Sampling Methods, Types of Sampling Methods

SAMPLING
SAMPLING
METHODS
METHODS
1

LEARNING OBJECTIVES
LEARNING OBJECTIVES
 Learn the reasons for sampling
 Develop an understanding about
different sampling methods
 Distinguish between probability & non
probability sampling
 Discuss the relative advantages &
disadvantages of each sampling
methods
2

SAMPLING
SAMPLING
 A sample is “a smaller (but hopefully
representative) collection of units from a population
used to determine truths about that population”
 Why sample?
◦ Resources (time, money) and workload
◦ Gives results with known accuracy that can be
calculated mathematically
 The sampling frame is the list from which the
potential respondents are drawn
◦ Registrar’s office
◦ Class rosters
3

SAMPLING……
SAMPLING……
 What is your population of interest?
 To whom do you want to generalize your results?
 All doctors
 School children
 Indians
 Women aged 15-45 years
 Other
 Can you sample the entire population?
4

SAMPLING
SAMPLING…….
…….
 3 factors that influence sample representative-ness
 Sampling procedure
 Sample size
 Participation (response)
 When might you sample the entire population?
 When your population is very small
 When you have extensive resources
 When you don’t expect a very high response
5

SAMPLING
SAMPLING…….
…….
TARGET POPULATION
STUDY POPULATION
SAMPLE

Types of Samples
Types of Samples
 Probability (Random) Samples
◦ Simple random sample
◦ Systematic random sample
◦ Stratified random sample
◦ Multistage sample
◦ Multiphase sample
◦ Cluster sample
 Non-Probability Samples
◦ Convenience sample
◦ Purposive sample
◦ Quota
8

Process
Process
 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
9

Population definition
Population definition
 A population can be defined as including all people
or items with the characteristic one wishes to
understand.
 Because there is very rarely enough time or money
to gather information from everyone or everything
in a population, the goal becomes finding a
representative sample (or subset) of that
population.
10

Population definition…….
Population definition…….
 Note also that the population from which the
sample is drawn may not be the same as the
population about which we actually want
information. Often there is large but not complete
overlap between these two groups due to frame
issues etc .
 Sometimes they may be entirely separate - for
instance, we might study rats in order to get a
better understanding of human health, or we might
study records from people born in 2008 in order to
make predictions about people born in 2009.
11

SAMPLING FRAME
SAMPLING FRAME
 In the most straightforward case, such as the sentencing
of a batch of material from production (acceptance
sampling by lots), it is possible to identify and measure
every single item in the population and to include any one
of them in our sample. However, in the more general
case this is not possible. There is no way to identify all
rats in the set of all rats. Where voting is not
compulsory, there is no way to identify which people will
actually vote at a forthcoming election (in advance of the
election)
 As a remedy, we seek a sampling frame which has the
property that we can identify every single element and
include any in our sample .
 The sampling frame must be representative of the
population
12

PROBABILITY SAMPLING
PROBABILITY SAMPLING
 A probability sampling scheme is one in which
every unit in the population has a chance
(greater than zero) of being selected in the
sample, and this probability can be accurately
determined.
 . When every element in the population does
have the same probability of selection, this is
known as an 'equal probability of selection'
(EPS) design. Such designs are also referred
to as 'self-weighting' because all sampled
units are given the same weight.
13

PROBABILITY SAMPLING…….
PROBABILITY SAMPLING…….
 Probability sampling includes:
 Simple Random Sampling,
 Systematic Sampling,
 Stratified Random Sampling,
 Cluster Sampling
 Multistage Sampling.
 Multiphase sampling
14

NON PROBABILITY SAMPLING
NON PROBABILITY SAMPLING
 Any sampling method where some elements of population
have no chance of selection (these are sometimes
referred to as 'out of coverage'/'undercovered'), or
where the probability of selection can't be accurately
determined. It involves the selection of elements based
on assumptions regarding the population of interest,
which forms the criteria for selection. 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.
15

NONPROBABILITY SAMPLING…….
NONPROBABILITY SAMPLING…….
• Nonprobability Sampling includes:
Accidental Sampling, Quota Sampling and
Purposive Sampling. In addition, nonresponse
effects may turn any probability design into a
nonprobability design if the characteristics of
nonresponse are not well understood, since
nonresponse effectively modifies each
element's probability of being sampled.
16

