1. Multiple Indicator Cluster Surveys Data Dissemination and Further Analysis Workshop Sample and Survey Characteristics, Data Quality and Sampling Error Tables in MICS Reports MICS4 Data Dissemination and Further Analysis Workshop

2. Response rates and background characteristics Set of 6 tables that: Presents sample coverage and characteristics of households and respondents Age and sex distribution of survey population Characteristics of Respondents

3. Table HH.1: Results of household, women's, men's and under-5 interviews Number of households, women, men, and children under 5 by results of the household, women's, men's and under-5's interviews, and household, women's, men's and under-5's response rates, Country, Year Residence Region UrbanRural Region 1Region 2Region 3Region 4Region 5Total Households Sampled Occupied Interviewed Household response rate Women Eligible Interviewed Women's response rate Women's overall response rate Men Eligible Interviewed Men's response rate Men's overall response rate Children under 5 Eligible Mothers/caretakers interviewed Under-5's response rate Under-5's overall response rate The denominator for the household response rate is the number of households found to be occupied during fieldwork (HH9 = 01, 02, 04, 07); the numerator is the number of households with complete household questionnaires (HH9 = 01). The denominator for the women’s response rate is the number of eligible women enumerated in the household listing form (HH12); the numerator is the number of women interviewed (HH13). The denominator for the men's response rate is the number of eligible men enumerated in the household listing form (HH13A); the numerator is the number of men interviewed (HH13B). The denominator for the response rate for the questionnaire for children under 5 is the number of under-5 children identified in the household listing form (HH14); the numerator is the number of complete questionnaires for children under 5 (HH15). Overall response rates are calculated for women, men and under-5's by multiplying the household response rate with the women's, men's and under-5's response rates, respectively.

4. Table HH.2: Household age distribution by sex Percent and frequency distribution of the household population by five-year age groups, dependency age groups, and by child (age 0- 17 years) and adult populations (age 18 or more), by sex, Country, Year Males Females Total NumberPercent NumberPercent NumberPercent Age 0-4 5-9 10-14 15-19 20-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64 65-69 70-74 75-79 80-84 85+ Missing/DK Dependency age groups 0-14 15-64 65+ Missing/DK Child and adult populations Children age 0-17 years Adults age 18+ years Missing/DK Total 100.0

5. Table HH.3: Household composition Percent and frequency distribution of households by selected characteristics, Country, Year Weighted percent Number of households WeightedUnweighted Sex of household head Male Female Region Region 1 Region 2 Region 3 Region 4 Region 5 Residence Urban Rural Number of household members 1 2 3 4 5 6 7 8 9 10+ Education of household head None Primary Secondary Higher Religion/Language/Ethnicity of household head Group 1 Group 2 Group 3 Total100.0 Households with at least One child age 0-4 years One child age 0-17 years One woman age 15-49 years One man age 15-59 years Mean household size Total weighted and unweighted numbers of households should be equal when normalized sample weights are used.

6. Table HH.5: Under-5's background characteristics Percent and frequency distribution of children under five years of age by selected characteristics, Country, Year Weighted percent Number of under-5 children WeightedUnweighted Sex Male Female Region Region 1 Region 2 Region 3 Region 4 Region 5 Residence Urban Rural Age 0-5 months 6-11 months 12-23 months 24-35 months 36-47 months 48-59 months Mother’s education* None Primary Secondary Higher Wealth index quintile Poorest Second Middle Fourth Richest Religion/Language/Ethnicity of household head Group 1 Group 2 Group 3 Total100.0 * Mother's education refers to educational attainment of mothers and caretakers of children under 5. Total weighted and unweighted numbers of children under 5 should be equal when normalized sample weights are used.

7. Data quality tables One of the MICS primary goals is to produce high quality, statistically sound and internationally comparable estimates of indicators. The quality of MICS data is assured by several processes: Recommended training and field work supervision Double data entry, consistency checks, secondary editing Field check tables generated on a regular bases with goal to indicate potential problems in the field, etc.

