Lecture Sampling Techniques for researchers.ppt

Defining the Population
A population refers to all the members of a
particular group.
The first task in selecting a sample is to define the
population of interest.
In Educational Research, the population of interest
is a group of persons who possess certain
characteristics.
A target population is the actual population that the
researcher would like to generalize.

 Why Sample?
Unable to study all members of a population
Reduce bias
Save time and money
Measurements may be better in sample than
in entire population
Feasibility

Padre Fred / MLT/CSHS: Research
Methodology; 2011 – 12
Sampling Concepts
 Population—a well-defined set (of what?)
that has certain properties
People
Animals
Objects
Events

Identifying Population Descriptors
 Specify inclusion (eligibility) criteria
 Specify exclusion (delimitations) criteria
. . . leads to sample selection

Let's consider a hypothetical research study for MBA students investigating the factors influencing job
satisfaction among employees in the technology sector.
Inclusion criteria:
1. Employment Status: Participants must be currently employed full-time in the technology sector.
2. Position: Participants must hold a professional position within the organization (e.g., engineer, manager,
analyst).
3. Experience: Participants must have at least one year of work experience in their current role.
4. Company Size: Participants' organizations must have at least 50 employees.
5. Consent: Participants must voluntarily agree to participate in the study.
Exclusion criteria:
1. Part-time Employment: Participants who work part-time or on a contractual basis are excluded from the
study.
2. Non-Professional Roles: Participants in administrative or support roles that are not directly related to
technology or management are excluded.
3. Less Than One Year of Experience: Participants with less than one year of experience in their current role
are excluded to ensure a baseline level of familiarity with the job.
4. Small Companies: Employees of companies with fewer than 50 employees are excluded to focus on
organizations with a significant presence in the technology sector.
5. Involuntary Participation: Employees who are pressured or coerced into participating in the study by their
employers are excluded to ensure voluntary participation and minimize bias.
These criteria help ensure that the sample population is relevant to the research question and provides
valuable insights into the factors influencing job satisfaction among employees in the technology sector.

Population Descriptor Examples
 Gender
 Age
 Marital status
 Religion
 Ethnicity
 Education
 Health status
 Diagnosis
 Co-morbidities

Sampling
Sampling is the process or
technique of selecting a
sample of appropriate
characteristics and adequate
size.

Representativeness (validity)
A sample should accurately reflect distribution of
relevant variable in population
 Person e.g. age, sex
 Place e.g. urban vs. rural
 Time e.g. seasonality
Representativeness essential to generalise
Ensure representativeness before starting,
Confirm once completed

Sampling and representativeness
Sample
Target Population
Sampling
Population
Target Population  Sampling Population  Sample

Definitions
 Population – group of things (people)
having one or more common
characteristics
 Sample – representative subgroup of the
larger population
Used to estimate something about a
population (generalize)
Must be similar to population on characteristic
being investigated

Population:
a set which includes all
measurements of interest
to the researcher
(The collection of all
responses, measurements, or
counts that are of interest)
Sample:
A subset of the population

Lecture Sampling Techniques for researchers.ppt

1. Sampling Frame:
- The sampling frame is the complete list or source from which the sample is chosen in a
research study.
- It should ideally encompass all the individuals or elements that make up the population being
studied.
- The sampling frame serves as the foundation for selecting potential participants for the
research.
Example:
- In a survey about customer satisfaction in a particular city, the sampling frame could be a list
of all registered residents in that city obtained from the municipal records.
2. Sampling Unit: - The sampling unit refers to the individual elements or entities from the
sampling frame that are selected for inclusion in the sample.
- It represents the specific units from which the research collects and analyses data.
- The sampling unit should be representative of the broader population under study.
Example:
- If the sampling frame is the list of registered residents, then each resident represents a
sampling unit. From this list, a subset of residents would be randomly selected to participate in
the survey.
The sampling frame provides the source or list from which the sample is drawn, while the
sampling unit represents the specific individuals or elements selected from that frame to
comprise the sample.

Sampling Error
This arises out of random sampling and is
the discrepancies between sample values
and the population value.
Sampling Variation
 Due to infinite variations among individuals
and their surrounding conditions.
 Produce differences among samples from
the population and is due to chance.
Def. – Cont.

