Abstract

The primary objective of the 2018 NDHS is to provide up-to-date estimates of basic demographic and health indicators. Specifically, the NDHS collected information on fertility, awareness and use of family planning methods, breastfeeding practices, nutritional status of women and children, maternal and child health, adult and childhood mortality, women’s empowerment, domestic violence, female genital cutting, prevalence of malaria, awareness and behaviour regarding HIV/AIDS and other sexually transmitted infections (STIs), disability, and other health-related issues such as smoking.

The information collected through the 2018 NDHS is intended to assist policymakers and programme managers in evaluating and designing programmes and strategies for improving the health of the country’s population. The 2018 NDHS also provides indicators relevant to the Sustainable Development Goals (SDGs) for Nigeria.

Geographic coverage

National coverage

Analysis unit

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

Universe

The survey covered all de jure household members (usual residents), all women aged 15-49 years resident in the household, and all children aged 0-5 years resident in the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling frame used for the 2018 NDHS is the Population and Housing Census of the Federal Republic of Nigeria (NPHC), which was conducted in 2006 by the National Population Commission. Administratively, Nigeria is divided into states. Each state is subdivided into local government areas (LGAs), and each LGA is divided into wards. In addition to these administrative units, during the 2006 NPHC each locality was subdivided into convenient areas called census enumeration areas (EAs). The primary sampling unit (PSU), referred to as a cluster for the 2018 NDHS, is defined on the basis of EAs from the 2006 EA census frame. Although the 2006 NPHC did not provide the number of households and population for each EA, population estimates were published for 774 LGAs. A combination of information from cartographic material demarcating each EA and the LGA population estimates from the census was used to identify the list of EAs, estimate the number of households, and distinguish EAs as urban or rural for the survey sample frame. Before sample selection, all localities were classified separately into urban and rural areas based on predetermined minimum sizes of urban areas (cut-off points); consistent with the official definition in 2017, any locality with more than a minimum population size of 20,000 was classified as urban.

The sample for the 2018 NDHS was a stratified sample selected in two stages. Stratification was achieved by separating each of the 36 states and the Federal Capital Territory into urban and rural areas. In total, 74 sampling strata were identified. Samples were selected independently in every stratum via a two-stage selection. Implicit stratifications were achieved at each of the lower administrative levels by sorting the sampling frame before sample selection according to administrative order and by using a probability proportional to size selection during the first sampling stage.

For further details on sample selection, see Appendix A of the final report.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

Four questionnaires were used for the 2018 NDHS: the Household Questionnaire, the Woman’s Questionnaire, the Man’s Questionnaire, and the Biomarker Questionnaire. The questionnaires, based on The DHS Program’s standard Demographic and Health Survey (DHS-7) questionnaires, were adapted to reflect the population and health issues relevant to Nigeria. Comments were solicited from various stakeholders representing government ministries and agencies, nongovernmental organisations, and international donors. In addition, information about the fieldworkers for the survey was collected through a self-administered Fieldworker Questionnaire.

Cleaning operations

The processing of the 2018 NDHS data began almost immediately after the fieldwork started. As data collection was completed in each cluster, all electronic data files were transferred via the IFSS to the NPC central office in Abuja. These data files were registered and checked for inconsistencies, incompleteness, and outliers. The field teams were alerted to any inconsistencies and errors. Secondary editing, carried out in the central office, involved resolving inconsistencies and coding the open-ended questions. The NPC data processor coordinated the exercise at the central office. The biomarker paper questionnaires were compared with electronic data files to check for any inconsistencies in data entry. Data entry and editing were carried out using the CSPro software package. The concurrent processing of the data offered a distinct advantage because it maximised the likelihood of the data being error-free and accurate. Timely generation of field check tables allowed for effective monitoring. The secondary editing of the data was completed in the second week of April 2019.

Response rate

A total of 41,668 households were selected for the sample, of which 40,666 were occupied. Of the occupied households, 40,427 were successfully interviewed, yielding a response rate of 99%. In the households interviewed, 42,121 women age 15-49 were identified for individual interviews; interviews were completed with 41,821 women, yielding a response rate of 99%. In the subsample of households selected for the male survey, 13,422 men age 15-59 were identified and 13,311 were successfully interviewed, yielding a response rate of 99%.

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 2018 Nigeria Demographic and Health Survey (NDHS) to minimise 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 2018 NDHS 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 among all possible samples. Although the degree of variability is not known exactly, it can be estimated from the survey results.

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% 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 2018 NDHS sample is the result of a multistage stratified design, and, consequently, it was necessary to use more complex formulas. Sampling errors are computed in SAS, using programs developed by ICF. These programs use the Taylor linearisation method to estimate variances for survey estimates that are means, proportions, or ratios. The Jackknife repeated replication method is used for variance estimation of more complex statistics such as fertility and mortality rates.

Note: A more detailed description of estimates of sampling errors are presented in APPENDIX B of the survey 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 years - Reporting of age at death in days - Reporting of age at death in months - Standardisation exercise results from anthropometry training - Height and weight data completeness and quality for children - Height measurements from random subsample of measured children - Sibship size and sex ratio of siblings - Pregnancy-related mortality trends - Data collection period - Malaria prevalence according to rapid diagnostic test (RDT)

Note: See detailed data quality tables in APPENDIX C of the report.

Abstract

The primary objective of the 2018 ZDHS was to provide up-to-date estimates of basic demographic and health indicators. Specifically, the ZDHS collected information on: - Fertility levels and preferences; contraceptive use; maternal and child health; infant, child, and neonatal mortality levels; maternal mortality; and gender, nutrition, and awareness regarding HIV/AIDS and other health issues relevant to the achievement of the Sustainable Development Goals (SDGs) - Ownership and use of mosquito nets as part of the national malaria eradication programmes - Health-related matters such as breastfeeding, maternal and childcare (antenatal, delivery, and postnatal), children’s immunisations, and childhood diseases - Anaemia prevalence among women age 15-49 and children age 6-59 months - Nutritional status of children under age 5 (via weight and height measurements) - HIV prevalence among men age 15-59 and women age 15-49 and behavioural risk factors related to HIV - Assessment of situation regarding violence against women

Geographic coverage

National coverage

Analysis unit

• Household
• Individual
• Children age 0-5
• Woman age 15-49
• Man age 15-59

Universe

The survey covered all de jure household members (usual residents), all women age 15-49, all men age 15-59, and all children age 0-5 years who are usual members of the selected households or who spent the night before the survey in the selected households.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling frame used for the 2018 ZDHS is the Census of Population and Housing (CPH) of the Republic of Zambia, conducted in 2010 by ZamStats. Zambia is divided into 10 provinces. Each province is subdivided into districts, each district into constituencies, and each constituency into wards. In addition to these administrative units, during the 2010 CPH each ward was divided into convenient areas called census supervisory areas (CSAs), and in turn each CSA was divided into enumeration areas (EAs). An enumeration area is a geographical area assigned to an enumerator for the purpose of conducting a census count; according to the Zambian census frame, each EA consists of an average of 110 households.

The current version of the EA frame for the 2010 CPH was updated to accommodate some changes in districts and constituencies that occurred between 2010 and 2017. The list of EAs incorporates census information on households and population counts. Each EA has a cartographic map delineating its boundaries, with identification information and a measure of size, which is the number of residential households enumerated in the 2010 CPH. This list of EAs was used as the sampling frame for the 2018 ZDHS.

The 2018 ZDHS followed a stratified two-stage sample design. The first stage involved selecting sample points (clusters) consisting of EAs. EAs were selected with a probability proportional to their size within each sampling stratum. A total of 545 clusters were selected.

The second stage involved systematic sampling of households. A household listing operation was undertaken in all of the selected clusters. During the listing, an average of 133 households were found in each cluster, from which a fixed number of 25 households were selected through an equal probability systematic selection process, to obtain a total sample size of 13,625 households. Results from this sample are representative at the national, urban and rural, and provincial levels.

For further details on sample selection, see Appendix A of the final report.

