SlideShare a Scribd company logo
1 of 27
COMPLEX SAMPLING
     Siti Haslinda Mohd Din
           Statistician
   Institute for Public Health
JUST A MINUTE
 One day some papers catch fire  in a wastebasket
 in the Dean’s office. Luckily, a physicist, a chemist
  and a statistician  happen to be nearby.
 Naturally, they are you to help.
         “What rush in doing????”
               the Dean demand
      The physicist whips out a notebook and starts
      to work on how much energy would have to be
      removed from the fire in order to stop the
      combustion.
Then a chemist  works statistician replies,
            To which the on determining
which solve a problem of this magnitude, you need a
  “To reagent would have to be added to
      LARGE SAMPLE SIZE.”
the fire to prevent oxidation.
           While they doing this, the statistician  is
           setting fires to all the other wastebaskets
           in the adjacent offices.
                      http://www.amstat.org/publications/ise/v10n3/friedman.html
Survey Sampling

• The subject of survey sampling is
  concern with the process of
  selecting members of the
  population to be included in the
  survey and the estimation.
• A sample design needs to be
  developed to meet the survey
  objectives.
Properties of complex sampling
A given complex sample can have some or all of the following features:



                         STRATIFICATION



                                    +
                                   CLUSTER



                                        +
                                   MULTISTAGE
Properties of complex sampling
Stratification
      - Selecting samples independently within non-
        overlapping subgroups of the population, or
        strata.
         For example,
             strata may be socioeconomic groups, job
             categories, age groups, or ethnic groups.
      - With stratification, you can ensure
         • adequate sample sizes for subgroups of
            interest,
         • improve the precision of overall
            estimates, and
         • use different sampling methods from
            stratum to stratum.
Properties of complex sampling

Clustering.

    • Involves the selection of groups of
      sampling units, or clusters.
        For example, clusters may be schools,
         hospitals, or geographical areas, and
         sampling units may be students, patients,
         or citizens.
    • Clustering is common in multistage
      designs and area (geographic) samples.
Properties of complex sampling
Multiple stages.
 •In multistage sampling,
    – a first-stage sample based on clusters.
    – a second-stage sample by drawing subsamples from
      the selected clusters.
    – If the second-stage sample is based on
      subclusters, then add a third stage to the sample.
           For example:
           • first stage of a survey, a sample of cities
           • from the selected cities, households could
             be sampled.
           • Finally, from the selected households,
             individuals could be polled.
Example : South Zone
        Johor               STRATIFIED
                             Negeri Sembilan                  Melaka



   STRATIFIED
  Urban    Rural                Urban     Rural
                                  STRATIFIED           Urban     Rural
                                                         STRATIFIED



                                           eb            eb

EB   EB      eb                 eb      eb   eb            eb      eb   eb
CLUSTER     CLUSTER       CLUSTER      CLUSTER       CLUSTER       CLUSTER
   EB       eb   eb        eb    eb                       eb       eb


                      Not selected                Selected enumeration
                      enumeration block           block
Sampling Weight
• Uniform in SRS but varies in unequal
  probabilities sampling
• Sampling weights are automatically
  computed while drawing a complex sample
  and ideally correspond to the “frequency”
  that each sampling unit represents in the
  target population. Therefore, the sum of
  the weights over the sample should
  estimate the population size.
Sampling Weight
• Used to compensate for
  – Unequal probabilities of selection
  – Nonresponse adjustment (a unit that fails to
    respond)
  – In post stratification to adjust weighted
    sample distribution for certain variables (eg
    age and sex) to make them conform to the
    known population distribution.

                To improved the precision of sample
                estimates and to compensate for
                noncoverage and nonresponse
Basic weighting approach
• Suppose sample element i was
  selected with probability ∏i.
• Then the sample element i
  represents 1/∏i elements in the
  population.
     W = 1/∏i
• Example : a sample element selected
  with probability 1/10 represents 10
  elements in the population
Weighting for Unequal
    Probabilities of Selection
• Consider an EPSEM (Equal Probability of
  Selection Method) sample of 6 household
  selected from 240 household. One adult is
  selected at random in each selected household.

    • The probability of selection of the βth adult is
       – P(αβ) = P(α).P(β|α)=f.1/Bα=1/wα
       – Which Bα = number of adults in household α
       if f=6/240 = 1/40 and Bα=3
       then P(αβ) = (1/40)X(1/3)=1/120
       Therefore each adults represents 120 adults
         from population; W=120
Non response
Sources of failure to obtain observations
(responses, measurements) on some elements
selected and designated for the sample;
                 •Not at homes
                 •Refusals
                 •Incapacitated or inability
                 •Not found
                 •Lost schedules

NR refer to eligible respondents and should exclude the
ineligibles but include vacant dwellings, household without
the specified kinds of population elements.

              NR rate computed for responses and
              nonresponses among the eligible only.
Disposition of the sample
   with components of                              Total Units
response and nonresponse                        (Initial Sample)
                                                        (1)



                                Resolved                                      Unresolved
                                   (2)                                            (3)



                                                                    Estimated Units      Estimated Units
             Units in Scope                    Units Out of Scope
                                                                       in Scope           Out of Scope
                   (4)                                 (5)
                                                                          (3A)                (3B)



  Respondents            Nonrespondents             Nonexistent Units   Response rate
      (6)                      (7)                         (8)          = [6]/([4]+[3A])

                                                    Units Temporarily
   Refusal Conversions           Refusals
           (11)                    (13)
                                                      Out of Scope       Non response rate
                                                           (9)           = ([7]+[3A])/([4]+[3A])
                                                    Units Permanently
   Other Respondents           Noncontacts
                                                      Out of Scope
          (12)                    (14)
                                                           (10)           Estimated Units in scope
                                                                          [3A] = [4]/[2]X[3]
                                   Other
                              Nonrespondents
                                    (15)
                                                                        Adapted from Hidiroglou et al (1993)
Weighting for Non response
• Compute weighted response rates in
  subgroups of the sample.
• Use the inverse of the subgroup response
  rates for non-response adjustment
• The weighted response rate=
   Weighted # completed interviews with eligible elements
   Weighted # eligible elements in sample
• Exclude empty dwellings, destroyed dwellings, addresses
  that are not dwellings and ineligible elements

           W2 = 1/ response rate
               = nh / nh’            nh = # of sample response
                                     nh’ = # of actual response
Total weight


    • W = W1 X W2
W1 = weight for unequal selection probabilities
W2 = weight for non-response
Weighting for Post Stratification

  • The weighted sample distribution conform
    to a known population distribution.
  • If known population of female of age 25-
    64 and stay in North area are 12,800,100
    where as total weighted sampled are
    11,325,553.
  • Therefore, the post stratification weight:
       W = 12,800,100/11,325,553
          = 1.13

