1. 1 STK 4600: Statistical methods for social sciences. Survey sampling and statistical demography Surveys for households and individuals

2. 2 Survey sampling: 4 major topics 1.Traditional design-based statistical inference 6 weeks 2.Likelihood considerations 1 weeks 3.Model-based statistical inference 3 weeks 4.Missing data - nonresponse 2 weeks

3. 3 Statistical demography Mortality Life expectancy Population projections 2-3 weeks

4. 4 Course goals Give students knowledge about: –planning surveys in social sciences –major sampling designs –basic concepts and the most important estimation methods in traditional applied survey sampling –Likelihood principle and its consequences for survey sampling –Use of modeling in sampling –Treatment of nonresponse –A basic knowledge of demography

5. 5 But first: Basic concepts in sampling Population (Target population): The universe of all units of interest for a certain study Denoted, with N being the size of the population: U = {1, 2,...., N} All units can be identified and labeled Ex: Political poll – All adults eligible to vote Ex: Employment/Unemployment in Norway– All persons in Norway, age 15 or more Ex: Consumer expenditure : Unit = household Sample: A subset of the population, to be observed. The sample should be ”representative” of the population

6. 6 Sampling design: The sample is a probability sample if all units in the sample have been chosen with certain probabilities, and such that each unit in the population has a positive probability of being chosen to the sample We shall only be concerned with probability sampling Example: simple random sample (SRS). Let n denote the sample size. Every possible subset of n units has the same chance of being the sample. Then all units in the population have the same probability n/N of being chosen to the sample. The probability distribution for SRS on all subsets of U is an example of a sampling design: The probability plan for selecting a sample s from the population:

7. 7 Basic statistical problem: Estimation A typical survey has many variables of interest Aim of a sample is to obtain information regarding totals or averages of these variables for the whole population Examples : Unemployment in Norway– Want to estimate the total number t of individuals unemployed. For each person i (at least 15 years old) in Norway:

8. 8 In general, variable of interest: y with y i equal to the value of y for unit i in the population, and the total is denoted The typical problem is to estimate t or t/N Sometimes, of interest also to estimate ratios of totals: Example- estimating the rate of unemployment: Unemployment rate:

9. 9 Sources of error in sample surveys 1.Target population U vs Frame population U F Access to the population is thru a list of units – a register U F. U and U F may not be the same: Three possible errors in U F : –Undercoverage: Some units in U are not in U F –Overcoverage: Some units in U F are not in U –Duplicate listings: A unit in U is listed more than once in U F U F is sometimes called the sampling frame

10. 10 2.Nonresponse - missing data Some persons cannot be contacted Some refuse to participate in the survey Some may be ill and incapable of responding In postal surveys: Can be as much as 70% nonresponse In telephone surveys: 50% nonresponse is not uncommon Possible consequences: –Bias in the sample, not representative of the population –Estimation becomes more inaccurate Remedies: –imputation, weighting

11. 11 3.Measurement error – the correct value of y i is not measured –In interviewer surveys: Incorrect marking interviewer effect: people may say what they think the interviewer wants to hear – underreporting of alcohol ute, tobacco use misunderstanding of the question, do not remember correctly.

12. 12 4.Sampling error –The error caused by observing a sample instead of the whole population –To assess this error- margin of error: measure sample to sample variation –Design approach deals with calculating sampling errors for different sampling designs –One such measure: 95% confidence interval: If we draw repeated samples, then 95% of the calculated confidence intervals for a total t will actually include t

13. 13 The first 3 errors: nonsampling errors –Can be much larger than the sampling error In this course: –Sampling error –nonresponse bias –Shall assume that the frame population is identical to the target population –No measurement error

14. 14 Summary of basic concepts Population, target population unit sample sampling design estimation –estimator –measure of bias –measure of variance –confidence interval

15. 15 survey errors: –register /frame population –mesurement error –nonresponse –sampling error

16. 16 Example – Psychiatric Morbidity Survey 1993 from Great Britain Aim: Provide information about prevalence of psychiatric problems among adults in GB as well as their associated social disabilities and use of services Target population: Adults aged 16-64 living in private households Sample: Thru several stages: 18,000 adresses were chosen and 1 adult in each household was chosen 200 interviewers, each visiting 90 households