Mode of data collection

Face-to-face [f2f]

Research instrument

Four questionnaires were used in the 2018 ZDHS: the Household Questionnaire, the Woman’s Questionnaire, the Man’s Questionnaire, and the Biomarker Questionnaire. The questionnaires, based on The DHS Program’s Model Questionnaires, were adapted to reflect the population and health issues relevant to Zambia. Input on questionnaire content was solicited from various stakeholders representing government ministries and agencies, nongovernmental organisations, and international cooperating partners. After all questionnaires were finalised in English, they were translated into seven local languages: Bemba, Kaonde, Lozi, Lunda, Luvale, Nyanja, and Tonga. In addition, information about the fieldworkers for the survey was collected through a self-administered Fieldworker Questionnaire.

Cleaning operations

All electronic data files were transferred via a secure internet file streaming system to the ZamStats central office in Lusaka, where they were stored on a password-protected computer. The data processing operation included secondary editing, which required resolution of computer-identified inconsistencies and coding of open-ended questions. The data were processed by two IT specialists and one secondary editor who took part in the main fieldwork training; they were supervised remotely by staff from The DHS Program. Data editing was accomplished using CSPro software. During the fieldwork, field-check tables were generated to check various data quality parameters, and specific feedback was given to the teams to improve performance. Secondary editing and data processing were initiated in July 2018 and completed in March 2019.

Response rate

Of the 13,595 households in the sample, 12,943 were occupied. Of these occupied households, 12,831 were successfully interviewed, yielding a response rate of 99%.

In the interviewed households, 14,189 women age 15-49 were identified as eligible for individual interviews; 13,683 women were interviewed, yielding a response rate of 96% (the same rate achieved in the 2013-14 survey). A total of 13,251 men were eligible for individual interviews; 12,132 of these men were interviewed, producing a response rate of 92% (a 1 percentage point increase from the previous survey).

Of the households successfully interviewed, 12,505 were interviewed in 2018 and 326 in 2019. As the large majority of households were interviewed in 2018 and the year for reference indicators is 2018.

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 2018 Zambia Demographic and Health Survey (ZDHS) to minimise 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 2018 ZDHS 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 among all possible samples. Although the degree of variability is not known exactly, it can be estimated from the survey results.

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% 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 2018 ZDHS sample is the result of a multi-stage stratified design, and, consequently, it was necessary to use more complex formulas. Sampling errors are computed in SAS, using programs developed by ICF. These programs use the Taylor linearisation method to estimate variances for survey estimates that are means, proportions, or ratios. The Jackknife repeated replication method is used for variance estimation of more complex statistics such as fertility and mortality rates.

Note: A more detailed description of estimates of sampling errors are presented in APPENDIX B of the survey 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 years - Reporting of age at death in days - Reporting of age at death in months - Completeness of information on siblings - Sibship size and sex ratio of siblings - Height and weight data completeness and quality for children - Number of enumeration areas completed by month, according to province, Zambia DHS 2018

Note: Data quality tables are presented in APPENDIX C of the report.

The Pakistan Demographic and Health Survey PDHS 2017-18 was the fourth of its kind in Pakistan, following the 1990-91, 2006-07, and 2012-13 PDHS surveys. The primary objective of the 2017-18 PDHS is to provide up-to-date estimates of basic demographic and health indicators. The PDHS provides a comprehensive overview of population, maternal, and child health issues in Pakistan. Specifically, the 2017-18 PDHS collected information on: Key demographic indicators, particularly fertility and under-5 mortality rates, at the national level, for urban and rural areas, and within the country’s eight regions Direct and indirect factors that determine levels and trends of fertility and child mortality Contraceptive knowledge and practice Maternal health and care including antenatal, perinatal, and postnatal care Child feeding practices, including breastfeeding, and anthropometric measures to assess the nutritional status of children under age 5 and women age 15-49 Key aspects of family health, including vaccination coverage and prevalence of diseases among infants and children under age 5 Knowledge and attitudes of women and men about sexually transmitted infections (STIs), including HIV/AIDS, and potential exposure to risk Women's empowerment and its relationship to reproductive health and family planning Disability level Extent of gender-based violence Migration patterns The information collected through the 2017-18 PDHS is intended to assist policymakers and program managers at the federal and provincial government levels, in the private sector, and at international organisations in evaluating and designing programs and strategies for improving the health of the country’s population. The data also provides information on indicators relevant to the Sustainable Development Goals.

Geographic coverage

National coverage

Analysis unit

households/individuals

Kind of data

survey

Frequency of data collection

Yearly

Sampling procedure

Sample size:

The primary objective of the 2017-18 Jordan Population and Family Health Survey (JPFHS) is to provide up-to-date estimates of basic demographic and health indicators. Specifically, the 2017-18 JPFHS: Collected data at the national level that allowed calculation of key demographic indicators Explored the direct and indirect factors that determine levels of and trends in fertility and childhood mortality Measured levels of contraceptive knowledge and practice Collected data on key aspects of family health, including immunisation coverage among children, the prevalence and treatment of diarrhoea and other diseases among children under age 5, and maternity care indicators such as antenatal visits and assistance at delivery among ever-married women Obtained data on child feeding practices, including breastfeeding, and conducted anthropometric measurements to assess the nutritional status of children under age 5 and ever-married women age 15-49 Conducted haemoglobin testing on children age 6-59 months and ever-married women age 15-49 to provide information on the prevalence of anaemia among these groups Collected data on knowledge and attitudes of ever-married women and men about sexually transmitted infections (STIs) and HIV/AIDS Obtained data on ever-married women’s experience of emotional, physical, and sexual violence Obtained data on household health expenditures

Abstract

The 2017-18 Bangladesh Demographic and Health Survey (2017-18 BDHS) is a nationwide survey with a nationally representative sample of approximately 20,250 selected households. All ever-married women age 15-49 who are usual members of the selected households or who spent the night before the survey in the selected households were eligible for individual interviews. The survey was designed to produce reliable estimates for key indicators at the national level as well as for urban and rural areas and each of the country’s eight divisions: Barishal, Chattogram, Dhaka, Khulna, Mymensingh, Rajshahi, Rangpur, and Sylhet.

The main objective of the 2017-18 BDHS is to provide up-to-date information on fertility and fertility preferences; childhood mortality levels and causes of death; awareness, approval, and use of family planning methods; maternal and child health, including breastfeeding practices and nutritional status; newborn care; women’s empowerment; selected noncommunicable diseases (NCDS); and availability and accessibility of health and family planning services at the community level.

This information is intended to assist policymakers and program managers in monitoring and evaluating the 4th Health, Population and Nutrition Sector Program (4th HPNSP) 2017-2022 of the Ministry of Health and Family Welfare (MOHFW) and to provide estimates for 14 major indicators of the HPNSP Results Framework (MOHFW 2017).

Geographic coverage

National coverage

Analysis unit

• Household
• Individual
• Children age 0-5
• Woman age 15-49
• Community

Universe

The survey covered all de jure household members (usual residents), all women aged 15-49 and all children aged 0-5 resident in the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample for the 2017-18 BDHS is nationally representative and covers the entire population residing in non-institutional dwelling units in the country. The survey used a list of enumeration areas (EAs) from the 2011 Population and Housing Census of the People’s Republic of Bangladesh, provided by the Bangladesh Bureau of Statistics (BBS), as a sampling frame (BBS 2011). The primary sampling unit (PSU) of the survey is an EA with an average of about 120 households.

Bangladesh consists of eight administrative divisions: Barishal, Chattogram, Dhaka, Khulna, Mymensingh, Rajshahi, Rangpur, and Sylhet. Each division is divided into zilas and each zila into upazilas. Each urban area in an upazila is divided into wards, which are further subdivided into mohallas. A rural area in an upazila is divided into union parishads (UPs) and, within UPs, into mouzas. These divisions allow the country as a whole to be separated into rural and urban areas.

The survey is based on a two-stage stratified sample of households. In the first stage, 675 EAs (250 in urban areas and 425 in rural areas) were selected with probability proportional to EA size. The sample in that stage was drawn by BBS, following the specifications provided by ICF that include cluster allocation and instructions on sample selection. A complete household listing operation was then carried out in all selected EAs to provide a sampling frame for the second-stage selection of households. In the second stage of sampling, a systematic sample of an average of 30 households per EA was selected to provide

statistically reliable estimates of key demographic and health variables for the country as a whole, for urban and rural areas separately, and for each of the eight divisions. Based on this design, 20,250 residential households were selected. Completed interviews were expected from about 20,100 ever-married women age 15-49. In addition, in a subsample of one-fourth of the households (about 7-8 households per EA), all ever-married women age 50 and older, never-married women age 18 and older, and men age 18 and older were weighed and had their height measured. In the same households, blood pressure and blood glucose testing were conducted for all adult men and women age 18 and older.

