Abstract

The 2002 Vietnam Demographic and Health Survey (VNDHS 2002) is a nationally representative sample survey of 5,665 ever-married women age 15-49 selected from 205 sample points (clusters) throughout Vietnam. It provides information on levels of fertility, family planning knowledge and use, infant and child mortality, and indicators of maternal and child health. The survey included a Community/ Health Facility Questionnaire that was implemented in each of the sample clusters.

The survey was designed to measure change in reproductive health indicators over the five years since the VNDHS 1997, especially in the 18 provinces that were targeted in the Population and Family Health Project of the Committee for Population, Family and Children. Consequently, all provinces were separated into “project” and “nonproject” groups to permit separate estimates for each. Data collection for the survey took place from 1 October to 21 December 2002.

The Vietnam Demographic and Health Survey 2002 (VNDHS 2002) was the third DHS in Vietnam, with prior surveys implemented in 1988 and 1997. The VNDHS 2002 was carried out in the framework of the activities of the Population and Family Health Project of the Committee for Population, Family and Children (previously the National Committee for Population and Family Planning).

The main objectives of the VNDHS 2002 were to collect up-to-date information on family planning, childhood mortality, and health issues such as breastfeeding practices, pregnancy care, vaccination of children, treatment of common childhood illnesses, and HIV/AIDS, as well as utilization of health and family planning services. The primary objectives of the survey were to estimate changes in family planning use in comparison with the results of the VNDHS 1997, especially on issues in the scope of the project of the Committee for Population, Family and Children.

VNDHS 2002 data confirm the pattern of rapidly declining fertility that was observed in the VNDHS 1997. It also shows a sharp decline in child mortality, as well as a modest increase in contraceptive use. Differences between project and non-project provinces are generally small.

Geographic coverage

The 2002 Vietnam Demographic and Health Survey (VNDHS 2002) is a nationally representative sample survey. The VNDHS 1997 was designed to provide separate estimates for the whole country, urban and rural areas, for 18 project provinces and the remaining nonproject provinces as well. Project provinces refer to 18 focus provinces targeted for the strengthening of their primary health care systems by the Government's Population and Family Health Project to be implemented over a period of seven years, from 1996 to 2002 (At the outset of this project there were 15 focus provinces, which became 18 by the creation of 3 new provinces from the initial set of 15). These provinces were selected according to criteria based on relatively low health and family planning status, no substantial family planning donor presence, and regional spread. These criteria resulted in the selection of the country's poorer provinces. Nine of these provinces have significant proportions of ethnic minorities among their population.

Analysis unit

• Household
• Women age 15-49

Universe

The population covered by the 2002 VNDHS is defined as the universe of all women age 15-49 in Vietnam.

Kind of data

Sample survey data

Sampling procedure

The sample for the VNDHS 2002 was based on that used in the VNDHS 1997, which in turn was a subsample of the 1996 Multi-Round Demographic Survey (MRS), a semi-annual survey of about 243,000 households undertaken regularly by GSO. The MRS sample consisted of 1,590 sample areas known as enumeration areas (EAs) spread throughout the 53 provinces/cities of Vietnam, with 30 EAs in each province. On average, an EA comprises about 150 households. For the VNDHS 1997, a subsample of 205 EAs was selected, with 26 households in each urban EA and 39 households for each rural EA. A total of 7,150 households was selected for the survey. The VNDHS 1997 was designed to provide separate estimates for the whole country, urban and rural areas, for 18 project provinces and the remaining nonproject provinces as well. Because the main objective of the VNDHS 2002 was to measure change in reproductive health indicators over the five years since the VNDHS 1997, the sample design for the VNDHS 2002 was as similar as possible to that of the VNDHS 1997.

Although it would have been ideal to have returned to the same households or at least the same sample points as were selected for the VNDHS 1997, several factors made this undesirable. Revisiting the same households would have held the sample artificially rigid over time and would not allow for newly formed households. This would have conflicted with the other major survey objective, which was to provide up-to-date, representative data for the whole of Vietnam. Revisiting the same sample points that were covered in 1997 was complicated by the fact that the country had conducted a population census in 1999, which allowed for a more representative sample frame.

In order to balance the two main objectives of measuring change and providing representative data, it was decided to select enumeration areas from the 1999 Population Census, but to cover the same communes that were sampled in the VNDHS 1997 and attempt to obtain a sample point as close as possible to that selected in 1997. Consequently, the VNDHS 2002 sample also consisted of 205 sample points and reflects the oversampling in the 20 provinces that fall in the World Bank-supported Population and Family Health Project. The sample was designed to produce about 7,000 completed household interviews and 5,600 completed interviews with ever-married women age 15-49.

Mode of data collection

Face-to-face

Research instrument

As in the VNDHS 1997, three types of questionnaires were used in the 2002 survey: the Household Questionnaire, the Individual Woman's Questionnaire, and the Community/Health Facility Questionnaire. The first two questionnaires were based on the DHS Model A Questionnaire, with additions and modifications made during an ORC Macro staff visit in July 2002. The questionnaires were pretested in two clusters in Hanoi (one in a rural area and another in an urban area). After the pretest and consultation with ORC Macro, the drafts were revised for use in the main survey.

a) The Household Questionnaire was used to enumerate all usual members and visitors in selected households and to collect information on age, sex, education, marital status, and relationship to the head of household. The main purpose of the Household Questionnaire was to identify persons who were eligible for individual interview (i.e. ever-married women age 15-49). In addition, the Household Questionnaire collected information on characteristics of the household such as water source, type of toilet facilities, material used for the floor and roof, and ownership of various durable goods.

b) The Individual Questionnaire was used to collect information on ever-married women aged 15-49 in surveyed households. These women were interviewed on the following topics:
- Respondent's background characteristics (education, residential history, etc.); - Reproductive history; - Contraceptive knowledge and use;
- Antenatal and delivery care; - Infant feeding practices; - Child immunization; - Fertility preferences and attitudes about family planning; - Husband's background characteristics; - Women's work information; and - Knowledge of AIDS.

c) The Community/Health Facility Questionnaire was used to collect information on all communes in which the interviewed women lived and on services offered at the nearest health stations. The Community/Health Facility Questionnaire consisted of four sections. The first two sections collected information from community informants on some characteristics such as the major economic activities of residents, distance from people's residence to civic services and the location of the nearest sources of health care. The last two sections involved visiting the nearest commune health centers and intercommune health centers, if these centers were located within 30 kilometers from the surveyed cluster. For each visited health center, information was collected on the type of health services offered and the number of days services were offered per week; the number of assigned staff and their training; medical equipment and medicines available at the time of the visit.

Cleaning operations

The first stage of data editing was implemented by the field editors soon after each interview. Field editors and team leaders checked the completeness and consistency of all items in the questionnaires. The completed questionnaires were sent to the GSO headquarters in Hanoi by post for data processing. The editing staff of the GSO first checked the questionnaires for completeness. The data were then entered into microcomputers and edited using a software program specially developed for the DHS program, the Census and Survey Processing System, or CSPro. Data were verified on a 100 percent basis, i.e., the data were entered separately twice and the two results were compared and corrected. The data processing and editing staff of the GSO were trained and supervised for two weeks by a data processing specialist from ORC Macro. Office editing and processing activities were initiated immediately after the beginning of the fieldwork and were completed in late December 2002.

Response rate

The results of the household and individual

The baseline survey in Tajikistan captures data in the Feed the Future Zones of Influence (ZOI), comprised of 12 of the 24 districts in Khatlon province. A total of 2,000 households in the ZOI were surveyed for the PBS data collection activity. These households are spread across 100 standard enumeration areas in the targeted districts. The survey is comprised of ten CSV files: a children's file, a household-level file, a household member level file, a women's file, several files describing consumption, and two files used to construct the Women's Empowerment in Agriculture Index. This file reports survey results related to households.

Feed the Future (FTF) initiative in Ghana is a collaborative effort that supports country-owned processes and plans for improving food security and nutrition, particularly in the northern part of the country. These datasets cover the interim survey that took place in 2015 and was designed as a follow-up to the baseline survey that happened from 2012 to 2013. The survey covered a range of indicators organized around four groups: (1) economic well-being; (2) women and children anthropometry; (3) hunger and diet diversity; and (4) women's empowerment. The survey design involved two stages in which enumeration areas were selected followed by households. Data was collected in a face-to-face fashion using well-designed questionnaires and other study materials.

The Annual Social and Economic Supplement or March CPS supplement is the primary source of detailed information on income and work experience in the United States. Numerous publications based on this survey are issued each year by the Bureaus of Labor Statistics and Census. A public-use microdata file is available for private researchers, who also produce many academic and policy-related documents based on these data. The Annual Social and Economic Supplement is used to generate the annual Population Profile of the United States, reports on geographical mobility and educational attainment, and detailed analysis of money income and poverty status. The labor force and work experience data from this survey are used to profile the U.S. labor market and to make employment projections. To allow for the same type of in-depth analysis of hispanics, additional hispanic sample units are added to the basic CPS sample in March each year. Additional weighting is also performed so that estimates can be made for households and families, in addition to persons.

Abstract

Different countries have different health outcomes that are in part due to the way respective health systems perform. Regardless of the type of health system, individuals will have health and non-health expectations in terms of how the institution responds to their needs. In many countries, however, health systems do not perform effectively and this is in part due to lack of information on health system performance, and on the different service providers.

The aim of the WHO World Health Survey is to provide empirical data to the national health information systems so that there is a better monitoring of health of the people, responsiveness of health systems and measurement of health-related parameters.

