sampling

SAMPLING
DESIGN
Geethakumary.V.P.

SAMPLING -TERMS
 SAMPLING THEORY-is a mathematical
method of decision making for
determining the most efficient means
of selecting a sample that represent the
population under study

SAMPLING -TERMS
Population : Is a group whose members
possess specific attributes that a
researcher is interested in studying.
Population is the entire aggregation of
cases that meet a designed set of
criteria.
May consist of events, places, objects,
animals or individuals

Sampling -terms
Target Population : is the population under
study / The population to which the researcher
wants to generalize the findings.
Is the entire set of individuals or elements who
meet the sampling criteria.
Accessible Population : is that part of target
population that is available to the researcher.

SAMPLE
 A portion of the population that has been
selected by the researcher to represent
the population of interest.
 Sample consists of a subset of the units
that compose the population.
 Element : Is a single member of the
population under study. (Subject /
Participant )

SAMPLING -TERMS
 Sampling criteria/eligibility criteria –sampling
criteria list the characteristics essential for
membership in target population.
The criteria that specify characteristics
that the people must possess to be
included into the sample.
Inclusion criteria
Exclusion criteria : The criteria that the
subjects must not possess.

Sampling criteria/eligibility criteria
Reflect considerations other than
substantive or theoretical interest
 Cost
 Practical constraints
 Peoples ability to participate in the study
 Design considerations

SAMPLING -TERMS
 REPRESENTATIVENESS-means that the sample must
like the population in as many ways as possible
 Representativeness – is how well the sample represents the
variables of interest in the target population
 Representative sample is one whose key characteristics
closely approximate those of population
 If sample is not representative ,external validity is at risk

REPRESENTATIVENESS contd
 No way make sure that a sample is
representative without obtaining information
from population.
 Certain sampling procedures are less likely to
result in biased samples than others ,but
representativeness can never be guaranteed.
 Minimise error and if possible estimate their
magnitude.

SAMPLING -TERMS
Sampling frame : A comprehensive
list of all the sampling elements in
the target population.
Strata : A stratum refers to a
mutually exclusive segment of a
population established by one or
more characteristics.

SAMPLING –TERMS contd
 Random selection : Is a process of
selecting a representative sample of the
target population. The purpose of which is to
ensure that every element in the target
population has an equal, independent non-
zero chance of being selected for inclusion in
the study.

SAMPLING –TERMS contd
 Sampling frame : A comprehensive
list of all the sampling elements in
the target population
 Strata : A stratum refers to a
mutually exclusive segment of a
population established by one or
more characteristics

SAMPLING -TERMS
Statistic and parameter
information collected from a sample is called
statistic
information collected from a population is called a
parameter
Data –data are information collected from a source

SAMPLING -TERMS
 Sampling error –is the difference or error
between the sample statistic and the
population parameter-estimated
statistically by calculating the standard
error.
 Standard error –is the measurement of the
standard deviation between a sample
measure and a population measure.

SAMPLING -TERMS
Standard error
random variation –expected difference
in value of different subjects from the
same sample
systematic variation –exclusion criteria
tend to increase the systematic bias in
sample

SAMPLING -TERMS
 Sampling bias –occurs when the
researcher shows a preference in
selecting one participant over the another.
Systematic over representation of
segment of the population in terms of a
characteristics relevant to the research
question.

Why Sample?
 Why not study everyone?
 Census vs. sampling

Why sampling?
 Sampling reduces demands on resources
-finance, manpower, materials.
 Reduces duration of study.
 Sampling may be the feasible method.
 Ethically sampling is the only permitted method
 Sometimes increases accuracy(non sampling
errors and non response rate can be kept
minimal)

Quantitative studies
 Seek to select samples that allow them
to generalize their results to broader
groups
 Sampling plan that specifies the
number & method of selection, in
advance

Qualitative studies
 Not concerned with issues of
generalizability
 Concerned with holistic understanding
of the phenomenon of interest
 Sampling decisions are based on
informational & theoretical needs
 Do not develop a formal sampling plan
in advance

Sampling plan
 Describes the strategies that will be
used to obtain a sample for the study
 Developed to
Enhance representativeness
Reduce systematic bias
Decrease sampling error
 Provides details of the use of a
sampling method in a specific study
 Must be described in detail for purpose
of critique, replication & meta analyses

Types of Sampling
Probability
Non-Probability

Probability Sampling methods
 Random selection in choosing elements
 Researcher can specify the probability
that each element of the population will
be included in the sample
 More respected of the two approaches
because of greater confidence in their
representativeness

Probability Sampling
 Is a random method of selecting
participants in a research study who are
the most representative of the population
 Involves a selection process in which each
element in the population has an equal,
independent chance of being selected

Probability Sampling
 Cannot guarantee “representativeness” on
all traits of interest
 A sampling plan with known statistical
properties
 Permits statements like: “The probability is
.99 that the true population correlation falls
between .46 and .56.”

