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Alam Nuzhathalam1 Associate Professor
Nuzhath Alam
Alam Nuzhathalam
2
 Sampling
 Introduction, terminologies
 Definition, characteristics of good sample
 Sampling criteria, factors influencing sampling process
 Sampling Steps/Design/Process
 Sampling Methods/Techniques/Types
 Probability Sampling
 Non-Probability Sampling
 Sampling Errors and Non-Sampling Errors
Alam Nuzhathalam3
 Population: The entire set of individuals or objects having
some common characteristics selected for a research study
 Target Population: Entire group of people or objects to
which the researcher wishes to generalize the findings of
the study
 Sample: A subset of the population study population
target population sample
 Study Population (Sampling): The population to be
studied/to which the investigator wants to generalize his
results
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Alam Nuzhathalam
 Sampling Unit: Smallest unit from which sample can be
selected
 Sampling Frame: List of all the sampling units from which
sample is drawn
 Sampling Scheme: Method of selecting sampling units
from sampling frame
 Sampling Fraction: Ratio between sample size and
population size
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Definition:
 Sampling is the process of selecting units (e.g., people,
organizations) from a population of interest so that by
studying the sample we may fairly generalize our results
back to the population from which they were chosen
 The process by which researchers select a representative
subset or part of the total population that could be
studied for their topic so that they will be able to draw
conclusions about the entire population
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Alam Nuzhathalam
 A sample should be reliable
 A sample should be economical
 A sample should be proportional
 A sample should be goal oriented
 A sample should be appropriate in size
 A sample should be selected at randomly
 A sample should be free from bias and errors
 A sample should be true representation of population
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Alam Nuzhathalam
 Sampling criteria refers to the essential characteristics
of a subject or respondent such as ability to read and
write responses on the data collection instruments
 For example, these criteria could include:
 Age (elderly, children, etc)
 Gender (male/female)
 Marital status
 Ability to understand English/Hindi, etc
 Ability to write
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Alam Nuzhathalam
Factors that influence sampling process:
 Element Type
 Research Type
 Population size
 Available Resources
 Constraints/ limitations
 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
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Alam Nuzhathalam
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SSCSM: Soft Skill Computer
Shaksharta Mission
 S - Simple
 S - Stratified
 C - Cluster
 S - Systematic
 M - Multi Stage
CQPSS: Continuous Quality
Process Software & Systems
 C - Convenience
 Q - Quota
 P - Purposive
 S - Self Selection
 S - Snow Ball
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Alam Nuzhathalam
Probability Sampling Non-Probability Sampling
Alam Nuzhathalam13
 Probability sampling is defined as a sampling technique
in which the researcher chooses samples from a larger
population using a method based on the theory of
probability. For a participant to be considered as a
probability sample, he/she must be selected using a
random selection
 Probability sampling uses statistical theory to randomly
select a small group of people (sample) from an existing
large population and then predict that all their responses
will match the overall population
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Alam Nuzhathalam15
Alam Nuzhathalam16
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 Simple random sampling is a sampling technique where
every item in the population has an even chance and
likelihood of being selected in the sample. Here the selection
of items completely depends on chance and therefore this
sampling technique is also sometimes known as a method of
chances (i.e., Lottery method, random table)
 Example: A simple random sample would be the names of
25 employees being chosen out of a hat from a company of
250 employees. In this case, the population is all 250
employees, and the sample is random because each
employee has an equal chance of being chosen
Advantages:
 It is a fair method of sampling and if applied appropriately it
helps to reduce any bias involved as compared to any other
sampling method involved
 Since it involves a large sample frame it is usually easy to pick
smaller sample size from the existing larger population
Disadvantages:
 It is a costlier method of sampling as it requires a complete list
of all potential respondents to be available beforehand
 This sampling method is not suitable for studies involving face-
to-face interviews as covering large geographical areas have
cost and time constraints

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Alam Nuzhathalam19
 Stratified sampling is a probability sampling technique
wherein the researcher divides the entire population into
different subgroups or strata, then randomly selects the
final subjects proportionally from the different strata
 A stratified sample is one that ensures that subgroups
(strata) of a given population are each adequately
represented within the whole sample population of
a research study. Example: one might divide a sample of
adults into subgroups by age, like 15–19, 20–24, 25–29,
30–34, and 35 and above
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Alam Nuzhathalam
Advantages:
 Assures representation of all groups in sample population
 Characteristics of all groups in same population
Disadvantages:
 Requires accurate information on proportions of each
stratum
 Stratified lists costly to prepare
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Alam Nuzhathalam
Alam Nuzhathalam22
 Cluster sampling refers to a type of sampling method.