17. 17 Result of the sampling process Sample of addresses18,000 Vacant premises 927 Institutions/business premises 573 Demolished 499 Second home/holiday flat 236 Private household addresses 15,765 Extra households found 669 Total private households 16,434 Households with no one 16-64 3,704 Eligible households 12,730 Nonresponse 2,622 Sample 10,108 households with responding adults aged 16-64

18. 18 Why sampling ? reduces costs for acceptable level of accuracy (money, manpower, processing time...) may free up resources to reduce nonsampling error and collect more information from each person in the sample –ex: 400 interviewers at $5 per interview: lower sampling error 200 interviewers at 10$ per interview: lower nonsampling error much quicker results

19. 19 When is sample representative ? Balance on gender and age: –proportion of women in sample @ proportion in population –proportions of age groups in sample @ proportions in population An ideal representative sample: –A miniature version of the population: –implying that every unit in the sample represents the characteristics of a known number of units in the population Appropriate probability sampling ensures a representative sample ”on the average”

20. 20 Alternative approaches for statistical inference based on survey sampling Design-based: –No modeling, only stochastic element is the sample s with known distribution Model-based: The values y i are assumed to be values of random variables Y i : –Two stochastic elements: Y = (Y 1, …,Y N ) and s –Assumes a parametric distribution for Y –Example : suppose we have an auxiliary variable x. Could be: age, gender, education. A typical model is a regression of Y i on x i.

21. 21 Statistical principles of inference imply that the model-based approach is the most sound and valid approach Start with learning the design-based approach since it is the most applied approach to survey sampling used by national statistical institutes and most research institutes for social sciences. –Is the easy way out: Do not need to model. All statisticians working with survey sampling in practice need to know this approach

22. 22 Design-based statistical inference Can also be viewed as a distribution-free nonparametric approach The only stochastic element: Sample s, distribution p(s) for all subsets s of the population U={1,..., N} No explicit statistical modeling is done for the variable y. All y i ’s are considered fixed but unknown Focus on sampling error Sets the sample survey theory apart from usual statistical analysis The traditional approach, started by Neyman in 1934

23. 23 Estimation theory-simple random sample Estimation of the population mean of a variable y: A natural estimator - the sample mean: Desirable properties: SRS of size n: Each sample s of size n has Can be performed in principle by drawing one unit at time at random without replacement

24. 24 The uncertainty of an unbiased estimator is measured by its estimated sampling variance or standard error (SE): Some results for SRS:

25. 25 usually unimportant in social surveys: n =10,000 and N = 5,000,000: 1- f = 0.998 n =1000 and N = 400,000: 1- f = 0.9975 n =1000 and N = 5,000,000: 1-f = 0.9998 effect of changing n much more important than effect of changing n/N

26. 26 The estimated variance Usually we report the standard error of the estimate: Confidence intervals for m is based on the Central Limit Theorem:

27. 27 Example N = 341 residential blocks in Ames, Iowa y i = number of dwellings in block i 1000 independent SRS for different values of n n Proportion of samples with |Z| <1.64 Proportion of samples with |Z| <1.96 300.880.93 500.880.93 700.880.94 900.900.95

28. 28 For one SRS with n = 90:

29. 29 The coefficient of variation for the estimate: A measure of the relative variability of an estimate. It does not depend on the unit of measurement. More stable over repeated surveys, can be used for planning, for example determining sample size More meaningful when estimating proportions Absolute value of sampling error is not informative when not related to value of the estimate For example, SE =2 is small if estimate is 1000, but very large if estimate is 3

30. 30 Estimation of a population proportion p with a certain characteristic A p = (number of units in the population with A)/N Let y i = 1 if unit i has characteristic A, 0 otherwise Then p is the population mean of the y i ’s. Let X be the number of units in the sample with characteristic A. Then the sample mean can be expressed as

31. 31 So the unbiased estimate of the variance of the estimator:

32. 32 Examples A political poll: Suppose we have a random sample of 1000 eligible voters in Norway with 280 saying they will vote for the Labor party. Then the estimated proportion of Labor votes in Norway is given by: Confidence interval requires normal approximation. Can use the guideline from binomial distribution, when N-n is large:

33. 33 In this example : n = 1000 and N = 4,000,000 Ex: Psychiatric Morbidity Survey 1993 from Great Britain p = proportion with psychiatric problems n = 9792 (partial nonresponse on this question: 316) N @ 40,000,000

34. 34 General probability sampling Sampling design: p(s) - known probability of selection for each subset s of the population U Actually: The sampling design is the probability distribution p(. ) over all subsets of U Typically, for most s: p(s) = 0. In SRS of size n, all s with size different from n has p(s) = 0. The inclusion probability:

35. 35 Illustration U = {1,2,3,4} Sample of size 2; 6 possible samples Sampling design: p({1,2}) = ½, p({2,3}) = 1/4, p({3,4}) = 1/8, p({1,4}) = 1/8 The inclusion probabilities:

36. 36 Some results

37. 37 Estimation theory probability sampling in general Problem: Estimate a population quantity for the variable y For the sake of illustration: The population total

38. 38 CV is a useful measure of uncertainty, especially when standard error increases as the estimate increases Because, typically we have that

39. 39 Some peculiarities in the estimation theory Example: N=3, n=2, simple random sample

40. 40 For this set of values of the y i ’s:

41. 41 Let y be the population vector of the y-values. This example shows that is not uniformly best ( minimum variance for all y) among linear design-unbiased estimators Example shows that the ”usual” basic estimators do not have the same properties in design-based survey sampling as they do in ordinary statistical models In fact, we have the following much stronger result: Theorem: Let p(. ) be any sampling design. Assume each y i can take at least two values. Then there exists no uniformly best design-unbiased estimator of the total t

42. 42 Proof: This implies that a uniformly best unbiased estimator must have variance equal to 0 for all values of y, which is impossible

43. 43 Determining sample size The sample size has a decisive effect on the cost of the survey How large n should be depends on the purpose for doing the survey In a poll for detemining voting preference, n = 1000 is typically enough In the quarterly labor force survey in Norway, n = 24000 Mainly three factors to consider: 1. Desired accuracy of the estimates for many variables. Focus on one or two variables of primary interest 2. Homogeneity of the population. Needs smaller samples if little variation in the population 3. Estimation for subgroups, domains, of the population

44. 44 It is often factor 3 that puts the highest demand on the survey If we want to estimate totals for domains of the population we should take a stratified sample A sample from each domain A stratified random sample: From each domain a simple random sample

45. 45 Assume the problem is to estimate a population proportion p for a certain stratum, and we use the sample proportion from the stratum to estimate p Let n be the sample size of this stratum, and assume that n/N is negligible Desired accuracy for this stratum: 95% CI for p should be The accuracy requirement:

46. 46 The estimate is unkown in the planning fase Use the conservative size 384 or a planning value p 0 with n = 1536 p 0 (1- p 0 ) F.ex.: With p 0 = 0.2: n = 246 In general with accuracy requirement d, 95% CI

47. 47 With e = 0.1, then we require approximately that

48. 48 Example: Monthly unemployment rate Important to detect changes in unemployment rates from month to month planning value p 0 = 0.05

49. 49 Two basic estimators: Ratio estimator Horvitz-Thompson estimator Ratio estimator in simple random samples H-T estimator for unequal probability sampling: The inclusion probabilities are unequal The goal is to estimate a population total t for a variable y

50. 50 Ratio estimator Suppose we have known auxiliary information for the whole population: Ex: age, gender, education, employment status The ratio estimator for the y-total t:

51. 51 We can express the ratio estimator on the following form: It adjusts the usual “sample mean estimator” in the cases where the x-values in the sample are too small or too large. Reasonable if there is a positive correlation between x and y Example: University of 4000 students, SRS of 400 Estimate the total number t of women that is planning a career in teaching, t=Np, p is the proportion y i = 1 if student i is a woman planning to be a teacher, t is the y-total

52. 52 Results : 84 out of 240 women in the sample plans to be a teacher HOWEVER: It was noticed that the university has 2700 women (67,5%) while in the sample we had 60% women. A better estimate that corrects for the underrepresentation of women is obtained by the ratio estimate using the auxiliary x = 1 if student is a woman