The overall aims of the survey is to examine the way populations report their health, understand how people value health states, measure the performance of health systems in relation to responsiveness and gather information on modes and extents of payment for health encounters through a nationally representative population based community survey. In addition, it addresses various areas such as health care expenditures, adult mortality, birth history, various risk factors, assessment of main chronic health conditions and the coverage of health interventions, in specific additional modules.

The objectives of the survey programme are to: 1. develop a means of providing valid, reliable and comparable information, at low cost, to supplement the information provided by routine health information systems. 2. build the evidence base necessary for policy-makers to monitor if health systems are achieving the desired goals, and to assess if additional investment in health is achieving the desired outcomes. 3. provide policy-makers with the evidence they need to adjust their policies, strategies and programmes as necessary.

Geographic coverage

The survey sampling frame must cover 100% of the country's eligible population, meaning that the entire national territory must be included. This does not mean that every province or territory need be represented in the survey sample but, rather, that all must have a chance (known probability) of being included in the survey sample.

There may be exceptional circumstances that preclude 100% national coverage. Certain areas in certain countries may be impossible to include due to reasons such as accessibility or conflict. All such exceptions must be discussed with WHO sampling experts. If any region must be excluded, it must constitute a coherent area, such as a particular province or region. For example if ¾ of region D in country X is not accessible due to war, the entire region D will be excluded from analysis.

Analysis unit

Households and individuals

Universe

The WHS will include all male and female adults (18 years of age and older) who are not out of the country during the survey period. It should be noted that this includes the population who may be institutionalized for health reasons at the time of the survey: all persons who would have fit the definition of household member at the time of their institutionalisation are included in the eligible population.

If the randomly selected individual is institutionalized short-term (e.g. a 3-day stay at a hospital) the interviewer must return to the household when the individual will have come back to interview him/her. If the randomly selected individual is institutionalized long term (e.g. has been in a nursing home the last 8 years), the interviewer must travel to that institution to interview him/her.

The target population includes any adult, male or female age 18 or over living in private households. Populations in group quarters, on military reservations, or in other non-household living arrangements will not be eligible for the study. People who are in an institution due to a health condition (such as a hospital, hospice, nursing home, home for the aged, etc.) at the time of the visit to the household are interviewed either in the institution or upon their return to their household if this is within a period of two weeks from the first visit to the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

SAMPLING GUIDELINES FOR WHS

Surveys in the WHS program must employ a probability sampling design. This means that every single individual in the sampling frame has a known and non-zero chance of being selected into the survey sample. While a Single Stage Random Sample is ideal if feasible, it is recognized that most sites will carry out Multi-stage Cluster Sampling.

The WHS sampling frame should cover 100% of the eligible population in the surveyed country. This means that every eligible person in the country has a chance of being included in the survey sample. It also means that particular ethnic groups or geographical areas may not be excluded from the sampling frame.

The sample size of the WHS in each country is 5000 persons (exceptions considered on a by-country basis). An adequate number of persons must be drawn from the sampling frame to account for an estimated amount of non-response (refusal to participate, empty houses etc.). The highest estimate of potential non-response and empty households should be used to ensure that the desired sample size is reached at the end of the survey period. This is very important because if, at the end of data collection, the required sample size of 5000 has not been reached additional persons must be selected randomly into the survey sample from the sampling frame. This is both costly and technically complicated (if this situation is to occur, consult WHO sampling experts for assistance), and best avoided by proper planning before data collection begins.

All steps of sampling, including justification for stratification, cluster sizes, probabilities of selection, weights at each stage of selection, and the computer program used for randomization must be communicated to WHO

STRATIFICATION

Stratification is the process by which the population is divided into subgroups. Sampling will then be conducted separately in each subgroup. Strata or subgroups are chosen because evidence is available that they are related to the outcome (e.g. health, responsiveness, mortality, coverage etc.). The strata chosen will vary by country and reflect local conditions. Some examples of factors that can be stratified on are geography (e.g. North, Central, South), level of urbanization (e.g. urban, rural), socio-economic zones, provinces (especially if health administration is primarily under the jurisdiction of provincial authorities), or presence of health facility in area. Strata to be used must be identified by each country and the reasons for selection explicitly justified.

Stratification is strongly recommended at the first stage of sampling. Once the strata have been chosen and justified, all stages of selection will be conducted separately in each stratum. We recommend stratifying on 3-5 factors. It is optimum to have half as many strata (note the difference between stratifying variables, which may be such variables as gender, socio-economic status, province/region etc. and strata, which are the combination of variable categories, for example Male, High socio-economic status, Xingtao Province would be a stratum).

Strata should be as homogenous as possible within and as heterogeneous as possible between. This means that strata should be formulated in such a way that individuals belonging to a stratum should be as similar to each other with respect to key variables as possible and as different as possible from individuals belonging to a different stratum. This maximises the efficiency of stratification in reducing sampling variance.

MULTI-STAGE CLUSTER SELECTION

A cluster is a naturally occurring unit or grouping within the population (e.g. enumeration areas, cities, universities, provinces, hospitals etc.); it is a unit for which the administrative level has clear, nonoverlapping boundaries. Cluster sampling is useful because it avoids having to compile exhaustive lists of every single person in the population. Clusters should be as heterogeneous as possible within and as homogenous as possible between (note that this is the opposite criterion as that for strata). Clusters should be as small as possible (i.e. large administrative units such as Provinces or States are not good clusters) but not so small as to be homogenous.

In cluster sampling, a number of clusters are randomly selected from a list of clusters. Then, either all members of the chosen cluster or a random selection from among them are included in the sample. Multistage sampling is an extension of cluster sampling where a hierarchy of clusters are chosen going from larger to smaller.

In order to carry out multi-stage sampling, one needs to know only the population sizes of the sampling units. For the smallest sampling unit above the elementary unit however, a complete list of all elementary units (households) is needed; in order to be able to randomly select among all households in the TSU, a list of all those households is required. This information may be available from the most recent population census. If the last census was >3 years ago or the information furnished by it was of poor quality or unreliable, the survey staff will have the task of enumerating all households in the smallest randomly selected sampling unit. It is very important to budget for this step if it is necessary and ensure that all households are properly enumerated in order that a representative sample is obtained.

It is always best to have as many clusters in the PSU as possible. The reason for this is that the fewer the number of respondents in each PSU, the lower will be the clustering effect which

The Bangladesh Demographic and Health Survey (BDHS) is part of the worldwide Demographic and Health Surveys program, which is designed to collect data on fertility, family planning, and maternal and child health. The BDHS is intended to serve as a source of population and health data for policymakers and the research community. In general, the objectives of the BDHS are to: assess the overall demographic situation in Bangladesh, assist in the evaluation of the population and health programs in Bangladesh, and advance survey methodology. More specifically, the objective of the BDHS is to provide up-to-date information on fertility and childhood mortality levels; nuptiality; fertility preferences; awareness, approval, and use of family planning methods; breastfeeding practices; nutrition levels; and maternal and child health. This information is intended to assist policymakers and administrators in evaluating and designing programs and strategies for improving health and family planning services in the country.

Abstract

The Bangladesh Demographic and Health Survey (BDHS) is the first of this kind of study conducted in Bangladesh. It provides rapid feedback on key demographic and programmatic indicators to monitor the strength and weaknesses of the national family planning/MCH program. The wealth of information collected through the 1993-94 BDHS will be of immense value to the policymakers and program managers in order to strengthen future program policies and strategies.

The BDHS is intended to serve as a source of population and health data for policymakers and the research community. In general, the objectives of the BDHS are to: - asses the overall demographic situation in Bangladesh, - assist in the evaluation of the population and health programs in Bangladesh, and - advance survey methodology.

More specifically, the BDHS was designed to: - provide data on the family planning and fertility behavior of the Bangladesh population to evaluate the national family planning programs, - measure changes in fertility and contraceptive prevalence and, at the same time, study the factors which affect these changes, such as marriage patterns, urban/rural residence, availability of contraception, breastfeeding patterns, and other socioeconomic factors, and - examine the basic indicators of maternal and child health in Bangladesh.

Geographic coverage

National

Analysis unit

• Household
• Children under five years
• Women age 10-49
• Men

Kind of data

Sample survey data

Sampling procedure

Bangladesh is divided into five administrative divisions, 64 districts (zillas), and 489 thanas. In rural areas, thanas are divided into unions and then mauzas, an administrative land unit. Urban areas are divided into wards and then mahallas. The 1993-94 BDHS employed a nationally-representative, two-stage sample. It was selected from the Integrated Multi-Purpose Master Sample (IMPS), newly created by the Bangladesh Bureau of Statistics. The IMPS is based on 1991 census data. Each of the five divisions was stratified into three groups: 1) statistical metropolitan areas (SMAs) 2) municipalities (other urban areas), and 3) rural areas. In rural areas, the primary sampling unit was the mauza, while in urban areas, it was the mahalla. Because the primary sampling units in the IMPS were selected with probability proportional to size from the 1991 census frame, the units for the BDHS were sub-selected from the IMPS with equal probability to make the BDHS selection equivalent to selection with probability proportional to size. A total of 304 primary sampling units were selected for the BDHS (30 in SMAs, 40 in municipalities, and 234 in rural areas), out of the 372 in the IMPS. Fieldwork in three sample points was not possible, so a total of 301 points were covered in the survey.

Since one objective of the BDHS is to provide separate survey estimates for each division as well as for urban and rural areas separately, it was necessary to increase the sampling rate for Barisal Division und for municipalities relative to the other divisions, SMAs, and rural areas. Thus, the BDHS sample is not self-weighting and weighting factors have been applied to the data in this report.