Probability Samples
 A probability sample is one in which each
element of the population has a known
non-zero probability of selection.
 Not a probability sample if probabilities of
selection are not known.

Types of Sampling
Probability
 Simple Random
 Stratified Random
 Cluster sampling
 Systematic sampling

Types of Sampling
Probability
 Simple Random
 Systematic Random
 Stratified Random
 Random Cluster
 Complex Multi-stage
Random (various kinds)
 Stratified Cluster

Simple random sampling
 The most basic of probability sampling
designs
 Each element has independent chance
for being included in to the study group
 Requires complete list of the accessible
population
 One stage selection process

Steps of simple random
sampling

Advantages
 The randomness offers the advantage of being
the most representative
 The only viable method of obtaining
representative sample in quantitative studies
 Allows for the use of inferential statistics
 Allows more accurate generalization to the
target population
 Allows the researcher to estimate the magnitude
of sampling error

Advantages
Eliminates researcher bias
Requires limited knowledge about the
population
Provides a means of estimating the
sampling error

Stratified random sampling
A variant of simple random sampling in
which the population is first divided into 2 or
more strata or subtypes
Used to increase the representativeness of
different groups within the population
Subdivides the population in to
homogenous subjects from which an
appropriate number of elements can be
selected at random

Stratified Random Sampling-1
 Divide population into groups that differ in
important ways
 Basis for grouping must be known before
sampling
 Select random sample from within each
group

Stratified Random Sampling
 For a given sample size, reduces error compared
to simple random sampling if the groups are
different from each other
 Probabilities of selection may be different for
different groups, as long as they are known
 Over sampling small groups improves inter group
comparisons

Proportional stratified random
sampling
 A method of increasing the
representativeness of the variable in the
sample
 The number of subjects taken from each
stratum would be proportional to the
number in the population

Advantages
More representative
Greater accuracy
Greater geographic concentration
Decreased sampling error, power
increased, data collection time reduced

Disadvantages
It requires extensive knowledge of the
population under study to stratify it
accurately
A complete list of target population is
needed
It can quickly become very complex
Knowledge of advanced statistical
methods & or assistance of a sampling
consultant will be needed

Disproportional stratified random
sampling
Disproportional stratified random sampling
In this sampling method the number of
subjects in each stratum would not be
proportional to the number in the
population

Cluster sampling
Suitable when the study population is
large
Takes place in stages –Multistage
sampling
The researcher begins with the largest
most inclusive sampling unit , then
progresses to the next most inclusive unit
until the final stage i.e.. The stage from
which the study subjects are randomly
drawn to the sample
Involves successive random sampling of

Cluster sampling
Merits
 Economical and time saving
 It allows probability sampling for a
population that is not listed in the sampling
frame
 Flexibility in the sampling method-existing
divisions/subdivisions can be used

Cluster sampling
Limitations
 less accurate
 possibility of sampling error in each stage

Systematic Sampling
Can be a probability or non-probability
sampling
To be a probability sampling design, the
elements in the sampling frame need to
be listed randomly
Involves the selection of every kth from
the sampling frame
The sampling interval, k= N/n
Where N is the accessible population & n is the
sample size

Systematic Random Sampling
 Each element has an equal probability of
selection, but combinations of elements have
different probabilities. Population size N, desired
sample size n, sampling interval k=N/n.
 Randomly select a number j between 1and k,
sample element j and then every kth element
thereafter, j+k, j+2k, etc.
 Example: N=64, n=8, k=64/8=8. Random j=3.

 Has same error rate as simple random
sample if the list is in random or
haphazard order
 Provides the benefits of implicit
stratification if the list is grouped

 Runs the risk of error if periodicity in the
list matches the sampling interval
 This is rare.
 In this example, every 4thelement is red,
and red never gets sampled. If j had been
4or 8, ONLY reds would be sampled.