8. Data quality tables After field work is completed 16 tables are produced for assessment of data quality. Intended to check distributions, heaping, understatement or overstatement, sex ratios, eligibility and coverage, out- transference of eligible persons, the extent of missing information, outliers, sex ratios, quality of anthropometric measurements. Useful for understanding quality issues, familiarity with issues in data sets, indicative of the quality of training and implementation.

9. Table DQ.1: Age distribution of household population Single-year age distribution of household population by sex, Country, Year MalesFemalesMalesFemales NumberPercent NumberPercent NumberPercent NumberPercent 045 146 247 348 449 550 651 752 853 954 1055 1156 1257 …..…. 3782 3883 3984 4085+ 41 42DK/Missing 43 44 Total100.0 Typical data quality issues: Heaping on ages with digits ending with 0 and 5. If age reporting is good, the distribution should be smooth. The table should also provide insights into overreporting or underreporting at certain age groups or intervals, and the extent of missing information on age. Deficits at ages 4, 15, and 49, excesses at ages 5 and 6, 14, and 50 might be indicative of out-transference of ages to avoid administering individual questionnaires.

10. Age distribution of household population, example country, 2010

11. Table DQ.2: Age distribution of eligible and interviewed women Household population of women age 10-54, interviewed women age 15-49, and percentage of eligible women who were interviewed, by five-year age groups, Country, Year Household population of women age 10-54 years Interviewed women age 15-49 years Percentage of eligible women interviewed (Completion rate) Number Percent Age 10-14na 15-19 20-24 25-29 30-34 35-39 40-44 45-49 50-54na Total (15-49)100.0 Ratio of 50-54 to 45-49 Typical data quality issues: In countries with growing populations, the percentages in each age group of women should decline with age (Column B). The last column shows whether the survey was equally effective in interviewing women in all age groups - typically, some surveys fail to interview the younger women, sometimes because of problems in sample implementation, sometimes because of interviewers' reluctance to interview young women. These figures should be high, preferably over 95 percent, or at least 90 percent, and should not vary much by age. The distribution in Column D should be similar to the distribution in Column B If completion rates vary greatly by age and fall below 85 percent in 2 or 3 groups, say for groups age 15 to 24, it may be necessary to re-calculate sample weights by taking age-specific non-response into account. Failure to do so may lead to biased estimates of indicators which typically vary by age of women. Weights used for both household population of women (Column B) and interviewed women (Column D) are household weights. Age is based on the household schedule. Table should be run unweighted if major problems are identified.

12. Table DQ.2: Age distribution of eligible and interviewed women Household population of women age 10-54, interviewed women age 15-49, and percentage of eligible women who were interviewed, by five-year age groups, example country, year Household population of women age 10-54 Interviewed women age 15-49 Percentage of eligible women interviewed (Completion rate) Number Percent Age10-146011... 15-193950331820.8 84.0 20-243423301118.9 88.0 25-293418307319.3 89.9 30-342607235014.7 90.2 35-392104191912.0 91.2 40-44147312898.1 87.5 45-49112110006.3 89.2 50-541407... Total (15-49) 1809515959100.0 88.2 Ratio of 50-54 to 45-491.26 Age distribution of eligible and interviewed women, example country, 2010

13. Table DQ.3: Age distribution of under-5s in household and under-5 questionnaires Household population of children age 0-7, children age 0-4 whose mothers/caretakers were interviewed, and percentage of under-5 children whose mothers/caretakers were interviewed, by single ages, Country, Year Household population of children 0-7 years Interviewed under-5 children Percentage of eligible under-5s interviewed (Completion rate) Number Percent Age 0 1 2 3 4 5na 6 7 Total (0-4)100.0 Ratio of 5 to 4 Typical data quality issues: In countries with growing populations, the numbers of children at each age (Column B) should be declining, The table is intended to provide information on the efficiency of the survey in collecting information on under-5s. Distribution of children by age in the household questionnaire should be smooth, with little or no heaping on age 5. Heaping on age 5 may be indicative of out-transference of children age 0-4 to outside the eligibility range. Percentages in the last column (completion rates) should be over 90, preferably over 95. Weights used for both household population of children and under-5 interviews are household weights. Age is based on the household schedule. Table should be run unweighted if major problems are identified.