After the selection of the BDHS sample points, field staffs were trained by Mitra and Associates and conducted a household listing operation in September and October 1993. A systematic sample of households was then selected from these lists, with an average "take" of 25 households in the urban clusters and 37 households in rural clusters. Every second household was identified as selected for the husband's survey, meaning that, in addition to interviewing all ever-married women age 10-49, interviewers also interviewed the husband of any woman who was successfully interviewed. It was expected that the sample would yield interviews with approximately 10,000 ever-married women age 10-49 and 4,200 of their husbands.

Note: See detailed in APPENDIX A of the survey final report.

Sampling deviation

Data collected for women 10-49, indicators calculated for women 15-49. A total of 304 primary sampling units were selected, but fieldwork in 3 sample points was not possible.

Mode of data collection

Face-to-face

Research instrument

Four types of questionnaires were used for the BDHS: a Household Questionnaire, a Women's Questionnaire, a Husbands' Questionnaire, and a Service Availability Questionnaire. The contents of these questionnaires were based on the DHS Model A Questionnaire, which is designed for use in countries with relatively high levels of contraceptive use. Additions and modifications to the model questionnaires were made during a series of meetings with representatives of various organizations, including the Asia Foundation, the Bangladesh Bureau of Statistics, the Cambridge Consulting Corporation, the Family Planning Association of Bangladesh, GTZ, the International Centre for Diarrhoeal Disease Research (ICDDR,B), Pathfinder International, Population Communications Services, the Population Council, the Social Marketing Company, UNFPA, UNICEF, University Research Corporation/Bangladesh, and the World Bank. The questionnaires were developed in English and then translated into and printed in Bangla.

The Household Questionnaire was used to list all the usual members and visitors of selected households. Some basic information was collected on the characteristics of each person listed, including his/her age, sex, education, and relationship to the head of the household. The main purpose of the Household Questionnaire was to identify women and men who were eligible for individual interview. In addition, information was collected about the dwelling itself, such as the source of water, type of toilet facilities, materials used to construct the house, and ownership of various consumer goods.

The Women's Questionnaire was used to collect information from ever-married women age 10-49. These women were asked questions on the following topics: - Background characteristics (age, education, religion, etc.), - Reproductive history, - Knowledge and use of family planning methods, - Antenatal and delivery care, - Breastfeeding and weaning practices, - Vaccinations and health of children under age three, - Marriage, - Fertility preferences, and - Husband's background and respondent's work.

The Husbands' Questionnaire was used to interview the husbands of a subsample of women who were interviewed. The questionnaire included many of the same questions as the Women's Questionnaire, except that it omitted the detailed birth history, as well as the sections on maternal care, breastfeeding and child health.

The Service Availability Questionnaire was used to collect information on the family planning and health services available in and near the sampled areas. It consisted of a set of three questionnaires: one to collect data on characteristics of the community, one for interviewing family welfare visitors and one for interviewing family planning field workers, whether government or non-governent supported. One set of service availability questionnaires was to be completed in each cluster (sample point).

Cleaning operations

All questionnaires for the BDHS were returned to Dhaka for data processing at Mitra and Associates. The processing operation consisted of office editing, coding of open-ended questions, data entry, and editing inconsistencies found by the computer programs. One senior staff member, 1 data processing supervisor, questionnaire administrator, 2 office editors, and 5 data entry operators were responsible for the data processing operation. The data were processed on five microcomputers. The DHS data entry and editing programs were written in ISSA (Integrated System for Survey Analysis). Data processing commenced in early February and was completed by late April 1994.

Response rate

A total of 9,681 households were selected for the sample, of which 9,174 were successfully interviewed. The shortfall is primarily due to dwellings that were vacant, or in which the inhabitants had left for an extended period at the time they were visited by the interviewing teams. Of the 9,255 households that were occupied, 99 percent were successfully interviewed. In these households, 9,900 women were identified as eligible for the individual interview and interviews were completed for 9,640 or 97 percent of these. In one-half of the households that were selected for inclusion in the husbands' survey, 3,874 eligible husbands were identified, of which 3,284 or 85 percent were interviewed.

The principal reason for non-response among eligible women and men was failure to find them at home despite repeated visits to the household. The refusal rate was very low (less than one-tenth of one percent among women and husbands). Since the main reason for interviewing husbands was to match the information with that from their wives, survey procedures called for interviewers not to interview husbands of women who were not interviewed. Such cases account for about one-third of the non-response among husbands. Where husbands and wives were both interviewed, they were interviewed simultaneously but separately.

Note: See summarized response rates by residence (urban/rural) in Table 1.1 of the survey final report.

Sampling error estimates

The estimates from a sample survey are affected by two types of errors: non-sampling errors and sampling errors. Non-sampling 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

Abstract

The primary objective of the 2017 Indonesia Dmographic and Health Survey (IDHS) is to provide up-to-date estimates of basic demographic and health indicators. The IDHS provides a comprehensive overview of population and maternal and child health issues in Indonesia. More specifically, the IDHS was designed to: - provide data on fertility, family planning, maternal and child health, and awareness of HIV/AIDS and sexually transmitted infections (STIs) to help program managers, policy makers, and researchers to evaluate and improve existing programs; - measure trends in fertility and contraceptive prevalence rates, and analyze factors that affect such changes, such as residence, education, breastfeeding practices, and knowledge, use, and availability of contraceptive methods; - evaluate the achievement of goals previously set by national health programs, with special focus on maternal and child health; - assess married men’s knowledge of utilization of health services for their family’s health and participation in the health care of their families; - participate in creating an international database to allow cross-country comparisons in the areas of fertility, family planning, and health.

Geographic coverage

National coverage

Analysis unit

• Household
• Individual
• Children age 0-5
• Woman age 15-49
• Man age 15-54

Universe

The survey covered all de jure household members (usual residents), all women age 15-49 years resident in the household, and all men age 15-54 years resident in the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

The 2017 IDHS sample covered 1,970 census blocks in urban and rural areas and was expected to obtain responses from 49,250 households. The sampled households were expected to identify about 59,100 women age 15-49 and 24,625 never-married men age 15-24 eligible for individual interview. Eight households were selected in each selected census block to yield 14,193 married men age 15-54 to be interviewed with the Married Man's Questionnaire. The sample frame of the 2017 IDHS is the Master Sample of Census Blocks from the 2010 Population Census. The frame for the household sample selection is the updated list of ordinary households in the selected census blocks. This list does not include institutional households, such as orphanages, police/military barracks, and prisons, or special households (boarding houses with a minimum of 10 people).

The sampling design of the 2017 IDHS used two-stage stratified sampling: Stage 1: Several census blocks were selected with systematic sampling proportional to size, where size is the number of households listed in the 2010 Population Census. In the implicit stratification, the census blocks were stratified by urban and rural areas and ordered by wealth index category.

Stage 2: In each selected census block, 25 ordinary households were selected with systematic sampling from the updated household listing. Eight households were selected systematically to obtain a sample of married men.

For further details on sample design, see Appendix B of the final report.

Mode of data collection

Face-to-face [f2f]

Research instrument

The 2017 IDHS used four questionnaires: the Household Questionnaire, Woman’s Questionnaire, Married Man’s Questionnaire, and Never Married Man’s Questionnaire. Because of the change in survey coverage from ever-married women age 15-49 in the 2007 IDHS to all women age 15-49, the Woman’s Questionnaire had questions added for never married women age 15-24. These questions were part of the 2007 Indonesia Young Adult Reproductive Survey Questionnaire. The Household Questionnaire and the Woman’s Questionnaire are largely based on standard DHS phase 7 questionnaires (2015 version). The model questionnaires were adapted for use in Indonesia. Not all questions in the DHS model were included in the IDHS. Response categories were modified to reflect the local situation.

Cleaning operations

All completed questionnaires, along with the control forms, were returned to the BPS central office in Jakarta for data processing. The questionnaires were logged and edited, and all open-ended questions were coded. Responses were entered in the computer twice for verification, and they were corrected for computer-identified errors. Data processing activities were carried out by a team of 34 editors, 112 data entry operators, 33 compare officers, 19 secondary data editors, and 2 data entry supervisors. The questionnaires were entered twice and the entries were compared to detect and correct keying errors. A computer package program called Census and Survey Processing System (CSPro), which was specifically designed to process DHS-type survey data, was used in the processing of the 2017 IDHS.

Response rate

Of the 49,261 eligible households, 48,216 households were found by the interviewer teams. Among these households, 47,963 households were successfully interviewed, a response rate of almost 100%.

In the interviewed households, 50,730 women were identified as eligible for individual interview and, from these, completed interviews were conducted with 49,627 women, yielding a response rate of 98%. From the selected household sample of married men, 10,440 married men were identified as eligible for interview, of which 10,009 were successfully interviewed, yielding a response rate of 96%. The lower response rate for men was due to the more frequent and longer absence of men from the household. In general, response rates in rural areas were higher than those in urban areas.

Sampling error estimates

The estimates from a sample survey are affected by two types of errors: (1) nonsampling errors and (2) sampling errors. Nonsampling errors result from mistakes made in implementing data collection and data processing, such as failure to locate and interview the correct household, misunderstanding 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 2017 Indonesia Demographic and Health Survey (2017 IDHS) 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 2017 IDHS is only one of many samples that could have been selected from the same population, using the same design and identical size. Each of these samples would yield results that differ somewhat from the results of the actual sample selected. Sampling error is a measure of the variability among 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 2017 IDHS sample is the result of a multi-stage stratified design, and, consequently, it was necessary to use more complex formulas. The computer software used to calculate sampling errors for the 2017 IDHS is a STATA program. This program used the Taylor linearization method for variance estimation for survey estimates that are means or proportions. The Jackknife repeated replication method is used for variance estimation of more complex statistics such as fertility and mortality rates.

