Sampling

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Sampling
Basic concepts
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Overview

• Why do sampling?
• Steps for deciding sampling methodology
• Sampling methods
• Representative vs. bias
• Probability vs. non-probability
• Simple, random, systematic and cluster sampling

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What is the objective of sampling?
The objective of sampling is to estimate an
indicator for the larger population if we cannot
measure everybody.
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Population of Papua New Guinea

• 726,680 children less than 5 years of age
• 1,298,503 women 15-49 years of age

With 6 teams who each measure 13 women and 13
children per day, data collection would take
16,648 days or 45.6 years
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What is necessary to achieve this objective?
The sample must be representative of the larger
population.
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Representative versus bias




Bias Some members have greater chance of being
included than others (e.g. interviewer bias, main
road bias). ? Results will differ from the actual
population prevalence ? This error cannot be
corrected during the analysis
Representative All members of a population have
an equal chance of being included in the sample ?
Results will be close to the populations true
value
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random or biased sample?
a survey of child malnutrition is conducted by measuring the children of women who were advised over the radio to bring their under-fives to the health clinic on Tuesday morning
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random or biased sample?

• Proportion of HIV/AIDS affected population is
  5.8 based on statistics from health facilities
  who frequently take blood samples from pregnant
  women

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Steps for deciding sampling methodology

• Define objectives and geographic area

• Identify what info to collect

• Determine sampling method

Calculate sample size
Additional factors time available, financial
resources, physical access (security)
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Types of sampling

• Non-probability sampling
• Probability sampling

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non-probability sampling

• sampling that doesnt use random selection to
  choose units to be examined or measured
  non-representative results

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non-probability samplingWhen is it used?

• Rapid appraisal methods (e.g. key
  informant/community group interviews/focus group
  discussions)
• Often used in rapid assessments
• Sampling with a purpose in mind generally one
  or more pre-defined groups or areas to assess
• Useful to reach targeted sample quickly

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probability sampling
b

• sampling that uses random selection to choose
  units. Results are representative of the larger
  population

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Pros and Cons of Probability and
Non-Probability Sampling
factor probability non- probability
precision
time
cost
if lack of access due to insecurity
skill requirements statistics skills needed qualitative analysis skills needed
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key concepts for probability sampling
population the group of people for which indicators are measured
sampling frame the population list from which the sample is to be drawn
sample the randomly selected subset of the population
sampling unit the unit that is selected during the process of sampling (e.g. first stage community, 2. stage household)
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Example
A food security and nutrition survey is conducted
in Flexiland. 100,000 households live in the area
in 1,000 villages. First, 30 villages will be
selected. In each village 15 households will be
visited. The head of household head or spouse
reports on all food items consumed by the
household over the last 7 days. In addition, all
children 6-59 months are measured. On average
household have 1.5 children in this age group.

• Identify
• Population
• Sampling frame
• Sample
• Respondent
• Sampling units

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Example cont.

• Population Flexiland
• Sampling frame
• First stage List of villages
• Second stage List of households within villages
• Sample
• 450 HHs (3015)
• 675 children (4501.5)
• Respondent Household head or spouse
• Sampling units
• Primary Villages
• Secondary Households, children (6-59 months)

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Types of probability sampling

• A Simple random
• B Systematic
• C Cluster

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A Simple Random Sampling

• Each household/person randomly is selected from
  population list.
• Easier to use when population of interest is
  small and confined to small geographic area.
• Steps
• Number each sampling unit
• Choose new random number for each selection
  (random number table or lottery)

