This document discusses sampling methods and their key aspects. It defines sampling as selecting a subset of individuals from a population to make inferences about the whole population. Probability sampling methods aim to give all population elements an equal chance of selection, while non-probability methods do not. Some common probability methods described include simple random sampling, systematic sampling, and stratified sampling. The document also discusses sampling frames, statistics versus parameters, confidence levels, and evaluating different sampling techniques.

2.  Sampling is the process of selecting
participants from the population.
 Sampling refers to the process used to select
any number of persons to represent the
population according to some rules or plan on
basis of some selected measures.
 In general statistics and survey methodology,
sampling is basically concerned with the
selection of a subset of individuals from within
a statistical population to estimate the
characteristics of the whole population.

3. THE CONCEPT OF SAMPLING
POPULATION ELEMENTS
SAMPLE
SUBJECTS

4.  A sample is the group of people who take part in the
investigation. The people who take part are referred to
as “participants”.
 The target population is the total group of individuals
from which the sample might be drawn.
 Generalisability refers to the extent to which we can
apply the findings of our research to the target
population we are interested in.
 Sampling is that part of statistical practice concerned
with the selection of individual observations intended to
yield some knowledge about a population of concern,
especially for the purposes of statistical inference

5. FUNDAMENTALS OF SAMPLING
 POPULATION
 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.

6. • 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.

7.  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

8.  STATISTIC AND PARAMETER
 A statistic is a numerical value based upon
sample, whereas parameter is the numerical
value based upon population.
 When we calculate mean from a sample this
is called a statistic because it describes the
characteristics of a sample.
 When the same mean is calculated from a
population it is called parameter because it
describes characteristics of a population.

9.  CONFIDENCE LEVEL AND SIGNIFICANCE LEVEL
 The confidence level is the expected percentage of
times stipulating that the actual value will fall within
the stated precision limits. Confidence level indicates
the likelihood that the answer will fall within the given
precision range.
 Significance level is that level that indicates the
likelihood that the answer will fall outside the range.
CONFIDENCE >>>>><<<<<SIGNIFICANCE

10.  SAMPLING DESIGN
 Sampling design refers to the procedure the researcher would
follow or adopt in selecting same sampling units from which
inferences about population is drawn.
 A sample design is made up of two elements.
 Sampling method. Sampling method refers to the rules and
procedures by which some elements of the population are
included in the sample. Some common sampling methods
aresimple random sampling , stratified sampling , and cluster
sampling .
 Estimator. The estimation process for calculating sample statistics
is called the estimator. Different sampling methods may use
different estimators. For example, the formula for computing a
mean score with a simple random sample is different from the
formula for computing a mean score with a stratified sample.
Similarly, the formula for the standard errormay vary from one
sampling method to the next.

11.  The sampling error is a number that describes the
precision of an estimate from any one of those
samples. It is usually expressed as a margin of
error associated with a statistical level of
confidence. For example, a presidential preference
poll may report that the incumbent is favored by
51% of the voters, with a margin of error of plus-
or-minus 3 points at a confidence level of 95%.
This means that if the same survey were
conducted with 100 different samples of voters, 95
of them would be expected to show the incumbent
favored by between 48% and 54% of the voters
(51% ± 3%).

12.  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

13. PROBABILITY
SAMPLING
NON-PROBABILITY
SAMPLING
 Simple random sampling
 Systematic random
sampling
 Stratified random
sampling
 Multistage sampling
 Multiphase sampling
 Cluster sampling
 Convenience sampling
 Purposive sampling
 Quota sampling
 Accidental sampling
 Systematic sampling
 Snowball sampling
 Saturation sampling
 Dense sampling

14.  The best sampling is probability sampling, because it
increases the likelihood of obtaining samples that are
representative of the population.
 Probability samples are selected in such a way as to
be representative of the population. They provide the most
valid or credible results because they reflect the
characteristics of the population from which they are
selected (e.g., residents of a particular community, students
at an elementary school, etc.). There are two types of
probability samples: random and stratified.
 Sampling methods that allow us to know in advance how likely it is that
any element of a population will be selected for the sample & THAT
EVERY ELEMENT HAS SOME KNOWN CHANCE OF BEING
SELECTED are termed probability sampling methods.