A more detailed description of estimates of sampling errors are presented in Appendix C of the survey final report.

Data appraisal

Data Quality Tables - Household age distribution - Age distribution of eligible and interviewed women - Age distribution of eligible and interviewed men - Completeness of reporting - Births by calendar year - Reporting of age at death in days - Reporting of age at death in months

See details of the data quality tables in Appendix D of the survey final report.

Abstract

The Armenia Demographic and Health Survey (ADHS) was a nationally representative sample survey designed to provide information on population and health issues in Armenia. The primary goal of the survey was to develop a single integrated set of demographic and health data, the first such data set pertaining to the population of the Republic of Armenia. In addition to integrating measures of reproductive, child, and adult health, another feature of the DHS survey is that the majority of data are presented at the marz level.

The ADHS was conducted by the National Statistical Service and the Ministry of Health of the Republic of Armenia during October through December 2000. ORC Macro provided technical support for the survey through the MEASURE DHS+ project. MEASURE DHS+ is a worldwide project, sponsored by the USAID, with a mandate to assist countries in obtaining information on key population and health indicators. USAID/Armenia provided funding for the survey. The United Nations Children’s Fund (UNICEF)/Armenia provided support through the donation of equipment.

The ADHS collected national- and regional-level data on fertility and contraceptive use, maternal and child health, adult health, and AIDS and other sexually transmitted diseases. The survey obtained detailed information on these issues from women of reproductive age and, on certain topics, from men as well. Data are presented by marz wherever sample size permits.

The ADHS results are intended to provide the information needed to evaluate existing social programs and to design new strategies for improving the health of and health services for the people of Armenia. The ADHS also contributes to the growing international database on demographic and health-related variables.

Geographic coverage

National

Analysis unit

• Household
• Children under five years
• Women age 15-49
• Men age 15-54

Kind of data

Sample survey data

Sampling procedure

The sample was designed to provide estimates of most survey indicators (including fertility, abortion, and contraceptive prevalence) for Yerevan and each of the other ten administrative regions (marzes). The design also called for estimates of infant and child mortality at the national level for Yerevan and other urban areas and rural areas.

The target sample size of 6,500 completed interviews with women age 15-49 was allocated as follows: 1,500 to Yerevan and 500 to each of the ten marzes. Within each marz, the sample was allocated between urban and rural areas in proportion to the population size. This gave a target sample of approximately 2,300 completed interviews for urban areas exclusive of Yerevan and 2,700 completed interviews for the rural sector. Interviews were completed with 6,430 women. Men age 15-54 were interviewed in every third household; this yielded 1,719 completed interviews.

A two-stage sample was used. In the first stage, 260 areas or primary sampling units (PSUs) were selected with probability proportional to population size (PPS) by systematic selection from a list of areas. The list of areas was the 1996 Data Base of Addresses and Households constructed by the National Statistical Service. Because most selected areas were too large to be directly listed, a separate segmentation operation was conducted prior to household listing. Large selected areas were divided into segments of which two segments were included in the sample. A complete listing of households was then carried out in selected segments as well as selected areas that were not segmented.

The listing of households served as the sampling frame for the selection of households in the second stage of sampling. Within each area, households were selected systematically so as to yield an average of 25 completed interviews with eligible women per area. All women 15-49 who stayed in the sampled households on the night before the interview were eligible for the survey. In each segment, a subsample of one-third of all households was selected for the men's component of the survey. In these households, all men 15-54 who stayed in the household on the previous night were eligible for the survey.

Note: See detailed description of sample design in APPENDIX A of the survey report.

Mode of data collection

Face-to-face [f2f]

Research instrument

Three questionnaires were used in the ADHS: a Household Questionnaire, a Women’s Questionnaire, and a Men’s Questionnaire. The questionnaires were based on the model survey instruments developed for the MEASURE DHS+ program. The model questionnaires were adapted for use during a series of expert meetings hosted by the Center of Perinatology, Obstetrics, and Gynecology. The questionnaires were developed in English and translated into Armenian and Russian. The questionnaires were pretested in July 2000.

The Household Questionnaire was used to list all usual members of and visitors to a household and to collect information on the physical characteristics of the dwelling unit. The first part of the household questionnaire collected information on the age, sex, residence, educational attainment, and relationship to the household head of each household member or visitor. This information provided basic demographic data for Armenian households. It also was used to identify the women and men who were eligible for the individual interview (i.e., women 15-49 and men 15-54). The second part of the Household Questionnaire consisted of questions on housing characteristics (e.g., the flooring material, the source of water, and the type of toilet facilities) and on ownership of a variety of consumer goods.

The Women’s Questionnaire obtained information on the following topics: - Background characteristics - Pregnancy history - Antenatal, delivery, and postnatal care - Knowledge and use of contraception - Attitudes toward contraception and abortion - Reproductive and adult health - Vaccinations, birth registration, and health of children under age five - Episodes of diarrhea and respiratory illness of children under age five - Breastfeeding and weaning practices - Height and weight of women and children under age five - Hemoglobin measurement of women and children under age five - Marriage and recent sexual activity - Fertility preferences - Knowledge of and attitude toward AIDS and other sexually transmitted infections.

The Men’s Questionnaire focused on the following topics: - Background characteristics - Health - Marriage and recent sexual activity - Attitudes toward and use of condoms - Knowledge of and attitude toward AIDS and other sexually transmitted infections.

Cleaning operations

After a team had completed interviewing in a cluster, questionnaires were returned promptly to the National Statistical Service in Yerevan for data processing. The office editing staff first checked that questionnaires for all selected households and eligible respondents had been received from the field staff. In addition, a few questions that had not been precoded (e.g., occupation) were coded at this time. Using the ISSA (Integrated System for Survey Analysis) software, a specially trained team of data processing staff entered the questionnaires and edited the resulting data set on microcomputers. The process of office editing and data processing was initiated soon after the beginning of fieldwork and was completed by the end of January 2001.

Response rate

A total of 6,524 households were selected for the sample, of which 6,150 were occupied at the time of fieldwork. The main reason for the difference is that some of the dwelling units that were occupied during the household listing operation were either vacant or the household was away for an extended period at the time of interviewing. Of the occupied households, 97 percent were successfully interviewed.

In these households, 6,685 women were identified as eligible for the individual interview (i.e., age 15-49). Interviews were completed with 96 percent of them. Of the 1,913 eligible men identified, 90 percent were successfully interviewed. The principal reason for non-response among eligible women and men was the failure to find them at home despite repeated visits to the household. The refusal rate was low.

The overall response rates, the product of the household and the individual response rates, were 94 percent for women and 87 percent for men.

Note: See summarized response rates by residence (urban/rural) in Table 1.1 of the survey report.

Sampling error estimates

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 2000 Armenia Demographic and Health Survey (ADHS) 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 ADHS 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

The Decennial Census provides population estimates and demographic information on residents of the United States.

The Census Summary Files contain detailed tables on responses to the decennial census. Data tables in Summary File 1 provide information on population and housing characteristics, including cross-tabulations of age, sex, households, families, relationship to householder, housing units, detailed race and Hispanic or Latino origin groups, and group quarters for the total population. Summary File 2 contains data tables on population and housing characteristics as reported by housing unit.

Researchers at NYU Langone Health can find guidance for the use and analysis of Census Bureau data on the Population Health Data Hub (listed under "Other Resources"), which is accessible only through the intranet portal with a valid Kerberos ID (KID).

The National Population Health Survey (NPHS) program is designed to collect information related to the health of the Canadian population. The first cycle of data collection began in 1994. The institutional component includes long-term residents (expected to stay longer than six months) in health care facilities with four or more beds in Canada with the principal exclusion of the Yukon and the Northwest Teritories. The document has been produced to facilitate the manipulation of the 1996-1997 microdata file containing survey results. The main variables include: demography, health status, chronic conditions, restriction of activity, socio-demographic, and others.

The Victorian Population Health Survey program was established in 1998 and collects quality information at the State, regional and local government area levels about the health, lifestyle and …Show full descriptionThe Victorian Population Health Survey program was established in 1998 and collects quality information at the State, regional and local government area levels about the health, lifestyle and wellbeing of adult Victorians aged 18 years and over. The survey follows an established method to collect relevant, timely and valid health information which is applied to policy development and strategic planning. Information is collected via computer assisted telephone interview on overall self-rated health status, level of psychological distress, body mass index (to determine weight status), the presence of chronic diseases, nutrition, physical activity, smoking and alcohol consumption. Information is also collected on participation in screening for bowel cancer, breast cancer, high blood pressure, cholesterol and high blood sugar in addition to community participation, levels of social support and connections with others. Interviews are also conducted in the major non-English languages in Victoria to ensure people of culturally and linguistically diverse backgrounds are included.

Abstract

The Thai Demographic and Health Survey (TDHS) was a nationally representative sample survey conducted from March through June 1988 to collect data on fertility, family planning, and child and maternal health. A total of 9,045 households and 6,775 ever-married women aged 15 to 49 were interviewed. Thai Demographic and Health Survey (TDHS) is carried out by the Institute of Population Studies (IPS) of Chulalongkorn University with the financial support from USAID through the Institute for Resource Development (IRD) at Westinghouse. The Institute of Population Studies was responsible for the overall implementation of the survey including sample design, preparation of field work, data collection and processing, and analysis of data. IPS has made available its personnel and office facilities to the project throughout the project duration. It serves as the headquarters for the survey.