            W3 =    # of population of specific category

                   # of weighted sampled of the specific category
Total weight


    • W = W1 X W2 X W3

W1 = weight for unequal selection probabilities
W2 = weight for non-response
W3 = weight for post stratification
Variance Estimation
• Linearization
  – Taylor Series approximation
     (Wolter 1985)
     • Best for simple statistics eg weighted mean
       (Frankel,1971)
• Replication (Resampling method)
  – Balanced Repeated Replication (BRR)
  – Jackknife estimation
     (Kish & Frankel 1974; Krewski and Rao 1981; Kovar, Rao
       and Wu 1988; Rao, Wu, and Yue 1992; Shao 1996)
     • Maximum-likelihood estimates (Brillinger, 1964)
     • Best for complex statistics like regression
       coefficients (Frankel,1971)
Available Software

•   Stata
•   SAS
•   SUDAAN (Research Triangle Institute)
•   WesVar
•   SPSS
•   NASSTIM&NASSVAR
•   etc
Comparison proportion of smoking
pregnant mother with years of schooling



 Years of schooling   Weighted proportion     Unweighted proportion

     < 12 years           0.315 ± 0.010            0.328 ± 0.007

      12 years            0.373 ± 0.012            0.332 ± 0.008

     > 12 years           0.202 ± 0.011            0.217± 0.008



         Data source : National Maternal and Child Survey 1988,US
Comparison of the highest prevalence
               by states and gender
                                                     Prevalens (%)
                       States             SPSS                       STATA
                                  Male           Female     Male         Female
                    Johor         26.97          29.62      25.39            28.75
                    Kedah         20.36          28.39      19.63            27.12
                    Kelantan      19.00          27.09      16.08            24.39
                    Melaka        29.06          34.84      29.67            33.99
                    N. Sembilan   30.99          34.56      28.40            34.18
                    Pahang        26.27          37.48      24.06            39.02
                    P. Pinang     24.81          28.80      24.40            27.09
Source : National   Perak         27.21          31.96      26.58            31.02
Health Morbidity
Survey 1996         Perlis        24.91          35.98      22.49            35.29
                    Selangor      25.65          28.66      25.26            26.73
                    Sarawak       21.46          26.73      17.41            28.18
                    Sabah         22.84          26.51      18.28            25.80
                    Terengganu    26.58          35.17      33.75            32.17
                    WPKL          30.80          29.94      30.29            29.39
The difference based on the highest
 prevalence of obesity among adults in Kedah
           by gender and ethnicity



                                                       Prevalens
               Gender Ethnic            S.E             (95% CI)
Without                                                 32.35
               Female   India          5.68         (21.22,43.48)
  weight
                                                        29.87
With weights   Female   Cina           4.54         (20.98,38.76)

                        Source : National Health Morbidity Survey 1996
Things to be considered if a design-
 based inference approach is chosen
• What is the nature of the sample design? Was is a
  stratified multistage sample design used? Was is a
  cluster sample design used? Were unequal prob. of
  selection applied?
   • Were there adjustments for nonresponse or coverage
     errors? Is there a weight or several weights that must
     be applied when different parts of the sample are
     analyzed?
       • Are there important measurement issues that
           could affect survey analyses? Is item
           nonresponse an important problem for some
           variables?
                 • How can the results be interpreted, and
                   what kind of inference are appropriate in
                   view of the complex survey design?
Steps required for performing
  a design-based analysis
Paul S. Levy and Stanley Lemeshow (1999)

              • Identify the following elements of the sample
                design:
                      • Stratification
                      • Clustering
                      • Population sizes required for determination of finite
                        population correction
              • Determine the sampling weight
              • Determine a final sampling weight; nonresponse, post
                stratification
              • Ensure data required for an appropriate design-
                based analysis
              • Determine the procedure and the set of commands
                for performing the required analysis
              • Run the analysis and carefully interpret findings
Further reading
•   C.J. Skinner, D.Holt, T.M.F.Smith, 1989, Analysis of Complex
    Surveys, New York: John Wiley and Sons.
•   P.S. Levy, S.Lemeshow. 1999, Sampling of Populations; Methods
    and Applications,, 3rd Ed.,John Wiley & Sons.
•   Cochran, W. G. 1977. Sampling Techniques. New York: John Wiley
    and Sons.
•   Kish, L. 1965. Survey Sampling. New York: John Wiley and Sons.
•   Kish, L. 1987. Statistical Design for Research. New York: John
    Wiley and Sons.
•   Murthy, M. N. 1967. Sampling Theory and Methods. Calcutta,
    India: Statistical Publishing Society.
•   E.L.Korn, B.I.Graubard. Examples of Differing Weighted and
    Unweighted Estimates From a Sample Survey, The American
    Statistician, Aug 1995, 49, No.3, 291-295.

             • E.S.Lee, R. N. Fourthofer, R.J. Lorimor. Analysis of
               Complex Sample Survey Data, Problem and Startegies,
               Sociological Methods & Research , Aug-Nov. 1986,15,69-
               100.
W. Hamilton, Cartoonist, 1995

More Related Content

What's hot

Sampling methods and sample size
Sampling methods and sample size  Sampling methods and sample size
Sampling methods and sample size mdanaee
 
Stochastic Process
Stochastic ProcessStochastic Process
Stochastic Processknksmart
 
Population census in india
Population census in indiaPopulation census in india
Population census in indiaDALAPATHI
 
Indirect standardisation biostatitics
Indirect standardisation biostatiticsIndirect standardisation biostatitics
Indirect standardisation biostatiticsRINSAVAHEED1
 
Mortality rates & standardization
Mortality rates &  standardizationMortality rates &  standardization
Mortality rates & standardizationVaishnavi Madhavan
 
Survey Surveillance Screening
Survey Surveillance Screening Survey Surveillance Screening
Survey Surveillance Screening MalihaQuader1
 
Tests of statistical significance : chi square and spss
Tests of statistical significance : chi square and spss Tests of statistical significance : chi square and spss
Tests of statistical significance : chi square and spss Drsnehas2
 
Measurements of trends
Measurements of trendsMeasurements of trends
Measurements of trendsKuriakose T D
 
Odds ratio and confidence interval
Odds ratio and confidence intervalOdds ratio and confidence interval
Odds ratio and confidence intervalUttamaTungkhang
 
Population Studies / Demography Introduction
Population Studies / Demography IntroductionPopulation Studies / Demography Introduction
Population Studies / Demography IntroductionMuteeullah
 

What's hot (20)

Sampling methods and sample size
Sampling methods and sample size  Sampling methods and sample size
Sampling methods and sample size
 
Field and Community Trials
Field and Community Trials Field and Community Trials
Field and Community Trials
 
Sample Size Determination
Sample Size DeterminationSample Size Determination
Sample Size Determination
 