The survey was successfully carried out in 672 clusters after elimination of three clusters (one urban and two rural) that were completely eroded by floodwater. These clusters were in Dhaka (one urban cluster), Rajshahi (one rural cluster), and Rangpur (one rural cluster). A total of 20,160 households were selected for the survey.

For further details on sample selection, see Appendix A of the final report.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The 2017-18 BDHS used six types of questionnaires: (1) the Household Questionnaire, (2) the Woman’s Questionnaire (completed by ever-married women age 15-49), (3) the Biomarker Questionnaire, (4) two verbal autopsy questionnaires to collect data on causes of death among children under age 5, (5) the Community Questionnaire, and the Fieldworker Questionnaire. The first three questionnaires were based on the model questionnaires developed for the DHS-7 Program, adapted to the situation and needs in Bangladesh and taking into account the content of the instruments employed in prior BDHS surveys. The verbal autopsy module was replicated from the questionnaires used in the 2011 BDHS, as the objectives of the 2011 BDHS and the 2017-18 BDHS were the same. The module was adapted from the standardized WHO 2016 verbal autopsy module. The Community Questionnaire was adapted from the version used in the 2014 BDHS. The adaptation process for the 2017-18 BDHS involved a series of meetings with a technical working group. Additionally, draft questionnaires were circulated to other interested groups and were reviewed by the TWG and SAC. The questionnaires were developed in English and then translated into and printed in Bangla. Back translations were conducted by people not involved with the Bangla translations.

Cleaning operations

Completed BDHS questionnaires were returned to Dhaka every 2 weeks for data processing at Mitra and Associates offices. Data processing began shortly after fieldwork commenced and consisted of office editing, coding of open-ended questions, data entry, and editing of inconsistencies found by the computer program. The field teams were alerted regarding any inconsistencies or errors found during data processing. Eight data entry operators and two data entry supervisors performed the work, which commenced on November 17, 2017, and ended on March 27, 2018. Data processing was accomplished using Census and Survey Processing System (CSPro) software, jointly developed by the United States Census Bureau, ICF, and Serpro S.A.

Response rate

Among the 20,160 households selected, 19,584 were occupied. Interviews were successfully completed in 19,457 (99%) of the occupied households. Among the 20,376 ever-married women age 15-49 eligible for interviews, 20,127 were interviewed, yielding a response rate of 99%. The principal reason for non-response among women was their absence from home despite repeated visits. Response rates did not vary notably by urbanrural residence.

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 2017-18 Bangladesh Demographic and Health Survey (BDHS) 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 2017-18 BDHS 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 among all possible samples. Although the degree of variability is not known exactly, it can be estimated from the survey results.

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% 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 2017-18 BDHS sample is the result of a multi-stage stratified design, and, consequently, it was necessary to use more complex formulas. Sampling errors are computed in SAS, using programs developed by ICF. These programs use the Taylor linearization method to estimate variances for survey estimates that are means, proportions, or ratios. The Jackknife repeated replication method is used for variance estimation of more complex statistics such as fertility and mortality rates.

Note: A more detailed description of estimates of sampling errors are presented in APPENDIX B of the survey report.

Data

Abstract

The 2017-18 Albania Demographic and Health Survey (2017-18 ADHS) is a nationwide survey with a nationally representative sample of approximately 17,160 households. All women age 15-49 who are usual residents of the selected households or who slept in the households the night before the survey were eligible for the survey. Women 50-59 years old were interviewed with an abbreviated questionnaire that only covered background characteristics and questions related to noncommunicable diseases.

The primary objective of the 2017-2018 ADHS was to provide estimates of basic sociodemographic and health indicators for the country as a whole and the twelve prefectures. Specifically, the survey collected information on basic characteristics of the respondents, fertility, family planning, nutrition, maternal and child health, knowledge of HIV behaviors, health-related lifestyle, and noncommunicable diseases (NCDs). The information collected in the ADHS will assist policymakers and program managers in evaluating and designing programs and in developing strategies for improving the health of the country’s population.

The sample for the 2017-18 ADHS was designed to produce representative results for the country as a whole, for urban and rural areas separately, and for each of the twelve prefectures known as Berat, Diber, Durres, Elbasan, Fier, Gjirokaster, Korce, Kukes, Lezhe, Shkoder, Tirana, and Vlore.

Geographic coverage

National coverage

Analysis unit

• Household
• Individual
• Children age 0-5
• Woman age 15-49
• Man age 15-59

Universe

The survey covered all de jure household members (usual residents), children age 0-4 years, women age 15-49 years and men age 15-59 years resident in the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

The ADHS surveys were done on a nationally representative sample that was representative at the prefecture level as well by rural and urban areas. A total of 715 enumeration areas (EAs) were selected as sample clusters, with probability proportional to each prefecture's population size. The sample design called for 24 households to be randomly selected in every sampling cluster, regardless of its size, but some of the EAs contained fewer than 24 households. In these EAs, all households were included in the survey. The EAs are considered the sample's primary sampling unit (PSU). The team of interviewers updated and listed the households in the selected EAs. Upon arriving in the selected clusters, interviewers spent the first day of fieldwork carrying out an exhaustive enumeration of households, recording the name of each head of household and the location of the dwelling. The listing was done with tablet PCs, using a digital listing application. When interviewers completed their respective sections of the EA, they transferred their files into the supervisor's tablet PC, where the information was automatically compiled into a single file in which all households in the EA were entered. The software and field procedures were designed to ensure there were no duplications or omissions during the household listing process. The supervisor used the software in his tablet to randomly select 24 households for the survey from the complete list of households.

All women age 15-49 who were usual residents of the selected households or who slept in the households the night before the survey were eligible for individual interviews with the full Woman's Questionnaire. Women age 50-59 were also interviewed, but with an abbreviated questionnaire that left out all questions related to reproductive health and mother and child health. A 50% subsample was selected for the survey of men. Every man age 15-59 who was a usual resident of or had slept in the household the night before the survey was eligible for an individual interview in these households.

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

Mode of data collection

Face-to-face [f2f]

Research instrument

Four questionnaires were used in the ADHS, one for the household and others for women age 15-49, for women age 50-59, and for men age 15-59. In addition to these four questionnaires, a form was used to record the vaccination information for children born in the 5 years preceding the survey whose mothers had been successfully interviewed.

Cleaning operations

Supervisors sent the accumulated fieldwork data to INSTAT’s central office via internet every day, unless for some reason the teams did not have access to the internet at the time. The data received from the various teams were combined into a single file, which was used to produce quality control tables, known as field check tables. These tables reveal systematic errors in the data such as omission of potential respondents, age displacement, inaccurate recording of date of birth and age at death, inaccurate measurement of height and weight, and other key indicators of data quality. These tables were reviewed and evaluated by ADHS senior staff, which in turn provided feedback and advice to the teams in the field.

Response rate

A total of 16,955 households were selected for the sample, of which 16,634 were occupied. Of the occupied households, 15,823 were successfully interviewed, which represents a response rate of 95%. In the interviewed households, 11,680 women age 15-49 were identified for individual interviews. Interviews were completed for 10,860 of these women, yielding a response rate of 93%. In the same households, 4,289 women age 50-59 were identified, of which 4,140 were successfully interviewed, yielding a 97% response rate. In the 50% subsample of households selected for the male survey, 7,103 eligible men age 15-59 were identified, of which 6,142 were successfully interviewed, yielding a response rate of 87%.

Response rates were higher in rural than in urban areas, which is a pattern commonly found in household surveys because in urban areas more people work and carry out activities outside the home.

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 2017-18 Albania 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 2017-18 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 among all possible samples. Although the degree of variability is not known exactly, it can be estimated from the survey results.