14. Table DQ.4: Women's completion rates by socio-economic characteristics of households Household population of women age 15-49, interviewed women age 15-49, and percentage of eligible women who were interviewed, by selected social and economic characteristics of the household, Country, Year Household population of women age 15-49 years Interviewed women age 15-49 years Percent of eligible women interviewed (Completion rates) NumberPercent NumberPercent Region Region 1 Region 2 Region 3 Region 4 Region 5 Area Urban Rural Household size 1-3 4-6 7+ Education of household head None Primary Secondary + Wealth index quintiles Poorest Second Middle Fourth Richest Religion/Language/Ethnicity of household head Group 1 Group 2 Group 3 Total 100.0 Typical data quality issues: Completion rates by socio-economic background characteristics should be similar across socio-economic groups. In cases when completion rates vary greatly by background characteristics, the sample may be biased. Completion rates by regions and urban-rural residence are reflected in sample weights when the sample design is based on regions and urban-rural residence. While this "corrects" for differential completion rates by these characteristics, it does not necessarily mean that the sample is no longer biased in terms of other socio-economic characteristics. Weights for both household population of women and interviewed women are household weights. Table should be run unweighted if major problems are identified.

15. Table DQ.5: Completion rates for under-5 questionnaires by socio-economic characteristics of households Household population of under-5 children, under-5 questionnaires completed, and percentage of under-5 children for whom interviews were completed, by selected socio-economic characteristics of the household, Country, Year Household population of under-5 children Interviewed under- 5 children Percent of eligible under-5s with completed under-5 questionnaires (Completion rates) NumberPercent NumberPercent Region Region 1 Region 2 Region 3 Region 4 Region 5 Area Urban Rural Household size 1-3 4-6 7+ Education of household head None Primary Secondary + Wealth index quintiles Poorest Second Middle Fourth Richest Religion/Language/Ethnicity of household head Group 1 Group 2 Group 3 Total 100.0 Typical data quality issues: Completion rates by socio-economic background characteristics should be similar across socio- economic groups. In cases when completion rates vary greatly by background characteristics, the sample may be biased. Completion rates by regions and urban-rural residence are reflected in sample weights when the sample design is based on regionsand urban-rural strata. While this "corrects" for differential response rates by these characteristics, it does not necessairly mean that the sample is no longer biased in terms of other socio-economic characteristics. Weights for both household population of children and interviewed children are household weights. Table should be run unweighted if major problems are identified.

16. Table DQ.5: Completion rates for under-5 questionnaires by socio-economic characteristics of households Household population of under-5 children, under-5 questionnaires completed, and percentage of under-5 children for whom interviews were completed, by selected socio-economic characteristics of the household, example country, 2010 Household population of under-5 children Interviewed under-5 children Percent of eligible under-5s with completed under-5 questionnaires (Completion rates) AreaUrbain376420.9353220.5 93.8 Rural1423579.11372879.5 96.4 Household size 1-3359920.014258.396.0 4-6927951.6725842.096.6 7+512028.4857749.795.3 Mother's education None1097861.01050860.995.7 Primary387821.5373321.696.3 Secondary +302716.8290516.896.0 Missing/DK116.6114.798.1 Wealth index quintiles Poorest349019.4337919.696.8 Second371020.6356120.696.0 Middle382821.3370421.596.8 Fourth379921.1365221.296.1 Richest317217.6296517.2 93.5 Total 17999100.017260100.095.9 Completion rates for under-5 questionnaires by socio-economic characteristics of households, example country, 2010