Stochastic Process
Stochastic ProcessStochastic Process
Stochastic Process
 
Introduction To Statistics
Introduction To StatisticsIntroduction To Statistics
Introduction To Statistics
 
Binary Logistic Regression
Binary Logistic RegressionBinary Logistic Regression
Binary Logistic Regression
 
Measures of disease burden
Measures of disease burdenMeasures of disease burden
Measures of disease burden
 
Population census in india
Population census in indiaPopulation census in india
Population census in india
 
Indirect standardisation biostatitics
Indirect standardisation biostatiticsIndirect standardisation biostatitics
Indirect standardisation biostatitics
 
Chi – square test
Chi – square testChi – square test
Chi – square test
 
Mortality rates & standardization
Mortality rates &  standardizationMortality rates &  standardization
Mortality rates & standardization
 
Survey Surveillance Screening
Survey Surveillance Screening Survey Surveillance Screening
Survey Surveillance Screening
 
Tests of statistical significance : chi square and spss
Tests of statistical significance : chi square and spss Tests of statistical significance : chi square and spss
Tests of statistical significance : chi square and spss
 
Biostatistics
BiostatisticsBiostatistics
Biostatistics
 
STATISTIC ESTIMATION
STATISTIC ESTIMATIONSTATISTIC ESTIMATION
STATISTIC ESTIMATION
 
Measurements of trends
Measurements of trendsMeasurements of trends
Measurements of trends
 
Odds ratio and confidence interval
Odds ratio and confidence intervalOdds ratio and confidence interval
Odds ratio and confidence interval
 
Population Studies / Demography Introduction
Population Studies / Demography IntroductionPopulation Studies / Demography Introduction
Population Studies / Demography Introduction
 
Direct standardisation ppt
Direct standardisation pptDirect standardisation ppt
Direct standardisation ppt
 
Disability Adjusted Life Years
Disability Adjusted Life YearsDisability Adjusted Life Years
Disability Adjusted Life Years
 

Viewers also liked

Difficulty Index, Discrimination Index, Reliability and Rasch Measurement Ana...
Difficulty Index, Discrimination Index, Reliability and Rasch Measurement Ana...Difficulty Index, Discrimination Index, Reliability and Rasch Measurement Ana...
Difficulty Index, Discrimination Index, Reliability and Rasch Measurement Ana...Azmi Mohd Tamil
 
How to run Pearson's Chi-square for SPSS
How to run Pearson's Chi-square for SPSSHow to run Pearson's Chi-square for SPSS
How to run Pearson's Chi-square for SPSSAzmi Mohd Tamil
 
How to run ANOVA on SPSS
How to run ANOVA on SPSSHow to run ANOVA on SPSS
How to run ANOVA on SPSSAzmi Mohd Tamil
 
How to run Student's t-test on SPSS
How to run Student's t-test on SPSSHow to run Student's t-test on SPSS
How to run Student's t-test on SPSSAzmi Mohd Tamil
 
Introduction to spss: define variables
Introduction to spss: define variablesIntroduction to spss: define variables
Introduction to spss: define variablesAzmi Mohd Tamil
 
How to draw Scatter plot on SPSS
How to draw Scatter plot on SPSSHow to draw Scatter plot on SPSS
How to draw Scatter plot on SPSSAzmi Mohd Tamil
 
Running Pearson's Correlation on SPSS
Running Pearson's Correlation on SPSSRunning Pearson's Correlation on SPSS
Running Pearson's Correlation on SPSSAzmi Mohd Tamil
 
How to run Simple Linear Regression on SPSS
How to run Simple Linear Regression on SPSSHow to run Simple Linear Regression on SPSS
How to run Simple Linear Regression on SPSSAzmi Mohd Tamil
 

Viewers also liked (8)

Difficulty Index, Discrimination Index, Reliability and Rasch Measurement Ana...
Difficulty Index, Discrimination Index, Reliability and Rasch Measurement Ana...Difficulty Index, Discrimination Index, Reliability and Rasch Measurement Ana...
Difficulty Index, Discrimination Index, Reliability and Rasch Measurement Ana...
 
How to run Pearson's Chi-square for SPSS
How to run Pearson's Chi-square for SPSSHow to run Pearson's Chi-square for SPSS
How to run Pearson's Chi-square for SPSS
 
How to run ANOVA on SPSS
How to run ANOVA on SPSSHow to run ANOVA on SPSS
How to run ANOVA on SPSS
 
How to run Student's t-test on SPSS
How to run Student's t-test on SPSSHow to run Student's t-test on SPSS
How to run Student's t-test on SPSS
 
Introduction to spss: define variables
Introduction to spss: define variablesIntroduction to spss: define variables
Introduction to spss: define variables
 
How to draw Scatter plot on SPSS
How to draw Scatter plot on SPSSHow to draw Scatter plot on SPSS
How to draw Scatter plot on SPSS
 
Running Pearson's Correlation on SPSS
Running Pearson's Correlation on SPSSRunning Pearson's Correlation on SPSS
Running Pearson's Correlation on SPSS
 
How to run Simple Linear Regression on SPSS
How to run Simple Linear Regression on SPSSHow to run Simple Linear Regression on SPSS
How to run Simple Linear Regression on SPSS
 

Similar to Weightage & Complex Sampling

Introduction to sampling
Introduction to samplingIntroduction to sampling
Introduction to samplingSituo Liu
 
Sampling techniques
Sampling techniquesSampling techniques
Sampling techniquesMukut Deori
 
Biostats Lec-2.pdf
Biostats Lec-2.pdfBiostats Lec-2.pdf
Biostats Lec-2.pdfPratikPhate2
 
Sampling Techniques.pptx
Sampling Techniques.pptxSampling Techniques.pptx
Sampling Techniques.pptxMostaque Ahmed
 
5. sampling design
5. sampling design5. sampling design
5. sampling designkbhupadhoj
 
Sampling design, sampling errors, sample size determination
Sampling design, sampling errors, sample size determinationSampling design, sampling errors, sample size determination
Sampling design, sampling errors, sample size determinationVishnupriya T H
 
Sampling by Mr Peng Kungkea
Sampling  by Mr Peng KungkeaSampling  by Mr Peng Kungkea
Sampling by Mr Peng KungkeaKungkea Peng
 
Experimental Design
Experimental  DesignExperimental  Design
Experimental DesignGreenAvatar
 
SamplingBigSlides.pdf
SamplingBigSlides.pdfSamplingBigSlides.pdf
SamplingBigSlides.pdfifuchfuhg
 
Understanding randomisation
Understanding randomisationUnderstanding randomisation
Understanding randomisationStephen Senn
 
Sampling Techniques, Scaling Techniques and Questionnaire Frame
Sampling Techniques, Scaling Techniques and Questionnaire FrameSampling Techniques, Scaling Techniques and Questionnaire Frame
Sampling Techniques, Scaling Techniques and Questionnaire FrameSonnappan Sridhar
 
