Sampling - everything you need to know in the basics of sampling!!!!

Sampling
Anju George, Associate Professor, SGCON,
Parumala
8/17/2020 1Anju George , SGCON

Basic terminologies
 Population- it is the entire aggregation
of cases in which a r/ser is interested.
 Eg
 Target population – entire population in
which a r/ser is interested.
 Accessible population – composed of
cases from target population that are
accessible to the r/ser as study
participants
 Sampling – process of selecting a
portion of the population to represent
the entire population

 Sample – subset of the population
elements.
 Element – basic units of research study
which is usually humans.
 Sampling frame – list of all the elements in
the population from which the sample is
drawn
 Sampling error ; the difference between
the sample mean and the population mean
 Sampling bias - distortion that arises when
the sample is not a representative of
population
 Sampling plan : plan that specifies8/17/2020 3Anju George , SGCON

Purposes
 Economical
 Improved quality of data
 Quick study results
 Precision and accuracy of data

Characteristics of good
sample
 Representative
 Free from bias and errors
 No substitution and incompleteness
 Appropriate sample size

Sampling process
 Identifying and defining the target
population
 Describing the accessible population
& ensuring sampling frame
 Specifying the sampling unit
 Specifying sample selection methods
 Determining the sample size
 Specifying the sampling plan
 Selecting a desired sample

Factors influencing the sampling
process
 Nature of the r/ser
◦ Inexperienced investigator
◦ Lack of interest
◦ Lack of honesty
◦ Intensive workload
◦ Inadequate supervision
 Nature of the sample
◦ Inappropriate sampling technique
◦ Sample size
◦ Defective sampling frame

Contn …
 Circumstances
◦ Lack of time
◦ Large geographic area
◦ Lack of co-operation
◦ Natural calamities

Types
Sampling technique- quantitative
Probability
1. Simple random
2. Stratified random
3. Systematic random
4. cluster /multistage
5. Sequential
Non probability
1. Purposive
2. Convenient
3. Consecutive
4. Quota
5. Snowball

Non probability sampling
 Des not give the population equal
chances to get selected into the study
 Elements are chosen by choice and
not by chance
 Non random technique is used
because of the r/sers constraint
towards time, money and work force
 Sample are not always the
representative part of the population

 Uses :
 This type of sampling can be utilized when :
 Showing that a particular trait is existent in the population
 The r/ser targets to make a qualitative ,pilot or
exploratory study
 The r/ser does not aim to produce results that will be
utilized to generate generalizations pertaining to the
entire population
 The r/ser has got limited time, work force and money

Purposive sampling
 Judgmental or authoritative sampling
 Subjects are chosen to be a part of the
samples based on a specific purpose in
mind
 In Purposive sampling , the r/ser
believes that some subjects are fit for r/s
compared to others. This is the reason
why they are purposively chosen .
 Experts who have in depth knowledge
about the accessible popln under study
may be asked to provide help to select
samples. 8/17/2020 12Anju George , SGCON

 Merits :
 Simple to draw samples
 Saves resources , less field work
 Demerits :
 Requires in-depth knowledge about the popln.
 Bias may exist
 Weakness - reliability of the authority and sampling
process lead to bias
 Subjects have no equal chance to participate in the
study

Convenience sampling
 Accidental sampling
 The samples are selected due to their
convenient accessibility and proximity
to the r/ser
 Eg:

 Merits :
◦ Easy, cheap, least time consuming
◦ Saves time , money and resources
 Demerits :
◦ Sampling bias can occur
◦ Sample is not representative
◦ Findings cannot be generalized

Consecutive sampling
 Total enumerative sampling
 The investigator picks up all the
available subjects who are meeting
the inclusion and exclusion criteria
 Eg :-

 Merits :
 Less effort in sampling process
 Not expensive, not time consuming and no work
force required
 More representativeness of the selected sample
 Demerits :
 No plan about the sample size and the sampling
schedule
 Does not guarantee representation
 Results cannot be generalized