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% 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 2017-18 ADHS sample is the result of a multi-stage stratified design, and, consequently, it was necessary to use more complex formulas. Sampling errors are computed in SAS, using programs developed by ICF. These programs use the Taylor linearization method to estimate variances for survey estimates that are means, proportions, or ratios. 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 B 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 years - Reporting of age at death in days - Reporting of age at death in months

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

Abstract

The survey provides statistically sound and internationally comparable data essential for developing evidence-based policies and programmes, and for monitoring progress toward national goals and global commitments. The Kiribati Social Development Indicator Survey (SDIS) results are critically important for the purposes of Sustainable Development Goal (SDG) monitoring, as the survey produces information on 32 global SDG indicators adopted by the National Development Indicators framework, either in their entirety or partially.

The 2018-19 Kiribati SDIS has as its primary objectives: • To provide high quality data for assessing the situation of children, adolescents, women and households in KSDIS; • To furnish data needed for monitoring progress toward national goals, as a basis for future action; • To collect disaggregated data for the identification of disparities, to inform policies aimed at social inclusion of the most vulnerable; • To validate data from other sources and the results of focused interventions; • To generate data on national and global SDG indicators; • To generate internationally comparable data for the assessment of the progress made in various areas, and to put additional efforts in those areas that require more attention; • To generate behavioural and attitudinal data not available in other data sources.

Geographic coverage

National Coverage: covering rural-urban areas and for the five district/island groups of the country (South Tarawa, Northern Gilbert, Central Gilbert, Southern Gilbert, and Line and Phoenix groups).

Analysis unit

-Household; -Household member; -Mosquito nets; -Women in reproductive age; -Birth history; -Men in reproductive age; -Mothers or primary caretakers of children under 5; -Mothers or primary caretakers of children age 5-17.

Universe

The survey covered all de jure household members (usual residents), all women aged between 15 to 49 years, all men aged between 15 to 49 years, all children under 5 and those aged 5 to 17 living in the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

-SELECTION PROCESS: The sample for the Kiribati Social Development Indicator Survey (SDIS) 2018-19 was designed to provide estimates for a large number of indicators on the situation of children and women at the national, rural-urban, South Tarawa, Northern Gilbert, Central Gilbert, Southern Gilbert and Line and Phoenix group. The urban and rural areas within each district were identified as the main sampling strata and the sample of households was selected in two stages. Within each stratum, a specified number of census Enumeration Areas (EAs) were selected systematically with probability proportional to size. After a household listing was carried out within the selected enumeration areas, a systematic sample of 3280 households was drawn in each sample enumeration area. All of the selected enumeration areas were visited during the fieldwork period.

A multi-stage, stratified cluster sampling approach was used for the selection of the survey sample. The sampling frame was based on the full/national household listing (mini-census) conducted in 2018 because the last census (2015) could not be used as a sampling frame as the EA boundaries differed from the 2010 Kiribati Census. The primary sampling units (PSUs) selected at the first stage were the enumeration areas (EAs) defined for the census enumeration.

-SAMPLE SIZE AND SAMPLE ALLOCATION: Since the overall sample size for the Kiribati SDIS partly depends on the geographic domains of analysis that are defined for the survey tables, the distribution of EAs and households in Kiribati from the 2018 Household Listing /Mini Census sampling frame was first examined by region, urban and rural strata.

The overall sample size for the Kiribati SDIS was calculated as 3,280 households. For the calculation of the sample size, the key indicator used was the underweight prevalence among children age 0-4 years. Since the survey results are tabulated at the regional level, it was necessary to determine the minimum sample size for each region.

For the calculation, r (underweight prevalence) was assumed to be 15 percent based on the national estimate from the Demographic and Health SUrvey (DHS) 2009. -The value of deff (design effect) was taken as 1.0 based on the estimate from the DHS 2009, -pb (percentage of children age 0-4 years in the total population) was taken as 12 percent, -AveSize (mean household size) was taken as 6.0 based on the 2018 mini-Census, and the response rate was assumed to be 98 percent, based on experience from the DHS 2009. -It was decided that an RME of at most 20 percent was needed for the district/island group estimates; this would result in an RME of 10 percent for the national estimate. The calculations resulted in a total sample size of 3,280 households, with the sample sizes in the districts varying between 515 and 780. The sample size in South Tarawa (urban) was adjusted upwards from 780 to 1,080 households in order to improve the precision in urban/rural comparisons. The sample sizes in the other districts/island groups were reduced by 75 households each.

The number of households selected per cluster for the Kiribati SDIS was determined as 20 households, based on several considerations, including the design effect, the budget available, and the time that would be needed per team to complete one cluster.

Census enumeration areas were selected from each of the sampling strata by using systematic probability proportional to size (pps) sampling procedures, based on the number of households in each enumeration area from the 2018 Mini- Census frame. The first stage of sampling was thus completed by selecting the required number of sample EAs (specified in Table SD.2) from each of the five district/Island groups.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

-QUESTIONNAIRE DESCRIPTION: Six questionnaires were used in the survey: 1) a household questionnaire to collect basic demographic information on all de jure household members (usual residents), the household, and the dwelling; 2) a water quality testing questionnaire administered in 4 households in each cluster of the sample; 3) a questionnaire for individual women administered in each household to all women age 15-49 years; 4) a questionnaire for individual men administered in every second household to all men age 15-49 years; 5) an under-5 questionnaire, administered to mothers (or caretakers) of all children under 5 living in the household; and 6) a questionnaire for children age 5-17 years, administered to the mother (or caretaker) of one randomly selected child age 5-17 years living in the household.

The questionnaires were based on the Multiple Indicator Cluster Surveys 6 (MICS6) standard questionnaires except for questionnaire for individual women/men had some add-on questions and/or modules from the Demographic and Health Surveys (DHS) programme. From the MICS6 model English version, the questionnaires were customised and translated into Kiribati language and were pre-tested in South Tarawa during September, 2018. Based on the results of the pre-test, modifications were made to the wording and translation of the questionnaires. A copy of the Kiribati Social Development Indicator Survey (SDIS) 2018-19 questionnaires is provided in the External Resources of this documentation.

-COMPOSITION OF THE QUESTIONNAIRES: The questionnaires included the following modules: -Household questionnaire: List of household members, Education, Household characteristics, Social transfers, Household energy use, Dengue, Water and sanitation, Handwashing, Salt iodisation.

-Water Quality Testing questionnaire: Water quality tests, Water quality testing results.

-Individual Women questionnaire: Background, ICT, Fertility/Birth history, Desire for last birth, Maternal and newborn health, Post-natal health checks, Contraception, Unmet need, Attitudes toward domestic violence, Victimisation, Marriage/union, Adult functioning, Sexual behaviour, HIV/AIDS, STI, Tobacco and alcohol use, Domestic violence, Life satisfaction.

-Individual Men questionnaire: Background, ICT, Fertility, Contraception, Unmet need, Attitudes toward domestic violence, Victimisation, Marriage/union, Adult functioning, Sexual behaviour, HIV/AIDS, STI, Circumcision, Tobacco and alcohol use, Life satisfaction.

-Children Under 5 questionnaire: Background, Birth registration, Early childhood development, Chil discipline, Child functioning, Breastfeeding and dietary intake, Immunisation, Care of illness, Anthropometry.

-Children Age 5-17 Years questionnaire: Background, Child labour, Child discipline, Child functioning, Parental involvment, Foundational learning skills.

Cleaning operations

Data were received at the National Statistical Office's central office via Internet File Streaming System (IFSS) integrated into the management application on the supervisors' tablets. Whenever logistically possible, synchronisation was daily. The central office communicated application updates to field teams through this system.

During data collection and following the completion of fieldwork, data were edited according to editing process described in detail in the Guidelines for Secondary Editing, a customised version of the standard MICS6 documentation.

Data editing took place at a number of stages throughout the processing (see Other processing), including: a) During data collection b) Structure checking and completeness c) Secondary editing d) Structural checking of SPSS data files

Detailed documentation of the editing of

Abstract

The 2017 Philippines National Demographic and Health Survey (NDHS 2017) is a nationwide survey with a nationally representative sample of approximately 30,832 housing units. The primary objective of the survey is to provide up-to-date estimates of basic demographic and health indicators. Specifically, the NDHS 2017 collected information on marriage, fertility levels, fertility preferences, awareness and use of family planning methods, breastfeeding, maternal and child health, child mortality, awareness and behavior regarding HIV/AIDS, women’s empowerment, domestic violence, and other health-related issues such as smoking.