Sampling design-WPS Office.pdf
Sampling design-WPS Office.pdfSampling design-WPS Office.pdf
Sampling design-WPS Office.pdfMuthuLakshmi124949
 

Similar to Weightage & Complex Sampling (20)

Sampling slides
Sampling slidesSampling slides
Sampling slides
 
Sampling bigslides
Sampling bigslidesSampling bigslides
Sampling bigslides
 
Introduction to sampling
Introduction to samplingIntroduction to sampling
Introduction to sampling
 
Sampling techniques
Sampling techniquesSampling techniques
Sampling techniques
 
Biostats Lec-2.pdf
Biostats Lec-2.pdfBiostats Lec-2.pdf
Biostats Lec-2.pdf
 
Sampling Techniques.pptx
Sampling Techniques.pptxSampling Techniques.pptx
Sampling Techniques.pptx
 
5. sampling design
5. sampling design5. sampling design
5. sampling design
 
Sampling design, sampling errors, sample size determination
Sampling design, sampling errors, sample size determinationSampling design, sampling errors, sample size determination
Sampling design, sampling errors, sample size determination
 
Sampling by Mr Peng Kungkea
Sampling  by Mr Peng KungkeaSampling  by Mr Peng Kungkea
Sampling by Mr Peng Kungkea
 
Experimental Design
Experimental  DesignExperimental  Design
Experimental Design
 
Sampling....
Sampling....Sampling....
Sampling....
 
SamplingBigSlides.pdf
SamplingBigSlides.pdfSamplingBigSlides.pdf
SamplingBigSlides.pdf
 
samplingdesignppt.pdf
samplingdesignppt.pdfsamplingdesignppt.pdf
samplingdesignppt.pdf
 
Sampling design ppt
Sampling design pptSampling design ppt
Sampling design ppt
 
SAMPLING-PROCEDURE.pdf
SAMPLING-PROCEDURE.pdfSAMPLING-PROCEDURE.pdf
SAMPLING-PROCEDURE.pdf
 
Understanding randomisation
Understanding randomisationUnderstanding randomisation
Understanding randomisation
 
Sampling Techniques, Scaling Techniques and Questionnaire Frame
Sampling Techniques, Scaling Techniques and Questionnaire FrameSampling Techniques, Scaling Techniques and Questionnaire Frame
Sampling Techniques, Scaling Techniques and Questionnaire Frame
 
Statistics - Basics
Statistics - BasicsStatistics - Basics
Statistics - Basics
 
4. Sampling.pptx
4. Sampling.pptx4. Sampling.pptx
4. Sampling.pptx
 
Sampling design-WPS Office.pdf
Sampling design-WPS Office.pdfSampling design-WPS Office.pdf
Sampling design-WPS Office.pdf
 

More from Azmi Mohd Tamil

Hybrid setup - How to conduct simultaneous face-to-face and online presentati...
Hybrid setup - How to conduct simultaneous face-to-face and online presentati...Hybrid setup - How to conduct simultaneous face-to-face and online presentati...
Hybrid setup - How to conduct simultaneous face-to-face and online presentati...Azmi Mohd Tamil
 
Audiovisual and technicalities from preparation to retrieval how to enhance m...
Audiovisual and technicalities from preparation to retrieval how to enhance m...Audiovisual and technicalities from preparation to retrieval how to enhance m...
Audiovisual and technicalities from preparation to retrieval how to enhance m...Azmi Mohd Tamil
 
Broadcast quality online teaching at zero budget
Broadcast quality online teaching at zero budgetBroadcast quality online teaching at zero budget
Broadcast quality online teaching at zero budgetAzmi Mohd Tamil
 
Video for Teaching & Learning: OBS
Video for Teaching & Learning: OBSVideo for Teaching & Learning: OBS
Video for Teaching & Learning: OBSAzmi Mohd Tamil
 
Bengkel 21-12-2020 - Etika atas Talian & Alat Minima
Bengkel 21-12-2020 - Etika atas Talian & Alat MinimaBengkel 21-12-2020 - Etika atas Talian & Alat Minima
Bengkel 21-12-2020 - Etika atas Talian & Alat MinimaAzmi Mohd Tamil
 
GIS & History of Mapping in Malaya (lecture notes circa 2009)
GIS & History of Mapping in Malaya (lecture notes circa 2009)GIS & History of Mapping in Malaya (lecture notes circa 2009)
GIS & History of Mapping in Malaya (lecture notes circa 2009)Azmi Mohd Tamil
 
Blended e-learning in UKMFolio
Blended e-learning in UKMFolioBlended e-learning in UKMFolio
Blended e-learning in UKMFolioAzmi Mohd Tamil
 
How to Compute & Recode SPSS Data
How to Compute & Recode SPSS DataHow to Compute & Recode SPSS Data
How to Compute & Recode SPSS DataAzmi Mohd Tamil
 
Introduction to Data Analysis With R and R Studio
Introduction to Data Analysis With R and R StudioIntroduction to Data Analysis With R and R Studio
Introduction to Data Analysis With R and R StudioAzmi Mohd Tamil
 
Hack#38 - How to Stream Zoom to Facebook & YouTube Without Using An Encoder o...
Hack#38 - How to Stream Zoom to Facebook & YouTube Without Using An Encoder o...Hack#38 - How to Stream Zoom to Facebook & YouTube Without Using An Encoder o...
Hack#38 - How to Stream Zoom to Facebook & YouTube Without Using An Encoder o...Azmi Mohd Tamil
 
Hack#37 - How to simultaneously live stream to 4 sites using a single hardwar...
Hack#37 - How to simultaneously live stream to 4 sites using a single hardwar...Hack#37 - How to simultaneously live stream to 4 sites using a single hardwar...
Hack#37 - How to simultaneously live stream to 4 sites using a single hardwar...Azmi Mohd Tamil
 
Cochran Mantel Haenszel Test with Breslow-Day Test & Quadratic Equation
Cochran Mantel Haenszel Test with Breslow-Day Test & Quadratic EquationCochran Mantel Haenszel Test with Breslow-Day Test & Quadratic Equation
Cochran Mantel Haenszel Test with Breslow-Day Test & Quadratic EquationAzmi Mohd Tamil
 
New Emerging And Reemerging Infections circa 2006
New Emerging And Reemerging Infections circa 2006New Emerging And Reemerging Infections circa 2006
New Emerging And Reemerging Infections circa 2006Azmi Mohd Tamil
 
Hacks#36 -Raspberry Pi 4 Mini Computer
Hacks#36 -Raspberry Pi 4 Mini ComputerHacks#36 -Raspberry Pi 4 Mini Computer
Hacks#36 -Raspberry Pi 4 Mini ComputerAzmi Mohd Tamil
 
