Lecture Sampling Methods by Prof: Dr Tauseef Jawaid.ppt

MBBS.USMLE, DPH, Dip-Card, M.Phil, FCPS
Professor Community Medicine
Gujranwala Medical College Gujranwala
Ex-Professor Community Medicine
UmulQurrah University Makka/King Khalid
University Saudi Arabia

The Scientific Method
1. Develop the problem
2. Develop a theoretical solution to the
problem
3. Formulate the hypothesis or question
4. Formulate the research plan (methods)
5. Collect and analyze the data
6. Interpret the results and form
conclusions
7. Refine the theory
8/10/2023 Prof Dr Muhammad Tauseef Jawaid

Formulation of the
Research Methods
A. Selecting the Appropriate Design
B. Selecting the Subjects
C. Selecting Measurement Methods &
Techniques
D. Selecting Instrumentation

Formulation of the
Research Methods
E. Developing Procedures & Protocol
F. Using a Pilot Study
G. Selecting the Appropriate Analysis
Techniques
H. Developing a Timeline & Budget
I. Collecting the Data

OBJECTIVES
 Identify and define the population(s) to be
studied.
 Identify and describe common methods of
sampling.
 Discuss problems of bias that should be
avoided when selecting a sample.
 List the issues to consider when deciding on
sample size.
 Decide on the sampling method(s) and
sample size(s) most appropriate for the
research design you are developing.

Sampling…
 The process of selecting a number of
individuals for a study in such a way
that the individuals represent the
larger group from which they were
selected

14-8
Why Sample?
Greater
accuracy
Availability of
elements
Greater speed
Sampling
provides
Lower cost

14-9
When Is a Census Appropriate?
Necessary
Feasible

14-10
What Is a Valid Sample?
Accurate Precise

Sample…
…the representatives selected for a
study whose characteristics
exemplify the larger group from
which they were selected

Population…
…the larger group from which
individuals are selected to participate
in a study

The purpose for sampling…
 To gather data about the population
in order to make an inference that
can be generalized to the population

The sampling process…
POPULATION
SAMPLE
INFERENCE

Regarding the sample…
POPULATION (N)
SAMPLE (n)
IS THE SAMPLE
REPRESENTATIVE?

Regarding the inference…
POPULATION (N)
SAMPLE (n)
INFERENCE
IS THE
INFERENCE
GENERALIZABLE?

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Sampling Design
within the Research Process

Mistakes to be conscious of...
2. Sampling bias
…which threaten to render a study’s
findings invalid
1. Sampling error

Sampling error…
…the chance and random variation in
variables that occurs when any sample
is selected from the population
…sampling error is to be expected

…to avoid sampling error, a census of
the entire population must be taken
…to control for sampling error,
researchers use various sampling
methods

Sampling bias…
…nonrandom differences, generally the
fault of the researcher, which cause the
sample is over-represent individuals or
groups within the population and
which lead to invalid findings
…sources of sampling bias include the
use of volunteers and available groups

Steps in sampling...
2. Determine sample size (n)
3. Control for bias and error
4. Select sample
1. Define population (N) to be sampled

1. Define population to be sampled...
 Identify the group of interest and
its characteristics to which the
findings of the study will be
generalized
…called the “target” population
(the ideal selection)
…oftentimes the “accessible” or
“available” population must be
used (the realistic selection)

2. Determine the sample size...
 The size of the sample influences
both the representativeness of the
sample and the statistical analysis
of the data
…larger samples are more likely
to detect a difference between
different groups
…smaller samples are more likely
not to be representative

Rules of thumb for determining the
sample size...
2. For smaller samples (N ‹ 100), there is
little point in sampling. Survey the
entire population.
1. The larger the population size, the
smaller the percentage of the
population required to get a
representative sample

4. If the population size is around 1500,
20% should be sampled.
3. If the population size is around 500
(give or take 100), 50% should be
sampled.
5. Beyond a certain point (N = 5000),
the population size is almost
irrelevant and a sample size of 400
may be adequate.