 Example: In a clinical trail of 200 patients
we find that the efficacy of a particular
drug is 75%
If we repeat the study using the same
drug in another group of similar 200
patients we will not get the same efficacy
of 75%. It could be 78% or 71%.
“Different results from different trails
though all of them conducted under the
same conditions”

In general, 2 requirements
1. Sampling frame must be available, otherwise
develop a sampling frame.
2. Choose an appropriate sampling method to
draw a sample from the sampling frame.
How to sample ?

The Sampling Design Process
Define the Population
Determine the Sampling Frame
Select Sampling Technique(s)
Determine the Sample Size
Execute the Sampling Process

Process
19
 The sampling process comprises several stages:
Defining the population of concern
Specifying a sampling frame, a set of items or
events possible to measure
Specifying a sampling method for selecting
items or events from the frame
Determining the sample size
Implementing the sampling plan
Sampling and data collecting
Reviewing the sampling process

Sampling Methods
Probability Sampling
 Simple random sampling
 Stratified random
sampling
 Systematic random
sampling
 Cluster (area) random
sampling
 Multistage random
sampling
Non-Probability Sampling
 Deliberate (quota)
sampling
 Convenience sampling
 Purposive sampling
 Snowball sampling
 Consecutive sampling

Factors Influencing Sample Size
 Type of design used
 Type of sampling procedure used
 Type of formula used for estimating optimum sample
size
 Degree of precision required
 Heterogeneity of the attributes under investigation
 Relative frequency that the phenomenon of interest
occurs in the population (i.e., common vs. rare health
problem)
 Projected cost of using a particular sampling strategy

Padre Fred / MLT/CSHS:
Research Methodology; 2011 –
12
Critical Thinking Decision Path: Assessing the
Relationship between the Type of Sampling Strategy
and the Appropriate Generalizability

Probability versus
Nonprobability
 Probability Samples: each member of the
population has a known non-zero probability of
being selected
 Methods include random sampling, systematic
sampling, and stratified sampling.
 Nonprobability Samples: members are
selected from the population in some nonrandom
manner
 Methods include convenience sampling, judgment
sampling, quota sampling, and snowball sampling

PROBABILITY SAMPLING
24
 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.

NON PROBABILITY SAMPLING
25
 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. Hence, because
the selection of elements is nonrandom, nonprobability
sampling not allows the estimation of sampling errors..
 Example: We visit every household in a given street, and
interview the first person to answer the door. In any
household with more than one occupant, this is a
nonprobability sample, because some people are more
likely to answer the door (e.g. an unemployed person who
spends most of their time at home is more likely to
answer than an employed housemate who might be at
work when the interviewer calls) and it's not practical to
calculate these probabilities.

Random Sampling
Random sampling is the purest form of
probability sampling.
 Each member of the population has an equal and known
chance of being selected.
 When there are very large populations, it is often ‘difficult’
to identify every member of the population, so the pool of
available subjects becomes biased.
 You can use software, such as minitab to generate random
numbers or to draw directly from the columns

SIMPLE RANDOM SAMPLING
27
• 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.
• It provides for greatest number of possible
samples. 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.

SIMPLE RANDOM
SAMPLING……..
28
 Estimates are easy to calculate.
 Simple random sampling is always an EPS design, but not all
EPS designs are simple random sampling.
 Disadvantages
 If sampling frame large, this method impracticable.
 Minority subgroups of interest in population may not be
present in sample in sufficient numbers for study.

REPLACEMENT OF SELECTED UNITS
29
 Sampling schemes may be without replacement ('WOR' -
no element can be selected more than once in the same
sample) or with replacement ('WR' - an element may
appear multiple times in the one sample).
 For example, if we catch fish, measure them, and
immediately return them to the water before continuing
with the sample, this is a WR design, because we might
end up catching and measuring the same fish more than
once. However, if we do not return the fish to the water
(e.g. if we eat the fish), this becomes a WOR design.