Hack#35 How to FB Live using a Video Encoder
Hack#35 How to FB Live using a Video EncoderHack#35 How to FB Live using a Video Encoder
Hack#35 How to FB Live using a Video EncoderAzmi Mohd Tamil
 
Hack#34 - Online Teaching with Microsoft Teams
Hack#34 - Online Teaching with Microsoft TeamsHack#34 - Online Teaching with Microsoft Teams
Hack#34 - Online Teaching with Microsoft TeamsAzmi Mohd Tamil
 
Skype for Business for UKM
Skype for Business for UKM Skype for Business for UKM
Skype for Business for UKM Azmi Mohd Tamil
 
Introduction to Structural Equation Modeling
Introduction to Structural Equation ModelingIntroduction to Structural Equation Modeling
Introduction to Structural Equation ModelingAzmi Mohd Tamil
 
Safe computing (circa 2004)
Safe computing (circa 2004)Safe computing (circa 2004)
Safe computing (circa 2004)Azmi Mohd Tamil
 

More from Azmi Mohd Tamil (20)

Hybrid setup - How to conduct simultaneous face-to-face and online presentati...
Hybrid setup - How to conduct simultaneous face-to-face and online presentati...Hybrid setup - How to conduct simultaneous face-to-face and online presentati...
Hybrid setup - How to conduct simultaneous face-to-face and online presentati...
 
Audiovisual and technicalities from preparation to retrieval how to enhance m...
Audiovisual and technicalities from preparation to retrieval how to enhance m...Audiovisual and technicalities from preparation to retrieval how to enhance m...
Audiovisual and technicalities from preparation to retrieval how to enhance m...
 
Broadcast quality online teaching at zero budget
Broadcast quality online teaching at zero budgetBroadcast quality online teaching at zero budget
Broadcast quality online teaching at zero budget
 
Video for Teaching & Learning: OBS
Video for Teaching & Learning: OBSVideo for Teaching & Learning: OBS
Video for Teaching & Learning: OBS
 
Bengkel 21-12-2020 - Etika atas Talian & Alat Minima
Bengkel 21-12-2020 - Etika atas Talian & Alat MinimaBengkel 21-12-2020 - Etika atas Talian & Alat Minima
Bengkel 21-12-2020 - Etika atas Talian & Alat Minima
 
GIS & History of Mapping in Malaya (lecture notes circa 2009)
GIS & History of Mapping in Malaya (lecture notes circa 2009)GIS & History of Mapping in Malaya (lecture notes circa 2009)
GIS & History of Mapping in Malaya (lecture notes circa 2009)
 
Blended e-learning in UKMFolio
Blended e-learning in UKMFolioBlended e-learning in UKMFolio
Blended e-learning in UKMFolio
 
How to Compute & Recode SPSS Data
How to Compute & Recode SPSS DataHow to Compute & Recode SPSS Data
How to Compute & Recode SPSS Data
 
Introduction to Data Analysis With R and R Studio
Introduction to Data Analysis With R and R StudioIntroduction to Data Analysis With R and R Studio
Introduction to Data Analysis With R and R Studio
 
Hack#38 - How to Stream Zoom to Facebook & YouTube Without Using An Encoder o...
Hack#38 - How to Stream Zoom to Facebook & YouTube Without Using An Encoder o...Hack#38 - How to Stream Zoom to Facebook & YouTube Without Using An Encoder o...
Hack#38 - How to Stream Zoom to Facebook & YouTube Without Using An Encoder o...
 
Hack#37 - How to simultaneously live stream to 4 sites using a single hardwar...
Hack#37 - How to simultaneously live stream to 4 sites using a single hardwar...Hack#37 - How to simultaneously live stream to 4 sites using a single hardwar...
Hack#37 - How to simultaneously live stream to 4 sites using a single hardwar...
 
Cochran Mantel Haenszel Test with Breslow-Day Test & Quadratic Equation
Cochran Mantel Haenszel Test with Breslow-Day Test & Quadratic EquationCochran Mantel Haenszel Test with Breslow-Day Test & Quadratic Equation
Cochran Mantel Haenszel Test with Breslow-Day Test & Quadratic Equation
 
New Emerging And Reemerging Infections circa 2006
New Emerging And Reemerging Infections circa 2006New Emerging And Reemerging Infections circa 2006
New Emerging And Reemerging Infections circa 2006
 
Hacks#36 -Raspberry Pi 4 Mini Computer
Hacks#36 -Raspberry Pi 4 Mini ComputerHacks#36 -Raspberry Pi 4 Mini Computer
Hacks#36 -Raspberry Pi 4 Mini Computer
 
Hack#35 How to FB Live using a Video Encoder
Hack#35 How to FB Live using a Video EncoderHack#35 How to FB Live using a Video Encoder
Hack#35 How to FB Live using a Video Encoder
 
Hack#34 - Online Teaching with Microsoft Teams
Hack#34 - Online Teaching with Microsoft TeamsHack#34 - Online Teaching with Microsoft Teams
Hack#34 - Online Teaching with Microsoft Teams
 
Hack#33 How To FB-Live
Hack#33 How To FB-LiveHack#33 How To FB-Live
Hack#33 How To FB-Live
 
Skype for Business for UKM
Skype for Business for UKM Skype for Business for UKM
Skype for Business for UKM
 
Introduction to Structural Equation Modeling
Introduction to Structural Equation ModelingIntroduction to Structural Equation Modeling
Introduction to Structural Equation Modeling
 
Safe computing (circa 2004)
Safe computing (circa 2004)Safe computing (circa 2004)
Safe computing (circa 2004)
 

Recently uploaded

EXERCISE PERFORMANCE.pptx, Lung function
EXERCISE PERFORMANCE.pptx, Lung functionEXERCISE PERFORMANCE.pptx, Lung function
EXERCISE PERFORMANCE.pptx, Lung functionkrishnareddy157915
 
Generative AI in Health Care a scoping review and a persoanl experience.
Generative AI in Health Care a scoping review and a persoanl experience.Generative AI in Health Care a scoping review and a persoanl experience.
Generative AI in Health Care a scoping review and a persoanl experience.Vaikunthan Rajaratnam
 
DNA nucleotides Blast in NCBI and Phylogeny using MEGA Xi.pptx
DNA nucleotides Blast in NCBI and Phylogeny using MEGA Xi.pptxDNA nucleotides Blast in NCBI and Phylogeny using MEGA Xi.pptx
DNA nucleotides Blast in NCBI and Phylogeny using MEGA Xi.pptxMAsifAhmad
 
Physiology of Smooth Muscles -Mechanics of contraction and relaxation
Physiology of Smooth Muscles -Mechanics of contraction and relaxationPhysiology of Smooth Muscles -Mechanics of contraction and relaxation
Physiology of Smooth Muscles -Mechanics of contraction and relaxationMedicoseAcademics
 