53. 53 In business surveys it is very common to use a ratio estimator. Ex: y i = amount spent on health insurance by business i x i = number of employees in business i We shall now do a comparison between the ratio estimator and the sample mean based estimator. We need to derive expectation and variance for the ratio estimator

54. 54 First: Must define the population covariance The population correlation coefficient:

55. 55

56. 56 It follows that Hence, in SRS, the absolute bias of the ratio estimator is small relative to the true SE of the estimator if the coefficient of variation of the x- sample mean is small Certainly true for large n

57. 57

58. 58 Note: The ratio estimator is very precise when the population points (y i, x i ) lie close around a straight line thru the origin with slope R. The regression model generates the ratio estimator

59. 59 The ratio estimator is more accurate if Rx i predicts y i better than m y does

60. 60 Estimated variance for the ratio estimator

61. 61 For large n, N-n: Approximate normality holds and an approximate 95% confidence interval is given by

62. 62 Unequal probability sampling Example: Psychiatric Morbidity Survey: Selected individuals from households Inclusion probabilities:

63. 63 Horvitz-Thompson estimator- unequal probability sampling Let’s try and use Bias is large if inclusion probabilities tend to increase or decrease systematically with y i

64. 64 Use weighting to correct for bias:

65. 65 Horvitz-Thompson estimator is widely used f.ex., in official statistics

66. 66 Note that the variance is small if we determine the inclusion probabilities such that Of course, we do not know the value of y i when planning the survey, use known auxiliary x i and choose

67. 67 Example: Population of 3 elephants, to be shipped. Needs an estimate for the total weight Weighing an elephant is no simple matter. Owner wants to estimate the total weight by weighing just one elephant. Knows from earlier: Elephant 2 has a weight y 2 close to the average weight. Wants to use this elephant and use 3y 2 as an estimate However: To get an unbiased estimator, all inclusion probabilities must be positive.

68. 68 Sampling design: The weights: 1,2, 4 tons, total = 7 tons H-T estimator: Hopeless! Always far from true total of 7 Can not be used, even though

69. 69 Problem: The planned estimator, even though not a SRS: Possible values: 3, 6, 12

70. 70

71. 71 Variance estimate for H-T estimator Assume the size of the sample is determined in advance to be n.

72. 72 Can always compute the variance estimate!! Since, necessarily p ij > 0 for all i,j in the sample s But: If not all p ij > 0, should not use this estimate! It can give very incorrect estimates The variance estimate can be negative, but for most sampling designs it is always positive

73. 73 A modified H-T estimator Consider first estimating the population mean An obvious choice: Alternative: Estimate N as well, whether N is known or not

74. 74

75. 75 If sample size varies then the “ratio” estimator performs better than the H-T estimator, the ratio is more stable than the numerator Example:

76. 76 H-T estimator varies because n varies, while the modified H-T is perfectly stable

77. 77 Review of Advantages of Probability Sampling Objective basis for inference Permits unbiased or approximately unbiased estimation Permits estimation of sampling errors of estimators –Use central limit theorem for confidence interval –Can choose n to reduce SE or CV for estimator

78. 78 Outstanding issues in design-based inference Estimation for subpopulations, domains Choice of sampling design – –discuss several different sampling designs –appropriate estimators More on use of auxiliary information to improve estimates More on variance estimation

79. 79 Estimation for domains Domain (subpopulation): a subset of the population of interest Ex: Population = all adults aged 16-64 Examples of domains: –Women –Adults aged 35-39 –Men aged 25-29 –Women of a certain ethnic group –Adults living in a certain city Partition population U into D disjoint domains U 1,…,U d,..., U D of sizes N 1,…,N d,…,N D

80. 80 Estimating domain means Simple random sample from the population e.g., proportion of divorced women with psychiatric problems. Note: n d is a random variable

81. 81 The estimator is a ratio estimator:

82. 82

83. 83 Can then treat s d as a SRS from U d Whatever size of n is, conditional on n d, s d is a SRS from U d – conditional inference Example: Psychiatric Morbidity Survey 1993 Proportions with psychiatric problems Domain d ndnd SE women 49330.18 Divorced women 3140.29