The Thai Demographic and Health Survey (TDHS) was undertaken for the main purpose of providing data concerning fertility, family planning and maternal and child health to program managers and policy makers to facilitate their evaluation and planning of programs, and to population and health researchers to assist in their efforts to document and analyze the demographic and health situation. It is intended to provide information both on topics for which comparable data is not available from previous nationally representative surveys as well as to update trends with respect to a number of indicators available from previous surveys, in particular the Longitudinal Study of Social Economic and Demographic Change in 1969-73, the Survey of Fertility in Thailand in 1975, the National Survey of Family Planning Practices, Fertility and Mortality in 1979, and the three Contraceptive Prevalence Surveys in 1978/79, 1981 and 1984.

Geographic coverage

National

Analysis unit

• Household
• Women age 15-49

Universe

The population covered by the 1987 THADHS is defined as the universe of all women Ever-married women in the reproductive ages (i.e., women 15-49). This covered women in private households on the basis of a de facto coverage definition. Visitors and usual residents who were in the household the night before the first visit or before any subsequent visit during the few days the interviewing team was in the area were eligible. Excluded were the small number of married women aged under 15 and women not present in private households.

Kind of data

Sample survey data

Sampling procedure

SAMPLE SIZE AND ALLOCATION

The objective of the survey was to provide reliable estimates for major domains of the country. This consisted of two overlapping sets of reporting domains: (a) Five regions of the country namely Bangkok, north, northeast, central region (excluding Bangkok), and south; (b) Bangkok versus all provincial urban and all rural areas of the country. These requirements could be met by defining six non-overlapping sampling domains (Bangkok, provincial urban, and rural areas of each of the remaining 4 regions), and allocating approximately equal sample sizes to them. On the basis of past experience, available budget and overall reporting requirement, the target sample size was fixed at 7,000 interviews of ever-married women aged 15-49, expected to be found in around 9,000 households. Table A.I shows the actual number of households as well as eligible women selected and interviewed, by sampling domain (see Table i.I for reporting domains).

THE FRAME AND SAMPLE SELECTION

The frame for selecting the sample for urban areas, was provided by the National Statistical Office of Thailand and by the Ministry of the Interior for rural areas. It consisted of information on population size of various levels of administrative and census units, down to blocks in urban areas and villages in rural areas. The frame also included adequate maps and descriptions to identify these units. The extent to which the data were up-to-date as well as the quality of the data varied somewhat in different parts of the frame. Basically, the multi-stage stratified sampling design involved the following procedure. A specified number of sample areas were selected systematically from geographically/administratively ordered lists with probabilities proportional to the best available measure of size (PPS). Within selected areas (blocks or villages) new lists of households were prepared and systematic samples of households were selected. In principle, the sampling interval for the selection of households from lists was determined so as to yield a self weighting sample of households within each domain. However, in the absence of good measures of population size for all areas, these sampling intervals often required adjustments in the interest of controlling the size of the resulting sample. Variations in selection probabilities introduced due to such adjustment, where required, were compensated for by appropriate weighting of sample cases at the tabulation stage.

SAMPLE OUTCOME

The final sample of households was selected from lists prepared in the sample areas. The time interval between household listing and enumeration was generally very short, except to some extent in Bangkok where the listing itself took more time. In principle, the units of listing were the same as the ultimate units of sampling, namely households. However in a small proportion of cases, the former differed from the latter in several respects, identified at the stage of final enumeration: a) Some units listed actually contained more than one household each b) Some units were "blanks", that is, were demolished or not found to contain any eligible households at the time of enumeration. c) Some units were doubtful cases in as much as the household was reported as "not found" by the interviewer, but may in fact have existed.

Mode of data collection

Face-to-face

Research instrument

The DHS core questionnaires (Household, Eligible Women Respondent, and Community) were translated into Thai. A number of modifications were made largely to adapt them for use with an ever- married woman sample and to add a number of questions in areas that are of special interest to the Thai investigators but which were not covered in the standard core. Examples of such modifications included adding marital status and educational attainment to the household schedule, elaboration on questions in the individual questionnaire on educational attainment to take account of changes in the educational system during recent years, elaboration on questions on postnuptial residence, and adaptation of the questionnaire to take into account that only ever-married women are being interviewed rather than all women. More generally, attention was given to the wording of questions in Thai to ensure that the intent of the original English-language version was preserved.

a) Household questionnaire

The household questionnaire was used to list every member of the household who usually lives in the household and as well as visitors who slept in the household the night before the interviewer's visit. Information contained in the household questionnaire are age, sex, marital status, and education for each member (the last two items were asked only to members aged 13 and over). The head of the household or the spouse of the head of the household was the preferred respondent for the household questionnaire. However, if neither was available for interview, any adult member of the household was accepted as the respondent. Information from the household questionnaire was used to identify eligible women for the individual interview. To be eligible, a respondent had to be an ever-married woman aged 15-49 years old who had slept in the household 'the previous night'.

Prior evidence has indicated that when asked about current age, Thais are as likely to report age at next birthday as age at last birthday (the usual demographic definition of age). Since the birth date of each household number was not asked in the household questionnaire, it was not possible to calculate age at last birthday from the birthdate. Therefore a special procedure was followed to ensure that eligible women just under the higher boundary for eligible ages (i.e. 49 years old) were not mistakenly excluded from the eligible woman sample because of an overstated age. Ever-married women whose reported age was between 50-52 years old and who slept in the household the night before birthdate of the woman, it was discovered that these women (or any others being interviewed) were not actually within the eligible age range of 15-49, the interview was terminated and the case disqualified. This attempt recovered 69 eligible women who otherwise would have been missed because their reported age was over 50 years old or over.

b) Individual questionnaire

The questionnaire administered to eligible women was based on the DHS Model A Questionnaire for high contraceptive prevalence countries. The individual questionnaire has 8 sections: - Respondent's background - Reproduction - Contraception - Health and breastfeeding - Marriage - Fertility preference - Husband's background and woman's work - Heights and weights of children and mothers

The questionnaire was modified to suit the Thai context. As noted above, several questions were added to the standard DHS core questionnaire not only to meet the interest of IPS researchers hut also because of their relevance to the current demographic situation in Thailand. The supplemental questions are marked with an asterisk in the individual questionnaire. Questions concerning the following items were added in the individual questionnaire: - Did the respondent ever

Please note: This is a Synthetic data file, also known as a Dummy file - it is not real data. This synthetic file should not be used for purposes other than to develop an test computer programs that are to be submitted by remote access. Each record in the synthetic file matches the format and content parameters of the real Statistics Canada Master File with which it is associated, but the data themselves have been 'made up'. They do NOT represent responses from real individuals and should NOT be used for actual analysis. These data are provided solely for the purpose of testing statistical package 'code' (e.g. SPSS syntax, SAS programs, etc.) in preperation for analysis using the associated Master File in a Research Data Centre, by Remote Job Submission, or by some other means of secure access. If statistical analysis 'code' works with the synthetic data, researchers can have some confidence that the same code will run successfully against the Master File data in the Resource Data Centres. In the fall of 1991, the National Health Information Council recommended that an ongoing national survey of population health be conducted. This recommendation was based on consideration of the economic and fiscal pressures on the health care systems and the requirement for information with which to improve the health status of the population in Canada. Commencing in April 1992, Statistics Canada received funding for development of a National Population Health Survey (NPHS). The NPHS collects information related to the health of the Canadian population and related socio-demographic information to: aid in the development of public policy by providing measures of the level, trend and distribution of the health status of the population, provide data for analytic studies that will assist in understanding the determinants of health, and collect data on the economic, social, demographic, occupational and environmental correlates of health. In addition the NPHS seeks to increase the understanding of the relationship between health status and health care utilization, including alternative as well as traditional services, and also to allow the possibility of linking survey data to routinely collected administrative data such as vital statistics, environmental measures, community variables, and health services utilization. The NPHS collects information related to the health of the Canadian population and related socio-demographic information. It is composed of three components: the Households, the Health Institutions, and the North components. The Household component started in 1994/1995 and is conducted every two years. The first three cycles (1994/1995, 1996/1997, 1997/1998) were both cross-sectional and longitudinal. The NPHS longitudinal sample includes 17,276 persons from all ages in 1994/1995 and these same persons are to be interviewed every two years. Beginning in Cycle 4 (2000/2001) the survey became strictly longitudinal (collecting health information from the same individuals each cycle). The cross-sectional and longitudinal documentation of the Household component is presented separately as well as the documentation for the Health Institutions and North components. The cross-sectional component of the Population Health Survey Program has been taken over by the Canadian Community Health Survey (CCHS). With the introduction of the Canadian Community Health Survey (CCHS), there were many changes to the 2000-2001 National Population Health Survey - Household questionnaire. Since NPHS is strictly a longitudinal survey, some content was migrated to the CCHS (such as the two-week disability section and certain questions on place where health care was provided) or was dropped (e.g. certain chronic conditions), while the order of the questionnaire changed. As only the longitudinal respondent is now surveyed, it was no longer necessary to distinguish between the General questionnaire and the Health component. Health Canada, Public Health Agency of Canada and provincial ministries of health use NPHS longitudinal data to plan, implement and evaluate programs and health policies to improve health and the efficiency of health services. Non-profit health organizations and researchers in the academic fields use the information to move research ahead and to improve health.

