SlideShare ist ein Scribd-Unternehmen logo
1 von 13
Downloaden Sie, um offline zu lesen
Slide 12- 1
Cluster and Multistage Sampling
 Sometimes stratifying isn’t practical and simple random
sampling is difficult.
 Splitting the population into similar parts or clusters can make
sampling more practical.
 Then we could select one or a few clusters at random and
perform a census within each of them.
 This sampling design is called cluster sampling.
 If each cluster fairly represents the full population, cluster
sampling will give us an unbiased sample.
Slide 12- 2
Cluster and Multistage Sampling
(cont.)
 Cluster sampling is not the same as stratified sampling.
 We stratify to ensure that our sample represents different
groups in the population, and sample randomly within each
stratum.
 Strata are homogeneous, but differ from one another.
 Clusters are more or less alike, each heterogeneous and
resembling the overall population.
 We select clusters to make sampling more practical or
affordable.
Slide 12- 3
Cluster and Multistage Sampling
(cont.)
 Sometimes we use a variety of sampling
methods together.
 Sampling schemes that combine several
methods are called multistage samples.
 Most surveys conducted by professional
polling organizations use some combination of
stratified and cluster sampling as well as
simple random sampling.
STEPS IN CLUSTER RANDOM
SAMPLING:
1. Identify and define the population.
2. Determine the desired sample size.
3. Identify and define a logical cluster.
STEPS IN CLUSTER RANDOM
SAMPLING:
4. List all clusters (or obtain a list) that make up
the population of clusters.
5. Estimate the average number of population
members per cluster.
6. Determine the number of clusters needed by
dividing the sample size by the estimated
size of a cluster.
STEPS IN CLUSTER RANDOM
SAMPLING:
7. Randomly select the needed number of
clusters by using a table of random
numbers.
8. Include in your study all population
members in each selected cluster.
Cluster sampling
Cluster 4
Cluster 5
Cluster 3
Cluster 2Cluster 1
 Types:
 One stage – when all units in the selected cluster are selected.
 Two stage – only some units from a selected cluster are taken using
simple random or systematic random sampling.
 Advantages
 Simple as complete list of sampling units within population not required
 Low cost
 Can estimate characteristics of both cluster and population
 Less travel/resources required
 Disadvantages
 Potential problem is that cluster members are more likely to be alike,
than those in another cluster (homogenous).
 Each stage in cluster sampling introduces sampling error—the
more stages there are, the more error there tends to be
 Usually less expensive than SRS but not as accurate
Cluster sampling (contd.)
 A special form of cluster sampling called the “30 X 7 cluster
sampling”, has been recommended by the WHO for field
studies in assessing vaccination coverage.
 In this a list of all villages (clusters) for a given geographical
area is made.
 30 clusters are selected using Probability Proportional to Size
(PPS).
 From each of the selected clusters, 7 subjects are randomly
chosen.
 Thus a total sample of 30 x 7 = 210 subjects is chosen.
 The advantage of cluster sampling is that sampling frame is
not required
Cluster sampling (contd.)
Multistage random sampling
 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
 In this method, the whole population is divided in first stage
sampling units from which a random sample is selected.
 The selected first stage is then subdivided into second stage units
from which another sample is selected.
 Third and fourth stage sampling is done in the same manner if
necessary.
 Example:
 NFHS data is collected by multistage sampling.
 Rural areas – 2 stage sampling – Villages from list by PPS,
Households from village
 Urban areas – Wards (PPS) – CEB (PPS) – 30 households
from each CEB
CEB
WARD
HOUSHOLD
ADVANTAGES OF TWO-STAGE
RANDOM SAMPLING:
 Less time-consuming

Weitere ähnliche Inhalte

Was ist angesagt?