With cluster sampling, the researcher divides the
population into separate groups, called clusters. Then, a
simple random sample of clusters is selected from the
population. The researcher conducts his analysis on data
from the sampled clusters
 For example, a researcher wants to survey academic
performance of nursing students in India. He can divide
the entire population into different cities. Then the
researcher selects a number of clusters depending on his
research through simple or systematic random sampling
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Advantages:
 Requires fewer resources; Since cluster sampling selects only
certain groups from the entire population, the method
requires fewer resources for the sampling process
 More feasible; The division of the entire population into
homogenous groups increases the feasibility of the sampling
Disadvantages:
 The cost to reach an element to sample is very high
 Each stage in cluster sampling introduces sample error. The
more stages there are more error tends to be
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Alam Nuzhathalam25
 Systematic sampling is a type of probability sampling
method in which sample members from a larger
population are selected according to a random starting
point but with a fixed, periodic interval. This interval,
called the sampling interval, is calculated by dividing the
population size by the desired sample size
 For example, if a researcher is seeking to form a
systematic sample of 50 volunteers from a nursing
students of 500, they can select every 10th student to
build a sample systematically
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Advantages:
 It’s extremely simple and convenient for the researchers to
create, conduct, analyze samples
 As there’s no need to number each member of a sample,
it is better for representing a population in a faster and
simpler manner
Disadvantages:
 Periodic ordering required
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Alam Nuzhathalam28
 Multistage sampling is defined as a sampling method that
divides the population into groups (or clusters) for
conducting research. During this sampling method, significant
clusters of the selected people are split into sub-groups at
various stages to make it simpler for primary data collection
 Example: Stratify the population by region of the country.
For each region, stratify by urban, suburban, and rural and
take a random sample of communities within those strata.
Divide the selected communities into city blocks as clusters,
and sample some blocks. Everyone on the block or within the
fixed area may then be sampled
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Advantages:
 More accurate than the cluster sampling for same size
population
 Less time consuming
Disadvantages:
 Costly
 Not as accurate as simple random sampling
 Each stage in sampling introduces sample error. The more
stages there are more error tends to be
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 Population divided into
few groups
 Homogeneity within sub-
groups
 Heterogeneity between
sub-groups
 Choice of elements from
within sub-groups
 Population divided into
many groups
 Heterogeneity within sub-
groups
 Homogeneity between
sub-groups
 Random choice of sub-
groups
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Alam Nuzhathalam
 Non-probability sampling is a sampling method in which not
all members of the population have an equal chance of
participating in the study
 It is most useful for exploratory studies like a pilot survey.