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Example Select 5 people out of 10
Random number table 2352 6959 7678 1937 2554
6804 9098 4316 4318 2346 7276 1880 7136
9603 0163 3152 7000 2865 8357 4475 9804
0042 1106 7949 2932 9958 9582 2235 1140
1164 7841 1688 4097 8995 5030 1785 5420
0125 4953 1332 5540 6278 1584 4392 3258
1374 1617 7427
Number 1 2 3 4 5 6 7 8 9 0
Household Edmond Daniel Jyoti Victor Anne Sheriff
Vandi Iye Victor Rauf
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Example 1. Person 2
Random number table 2352 6959 7678 1937 2554
6804 9098 4316 4318 2346 7276 1880 7136
9603 0163 3152 7000 2865 8357 4475 9804
0042 1106 7949 2932 9958 9582 2235 1140
1164 7841 1688 4097 8995 5030 1785 5420
0125 4953 1332 5540 6278 1584 4392 3258
1374 1617 7427
Number 1 2 3 4 5 6 7 8 9 0
Household Edmond Daniel Jyoti Victor Anne Sheriff
Vandi Iye Victor Rauf
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Example 2. Person 3
Random number table 2352 6959 7678 1937 2554
6804 9098 4316 4318 2346 7276 1880 7136
9603 0163 3152 7000 2865 8357 4475 9804
0042 1106 7949 2932 9958 9582 2235 1140
1164 7841 1688 4097 8995 5030 1785 5420
0125 4953 1332 5540 6278 1584 4392 3258
1374 1617 7427
Number 1 2 3 4 5 6 7 8 9 0
Household Edmond Daniel Jyoti Victor Anne Sheriff
Vandi Iye Victor Rauf
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Example 3. Person 5
Random number table 2352 6959 7678 1937 2554
6804 9098 4316 4318 2346 7276 1880 7136
9603 0163 3152 7000 2865 8357 4475 9804
0042 1106 7949 2932 9958 9582 2235 1140
1164 7841 1688 4097 8995 5030 1785 5420
0125 4953 1332 5540 6278 1584 4392 3258
1374 1617 7427
Number 1 2 3 4 5 6 7 8 9 0
Household Edmond Daniel Jyoti Victor Anne Sheriff
Vandi Iye Victor Rauf
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Example 4. Person 6
Random number table 2352 6959 7678 1937 2554
6804 9098 4316 4318 2346 7276 1880 7136
9603 0163 3152 7000 2865 8357 4475 9804
0042 1106 7949 2932 9958 9582 2235 1140
1164 7841 1688 4097 8995 5030 1785 5420
0125 4953 1332 5540 6278 1584 4392 3258
1374 1617 7427
Number 1 2 3 4 5 6 7 8 9 0
Household Edmond Daniel Jyoti Victor Anne Sheriff
Vandi Iye Victor Rauf
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Example 5. Person 9
Random number table 2352 6959 7678 1937 2554
6804 9098 4316 4318 2346 7276 1880 7136
9603 0163 3152 7000 2865 8357 4475 9804
0042 1106 7949 2932 9958 9582 2235 1140
1164 7841 1688 4097 8995 5030 1785 5420
0125 4953 1332 5540 6278 1584 4392 3258
1374 1617 7427
Number 1 2 3 4 5 6 7 8 9 0
Household Edmond Daniel Jyoti Victor Anne Sheriff
Vandi Iye Victor Rauf
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Using Random Number Tables

• If units lt 10, then use 1 digit of table numbers
• If units lt 100, then use 2 digits of table
  numbers
• If units lt 1000, then use 3 digits of table
  numbers

• Example You want to randomly select 6 out of 71
  towns
• You number them from 1 to 71.
• Close eyes and place fingertip on the table to
  start
• Decide if you want to move right, left, up or
  down
• Select first two digits of each number in the
  table
• Cross out those that start with 72 or higher

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TABLE OF RANDOM NUMBERS
39634 62349 74088 65564 16379 19713 39153 69459 17986 24537
14595 35050 40469 27478 44526 67331 93365 54526 22356 93208
30734 71571 83722 79712 25775 65178 07763 82928 31131 30196
64628 89126 91254 99090 25752 03091 39411 73146 06089 15630
42831 95113 43511 42082 15140 34733 68076 18292 69486 80468
80583 70361 41047 26792 78466 03395 17635 09697 82447 31405
00209 90404 99457 72570 42194 49043 24330 14939 09865 45906
05409 20830 01911 60767 55248 79253 12317 84120 77772 50103
95836 22530 91785 80210 34361 52228 33869 94332 83868 61672
65358 70469 87149 89509 72176 18103 55169 79954 72002 20582
6 villages are selected
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Class exercise

• Select randomly 4 members in this class using the
  random number table

Random number table 3647 2352 6959 1937 2554
6804 9098 4316 4318 2346 7276 1880 7136
9603 0163 3152 7000 2865 8357 4475 9804
0042 1106 7949 2932 9958 9582 2235 1140
1164 7841 1688 4097 8995 5030 1785 5420
0125 4953 1332 5540 6278 1584 4392 3258
1374 1617 7427 3320
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Using SPSS

• SPSS can help to randomly select cases by using
  the select cases function
• ? Data ? Select cases ? Random sample of cases
  (option 1 xx of all cases option 2 x cases
  from the first x cases)

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Simple Random Sampling

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B Systematic Random Sampling

• Similar to simple random sampling, works well in
  well-organized refugee/IDP camps or neighborhoods
• First person chosen randomly
• Systematic selection of subsequent people
• Statistics same as simple random sampling
• Steps
• List or map all units in the population
• Compute sampling interval (Number of population
  / Sample size)
• Select random start between 1 and sampling
  interval
• Repeatedly add sampling interval to select
  subsequent sampling units