84. 84 Estimating domain totals N d is known: Use N d unknown, must be estimated

85. 85 Stratified sampling Basic idea: Partition the population U into H subpopulations, called strata. N h = size of stratum h, known Draw a separate sample from each stratum, s h of size n h from stratum h, independently between the strata In social surveys: Stratify by geographic regions, age groups, gender Ex –business survey. Canadian survey of employment. Establishments stratified by oStandard Industrial Classification – 16 industry divisions oSize – number of employees, 4 groups, 0-19, 20-49, 50- 199, 200+ oProvince – 12 provinces Total number of strata: 16x4x12=768

86. 86 Reasons for stratification 1.Strata form domains of interest for which separate estimates of given precision is required, e.g. strata = geographical regions 2.To “spread” the sample over the whole population. Easier to get a representative sample 3.To get more accurate estimates of population totals, reduce sampling variance 4.Can use different modes of data collection in different strata, e.g. telephone versus home interviews

87. 87 Stratified simple random sampling The most common stratified sampling design SRS from each stratum Notation:

88. 88 t h = y-total for stratum h: Consider estimation of t h : Assuming no auxiliary information in addition to the “stratifying variables” The stratified estimator of t:

89. 89 A weighted average of the sample stratum means. Properties of the stratified estimator follows from properties of SRS estimators. Notation:

90. 90 Estimated variance is obtained by estimating the stratum variance with the stratum sample variance Approximate 95% confidence interval if n and N-n are large:

91. 91 Estimating population proportion in stratified simple random sampling p h : proportion in stratum h with a certain characteristic A p is the population mean: p = t/N Stratum mean estimator: Stratified estimator of the total t = number of units in the with characteristic A:

92. 92 Estimated variance:

93. 93 Allocation of the sample units Important to determine the sizes of the stratum samples, given the total sample size n and given the strata partitioning –how to allocate the sample units to the different strata Proportional allocation –A representative sample should mirror the population –Strata proportions: W h =N h /N –Strata sample proportions should be the same: n h /n = W h –Proportional allocation:

94. 94 The stratified estimator under proportional allocation The equally weighted sample mean ( sample is self- weighting: Every unit in the sample represents the same number of units in the population, N/n)

95. 95 Variance and estimated variance under proportional allocation

96. 96 The estimator in simple random sample: Under proportional allocation: but the variances are different:

97. 97 Total variance = variance within strata + variance between strata Implications: 1.No matter what the stratification scheme is: Proportional allocation gives more accurate estimates of population total than SRS 2.Choose strata with little variability, smaller strata variances. Then the strata means will vary more and between variance becomes larger and precision of estimates increases compared to SRS

98. 98 Optimal allocation If the only concern is to estimate the population total t: Choose n h such that the variance of the stratified estimator is minimum Solution depends on the unkown stratum variances If the stratum variances are approximately equal, proportional allocation minimizes the variance of the stratified estimator

99. 99 Result follows since the sample sizes must add up to n

100. 100 Called Neyman allocation (Neyman, 1934) Should sample heavily in strata if –The stratum accounts for a large part of the population –The stratum variance is large If the stratum variances are equal, this is proportional allocation Problem, of course: Stratum variances are unknown –Take a small preliminary sample (pilot) –The variance of the stratified estimator is not very sensitive to deviations from the optimal allocation. Need just crude approximations of the stratum variances

101. 101 Optimal allocation when considering the cost of a survey C represents the total cost of the survey, fixed – our budget c 0 : overhead cost, like maintaining an office c h : cost of taking an observation in stratum h –Home interviews: traveling cost +interview –Telephone or postal surveys: c h is the same for all strata – In some strata: telephone, in others home interviews Minimize the variance of the stratified estimator for a given total cost C

102. 102 Solution:

103. 103 We can express the optimal sample sizes in relation to n In particular, if c h = c for all h:

104. 104 Other issues with optimal allocation Many survey variables Each variable leads to a different optimal solution –Choose one or two key variables –Use proportional allocation as a compromise If n h > N h, let n h =N h and use optimal allocation for the remaining strata If n h =1, can not estimate variance. Force n h =2 or collapse strata for variance estimation Number of strata: For a given n often best to increase number of strata as much as possible. Depends on available information