CONNECTIVE TISSUE (ANATOMY AND PHYSIOLOGY).pdf
CONNECTIVE TISSUE (ANATOMY AND PHYSIOLOGY).pdfCONNECTIVE TISSUE (ANATOMY AND PHYSIOLOGY).pdf
CONNECTIVE TISSUE (ANATOMY AND PHYSIOLOGY).pdfDolisha Warbi
 
Pharmacokinetic Models by Dr. Ram D. Bawankar.ppt
Pharmacokinetic Models by Dr. Ram D.  Bawankar.pptPharmacokinetic Models by Dr. Ram D.  Bawankar.ppt
Pharmacokinetic Models by Dr. Ram D. Bawankar.pptRamDBawankar1
 
BENIGN BREAST DISEASE
BENIGN BREAST DISEASE BENIGN BREAST DISEASE
BENIGN BREAST DISEASE Mamatha Lakka
 
CPR.nursingoutlook.pdf , Bsc nursing student
CPR.nursingoutlook.pdf , Bsc nursing studentCPR.nursingoutlook.pdf , Bsc nursing student
CPR.nursingoutlook.pdf , Bsc nursing studentsaileshpanda05
 
Using Data Visualization in Public Health Communications
Using Data Visualization in Public Health CommunicationsUsing Data Visualization in Public Health Communications
Using Data Visualization in Public Health Communicationskatiequigley33
 
Physiotherapy Management of Rheumatoid Arthritis
Physiotherapy Management of Rheumatoid ArthritisPhysiotherapy Management of Rheumatoid Arthritis
Physiotherapy Management of Rheumatoid ArthritisNilofarRasheed1
 
ANATOMICAL FAETURES OF BONES FOR NURSING STUDENTS .pptx
ANATOMICAL FAETURES OF BONES  FOR NURSING STUDENTS .pptxANATOMICAL FAETURES OF BONES  FOR NURSING STUDENTS .pptx
ANATOMICAL FAETURES OF BONES FOR NURSING STUDENTS .pptxWINCY THIRUMURUGAN
 
SGK RỐI LOẠN TOAN KIỀM ĐHYHN RẤT HAY VÀ ĐẶC SẮC.pdf
SGK RỐI LOẠN TOAN KIỀM ĐHYHN RẤT HAY VÀ ĐẶC SẮC.pdfSGK RỐI LOẠN TOAN KIỀM ĐHYHN RẤT HAY VÀ ĐẶC SẮC.pdf
SGK RỐI LOẠN TOAN KIỀM ĐHYHN RẤT HAY VÀ ĐẶC SẮC.pdfHongBiThi1
 
concept of total quality management (TQM).
concept of total quality management (TQM).concept of total quality management (TQM).
concept of total quality management (TQM).kishan singh tomar
 
Basic structure of hair and hair growth cycle.pptx
Basic structure of hair and hair growth cycle.pptxBasic structure of hair and hair growth cycle.pptx
Basic structure of hair and hair growth cycle.pptxkomalt2001
 
introduction to neurology (nervous system, areas, motor and sensory systems)
introduction to neurology (nervous system, areas, motor and sensory systems)introduction to neurology (nervous system, areas, motor and sensory systems)
introduction to neurology (nervous system, areas, motor and sensory systems)Mohamed Rizk Khodair
 
Breast cancer -ONCO IN MEDICAL AND SURGICAL NURSING.pptx
Breast cancer -ONCO IN MEDICAL AND SURGICAL NURSING.pptxBreast cancer -ONCO IN MEDICAL AND SURGICAL NURSING.pptx
Breast cancer -ONCO IN MEDICAL AND SURGICAL NURSING.pptxNaveenkumar267201
 
Neurological history taking (2024) .
Neurological  history  taking  (2024)  .Neurological  history  taking  (2024)  .
Neurological history taking (2024) .Mohamed Rizk Khodair
 
Role of Soap based and synthetic or syndets bar
Role of  Soap based and synthetic or syndets barRole of  Soap based and synthetic or syndets bar
Role of Soap based and synthetic or syndets barmohitRahangdale
 
Bulimia nervosa ( Eating Disorders) Mental Health Nursing.
Bulimia nervosa ( Eating Disorders) Mental Health Nursing.Bulimia nervosa ( Eating Disorders) Mental Health Nursing.
Bulimia nervosa ( Eating Disorders) Mental Health Nursing.aarjukhadka22
 
MedMatch: Your Health, Our Mission. Pitch deck.
MedMatch: Your Health, Our Mission. Pitch deck.MedMatch: Your Health, Our Mission. Pitch deck.
MedMatch: Your Health, Our Mission. Pitch deck.whalesdesign
 

Recently uploaded (20)

EXERCISE PERFORMANCE.pptx, Lung function
EXERCISE PERFORMANCE.pptx, Lung functionEXERCISE PERFORMANCE.pptx, Lung function
EXERCISE PERFORMANCE.pptx, Lung function
 
Generative AI in Health Care a scoping review and a persoanl experience.
Generative AI in Health Care a scoping review and a persoanl experience.Generative AI in Health Care a scoping review and a persoanl experience.
Generative AI in Health Care a scoping review and a persoanl experience.
 
DNA nucleotides Blast in NCBI and Phylogeny using MEGA Xi.pptx
DNA nucleotides Blast in NCBI and Phylogeny using MEGA Xi.pptxDNA nucleotides Blast in NCBI and Phylogeny using MEGA Xi.pptx
DNA nucleotides Blast in NCBI and Phylogeny using MEGA Xi.pptx
 
Physiology of Smooth Muscles -Mechanics of contraction and relaxation
Physiology of Smooth Muscles -Mechanics of contraction and relaxationPhysiology of Smooth Muscles -Mechanics of contraction and relaxation
Physiology of Smooth Muscles -Mechanics of contraction and relaxation
 
CONNECTIVE TISSUE (ANATOMY AND PHYSIOLOGY).pdf
CONNECTIVE TISSUE (ANATOMY AND PHYSIOLOGY).pdfCONNECTIVE TISSUE (ANATOMY AND PHYSIOLOGY).pdf
CONNECTIVE TISSUE (ANATOMY AND PHYSIOLOGY).pdf
 
Pharmacokinetic Models by Dr. Ram D. Bawankar.ppt
Pharmacokinetic Models by Dr. Ram D.  Bawankar.pptPharmacokinetic Models by Dr. Ram D.  Bawankar.ppt
Pharmacokinetic Models by Dr. Ram D. Bawankar.ppt
 
BENIGN BREAST DISEASE
BENIGN BREAST DISEASE BENIGN BREAST DISEASE
BENIGN BREAST DISEASE
 
CPR.nursingoutlook.pdf , Bsc nursing student
CPR.nursingoutlook.pdf , Bsc nursing studentCPR.nursingoutlook.pdf , Bsc nursing student
CPR.nursingoutlook.pdf , Bsc nursing student
 