Researchers use this method in studies where it is not
possible to draw random probability sampling due to time
or cost considerations
 It is defined as a sampling technique in which the researcher
selects samples based on the subjective judgment of the
researcher rather than random selection. It is a less stringent
method
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Alam Nuzhathalam33
Alam Nuzhathalam34
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Alam Nuzhathalam
 Convenience sampling (grab sampling, accidental sampling,
or opportunity sampling) is a type of non-probability
sampling that involves the sample being drawn from that
part of the population that is close to hand. This type of
sampling is most useful for pilot testing. Convenience
sampling involves choosing respondents all the convenience
of the researcher
 A convenience sample is a type of non-probability sampling
method where the sample is taken from a group of people
easy to contact or to reach. Example is using subjects that
are selected from a clinic, a class or an institution that is
easily accessible to the researcher
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Advantages:
 Quick
 Convenient
 Economical
 Extensivelyused
Disadvantages:
 Restriction of Generalization
 Sample may not be representative
 Projecting data beyond sample not justified
 Variability and bias cannot be measured or controlled
Alam Nuzhathalam37
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Alam Nuzhathalam
 Quota sampling is a method for selecting survey participants
that is a non-probabilistic version of stratified sampling. The
population is first segmented into mutually exclusive sub-groups, just
asin stratified sampling
 In quota sampling, a population is first segmented into mutually
exclusive sub-groups, just as in stratified sampling. Then
judgment is used to select the subjects or units from each
segment based on a specified proportion
 Example, interviewers might be tempted to interview those
people in the street who look most helpful, or may choose to
use accidental sampling to question those closest to them, to
save time
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Advantages:
 Used when research budget is limited
 No need for list of population elements
 Easy to carry out than stratifies sampling
Disadvantages:
 Time consuming
 Selection of sample upon accessibility, prone to bias
Alam Nuzhathalam40
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Alam Nuzhathalam
 Purposive sampling, also known as judgmental, selective,
or subjective sampling, is a form of non-
probability sampling in which researchers rely on their
own judgment when choosing members of the population
to participate in their study
 Participants are selected according to the needs of the
study. Applicants who do not meet the profile are
rejected. For example, Conducting a study on why high
school students choose community college over university
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Advantages:
 Low cost
 Less time involved
 Meet the specific objective
 There is a assurance of quality response
Disadvantages:
 Time consuming process
 Bias selection of sample may occur
Alam Nuzhathalam43
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Alam Nuzhathalam
 Self-selection sampling (volunteer sampling) is a non-
probability technique, that is based on the judgement of
the researcher. This is a useful tool for researchers, who
want people or organizations, to participate as part of
a study on their own accord
 As a sampling strategy, self section sampling can be used
with a wide range of research designs
and research methods
 Example, Survey researchers may put a questionnaire
online and subsequently invite anyone within a particular
organisation to take part
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Alam Nuzhathalam
Advantages:
 More accurate
 Access to a variety of participants
 Useful in specific circumstances to serve the purpose
Disadvantages:
 Massare left
 Volunteer based
 More costly due to Advertizing
Alam Nuzhathalam46
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Alam Nuzhathalam
 Snowball sampling is also known as chain sampling,
chain-referral sampling, referral sampling. It is a non-
probability sampling technique where existing study
subjects recruit future subjects from among their
acquaintances
 The research starts with a key person and introduce the
next one to becomeachain
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Advantages:
 Lowcost
 Locate hidden population
 Useful in specific circumstances & for locating rare populations
Disadvantages:
 Not Random
 Not independent
 Projecting data beyond sample not justified
Alam Nuzhathalam49
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Alam Nuzhathalam
 Error caused by act of taking sample
 Sampling error is the error caused by observing a
sample instead of the whole population. The sampling
error is the difference between a sample statistic used to
estimate a population parameter and the actual but
unknown value of the parameter
Twotypes of samplingerrors
 Biased Errors: Due to selection of sampling technique; size of
the sample
 Unbiased Errors/Random sampling errors: Differences between
the members of the population included or notincluded
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Alam Nuzhathalam
Sampling errors can be reduced by the following methods:
 Increase the sample size. A larger sample size leads to a
more precise result because the study gets closer to the