Users can download data or view data tables on topics related to the labor force of the United States. Background Current Population Survey is a joint effort between the Bureau of Labor Statistics and the Census Bureau. It provides information and data on the labor force of the United States, such as: employment, unemployment, earnings, hours of work, school enrollment, health, employee benefits and income. The CPS is conducted monthly and has a sample of approximately 50,000 households. It is representative of the non-institutionalized US population. The sample provides estimates for the nation as a whole and serves as part of model-based estimates for individual states and other geographic areas. User Functionality Users can download data sets or view data tables on their topic of interest. Data can be organized by a variety of demographic variables, including: sex, age, race, marital status and educational attainment. Data is available on a national or state level. Data Notes The CPS is conducted monthly and has a sample of approximately 50,000 households. It is representative of the non-institutionalized US population. The sample provides estimates for th e nation as a whole and serves as part of model-based estimates for individual states and other geographic areas.

Abstract

Different countries have different health outcomes that are in part due to the way respective health systems perform. Regardless of the type of health system, individuals will have health and non-health expectations in terms of how the institution responds to their needs. In many countries, however, health systems do not perform effectively and this is in part due to lack of information on health system performance, and on the different service providers.

The aim of the WHO World Health Survey is to provide empirical data to the national health information systems so that there is a better monitoring of health of the people, responsiveness of health systems and measurement of health-related parameters.

The overall aims of the survey is to examine the way populations report their health, understand how people value health states, measure the performance of health systems in relation to responsiveness and gather information on modes and extents of payment for health encounters through a nationally representative population based community survey. In addition, it addresses various areas such as health care expenditures, adult mortality, birth history, various risk factors, assessment of main chronic health conditions and the coverage of health interventions, in specific additional modules.

The objectives of the survey programme are to: 1. develop a means of providing valid, reliable and comparable information, at low cost, to supplement the information provided by routine health information systems. 2. build the evidence base necessary for policy-makers to monitor if health systems are achieving the desired goals, and to assess if additional investment in health is achieving the desired outcomes. 3. provide policy-makers with the evidence they need to adjust their policies, strategies and programmes as necessary.

Geographic coverage

The survey sampling frame must cover 100% of the country's eligible population, meaning that the entire national territory must be included. This does not mean that every province or territory need be represented in the survey sample but, rather, that all must have a chance (known probability) of being included in the survey sample.

There may be exceptional circumstances that preclude 100% national coverage. Certain areas in certain countries may be impossible to include due to reasons such as accessibility or conflict. All such exceptions must be discussed with WHO sampling experts. If any region must be excluded, it must constitute a coherent area, such as a particular province or region. For example if ¾ of region D in country X is not accessible due to war, the entire region D will be excluded from analysis.

Analysis unit

Households and individuals

Universe

The WHS will include all male and female adults (18 years of age and older) who are not out of the country during the survey period. It should be noted that this includes the population who may be institutionalized for health reasons at the time of the survey: all persons who would have fit the definition of household member at the time of their institutionalisation are included in the eligible population.

If the randomly selected individual is institutionalized short-term (e.g. a 3-day stay at a hospital) the interviewer must return to the household when the individual will have come back to interview him/her. If the randomly selected individual is institutionalized long term (e.g. has been in a nursing home the last 8 years), the interviewer must travel to that institution to interview him/her.

The target population includes any adult, male or female age 18 or over living in private households. Populations in group quarters, on military reservations, or in other non-household living arrangements will not be eligible for the study. People who are in an institution due to a health condition (such as a hospital, hospice, nursing home, home for the aged, etc.) at the time of the visit to the household are interviewed either in the institution or upon their return to their household if this is within a period of two weeks from the first visit to the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

SAMPLING GUIDELINES FOR WHS

Surveys in the WHS program must employ a probability sampling design. This means that every single individual in the sampling frame has a known and non-zero chance of being selected into the survey sample. While a Single Stage Random Sample is ideal if feasible, it is recognized that most sites will carry out Multi-stage Cluster Sampling.

The WHS sampling frame should cover 100% of the eligible population in the surveyed country. This means that every eligible person in the country has a chance of being included in the survey sample. It also means that particular ethnic groups or geographical areas may not be excluded from the sampling frame.

The sample size of the WHS in each country is 5000 persons (exceptions considered on a by-country basis). An adequate number of persons must be drawn from the sampling frame to account for an estimated amount of non-response (refusal to participate, empty houses etc.). The highest estimate of potential non-response and empty households should be used to ensure that the desired sample size is reached at the end of the survey period. This is very important because if, at the end of data collection, the required sample size of 5000 has not been reached additional persons must be selected randomly into the survey sample from the sampling frame. This is both costly and technically complicated (if this situation is to occur, consult WHO sampling experts for assistance), and best avoided by proper planning before data collection begins.

All steps of sampling, including justification for stratification, cluster sizes, probabilities of selection, weights at each stage of selection, and the computer program used for randomization must be communicated to WHO

STRATIFICATION

Stratification is the process by which the population is divided into subgroups. Sampling will then be conducted separately in each subgroup. Strata or subgroups are chosen because evidence is available that they are related to the outcome (e.g. health, responsiveness, mortality, coverage etc.). The strata chosen will vary by country and reflect local conditions. Some examples of factors that can be stratified on are geography (e.g. North, Central, South), level of urbanization (e.g. urban, rural), socio-economic zones, provinces (especially if health administration is primarily under the jurisdiction of provincial authorities), or presence of health facility in area. Strata to be used must be identified by each country and the reasons for selection explicitly justified.

Stratification is strongly recommended at the first stage of sampling. Once the strata have been chosen and justified, all stages of selection will be conducted separately in each stratum. We recommend stratifying on 3-5 factors. It is optimum to have half as many strata (note the difference between stratifying variables, which may be such variables as gender, socio-economic status, province/region etc. and strata, which are the combination of variable categories, for example Male, High socio-economic status, Xingtao Province would be a stratum).

Strata should be as homogenous as possible within and as heterogeneous as possible between. This means that strata should be formulated in such a way that individuals belonging to a stratum should be as similar to each other with respect to key variables as possible and as different as possible from individuals belonging to a different stratum. This maximises the efficiency of stratification in reducing sampling variance.

MULTI-STAGE CLUSTER SELECTION

A cluster is a naturally occurring unit or grouping within the population (e.g. enumeration areas, cities, universities, provinces, hospitals etc.); it is a unit for which the administrative level has clear, nonoverlapping boundaries. Cluster sampling is useful because it avoids having to compile exhaustive lists of every single person in the population. Clusters should be as heterogeneous as possible within and as homogenous as possible between (note that this is the opposite criterion as that for strata). Clusters should be as small as possible (i.e. large administrative units such as Provinces or States are not good clusters) but not so small as to be homogenous.

In cluster sampling, a number of clusters are randomly selected from a list of clusters. Then, either all members of the chosen cluster or a random selection from among them are included in the sample. Multistage sampling is an extension of cluster sampling where a hierarchy of clusters are chosen going from larger to smaller.

In order to carry out multi-stage sampling, one needs to know only the population sizes of the sampling units. For the smallest sampling unit above the elementary unit however, a complete list of all elementary units (households) is needed; in order to be able to randomly select among all households in the TSU, a list of all those households is required. This information may be available from the most recent population census. If the last census was >3 years ago or the information furnished by it was of poor quality or unreliable, the survey staff will have the task of enumerating all households in the smallest randomly selected sampling unit. It is very important to budget for this step if it is necessary and ensure that all households are properly enumerated in order that a representative sample is obtained.

It is always best to have as many clusters in the PSU as possible. The reason for this is that the fewer the number of respondents in each PSU, the lower will be the clustering effect which

Abstract

Different countries have different health outcomes that are in part due to the way respective health systems perform. Regardless of the type of health system, individuals will have health and non-health expectations in terms of how the institution responds to their needs. In many countries, however, health systems do not perform effectively and this is in part due to lack of information on health system performance, and on the different service providers.

The aim of the WHO World Health Survey is to provide empirical data to the national health information systems so that there is a better monitoring of health of the people, responsiveness of health systems and measurement of health-related parameters.

The overall aims of the survey is to examine the way populations report their health, understand how people value health states, measure the performance of health systems in relation to responsiveness and gather information on modes and extents of payment for health encounters through a nationally representative population based community survey. In addition, it addresses various areas such as health care expenditures, adult mortality, birth history, various risk factors, assessment of main chronic health conditions and the coverage of health interventions, in specific additional modules.

The objectives of the survey programme are to: 1. develop a means of providing valid, reliable and comparable information, at low cost, to supplement the information provided by routine health information systems. 2. build the evidence base necessary for policy-makers to monitor if health systems are achieving the desired goals, and to assess if additional investment in health is achieving the desired outcomes. 3. provide policy-makers with the evidence they need to adjust their policies, strategies and programmes as necessary.

Geographic coverage

The survey sampling frame must cover 100% of the country's eligible population, meaning that the entire national territory must be included. This does not mean that every province or territory need be represented in the survey sample but, rather, that all must have a chance (known probability) of being included in the survey sample.

There may be exceptional circumstances that preclude 100% national coverage. Certain areas in certain countries may be impossible to include due to reasons such as accessibility or conflict. All such exceptions must be discussed with WHO sampling experts. If any region must be excluded, it must constitute a coherent area, such as a particular province or region. For example if ¾ of region D in country X is not accessible due to war, the entire region D will be excluded from analysis.