3. Control for sampling bias and error...
 Be aware of the sources of sampling
bias and identify how to avoid it
 Decide whether the bias is so severe
that the results of the study will be
seriously affected
 In the final report, document
awareness of bias, rationale for
proceeding, and potential effects

4. Select the sample...
 A process by which the researcher
attempts to ensure that the sample
is representative of the population
from which it is to be selected
…requires identifying the sampling
method that will be used

Approaches to quantitative sampling...
2. Nonrandom (“nonprobability”): does
not have random sampling at any
state of the sample selection;
increases probability of sampling bias
1. Random: (Probability) allows a
procedure governed by chance to
select the sample; controls for
sampling bias

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Types of Sampling Designs
Element
Selection
Probability Nonprobability
Unrestricted Simple random Convenience
Restricted Complex random Purposive
Systematic Judgment
Cluster Quota
Stratified Snowball
Double

Types of Sampling Methods
Quota
Sampling
Non-Probability
Samples
Convenience Snow ball
Probability Samples
Simple
Random
Systematic
Stratified
Cluster
Purposive

Probability
Sampling

Probability Sampling
 This is one in which each person in the
population has a chance/probability of being
selected
Probability Sample
Simple
Random
Systematic Stratified Cluster

14-34
Steps in Sampling Design
What is the target population?
What are the parameters of
interest?
What is the sampling frame?
What is the appropriate sampling
method?
What size sample is needed?

Random sampling strategies to
collect quantitative data
 If the aim of a study is to measure
variables distributed in a population (e.g.,
diseases) or to test hypotheses about
which factors are contributing
significantly to a certain problem, we have
to be sure that we can generalize the
findings obtained from a sample to the
total study population. Then, purposeful
sampling methods are inadequate, and
probability- or random sampling methods
have to be used.

Random sampling methods...
2. Stratified sampling
3. Cluster sampling
4. Systematic sampling
1. Simple random sampling

 PROBABILITY SAMPLING involves
using random selection procedures to
ensure that each unit of the sample is
chosen on the basis of chance. All
units of the study population should
have an equal, or at least a known
chance of being included in the sample.
 Probability sampling requires that a
listing of all study units exists or can
be compiled. This listing is called the
sampling frame.

Types of
probability sampling methods
Simple random sampling
Systematic sampling
Stratified sampling
Cluster sampling
Multistage sampling

Simple Random Samples
 Every individual or item from the frame has an
equal chance of being selected
 Selection may be with replacement or, without
replacement
 Samples obtained from table of random numbers
or computer random number generators
 Random samples are unbiased and, on average,
representative of the population

1. Simple random sampling: the process
of selecting a sample that allows
individual in the defined population to
have an equal and independent
chance of being selected for the
sample

Steps in random sampling...
2. Determine the desired sample size.
3. List all members of the population.
4. Assign all individuals on the list a
consecutive number from zero to the
required number. Each individual
must have the same number of digits
as each other individual.
1. Identify and define the population.

6. For the selected number, look only at
the number of digits assigned to each
population member.
5. Select an arbitrary number in the table
of random numbers.

8. Go to the next number in the column
and repeat step #7 until the desired
number of individuals has been
selected for the sample.
7. If the number corresponds to the
number assigned to any of the
individuals in the population, then that
individual is included in the sample.

Simple Random Sampling
 In this method, all elements may have
an equal chance of being selected in
the sample:
– Lottery Method
• Small sample sizes, with numbers of
population written on pieces of papers, drawn
from a basket without looking.
• With and Without Replacement.
– Random Numbers Table
– Computer Generated Random Sample

Random Numbers Table
•Select starting point by tossing a
die.
•First toss gets a row block, and an
individual row within the block,
e.g., 2 & 3
•Second toss gets a column block
and a column within the block, e.g.,
3 & 1
•Starting point is where row (83,
no. 8) and column (16, no. 1)
intersect (at 6).
•Select number-of-digits-column
from 6, based on population size N
(e.g., 4 digit column for 1000).
•Proceed & select the numbers
equal to sample size from N (e.g.,
first 200 including 1000).
HOW TO USE

advantages…
…easy to conduct
…strategy requires minimum knowledge
of the population to be sampled

disadvantages…
…need names of all population members
…may over- represent or under- estimate
sample members
…there is difficulty in reaching all selected
in the sample

14-48
Simple Random
Advantages
 Easy to implement
with random
dialing
Disadvantages
 Requires list of
population
elements
 Time consuming
 Uses larger sample
sizes
 Produces larger
errors
 High cost

Systematic Random Sampling
 Here, after knowing the population size (N), a
sampling interval is made (every Kth), depending on
the required sample size (population / Kth interval)
(1000 / 5 = 200).
 If sample size is known, then calculate Kth interval
(Population / Sample size = Kth interval) (1000 / 200 =
5).
 The first sample number is selected from the Kth
interval by simple random sampling (numbers 1 – 5
included).
 Thereafter every Kth person is included in the
sample till the population limit.
 Useful for hospital-based studies.