Random Cluster Sampling
 Done correctly, this is a form of random
sampling
 Population is divided into groups, usually
geographic or organizational
 Some of the groups are randomly chosen
 In pure cluster sampling, whole cluster is
sampled.
 In simple multistage cluster, there is random
sampling within each randomly chosen cluster

Random Cluster Sampling
 Population is divided into groups
 Some of the groups are randomly selected
 For given sample size, a cluster sample
has more error than a simple random
sample
 Cost savings of clustering may permit
larger sample
 Error is smaller if the clusters are similar
to each other

Random Cluster Sampling -
 Cluster sampling has very high error if the
clusters are different from each other
 Cluster sampling is NOT desirable if the
clusters are different
 It IS random sampling: you randomly
choose the clusters
 But you will tend to omit some kinds of
subjects

Stratified Cluster Sampling
 Reduce the error in cluster sampling by
creating strata of clusters
 Sample one cluster from each stratum
 The cost-savings of clustering with the
error reduction of stratification

Stratification vs. Clustering
Stratification
 Divide population into
groups different from
each other: sexes, races,
ages
 Sample randomly from
each group
 Less error compared to
simple random
 More expensive to obtain
stratification information
before sampling
Clustering
 Divide population into
comparable groups
:schools, cities
 Randomly sample some
of the groups
 More error compared to
simple random
 Reduces costs to sample
only some areas or
organizations

Stratified Cluster Sampling
 Combines elements of stratification and
clusteringFirst you define the clusters
 Then you group the clusters into strata of
clusters,putting similar clusters together in a
stratum
 Then you randomly pick one (or more)
clusterfrom each of the strata of clusters
 Then you sample the subjects within the
sampledclusters (either all the subjects, or a
simple random sample of them)

Multi-stage Probability Samples
 Large national probability samples involve
several stages of stratified cluster sampling
 The whole country is divided into geographic
clusters, metropolitan and rural
 Some large metropolitan areas are selected with
certainty (certainty is a non-zero probability!)
 Other areas are formed into strata of areas (e.g.
middle-sized cities, rural counties); clusters are
selected randomly from these strata

 Within each sampled area, the clusters
are defined, and the process is repeated,
perhaps several times, until blocks or
telephone exchanges are selected
 At the last step, households and
individuals within household are randomly
selected
 Random samples make multiple call-
backs to people not at home.

Non-probability Sampling
An alternative approach to probability
sampling
Each element in this study does not have
an independent chance of being included
in the study

Non Probability Sampling methods
 Elements are selected by non random
method
 No way to estimate the probability that
each element has of being included
 Every element usually does not have a
chance for inclusion
 Less likely to produce accurate &
representative sample

Non-probability Samples
 • Convenience
 • Purposive
 • Quota

Convenience Sampling
Uses participants who are easily
accessible to the researcher & who meet
the criteria of the study
Entails the use of the most conveniently
available people / objects as subjects in a
study
Also known as Accidental sampling

Also called CHUNK
refers to fraction of the population
being investigated which is selected
neither by probability nor by judgement
but by convenience .

Convenience Sample
 Subjects selected because it is easy to
access them.
 No reason tied to purposes of research.
 Students in your class, people on State
Street, friends

Advantages: Easy, Saves money & time
Disadvantages: Potential for sampling
bias, Limited generalizability of the results

Purposive Samples
 Subjects selected for a good reason tied to
purposes of research
 Small samples < 30, not large enough for power
of probability sampling.
 Nature of research requires small sample
 Choose subjects with appropriate variability in what
you are studying
 Hard-to-get populations that cannot be found
through screening general population

Quota Sampling
 Pre-plan number of subjects in specified
categories (e.g. 100 men, 100 women)
 In uncontrolled quota sampling, the subjects
chosen for those categories are a convenience
sample, selected any way the interviewer
chooses
 In controlled quota sampling, restrictions are
imposed to limit interviewer’s choice
 No call-backs or other features to eliminate
convenience factors in sample selection

Quota Vs Stratified Sampling
 In Stratified Sampling,
selection of subject is
random. Call-backs are
used to get that particular
subject.
 Stratified sampling
without call-backs may
not, in practice, be much
different from quota
sampling.
 In Quota Sampling,
interviewer selects first
available subject who
meets criteria: is a
convenience sample.
 Highly controlled quota
sampling uses probability
sampling down to the last
block or telephone
exchange
•But you should know the difference for the test!!