SIMPLE RANDOM SAMPLING
SIMPLE RANDOM SAMPLING
• Applicable when population is small,
homogeneous & readily available
• All subsets of the frame are given an equal
probability. Each element of the frame thus
has an equal probability of selection.
• 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.
17

SIMPLE RANDOM SAMPLING……..
SIMPLE RANDOM SAMPLING……..
 Estimates are easy to calculate.
 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.
18

REPLACEMENT OF SELECTED UNITS
REPLACEMENT OF SELECTED UNITS
 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.
19

SYSTEMATIC SAMPLING
SYSTEMATIC SAMPLING
 Systematic sampling relies on arranging the target
population according to some ordering scheme and
then selecting elements at regular intervals through
that ordered list.
 Systematic sampling involves a random start and then
proceeds with the selection of every kth element from
then onwards. In this case, k=(population size/sample
size).
 It is important that the starting point is not
automatically the first in the list, but is instead
randomly chosen from within the first to the kth
element in the list.
 A simple example would be to select every 10th name
from the telephone directory (an 'every 10th' sample,
also referred to as 'sampling with a skip of 10').
20

SYSTEMATIC SAMPLING……
As described above, systematic sampling is an EPS method, because all
elements have the same probability of selection (in the example
given, one in ten). It is not 'simple random sampling' because
different subsets of the same size have different selection
probabilities - e.g. the set {4,14,24,...,994} has a one-in-ten
probability of selection, but the set {4,13,24,34,...} has zero
probability of selection.
21

 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.
22

STRATIFIED SAMPLING
STRATIFIED SAMPLING
Where population embraces a number of distinct
categories, the frame can be organized into
separate "strata." Each stratum is then sampled
as an independent sub-population, out of which
individual elements can be randomly selected.
 Every unit in a stratum has same chance of being
selected.
 Using same sampling fraction for all strata
ensures proportionate representation in the
sample.
 Adequate representation of minority subgroups of
interest can be ensured by stratification & varying
sampling fraction between strata as required.
23

STRATIFIED SAMPLING……
STRATIFIED SAMPLING……
 Finally, since each stratum is treated as an
independent population, different sampling
approaches can be applied to different strata.
 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
24

STRATIFIED SAMPLING…….
STRATIFIED SAMPLING…….
25
Draw a sample from each stratum

POSTSTRATIFICATION
POSTSTRATIFICATION
 Stratification is sometimes introduced after the sampling
phase in a process called "poststratification“.
 This approach is typically implemented due to a lack of prior
knowledge of an appropriate stratifying variable or when
the experimenter lacks the necessary information to create
a stratifying variable during the sampling phase. Although
the method is susceptible to the pitfalls of post hoc
approaches, it can provide several benefits in the right
situation. Implementation usually follows a simple random
sample. In addition to allowing for stratification on an
ancillary variable, poststratification can be used to
implement weighting, which can improve the precision of a
sample's estimates.
26

OVERSAMPLING
OVERSAMPLING
 Choice-based sampling is one of the stratified
sampling strategies. In this, data are stratified
on the target and a sample is taken from each
strata so that the rare target class will be more
represented in the sample. The model is then
built on this biased sample. The effects of the
input variables on the target are often
estimated with more precision with the choice-
based sample even when a smaller overall sample
size is taken, compared to a random sample. The
results usually must be adjusted to correct for
the oversampling.
27

CLUSTER SAMPLING…….
 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
28

CLUSTER SAMPLING
CLUSTER SAMPLING
 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.
29

• 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
30

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.
31

Difference Between Strata and Clusters
Difference Between Strata and Clusters
 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
32

MULTISTAGE SAMPLING
MULTISTAGE SAMPLING
 Complex form of cluster sampling in which two or more levels of
units are embedded one in the other.
 First stage, random number of districts chosen in all
states.
 Followed by random number of talukas, villages.
 Then third stage units will be houses.
 All ultimate units (houses, for instance) selected at last step
are surveyed.
33