Systematic sample
 This method is referred to as a systematic
sample with a random start.
 This is done by picking every 5th or 10th unit
at regular intervals.
 For example to carry out a Malria survey in
a town, we take 10% sample. If the total
population of the town is about 5000. The
sample comes to 500.

 Randomly select one individual from the 1st group
 Select every k-th individual thereafter
 We number the houses first. Then a number is taken at
random; say 3.Than every 10th number is selected from
that point onward like 3, 13, 23, 33 etc.
Systematic Samples
N = 500
n = 3
k = 10
First Group

4. Systematic sampling: the process of
selecting individuals within the
defined population from a list by
taking every Kth name.

Steps in systematic sampling...
3. Obtain a list of the population.
4. Determine what K is equal to by
dividing the size of the population by
the desired sample size.

6. Starting at that point, take every Kth
name on the list until the desired
sample size is reached.
5. Start at some random place in the
population list. Close you eyes and
point your finger to a name.
7. If the end of the list is reached before
the desired sample is reached, go
back to the top of the list.

advantages…
…sample selection is simple

disadvantages…
…all members of the population do not
have an equal chance of being selected
…the Kth person may be related to a
periodical order in the population list,
producing unrepresentativeness in the
sample

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Systematic
Advantages
 Simple to design
 Easier than simple
random
 Easy to determine
sampling distribution of
mean or proportion
Disadvantages
 Periodicity within
population may skew
sample and results
 Trends in list may bias
results
 Moderate cost

2. Stratified sampling: the process of
selecting a sample that allows
identified subgroups in the defined
population to be represented in the
same proportion that they exist in the
population

Stratified Random Sampling
 The population is first divided into well-
defined (non-overlapping) strata, so that
strata are heterogeneous, but elements are
homogeneous within each stratum, e.g., S-
E. status, mild, moderate, severe dysplasia,
etc.
 The elements in each stratum are then
randomly sampled by either simple or
systematic sampling methods.
 Sample size for each stratum is determined
based on study requirements.
 Useful for ordinal level sampling.

Stratified Samples
 Procedure: Divide the population into strata (mutually
exclusive classes), such as men and women. Then randomly
sample within strata.
 Suppose a population is 30% male and 70% female. To get a
sample of 100 people, we randomly choose males (from the
population of all males) and, separately, choose females. Our
sample is then guaranteed to have exactly the correct
proportion of sexes.

Steps in stratified sampling...
3. Identify the variable and subgroups
(strata) for which you want to
guarantee appropriate, equal
representation.

5. Randomly select, using a table of
random numbers) an “appropriate”
number of individuals from each of
the subgroups, appropriate meaning
an equal number of individuals
4. Classify all members of the population
as members of one identified
subgroup.

advantages…
…more precise sample
…can be used for both proportions and
stratification sampling
…sample represents the desired strata

disadvantages…
…need names of all population members
…there is difficulty in reaching all selected
in the sample
…researcher must have names of all
populations

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Stratified
Advantages
 Control of sample size
in strata
 Increased statistical
efficiency
 Provides data to
represent and analyze
subgroups
 Enables use of different
methods in strata
Disadvantages
 Increased error will
result if subgroups are
selected at different
rates
 Especially expensive if
strata on population
must be created
 High cost

3. Cluster sampling: the process of
randomly selecting intact groups, not
individuals, within the defined
population sharing similar
characteristics

Cluster Random Sampling
 The population is divided into clusters, which are
homogeneous, but the elements within the clusters
are heterogeneous, e.g., Provinces, Districts,
Streets, Wards, etc.
 Sampling is done randomly (simple / systematic):
– Of the clusters, then including all elements as
part of sample (if clusters are too many but
elements are few).
– Of the elements (if clusters are few, but elements
in clusters are too large).
 Ideal for surveys – community, hospital based, etc.