Using Data Visualization in Public Health Communications
Using Data Visualization in Public Health CommunicationsUsing Data Visualization in Public Health Communications
Using Data Visualization in Public Health Communications
 
Physiotherapy Management of Rheumatoid Arthritis
Physiotherapy Management of Rheumatoid ArthritisPhysiotherapy Management of Rheumatoid Arthritis
Physiotherapy Management of Rheumatoid Arthritis
 
ANATOMICAL FAETURES OF BONES FOR NURSING STUDENTS .pptx
ANATOMICAL FAETURES OF BONES  FOR NURSING STUDENTS .pptxANATOMICAL FAETURES OF BONES  FOR NURSING STUDENTS .pptx
ANATOMICAL FAETURES OF BONES FOR NURSING STUDENTS .pptx
 
SGK RỐI LOẠN TOAN KIỀM ĐHYHN RẤT HAY VÀ ĐẶC SẮC.pdf
SGK RỐI LOẠN TOAN KIỀM ĐHYHN RẤT HAY VÀ ĐẶC SẮC.pdfSGK RỐI LOẠN TOAN KIỀM ĐHYHN RẤT HAY VÀ ĐẶC SẮC.pdf
SGK RỐI LOẠN TOAN KIỀM ĐHYHN RẤT HAY VÀ ĐẶC SẮC.pdf
 
concept of total quality management (TQM).
concept of total quality management (TQM).concept of total quality management (TQM).
concept of total quality management (TQM).
 
Basic structure of hair and hair growth cycle.pptx
Basic structure of hair and hair growth cycle.pptxBasic structure of hair and hair growth cycle.pptx
Basic structure of hair and hair growth cycle.pptx
 
introduction to neurology (nervous system, areas, motor and sensory systems)
introduction to neurology (nervous system, areas, motor and sensory systems)introduction to neurology (nervous system, areas, motor and sensory systems)
introduction to neurology (nervous system, areas, motor and sensory systems)
 
Breast cancer -ONCO IN MEDICAL AND SURGICAL NURSING.pptx
Breast cancer -ONCO IN MEDICAL AND SURGICAL NURSING.pptxBreast cancer -ONCO IN MEDICAL AND SURGICAL NURSING.pptx
Breast cancer -ONCO IN MEDICAL AND SURGICAL NURSING.pptx
 
Neurological history taking (2024) .
Neurological  history  taking  (2024)  .Neurological  history  taking  (2024)  .
Neurological history taking (2024) .
 
Role of Soap based and synthetic or syndets bar
Role of  Soap based and synthetic or syndets barRole of  Soap based and synthetic or syndets bar
Role of Soap based and synthetic or syndets bar
 
Bulimia nervosa ( Eating Disorders) Mental Health Nursing.
Bulimia nervosa ( Eating Disorders) Mental Health Nursing.Bulimia nervosa ( Eating Disorders) Mental Health Nursing.
Bulimia nervosa ( Eating Disorders) Mental Health Nursing.
 
MedMatch: Your Health, Our Mission. Pitch deck.
MedMatch: Your Health, Our Mission. Pitch deck.MedMatch: Your Health, Our Mission. Pitch deck.
MedMatch: Your Health, Our Mission. Pitch deck.
 