Table of random numbers
6 8 4 2 5 7 9 5 4 1 2 5 6 3 2 1 4 0
5 8 2 0 3 2 1 5 4 7 8 5 9 6 2 0 2 4
3 6 2 3 3 3 2 5 4 7 8 9 1 2 0 3 2 5
9 8 5 2 6 3 0 1 7 4 2 4 5 0 3 6 8 6

49486 93775 88744 80091 92732
94860 36746 04571 13150 65383
10169 95685 47585 53247 60900
12018 45351 15671 23026 55344
45611 71585 61487 87434 07498
89137 30984 18842 69619 53872
94541 12057 30771 19598 96069
89920 28843 87599 30181 26839
32472 32796 15255 39636 90819
1 2 3 4 5
Random Number table

How to select a simple random
sample
1. Define the population
2. Determine the desired sample size
3. List all members of the population or the
potential subjects
 For example:
 4th grade boys who have demonstrated
problem behaviors
 Lets select 10 boys from the list

SYSTEMATIC SAMPLING
33
 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 random Sampling
 Technique
 Use “system” to select sample
(e.g., every 5th item in
alphabetized list, every 10th
name in phone book)
 Advantage
 Quick, efficient, saves time
and energy
 Disadvantage
 Not entirely bias free; each
item does not have equal
chance to be selected
 System for selecting subjects
may introduce systematic
error
 Cannot generalize beyond
population actually sampled
. It is also called an Nth name selection
technique.
After calculating the required sample size,
every Nth record is selected from a list of
population members.
As long as the list contains no hidden
order, this sampling method is as good as
the random sampling method.
Its only advantage over the random
sampling technique is simplicity (and
possibly cost-effectiveness).

SYSTEMATIC SAMPLING……
35
 ADVANTAGES:
 Sample easy to select
 Suitable sampling frame can be identified easily
 Sample evenly spread over entire reference population
 DISADVANTAGES:
 Sample may be biased if hidden periodicity in population
coincides with that of selection.
 Difficult to assess precision of estimate from one survey.

Example
 If a systematic sample of 500 students were to be
carried out in a university with an enrolled population of
10,000, the sampling interval would be:
 I = N/n = 10,000/500 =20
 All students would be assigned sequential numbers. The
starting point would be chosen by selecting a random
number between 1 and 20. If this number was 9, then
the 9th student on the list of students would be selected
along with every following 20th student. The sample of
students would be those corresponding to student
numbers 9, 29, 49, 69, ........ 9929, 9949, 9969 and
9989.

Stratified Random Sampling
 Technique
 Divide population into various strata
 Randomly sample within each strata
 Sample from each strata should be proportional
 Advantage
 Better in achieving representativeness on control variable
 Disadvantage
 Difficult to pick appropriate strata
 Difficult to Identify every member in population
A stratum is a subset of the population that share at least one common
characteristic; such as males and females.
Identify relevant stratums and their actual representation in the
population.
Random sampling is then used to select a sufficient number of subjects
from each stratum.
Stratified sampling is often used when one or more of the stratums in
the population have a low incidence relative to the other stratums.

STRATIFIED SAMPLING
39
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……
40
 Drawbacks to using stratified sampling.
 First, sampling frame of entire population has to be prepared
separately for each stratum
 Second, when examining multiple criteria, stratifying variables
may be related to some, but not to others, further complicating
the design, and potentially reducing the utility of the strata.
 Finally, in some cases (such as designs with a large number of
strata, or those with a specified minimum sample size per
group), stratified sampling can potentially require a larger
sample than would other methods

STRATIFIED SAMPLING…….
41
Draw a sample from each stratum

Stratified Random selection Example

Cluster (Area) random sampling
 Randomly select groups (cluster) – all members
of groups are subjects
 Appropriate when
 you can’t obtain a list of the members of the
population
 have little knowledge of population characteristics
 Population is scattered over large geographic
area

Cluster sampling
Section 4
Section 5
Section 3
Section 2
Section 1

CLUSTER SAMPLING
45
 Cluster sampling is an example of 'two-stage
sampling' .
 First stage a sample of areas is chosen;
 Second stage a sample of respondents within
those areas is selected.
 Population divided into clusters of homogeneous
units, usually based on geographical contiguity.
 Sampling units are groups rather than individuals.
 A sample of such clusters is then selected.
 All units from the selected clusters are studied.

