Vietnam Population and AIDS Indicator Survey 2005 ... - Measure DHS

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ESTIMATES OF SAMPLING ERRORS Appendix B The estimates from a sample survey are affected by two types of errors: (1) nonsampling errors, and (2) sampling errors. Nonsampling errors are the results of mistakes made in implementing data collection and data processing, such as failure to locate and interview the correct household, misunderstanding of the questions on the part of either the interviewer or the respondent, and data entry errors. Although numerous efforts were made during the implementation of the 2005 Vietnam Population and AIDS Indicator Survey (VPAIS) to minimize this type of error, nonsampling errors are impossible to avoid and difficult to evaluate statistically. Sampling errors, on the other hand, can be evaluated statistically. The sample of respondents selected in the 2005 VPAIS is only one of many samples that could have been selected from the same population, using the same design and expected size. Each of these samples would yield results that differ somewhat from the results of the actual sample selected. Sampling errors are a measure of the variability between all possible samples. Although the degree of variability is not known exactly, it can be estimated from the survey results. A sampling error is usually measured in terms of the standard error for a particular statistic (mean, percentage, etc.), which is the square root of the variance. The standard error can be used to calculate confidence intervals within which the true value for the population can reasonably be assumed to fall. For example, for any given statistic calculated from a sample survey, the value of that statistic will fall within a range of plus or minus two times the standard error of that statistic in 95 percent of all possible samples of identical size and design. If the sample of respondents had been selected as a simple random sample, it would have been possible to use straightforward formulas for calculating sampling errors. However, the 2005 VPAIS sample is the result of a multi-stage stratified design and, consequently, it was necessary to use more complex formulae. The computer software used to calculate sampling errors for the 2005 VPAIS is the ISSA Sampling Error Module. This module used the Taylor linearization method of variance estimation for survey estimates that are means or proportions. The Taylor linearization method treats any percentage or average as a ratio estimate, r = y/x, where y represents the total sample value for variable y, and x represents the total number of cases in the group or subgroup under consideration. The variance of r is computed using the formula given below, with the standard error being the square root of the variance: 2 SE ( r) = var( r) = 1 − f 2 x H ∑ h = 1 ⎡ ⎢ ⎢⎣ m h m h − 1 ⎛ ⎜ ⎝ m h 2 ∑ z i = 1 hi − 2 z h m h ⎞⎤ ⎟ ⎥ ⎠⎥⎦ in which z hi = y − rx = yhi − rxhi , and h h h z Appendix B | 109
where h represents the stratum which varies from 1 to H, m h is the total number of clusters selected in the h th stratum, y hi is the sum of the weighted values of variable y in the i th cluster in the h th stratum, x hi is the sum of the weighted number of cases in the i th cluster in the h th stratum, and f is the overall sampling fraction, which is so small that it is ignored. In addition to the standard error, ISSA computes the design effect (DEFT) for each estimate, which is defined as the ratio between the standard error using the given sample design and the standard error that would result if a simple random sample had been used. A DEFT value of 1.0 indicates that the sample design is as efficient as a simple random sample, while a value greater than 1.0 indicates the increase in the sampling error due to the use of a more complex and less statistically efficient design. ISSA also computes the relative error and confidence limits for the estimates. Sampling errors for the 2005 VPAIS are calculated for selected variables considered to be of primary interest for the women’s survey and for men’s surveys, respectively. The results are presented in this appendix for the country as a whole, for urban and rural areas, for 3 geographical regions and for 4 individual provinces. For each variable, the type of statistic (mean, proportion, or rate) and the base population are given in Table B.1. Tables B.2 to B.11 present the value of the statistic (R), its standard error (SE), the number of unweighted (N) and weighted (WN) cases, the design effect (DEFT), the relative standard error (SE/R), and the 95 percent confidence limits (R±2SE), for each variable. The DEFT is considered undefined when the standard error considering simple random sample is zero (when the estimate is close to 0 or 1). The confidence interval (e.g., as calculated for accepting attitudes towards people with HIV) can be interpreted as follows: the overall proportion from the national sample for women 15-49 who answered that they would like to care and help for a relative sick with HIV is 0.230 and its standard error is 0.009. Therefore, to obtain the 95 percent confidence limits, one adds and subtracts twice the standard error to the sample estimate, i.e., 0.230±2×0.009. There is a high probability (95 percent) that the true proportion of women who had used a condom at last high-risk sex is between 0.212 and 0.248. Sampling errors are analyzed for the national women’s sample. The relative standard errors (SE/R) at the national level range between 0.1 percent and 80.3 percent, with an average value of 11.8 percent. The highest relative standard errors are for estimates of very low values (e.g., had higher-risk sex with non-marital non-cohabiting partners in past 12 months). If estimates of very low values (less than 10 percent) were removed, then the average drops to 3 percent. So in general, the relative standard error for most estimates for the country as a whole is small, except for estimates of very small proportions. For the total sample, the value of the design effect (DEFT), averaged over all variables, is 1.73 which means that, due to multi-stage clustering of the sample, the average standard error is increased by a factor of 1.73 over that in an equivalent simple random sample. 110 | Appendix B
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VIETNAM POPULATION AND AIDS INDICAT
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CONTENTS TABLES AND FIGURES .......
