the elements in each of the randomly chosen
clusters
• Cluster sampling can also be done in several
stages and is then known as multistage cluster
sampling.
Probability Sampling
Example: If we were to do a national survey of the
average monthly bank deposits, cluster sampling
would be used to select the urban, semi urban and
rural geographical location for study. At the next
stage particular areas in each of these locations
would be chosen. At the third stage, banks within
each area would be chosen.
Example:
Probability Sampling
Double sampling:
• A sampling design where initially a sample is
used in a study to collect some preliminary
information of interest, and later a subsample of
this primary sample is use to examine the matter
in more detail.
Probability Sampling
Double sampling
Example: A structured interview might indicate that
a subgroup of respondents has more insight into
the problems of the organization. These
respondents might be interviewed again and again
and asked additional questions.
Non-Probability Sampling
Convenience Sampling:
• Sampling technique which selects those
sampling units most conveniently available at a
certain point in, or over a period, of time.
Non-Probability Sampling
Convenience Sampling:
• Major advantages of convenience sampling is
that is quick, convenient and economical; a
major disadvantage is that the sample may not
be representative.
• Convenience sampling is best used for the
purpose
of
exploratory
research
and
supplemented subsequently with probability
sampling.
Non-Probability Sampling
Judgment (purposive) Sampling:
• Sampling technique in which the business
researcher selects the sample based on
judgment about some appropriate characteristic
of the sample members.
Example: Selection of certain students who are
active in the university activities to inquire about
the sports and recreation facilities at the university.
Recap
• Simple random sampling and restricted
sampling are two basic types of probability
sampling.
• Probability ( Simple Random, Systematic,
Cluster,
Single
stage/multistage,
Double
sampling)
• Non Probability (Convenience, judgment)