Analysis unit

Households and individuals

Universe

The WHS will include all male and female adults (18 years of age and older) who are not out of the country during the survey period. It should be noted that this includes the population who may be institutionalized for health reasons at the time of the survey: all persons who would have fit the definition of household member at the time of their institutionalisation are included in the eligible population.

If the randomly selected individual is institutionalized short-term (e.g. a 3-day stay at a hospital) the interviewer must return to the household when the individual will have come back to interview him/her. If the randomly selected individual is institutionalized long term (e.g. has been in a nursing home the last 8 years), the interviewer must travel to that institution to interview him/her.

The target population includes any adult, male or female age 18 or over living in private households. Populations in group quarters, on military reservations, or in other non-household living arrangements will not be eligible for the study. People who are in an institution due to a health condition (such as a hospital, hospice, nursing home, home for the aged, etc.) at the time of the visit to the household are interviewed either in the institution or upon their return to their household if this is within a period of two weeks from the first visit to the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

SAMPLING GUIDELINES FOR WHS

Surveys in the WHS program must employ a probability sampling design. This means that every single individual in the sampling frame has a known and non-zero chance of being selected into the survey sample. While a Single Stage Random Sample is ideal if feasible, it is recognized that most sites will carry out Multi-stage Cluster Sampling.

The WHS sampling frame should cover 100% of the eligible population in the surveyed country. This means that every eligible person in the country has a chance of being included in the survey sample. It also means that particular ethnic groups or geographical areas may not be excluded from the sampling frame.

The sample size of the WHS in each country is 5000 persons (exceptions considered on a by-country basis). An adequate number of persons must be drawn from the sampling frame to account for an estimated amount of non-response (refusal to participate, empty houses etc.). The highest estimate of potential non-response and empty households should be used to ensure that the desired sample size is reached at the end of the survey period. This is very important because if, at the end of data collection, the required sample size of 5000 has not been reached additional persons must be selected randomly into the survey sample from the sampling frame. This is both costly and technically complicated (if this situation is to occur, consult WHO sampling experts for assistance), and best avoided by proper planning before data collection begins.

All steps of sampling, including justification for stratification, cluster sizes, probabilities of selection, weights at each stage of selection, and the computer program used for randomization must be communicated to WHO

STRATIFICATION

Stratification is the process by which the population is divided into subgroups. Sampling will then be conducted separately in each subgroup. Strata or subgroups are chosen because evidence is available that they are related to the outcome (e.g. health, responsiveness, mortality, coverage etc.). The strata chosen will vary by country and reflect local conditions. Some examples of factors that can be stratified on are geography (e.g. North, Central, South), level of urbanization (e.g. urban, rural), socio-economic zones, provinces (especially if health administration is primarily under the jurisdiction of provincial authorities), or presence of health facility in area. Strata to be used must be identified by each country and the reasons for selection explicitly justified.

Stratification is strongly recommended at the first stage of sampling. Once the strata have been chosen and justified, all stages of selection will be conducted separately in each stratum. We recommend stratifying on 3-5 factors. It is optimum to have half as many strata (note the difference between stratifying variables, which may be such variables as gender, socio-economic status, province/region etc. and strata, which are the combination of variable categories, for example Male, High socio-economic status, Xingtao Province would be a stratum).

Strata should be as homogenous as possible within and as heterogeneous as possible between. This means that strata should be formulated in such a way that individuals belonging to a stratum should be as similar to each other with respect to key variables as possible and as different as possible from individuals belonging to a different stratum. This maximises the efficiency of stratification in reducing sampling variance.

MULTI-STAGE CLUSTER SELECTION

A cluster is a naturally occurring unit or grouping within the population (e.g. enumeration areas, cities, universities, provinces, hospitals etc.); it is a unit for which the administrative level has clear, nonoverlapping boundaries. Cluster sampling is useful because it avoids having to compile exhaustive lists of every single person in the population. Clusters should be as heterogeneous as possible within and as homogenous as possible between (note that this is the opposite criterion as that for strata). Clusters should be as small as possible (i.e. large administrative units such as Provinces or States are not good clusters) but not so small as to be homogenous.

In cluster sampling, a number of clusters are randomly selected from a list of clusters. Then, either all members of the chosen cluster or a random selection from among them are included in the sample. Multistage sampling is an extension of cluster sampling where a hierarchy of clusters are chosen going from larger to smaller.

In order to carry out multi-stage sampling, one needs to know only the population sizes of the sampling units. For the smallest sampling unit above the elementary unit however, a complete list of all elementary units (households) is needed; in order to be able to randomly select among all households in the TSU, a list of all those households is required. This information may be available from the most recent population census. If the last census was >3 years ago or the information furnished by it was of poor quality or unreliable, the survey staff will have the task of enumerating all households in the smallest randomly selected sampling unit. It is very important to budget for this step if it is necessary and ensure that all households are properly enumerated in order that a representative sample is obtained.

It is always best to have as many clusters in the PSU as possible. The reason for this is that the fewer the number of respondents in each PSU, the lower will be the clustering effect which

The Uganda Population-Based Survey (PBS) provides a comprehensive assessment of the current status of agriculture and food security in 38 districts across eight regions of the country. The PBS was conducted from October 25 to December 30, 2012. The overall objective of the survey is to provide baseline data on living standards, nutritional status, and women's empowerment in agriculture in the Zone Of Influence. This dataset is the second of two datasets needed to calculate the Women's Empowerment in Agriculture Index-related measures.

Abstract

Different countries have different health outcomes that are in part due to the way respective health systems perform. Regardless of the type of health system, individuals will have health and non-health expectations in terms of how the institution responds to their needs. In many countries, however, health systems do not perform effectively and this is in part due to lack of information on health system performance, and on the different service providers.

The aim of the WHO World Health Survey is to provide empirical data to the national health information systems so that there is a better monitoring of health of the people, responsiveness of health systems and measurement of health-related parameters.

The overall aims of the survey is to examine the way populations report their health, understand how people value health states, measure the performance of health systems in relation to responsiveness and gather information on modes and extents of payment for health encounters through a nationally representative population based community survey. In addition, it addresses various areas such as health care expenditures, adult mortality, birth history, various risk factors, assessment of main chronic health conditions and the coverage of health interventions, in specific additional modules.

The objectives of the survey programme are to: 1. develop a means of providing valid, reliable and comparable information, at low cost, to supplement the information provided by routine health information systems. 2. build the evidence base necessary for policy-makers to monitor if health systems are achieving the desired goals, and to assess if additional investment in health is achieving the desired outcomes. 3. provide policy-makers with the evidence they need to adjust their policies, strategies and programmes as necessary.

Geographic coverage

The survey sampling frame must cover 100% of the country's eligible population, meaning that the entire national territory must be included. This does not mean that every province or territory need be represented in the survey sample but, rather, that all must have a chance (known probability) of being included in the survey sample.

There may be exceptional circumstances that preclude 100% national coverage. Certain areas in certain countries may be impossible to include due to reasons such as accessibility or conflict. All such exceptions must be discussed with WHO sampling experts. If any region must be excluded, it must constitute a coherent area, such as a particular province or region. For example if ¾ of region D in country X is not accessible due to war, the entire region D will be excluded from analysis.

Analysis unit

Households and individuals

Universe

The WHS will include all male and female adults (18 years of age and older) who are not out of the country during the survey period. It should be noted that this includes the population who may be institutionalized for health reasons at the time of the survey: all persons who would have fit the definition of household member at the time of their institutionalisation are included in the eligible population.

If the randomly selected individual is institutionalized short-term (e.g. a 3-day stay at a hospital) the interviewer must return to the household when the individual will have come back to interview him/her. If the randomly selected individual is institutionalized long term (e.g. has been in a nursing home the last 8 years), the interviewer must travel to that institution to interview him/her.

The target population includes any adult, male or female age 18 or over living in private households. Populations in group quarters, on military reservations, or in other non-household living arrangements will not be eligible for the study. People who are in an institution due to a health condition (such as a hospital, hospice, nursing home, home for the aged, etc.) at the time of the visit to the household are interviewed either in the institution or upon their return to their household if this is within a period of two weeks from the first visit to the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

SAMPLING GUIDELINES FOR WHS

Surveys in the WHS program must employ a probability sampling design. This means that every single individual in the sampling frame has a known and non-zero chance of being selected into the survey sample. While a Single Stage Random Sample is ideal if feasible, it is recognized that most sites will carry out Multi-stage Cluster Sampling.

The WHS sampling frame should cover 100% of the eligible population in the surveyed country. This means that every eligible person in the country has a chance of being included in the survey sample. It also means that particular ethnic groups or geographical areas may not be excluded from the sampling frame.

The sample size of the WHS in each country is 5000 persons (exceptions considered on a by-country basis). An adequate number of persons must be drawn from the sampling frame to account for an estimated amount of non-response (refusal to participate, empty houses etc.). The highest estimate of potential non-response and empty households should be used to ensure that the desired sample size is reached at the end of the survey period. This is very important because if, at the end of data collection, the required sample size of 5000 has not been reached additional persons must be selected randomly into the survey sample from the sampling frame. This is both costly and technically complicated (if this situation is to occur, consult WHO sampling experts for assistance), and best avoided by proper planning before data collection begins.

All steps of sampling, including justification for stratification, cluster sizes, probabilities of selection, weights at each stage of selection, and the computer program used for randomization must be communicated to WHO

STRATIFICATION

Stratification is the process by which the population is divided into subgroups. Sampling will then be conducted separately in each subgroup. Strata or subgroups are chosen because evidence is available that they are related to the outcome (e.g. health, responsiveness, mortality, coverage etc.). The strata chosen will vary by country and reflect local conditions. Some examples of factors that can be stratified on are geography (e.g. North, Central, South), level of urbanization (e.g. urban, rural), socio-economic zones, provinces (especially if health administration is primarily under the jurisdiction of provincial authorities), or presence of health facility in area. Strata to be used must be identified by each country and the reasons for selection explicitly justified.