Sampling methods 16
Sampling methods   16Sampling methods   16
Sampling methods 16Raj Selvam
 
probability and non-probability samplings
probability and non-probability samplingsprobability and non-probability samplings
probability and non-probability samplingsn1a2g3a4j5a6i7
 
Types of random sampling
Types of random samplingTypes of random sampling
Types of random samplingStudying
 
Simple random sampling
Simple random samplingSimple random sampling
Simple random samplingsuncil0071
 
Systematic ranom sampling for slide share
Systematic ranom sampling for slide shareSystematic ranom sampling for slide share
Systematic ranom sampling for slide shareIVenkatReddyGaaru
 
Survival analysis
Survival analysisSurvival analysis
Survival analysisHar Jindal
 
Stratified random sampling
Stratified random samplingStratified random sampling
Stratified random samplingwaiton sherekete
 
Sampling Design
Sampling DesignSampling Design
Sampling DesignJale Nonan
 
Systematic sampling in probability sampling
Systematic sampling in probability sampling Systematic sampling in probability sampling
Systematic sampling in probability sampling Sachin H
 
sampling simple random sampling
sampling simple random samplingsampling simple random sampling
sampling simple random samplingDENNY VARGHESE
 
an introduction and characteristics of sampling, types of sampling and errors
an introduction and characteristics of sampling, types of sampling and errorsan introduction and characteristics of sampling, types of sampling and errors
an introduction and characteristics of sampling, types of sampling and errorsGunjan Verma
 
Sample and sampling techniques
Sample and sampling techniquesSample and sampling techniques
Sample and sampling techniquesAnupam Ghosh
 
Sampling and sampling techniques PPT
Sampling and sampling techniques PPTSampling and sampling techniques PPT
Sampling and sampling techniques PPTsabari123vel
 
Sampling techniques
Sampling techniquesSampling techniques
Sampling techniqueschetan1923
 
Non Probability Sampling
Non Probability SamplingNon Probability Sampling
Non Probability SamplingMuhammad Farooq
 

Was ist angesagt? (20)

Sampling methods 16
Sampling methods   16Sampling methods   16
Sampling methods 16
 
probability and non-probability samplings
probability and non-probability samplingsprobability and non-probability samplings
probability and non-probability samplings
 
Types of random sampling
Types of random samplingTypes of random sampling
Types of random sampling
 
sampling
samplingsampling
sampling
 
Simple random sampling
Simple random samplingSimple random sampling
Simple random sampling
 
Systematic ranom sampling for slide share
Systematic ranom sampling for slide shareSystematic ranom sampling for slide share
Systematic ranom sampling for slide share
 
Survival analysis
Survival analysisSurvival analysis
Survival analysis
 
SAMPLING
SAMPLINGSAMPLING
SAMPLING
 
Stratified random sampling
Stratified random samplingStratified random sampling
Stratified random sampling
 
Sampling Design
Sampling DesignSampling Design
Sampling Design
 
Systematic sampling in probability sampling
Systematic sampling in probability sampling Systematic sampling in probability sampling
Systematic sampling in probability sampling
 
sampling simple random sampling
sampling simple random samplingsampling simple random sampling
sampling simple random sampling
 
Sampling
SamplingSampling
Sampling
 
an introduction and characteristics of sampling, types of sampling and errors
an introduction and characteristics of sampling, types of sampling and errorsan introduction and characteristics of sampling, types of sampling and errors
an introduction and characteristics of sampling, types of sampling and errors
 
Sampling in Research
Sampling in ResearchSampling in Research
Sampling in Research
 
Sample and sampling techniques
Sample and sampling techniquesSample and sampling techniques
Sample and sampling techniques
 
Sampling and sampling techniques PPT
Sampling and sampling techniques PPTSampling and sampling techniques PPT
Sampling and sampling techniques PPT
 
Sampling
SamplingSampling
Sampling
 
Sampling techniques
Sampling techniquesSampling techniques
Sampling techniques
 
Non Probability Sampling
Non Probability SamplingNon Probability Sampling
Non Probability Sampling
 

Andere mochten auch

Andere mochten auch (10)

3 stage cluster sampling
3 stage cluster sampling3 stage cluster sampling
3 stage cluster sampling
 
Cluster & multi satge random sampling
Cluster & multi satge random samplingCluster & multi satge random sampling
Cluster & multi satge random sampling
 
Sampling
SamplingSampling
Sampling
 
Bsm presentation cluster sampling
Bsm presentation cluster samplingBsm presentation cluster sampling
Bsm presentation cluster sampling
 
Statistical sampling
Statistical samplingStatistical sampling
Statistical sampling
 