105. 105 Sometimes the main interest is in precision of the estimates for stratum totals and less interest in the precision of the estimate for the population total Need to decide n h to achieve desired accuracy for estimate of t h, discussed earlier –If we decide to do proportional allocation, it can mean in small strata (small N h ) the sample size n h must be increased

106. 106 Poststratification Stratification reduces the uncertainty of the estimator compared to SRS In many cases one wants to stratify according to variables that are not known or used in sampling Can then stratify after the data have been collected Hence, the term poststratification The estimator is then the usual stratified estimator according to the poststratification If we take a SRS and N-n and n are large, the estimator behaves like the stratified estimator with proportional allocation

107. 107 Poststratification to reduce nonresponse bias Poststratification is mostly used to correct for nonresponse Choose strata with different response rates Poststratification amounts to assuming that the response sample in poststratum h is representative for the nonresponse group in the sample from poststratum h

108. 108 Systematic sampling Idea:Order the population and select every kth unit Procedure: U = {1,…,N} and N=nk + c, c < n 1.Select a random integer r between 1 and k, with equal probability 2.Select the sample s r by the systematic rule s r = {i: i = r + (j-1)k: j= 1, …, n r } where the actual sample size n r takes values [N/k] or [N/k] +1 k : sampling interval = [N/n] Very easy to implement: Visit every 10 th house or interview every 50 th name in the telephone book

109. 109 k distinct samples each selected with probability 1/k Unlike in SRS, many subsets of U have zero probability Examples: 1) N =20, n=4. Then k=5 and c=0. Suppose we select r =1. Then the sample is {1,6,11,16} 5 possible distinct samples. In SRS: 4845 distinct samples 2) N= 149, n = 12. Then k = 12, c=5. Suppose r = 3. s 3 = {3,15,27,39,51,63,75,87,99,111,123,135,147} and sample size is 13 3) N=20, n=8. Then k=2 and c = 4. Sample size is n r =10 4) N= 100 000, n = 1500. Then k = 66, c=1000 and c/k =15.15 with [c/k]=15. n r = 1515 or 1516

110. 110 Estimation of the population total These estimators are approximately the same:

111. 111 Advantage of systematic sampling: Can be implemented even where no population frame exists E.g. sample every 10 th person admitted to a hospital, every 100 th tourist arriving at LA airport.

112. 112 The variance is small if Or, equivalently, if the values within the possible samples s r are very different; the samples are heterogeneous Problem: The variance cannot be estimated properly because we have only one observation of t(s r )

113. 113 Systematic sampling as Implicit Stratification In practice: Very often when using systematic sampling (common design in national statistical institutes): The population is ordered such that the first k units constitute a homogeneous “stratum”, the second k units another “stratum”, etc. Implicit strataUnits 11,2….,k 2k+1,…,2k :: n = N/k assumed(n-1)k+1,.., nk Systematic sampling selects 1 unit from each stratum at random

114. 114 Systematic sampling vs SRS Systematic sampling is more efficient if the study variable is homogeneous within the implicit strata –Ex: households ordered according to house numbers within neighbourhooods and study variable related to income Households in the same neighbourhood are usually homogeneous with respect socio-economic variables If population is in random order (all N! permutations are equally likely): systematic sampling is similar to SRS Systematic sampling can be very bad if y has periodic variation relative to k: –Approximately: y 1 = y k+1, y 2 = y k+2, etc

115. 115 Variance estimation No direct estimate, impossible to obtain unbiased estimate If population is in random order: can use the variance estimate form SRS as an approximation Develop a conservative variance estimator by collapsing the “implicit strata”, overestimate the variance The most promising approach may be: Under a statistical model, estimate the expected value of the design variance Typically, systematic sampling is used in the second stage of two-stage sampling (to be discussed later), may not be necessary to estimate this variance then.