Stratification is strongly recommended at the first stage of sampling. Once the strata have been chosen and justified, all stages of selection will be conducted separately in each stratum. We recommend stratifying on 3-5 factors. It is optimum to have half as many strata (note the difference between stratifying variables, which may be such variables as gender, socio-economic status, province/region etc. and strata, which are the combination of variable categories, for example Male, High socio-economic status, Xingtao Province would be a stratum).

Strata should be as homogenous as possible within and as heterogeneous as possible between. This means that strata should be formulated in such a way that individuals belonging to a stratum should be as similar to each other with respect to key variables as possible and as different as possible from individuals belonging to a different stratum. This maximises the efficiency of stratification in reducing sampling variance.

MULTI-STAGE CLUSTER SELECTION

A cluster is a naturally occurring unit or grouping within the population (e.g. enumeration areas, cities, universities, provinces, hospitals etc.); it is a unit for which the administrative level has clear, nonoverlapping boundaries. Cluster sampling is useful because it avoids having to compile exhaustive lists of every single person in the population. Clusters should be as heterogeneous as possible within and as homogenous as possible between (note that this is the opposite criterion as that for strata). Clusters should be as small as possible (i.e. large administrative units such as Provinces or States are not good clusters) but not so small as to be homogenous.

In cluster sampling, a number of clusters are randomly selected from a list of clusters. Then, either all members of the chosen cluster or a random selection from among them are included in the sample. Multistage sampling is an extension of cluster sampling where a hierarchy of clusters are chosen going from larger to smaller.

In order to carry out multi-stage sampling, one needs to know only the population sizes of the sampling units. For the smallest sampling unit above the elementary unit however, a complete list of all elementary units (households) is needed; in order to be able to randomly select among all households in the TSU, a list of all those households is required. This information may be available from the most recent population census. If the last census was >3 years ago or the information furnished by it was of poor quality or unreliable, the survey staff will have the task of enumerating all households in the smallest randomly selected sampling unit. It is very important to budget for this step if it is necessary and ensure that all households are properly enumerated in order that a representative sample is obtained.

It is always best to have as many clusters in the PSU as possible. The reason for this is that the fewer the number of respondents in each PSU, the lower will be the clustering effect which

Abstract

The 2013 Liberia Demographic and Health Survey (LDHS) is designed to provide data for monitoring the population and health situation in Liberia. The 2013 LDHS is the fourth Demographic and Health Survey conducted in Liberia since 1986. The primary objective of the 2013 LDHS is to provide up-to-date estimates of basic demographic and health indicators. Specifically, the 2013 LDHS collected information on fertility levels, marriage, sexual activity, fertility preferences, family planning methods, breastfeeding practices, nutrition, childhood and maternal mortality, maternal and child health, and HIV/AIDS and other sexually transmitted infections (STIs). In addition, the 2013 LDHS provides estimates on HIV prevalence among adult Liberians.

Geographic coverage

National coverage

Analysis unit

• Household
• Individual/ person
• Children age 0-5 years
• Woman age 15 to 49 years
• Man age 15 to 49 years

Kind of data

Sample survey data [ssd]

Sampling procedure

Sample Design The sampling frame for the 2013 LDHS was developed by the Liberia Institute of Statistics and Geo-Information Services (LISGIS) after the 2008 National Population and Housing Census (NPHC). The sampling frame is similar to that used for the 2009 and 2011 Liberia Malaria Indicator Surveys (LMIS), except that the classification of localities as urban or rural was updated through the application of standardized definitions. The sampling frame excluded nomadic and institutional populations such as residents of hotels, barracks, and prisons. Notably, the sampling frame for the 2013 LDHS differs markedly from that used for the 2007 LDHS, which was based on the 1984 NPHC. Taken together, these differences may complicate data comparisons between surveys.

The 2013 LDHS followed a two-stage sample design that allowed estimates of key indicators for the country as a whole, for urban and rural areas separately, for Greater Monrovia and other urban areas separately, and for each of 15 counties. To facilitate estimates of geographical differentials for certain demographic indicators, the 15 counties were collapsed into five regions as follows: North Western: Bomi, Grand Cape Mount, and Gbarpolu South Central: Montserrado, Margibi, and Grand Bassa South Eastern A: River Cess, Sinoe, and Grand Gedeh South Eastern B: River Gee, Grand Kru, and Maryland North Central: Bong, Nimba, and Lofa

Regional data were presented in the 2007 LDHS, the 2009 LMIS, and the 2011 LMIS. However, in contrast with these past surveys, the South Central region now includes Monrovia. Thus, data presented for the South Central region in this report is not directly comparable to that presented in the 2007 LDHS, the 2009 LMIS, or the 2011 LMIS.

The first stage of sample selection involved selecting sample points (clusters) consisting of enumeration areas (EAs) delineated for the 2008 NPHC. Overall, the sample included 322 sample points, 119 in urban areas and 203 in rural areas. To allow for separate estimates of Greater Monrovia and Montserrado as a whole, 44 sample points were selected in Montserrado; 16 to 26 sample points were selected in each of the other 14 counties.

The second stage of selection involved the systemic sampling of households. A household listing operation was undertaken in all the selected EAs from mid-September to mid-October 2012. From these lists, households to be included in the survey were selected. Approximately 30 households were selected from each sample point for a total sample size of 9,677 households. During the listing, geographic coordinates (latitude and longitude) were taken in the center of the populated area of each EA using global positioning system (GPS) units.

Because of the approximately equal sample sizes in each region, the sample is not self-weighting at the national level, and weighting factors have been added to the data file so that the results will be proportional at the national level.

All women age 15-49 who were either permanent residents of the selected households or visitors who stayed in the household the night before the survey were eligible to be interviewed. In half of the households, all men age 15-49 who were either permanent residents of the selected households or visitors who stayed in the household the night before the survey were eligible to be interviewed. In the subsample of households selected for the male survey, blood samples were collected for laboratory testing to detect HIV from eligible women and men who consented; in this same subsample of households, height and weight information was collected from eligible women, men, and children 0-59 months.

Further details on the sample design and implementation are given in Appendix A of the final report.

Mode of data collection

Face-to-face [f2f]

Research instrument

Three questionnaires were used for the 2013 LDHS: the Household Questionnaire, the Woman’s Questionnaire, and the Man’s Questionnaire. These questionnaires are based on MEASURE DHS standard survey questionnaires and were adapted to reflect the population and health issues relevant to Liberia. Input was solicited from various stakeholders representing government ministries and agencies, nongovernmental organizations, and international donors.

Given that there are dozens of local languages in Liberia, most of which have no accepted written script and are not taught in the schools, and given that English is widely spoken, it was decided not to attempt to translate the questionnaires into vernaculars. However, many of the questions were broken down into a simpler form of Liberian English that interviewers could use with respondents.

The Household Questionnaire was used to list all the usual members of and visitors to selected households. Some basic demographic information was collected on the characteristics of each person listed, including his or her age, sex, education, and relationship to the head of the household. For children under age 18, survival status of the parents was determined. The data on age and sex of household members obtained in the Household Questionnaire were used to identify women and men who were eligible for individual interview and HIV testing. The Household Questionnaire also collected information on characteristics of the household’s dwelling unit, such as the source of water, type of toilet facility, materials used for the floor of the house, ownership of various durable goods, ownership and use of mosquito nets, and information on household out-of-pocket health-related expenditures. The Household Questionnaire was also used to record height and weight measurements of children 0-59 months and eligible adults. Also recorded was whether or not eligible adults consented to HIV testing.

The Woman’s Questionnaire was used to collect information from all eligible women age 15-49.

The Man’s Questionnaire was administered to all men age 15-49 in the subsample of households selected for the male survey in the 2013 LDHS sample. The Man’s Questionnaire collected much of the same information as the Woman’s Questionnaire, but was shorter because it did not contain a detailed reproductive history or questions on maternal and child health.

Cleaning operations

All questionnaires were returned to the LISGIS central office in Monrovia for data processing, which consisted of office editing, coding of open-ended questions, data entry, and editing computer-identified errors. The data were processed by a team of 12 data entry clerks, two data editors, one data entry supervisor, and two administrators of questionnaires; the latter checked that the clusters were completed according to the sample selection and that all members of the household eligible for individual interview were identified. Secondary editing was led by an LDHS coordinator. Several LISGIS staff took on the responsibility of receiving the blood samples from the field and checking them before sending them to the Montserrado Regional Blood Bank for storage. Data entry and editing using CSPro software was initiated in April 2013 and completed in late August 2013.

Response rate

A total of 9,677 households were selected for the sample, of which 9,386 were occupied. Of the occupied households, 9,333 were successfully interviewed, yielding a response rate of 99 percent.

In the interviewed households, 9,462 eligible women were identified for individual interview; of these, complete interviews were conducted with 9,239 women, yielding a response rate of 98 percent. In the subsample of households selected for the male survey, 4,318 eligible men were identified and 4,118 were successfully interviewed, yielding a response rate of 95 percent. The lower response rate for men was likely due to their more frequent and longer absences from the household.

Sampling error estimates

The estimates from a sample survey are affected by two types of errors: nonsampling errors and 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 2013 Liberia Demographic and Health Survey 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 2013 LDHS is only one of many samples that could have been selected from the same population,