actual population size
 Randomize selection to eliminate bias
 Divide the population into groups
 Perform an external record check
 Know your population
 Train your team
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Alam Nuzhathalam
 Any error or inaccuracies caused by factors other
than sampling error
 Non-sampling errors to biases and mistakes in selection of
sample
Examples of non-sampling errors are:
 Selection bias
 Interviewer error
 Population mis-specification error
 Respondent error, non-response error
 Sampling frame error, processing error
Alam Nuzhathalam
53
Causes for non-sampling error:
 Sample size
 Processing error
 Loaded questions
 Lack of knowledge
 Sampling operations
 Inadequate of response
 Concealment of the truth
 Misunderstanding the concept
Alam Nuzhathalam
54
 Probability sampling can generalize to population
 Probability sampling should be attempted as it has lowest bias
and more importantly significance of the result
 Non-probability sampling can be generalize to the institution
or place where the sample was studied
 Using a sample in research saves mainly on money and time, if
a suitable sample strategy is used appropriate size selected
and necessary precautions to reduce on sampling and
measurement errors, then a sample should yield valid and
reliable information
Alam Nuzhathalam55

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Sampling Techniques and Sampling Methods (Sampling Types - Probability Sampling and Non Probability Sampling)

  • 1. Alam Nuzhathalam1 Associate Professor Nuzhath Alam
  • 2. Alam Nuzhathalam 2  Sampling  Introduction, terminologies  Definition, characteristics of good sample  Sampling criteria, factors influencing sampling process  Sampling Steps/Design/Process  Sampling Methods/Techniques/Types  Probability Sampling  Non-Probability Sampling  Sampling Errors and Non-Sampling Errors
  • 4.  Population: The entire set of individuals or objects having some common characteristics selected for a research study  Target Population: Entire group of people or objects to which the researcher wishes to generalize the findings of the study  Sample: A subset of the population study population target population sample  Study Population (Sampling): The population to be studied/to which the investigator wants to generalize his results 4 Alam Nuzhathalam
  • 5.  Sampling Unit: Smallest unit from which sample can be selected  Sampling Frame: List of all the sampling units from which sample is drawn  Sampling Scheme: Method of selecting sampling units from sampling frame  Sampling Fraction: Ratio between sample size and population size 5 Alam Nuzhathalam
  • 7. Definition:  Sampling is the process of selecting units (e.g., people, organizations) from a population of interest so that by studying the sample we may fairly generalize our results back to the population from which they were chosen  The process by which researchers select a representative subset or part of the total population that could be studied for their topic so that they will be able to draw conclusions about the entire population 7 Alam Nuzhathalam
  • 8.  A sample should be reliable  A sample should be economical  A sample should be proportional  A sample should be goal oriented  A sample should be appropriate in size  A sample should be selected at randomly  A sample should be free from bias and errors  A sample should be true representation of population 8 Alam Nuzhathalam
  • 9.  Sampling criteria refers to the essential characteristics of a subject or respondent such as ability to read and write responses on the data collection instruments  For example, these criteria could include:  Age (elderly, children, etc)  Gender (male/female)  Marital status  Ability to understand English/Hindi, etc  Ability to write 9 Alam Nuzhathalam
  • 10. Factors that influence sampling process:  Element Type  Research Type  Population size  Available Resources  Constraints/ limitations  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 10 Alam Nuzhathalam
  • 11. 11
  • 12. SSCSM: Soft Skill Computer Shaksharta Mission  S - Simple  S - Stratified  C - Cluster  S - Systematic  M - Multi Stage CQPSS: Continuous Quality Process Software & Systems  C - Convenience  Q - Quota  P - Purposive  S - Self Selection  S - Snow Ball 12 Alam Nuzhathalam Probability Sampling Non-Probability Sampling
  • 14.  Probability sampling is defined as a sampling technique in which the researcher chooses samples from a larger population using a method based on the theory of probability. For a participant to be considered as a probability sample, he/she must be selected using a random selection  Probability sampling uses statistical theory to randomly select a small group of people (sample) from an existing large population and then predict that all their responses will match the overall population 14 Alam Nuzhathalam
  • 17. 17 Alam Nuzhathalam  Simple random sampling is a sampling technique where every item in the population has an even chance and likelihood of being selected in the sample. Here the selection of items completely depends on chance and therefore this sampling technique is also sometimes known as a method of chances (i.e., Lottery method, random table)  Example: A simple random sample would be the names of 25 employees being chosen out of a hat from a company of 250 employees. In this case, the population is all 250 employees, and the sample is random because each employee has an equal chance of being chosen