15.  SOME PROCESSES ARE USED IN ORDER TO
MAKE PROBABILITY SAMPLING A
SUCCESS……….
Randomization: a technique for insuring that any
member of a population has an equal chance of
appearing in a sample.
 With randomization, sample statistics will on average
have the same values as the population parameters.
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.

17.  Non-probability sampling methods cannot
specify the probability that a given sample will
be selected.
 Example: snowball sampling methods (Edin and
Lein)
 Why use such methods?
 They are often inexpensive
 They can provide information about groups that are
difficult to sample or require great trust or will get
lengthy unstructured interviews.
 Some social variables and their relationships are
universal, which makes sampling method irrelevant!
 This is assumed for many psychology studies and medical
studies.

18.  Critical evaluation of Sampling Methods

19.  Every subset of a specified size n from the
population has an equal chance of being
selected.
STEPS INVOLVED;
 Get a list or “sampling frame”
a. This is the hard part! It must not
systematically exclude anyone.
 Generate random numbers
 Select one person per random numbers

20. • 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.
 Advantages
 Estimates are easy to calculate.
 Simple random sampling is always an EPS design, but
not all EPS designs are simple random sampling.
 Disadvantages
 If sampling frame large, this method impracticable.
 Minority subgroups of interest in population may not be
present in sample in sufficient numbers for study

21.  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').

22.  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.

23.  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.

24.  The population is divided into two or more groups
called strata, according to some criterion, such as
geographic location, grade level, age, or income,
and subsamples are randomly selected from each
strata.
 Steps invoved:
1. Separate your population into groups or “strata”
2. Do either a simple random sample or systematic random
sample from there
a. Note you must know easily what the “strata” are before
attempting this
b. If your sampling frame is sorted by, say, school district,
then you’re able to use this method

25. 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.

26.  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

27. 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.

28.  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
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.

30.  Cons
 Larger sampling error
[variation in score
form sample to
sample]
 Typically “requires”
larger sample
 Pros
 Saves time
 Saves money
 Should allow for
closer supervision in
the field
 Requires enumerating
only part of the
population

31.  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.  Complex form of cluster sampling in which two or more
levels of units are embedded one in the other.
 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.

33. 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.

34.  Advantages
 Selection of whichever individuals are easiest to reach.
 It is done at the “convenience” of the researcher.
 Use results that are easy to get.
 Disadvantages
 Results may be contaminated.

35.  Quota sampling is intended to overcome the most obvious flaw of
availability sampling—that the sample will just consist of whoever
or whatever is available, without any concern for its similarity to the
population of interest.
 The distinguishing feature of a quota sample is that quotas are set
to ensure that the sample represents certain characteristics in
proportion to their prevalence in the population.
Apparently, many reputable national polls use quota sampling and have been
doing quite well.
Cons
 Take more effort
than many
 Need trustworthy
poll takers
Pros
 Although don’t
know “odds…”
experience shows
it’s typically “pretty
good”

36.  In purposive sampling, each sample element is selected for a
purpose, usually because of the unique position of the sample
elements.
 Purposive sampling may involve studying the entire population of
some limited group (directors of shelters for homeless adults) or a sub-
set of a population (mid-level managers with a reputation for
efficiency).
 Or a purposive sample may be a “key informant survey,” which targets
individuals who are particularly knowledgeable about the issues under
investigation.
 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

37.  It may be defined as drawing or selecting every
Nth person from a predetermined list of
elements or indivisuals.
 This sampling plan possesses certain
characteristics of randomness (first item
selected is a random one), at the same time
possesses some non probability traits as
well(such as excluding all persons between
every Nth element chosen.

38.  Snowball sampling is useful for hard-to-reach or
hard-to-identify populations for which there is no
sampling frame, but the members of which are
somewhat interconnected (at least some members of
the population know each other).
 It can be used to sample members of such groups as
drug dealers, prostitutes, practicing criminals,
participants in Alcoholics Anonymous groups, gang
leaders, informal organizational leaders, and
homeless persons.
 Pros
 May be able to find difficult to locate groups
 Cons
 May run into a “vein” or network which isn’t at all
representative of such kinds of people

39. That’s all folks
THANK YOU