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BY
SHARADA
(RESEARCH SCHOLAR)
DEPTT. OF HOME SCIENCE
MAHILA MAHA VIDYALAYA
BHU, VARANASI
SAMPLING: A Scientific Method of
Data Collection
OUTLINE OF PRESENTATION
 SAMPLE
SAMPLING
SAMPLING METHOD
TYPES OF SAMPLING METHOD
SAMPLING ERROR
SAMPLE
•It is a Unit that selected from population
•Representers of the population
•Purpose to draw the inference
Very difficult to study each and every unit of the
population when population unit are heterogeneous
WHY SAMPLE ?
Time Constraints
Finance
It is very easy and convenient to draw the sample from
homogenous population
The population having significant variations (Heterogeneous),
observation of multiple individual needed to find all possible
characteristics that may exist
Population
The entire group of people of interest from whom the
researcher needs to obtain information
Element (sampling unit)
One unit from a population
Sampling
The selection of a subset of the population through various
sampling techniques
Sampling Frame
Listing of population from which a sample is chosen. The
sampling frame for any probability sample is a complete list of
all the cases in the population from which your sample will be
drown
Parameter
The variable of interest
Statistic
The information obtained from the sample
about the parameter
Population Vs. Sample
Population of
Interest
Sample
Population Sample
Parameter Statistic
We measure the sample using statistics in order to draw
inferences about the population and its parameters.
Universe
Census
Sample Population
Sample Frame
Elements
Characteristics of Good Samples
Representative
Accessible
Low cost
Process by which the sample are taken from
population to obtain the information
Sampling is the process of selecting observations (a
sample) to provide an adequate description and
inferences of the population
SAMPLING
Population
SampleSampling
Frame
Sampling Process
What you
want to talk
about
What you
actually
observe in
the data
Inference
Steps in Sampling Process
Define the population
Identify the sampling frame
Select a sampling design or
procedure
Determine the sample size
Draw the sample
Sampling Design Process
Define Population
Determine Sampling Frame
Determine Sampling Procedure
Probability Sampling
Simple Random Sampling
Stratified Sampling
Cluster Sampling
Systematic Sampling
Multistage Sampling
Non-Probability Sampling
Convenient
Judgmental
Quota
Snow ball Sampling
Determine Appropriate
Sample Size
Execute Sampling
Design
Classification of Sampling
Methods
Sampling
Methods
Probability
Samples
Simple
Random
Cluster
Systematic Stratified
Non-
probability
QuotaJudgment
Convenience Snowball
Multista
ge
Probability Sampling
Each and every unit of the population has the
equal chance for selection as a sampling unit
Also called formal sampling or random sampling
Probability samples are more accurate
Probability samples allow us to estimate the
accuracy of the sample
Types of Probability Sampling
Simple Random Sampling
Stratified Sampling
Cluster Sampling
Systematic Sampling
Multistage Sampling
Simple Random Sampling
 The purest form of probability sampling
 Assures each element in the population has an
equal chance of being included in the sample
 Random number generators
Simple random sampling
Types of Simple Random Sample
With replacement
Without replacement
With replacement
The unit once selected has the chance
for again selection
Without replacement
The unit once selected can not be
selected again
Methods of SRS
 Tippet method
Lottery Method
Random Table
Random numbers of table
6 8 4 2 5 7 9 5 4 1 2 5 6 3 2 1
4 0
5 8 2 0 3 2 1 5 4 7 8 5 9 6 2 0
2 4
3 6 2 3 3 3 2 5 4 7 8 9 1 2 0 3
2 5
9 8 5 2 6 3 0 1 7 4 2 4 5 0 3 6
Advantages of SRS
 Minimal knowledge of population
needed
 External validity high; internal
validity high; statistical estimation
of error
 Easy to analyze data
Disadvantage
 High cost; low frequency of use
 Requires sampling frame
 Does not use researchers’ expertise
 Larger risk of random error than
stratified
Stratified Random Sampling
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.
Elements within each strata are homogeneous,
but are heterogeneous across strata
Stratified Random Sampling
Types of Stratified Random Sampling
Proportionate Stratified Random Sampling
Equal proportion of sample unit are selected from each
strata
Disproportionate Stratified Random Sampling
Also called as equal allocation technique and sample unit
decided according to analytical consideration
Advantage
 Assures representation of all groups in
sample population needed
 Characteristics of each stratum can be
estimated and comparisons made
 Reduces variability from systematic
Disadvantage
 Requires accurate information on
proportions of each stratum
 Stratified lists costly to prepare
The population is divided into subgroups
(clusters) like families. A simple random sample
is taken of the subgroups and then all members of
the cluster selected are surveyed.