17. Table DQ.6: Completeness of reporting Percentage of observations that are missing information for selected questions and indicators, Country, Year Questionnaire and type of missing informationReference group Percent with missing/incomplete information* Number of cases Household AgeAll household members Salt test resultAll households interviewed that have salt Starting time of interviewAll households interviewed Ending time of interviewAll households interviewed Women Woman's date of birthAll women age 15-49 Only month Both month and year Date of first birthAll women age 15-49 with at least one live birth Only month Both month and year Completed years since first birth All women age 15-49 with at least one live birth with year of first birth unknown Date of last birthAll women age 15-49 with a live birth in last 2 years Only month Both month and year Date of first marriage/unionAll ever married women age 15-49 Only month Both month and year Age at first marriage/union All ever married women age 15-49 with year of first marriage not known Age at first intercourseAll women age 15-24 who have ever had sex Time since last intercourseAll women age 15-24 who have ever had sex Starting time of interviewAll women interviewed Ending time of interviewAll women interviewed Under-5 Date of birthAll under-5 children Only month Both month and year Anthropometric measurementsAll under-5 children Weight Height Both weight and height Starting time of interviewAll under-5 children Ending time of interviewAll under-5 children * Includes "Don't know" responses Typical data quality issues: Surveys always have cases with missing information. The extent of missing information is important, because it can result in biased results if such proportions are high. Particularly informative about the quality of survey is the extent of missing information on measurements, ages, and dates of events.

18. Completeness of reporting, example country, year Under 5 questionnaire Women questionnaire

19. Table DQ.7: Completeness of information for anthropometric indicators Distribution of children under 5 by completeness of information for anthropometric indicators, Country, Year Valid weight and date of birth Reason for exclusion from analysis Total Percent of children excluded from analysis Number of children under 5 Weight not measured Incomplete date of birth Weight not measured, incomplete date of birth Flagged cases (outliers) Weight by age <6 months100.0 6-11 months100.0 12-23 months100.0 24-35 months100.0 36-47 months100.0 48-59 months100.0 Total Valid height and date of birth Reason for exclusion from analysis Total Percent of children excluded from analysis Number of children under 5 Height not measured Incomplete date of birth Height not measured, incomplete date of birth Flagged cases (outliers) Height by age <6 months100.0 6-11 months100.0 12-23 months100.0 24-35 months100.0 36-47 months100.0 48-59 months100.0 Total Valid weight and height Reason for exclusion from analysis Total Percent of children excluded from analysis Number of children under 5 Weight not measured Height not measured Weight not measured, height not measured Flagged cases (outliers) Weight by height <6 months100.0 6-11 months100.0 12-23 months100.0 24-35 months100.0 36-47 months100.0 48-59 months100.0 Total Typical data quality issues:Under-5 children may be excluded from anthropometric analysis due to a number of reasons. Column B shows the percentage of under-5 children who are included in anthropometric analysis for each of the three anthropometric indicators (underweight, stunting and wasting). Both in terms of the total rows and across age groups, these percentages should be above 90 percent, preferably 95 percent. Column H shows the percentage of under-5 children excluded from analyses.

20. Completeness of information for anthropometric indicators, example country, year Table DQ.7: Completeness of information for anthropometric indicators Distribution of children under 5 by completeness of information for anthropometric indicators, country, year Valid weight and date of birth Reason for exclusion from analysis Total Percent of children excluded from analysis Number of children under 5 Weight not measured Incomplete date of birth Weight not measured, incomplete date of birth Flagged cases (outliers) Weight by age<6 months84.2.3.0 15.1 100.015.8304 6-11 months90.0.0 10.0 100.010.0350 12-23 months87.5.4.1.0 12.0 100.012.5711 24-35 months83.2.3.5.0 16.1 100.016.8654 36-47 months82.4.9.6.0 16.2 100.017.6697 48-59 months84.3.2.9.0 14.7 100.015.7586 Total 84.9.4.0 14.2 100.015.13302 Valid weight and date of birth Reason for exclusion from analysis Total Percent of children excluded from analysis Number of children under 5 Weight not measured Incomple te date of birth Weight not measured, incomplete date of birth Flagged cases (outliers) Weight by age<6 months86.8.1 3.8.0 9.4 100.013.21867 6-11 months86.7.3 6.0.0 6.9 100.013.31615 12-23 months82.1.1 11.1.0 6.7 100.017.92964 24-35 months72.8.1 18.5.0 8.6 100.027.23421 36-47 months69.5.1 21.6.1 8.7 100.030.53670 48-59 months65.8.1 24.1.0 10.0 100.034.23469 Total 75.1.1 16.2.0 8.5 100.024.917006 Example 1 Example 2