Quota sampling
 The r/s er ensures equal or
proportionate representation of the
samples, depending on which trait is
considered as the basis for quota
 Bases of quota are usually age,
gender, education , race, religion , s/e
status
 Steps :
◦ Divide the population into subgroups
◦ Recognize the proportions of the
subgroups
◦ Choosing subjects from the sub groups8/17/2020 18Anju George , SGCON

 Merits :
◦ Cheap
◦ Suitable for studies where field work has to be
carried out
 Demerits :
◦ May not be always representative
◦ Other traits may be over represented
◦ Bias is possible

Snow ball sampling
 Chain referral sampling
 It is used by the r/s ers to identify potential
subjects n studies where subjects are hard
to locate , such as commercial sex workers ,
drug abusers etc
 The r/s er asks for assistance from the
subjects to identify people with similar trait
of interest after observing the initial subject
 Types :
 Linear snowball sampling
 Exponential snow ball sampling
 Non exponential snow ball sampling

 Merits :
◦ Helps the r/ser to reach the popln that are
difficult to sample
◦ Simple, cheap and cost efficient
 Demerits
◦ Less control over sampling method
◦ No guarantee over representation
◦ Sampling bias

Probability sampling
 Based on theory of probability
 Provides equal chance to all
individuals in the population to get
selected into the study
 Possible only throu randomization
 Sampling and systematic bias is less
 Representative sample is obtained

Simple random sampling
 Pre requisites
◦ Homogenous population & a list of the
elements of accessible population
(sampling frame )
 Drawing of sample is throu :
◦ The lottery method
◦ The use of table of random numbers
◦ The use of computer

 Merits :
 Sample can be easily assembled
 Fair way of selecting sample
 Require min. kno. about the population
 Unbiased
 Free from sampling errors
 Sample errors can be easily computed and
accuracy of estimate is easily assessed
 Demerits
 Require complete and up to date list of all members
of the popln.
 Expensive & time consuming
 Lot of procedure before sample is drawn
 Does not make use of the r/sers knowledge about
the popln.

Stratified random sampling
 Used for heterogeneous popln.
 r/ser divide the entire popln into
different homogeneous subgps or
strata and then randomly selects the
final sample
 Strata are divided according to the
selected traits of the population
 Types : proportionate stratified random
sampling & disproportionate stratified
random sampling8/17/2020 25Anju George , SGCON

 Merits
◦ Ensure representation of all gps of the popln
◦ Comparison is possible b/w subgps
◦ Can include the in accessible and smallest
subgroups in the popln
◦ High statistical precision
◦ Hence require less sample size - save time,
money and effort
 Demerits
◦ Require more accurate information reg the
samples in proportionate sampling
◦ Large population must be available
◦ Possibility of faulty classification

Systematic random sampling
 Involves selection of every Kth case
from the list of the group.
 K = N/n
 Where N is the no. of the subjects in
the target population and n is the size
of the sample
 Eg:-

 Method of selection is :
 The sampling frame is prepared (list of
subjects in the target population ) with
random distribution of subjects
 Randomly select the first subject and then
every Kth case is selected

 Merits :
◦ Convenient & simple
◦ Distribution of sample is even throughout the
population
◦ Less time consuming and cheaper than simple
random sampling
◦ Statistically more efficient and provides a
representative sample
 Demerits :
◦ If the first subject is not randomly selected then it
becomes a non random sampling technique
◦ It can sometimes be biased
◦ If sampling frame does not have randomly distributed
sample, it may not be appropriate to select a
representative sample 8/17/2020 29Anju George , SGCON

Cluster or multistage
sampling
 Done usually when population size is
large to carry out a simple ran.
sampling.
 Usually done when the r/s is focused
on a wide geographic area
 Eg: a r/ser needs to survey the
academic performance of Indian high
school students

 Types of cluster sampling are:
◦ One stage cluster sampling
 Sampling is done only once. For eg: if the r/ser
wants to study about homeless people who live in
shelters, and if there are shelters , the r/ser would
randomly select one shelter and may include those
people into the study.
◦ Two stage cluster sampling
 Sub sampling is done in this type. I.e., for eg. In the
above case the r/ser will select the shelter randomly
and will also select the people randomly for
including in the study.