Cluster Sampling
Cluster SamplingCluster Sampling
Cluster Sampling
 
sampling ppt
sampling pptsampling ppt
sampling ppt
 
Sampling
SamplingSampling
Sampling
 
Sampling and Sample Types
Sampling  and Sample TypesSampling  and Sample Types
Sampling and Sample Types
 
Experimental research design
Experimental research designExperimental research design
Experimental research design
 

Ähnlich wie Cluster and multistage sampling

Ähnlich wie Cluster and multistage sampling (20)

43911
4391143911
43911
 
Sampling techniques
Sampling techniquesSampling techniques
Sampling techniques
 
Random sampling
Random samplingRandom sampling
Random sampling
 
Sampling techniques
Sampling techniques  Sampling techniques
Sampling techniques
 
Probablistic sampling group 3 assighnment
Probablistic sampling group 3 assighnmentProbablistic sampling group 3 assighnment
Probablistic sampling group 3 assighnment
 
Probablistic sampling design
Probablistic sampling designProbablistic sampling design
Probablistic sampling design
 
Probablistic sampling design
Probablistic sampling designProbablistic sampling design
Probablistic sampling design
 
Probablistic sampling group 3 assighnment
Probablistic sampling group 3 assighnmentProbablistic sampling group 3 assighnment
Probablistic sampling group 3 assighnment
 
Kamrizzaman sir 4, 5 & 6 chapter, 5011
Kamrizzaman sir 4, 5 & 6 chapter, 5011Kamrizzaman sir 4, 5 & 6 chapter, 5011
Kamrizzaman sir 4, 5 & 6 chapter, 5011
 
Sampling techniques new
Sampling techniques newSampling techniques new
Sampling techniques new
 
Sampling techniques new
Sampling techniques newSampling techniques new
Sampling techniques new
 
Sampling and its variability
Sampling  and its variabilitySampling  and its variability
Sampling and its variability
 
sampling techniques.pptx
sampling techniques.pptxsampling techniques.pptx
sampling techniques.pptx
 
sampling techniques.pptx
sampling techniques.pptxsampling techniques.pptx
sampling techniques.pptx
 
Sampling design
Sampling designSampling design
Sampling design
 
2.7.21 sampling methods data analysis
2.7.21 sampling methods data analysis2.7.21 sampling methods data analysis
2.7.21 sampling methods data analysis
 
Sampling.ppt
Sampling.pptSampling.ppt
Sampling.ppt
 
6152935.ppt
6152935.ppt6152935.ppt
6152935.ppt
 
Rm 6 Sampling Design
Rm   6   Sampling DesignRm   6   Sampling Design
Rm 6 Sampling Design
 
Sampling Design
Sampling DesignSampling Design
Sampling Design
 

Mehr von suncil0071

Conditional probability
Conditional probabilityConditional probability
Conditional probabilitysuncil0071
 
Basics of probability
Basics of probabilityBasics of probability
Basics of probabilitysuncil0071
 
Simple random sampling
Simple random samplingSimple random sampling
Simple random samplingsuncil0071
 
Ideas to understand
Ideas to understandIdeas to understand
Ideas to understandsuncil0071
 
Understanding randomness
Understanding randomnessUnderstanding randomness
Understanding randomnesssuncil0071
 
Linear regression
Linear regressionLinear regression
Linear regressionsuncil0071
 
Linear regression
Linear regressionLinear regression
Linear regressionsuncil0071
 
Linear regression
Linear regressionLinear regression
Linear regressionsuncil0071
 
Measures of central tendency mean
Measures of central tendency  meanMeasures of central tendency  mean
Measures of central tendency meansuncil0071
 
Histograms & stem plots
Histograms  & stem plotsHistograms  & stem plots
Histograms & stem plotssuncil0071
 

Mehr von suncil0071 (12)

Conditional probability
Conditional probabilityConditional probability
Conditional probability
 
Basics of probability
Basics of probabilityBasics of probability
Basics of probability
 
Who’s who
Who’s whoWho’s who
Who’s who
 
Simple random sampling
Simple random samplingSimple random sampling
Simple random sampling
 
Ideas to understand
Ideas to understandIdeas to understand
Ideas to understand
 
Understanding randomness
Understanding randomnessUnderstanding randomness
Understanding randomness
 