MULTISTAGE SAMPLING……..
MULTISTAGE SAMPLING……..
 This technique, is essentially the process of taking
random samples of preceding random samples.
 Not as effective as true random sampling, but probably
solves more of the problems inherent to random
sampling.
 An effective strategy because it banks on multiple
randomizations. As such, extremely useful.
 Multistage sampling used frequently when a complete
list of all members of the population not exists and is
inappropriate.
 Moreover, by avoiding the use of all sample units in all
selected clusters, multistage sampling avoids the large,
and perhaps unnecessary, costs associated with
traditional cluster sampling.
34

MULTI PHASE SAMPLING
MULTI PHASE SAMPLING
 Part of the information collected from whole sample & part from
subsample.
 In Tb survey MT in all cases – Phase I
 X –Ray chest in MT +ve cases – Phase II
 Sputum examination in X – Ray +ve cases - Phase III
 Survey by such procedure is less costly, less laborious & more
purposeful
35

MATCHED RANDOM SAMPLING
MATCHED RANDOM SAMPLING
A method of assigning participants to groups in which
pairs of participants are first matched on some
characteristic and then individually assigned randomly to
groups.
 The Procedure for Matched random sampling can be
briefed with the following contexts,
 Two samples in which the members are clearly paired, or
are matched explicitly by the researcher. For example,
IQ measurements or pairs of identical twins.
 Those samples in which the same attribute, or variable,
is measured twice on each subject, under different
circumstances. Commonly called repeated measures.
 Examples include the times of a group of athletes for
1500m before and after a week of special training; the
milk yields of cows before and after being fed a
particular diet.
36

QUOTA SAMPLING
QUOTA SAMPLING
 The population is first segmented into mutually
exclusive sub-groups, just as in stratified sampling.
 Then judgment used to select subjects or units from
each segment based on a specified proportion.
 For example, an interviewer may be told to sample 200
females and 300 males between the age of 45 and 60.
 It is this second step which makes the technique one of
non-probability sampling.
 In quota sampling the selection of the sample is non-
random.
 For example interviewers might be tempted to interview
those who look most helpful. The problem is that these
samples may be biased because not everyone gets a
chance of selection. This random element is its greatest
weakness and quota versus probability has been a matter
of controversy for many years
37

CONVENIENCE SAMPLING
CONVENIENCE SAMPLING
 Sometimes known as grab or opportunity sampling or accidental
or haphazard sampling.
 A type of nonprobability sampling which involves the sample being
drawn from that part of the population which is close to hand.
That is, readily available and convenient.
 The researcher using such a sample cannot scientifically make
generalizations about the total population from this sample
because it would not be representative enough.
 For example, if the interviewer was to conduct a survey at a
shopping center early in the morning on a given day, the people
that he/she could interview would be limited to those given there
at that given time, which would not represent the views of other
members of society in such an area, if the survey was to be
conducted at different times of day and several times per week.
 This type of sampling is most useful for pilot testing.
 In social science research, snowball sampling is a similar
technique, where existing study subjects are used to recruit more
subjects into the sample.
38

39
CONVENIENCE SAMPLING…….
CONVENIENCE SAMPLING…….
◦ Use results that are easy to get
39

Judgmental sampling or
Judgmental sampling or
Purposive sampling
Purposive sampling
 - The researcher chooses the
sample based on who they think
would be appropriate for the study.
This is used primarily when there is
a limited number of people that
have expertise in the area being
researched
40

PANEL SAMPLING
PANEL SAMPLING
 Method of first selecting a group of participants through a
random sampling method and then asking that group for the same
information again several times over a period of time.
 Therefore, each participant is given same survey or interview at
two or more time points; each period of data collection called a
"wave".
 This sampling methodology often chosen for large scale or
nation-wide studies in order to gauge changes in the population
with regard to any number of variables from chronic illness to job
stress to weekly food expenditures.
 Panel sampling can also be used to inform researchers about
within-person health changes due to age or help explain changes
in continuous dependent variables such as spousal interaction.
 There have been several proposed methods of analyzing panel
sample data, including growth curves.
41

What sampling method u recommend?
What sampling method u recommend?
 Determining proportion of undernourished five
year olds in a village.
 Investigating nutritional status of preschool
children.
 Selecting maternity records for the study of
previous abortions or duration of postnatal
stay.
 In estimation of immunization coverage in a
province, data on seven children aged 12-23
months in 30 clusters are used to determine
proportion of fully immunized children in the
province.
 Give reasons why cluster sampling is used in
this survey.
42