Snowball Sampling
A particular type of convenience sampling
Useful for studies in which the criteria for
inclusion in the study specify a specific
trait i.e. difficult to find by ordinary means
In this early members are asked to identify
& refer other people who meet the
eligibility criteria
Also known as Network sampling

Non-probability Sampling
Advantages: Less complicated, less
expensive& allows the researcher to be
more spontaneous when research
situation arises
Disadvantages: Sample may not be a
representative of the population, Cannot
be generalized beyond the study sample
& sampling error can not be estimated

STEPS IN SAMPLING
 IDENTIFY THE POPULATION –IDENTIFY THE POPULATION –
TARGET,ACCESSIBLETARGET,ACCESSIBLE
 SPECIFY THE INCLUSION ANDSPECIFY THE INCLUSION AND
EXCLUSION CRITERIAEXCLUSION CRITERIA
 SPECIFY THE SAMPLING PLAN –SPECIFY THE SAMPLING PLAN –
METHOD,SIZE.METHOD,SIZE.
 IDENTIFY THE ELEMENTIDENTIFY THE ELEMENT

STEPS IN SAMPLING-Contd
 RECRUIT THE SAMPLERECRUIT THE SAMPLE
USE A SCREENING INSTRUMENTUSE A SCREENING INSTRUMENT
IDENTIFY ELIGIBLE CANDIDATESIDENTIFY ELIGIBLE CANDIDATES
GAIN COOPERATIONGAIN COOPERATION
EnjoyableEnjoyable
worthwhileworthwhile
convenientconvenient
pleasantpleasant
nonthreateningnonthreatening

 RECRUIT THE SAMPLERECRUIT THE SAMPLE
 RECRUITMENT METHODRECRUITMENT METHOD
face to face more effectiveface to face more effective
CourtesyCourtesy
PersistencePersistence
IncentivesIncentives
Research benefitsResearch benefits
Sharing resultsSharing results
Endorsements, confidentiality assuranceEndorsements, confidentiality assurance

 RETAINING SUBJECTS
get contact details
reimbursement for participation
bonus payment
Use subjects time preciously
Do not take for granted
nurture subjects-refreshment, surrounding
Maintain a pleasant climate

ENSURE THESE DO NOT
INFLUENCE THE DATA

SAMPLE SIZE
 No simple formula
 Use the largest sample
 Larger the sample more representative it is
likely to be
 Smaller sample tend to produce less
accurate estimates than larger ones
 Larger the sample smaller the sampling
error

SAMPLE SIZE -contd
 Size increases probability of deviant sample
diminishes
 Larger sample provide opportunity to
counter balance atypical value
 Larger sample are no assurance of
accuracy
 Large sample can harbor extensive bias

HOW TO ESTIMATE
SAMPLE SIZE ?
CONSIDER
Main research question, outcome measure
statistical procedure
statistical and clinical assumption
type 1,type 2 error- less; more subjects
level of significance-high; more subjects
precision - high ;more subjects
one sides/two sides

HOW TO ESTIMATE
SAMPLE SIZE ?
CONSIDER
 study constraints
Availability of resources- finance
material, manpower, logistic support,
Time, Ethical consideration
 level of significance, power and effect size

HOW TO ESTIMATE
SAMPLE SIZE ?
LEVEL OF SIGNIFICANCE –the more
stringent the greater the necessary sample
size
POWER
- is the capacity of the study to detect
differences or relationships that actually
exist in the population
-is the capacity to correctly reject a null
hypothesis

HOW TO ESTIMATE
SAMPLE SIZE ?
EFFECT SIZE
Effect is the presence of a phenomenon
 If a phenomenon exists;it exist to some
degree
 Effect size is the extent of the presence of
a phenomenon

HOW TO ESTIMATE
SAMPLE SIZE ?
 It is easier to detect large difference
 Smaller sample can detect large difference
 Smaller effect size require large sample
 Effect size is smaller with small samples and
thus more difficult to detect
 Increasing sample size increase effect size
making it more likely that the effect will be
detected
 Extremely small effect size may not be

Factors determining sample
size
 Size of population
 The resource available
 The degree of accuracy or precision
desired
 Homogeneity / heterogeneity of population
 Nature of the study
 Sampling plan adopted

