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Sampling Frames and Sample Design Pres. 5

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Title: Sampling Frames and Sample Design Pres. 5


1
Sampling Frames andSample DesignPres. 5
2
Sample Frames Sample Design
  • Objectives Important to define objectives before
    designing a sample
  • Items to estimate coverage error, duplication,
    omissions, etc.
  • Geographic level national, sub-national
    (province or district, urban/rural, etc.)
  • Demographic characteristics sex, age, person,
    household, etc.
  • Confidence level
  • Margin of error

3
Sample Frames Sample Design
  • Frames Material from which a sample is drawn
  • Each unit to be included in the universe
  • There should be no duplicates
  • Each unit should be well defined and
    distinguishable from other units (it should be
    unique)
  • Should be updated

4
Sampling Strategies
  • Probability household surveys
  • It is usual to make inferences in a PES for a
    number of analytical domains
  • Relatively large samples necessary in each domain
    for reliable estimates
  • Stratified cluster sample design-common
  • First-stage units or Primary Sampling Units
    (PSUs) - many countries use geographically
    contiguous land areas usually called area
    clusters or EAs
  • PPS systematic sample selection
  • Second-stage, common to canvass all persons in
    selected households

5
Importance of Stratification
  • Population subdivided into heterogeneous groups
    that are internally homogenous
  • Stratification based on variables correlated with
    the extent of coverage-geopolitical subdivisions
  • Internal homogeneity can be maintained with
    regard to socio-demographic variables e.g. urban
    stratum
  • Common strata may include rural, urban,
    provinces etc.

6
Multi-stage Cluster Sampling
  • Usually used when sampling hierarchical
    populations
  • The hierarchical levels are called stages
  • First stage units are called primary sampling
    units (PSUs) e.g. EAs
  • Second stage units are called secondary sampling
    units (SSUs) e.g. households
  • Last stage units are called ultimate sampling
    units (USUs) e.g. persons within households which
    can be selected from EAs

7
Why Area sampling?
  • At national level only a frame of EAs is required
  • Data collection is more efficient
  • Lower costs compared to simple random sampling
    (SRS)
  • Supervision is easier
  • However, estimates are prone to higher
    variability compared to SRS

8
Choices of PSUs
  • Must have clearly identifiable and stable
    boundaries
  • Must completely cover the relevant population
  • Preferably must have measures of size
  • They should be mapped
  • Must cover the whole country
  • The number of PSUs must be relatively large

9
Common problems with EAs
  • Incomplete coverage
  • Inadequate maps
  • Poor measures of size or lack of them

10
PES sample design
  • A single-stage stratified clustered sample
    design is commonly adopted
  • When the PSUs i.e. EAs are selected all
    households in selected EAs are canvassed, or
    more rarely only a sample (e.g. 1 every 5).
  • This is beneficial for matching operation

11
Sample Size
  • Sample size depends on estimate requirements
  • Geographic level (national, province,
    urban/rural)
  • Demographic (sex, age)
  • Reliability
  • Confidence level

12
Sample Size
  • To estimate sample size in the case of
    proportions you must
  • Know the occurrence of the event in the
    population by domain of estimation
  • Specify a confidence interval (e.g 95)
  • Specify the margin of error or precision (e.g 1)

13
Sample Size (contd.)
  • To estimate sample size in the case of
    proportions, the following formula can be used

14
Sample size (contd.)
  • From that it is deduced

15
Sample Size (contd.)
  • Example
  • To estimate percentage of households omitted in
    the census (expected about 5) confidence
    interval at 95 (t1.96) for a margin of error of
    2
  • The sample size works out to be

16
Sample Size (contd.)
  • Adjusting for non-response, e.g. 10
  • Adjusting for the design effect for a complex
    sample design
  • Design effect of 2 is a default value 2 x 507
    1,014
  • This may apply to each province (analysis)
    domains. If they are five provinces
  • Sample size will be 5 x 1,014 5,070

17
Sample selection procedures
  • For greater convenience and efficiency, the
    sample of PSUs should be selected using a
    systematic procedure.
  • If there are good measures of size, probability
    proportional to size (PPS) should be used to
    increase the efficiency of the sample design.
  • Otherwise, the selection should be made with
    equal probabilities

18
Sample selection procedures -- PPS
  • 1) Order the EAs geographically (and, if
    applicable, by other stratification
    characteristic) to allow implicit stratification
  •  2) Record for each EA i of the stratum h the
    measure of size Mhi, typically the number of
    households or persons from the census mapping
    operation
  •  3) Cumulate the size measures down the list of
    EAs, the last cumulated number will be equal to
    the total number of households (or persons) in
    stratum h (Mh)
  •  4) Determine the number of EAs (nh) to be
    selected in a stratum according to the allocation

19
Sample selection procedures - PPS (contd.)
  •  5) Determine the sampling interval (Ih) by
  •  6) Obtain a random number (Ah) between 1 and Ih
    inclusively
  •  7) Determine the selected EAs as follows
  •  ShiAh (i-1) x Ih, for i 1,...,nh, rounded
    up to the next integer
  • The i-th EA selected will be the one for which
    the cumulated measure is closest to Shi without
    exceeding it.

20
Illustration Selection of Eight EAs with
probability Proportional to size
21
Sample Allocation 2009 Kenyan PES
22
  • Thank You!
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