Probability proportional to size sampling
Probability proportional to size sampling
 In some cases the sample designer has access to an "auxiliary
variable" or "size measure", believed to be correlated to the
variable of interest, for each element in the population. This
data can be used to improve accuracy in sample design. One
option is to use the auxiliary variable as a basis for
stratification, as discussed above.
 Another option is probability-proportional-to-size ('PPS')
sampling, in which the selection probability for each element
is set to be proportional to its size measure, up to a
maximum of 1. In a simple PPS design, these selection
probabilities can then be used as the basis for Poisson
sampling. However, this has the drawbacks of variable sample
size, and different portions of the population may still be
over- or under-represented due to chance variation in
selections. To address this problem, PPS may be combined
with a systematic approach.
43

Contd.
Contd.
 Example: Suppose we have six schools with populations of 150, 180, 200, 220,
260, and 490 students respectively (total 1500 students), and we want to use
student population as the basis for a PPS sample of size three. To do this, we
could allocate the first school numbers 1 to 150, the second school 151 to
330 (= 150 + 180), the third school 331 to 530, and so on to the last school
(1011 to 1500). We then generate a random start between 1 and 500 (equal
to 1500/3) and count through the school populations by multiples of 500. If
our random start was 137, we would select the schools which have been
allocated numbers 137, 637, and 1137, i.e. the first, fourth, and sixth schools.
 The PPS approach can improve accuracy for a given sample size by
concentrating sample on large elements that have the greatest impact on
population estimates. PPS sampling is commonly used for surveys of
businesses, where element size varies greatly and auxiliary information is often
available - for instance, a survey attempting to measure the number of guest-
nights spent in hotels might use each hotel's number of rooms as an auxiliary
variable. In some cases, an older measurement of the variable of interest can
be used as an auxiliary variable when attempting to produce more current
estimates.
44

Event sampling
Event sampling
 Event Sampling Methodology (ESM) is a new form of
sampling method that allows researchers to study ongoing
experiences and events that vary across and within days in its
naturally-occurring environment. Because of the frequent
sampling of events inherent in ESM, it enables researchers to
measure the typology of activity and detect the temporal and
dynamic fluctuations of work experiences. Popularity of ESM
as a new form of research design increased over the recent
years because it addresses the shortcomings of cross-
sectional research, where once unable to, researchers can
now detect intra-individual variances across time. In ESM,
participants are asked to record their experiences and
perceptions in a paper or electronic diary.
 There are three types of ESM:# Signal contingent – random
beeping notifies participants to record data. The advantage of
this type of ESM is minimization of recall bias.
 Event contingent – records data when certain events occur
45

Contd.
Contd.
 Event contingent – records data when certain events occur
 Interval contingent – records data according to the passing of
a certain period of time
 ESM has several disadvantages. One of the disadvantages of
ESM is it can sometimes be perceived as invasive and
intrusive by participants. ESM also leads to possible self-
selection bias. It may be that only certain types of individuals
are willing to participate in this type of study creating a non-
random sample. Another concern is related to participant
cooperation. Participants may not be actually fill out their
diaries at the specified times. Furthermore, ESM may
substantively change the phenomenon being studied.
Reactivity or priming effects may occur, such that repeated
measurement may cause changes in the participants'
experiences. This method of sampling data is also highly
vulnerable to common method variance.[6]
46

contd.
contd.
 Further, it is important to think about whether or not
an appropriate dependent variable is being used in an
ESM design. For example, it might be logical to use ESM
in order to answer research questions which involve
dependent variables with a great deal of variation
throughout the day. Thus, variables such as change in
mood, change in stress level, or the immediate impact
of particular events may be best studied using ESM
methodology. However, it is not likely that utilizing ESM
will yield meaningful predictions when measuring
someone performing a repetitive task throughout the
day or when dependent variables are long-term in
nature (coronary heart problems).
47

Sampling Methods, Types of Sampling Methods

More Related Content

Similar to Sampling Methods, Types of Sampling Methods (20)

Recently uploaded (20)

Sampling Methods, Types of Sampling Methods

Editor's Notes