Cluster Sampling
Cluster sampling
Section 4
Section 5
Section 3
Section 2Section 1
Advantage
 Low cost/high frequency of use
 Requires list of all clusters, but only of individuals within
chosen clusters
 Can estimate characteristics of both cluster and
population
 For multistage, has strengths of used methods
 Researchers lack a good sampling frame for a dispersed
population
Disadvantage
The cost to reach an element to sample is very
high
Usually less expensive than SRS but not as
accurate
Each stage in cluster sampling introduces
sampling error—the more stages there are, the
more error there tends to be
Systematic Random Sampling
Order all units in the sampling frame based
on some variable and then every nth number
on the list is selected
Gaps between elements are equal and
Constant There is periodicity.
N= Sampling Interval
Systematic Random Sampling
Advantage
 Moderate cost; moderate usage
 External validity high; internal validity
high; statistical estimation of error
 Simple to draw sample; easy to verify
Disadvantage
Periodic ordering
Requires sampling
frame
Multistage sampling refers to sampling plans
where the sampling is carried out in stages
using smaller and smaller sampling units at each
stage.
Not all Secondary Units Sampled normally used
to overcome problems associated with a
geographically dispersed population
Multistage Random Sampling
1
2
3
4
5
6
7
8
9
10
Primary
Clusters
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
Secondary
Clusters Simple Random Sampling within Secondary Clusters
Multistage Random Sampling
Select all schools; then sample within
schools
Sample schools; then measure all
students
Sample schools; then sample students
The probability of each case being selected from
the total population is not known
Units of the sample are chosen on the basis of
personal judgment or convenience
There are NO statistical techniques for measuring
random sampling error in a non-probability
sample. Therefore, generalizability is never
statistically appropriate.
Non Probability Sampling
Non Probability Sampling
 Involves non random methods in selection of
sample
All have not equal chance of being selected
Selection depend upon situation
Considerably less expensive
Convenient
Sample chosen in many ways
Types of Non probability Sampling
 Purposive Sampling
 Quota sampling (larger populations)
Snowball sampling
Self-selection sampling
Convenience sampling
Purposive Sampling
Also called judgment Sampling
The sampling procedure in which an experienced
research selects the sample based on some
appropriate characteristic of sample
members… to serve a purpose
When taking sample reject, people who do not
fit for a particular profile
Start with a purpose in mind
Sample are chosen well based on the
some criteria
There is a assurance of Quality
response
Meet the specific objective
Advantage
Demerit
Bias selection of sample may
occur
 Time consuming process
Quota Sampling
The population is divided into cells on the basis
of relevant control characteristics.
A quota of sample units is established for each
cell
A convenience sample is drawn for each cell
until the quota is met
It is entirely non random and it is normally
used for interview surveys
Advantage
 Used when research budget limited
 Very extensively used/understood
 No need for list of population elements
 Introduces some elements of stratification
Demerit
 Variability and bias cannot be measured
or controlled
 Time Consuming
 Projecting data beyond sample not
justified
Snowball Sampling
The research starts with a key person and
introduce the next one to become a chain
Make contact with one or two cases in the
population
Ask these cases to identify further cases.
 Stop when either no new cases are given or the
sample is as large as manageable
Advantage
Demerit
 low cost
 Useful in specific circumstances
 Useful for locating rare populations
 Bias because sampling units not independent
 Projecting data beyond sample not justified
Self selection Sampling
It occurs when you allow each case usually
individuals, to identify their desire to take part
in the research you therefore
Publicize your need for cases, either by
advertising through appropriate media or by
asking them to take part
Collect data from those who respond
Advantage
Demerit
 More accurate
 Useful in specific circumstances to serve the
purpose
 More costly due to Advertizing
 Mass are left
Called as Accidental / Incidental
Sampling
Selecting haphazardly those cases that
are easiest to obtain
Sample most available are chosen
It is done at the “convenience” of the
researcher
Convenience Sampling
Merit
 Very low cost
 Extensively used/understood
 No need for list of population elements
Demerit
 Variability and bias cannot be measured or
controlled
 Projecting data beyond sample not
justified
Sampling Error
Sampling error refers to differences
between the sample and the population
that exist only because of the observations
that happened to be selected for the
sample
Increasing the sample size will reduce this
type of error
Types of Sampling Error
Sample Errors
Non Sample Errors
Sample Errors
Error caused by the act of taking a sample
They cause sample results to be different from the
results of census
Differences between the sample and the population
that exist only because of the observations that
happened to be selected for the sample
Statistical Errors are sample error
We have no control over
Non Sample Errors
Non Response Error
Response Error
Not Control by Sample Size
Non Response Error
A non-response error occurs when
units selected as part of the sampling
procedure do not respond in whole
or in part
Response Errors
Respondent error (e.g., lying, forgetting, etc.)