21. Table DQ.8: Heaping in anthropometric measurements Distribution of weight and height/length measurements by digits reported for decimals, Country, Year WeightHeight or length DigitsNumberPercent NumberPercent 0 1 2 3 4 5 6 7 8 9 0 or 5 Total 100.0 Typical data quality issues: Under normal circumstances, approximately 10 percent of anthropometric measurements should be reported for each of the digits for the decimals. Significant excesses over 10 percent are indicative of heaping, and therefore quality problems in anthropometric measurements, either due to truncation or rounding. Typically, more heaping is expected in height/length than weight measurements.

22. Table DQ.8: Heaping in anthropometric measurements Distribution of weight and height/length measurements by digits reported for decimals, country, year WeightHeight NumberPercentNumberPercent Digits 0199712.8423427.1 115019.612137.8 2163810.5188512.1 314839.515279.8 413918.911627.4 514869.5187612.0 614919.610046.4 7156710.010046.4 815079.78255.3 915399.98875.7 0 or 5348322.3611039.1 Total15600100.015617100.0 Heaping in anthropometric measurements, example country, year

23. Table DQ.9: Observation of bednets and places for hand washing Percentage of bednets in all households interviewed observed by the interviewer, and percentage of places for handwashing observed by the interviewer in all interviewed households, Country, Year Percentage of bednets observed by interviewer Total number of bednets Place for handwashing Total Number of households interviewed Observed Not observed Not in the dwelling, plot or yard No permission to seeOther Region Region 1 Region 2 Region 3 Region 4 Region 5 Area Urban Rural Wealth index quintiles Poorest Second Middle Fourth Richest Total Typical data quality issues: Interviewers are required to observe and record the type of bednets in households. Observation of bednets is likely to lead to improved data quality. Interviewers are also required to observe the place for handwashing for the presence of water and soap. Both Columns B and D should not be less than 90 percent. Household members may be reluctant to let interviewers observe places for handwashing or bednets in the rooms of the house, particularly bedrooms. This might in turn be related to cultural and social characteristics of the households. For this reason, percentages of bednets and places for handwashing are provided here by regions and urban-rural areas in this table.

24. Table DQ.10: Observation of women's health cards Percent distribution of women with a live birth in the last 2 years by presence of a health card, and the percentage of health cards seen by the interviewers, Country, Year Woman does not have health card Woman has health card Missing/D KTotal Percent of health cards seen by the interviewer (1)/(1+2)*100 Number of women with a live birth in the last two years Seen by the interviewer (1) Not seen by the interviewer (2) Region Region 1100.0 Region 2100.0 Region 3100.0 Region 4100.0 Region 5100.0 Area Urban100.0 Rural100.0 Wealth index quintiles Poorest 100.0 Second 100.0 Middle 100.0 Fourth 100.0 Richest 100.0 Total 100.0 Typical data issues: Interviewers are required to ask respondents if they have health cards, and if so, ask to see these cards (MN5 in Women;s Questionnaire). These cards are then used by the interviewer to record information on tetanus toxoid vaccinations during pregnancy, or any other useful information on the card. Observation of cards is likely to improve the quality of information collected, as the data collected becomes less dependent on the recall of the respondent.