CLUSTER SAMPLING…….
46
 Advantages :
 Cuts down on the cost of preparing a
sampling frame.
 This can reduce travel and other
administrative costs.
 Disadvantages: sampling error is higher
for a simple random sample of same
size.
 Often used to evaluate vaccination
coverage in EPI

47
• Identification of clusters
– List all cities, towns, villages & wards of cities with
their population falling in target area under study.
– Calculate cumulative population & divide by 30, this
gives sampling interval.
– Select a random no. less than or equal to sampling
interval having same no. of digits. This forms 1st
cluster.
– Random no.+ sampling interval = population of 2nd
cluster.
– Second cluster + sampling interval = 4th cluster.
– Last or 30th cluster = 29th cluster + sampling interval

48
Two types of cluster sampling methods.
One-stage sampling. All of the elements
within selected clusters are included in
the sample.
Two-stage sampling. A subset of
elements within selected clusters are
randomly selected for inclusion in the
sample.

Difference Between Strata and Clusters
49
 Although strata and clusters are both non-
overlapping subsets of the population, they
differ in several ways.
 All strata are represented in the sample; but
only a subset of clusters are in the sample.
 With stratified sampling, the best survey results
occur when elements within strata are internally
homogeneous. However, with cluster sampling,
the best results occur when elements within
clusters are internally heterogeneous

Cluster (Area) Sampling
 Advantage
More practical, less costly
 Conclusions should be stated in terms of
cluster (sample unit – school)
 Sample size is number of clusters

Multistage random sampling
 Stage 1
randomly sample clusters (schools)
 Stage 2
randomly sample individuals from the schools
selected

Sampling Methods
Non-Probability Sampling
 Deliberate (quota)
sampling
 Convenience sampling
 Purposive sampling
 Snowball sampling
 Consecutive sampling

Deliberate (Quota) Sampling
 Similar to stratified random sampling
 Technique
 Quotas set using some characteristic of the
population thought to be relevant
 Subjects selected non-randomly to meet quotas (usu.
convenience sampling)
 Disadvantage
 selection bias
 Cannot set quotas for all characteristics important to
study

Convenience Sampling
 “Take them where you find them” -
nonrandom
 Intact classes, volunteers, survey
respondents (low return), a typical group,
a typical person
 Disadvantage: Selection bias

Convenience Sampling
 Convenience sampling is used in exploratory
research where the researcher is interested in
getting an inexpensive approximation.
 The sample is selected because they are
convenient.
 It is a nonprobability method.
 Often used during preliminary research efforts to get
an estimate without incurring the cost or time required
to select a random sample

Judgment sampling is a common
nonprobability method.
The sample is selected based upon judgment.
an extension of convenience sampling
When using this method, the researcher must be
confident that the chosen sample is truly
representative of the entire population.
Judgment Sampling

Purposive Sampling
 Purposive sampling (criterion-based sampling)
 Establish criteria necessary for being included in
study and find sample to meet criteria
 Solution: Screening
 Use random sampling to obtain a representative
sample of larger population and then those subjects
that are not members of the desired population are
screened or filtered out
 EX: want to study smokers but can’t identify all
smokers

Snowball Sampling
In snowball sampling, an initial group of respondents is
selected.
 After being interviewed, these respondents are asked
to identify others who belong to the target population
of interest.
 Subsequent respondents are selected based on the
referrals.

Choosing probability vs. non-probability sampling
method
Probability Evaluation Criteria Non-probability
sampling sampling
Conclusive Nature of research Exploratory
Larger sampling Relative magnitude Larger non-sampling
errors sampling vs. error
non-sampling error
High Population variability Low
[Heterogeneous] [Homogeneous]
Favorable Statistical Considerations Unfavorable
High Sophistication Needed Low
Relatively Longer Time Relatively shorter
High Budget Needed Low

17 general practice group
Random sampling
7 were selected
People age 65 or older were registered with the
general practices. Total 750-850 in each Gen Pract
One third in each practices were selected to form survey sample
Use SRS to select eligible people in each practice
Sampling
Procedure

In Conclusion,
For any research, based on its study design
and objectives an appropriate random
sampling technique should be used, so as
to generalize the findings.

Lecture Sampling Techniques for researchers.ppt

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