Linear regression
Linear regressionLinear regression
Linear regression
 
Linear regression
Linear regressionLinear regression
Linear regression
 
Linear regression
Linear regressionLinear regression
Linear regression
 
Correlation
CorrelationCorrelation
Correlation
 
Measures of central tendency mean
Measures of central tendency  meanMeasures of central tendency  mean
Measures of central tendency mean
 
Histograms & stem plots
Histograms  & stem plotsHistograms  & stem plots
Histograms & stem plots
 

Kürzlich hochgeladen

TINJUAN PEMROSESAN TRANSAKSI DAN ERP.pptx
TINJUAN PEMROSESAN TRANSAKSI DAN ERP.pptxTINJUAN PEMROSESAN TRANSAKSI DAN ERP.pptx
TINJUAN PEMROSESAN TRANSAKSI DAN ERP.pptxDwiAyuSitiHartinah
 
Strategic CX: A Deep Dive into Voice of the Customer Insights for Clarity
Strategic CX: A Deep Dive into Voice of the Customer Insights for ClarityStrategic CX: A Deep Dive into Voice of the Customer Insights for Clarity
Strategic CX: A Deep Dive into Voice of the Customer Insights for ClarityAggregage
 
YourView Panel Book.pptx YourView Panel Book.
YourView Panel Book.pptx YourView Panel Book.YourView Panel Book.pptx YourView Panel Book.
YourView Panel Book.pptx YourView Panel Book.JasonViviers2
 
Persuasive E-commerce, Our Biased Brain @ Bikkeldag 2024
Persuasive E-commerce, Our Biased Brain @ Bikkeldag 2024Persuasive E-commerce, Our Biased Brain @ Bikkeldag 2024
Persuasive E-commerce, Our Biased Brain @ Bikkeldag 2024Guido X Jansen
 
The Universal GTM - how we design GTM and dataLayer
The Universal GTM - how we design GTM and dataLayerThe Universal GTM - how we design GTM and dataLayer
The Universal GTM - how we design GTM and dataLayerPavel Šabatka
 
ChistaDATA Real-Time DATA Analytics Infrastructure
ChistaDATA Real-Time DATA Analytics InfrastructureChistaDATA Real-Time DATA Analytics Infrastructure
ChistaDATA Real-Time DATA Analytics Infrastructuresonikadigital1
 
How is Real-Time Analytics Different from Traditional OLAP?
How is Real-Time Analytics Different from Traditional OLAP?How is Real-Time Analytics Different from Traditional OLAP?
How is Real-Time Analytics Different from Traditional OLAP?sonikadigital1
 
SFBA Splunk Usergroup meeting March 13, 2024
SFBA Splunk Usergroup meeting March 13, 2024SFBA Splunk Usergroup meeting March 13, 2024
SFBA Splunk Usergroup meeting March 13, 2024Becky Burwell
 
Master's Thesis - Data Science - Presentation
Master's Thesis - Data Science - PresentationMaster's Thesis - Data Science - Presentation
Master's Thesis - Data Science - PresentationGiorgio Carbone
 
MEASURES OF DISPERSION I BSc Botany .ppt
MEASURES OF DISPERSION I BSc Botany .pptMEASURES OF DISPERSION I BSc Botany .ppt
MEASURES OF DISPERSION I BSc Botany .pptaigil2
 
Virtuosoft SmartSync Product Introduction
Virtuosoft SmartSync Product IntroductionVirtuosoft SmartSync Product Introduction
Virtuosoft SmartSync Product Introductionsanjaymuralee1
 
5 Ds to Define Data Archiving Best Practices
5 Ds to Define Data Archiving Best Practices5 Ds to Define Data Archiving Best Practices
5 Ds to Define Data Archiving Best PracticesDataArchiva
 
CI, CD -Tools to integrate without manual intervention
CI, CD -Tools to integrate without manual interventionCI, CD -Tools to integrate without manual intervention
CI, CD -Tools to integrate without manual interventionajayrajaganeshkayala
 
AI for Sustainable Development Goals (SDGs)
AI for Sustainable Development Goals (SDGs)AI for Sustainable Development Goals (SDGs)
AI for Sustainable Development Goals (SDGs)Data & Analytics Magazin
 