Interviewer bias
Recording errors
Poorly designed questionnaires
Measurement error
A response or data error is any systematic bias
that occurs during data collection, analysis or
interpretation
Respondent error
 respondent gives an incorrect answer, e.g. due to prestige
or competence implications, or due to sensitivity or social
undesirability of question
 respondent misunderstands the requirements
 lack of motivation to give an accurate answer
 “lazy” respondent gives an “average” answer
 question requires memory/recall
 proxy respondents are used, i.e. taking answers from
someone other than the respondent
Interviewer bias
 Different interviewers administer a survey in different
ways
 Differences occur in reactions of respondents to
different interviewers, e.g. to interviewers of their
own sex or own ethnic group
 Inadequate training of interviewers
 Inadequate attention to the selection of interviewers
 There is too high a workload for the interviewer
Measurement Error
 The question is unclear, ambiguous or difficult to
answer
 The list of possible answers suggested in the recording
instrument is incomplete
 Requested information assumes a framework
unfamiliar to the respondent
 The definitions used by the survey are different from
those used by the respondent (e.g. how many part-time
employees do you have? See next slide for an example)
Key Points on Errors
Non-sampling errors are inevitable in production of
national statistics. Important that:-
 At planning stage, all potential non-sampling errors are
listed and steps taken to minimise them are considered.
 If data are collected from other sources, question
procedures adopted for data collection, and data
verification at each step of the data chain.
 Critically view the data collected and attempt to resolve
queries immediately they arise.
 Document sources of non-sampling errors so that results
presented can be interpreted meaningfully.
SAMPLING AND SAMPLING ERRORS

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SAMPLING AND SAMPLING ERRORS

  • 1.
  • 2. BY SHARADA (RESEARCH SCHOLAR) DEPTT. OF HOME SCIENCE MAHILA MAHA VIDYALAYA BHU, VARANASI SAMPLING: A Scientific Method of Data Collection
  • 3. OUTLINE OF PRESENTATION  SAMPLE SAMPLING SAMPLING METHOD TYPES OF SAMPLING METHOD SAMPLING ERROR
  • 4. SAMPLE •It is a Unit that selected from population •Representers of the population •Purpose to draw the inference
  • 5. Very difficult to study each and every unit of the population when population unit are heterogeneous WHY SAMPLE ? Time Constraints Finance
  • 6. It is very easy and convenient to draw the sample from homogenous population
  • 7. The population having significant variations (Heterogeneous), observation of multiple individual needed to find all possible characteristics that may exist
  • 8. Population The entire group of people of interest from whom the researcher needs to obtain information Element (sampling unit) One unit from a population Sampling The selection of a subset of the population through various sampling techniques Sampling Frame Listing of population from which a sample is chosen. The sampling frame for any probability sample is a complete list of all the cases in the population from which your sample will be drown
  • 9. Parameter The variable of interest Statistic The information obtained from the sample about the parameter
  • 10. Population Vs. Sample Population of Interest Sample Population Sample Parameter Statistic We measure the sample using statistics in order to draw inferences about the population and its parameters.
  • 12. Characteristics of Good Samples Representative Accessible Low cost
  • 13. Process by which the sample are taken from population to obtain the information Sampling is the process of selecting observations (a sample) to provide an adequate description and inferences of the population SAMPLING
  • 14. Population SampleSampling Frame Sampling Process What you want to talk about What you actually observe in the data Inference
  • 15.
