Sampling

1. Sampling Basic concepts

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

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

4. 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

5. What is necessary to achieve this objective? The sample must be representativeof the larger population.

6. 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 population’s true value

7. BIASED random or biased sample?

8. BIASED 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

9. 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)

10. Types of sampling • Non-probability sampling • Probability sampling

11. non-probability sampling… sampling that doesn’t use random selection to choose units to be examined or measured: non-representative results

12. non-probability sampling…When 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

13. probability sampling… b sampling that uses random selection to choose units. Results are representative of the larger population

14. Pro’s and Con’s of Probability and Non-Probability Sampling

15. key concepts for probability sampling

16. 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

17. Example cont. • Population: Flexiland • Sampling frame: • First stage: List of villages • Second stage: List of households within villages • Sample: • 450 HHs (30*15) • 675 children (450*1.5) • Respondent: Household head or spouse • Sampling units: • Primary: Villages • Secondary: Households, children (6-59 months)

18. Types of probability sampling A: Simple random B: Systematic C: Cluster

19. 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)

20. Example: Select 5 people out of 10 Number 1 2 3 4 5 6 7 8 9 0 Household Edmond Daniel Jyoti Victor Anne Sheriff Vandi Iye Victor Rauf 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

21. Example: 1. Person = 2 Number 1 2 3 4 5 6 7 8 9 0 Household Edmond Daniel Jyoti Victor Anne Sheriff Vandi Iye Victor Rauf 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

22. Example: 2. Person = 3 Number 1 2 3 4 5 6 7 8 9 0 Household Edmond Daniel Jyoti Victor Anne Sheriff Vandi Iye Victor Rauf 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

23. Example: 3. Person = 5 Number 1 2 3 4 5 6 7 8 9 0 Household Edmond Daniel Jyoti Victor Anne Sheriff Vandi Iye Victor Rauf 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

24. Example: 4. Person = 6 Number 1 2 3 4 5 6 7 8 9 0 Household Edmond Daniel Jyoti Victor Anne Sheriff Vandi Iye Victor Rauf 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

25. Example: 5. Person = 9 Number 1 2 3 4 5 6 7 8 9 0 Household Edmond Daniel Jyoti Victor Anne Sheriff Vandi Iye Victor Rauf 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

26. Using Random Number Tables • If units < 10, then use 1 digit of table numbers • If units < 100, then use 2 digits of table numbers • If units < 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

27. 6 villages are selected

28. 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

29. 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)

33. 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

34. 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 19. Sayba Kolubah 20. James Tamba 21. Clifford Howard 22. Thomas Tarr 23. Jerry Morris 24. Lisa Miller 25. David Harper 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 44. Thomas Tarr 45. Jerry Morris 46. Lisa Miller 47. David Harper Example 1 (household list): selection of 15 households in a community of 47 households Sampling interval: 47/15 = 3 Select randomly starting point: 1, 2 or 3 (counting, lottery)

35. 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 19. Sayba Kolubah 20. James Tamba 21. Clifford Howard 22. Thomas Tarr 23. Jerry Morris 24. Lisa Miller 25. David Harper 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 44. Thomas Tarr 45. Jerry Morris 46. Lisa Miller 47. David Harper Example 1: selection of 15 households in a community of 47 households  15 HHs are selected

36. Example 2 (refugee camp): selection of 40 households in a camp made up of 480 households 480/40 = 12 Interval = 12

37. Example 1: Which sampling method if no registration took place yet? Stankovic I camp, Macedonia

38. Example 2: Which sampling method if registration already took place? Chaman camp, Pakistan

39. Example 3: Which sampling method? Kabumba camp, Zaire

40. What is required for both simple and systematic random sampling? Both require a complete list of sampling units arranged in some order.

41. What do we do when no accurate list of all basic sampling units is available? 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

42. 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

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

44. 1500 kms Example 4: Which sampling method?

45. 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)

46. Example 5: How many strata?

47. Example 6: How many strata?

48. 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?

49. Questions