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CHAPTER 8 CHILDREN AT RISK 8.1 Key
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CHAPTER 5 ATTITUDES RELATING TO HIV
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PREFACE The 2005 Vietnam Population
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SUMMARY OF INDICATORS Program area
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Map of Administrative Units of Viet
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international commitments to the pr
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model AIDS Indicator Survey (AIS) q
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All team members assigned to Hai Ph
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HOUSEHOLD POPULATION AND HOUSING CH
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of tile or cement (96 percent), whi
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asset information was collected in
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Figure 2.1 Age Pyramid, Vietnam 200
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Table 2.7 Educational attainment of
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Table 2.8 Household possession of m
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Table 2.10 Use of mosquito nets by
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Table 3.1 Background characteristic
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Table 3.2 Educational attainment of
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Table 3.3 Exposure to mass media Pe
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Table 3.4.2 Employment status: men
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Table 3.6 Age at first marriage Per
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Figure 3.1 Percent Distribution of
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General awareness of AIDS is high a
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Knowledge of all three means of avo
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Table 4.3.2 Beliefs about AIDS: men
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Table 4.4 Knowledge of prevention o
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and men) who reported that the HIV
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5.4 ATTITUDES TOWARDS NEGOTIATING S
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Table 5.3 Adult support of educatio
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HIV/AIDS-RELATED BEHAVIOR 6 6.1 KEY
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Table 6.1.1 Recent sexual activity:
Page 66 and 67: 6.4 MULTIPLE SEXUAL PARTNERS Among
Page 68 and 69: Table 6.2.2 Multiple sexual partner
Page 70 and 71: 6.7 VOLUNTARY HIV COUNSELING AND TE
Page 72 and 73: Women who become pregnant have an o
Page 74 and 75: Table 6.6 Self-reported prevalence
Page 76 and 77: Table 6.7 Prevalence of injections
Page 78 and 79: Table 7.1 Comprehensive knowledge a
Page 80 and 81: Table 7.2 Age at first sex among yo
Page 82 and 83: Table 7.3 Condom use at first sexua
Page 84 and 85: Practically all never-married women
Page 86 and 87: 7.8 ALCOHOL USE DURING SEX Research
Page 88 and 89: Table 7.7 Recent HIV tests among yo
Page 90 and 91: Table 8.1 Children’s living arran
Page 92 and 93: Table 8.3 also shows the same infor
Page 94 and 95: Table 8.5 Succession planning Perce
Page 96 and 97: HIV PREVALENCE IN HAI PHONG PROVINC
Page 98 and 99: Table 9.2 Coverage of HIV testing b
Page 100 and 101: 9.6 HIV PREVALENCE BY BACKGROUND CH
Page 102 and 103: Rutstein, S., K. Johnson, and D. Gw
Page 104 and 105: A.2 SAMPLING FRAME The sampling fra
Page 106 and 107: Table A.5 Sample allocation of expe
Page 108 and 109: Table A.7 Total number of EAs and n
Page 110 and 111: Table A.9 Population distribution a
Page 112 and 113: Table A.11 Sample implementation Pe
Page 114 and 115: Table A.14 Coverage of HIV testing
Page 118 and 119: Table B.1 List of selected variable
Page 120 and 121: Table B.3 Sampling errors for Urban
Page 122 and 123: Table B.5 Sampling errors for North
Page 124 and 125: Table B.7 Sampling errors for South
Page 126 and 127: Table B.9 Sampling errors for Ho Ch
Page 128 and 129: Table B.11 Sampling errors for Quan
Page 130 and 131: Team 4: Responsible for fieldwork i
Page 132 and 133: QUESTIONNAIRES Appendix D Appendix
Page 134 and 135: A. household schedule Now we would
Page 136 and 137: no Questions and filters Coding cat
Page 141 and 142: Department of population and labour
Page 145 and 146: Section2. reproduction no Questions
Page 147 and 148: Section 3. marriage and sexual acti
Page 151 and 152: Last sexual partner 334 check 103:
Page 153 and 154: Section 4. hiv/aids no Questions an
Page 163 and 164: Questions and filters Coding catego
Page 165: Comments about Respondent : Intervi
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