◦ Multistage cluster sampling
 Involves the repetition of two basic steps: listing and
sampling
 At each stage the sampling technique can differ.
 Theses sampling units are referred to as primary sampling
unit, secondary sampling unit, tertiary sampling unit etc until
one gets the final sampling unit
◦ Probability proportion to size cluster sampling
 Involves selecting clusters after considering the proportional
distribution of the elements in the target population8/17/2020 32Anju George , SGCON

 Merits :-
 Cheap, quick, and easy for large population
 Large population can be studied and require only
list of the members
 Enables the investigators to use existing division
such as districts, villages, towns etc
 Demerits :-
 Gives the least representative sample
 High possibility of having sampling error
 Not useful for a small homogeneous population

Sequential sampling
 In this the sample size is not fixed
 The investigator selects small sample
and tries to make out inferences; if not
able to draw results , the r/s er adds
more subjects until clear cut
inferences can be drawn.
 Eg :

 Merits :
 Helps to conduct study on a small representative
sample
 Help in finding inferences of a study
 Demerits:
 Requires repeated entry into the field for collecting
samples

Sample size determination
 Sample size in qualitative studies
◦ Data saturation
◦ In ethnography 25-50 key informants is
needed , in phenomenology 10 or less
samples is required , grounded theory uses
generally 20 to 30
 Sample size in quantitative studies
◦ Choose largest size of samples because
sample error is inversely proportional to
sample size
◦ Power analysis

 Thumb rules for estimating sample size
 Degree of precision required
 Type of sampling
 Homogeneity of population
 Cost and convenience
 Sample size determination using a table
 Power analysis
◦ Power – r/ser specifies the power he/she
wishes to achieve. Then the sample size for
that level of power can be estimated.
◦ effect size – it is the actual size of effect the
r/ser is looking for. ie, it is the effect of an
independent variable on the DV.

Statistical determination of the
sample size
The r/ser tries to set a sample size that
minimizes making two types of errors :
◦ To claim that variables are related when
they are not (type I error )
◦ To conclude the opposite that two
variables are unrelated when in fact they
are related (type II error )

Sample size for descriptive
study
 n = (1-n/N) x t ² (p x q)
d²
Where :
n = sample size
N = size of the eligible population
t ² = square value of SD score that refers to the
area under a normal distribution of values
p = percentage category for which the sample
size is computed
q= 1-p
d² = square value of one half of the precision
internal around the sample estimate

Finite population correction factor is not
considered as it has little effect when
sample size is < 5% of the total
population .
3 components left out : probability level,
confidence interval and a variance.

t= probability level
It is the SD score that expresses the % of a
variables value that falls within a set
interval when variables are normally
distributed.
1 SD includes approx. 68 % of the sample
value and the score is 1.0
value and the score is 1.96
value and the score is 2.88/17/2020 41Anju George , SGCON

 Variance (p&q)
◦ a variable expressed in 2 categories- those
who do and those who do not
◦ The proportion that would agree (p+q) has to
be summed up as 1.0

◦ For eg. If we want to carry out a study to
estimate the number of adults smoking in
Ludhiana . To determine the sample size we
need to come up with a good guess or
estimate of the %of smokers before we do the
study .
◦ For that we do:
 Take the estimate
 Guess
 Conduct a small pilot study ie, call for a random
sample of 25-50 houses. P is the proportion of adult
who smoke and q=1-p

 Confidence interval (d)- is the margin
of error that the r/ser will tolerate

Sample size determination using
calculator
 www. Raosoft.com/samplesize.html

Factors affecting sample size
 Design effect- quantitative r/s require a
large sample size
qualitative r/s require a small
sample size
 Resources available – see all resources
are available when large sample size is
selected
 Nature of the study – longi. studies-
small sample size &one time studies –
large sample size
 Sampling methods used – smaller
efficiently selected samples are better
than badly selected larger samples