  • 18. Advantages:  It is a fair method of sampling and if applied appropriately it helps to reduce any bias involved as compared to any other sampling method involved  Since it involves a large sample frame it is usually easy to pick smaller sample size from the existing larger population Disadvantages:  It is a costlier method of sampling as it requires a complete list of all potential respondents to be available beforehand  This sampling method is not suitable for studies involving face- to-face interviews as covering large geographical areas have cost and time constraints  18 Alam Nuzhathalam
  • 20.  Stratified sampling is a probability sampling technique wherein the researcher divides the entire population into different subgroups or strata, then randomly selects the final subjects proportionally from the different strata  A stratified sample is one that ensures that subgroups (strata) of a given population are each adequately represented within the whole sample population of a research study. Example: one might divide a sample of adults into subgroups by age, like 15–19, 20–24, 25–29, 30–34, and 35 and above 20 Alam Nuzhathalam
  • 21. Advantages:  Assures representation of all groups in sample population  Characteristics of all groups in same population Disadvantages:  Requires accurate information on proportions of each stratum  Stratified lists costly to prepare 21 Alam Nuzhathalam
  • 23.  Cluster sampling refers to a type of sampling method. With cluster sampling, the researcher divides the population into separate groups, called clusters. Then, a simple random sample of clusters is selected from the population. The researcher conducts his analysis on data from the sampled clusters  For example, a researcher wants to survey academic performance of nursing students in India. He can divide the entire population into different cities. Then the researcher selects a number of clusters depending on his research through simple or systematic random sampling 23 Alam Nuzhathalam
  • 24. Advantages:  Requires fewer resources; Since cluster sampling selects only certain groups from the entire population, the method requires fewer resources for the sampling process  More feasible; The division of the entire population into homogenous groups increases the feasibility of the sampling Disadvantages:  The cost to reach an element to sample is very high  Each stage in cluster sampling introduces sample error. The more stages there are more error tends to be 24 Alam Nuzhathalam
  • 26.  Systematic sampling is a type of probability sampling method in which sample members from a larger population are selected according to a random starting point but with a fixed, periodic interval. This interval, called the sampling interval, is calculated by dividing the population size by the desired sample size  For example, if a researcher is seeking to form a systematic sample of 50 volunteers from a nursing students of 500, they can select every 10th student to build a sample systematically 26 Alam Nuzhathalam
  • 27. Advantages:  It’s extremely simple and convenient for the researchers to create, conduct, analyze samples  As there’s no need to number each member of a sample, it is better for representing a population in a faster and simpler manner Disadvantages:  Periodic ordering required 27 Alam Nuzhathalam
  • 29.  Multistage sampling is defined as a sampling method that divides the population into groups (or clusters) for conducting research. During this sampling method, significant clusters of the selected people are split into sub-groups at various stages to make it simpler for primary data collection  Example: Stratify the population by region of the country. For each region, stratify by urban, suburban, and rural and take a random sample of communities within those strata. Divide the selected communities into city blocks as clusters, and sample some blocks. Everyone on the block or within the fixed area may then be sampled 29 Alam Nuzhathalam
  • 30. Advantages:  More accurate than the cluster sampling for same size population  Less time consuming Disadvantages:  Costly  Not as accurate as simple random sampling  Each stage in sampling introduces sample error. The more stages there are more error tends to be 30 Alam Nuzhathalam
  • 31.  Population divided into few groups  Homogeneity within sub- groups  Heterogeneity between sub-groups  Choice of elements from within sub-groups  Population divided into many groups  Heterogeneity within sub- groups  Homogeneity between sub-groups  Random choice of sub- groups 31 Alam Nuzhathalam
  • 32.  Non-probability sampling is a sampling method in which not all members of the population have an equal chance of participating in the study  It is most useful for exploratory studies like a pilot survey. Researchers use this method in studies where it is not possible to draw random probability sampling due to time or cost considerations  It is defined as a sampling technique in which the researcher selects samples based on the subjective judgment of the researcher rather than random selection. It is a less stringent method 32 Alam Nuzhathalam
  • 35. 35 Alam Nuzhathalam  Convenience sampling (grab sampling, accidental sampling, or opportunity sampling) is a type of non-probability sampling that involves the sample being drawn from that part of the population that is close to hand. This type of sampling is most useful for pilot testing. Convenience sampling involves choosing respondents all the convenience of the researcher  A convenience sample is a type of non-probability sampling method where the sample is taken from a group of people easy to contact or to reach. Example is using subjects that are selected from a clinic, a class or an institution that is easily accessible to the researcher