Cash Is Still King: ATM market research '2023
Cash Is Still King: ATM market research '2023Cash Is Still King: ATM market research '2023
Cash Is Still King: ATM market research '2023Vladislav Solodkiy
 
Elements of language learning - an analysis of how different elements of lang...
Elements of language learning - an analysis of how different elements of lang...Elements of language learning - an analysis of how different elements of lang...
Elements of language learning - an analysis of how different elements of lang...PrithaVashisht1
 
Mapping the pubmed data under different suptopics using NLP.pptx
Mapping the pubmed data under different suptopics using NLP.pptxMapping the pubmed data under different suptopics using NLP.pptx
Mapping the pubmed data under different suptopics using NLP.pptxVenkatasubramani13
 

Kürzlich hochgeladen (17)

TINJUAN PEMROSESAN TRANSAKSI DAN ERP.pptx
TINJUAN PEMROSESAN TRANSAKSI DAN ERP.pptxTINJUAN PEMROSESAN TRANSAKSI DAN ERP.pptx
TINJUAN PEMROSESAN TRANSAKSI DAN ERP.pptx
 
Strategic CX: A Deep Dive into Voice of the Customer Insights for Clarity
Strategic CX: A Deep Dive into Voice of the Customer Insights for ClarityStrategic CX: A Deep Dive into Voice of the Customer Insights for Clarity
Strategic CX: A Deep Dive into Voice of the Customer Insights for Clarity
 
YourView Panel Book.pptx YourView Panel Book.
YourView Panel Book.pptx YourView Panel Book.YourView Panel Book.pptx YourView Panel Book.
YourView Panel Book.pptx YourView Panel Book.
 
Persuasive E-commerce, Our Biased Brain @ Bikkeldag 2024
Persuasive E-commerce, Our Biased Brain @ Bikkeldag 2024Persuasive E-commerce, Our Biased Brain @ Bikkeldag 2024
Persuasive E-commerce, Our Biased Brain @ Bikkeldag 2024
 
The Universal GTM - how we design GTM and dataLayer
The Universal GTM - how we design GTM and dataLayerThe Universal GTM - how we design GTM and dataLayer
The Universal GTM - how we design GTM and dataLayer
 
ChistaDATA Real-Time DATA Analytics Infrastructure
ChistaDATA Real-Time DATA Analytics InfrastructureChistaDATA Real-Time DATA Analytics Infrastructure
ChistaDATA Real-Time DATA Analytics Infrastructure
 
How is Real-Time Analytics Different from Traditional OLAP?
How is Real-Time Analytics Different from Traditional OLAP?How is Real-Time Analytics Different from Traditional OLAP?
How is Real-Time Analytics Different from Traditional OLAP?
 
SFBA Splunk Usergroup meeting March 13, 2024
SFBA Splunk Usergroup meeting March 13, 2024SFBA Splunk Usergroup meeting March 13, 2024
SFBA Splunk Usergroup meeting March 13, 2024
 
Master's Thesis - Data Science - Presentation
Master's Thesis - Data Science - PresentationMaster's Thesis - Data Science - Presentation
Master's Thesis - Data Science - Presentation
 
MEASURES OF DISPERSION I BSc Botany .ppt
MEASURES OF DISPERSION I BSc Botany .pptMEASURES OF DISPERSION I BSc Botany .ppt
MEASURES OF DISPERSION I BSc Botany .ppt
 
Virtuosoft SmartSync Product Introduction
Virtuosoft SmartSync Product IntroductionVirtuosoft SmartSync Product Introduction
Virtuosoft SmartSync Product Introduction
 
5 Ds to Define Data Archiving Best Practices
5 Ds to Define Data Archiving Best Practices5 Ds to Define Data Archiving Best Practices
5 Ds to Define Data Archiving Best Practices
 
CI, CD -Tools to integrate without manual intervention
CI, CD -Tools to integrate without manual interventionCI, CD -Tools to integrate without manual intervention
CI, CD -Tools to integrate without manual intervention
 
AI for Sustainable Development Goals (SDGs)
AI for Sustainable Development Goals (SDGs)AI for Sustainable Development Goals (SDGs)
AI for Sustainable Development Goals (SDGs)
 
Cash Is Still King: ATM market research '2023
Cash Is Still King: ATM market research '2023Cash Is Still King: ATM market research '2023
Cash Is Still King: ATM market research '2023
 
Elements of language learning - an analysis of how different elements of lang...
Elements of language learning - an analysis of how different elements of lang...Elements of language learning - an analysis of how different elements of lang...
Elements of language learning - an analysis of how different elements of lang...
 