Cluster Samples
 Population divided into several “clusters,” each
 Simple random sample selected from each
 The samples are combined into one
Population
divided
into 4
clusters.

Steps in cluster sampling...
3. Identify and define a logical cluster.
4. List all clusters (or obtain a list) that
make up the population of clusters.
5. Estimate the average number of
population members per cluster.

7. Randomly select the needed number
of clusters by using a table of random
numbers.
6. Determine the number of clusters
needed by dividing the sample size by
the estimated size of a cluster.
8. Include in your study all population
members in each selected cluster.

advantages…
…efficient
…researcher doesn’t need names of all
population members
…reduces travel to site
…useful for educational research

disadvantages…
…fewer sampling points make it less like
that the sample is representative

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Cluster
Advantages
 Provides an unbiased
estimate of population
parameters if properly
done
 Economically more
efficient than simple
random
 Lowest cost per sample
 Easy to do without list
Disadvantages
 Often lower statistical
efficiency due to
subgroups being
homogeneous rather
than heterogeneous
 Moderate cost

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Stratified and Cluster Sampling
Stratified
 Population divided
into few subgroups
 Homogeneity within
subgroups
 Heterogeneity
between subgroups
 Choice of elements
from within each
subgroup
Cluster
 Population divided
into many
subgroups
 Heterogeneity
within subgroups
 Homogeneity
between subgroups
 Random choice of
subgroups

Multi - Stage Random Sampling
 If sampling is to be done in multiple
stages, e.g.,
District Tehsil Village
Households,
then each level is randomly sampled,
usually by systematic random method.
 Useful for surveys with large sample
sizes, e.g., national surveys, provincial
surveys, etc.

Non Probability
Sampling

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Nonprobability Samples
Cost
Feasibility
Time
No need to
generalize
Limited
objectives

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Nonprobability
Sampling Methods
Convenience
Judgment
Quota
Snowball

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Key Terms
 Area sampling
 Census
 Cluster sampling
 Convenience
sampling
 Disproportionate
stratified sampling
 Double sampling
 Judgment sampling
 Multiphase sampling
 Nonprobability sampling
 Population
 Population element
 Population parameters
 Population proportion of
incidence
 Probability sampling

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Key Terms
 Proportionate
stratified sampling
 Quota sampling
 Sample statistics
 Sampling
 Sampling error
 Sampling frame
 Sequential sampling
 Simple random sample
 Skip interval
 Snowball sampling
 Stratified random
sampling
 Systematic sampling
 Systematic variance

Key Definitions
 A population (universe) is the collection of
things under consideration
 A sample is a portion of the population
selected for analysis
 A parameter is a summary measure computed
to describe a characteristic of the population
 A statistic is a summary measure computed to
describe a characteristic of the sample

Sampling concepts and terminologies
 Population/Target population
 Sampling unit
 Sampling frame

Population/Target Population
 Target Population is the collection
of all individuals, families, groups
organizations or events that we are
interested in finding out about.
 Is the population to which the
researcher would like to generalize
the results. For example, all adults
population of Lahore aged 65 or
older

Sampling unit/Element/ Unit of
analysis
 Sampling unit is the unit about which
information is collected.
 Unit of analysis is the unit that
provides the basis of analysis.
 Each member of a population is an
element. (e.g. a child under 5)
 Sometimes it is household, e.g. any
injury in the household in the last
three months.

Sampling Frame
 The actual list of sampling units from
which the sample, or some stage of
the sample, is collected
 It is simply a list of the study
population

Sample Design
 A set of rules or procedures that
specify how a sample is to be
selected
 This can either be probability or non-
probability
 Sample size: The number of
elements in the obtained sample

Non Probability Sampling
 These are non random methods of
sampling and are open to a number of
sampling / selection biases.
 The common types are:
– Accidental or Convenience Sampling
– Purposive Sampling.
– Quota Sampling.
– Snowball Sampling.
– Temporal Sampling

Accidental / Convenience
Sampling
 Most commonly used non random
method.
– Researcher merely decides to include all
cases consecutively, or whenever he / she
wishes during the study period, i.e.,
haphazardly.
– Also includes sampling of community at
allotted times or through volunteers,
camps, etc.
– Such sampling is never representative.