 Homogeneity - if samples are
homogenous- small SS required
 Effect size – if relationship b/w IDV &DV is
strong- SS is required
 Degree of accuracy desired from the
estimate- limit of tolerable errors that exist
in sample estimates
 Degree of confidence –the margin of error
that is allowed

 Cooperation and attrition
 Subgroup analysis
 Measurement factors

Sampling errors
Sampling error is the deviation of the
characteristics, traits, behaviors, qualities
or figures of the entire population .
 Reasons for sampling error : -
◦ r/s ers draw different subjects from the same
population, but the subjects have individual
differences
◦ Because of biased sampling procedure
◦ Chance – eventhough randomization is done
there can be still chance that the sample is
not a true representative
◦ Systematic error – ie, the result of the sample
differ from the result of the entire population

Two basic reasons for sampling
error are:
 Chance error – error occur by chance
for eg. Some one did a comparative
study on malnutrition in under five
children in 2 cities A & B. unfortunately
city B had more no. of slum dwellers, so
it comprised of more malnourished
children and hence the skewing of the
result
 Sampling bias- tendency to favor the
selection of sampling units that possess
a particular characteristic leading to
over representation .

Types of sampling bias are:
 Self selection bias- happens in situation when
participants in the study have some kind of
control over the study to participate or not
 Exclusion bias – happens when some people
of the group are eliminated
 Healthy user bias- happens when the selected
sample has more likelihood to be healthier as
compared to the general population .
To minimize sampling error , maximize
sample size

Problems of sampling
 Sampling errors
 Lack of sample representativeness
 Difficulty in estimation of sample size
 Lack of knowledge about the sampling
process
 Lack of resources
 Lack of co-operation
 Lack of existing appropriate sampling
frames for larger population
 Callous approach of the r/s er towards
sampling process

Sampling – qualitative

Selection of samples…
 Participants are not selected randomly
 Samples tend to be small
 Sample members are not wholly
prespecified : they are emergent
 Sample selection is driven to a great
extent by conceptual requirements rather
than the desire for representativeness.

Convenience sampling
 also called as Volunteer sampling
 Easy & efficient method
Snowball sampling
Purposive sampling
◦ Maximum variation sampling – involves selection
of samples with a wide range of variations on
dimensions of interest. (ie ensuring that there is
men & women, rich&poor)
◦ Homogenous sampling – it deliberately reduces
variation and permits a more focused inquiry

◦ Typical case sampling – involves selecting of samples that
illustrate or highlight what is typical, average, normal or
representative
 Stratified purposive sampling – this strategy approaches
max variation sampling , but it is typically done along a
single dimension. (eg. Income, age )
 Extreme (deviant) case sampling / outlier sampling – this
approach provides opportunities for learning from the
most unusual and extreme cases (eg, outstanding
success or notable failure)
 Intensity sampling – involves information rich cases that
manifest the phenomenon of interest intensely.
 Reputational case sampling - involves selecting cases
based on recommendation of an expert or a key8/17/2020 56Anju George , SGCON

Special sampling cases
◦ Critical case sampling – involves selection of samples regarding
phenomenon of interest
◦ Criterion sampling – involves selection of a sample that meets a
predetermined criterion of importance
◦ Revelatory case sampling - involves identifying and gaining
access to a single case representing a phenomenon that was
previously inaccessible to the research scrutiny.
◦ Sampling of politically important cases – used to select or search
for politically sensitive cases or sites for analysis

 Sampling sequentially
Opportunistic sampling / emergent sampling – involves adding of
new cases to a sample based on changes in r/s circumstances
as data is being collected.
Confirming and disconfirming cases – confirming cases are that
additional cases which fit in to the r/s ers conceptualization with
new data. Disconfirming cases are those which do not fit and
poses threat to the r/sers interpretation
Theoretical sampling - is a strategy involving the selection of
incidents, slices of life, time periods, or people on the basis of
their potential manifestation of important theoretical constructs.

Sampling in three main
qualitative traditions
 Sampling in ethnography
 Sampling in phenomenological studies
 Sampling in grounded theory

8/17/2020 Anju George , SGCON 60

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