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Example 1 (household list) selection of 15
households in a community of 47 households

• 1. Peter Smith
• 2. John Edward
• 3. Mary McLean
• 4. George Williams
• 5. Morris Tamba
• 6. Sayba Kolubah
• 7. James Tamba
• 8. Clifford Howard
• 9. Thomas Tarr
• 10. Jerry Morris
• 11. Jules Sana
• 12. Lisa Miller
• 13. David Harper
• 14. Peter Smith
• 15. John Edward
• 16. Mary McLean
• 17. George Williams
• 18. Morris Tamba

• 26. Hilary Scott
• 27. Smith Suba
• 28. Zoe Mulbah
• 29. Roosevelt Hill
• 30. Johnson Snow
• 31. Salif Jensen
• 32. Fassou Clements
• 33. Massa Kru
• 34. Emanuel Liberty
• 35. Stella Morris
• 36. Peter Smith
• 37. John Edward
• 38. Mary McLean
• 39. George Williams
• 40. Morris Tamba
• 41. Sayba Kolubah
• 42. James Tamba
• 43. Clifford Howard

Sampling interval 47/15 3
Select randomly starting point 1, 2 or 3
(counting, lottery)
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Example 1 selection of 15 households in a
community of 47 households

• 1. Peter Smith
• 2. John Edward
• 3. Mary McLean
• 4. George Williams
• 5. Morris Tamba
• 6. Sayba Kolubah
• 7. James Tamba
• 8. Clifford Howard
• 9. Thomas Tarr
• 10. Jerry Morris
• 11. Jules Sana
• 12. Lisa Miller
• 13. David Harper
• 14. Peter Smith
• 15. John Edward
• 16. Mary McLean
• 17. George Williams
• 18. Morris Tamba

• 26. Hilary Scott
• 27. Smith Suba
• 28. Zoe Mulbah
• 29. Roosevelt Hill
• 30. Johnson Snow
• 31. Salif Jensen
• 32. Fassou Clements
• 33. Massa Kru
• 34. Emanuel Liberty
• 35. Stella Morris
• 36. Peter Smith
• 37. John Edward
• 38. Mary McLean
• 39. George Williams
• 40. Morris Tamba
• 41. Sayba Kolubah
• 42. James Tamba
• 43. Clifford Howard

? 15 HHs are selected
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Example 2 (refugee camp) selection of 40
households in a camp made up of 480 households
Systematic Sampling

480/40 12 Interval 12
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Example 1 Which sampling method if no
registration took place yet?
Stankovic I camp, Macedonia
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Example 2 Which sampling method if registration
already took place?
Chaman camp, Pakistan
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Example 3 Which sampling method?
Kabumba camp, Zaire
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What is required for both simple and systematic
random sampling?
Both require a complete list of sampling units
arranged in some order.
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What do we do when no accurate list of all
basic sampling units is available?
C Cluster Sampling

Used when sampling frame or geographic area is
large ? Saves time and resources Objective To
choose smaller geographic areas in which simple
or systematic random sampling can be done
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Two-stage Cluster Sampling


1st stage sites are selected using probability
proportion to size (PPS) methodology (
clusters) 2nd stage within each cluster,
households are randomly selected Example 1 25
clusters per district, 15 households per cluster
375 households in each district
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Two-stage Cluster Sampling in Flexiland

1. Step Select randomly 25 communities
Flexiland
2. Step Within each cluster (community), select
15 households using random or systematic random
sampling
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Pros and cons of cluster sampling

• Disadvantages
• Decreased precision of estimate
• Calculation of p values and confidence limits
  more complicated

• Advantages
• Cheaper - basic sampling units closer together
• Does not need complete list of basic sampling
  units

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Example 4 Which sampling method?
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Stratification

• Stratification is the process of grouping members
  of the population into relatively homogeneous
  subgroups (e.g. regions, districts, livelihood
  zones)
• The strata should be mutually exclusive every
  element in the population must be assigned to
  only one stratum
• Within each stratum, random, systematic or two
  stage cluster sampling is applied
• Advantages
• Sub-groups can be compared
• Representativeness is improved as the sample is
  more homogeneous
• During the analysis, weighting is used to
  generate results that are representative at the
  aggregate level (e.g. nation, rural/urban
  population)

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Example 5 How many strata?
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Example 6 How many strata?
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Final panel exercise

• Which sampling method would you choose?
• Rapid emergency food security assessments
  following a flood in the Northern Atlantic Coast
  region of Nicaragua?
• Nutrition survey in IDP-camp in Darfur?
• Comprehensive Food Security and Vulnerability
  Analysis (CFSVAs) in Zambia?
• Market assessment in Yemen?

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Questions