1. Convenience sampling: the process
of including whoever happens to be
available at the time
…called “accidental” or “haphazard”
sampling

disadvantages…
…difficulty in determining how much of
the effect (dependent variable) results
from the cause (independent variable)

2. Purposive sampling: the process
whereby the researcher selects a
sample based on experience or
knowledge of the group to be sampled
…called “judgment” sampling

Purposive Sampling
 Researcher purposely includes / excludes
subjects according to his / her wishes or
desired study objectives or results.
 It is obviously the most biased kind of
sampling, as the researcher can manipulate
the results any way merely by selection /
rejection of subjects.
 Sometimes it is done after results have
shown that the researcher’s hypothesis is
not supported – then selective exclusion of
undesirable subjects and inclusion /
addition of desirable subjects is done.

disadvantages…
…potential for inaccuracy in the
researcher’s criteria and resulting
sample selections

3. Quota sampling: the process whereby
a researcher gathers data from
individuals possessing identified
characteristics and quotas

Quota Sampling
 Researcher can choose only a fixed
number of subjects, either because of
limited funds, time period, political
constraints or some other reason.
 Often a known ratio, e.g., male : female
ratio, of say 2:3, makes researcher
include males and females on this
basis.
 It includes, among other biases, that of
sample size as well.

disadvantages…
…people who are less accessible (more
difficult to contact, more reluctant to
participate) are under-represented

Snowball Sampling
 A type of sampling where researcher
extends his / her selection of subjects by
asking from the current subject(s) about
future prospective subjects, i.e., word-of-
mouth selection.
 Commonly used for studies on drug
addiction, social vices, etc., where subject
secrecy is desirable.
 Subject to the snowball effect, where only
people known to each other are selected;
can also result in duplication of subjects
selected.

Snowball
 Recruiting people
based on
recommendation of
people you have just
interviewed
 Useful for studying
invisible/illegal
populations, such as
drug addicts
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8/10/2023 98
Prof Dr Muhammad Tauseef Jawaid

Temporal Sampling
 Here there is a time limit that determines the
period of sampling – subjects are selected
within that time period and not before or
afterwards.
 Basically a variety of convenience sampling.
 The obvious bias is that seasonal or natural
(annual) variations of disease are not taken
into account.
 In random sampling, the time period of
sampling is determined automatically by the
sample size determined.

Random vs non Random sampling
Advantages:
 Minimal Bias
 Ideal for inferential
statistics
 Studies more valid.
Disadvantages:
 Expensive
 Inconvenient and time
consuming.
 Some limitation of
sample size
Advantages:
 Convenient.
 Economical.
 Require less time and
skill.
Disadvantages:
 Non representative
 No inferential statistics
 Validity not accepted
 Weaker studies.
Probability Sampling Non Probability Sampling

Approaches to qualitative sampling...
…qualitative research is characterized
by in-depth inquiry, immersion in a
setting, emphasis on context, concern
with participants’ perspectives, and
description of a single setting, not
generalization to many settings

…because samples need to be small and
many potential participants are
unwilling to undergo the demands of
participation, most qualitative
research samples are purposive

…representativeness is secondary to
the quality of the participants’ ability
to provide the desired information
about self and setting

2. Homogeneous sampling: selecting
participants who are very similar in
experience, perspective, or outlook
1. Intensity sampling: selecting
participants who permit study of
different levels of the research topic

4. Snowball sampling: selecting a few
individuals who can identify other
individuals who can identify still other
individuals who might be good
participants for a study
3. Criterion sampling: selecting all cases
that meet some pre-defined
characteristic

5. Random purposive sampling: with a
small sample, selecting by random
means participants who were
purposively selected and are too
numerous to include all in the study

Mini-Quiz…
 True or false…
…there is no significant difference
between convenience sampling and
purposive sampling
false

…both quantitative and qualitative
researchers who use samples must
provide detailed information about
the purposive research participants
and how they were chosen
true

…the size of the sample influences
both the representativeness of the
sample itself and the statistical
analysis of study data
true

…sampling error reflects sloppy
research
false

…a good researcher can avoid
sampling bias
true

…the important difference between
convenience sampling and
purposive sampling is that, in the
latter, clear criteria guide selection of
the sample
true