The information collected through the NDHS 2017 is intended to assist policymakers and program managers in the Department of Health (DOH) and other organizations in designing and evaluating programs and strategies for improving the health of the country’s population.

Geographic coverage

National coverage

Analysis unit

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

Universe

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

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling scheme provides data representative of the country as a whole, for urban and rural areas separately, and for each of the country’s administrative regions. The sample selection methodology for the NDHS 2017 is based on a two-stage stratified sample design using the Master Sample Frame (MSF), designed and compiled by the PSA. The MSF is constructed based on the results of the 2010 Census of Population and Housing and updated based on the 2015 Census of Population. The first stage involved a systematic selection of 1,250 primary sampling units (PSUs) distributed by province or HUC. A PSU can be a barangay, a portion of a large barangay, or two or more adjacent small barangays.

In the second stage, an equal take of either 20 or 26 sample housing units were selected from each sampled PSU using systematic random sampling. In situations where a housing unit contained one to three households, all households were interviewed. In the rare situation where a housing unit contained more than three households, no more than three households were interviewed. The survey interviewers were instructed to interview only the pre-selected housing units. No replacements and no changes of the preselected housing units were allowed in the implementing stage in order to prevent bias. Survey weights were calculated, added to the data file, and applied so that weighted results are representative estimates of indicators at the regional and national levels.

All women age 15-49 who were either permanent residents of the selected households or visitors who stayed in the households the night before the survey were eligible to be interviewed. Among women eligible for an individual interview, one woman per household was selected for a module on domestic violence.

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

Mode of data collection

Face-to-face [f2f]

Research instrument

Two questionnaires were used for the NDHS 2017: the Household Questionnaire and the Woman’s Questionnaire. Both questionnaires, based on The DHS Program’s standard Demographic and Health Survey (DHS-7) questionnaires, were adapted to reflect the population and health issues relevant to the Philippines. Input was solicited from various stakeholders representing government agencies, universities, and international agencies.

Cleaning operations

The processing of the NDHS 2017 data began almost as soon as fieldwork started. As data collection was completed in each PSU, all electronic data files were transferred via an Internet file streaming system (IFSS) to the PSA central office in Quezon City. These data files were registered and checked for inconsistencies, incompleteness, and outliers. The field teams were alerted to any inconsistencies and errors while still in the PSU. Secondary editing involved resolving inconsistencies and the coding of openended questions; the former was carried out in the central office by a senior data processor, while the latter was taken on by regional coordinators and central office staff during a 5-day workshop following the completion of the fieldwork. Data editing was carried out using the CSPro software package. The concurrent processing of the data offered a distinct advantage, because it maximized the likelihood of the data being error-free and accurate. Timely generation of field check tables allowed for more effective monitoring. The secondary editing of the data was completed by November 2017. The final cleaning of the data set was carried out by data processing specialists from The DHS Program by the end of December 2017.

Response rate

A total of 31,791 households were selected for the sample, of which 27,855 were occupied. Of the occupied households, 27,496 were successfully interviewed, yielding a response rate of 99%. In the interviewed households, 25,690 women age 15-49 were identified for individual interviews; interviews were completed with 25,074 women, yielding a response rate of 98%.

The household response rate is slightly lower in urban areas than in rural areas (98% and 99%, respectively); however, there is no difference by urban-rural residence in response rates among women (98% for each).

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 Philippines National Demographic and Health Survey (NDHS) 2017 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 NDHS 2017 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 among all possible samples. Although the degree of variability is not known exactly, it can be estimated from the survey results.

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% 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 NDHS 2017 sample is the result of a multi-stage stratified design, and, consequently, it was necessary to use more complex formulas. Sampling errors are computed in SAS, using programs developed by ICF. These programs use the Taylor linearization method to estimate variances for survey estimates that are means, proportions, or ratios. 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 B of the survey final report.

Data appraisal

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

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

Abstract

The SHDS is a national sample survey designed to provide information on population, birth spacing, reproductive health, nutrition, maternal and child health, child survival, HIV/AIDS and sexually transmitted infections (STIs), in Somalia.. The main objective of the SHDS was to provide evidence on the health and demographic characteristics of the Somali population that will guide the development of programmes and formulation of effective policies. This information would also help monitor and evaluate national, sub-national and sector development plans, including the Sustainable Development Goals (SDGs), both by the government and development partners. The target population for SHDS was the women between 15 and 49 years of age, and the children less than the age of 5 years

Geographic coverage

The SHDS 2020 was a nationally representative household survey.

Analysis unit

The unit analysis of this survey are households, women aged 15-49 and children aged 0-5

Universe

This sample survey covered Women aged 15-49 and Children aged 0-5 years.

Kind of data

Sample survey data [ssd]

Sampling procedure

Sample Design The sample for the SHDS was designed to provide estimates of key indicators for the country as a whole, for each of the eighteen pre-war geographical regions, which are the country's first-level administrative divisions, as well as separately for urban, rural and nomadic areas. With the exception of Banadir region, which is considered fully urban, each region was stratified into urban, rural and nomadic areas, yielding a total of 55 sampling strata. All three strata of Lower Shabelle and Middle Juba regions, as well as the rural and nomadic strata of Bay region, were completely excluded from the survey due to security reasons. A final total of 47 sampling strata formed the sampling frame. Through the use of up-to-date, high-resolution satellite imagery, as well as on-the-ground knowledge of staff from the respective ministries of planning, all dwelling structures were digitized in urban and rural areas. Enumeration Areas (EAs) were formed onscreen through a spatial count of dwelling structures in a Geographic Information System (GIS) software. Thereafter, a sample ground verification of the digitized structures was carried out for large urban and rural areas and necessary adjustments made to the frame.

Each EA created had a minimum of 50 and a maximum of 149 dwelling structures. A total of 10,525 EAs were digitized: 7,488 in urban areas and 3,037 in rural areas. However, because of security and accessibility constraints, not all digitized areas were included in the final sampling frame-9,136 EAs (7,308 in urban and 1,828 in rural) formed the final frame. The nomadic frame comprised an updated list of temporary nomadic settlements (TNS) obtained from the nomadic link workers who are tied to these settlements. A total of 2,521 TNS formed the SHDS nomadic sampling frame. The SHDS followed a three-stage stratified cluster sample design in urban and rural strata with a probability proportional to size, for the sampling of Primary Sampling Units (PSU) and Secondary Sampling Units (SSU) (respectively at the first and second stage), and systematic sampling of households at the third stage. For the nomadic stratum, a two-stage stratified cluster sample design was applied with a probability proportional to size for sampling of PSUs at the first stage and systematic sampling of households at the second stage. To ensure that the survey precision is comparable across regions, PSUs were allocated equally to all regions with slight adjustments in two regions. Within each stratum, a sample of 35 EAs was selected independently, with probability proportional to the number of digitized dwelling structures. In this first stage, a total of 1,433 EAs were allocated (to urban - 770 EAs, rural - 488 EAs, and nomadic - 175 EAs) representing about 16 percent of the total frame of EAs. In the urban and rural selected EAs, all households were listed and information on births and deaths was recorded through the maternal mortality questionnaire. The data collected in this first phase was cleaned and a summary of households listed per EA formed the sampling frames for the second phase. In the second stage, 10 EAs were sampled out of the possible 35 that were listed, using probability proportional to the number of households. All households in each of these 10 EAs were serialized based on their location in the EA and 30 of these households sampled for the survey. The serialization was done to ensure distribution of the households interviewed for the survey in the EA sampled. A total of 220 EAs and 150 EAs were allocated to urban and rural strata respectively, while in the third stage, an average of 30 households were selected from the listed households in every EA to yield a total of 16,360 households from 538 EAs covered (220 EAs in urban, 147 EAs in rural and 171 EAs in nomadic) out of the sampled 545 EAs. In nomadic areas, a sample of 10 EAs (in this case TNS) were selected from each nomadic stratum, with probability proportional to the number of estimated households. A complete listing of households was carried out in the selected TNS followed by the selection of 30 households for the main survey interview. In those TNS with less than 30 households, all households were interviewed for the main survey. All eligible ever-married women aged 12 to 49 and never-married women aged 15 to 49 were interviewed in the selected households, while the household questionnaire was administered to all households selected. The maternal mortality questionnaire was administered to all households in each sampled TNS.