Mapping the pubmed data under different suptopics using NLP.pptx
Mapping the pubmed data under different suptopics using NLP.pptxMapping the pubmed data under different suptopics using NLP.pptx
Mapping the pubmed data under different suptopics using NLP.pptx
 

Cluster and multistage sampling

  • 1. Slide 12- 1 Cluster and Multistage Sampling  Sometimes stratifying isn’t practical and simple random sampling is difficult.  Splitting the population into similar parts or clusters can make sampling more practical.  Then we could select one or a few clusters at random and perform a census within each of them.  This sampling design is called cluster sampling.  If each cluster fairly represents the full population, cluster sampling will give us an unbiased sample.
  • 2. Slide 12- 2 Cluster and Multistage Sampling (cont.)  Cluster sampling is not the same as stratified sampling.  We stratify to ensure that our sample represents different groups in the population, and sample randomly within each stratum.  Strata are homogeneous, but differ from one another.  Clusters are more or less alike, each heterogeneous and resembling the overall population.  We select clusters to make sampling more practical or affordable.
  • 3. Slide 12- 3 Cluster and Multistage Sampling (cont.)  Sometimes we use a variety of sampling methods together.  Sampling schemes that combine several methods are called multistage samples.  Most surveys conducted by professional polling organizations use some combination of stratified and cluster sampling as well as simple random sampling.
  • 4. STEPS IN CLUSTER RANDOM SAMPLING: 1. Identify and define the population. 2. Determine the desired sample size. 3. Identify and define a logical cluster.
  • 5. STEPS IN CLUSTER RANDOM SAMPLING: 4. List all clusters (or obtain a list) that make up the population of clusters. 5. Estimate the average number of population members per cluster. 6. Determine the number of clusters needed by dividing the sample size by the estimated size of a cluster.
  • 6. STEPS IN CLUSTER RANDOM SAMPLING: 7. Randomly select the needed number of clusters by using a table of random numbers. 8. Include in your study all population members in each selected cluster.
  • 7. Cluster sampling Cluster 4 Cluster 5 Cluster 3 Cluster 2Cluster 1
  • 8.  Types:  One stage – when all units in the selected cluster are selected.  Two stage – only some units from a selected cluster are taken using simple random or systematic random sampling.  Advantages  Simple as complete list of sampling units within population not required  Low cost  Can estimate characteristics of both cluster and population  Less travel/resources required  Disadvantages  Potential problem is that cluster members are more likely to be alike, than those in another cluster (homogenous).  Each stage in cluster sampling introduces sampling error—the more stages there are, the more error there tends to be  Usually less expensive than SRS but not as accurate Cluster sampling (contd.)
  • 9.  A special form of cluster sampling called the “30 X 7 cluster sampling”, has been recommended by the WHO for field studies in assessing vaccination coverage.  In this a list of all villages (clusters) for a given geographical area is made.  30 clusters are selected using Probability Proportional to Size (PPS).  From each of the selected clusters, 7 subjects are randomly chosen.  Thus a total sample of 30 x 7 = 210 subjects is chosen.  The advantage of cluster sampling is that sampling frame is not required Cluster sampling (contd.)
  • 10. Multistage random sampling  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
  • 11. Multistage random sampling  In this method, the whole population is divided in first stage sampling units from which a random sample is selected.  The selected first stage is then subdivided into second stage units from which another sample is selected.  Third and fourth stage sampling is done in the same manner if necessary.  Example:  NFHS data is collected by multistage sampling.  Rural areas – 2 stage sampling – Villages from list by PPS, Households from village  Urban areas – Wards (PPS) – CEB (PPS) – 30 households from each CEB
  • 13. ADVANTAGES OF TWO-STAGE RANDOM SAMPLING:  Less time-consuming