25. Table DQ.11: Observation of under-5s birth certificates Percent distribution of children under 5 by presence of birth certificates,and percentage of birth calendar seen, Country, Year Child does not have birth certificate Child has birth certificate Don't know/MissingTotal Percent of birth certificates seen by the interviewer (1)/(1+2)*100 Number of children under age 5 Seen by the interviewer (1) Not seen by the interviewer (2) Region Region 1100.0 Region 2100.0 Region 3100.0 Region 4100.0 Region 5100.0 Area Urban100.0 Rural100.0 Child's age 0 100.0 1 2 3 4 Total 100.0 Typical data quality issues: Interviewers are required to ask and see the birth certificates of children. This is important for the completion of the Birth Registration module in the Under-5 questionnaire, but may also be useful for obtaining accurate information on children's dates of birth and ages. Percent of birth certificates seen by the interviewer (Column G) are desired to be as high as possible, preferably over 90 percent.

26. Table DQ.12: Observation of vaccination cards Percent distribution of children under 5 by presence of a vaccination card, and the percentage of vaccination cards seen by the interviewers, Country, Year Child does not have vaccination card Child has vaccination card Total Percent of vaccination cards seen by the interviewer (1)/(1+2)*100 Number of children under age 5 Had vaccination card previously Never had vaccinatio n card Seen by the interviewe r (1) Not seen by the interviewer (2) Don't know/Miss ing Region Region 1100.0 Region 2100.0 Region 3100.0 Region 4100.0 Region 5100.0 Area Urban100.0 Rural100.0 Child's age 0 100.0 1 2 3 4 Total 100.0 Typical data quality issues: Interviewers are required to ask to see the vaccination cards of under-5s from the respondent, and copy the information on the cards to the under-5 questionnaire. Information on vaccination cards is believed to be more accurate than information that would be provided by mothers or caretakers, in the absence of vaccination cards. Percentages in Column G is desired to be as high as possible. Particularly important are the results for children age 1, as immunization indicators are based on these children in most countries.

27. Observation of vaccination cards, example country, 2010

28. Table DQ.13: Presence of mother in the household and the person interviewed for the under-5 questionnaire Distribution of children under five by whether the mother lives in the same household, and the person interviewed for the under-5 questionnaire, Country, Year Mother in the householdMother not in the household Total Number of children under 5 Mother interviewed Father interviewed Other adult female interviewed Other adult male interviewed Father interviewed Other adult female interviewed Other adult male interviewed Age 0100.0 1 2 3 4 Total 100.0 Typical data quality issues: The under-5 questionnaire should be administered to the mother, if the mother is listed the household roster. The table is informative on whether the questionnaire was administered to the right person during the fieldwork. Not all information will have been collected from mothers, but cases where the mother is in the household but somebody else was interviewed can be problematic (Columns C, D, and E). "Adult" males and females are defined as those age 15 and above.

29. Table DQ.14: Selection of children age 2-14 years for the child discipline module Percent of households with at least two children age 2-14 years where correct selection of one child for the child discipline module was performed, Country, Year Percent of households where correct selection was performed Number of households with 2 or more children age 2-14 years Region Region 1 Region 2 Region 3 Region 4 Region 5 Area Urban Rural Number of children age 2-14 years 2 3 4 5+ Total Typical data quality issues: In households where 2 or more children age 2-14 years live, interviewers are required to select, according to pre-determined random selection procedures, one child for the child discipline module. Percentages with correct selection should be close to 100.0

30. Table DQ.14: Selection of children age 2-14 years for the child discipline module Percent of households with at least two children age 2-14 years where correct selection of one child for the child discipline module was performed, country, year Percent of households where correct selection was performed Number of households with 2 or more children age 2-14 years AreaUrban 83.5 3656 Rural 85.5 6183 Number of households by number of children 2-14 2 89.3 2746 3 89.1 2430 4 79.8 4663 Total 84.7 9839 Selection of children age 2-14 years for the child discipline module, example country, 2010