  • 16. Steps in Sampling Process Define the population Identify the sampling frame Select a sampling design or procedure Determine the sample size Draw the sample
  • 17. Sampling Design Process Define Population Determine Sampling Frame Determine Sampling Procedure Probability Sampling Simple Random Sampling Stratified Sampling Cluster Sampling Systematic Sampling Multistage Sampling Non-Probability Sampling Convenient Judgmental Quota Snow ball Sampling Determine Appropriate Sample Size Execute Sampling Design
  • 18. Classification of Sampling Methods Sampling Methods Probability Samples Simple Random Cluster Systematic Stratified Non- probability QuotaJudgment Convenience Snowball Multista ge
  • 19. Probability Sampling Each and every unit of the population has the equal chance for selection as a sampling unit Also called formal sampling or random sampling Probability samples are more accurate Probability samples allow us to estimate the accuracy of the sample
  • 20. Types of Probability Sampling Simple Random Sampling Stratified Sampling Cluster Sampling Systematic Sampling Multistage Sampling
  • 21. Simple Random Sampling  The purest form of probability sampling  Assures each element in the population has an equal chance of being included in the sample  Random number generators
  • 23. Types of Simple Random Sample With replacement Without replacement
  • 24. With replacement The unit once selected has the chance for again selection Without replacement The unit once selected can not be selected again
  • 25. Methods of SRS  Tippet method Lottery Method Random Table
  • 26. Random numbers of table 6 8 4 2 5 7 9 5 4 1 2 5 6 3 2 1 4 0 5 8 2 0 3 2 1 5 4 7 8 5 9 6 2 0 2 4 3 6 2 3 3 3 2 5 4 7 8 9 1 2 0 3 2 5 9 8 5 2 6 3 0 1 7 4 2 4 5 0 3 6
  • 27. Advantages of SRS  Minimal knowledge of population needed  External validity high; internal validity high; statistical estimation of error  Easy to analyze data
  • 28. Disadvantage  High cost; low frequency of use  Requires sampling frame  Does not use researchers’ expertise  Larger risk of random error than stratified
  • 29. Stratified Random Sampling 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. Elements within each strata are homogeneous, but are heterogeneous across strata
  • 31. Types of Stratified Random Sampling Proportionate Stratified Random Sampling Equal proportion of sample unit are selected from each strata Disproportionate Stratified Random Sampling Also called as equal allocation technique and sample unit decided according to analytical consideration
  • 32. Advantage  Assures representation of all groups in sample population needed  Characteristics of each stratum can be estimated and comparisons made  Reduces variability from systematic
  • 33. Disadvantage  Requires accurate information on proportions of each stratum  Stratified lists costly to prepare
  • 34. The population is divided into subgroups (clusters) like families. A simple random sample is taken of the subgroups and then all members of the cluster selected are surveyed. Cluster Sampling
  • 35.
  • 36. Cluster sampling Section 4 Section 5 Section 3 Section 2Section 1
  • 37. Advantage  Low cost/high frequency of use  Requires list of all clusters, but only of individuals within chosen clusters  Can estimate characteristics of both cluster and population  For multistage, has strengths of used methods  Researchers lack a good sampling frame for a dispersed population
  • 38. Disadvantage The cost to reach an element to sample is very high Usually less expensive than SRS but not as accurate Each stage in cluster sampling introduces sampling error—the more stages there are, the more error there tends to be
  • 39. Systematic Random Sampling Order all units in the sampling frame based on some variable and then every nth number on the list is selected Gaps between elements are equal and Constant There is periodicity. N= Sampling Interval
  • 41.