Weightage & Complex Sampling

  • 1. COMPLEX SAMPLING Siti Haslinda Mohd Din Statistician Institute for Public Health
  • 2. JUST A MINUTE One day some papers catch fire  in a wastebasket in the Dean’s office. Luckily, a physicist, a chemist  and a statistician  happen to be nearby. Naturally, they are you to help. “What rush in doing????” the Dean demand The physicist whips out a notebook and starts to work on how much energy would have to be removed from the fire in order to stop the combustion. Then a chemist  works statistician replies, To which the on determining which solve a problem of this magnitude, you need a “To reagent would have to be added to LARGE SAMPLE SIZE.” the fire to prevent oxidation. While they doing this, the statistician  is setting fires to all the other wastebaskets in the adjacent offices. http://www.amstat.org/publications/ise/v10n3/friedman.html
  • 3. Survey Sampling • The subject of survey sampling is concern with the process of selecting members of the population to be included in the survey and the estimation. • A sample design needs to be developed to meet the survey objectives.
  • 4. Properties of complex sampling A given complex sample can have some or all of the following features: STRATIFICATION + CLUSTER + MULTISTAGE
  • 5. Properties of complex sampling Stratification - Selecting samples independently within non- overlapping subgroups of the population, or strata. For example, strata may be socioeconomic groups, job categories, age groups, or ethnic groups. - With stratification, you can ensure • adequate sample sizes for subgroups of interest, • improve the precision of overall estimates, and • use different sampling methods from stratum to stratum.
  • 6. Properties of complex sampling Clustering. • Involves the selection of groups of sampling units, or clusters. For example, clusters may be schools, hospitals, or geographical areas, and sampling units may be students, patients, or citizens. • Clustering is common in multistage designs and area (geographic) samples.
  • 7. Properties of complex sampling Multiple stages. •In multistage sampling, – a first-stage sample based on clusters. – a second-stage sample by drawing subsamples from the selected clusters. – If the second-stage sample is based on subclusters, then add a third stage to the sample. For example: • first stage of a survey, a sample of cities • from the selected cities, households could be sampled. • Finally, from the selected households, individuals could be polled.
  • 8. Example : South Zone Johor STRATIFIED Negeri Sembilan Melaka STRATIFIED Urban Rural Urban Rural STRATIFIED Urban Rural STRATIFIED eb eb EB EB eb eb eb eb eb eb eb CLUSTER CLUSTER CLUSTER CLUSTER CLUSTER CLUSTER EB eb eb eb eb eb eb Not selected Selected enumeration enumeration block block
  • 9. Sampling Weight • Uniform in SRS but varies in unequal probabilities sampling • Sampling weights are automatically computed while drawing a complex sample and ideally correspond to the “frequency” that each sampling unit represents in the target population. Therefore, the sum of the weights over the sample should estimate the population size.
  • 10. Sampling Weight • Used to compensate for – Unequal probabilities of selection – Nonresponse adjustment (a unit that fails to respond) – In post stratification to adjust weighted sample distribution for certain variables (eg age and sex) to make them conform to the known population distribution. To improved the precision of sample estimates and to compensate for noncoverage and nonresponse
  • 11. Basic weighting approach • Suppose sample element i was selected with probability ∏i. • Then the sample element i represents 1/∏i elements in the population. W = 1/∏i • Example : a sample element selected with probability 1/10 represents 10 elements in the population
  • 12. Weighting for Unequal Probabilities of Selection • Consider an EPSEM (Equal Probability of Selection Method) sample of 6 household selected from 240 household. One adult is selected at random in each selected household. • The probability of selection of the βth adult is – P(αβ) = P(α).P(β|α)=f.1/Bα=1/wα – Which Bα = number of adults in household α if f=6/240 = 1/40 and Bα=3 then P(αβ) = (1/40)X(1/3)=1/120 Therefore each adults represents 120 adults from population; W=120
  • 13. Non response Sources of failure to obtain observations (responses, measurements) on some elements selected and designated for the sample; •Not at homes •Refusals •Incapacitated or inability •Not found •Lost schedules NR refer to eligible respondents and should exclude the ineligibles but include vacant dwellings, household without the specified kinds of population elements. NR rate computed for responses and nonresponses among the eligible only.
  • 14. Disposition of the sample with components of Total Units response and nonresponse (Initial Sample) (1) Resolved Unresolved (2) (3) Estimated Units Estimated Units Units in Scope Units Out of Scope in Scope Out of Scope (4) (5) (3A) (3B) Respondents Nonrespondents Nonexistent Units Response rate (6) (7) (8) = [6]/([4]+[3A]) Units Temporarily Refusal Conversions Refusals (11) (13) Out of Scope Non response rate (9) = ([7]+[3A])/([4]+[3A]) Units Permanently Other Respondents Noncontacts Out of Scope (12) (14) (10) Estimated Units in scope [3A] = [4]/[2]X[3] Other Nonrespondents (15) Adapted from Hidiroglou et al (1993)
  • 15. Weighting for Non response • Compute weighted response rates in subgroups of the sample. • Use the inverse of the subgroup response rates for non-response adjustment • The weighted response rate= Weighted # completed interviews with eligible elements Weighted # eligible elements in sample • Exclude empty dwellings, destroyed dwellings, addresses that are not dwellings and ineligible elements W2 = 1/ response rate = nh / nh’ nh = # of sample response nh’ = # of actual response
  • 16. Total weight • W = W1 X W2 W1 = weight for unequal selection probabilities W2 = weight for non-response
  • 17. Weighting for Post Stratification • The weighted sample distribution conform to a known population distribution. • If known population of female of age 25- 64 and stay in North area are 12,800,100 where as total weighted sampled are 11,325,553. • Therefore, the post stratification weight: W = 12,800,100/11,325,553 = 1.13 W3 = # of population of specific category # of weighted sampled of the specific category
  • 18. Total weight • W = W1 X W2 X W3 W1 = weight for unequal selection probabilities W2 = weight for non-response W3 = weight for post stratification
  • 19. Variance Estimation • Linearization – Taylor Series approximation (Wolter 1985) • Best for simple statistics eg weighted mean (Frankel,1971) • Replication (Resampling method) – Balanced Repeated Replication (BRR) – Jackknife estimation (Kish & Frankel 1974; Krewski and Rao 1981; Kovar, Rao and Wu 1988; Rao, Wu, and Yue 1992; Shao 1996) • Maximum-likelihood estimates (Brillinger, 1964) • Best for complex statistics like regression coefficients (Frankel,1971)
  • 20. Available Software • Stata • SAS • SUDAAN (Research Triangle Institute) • WesVar • SPSS • NASSTIM&NASSVAR • etc
  • 21. Comparison proportion of smoking pregnant mother with years of schooling Years of schooling Weighted proportion Unweighted proportion < 12 years 0.315 ± 0.010 0.328 ± 0.007 12 years 0.373 ± 0.012 0.332 ± 0.008 > 12 years 0.202 ± 0.011 0.217± 0.008 Data source : National Maternal and Child Survey 1988,US
  • 22. Comparison of the highest prevalence by states and gender Prevalens (%) States SPSS STATA Male Female Male Female Johor 26.97 29.62 25.39 28.75 Kedah 20.36 28.39 19.63 27.12 Kelantan 19.00 27.09 16.08 24.39 Melaka 29.06 34.84 29.67 33.99 N. Sembilan 30.99 34.56 28.40 34.18 Pahang 26.27 37.48 24.06 39.02 P. Pinang 24.81 28.80 24.40 27.09 Source : National Perak 27.21 31.96 26.58 31.02 Health Morbidity Survey 1996 Perlis 24.91 35.98 22.49 35.29 Selangor 25.65 28.66 25.26 26.73 Sarawak 21.46 26.73 17.41 28.18 Sabah 22.84 26.51 18.28 25.80 Terengganu 26.58 35.17 33.75 32.17 WPKL 30.80 29.94 30.29 29.39
  • 23. The difference based on the highest prevalence of obesity among adults in Kedah by gender and ethnicity Prevalens Gender Ethnic S.E (95% CI) Without 32.35 Female India 5.68 (21.22,43.48) weight 29.87 With weights Female Cina 4.54 (20.98,38.76) Source : National Health Morbidity Survey 1996
  • 24. Things to be considered if a design- based inference approach is chosen • What is the nature of the sample design? Was is a stratified multistage sample design used? Was is a cluster sample design used? Were unequal prob. of selection applied? • Were there adjustments for nonresponse or coverage errors? Is there a weight or several weights that must be applied when different parts of the sample are analyzed? • Are there important measurement issues that could affect survey analyses? Is item nonresponse an important problem for some variables? • How can the results be interpreted, and what kind of inference are appropriate in view of the complex survey design?
  • 25. Steps required for performing a design-based analysis Paul S. Levy and Stanley Lemeshow (1999) • Identify the following elements of the sample design: • Stratification • Clustering • Population sizes required for determination of finite population correction • Determine the sampling weight • Determine a final sampling weight; nonresponse, post stratification • Ensure data required for an appropriate design- based analysis • Determine the procedure and the set of commands for performing the required analysis • Run the analysis and carefully interpret findings
  • 26. Further reading • C.J. Skinner, D.Holt, T.M.F.Smith, 1989, Analysis of Complex Surveys, New York: John Wiley and Sons. • P.S. Levy, S.Lemeshow. 1999, Sampling of Populations; Methods and Applications,, 3rd Ed.,John Wiley & Sons. • Cochran, W. G. 1977. Sampling Techniques. New York: John Wiley and Sons. • Kish, L. 1965. Survey Sampling. New York: John Wiley and Sons. • Kish, L. 1987. Statistical Design for Research. New York: John Wiley and Sons. • Murthy, M. N. 1967. Sampling Theory and Methods. Calcutta, India: Statistical Publishing Society. • E.L.Korn, B.I.Graubard. Examples of Differing Weighted and Unweighted Estimates From a Sample Survey, The American Statistician, Aug 1995, 49, No.3, 291-295. • E.S.Lee, R. N. Fourthofer, R.J. Lorimor. Analysis of Complex Sample Survey Data, Problem and Startegies, Sociological Methods & Research , Aug-Nov. 1986,15,69- 100.