116. 116 Cluster sampling and multistage sampling Sampling designs so far: Direct sampling of the units in a single stage of sampling Of economial and practical reasons: may be necessary to modify these sampling designs –There exists no population frame (register: list of all units in the population), and it is impossible or very costly to produce such a register –The population units are scattered over a wide area, and a direct sample will also be widely scattered. In case of personal interviews, the traveling costs would be very high and it would not be possible to visit the whole sample

117. 117 Modified sampling can be done by 1.Selecting the sample indirectly in groups, called clusters; cluster sampling –Population is grouped into clusters –Sample is obtained by selecting a sample of clusters and observing all units within the clusters –Ex: In Labor Force Surveys: Clusters = Households, units = persons 2.Selecting the sample in several stages; multistage sampling

118. 118 3.In two-stage sampling: Population is grouped into primary sampling units (PSU) Stage 1: A sample of PSUs Stage 2: For each PSU in the sample at stage 1, we take a sample of population units, now also called secondary sampling units (SSU) Ex: PSUs are often geographical regions

119. 119 Examples 1.Cluster sampling. Want a sample of high school students in a certain area, to investigate smoking and alcohol use. If a list of high school classes is available,we can then select a sample of high school classes and give the questionaire to every student in the selected classes; cluster sampling with high school class being the clusters 2.Two-stage cluster sampling. If a list of classes is not available, we can first select high schools, then classes and finally all students in the selected classes. Then we have 2-stage cluster sample. 1.PSU = high school 2.SSU = classes 3.Units = students

120. 120 Psychiatric Morbidity Survey is a 4-stage sample –Population: adults aged 16-64 living in private households in Great Britain –PSUs = postal sectors –SSUs = addresses –3SUs = households –Units = individuals Sampling process: 1)200 PSUs selected 2)90 SSUs selected within each sampled PSU (interviewer workload) 3)All households selected per SSU 4)1 adult selected per household

121. 121 Cluster sampling Number of clusters in the population : N Number of units in cluster i: M i

122. 122 Simple random cluster sampling Ratio-to-size estimator Use auxiliary information: Size of the sampled clusters Approximately unbiased with approximate variance

123. 123 Note that this ratio estimator is in fact the usual sample mean based estimator with respect to the y- variable And corresponding estimator of the population mean of y is Can be used also if M is unknown

124. 124 Estimator’s variance is highly influenced by how the clusters are constructed. Note: The opposite in stratified sampling Typically, clusters are formed by “nearby units’ like households, schools, hospitals because of economical and practical reasons, with little variation within the clusters: Simple random cluster sampling will lead to much less precise estimates compared to SRS, but this is offset by big cost reductions Sometimes SRS is not possible; information only known for clusters

125. 125 Design Effects A design effect (deff) compares efficiency of two design- estimation strategies (sampling design and estimator) for same sample size Now: Compare Strategy 1:simple random cluster sampling with ratio estimator Strategy 2: SRS, of same sample size m, with usual sample mean estimator In terms of estimating population mean:

126. 126 The design effect of simple random cluster sampling, SCS, is then Estimated deff: In probation example: Conclusion: Cluster sampling is much less efficient

127. 127 Two-stage sampling Basic justification: With homogeneous clusters and a given budget, it is inefficient to survey all units in the cluster- can instead select more clusters Populations partioned into N primary sampling units (PSU) Stage 1: Select a sample s I of PSUs Stage 2: For each selected PSU i in s I : Select a sample s i of units (secondary sampling units, SSU) The cluster totals t i must be estimated from the sample

128. 128 General two-stage sampling plan:

129. 129 Suggested estimator for population total t : Unbiased estimator

130. 130 1.The first component expresses the sampling uncertainty on stage 1, since we are selecting a sample of PSU’s. It is the variance of the HT-estimator with t i as observations 2.The second component is stage 2 variance and tells us how well we are able to estimate each t i in the whole population 3.The second component is often negligible because of little variability within the clusters

131. 131 A special case: Clusters of equal size and SRS on stage 1 and stage 2 Self-weighting sample: equal inclusion probabilities for all units in the population

132. 132 Unequal cluster sizes. PPS – SRS sampling In social surveys: good reasons to have equal inclusion probabilities (self-weighting sample) for all units in the population (similar representation to all domains) Stage 1: Select PSUs with probability proportional to size M i Stage 2: SRS (or systematic sample) of SSUs Such that sample is self-weighting m i = m/n = equal sample sizes in all selected PSUs

133. 133 Remarks Usually one interviewer for each selected PSU First stage sampling is often stratified PPS With self-weighting PPS-SRS: –equal workload for each interviewer –Total sample size m is fixed