  • 42. Advantage  Moderate cost; moderate usage  External validity high; internal validity high; statistical estimation of error  Simple to draw sample; easy to verify
  • 44. Multistage sampling refers to sampling plans where the sampling is carried out in stages using smaller and smaller sampling units at each stage. Not all Secondary Units Sampled normally used to overcome problems associated with a geographically dispersed population Multistage Random Sampling
  • 46. Multistage Random Sampling Select all schools; then sample within schools Sample schools; then measure all students Sample schools; then sample students
  • 47. The probability of each case being selected from the total population is not known Units of the sample are chosen on the basis of personal judgment or convenience There are NO statistical techniques for measuring random sampling error in a non-probability sample. Therefore, generalizability is never statistically appropriate. Non Probability Sampling
  • 48. Non Probability Sampling  Involves non random methods in selection of sample All have not equal chance of being selected Selection depend upon situation Considerably less expensive Convenient Sample chosen in many ways
  • 49. Types of Non probability Sampling  Purposive Sampling  Quota sampling (larger populations) Snowball sampling Self-selection sampling Convenience sampling
  • 50. Purposive Sampling Also called judgment Sampling The sampling procedure in which an experienced research selects the sample based on some appropriate characteristic of sample members… to serve a purpose When taking sample reject, people who do not fit for a particular profile Start with a purpose in mind
  • 51. Sample are chosen well based on the some criteria There is a assurance of Quality response Meet the specific objective Advantage
  • 52. Demerit Bias selection of sample may occur  Time consuming process
  • 53. Quota Sampling The population is divided into cells on the basis of relevant control characteristics. A quota of sample units is established for each cell A convenience sample is drawn for each cell until the quota is met It is entirely non random and it is normally used for interview surveys
  • 54. Advantage  Used when research budget limited  Very extensively used/understood  No need for list of population elements  Introduces some elements of stratification Demerit  Variability and bias cannot be measured or controlled  Time Consuming  Projecting data beyond sample not justified
  • 55. Snowball Sampling The research starts with a key person and introduce the next one to become a chain Make contact with one or two cases in the population Ask these cases to identify further cases.  Stop when either no new cases are given or the sample is as large as manageable
  • 56. Advantage Demerit  low cost  Useful in specific circumstances  Useful for locating rare populations  Bias because sampling units not independent  Projecting data beyond sample not justified
  • 57. Self selection Sampling It occurs when you allow each case usually individuals, to identify their desire to take part in the research you therefore Publicize your need for cases, either by advertising through appropriate media or by asking them to take part Collect data from those who respond
  • 58. Advantage Demerit  More accurate  Useful in specific circumstances to serve the purpose  More costly due to Advertizing  Mass are left
  • 59. Called as Accidental / Incidental Sampling Selecting haphazardly those cases that are easiest to obtain Sample most available are chosen It is done at the “convenience” of the researcher Convenience Sampling
  • 60.
  • 61. Merit  Very low cost  Extensively used/understood  No need for list of population elements Demerit  Variability and bias cannot be measured or controlled  Projecting data beyond sample not justified
  • 62. Sampling Error Sampling error refers to differences between the sample and the population that exist only because of the observations that happened to be selected for the sample Increasing the sample size will reduce this type of error
  • 63.
  • 64. Types of Sampling Error Sample Errors Non Sample Errors
  • 65. Sample Errors Error caused by the act of taking a sample They cause sample results to be different from the results of census Differences between the sample and the population that exist only because of the observations that happened to be selected for the sample Statistical Errors are sample error We have no control over
  • 66. Non Sample Errors Non Response Error Response Error Not Control by Sample Size
  • 67. Non Response Error A non-response error occurs when units selected as part of the sampling procedure do not respond in whole or in part
  • 68. Response Errors Respondent error (e.g., lying, forgetting, etc.) Interviewer bias Recording errors Poorly designed questionnaires Measurement error A response or data error is any systematic bias that occurs during data collection, analysis or interpretation
  • 69. Respondent error  respondent gives an incorrect answer, e.g. due to prestige or competence implications, or due to sensitivity or social undesirability of question  respondent misunderstands the requirements  lack of motivation to give an accurate answer  “lazy” respondent gives an “average” answer  question requires memory/recall  proxy respondents are used, i.e. taking answers from someone other than the respondent
  • 70. Interviewer bias  Different interviewers administer a survey in different ways  Differences occur in reactions of respondents to different interviewers, e.g. to interviewers of their own sex or own ethnic group  Inadequate training of interviewers  Inadequate attention to the selection of interviewers  There is too high a workload for the interviewer
  • 71. Measurement Error  The question is unclear, ambiguous or difficult to answer  The list of possible answers suggested in the recording instrument is incomplete  Requested information assumes a framework unfamiliar to the respondent  The definitions used by the survey are different from those used by the respondent (e.g. how many part-time employees do you have? See next slide for an example)
  • 72. Key Points on Errors Non-sampling errors are inevitable in production of national statistics. Important that:-  At planning stage, all potential non-sampling errors are listed and steps taken to minimise them are considered.  If data are collected from other sources, question procedures adopted for data collection, and data verification at each step of the data chain.  Critically view the data collected and attempt to resolve queries immediately they arise.  Document sources of non-sampling errors so that results presented can be interpreted meaningfully.