SAMPLING

METHODS

Dr. Pius Ochwo

 LEARNING OBJECTIVES

Learn the reasons for sampling

Develop an understanding about different

sampling methods

Distinguish between probability & non probability

sampling

Discuss the relative advantages & disadvantages

of each sampling methods
What is research?
• “Scientific research is systematic, controlled,
empirical, and critical investigation of natural
phenomena guided by theory and hypotheses
about the presumed relations among such
phenomena.”
– Kerlinger, 1986

• Research is an organized and systematic way of

finding answers to questions
Important Components of Empirical Research

Problem statement, research questions, purposes,

benefits
Theory, assumptions, background literature
Variables and hypotheses
Operational definitions and measurement
Research design and methodology
Instrumentation, sampling
Data analysis
Conclusions, interpretations, recommendations
 SAMPLING
A sample is “a smaller (but hopefully
representative) collection of units from a
population used to determine truths about that
population” (Field, 2005)
Why sample?
Resources (time, money) and workload
Gives results with known accuracy that can be
calculated mathematically
The sampling frame is the list from which the
potential respondents are drawn:
Teachers
Students
Parents
SAMPLING……
What is your population of interest?
To whom do you want to generalize your
results?
All doctors
School children
Indians
Women aged 15-45 years
Other
Can you sample the entire population?
SAMPLING…….

3 factors that influence sample representative-

ness
 Sampling procedure
 Sample size
 Participation (response)

When might you sample the entire population?

 When your population is very small
 When you have extensive resources
 When you don’t expect a very high response
SAMPLING…….

STUDY POPULATION

SAMPLE

TARGET POPULATION
 Types of Samples

Probability (Random) Samples

 Simple random sample
Systematic random sample
Stratified random sample
Cluster sample
Non-Probability Samples
Convenience sample
Purposive sample
Quota
PROBABILITY SAMPLING

 A probability sampling scheme is one in which every

unit in the population has a chance (greater than zero)
of being selected in the sample, and this probability
can be accurately determined.

 . When every element in the population does have the

same probability of selection, this is known as an
'equal probability of selection' (EPS) design. Such
designs are also referred to as 'self-weighting'
because all sampled units are given the same weight.
PROBABILITY SAMPLING…….

Probability sampling includes:

Simple Random Sampling,
Systematic Sampling,
Stratified Random Sampling,
Cluster Sampling
NON PROBABILITY SAMPLING
 Any sampling method where some elements of
population have no chance of selection (these
are sometimes referred to as 'out of
coverage'/'undercovered'), or where the
probability of selection can't be accurately
determined. It involves the selection of
elements based on assumptions regarding the
population of interest, which forms the criteria
for selection.

 Example: We visit every household in a given

street, and interview the first person to open
the door. In any household with more than one
occupant, this is a nonprobability sample,
because some people are more likely to open the
door (e.g. an unemployed person who spends
most of their time at home is more likely to
open than an employed housemate who might be
at work when the interviewer calls).
NONPROBABILITY SAMPLING…….
• Nonprobability Sampling includes:
Accidental Sampling, Quota Sampling and
Purposive Sampling. In addition, nonresponse
effects may turn any probability design into a
nonprobability design if the characteristics of
nonresponse are not well understood, since
nonresponse effectively modifies each
element's probability of being sampled.
SIMPLE RANDOM SAMPLING
• Applicable when population is small,
homogeneous & readily available
• All subsets of the frame are given an equal
probability. Each element of the frame
thus has an equal probability of selection.
• This is done by assigning a number to each
unit in the sampling frame.
• A table of random number or lottery
system is used to determine which units
are to be selected.
SYSTEMATIC SAMPLING
 Systematic sampling relies on arranging the target
population according to some ordering scheme and then
selecting elements at regular intervals through that
ordered list.
 Systematic sampling involves a random start and then
proceeds with the selection of every kth element from
then onwards. In this case, k=(population size/sample
size).
 It is important that the starting point is not
automatically the first in the list, but is instead
randomly chosen from within the first to the kth
element in the list.
 A simple example would be to select every 10th name
from the telephone directory (an 'every 10th' sample,
also referred to as 'sampling with a skip of 10').
SYSTEMATIC SAMPLING……
As described above, systematic sampling is an EPS method, because all
elements have the same probability of selection (in the example
given, one in ten). It is not 'simple random sampling' because
different subsets of the same size have different selection
probabilities - e.g. the set {4,14,24,...,994} has a one-in-ten
probability of selection, but the set {4,13,24,34,...} has zero
probability of selection.
 STRATIFIED SAMPLING
Where population embraces a number of distinct
categories, the frame can be organized into separate
"strata." Each stratum is then sampled as an
independent sub-population, out of which individual
elements can be randomly selected.
Every unit in a stratum has same chance of being
selected.
Using same sampling fraction for all strata ensures
proportionate representation in the sample.
Adequate representation of minority subgroups of
interest can be ensured by stratification & varying
sampling fraction between strata as required.
STRATIFIED SAMPLING…….

Draw a sample from each stratum

 CLUSTER SAMPLING
In cluster sampling, researchers divide a
population into smaller groups known as
clusters. They then randomly select
among these clusters to form a sample.
Cluster sampling is a method of
probability sampling that is often used to
study large populations, particularly
those that are widely geographically
dispersed.
 CONVENIENCE SAMPLING
 Sometimes known as grab or opportunity sampling or accidental
or haphazard sampling.
 A type of nonprobability sampling which involves the sample being
drawn from that part of the population which is close to hand.
That is, readily available and convenient.
 The researcher using such a sample cannot scientifically make
generalizations about the total population from this sample
because it would not be representative enough.
 For example, if the interviewer was to conduct a survey at a
shopping center early in the morning on a given day, the people
that he/she could interview would be limited to those given there
at that given time, which would not represent the views of other
members of society in such an area, if the survey was to be
conducted at different times of day and several times per week.
 This type of sampling is most useful for pilot testing.
 In social science research, snowball sampling is a similar technique,
where existing study subjects are used to recruit more subjects
into the sample.
CONVENIENCE SAMPLING…….

 Use results that are easy to get

21
Judgmental sampling or Purposive sampling
- The researcher chooses the sample based on
who they think would be appropriate for the
study. This is used primarily when there is a
limited number of people that have expertise
in the area being researched
 PANEL SAMPLING
 Method of first selecting a group of participants through a
random sampling method and then asking that group for the same
information again several times over a period of time.
 Therefore, each participant is given same survey or interview at
two or more time points; each period of data collection called a
"wave".
 This sampling methodology often chosen for large scale or nation-
wide studies in order to gauge changes in the population with
regard to any number of variables from chronic illness to job
stress to weekly food expenditures.
 Panel sampling can also be used to inform researchers about
within-person health changes due to age or help explain changes in
continuous dependent variables such as spousal interaction.
 There have been several proposed methods of analyzing panel
sample data, including growth curves.
Questions???