Mode of data collection

Face-to-face [f2f]

Response rate

A total of 16,360 households were selected for the sample, of which 15,870 were occupied. Of the occupied households, 15,826 were successfully interviewed, yielding a response rate of 99.7 percent. The SHDS 2020 interviewed 16,486 women-11,876 ever-married women and 4,610 never-married women.

Sampling error estimates

Sampling errors are important data quality parameters which give measure of the precision of the survey estimates. They aid in determining the statistical reliability of survey 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 on the part of either the interviewer or the respondent, and data entry errors. Although numerous efforts were made during the implementation of the Somaliland Health and Demographic Survey ( SHDS 2020) to minimise this type of error, non-sampling 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 SHDS 2020 is only one of many samples that could have been selected from the same population, using the same design and sample 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 among all possible samples. Although the degree of variability is not known exactly, it can be estimated from the survey results. 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% of all possible samples of identical size and design. If the sample of respondents had been selected by simple random sampling, it would have been possible to use straightforward formulas for calculating sampling errors. However, the SHDS 2020 sample was the result of a multi-stage stratified design, and, consequently, it was necessary to use more complex formulas. The variance approximation procedure that account for the complex sample design used R program was estimated sampling errors in SHDS which is Taylor series linearization. The non-linear estimates are approximated by linear ones for estimating variance. The linear approximation is derived by taking the first-order Tylor series approximation. Standard variance estimation methods for linear statistics are then used to estimate the variance of the linearized estimator. The Taylor linearisation method treats any linear statistic such as a percentage or mean as a ratio estimate, r = y/x, where y represents the total sample value for variable y and x represents the total number of cases in the group or subgroup under consideration

Data appraisal

• Household age distribution
• Age distribution of eligible and interviewed women
• Pregnancy- related mortality trends Note: See detailed data quality tables in APPENDIX C of the report.

This Dataset compares certain Health issues(Neonatal Mortality per 1000,Postneonatal Mortality per 1000, Infant Mortality per 1000,Child Mortality per 1000,Under-5 Mortality per 1000,Percentage Vaccinated, Percentage with Vaccination Card) of Nasarawa state, to the state zone and on national level for the Year 2018. Source : Nigeria Demographic and Health Survey.

Abstract

The UNPS aims at producing annual estimates in key policy areas; and providing a platform for experimenting with and assessing national policies and programs. Explicitly, the objectives of the UNPS include:

1. To provide information required for monitoring the National Development Strategy, of major programs such as National Agricultural Advisory Services (NAADS) and General Budget Support, and also to provide information for the compilation of the National Accounts (e.g. agricultural production);

2. To provide high quality nationally representative information on income dynamics at the household level as well as information on service delivery and consumption expenditure estimates annually; to monitor poverty and service outcomes in interim years of other national survey efforts, such as the Uganda National Household Survey (UNHS), Uganda Demographic and Health Survey (UDHS) and National Service Delivery Surveys (NSDS);

3. To provide a framework for low-cost experimentation with different policy interventions to e.g. reduce teacher absenteeism, improve ante-natal and post-natal care, and assess the effect of subsidies on agricultural inputs among others;

4. To provide a framework for policy oriented analysis and capacity building substantiated with the UGDR and support to other research which feed into the Annual Policy Implementation Review; and

5. To facilitate randomized impact evaluations of interventions whose effects cannot currently be readily assessed through the existing system of national household surveys.

Geographic coverage

National coverage

Analysis unit

Households

Kind of data

Sample survey data [ssd]

Sampling procedure

The UNPS is carried out over a twelve-month period (a "wave") on a nationally representative sample of households, for the purpose of accommodating the seasonality associated with the composition of and expenditures on consumption. The survey is conducted in two visits in order to better capture agricultural outcomes associated with the two cropping seasons of the country. The UNPS therefore interviews each household twice in a year, in visits approximately six months apart.In 2009/10, the UNPS set out to track and interview 3,123 households that were distributed over 322 Enumeration Areas (EAs), selected out of 783 EAs that had been visited during the Uganda National Household Survey (UNHS) in 2005/06. The distribution of the EAs covered by the 2009/10 UNPS was such that it included all 34 EAs in Kampala District, and 72 EAs (58 rural and 14 urban) in each of the other regions i.e. Central excluding Kampala, Eastern, Western and Northern which make up the strata.

Within each stratum, the EAs were selected with equal probability with implicit stratification by urban/rural and district (in this order). However, the probabilities of selection for the rural portions of ten districts that had been oversampled by the UNHS 2005/06 were adjusted accordingly. Since most IDP (Internally Displaced People) camps in the Northern region are currently unoccupied, the EAs that constituted IDP camps were not part of the UNPS sample. This allocation allows for reliable estimates at the national, rural-urban and regional levels i.e. at level of strata representativeness which includes: (i) Kampala City, (ii) Other Urban Areas, (iii) Central Rural, (iv) Eastern Rural, (v) Western Rural, and (vi) Northern Rural. In the UNPS 2010/11, the concept of Clusters instead of EAs was introduced. A cluster represents a group of households that are within a particular geographical area up to parish level. This was done due to split-off households that fell outside the selected EAs but could still be reached and interviewed if they still resided within the same parish as the selected EA. Consequently, in each subsequent survey wave, a subset of individuals was selected for tracking.

The UNPS is part of the long term Census and Household Survey Program hence questionnaires and the timing of data collection are coordinated with the current surveys and census implemented by UBOS.

SAMPLE REFRESH Starting with the UNPS 2013/14 (Wave 4) fieldwork, one third of the initial UNPS sample was refreshed with the intention to balance the advantages and shortcomings of panel surveys. New households were identified using the updated sample frames developed by the UBOS in 2013 as part of the preparations for the 2014 Uganda Population and Housing Census. This same sample was used for the UNPS 2015/16 (Wave 5) and the UNPS 2018/19 (Wave 7).

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The Uganda NPS had six questionnaires namely:

Household Questionnaire; Woman Questionnaire; Agriculture & Livestock Questionnaire; Fisheries Questionnaire; Community Questionnaire Market Questionnaire.

Each of these questionnaires is divided into a number of sections and the number of questions in each section varies accordingly. It should be noted that in 2013/14, 2015/16 and 2018/19, all questionnaires were administered using the CAPI software except the Fisheries and Market Questionnaires which were not administered.

Analysis unit

Woman, Birth, Child, Birth, Man, Household Member

Universe

Ever-married women age 15-49, Births, Children age 0-4, Ever-married men age 15-49, All persons

Kind of data

Demographic and Household Survey [hh/dhs]

Sampling procedure

MICRODATA SOURCE: National Institute of Population Studies (NIPS) [Pakistan], and ICF.

SAMPLE UNIT: Woman SAMPLE SIZE: 15068

SAMPLE UNIT: Birth SAMPLE SIZE: 50495

SAMPLE UNIT: Child SAMPLE SIZE: 12708

SAMPLE UNIT: Man SAMPLE SIZE: 3691

SAMPLE UNIT: Member SAMPLE SIZE: 100869

Mode of data collection

Face-to-face [f2f]

This report presents findings on the health and health-related behaviours of the Lesbian, Gay and Bisexual (LGB) population in England. These are analysed by age, sex and ethnicity. The data are based on a representative sample of adults, aged 16 and over, who participated in the Health Survey for England from 2011–2018. 2% of adults surveyed in 2011-2018 identified as lesbian, gay or bisexual (LGB) The Health Survey for England series was designed to monitor trends in the health, and health related behaviours, of adults and children in England.

Abstract

The main objectives of the 2018/19 NLSS are: i) to provide critical information for production of a wide range of socio-economic and demographic indicators, including for benchmarking and monitoring of SDGs; ii) to monitor progress in population's welfare; iii) to provide statistical evidence and measure the impact on households of current and anticipated government policies. In addition, the 2018/19 NLSS could be utilized to improve other non-survey statistical information, e.g. to determine and calibrate the contribution of final consumption expenditures of households to GDP; to update the weights and determine the basket for the national Consumer Price Index (CPI); to improve the methodology and dissemination of micro-economic and welfare statistics in Nigeria.