Factors determining sample
size
 Nature of respondent/attrition
 Effect size –strength of relationship
between variables
 Subgroup analysis
 Sensitivity of measures

Type of study
 Qualitative & case studies: use very
small samples, comparisons are not
being made and sampling error &
generalizations have little relevance

Type of study
 Descriptive studies using survey
questionnaires & correlational studies
often require very large samples
 Multiple variables are examined,
extraneous variables

Type of study
 Quasi & experimental studies must use
sample size sufficient to achieve an
acceptable level of power (.08) to
reduce the risk of Type II error
 These studies use controls & refined
instruments, thus improving precision

Type of study
 The study design influences power, but
the design with the greatest power may
not always be the most valid design to
use

Number of variables
 If the variables are highly correlated
with the DV, the effect size will increase
& sample size can be reduced
 Therefore the variables in a study must
be carefully selected
 They should be essential to the
question
 Or should have a documented strong
relationship with the DV

Measurement Sensitivity
 As variance in instrument scores
increases, the sample size needed to
gain an accurate understanding of the
phenomenon under study increases

Data analysis techniques
 Large samples must be used when the
power of the planned statistical analysis
is low
 For some procedures having equal
group size increases power, because
the effect size is maximized
 The more unequal the group sizes are,
the smaller the effect size. Therefore, in
unequal groups the total sample size
must be larger

Attrition
 Number of subjects decline over time
 More likely if time lag between data
collection points is great
 Mobile population, difficult to trace
 Vulnerable or ‘at risk’ population
 Anticipate loss of subjects over time

Sample Size
 Heterogeneity: need larger sample to study
more diverse population
 Desired precision: need larger sample to get
smaller error
 Sampling design: smaller if stratified, larger if
cluster
 Nature of analysis: complex multivariate
statistics need larger samples
 Accuracy of sample depends upon sample size,
not ratio of sample to population

Sampling in Practice
 Often a non-random selection of basic sampling
frame (city, organization etc.)
 Fit between sampling frame and research goals
must be evaluated
 Sampling frame as a concept is relevant to all
kinds of research (including non probability)
 Non probability sampling means you cannot
generalize beyond the sample
 Probability sampling means you can generalize
to the population defined by the sampling frame

Evaluation of sampling design
 Nonprobability
- rarely representative of the population
some section will be underrepresented
-Advantage lies in their
economy,convenience

Evaluation of non-probability
sampling
 Under or over representation of some
segments
 Convenient & economical
 Sometimes there is no option but to use
non probability approach

Evaluation of non-probability
sampling
 Need to be cautious in drawing
inferences & conclusions from the data

 Nonprobability
with care in selection of sample,
conservative interpretation of the results
and replication of the study with new
sample NONPROBABILITY SAMPLE
work well.

 probability
-only viable method of obtaining
representative sample.
-helps to estimate sampling error

Problems in Sampling?
 What problems do you know about?
 What issues are you aware of?
 What questions do you have?

Problems of data collection
Expect more than expected
time
difficulty level
changes
reaction of people

PEOPLE –unpredictable
bystanders /others
SAMPLE participation
disappearance
mortality
external influence in decision
passive resistance

RESEARCHER- Lack of staff
interaction
role conflict
control of emotion
INSTITUTIONAL –POLICY
Events-season
,climate,calamity, local events, national
events

SUPPORT SYSTEM FOR
DATA COLLECTION
 PURPOSE
 TYPE OF ASSISTANCE
-Physical
-Money and material
-Emotional
 ACADEMIC COMMITTEE
 INSTITUTIONAL SUPPORT
 PERSONAL AND SOCIAL SUPPORT

KEY TASKS OF
DATACOLLECTION
RECRUIT SUBJECT
MAINTAINING CONSISTENCY,CONTROL,
RELIABILITY
SOLVING PROBLEMS AS THEY COME

CRITIQUING SAMPLING PLAN
ISSUES
WHETHER DESCRIBED
ADEQUATELY
WHETHER THE RESEARCHER
MADE GOOD SAMPLING DECISION

DESCRIPTION
description of main characteristics
number and characteristics of
potential subjects who decline to
participate in the study

DESCRIPTION
the type of approach
population under study
eligibility criteria
setting
sample size,rationale
description of main charecteristics

sampling

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