31. Table DQ.15: School attendance by single age Distribution of household population age 5-24 by educational level and educational level and grade attended in the current (or most recent) school year, Country, Year Currently attending Number of household members Not attending school Prescho ol Primary school Grade Secondary school Grade Higher than secondary Missing/D K 123456 123456Total Age at beginning of school year 5100.0 6 7 8 9 10100.0 11100.0 12100.0 13100.0 14100.0 15100.0 16100.0 17100.0 18100.0 19100.0 20100.0 21100.0 22100.0 23100.0 24 100.0 Typical data quality issues: The table could be used to look at outliers. Data entry programs do not check age versus educational grade in detail. If data has been collected and entered correctly, one should see cases concentrated over the diagonal, and should not expect such cases as 22 year old persons attending grades in primary school, very young people at grade 6 of secondary school etc. Many cases outside the diagonal would be indicative of both poor fieldwork supervision, as well as poor data entry and (lack of) verification. Before running the table, grades should be adapted to the system in the country. The table assumes 6 years of primary school and 6 years of secondary school. Age at the beginning of the school year is calculated from dates of birth of household members or by rejuvenating household members based on the date of the survey and current age. Levels and grades refer to the current school year, or the most recent school year if data collection was completed between school years.

32. Table DQ.16: Sex ratio at birth among children ever born and living Sex ratio (number of males per 100 females) among children ever born (at birth), children living, and deceased children, by age of women, Country, Year Children Ever BornChildren LivingChildren Deceased Number of women Number of sons ever born Number of daughters ever born Sex ratio at birth Number of sons living Number of daughters livingSex ratio Number of deceased sons Number of deceased daughtersSex ratio Age 15-19 20-24 25-29 30-34 35-39 40-44 45-49 Total Typical data quality issues: Universally, the sex ratio among live births is around 105 males per 100 females, typically ranging from 103 to 107 in sizeable populations (with the exception of populations where sex-selective abortions is widely practiced). The values in column D should be within these ranges. However, since surveys are influenced by chance fluctuations, one should be looking for systematically low or high ratios in all or most of the age groups (in several countries, very young daughters may not be reported, or deaths of males may not be reported). In most populations, death rates at early ages are higher for males than females - hence, the sex ratios among deceased children (Column L) should also be above 100.

33. Sampling Error Tables: Background The sample selected in a survey is one of the many samples that could have been selected (with same design and size). Sampling errors are measures of the variability between all possible samples, which can be estimated from survey results.

34. Sampling Error Tables: Background Calculation of sampling errors is very important; -Provides information on the reliability of your results -Tells you the ranges within which your estimates most probably fall - Provides clues as to the sample sizes (and designs) to be selected in forthcoming surveys

35. Sampling Error Tables: Background MICS4 sample designs are complex designs, usually based on stratified, multi-stage, cluster samples. It is not possible to use straightforward formula for the calculation of sampling errors. Sophisticated approaches have to be used. Versions 13 and above of SPSS are used for this purpose. SPSS uses Taylor linearization method of variance estimation for survey estimates that are means or proportions. This approach is used by most other package programs: Wesvar, Sudaan, Systat, EpiInfo, SAS

36. Sampling Error Tables: Background In MICS4, the objective is to calculate sampling errors for a selection of variables, for the national sample, as well as selected sub-populations, such as urban and rural areas, and regions.

37. Standard error is the square root of the variance – a measure of the variability between all possible samples

38. Coefficient of variation (relative error) is the ratio of SE to the estimate

39. Design effect is the ratio between the SE using the current design and the SE that would result if a simple random sample was used. A DEFT value of 1.0 indicates that the sample is as efficient as a SRS

40. Weighted and unweighted counts

41. Upper and lower confidence limits are calculated as p +/- 2.SE Indicate the ranges within which the estimate would fall in 95 percent of all possible samples of identical design and size

42. Comprehensive knowledge about HIV prevention among young people

43. Thank you