…a “good” sample is one that is
from which it was selected
true

…a simple stratified random sample
guarantees that each subgroup is
represented in the same proportion
that it exists in the population
false

…in a systematic sample, the
researcher selects K
false

…a table of random numbers selects
the sample through a purely random,
or chance, basis
true

…purposive sampling does not require
the researcher to describe in detail
the methods used to select a sample
false

…it is possible to defend purposive
samples because the researcher
uses clear criteria (e.g., experience
and prior knowledge) to identify
criteria for selecting the sample
true

…qualitative research uses sampling
strategies that produce samples
which are predominantly small and
nonrandom
true

…a good sample has a composition
precisely identical to that of the
population
false

…cluster sampling oftentimes is the
only feasible method of selecting a
sample because the population is
very large or spread out over a wide
geographic area
true

 Fill in the blank…
…a group which differs in the
characteristics of is members
heterogeneous

…the process of cluster sampling that
is completed in stages, involving the
selection of clusters within clusters
multistage

…the mental process by which
findings from a smaller group are
generalized to a larger group
inference

…the characteristics or variables of
the sample
demographics

…a group that shares similar
characteristics
homogeneous

…the group to which research findings
are generalizable
population

…any location within which a
researcher finds an intact group of
similar characteristics (i.e.,
population members)
cluster

…the extent to which the results of
one study can be applied to other
populations or situations
generalizability

 Which type of sample…
stratified
…identified subgroups in the
population are represented in the
same proportion that they exist in
the population

snowball
…selecting a few individuals who can
identify other individuals who can
identify still other individuals who
might be good participants for a
study

intensity
…selecting participants who permit
study of different levels of the
research topic

cluster
…selects intact groups, not individuals
having similar characteristics

random purposive
…selecting by random means
participants who are selected upon
defined criteria and not who are too
numerous to include all participants
in the study

homogeneous
…selecting participants who are very
similar in experience, perspective,
or outlook

random
…all individuals in the defined
population have an equal and
independent chance of being
selected for the sample

systematic
…a sampling process in which
individuals are selected from a
list by taking every Kth name

criterion
…selecting all cases that meet some
specific characteristic

This module has focused on...
…which describes the procedures
researchers use to select individuals
to participate in a study
sampling a population

The next module will focus on...
...the tools researchers use to gather
data for a study
instruments

Types of Survey Errors
 Coverage error
 Non response error
 Sampling error
 Measurement error
Excluded from
frame.
Follow up on
non responses.
Chance
differences from
sample to sample.
Bad Question!

Improving Response Rates
Prior
Notification
Motivating
Respondents
IncentivesQuestionnair
e Design
and
Administrati
on
Follow-Up Other
Facilitators
Callbacks
Methods of Improving
Response Rates
Reducing
Refusals
Reducing
Not-at-Homes

Evaluating Survey Worthiness
 What is the purpose of the survey?
 Is the survey based on a probability
sample?
 Coverage error – appropriate frame
 Non-response error – follow up
 Measurement error – good questions
elicit good responses
 Sampling error – always exists

Sample Size
Sample size relates to how many people
to pick up for the study
The question often asked is: How big a
sample is necessary for a good survey?
The main objective is to obtain both a
desirable accuracy and a desirable
confidence level with minimum cost.

Determination of Sample Size
 Type of analysis to be employed
 The level of precision needed
 Population homogeneity
/heterogeneity
 Available resources
 Sampling technique used

 n: the desired sample size
 z: the standard normal deviate usually set at 1.96
(which corresponds to the 95% confidence level)
 p: the proportion in the target population to have a
specific characteristic. If no estimate available set at
50% (or 0.50)
 q:1-p
 d: absolute precision or accuracy, normally set at
0.05.
Sample Size Calculation

Sample Size Calculation
n = (1.96)2 (0.5) (0.5)
(0.05) 2
n =384

Before you go to the field…
 Work plan
– Time lines
– Field work logistics
 Financing and budget
 Develop instruments
 Drawing a sample of household
 Training manual
 Pilot test

Sample Size Formula
Use WHO sample Size software

Lecture Sampling Methods by Prof: Dr Tauseef Jawaid.ppt

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Lecture Sampling Methods by Prof: Dr Tauseef Jawaid.ppt