  • 36. 36 Alam Nuzhathalam Advantages:  Quick  Convenient  Economical  Extensivelyused Disadvantages:  Restriction of Generalization  Sample may not be representative  Projecting data beyond sample not justified  Variability and bias cannot be measured or controlled
  • 38. 38 Alam Nuzhathalam  Quota sampling is a method for selecting survey participants that is a non-probabilistic version of stratified sampling. The population is first segmented into mutually exclusive sub-groups, just asin stratified sampling  In quota sampling, a population is first segmented into mutually exclusive sub-groups, just as in stratified sampling. Then judgment is used to select the subjects or units from each segment based on a specified proportion  Example, interviewers might be tempted to interview those people in the street who look most helpful, or may choose to use accidental sampling to question those closest to them, to save time
  • 39. 39 Alam Nuzhathalam Advantages:  Used when research budget is limited  No need for list of population elements  Easy to carry out than stratifies sampling Disadvantages:  Time consuming  Selection of sample upon accessibility, prone to bias
  • 41. 41 Alam Nuzhathalam  Purposive sampling, also known as judgmental, selective, or subjective sampling, is a form of non- probability sampling in which researchers rely on their own judgment when choosing members of the population to participate in their study  Participants are selected according to the needs of the study. Applicants who do not meet the profile are rejected. For example, Conducting a study on why high school students choose community college over university
  • 42. 42 Alam Nuzhathalam Advantages:  Low cost  Less time involved  Meet the specific objective  There is a assurance of quality response Disadvantages:  Time consuming process  Bias selection of sample may occur
  • 44. 44 Alam Nuzhathalam  Self-selection sampling (volunteer sampling) is a non- probability technique, that is based on the judgement of the researcher. This is a useful tool for researchers, who want people or organizations, to participate as part of a study on their own accord  As a sampling strategy, self section sampling can be used with a wide range of research designs and research methods  Example, Survey researchers may put a questionnaire online and subsequently invite anyone within a particular organisation to take part
  • 45. 45 Alam Nuzhathalam Advantages:  More accurate  Access to a variety of participants  Useful in specific circumstances to serve the purpose Disadvantages:  Massare left  Volunteer based  More costly due to Advertizing
  • 47. 47 Alam Nuzhathalam  Snowball sampling is also known as chain sampling, chain-referral sampling, referral sampling. It is a non- probability sampling technique where existing study subjects recruit future subjects from among their acquaintances  The research starts with a key person and introduce the next one to becomeachain
  • 48. 48 Alam Nuzhathalam Advantages:  Lowcost  Locate hidden population  Useful in specific circumstances & for locating rare populations Disadvantages:  Not Random  Not independent  Projecting data beyond sample not justified
  • 50. 50 Alam Nuzhathalam  Error caused by act of taking sample  Sampling error is the error caused by observing a sample instead of the whole population. The sampling error is the difference between a sample statistic used to estimate a population parameter and the actual but unknown value of the parameter Twotypes of samplingerrors  Biased Errors: Due to selection of sampling technique; size of the sample  Unbiased Errors/Random sampling errors: Differences between the members of the population included or notincluded
  • 51. 51 Alam Nuzhathalam Sampling errors can be reduced by the following methods:  Increase the sample size. A larger sample size leads to a more precise result because the study gets closer to the actual population size  Randomize selection to eliminate bias  Divide the population into groups  Perform an external record check  Know your population  Train your team
  • 52. 52 Alam Nuzhathalam  Any error or inaccuracies caused by factors other than sampling error  Non-sampling errors to biases and mistakes in selection of sample Examples of non-sampling errors are:  Selection bias  Interviewer error  Population mis-specification error  Respondent error, non-response error  Sampling frame error, processing error
  • 53. Alam Nuzhathalam 53 Causes for non-sampling error:  Sample size  Processing error  Loaded questions  Lack of knowledge  Sampling operations  Inadequate of response  Concealment of the truth  Misunderstanding the concept
  • 54. Alam Nuzhathalam 54  Probability sampling can generalize to population  Probability sampling should be attempted as it has lowest bias and more importantly significance of the result  Non-probability sampling can be generalize to the institution or place where the sample was studied  Using a sample in research saves mainly on money and time, if a suitable sample strategy is used appropriate size selected and necessary precautions to reduce on sampling and measurement errors, then a sample should yield valid and reliable information