The 2018/19 NLSS collected a comprehensive and diverse set of socio-economic and demographic data pertaining to the basic needs and conditions under which households live on a day to day basis. The 2018/19 NLSS questionnaire includes wide-ranging modules, covering demographic indicators, education, health, labour, expenditures on food and non-food goods, non-farm enterprises, household assets and durables, access to safety nets, housing conditions, economic shocks, exposure to crime and farm production indicators.

Geographic coverage

National coverage

Analysis unit

Households

Universe

The survey covered all de jure households excluding prisons, hospitals, military barracks, and school dormitories.

Kind of data

Sample survey data [ssd]

Sampling procedure

SAMPLING PROCEDURE The 2018/19 NLSS sample is designed to provide representative estimates for the 36 states and the Federal Capital Territory (FCT), Abuja. By extension. The sample is also representative at the national and zonal levels. Although the sample is not explicitly stratified by urban and rural areas, it is possible to obtain urban and rural estimates from the NLSS data at the national level. At all stages, the relative proportion of urban and rural EAs as has been maintained. Before designing the sample for the 2018/19 NLSS, the results from the 2009/10 HNLSS were analysed to extract the sampling properties (variance, design effect, etc.) and estimate the required sample size to reach a desired precision for poverty estimates in the 2018/19 NLSS.

EA SELECTION: The sampling frame for the 2018/19 NLSS was based on the national master sample developed by the NBS, referred to as the NISH2 (Nigeria Integrated Survey of Households 2). This master sample was based on the enumeration areas (EAs) defined for the 2006 Nigeria Census Housing and Population conducted by National Population Commission (NPopC). The NISH2 was developed by the NBS to use as a frame for surveys with state-level domains. NISH2 EAs were drawn from another master sample that NBS developed for surveys with LGA-level domains (referred to as the “LGA master sample”). The NISH2 contains 200 EAs per state composed of 20 replicates of 10 sample EAs for each state, selected systematically from the full LGA master sample. Since the 2018/19 NLSS required domains at the state-level, the NISH2 served as the sampling frame for the survey. Since the NISH2 is composed of state-level replicates of 10 sample EAs, a total of 6 replicates were selected from the NISH2 for each state to provide a total sample of 60 EAs per state. The 6 replicates selected for the 2018/19 NLSS in each state were selected using random systematic sampling. This sampling procedure provides a similar distribution of the sample EAs within each state as if one systematic sample of 60 EAs had been selected directly from the census frame of EAs.

A fresh listing of households was conducted in the EAs selected for the 2018/19 NLSS. Throughout the course of the listing, 139 of the selected EAs (or about 6%) were not able to be listed by the field teams. The primary reason the teams were not able to conduct the listing in these EAs was due to security issues in the country. The fieldwork period of the 2018/19 NLSS saw events related to the insurgency in the north east of the country, clashes between farmers and herdsman, and roving groups of bandits. These events made it impossible for the interviewers to visit the EAs in the villages and areas affected by these conflict events. In addition to security issues, some EAs had been demolished or abandoned since the 2006 census was conducted. In order to not compromise the sample size and thus the statistical power of the estimates, it was decided to replace these 139 EAs. Additional EAs from the same state and sector were randomly selected from the remaining NISH2 EAs to replace each EA that could not be listed by the field teams. This necessary exclusion of conflict affected areas implies that the sample is representative of areas of Nigeria that were accessible during the 2018/19 NLSS fieldwork period. The sample will not reflect conditions in areas that were undergoing conflict at that time. This compromise was necessary to ensure the safety of interviewers.

HOUSEHOLD SELECTION: Following the listing, the 10 households to be interviewed were selected from the listed households. These households were selected systemically after sorting by the order in which the households were listed. This systematic sampling helped to ensure that the selected households were well dispersed across the EA and thereby limit the potential for clustering of the selected households within an EA. Occasionally, interviewers would encounter selected households that were not able to be interviewed (e.g. due to migration, refusal, etc.). In order to preserve the sample size and statistical power, households that could not be interviewed were replaced with an additional randomly selected household from the EA. Replacement households had to be requested by the field teams on a case-by-case basis and the replacement household was sent by the CAPI managers from NBS headquarters. Interviewers were required to submit a record for each household that was replaced, and justification given for their replacement. These replaced households are included in the disseminated data. However, replacements were relatively rare with only 2% of sampled households not able to be interviewed and replaced.

Sampling deviation

Although a sample was initially drawn for Borno state, the ongoing insurgency in the state presented severe challenges in conducting the survey there. The situation in the state made it impossible for the field teams to reach large areas of the state without compromising their safety. Given this limitation it was clear that a representative sample for Borno was not possible. However, it was decided to proceed with conducting the survey in areas that the teams could access in order to collect some information on the parts of the state that were accessible.

The limited area that field staff could safely operate in in Borno necessitated an alternative sample selection process from the other states. The EA selection occurred in several stages. Initially, an attempt was made to limit the frame to selected LGAs that were considered accessible. However, after selection of the EAs from the identified LGAs, it was reported by the NBS listing teams that a large share of the selected EAs were not safe for them to visit. Therefore, an alternative approach was adopted that would better ensure the safety of the field team but compromise further the representativeness of the sample. First, the list of 788 EAs in the LGA master sample for Borno were reviewed by NBS staff in Borno and the EAs they deemed accessible were identified. The team identified 359 EAs (46%) that were accessible. These 359 EAs served as the frame for the Borno sample and 60 EAs were randomly selected from this frame. However, throughout the course of the NLSS fieldwork, additional insurgency related events occurred which resulted in 7 of the 60 EAs being inaccessible when they were to be visited. Unlike for the main sample, these EAs were not replaced. Therefore, 53 EAs were ultimately covered from the Borno sample. The listing and household selection process that followed was the same as for the rest of the states.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

Two sets of questionnaires – household and community – were used to collect information in the NLSS2018/19. The Household Questionnaire was administered to all households in the sample. The Community Questionnaire was administered to the community to collect information on the socio-economic indicators of the enumeration areas where the sample households reside.

Household Questionnaire: The Household Questionnaire provides information on demographics; education; health; labour; food and non-food expenditure; household nonfarm income-generating activities; food security and shocks; safety nets; housing conditions; assets; information and communication technology; agriculture and land tenure; and other sources of household income.

Community Questionnaire: The Community Questionnaire solicits information on access to transported and infrastructure; community organizations; resource management; changes in the community; key events; community needs, actions and achievements; and local retail price information.

Cleaning operations

CAPI: The 2018/19 NLSS was conducted using the Survey Solutions Computer Assisted Person Interview (CAPI) platform. The Survey Solutions software was developed and maintained by the Development Economics Data Group (DECDG) at the World Bank. Each interviewer and supervisor was given a tablet which they used to

The East Asian Social Survey (EASS) is a biennial social survey project that serves as a cross-national network of the following four General Social Survey type surveys in East Asia: the Chinese General Social Survey (CGSS), the Japanese General Social Survey (JGSS), the Korean General Social Survey (KGSS), and the Taiwan Social Change Survey (TSCS), and comparatively examines diverse aspects of social life in these regions. Since its 1st module survey in 2006, EASS produces and disseminates its module survey datasets and this is the harmonized data for the 6th module survey, called 'Families in East Asia'. Survey information in this module is the same topic as the first module of the EASS 2006, and it focuses on family dynamics and relations. Respondents were asked about details of their family members; such as, the number of family members, age, sex, birth order, marital status, employment status, whether they co-resides with and whether they are alive or deceased. Other information collected includes contact frequency, intergenerational support exchanges, and attitudes toward financial support from family members. Questions also include opinions regarding household chores, lifestyle preferences, health of respondent and parents, as well as family obligations. Demographic and other background information includes age, sex, marital status, religion, years of education completed, employment status, income, and household size and composition.

Abstract

The Benin EHCVM 2018/19 is implemented by the National institute of Statistics and Economical Analysis (INSAE) with support from the World Bank and the WAEMU Commission. The objective of the program is to strengthen the capacity of its member countries (Benin, Burkina Faso, Cote d’Ivoire, Guinee Bissau, Mali, Niger, Senegal, and Togo) to conduct living conditions surveys that meet harmonized, regional standards and to make the collected micro-data publicly accessible. The EHCVM is a nationally representative survey of 8,000 households, which are also representative of the geopolitical zones (at both the urban and rural level).

The survey uses two main survey instruments: a household/individual questionnaire, and a community-level questionnaire. The surveys took place in two waves with each wave covering half of the sample. The first wave was fielded between October 2018 and December 2018, while the second wave occurred between April 2019 and July 2019. The two-wave approach was chosen to account for seasonality of consumption.

Geographic coverage

National coverage

Analysis unit

• Household
• Individual
• Community

Universe

The survey covered all de jure households excluding prisons, hospitals, military barracks, and school dormitories.

Kind of data

Sample survey data [ssd]

Sampling procedure

The Benin EHCVM 2018/19 used the 2013 Census of Population and Housing (RGPH) as the sampling frame. This frame contains 10032 enumeration areas, is nationally representative, and covers all regions with urban and rural areas surveyed in all regions apart from Littoral, a purely urban region. In Benin, the survey design decided on the sample size using the poverty rate - obtained from the 2011 Integrated Modular Survey on living conditions of households - as a variable of interest. Then the survey design split the decided sample size among regions considering the number of households in the region and the necessity to minimize the relative error. The survey design also defined the domains as country, urban and rural areas, and each of the 12 regions. Taking this into account, 23 explicit sample strata were selected.

Upon deciding on the sample size and repartition, the survey design team implemented a 2-stage sampling methodology. At the first stage, 670 enumeration areas (EAs) were selected with Probability Proportional to Size (PPS) using the 2013 RGPH and the number of households as a measure of size. In the second stage, 12 households were selected in each enumeration area randomly.

The total estimated survey sample size was 8040 households - 3960 from urban areas and 4080 from rural areas. After that, the survey design randomly divided each enumeration area into two equal groups. The survey team interrogated the first group in wave 1 and the other in wave 2. Finally, for various reasons, including availability and quality monitoring, the final sample size comprises 8012 households, including 3940 households from urban areas and 4072 households from rural areas. In wave one, the survey teams interviewed 3997 households (1940 in urban areas and 2057 in rural areas. In wave two, the teams interviewed 4015 households (2000 in urban areas and 2015 in rural areas).

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The Benin ECHVM 2018/19 consists of two questionnaires for each of the two visits. The Household Questionnaires was administered to all households in the sample. The Community Questionnaire was administered to the community to collect information on the socio-economic indicators of the enumeration areas where the sample households reside.

EHCVM 2018/19 Household Questionnaire: The Households Questionnaire provides information on demographics; education; health; employment (including activity-related information, primary and secondary employments); nonjob revenues; saving and credit (including information for payments due for 15 years old members of the household); food consumption; food security; nonfood consumption; nonagricultural enterprises; housing; household’s assets; transfers (received and sent); shocks and survival strategies; safety nets; agriculture (including information on plots, costs of inputs, and crops); livestock; fishing; agricultural equipment; and a module that provides indicators to helps users situate the household on the poverty spectrum based on subjective considerations and comparative indicators.

EHCVM 2018/19 Community Questionnaire: The Community Questionnaire solicits information on general community’s characteristics; community access to infrastructure and to social services; community agricultural activity; community participation; and local retail price information.

Determinants of use of and unmet need for modern methods of family planning in Cameroon, DHS 2018.

Liver steatosis scores (CAP Score) were investigated using data from the National Center for Health Statistics examination survey (NHANES) for 2017-2018 and 2017-March 2020. The datasets include demographics, dietary, examination, laboratory, and questionnaire databases. Each dataset consists of a set of SAS files converted to CSV files using the SAS Viewer software. The selected variables for this analysis are systolic and diastolic blood pressure, total cholesterol, insulin, triglycerides, elasticity score, and CAP scores. Before data cleaning, the total number of records was 8,056 with ten columns. However, after data cleaning, the remaining records used in the analysis were 6,394, with only six columns.

Abstract

Background: Adolescent girls in Kenya are disproportionately affected by early and unintended pregnancies, unsafe abortion and HIV infection. The In Their Hands (ITH) programme in Kenya aims to increase adolescents' use of high-quality sexual and reproductive health (SRH) services through targeted interventions. ITH Programme aims to promote use of contraception and testing for sexually transmitted infections (STIs) including HIV or pregnancy, for sexually active adolescent girls, 2) provide information, products and services on the adolescent girl's terms; and 3) promote communities support for girls and boys to access SRH services.

Objectives: The objectives of the evaluation are to assess: a) to what extent and how the new Adolescent Reproductive Health (ARH) partnership model and integrated system of delivery is working to meet its intended objectives and the needs of adolescents; b) adolescent user experiences across key quality dimensions and outcomes; c) how ITH programme has influenced adolescent voice, decision-making autonomy, power dynamics and provider accountability; d) how community support for adolescent reproductive and sexual health initiatives has changed as a result of this programme.

Methodology ITH programme is being implemented in two phases, a formative planning and experimentation in the first year from April 2017 to March 2018, and a national roll out and implementation from April 2018 to March 2020. This second phase is informed by an Annual Programme Review and thorough benchmarking and assessment which informed critical changes to performance and capacity so that ITH is fit for scale. It is expected that ITH will cover approximately 250,000 adolescent girls aged 15-19 in Kenya by April 2020. The programme is implemented by a consortium of Marie Stopes Kenya (MSK), Well Told Story, and Triggerise. ITH's key implementation strategies seek to increase adolescent motivation for service use, create a user-defined ecosystem and platform to provide girls with a network of accessible subsidized and discreet SRH services; and launch and sustain a national discourse campaign around adolescent sexuality and rights. The 3-year study will employ a mixed-methods approach with multiple data sources including secondary data, and qualitative and quantitative primary data with various stakeholders to explore their perceptions and attitudes towards adolescents SRH services. Quantitative data analysis will be done using STATA to provide descriptive statistics and statistical associations / correlations on key variables. All qualitative data will be analyzed using NVIVO software.

Study Duration: 36 months - between 2018 and 2020.

Geographic coverage

Narok and Homabay counties

Analysis unit

Households

Universe

All adolescent girls aged 15-19 years resident in the household.

Sampling procedure

The sampling of adolescents for the household survey was based on expected changes in adolescent's intention to use contraception in future. According to the Kenya Demographic and Health Survey 2014, 23.8% of adolescents and young women reported not intending to use contraception in future. This was used as a baseline proportion for the intervention as it aimed to increase demand and reduce the proportion of sexually active adolescents who did not intend to use contraception in the future. Assuming that the project was to achieve an impact of at least 2.4 percentage points in the intervention counties (i.e. a reduction by 10%), a design effect of 1.5 and a non- response rate of 10%, a sample size of 1885 was estimated using Cochran's sample size formula for categorical data was adequate to detect this difference between baseline and end line time points. Based on data from the 2009 Kenya census, there were approximately 0.46 adolescents girls per a household, which meant that the study was to include approximately 4876 households from the two counties at both baseline and end line surveys.

We collected data among a representative sample of adolescent girls living in both urban and rural ITH areas to understand adolescents' access to information, use of SRH services and SRH-related decision making autonomy before the implementation of the intervention. Depending on the number of ITH health facilities in the two study counties, Homa Bay and Narok that, we sampled 3 sub-Counties in Homa Bay: West Kasipul, Ndhiwa and Kasipul; and 3 sub-Counties in Narok, Narok Town, Narok South and Narok East purposively. In each of the ITH intervention counties, there were sub-counties that had been prioritized for the project and our data collection focused on these sub-counties selected for intervention. A stratified sampling procedure was used to select wards with in the sub-counties and villages from the wards. Then households were selected from each village after all households in the villages were listed. The purposive selection of sub-counties closer to ITH intervention facilities meant that urban and semi-urban areas were oversampled due to the concentration of health facilities in urban areas.

Qualitative Sampling

Focus Group Discussion participants were recruited from the villages where the ITH adolescent household survey was conducted in both counties. A convenience sample of consenting adults living in the villages were invited to participate in the FGDS. The discussion was conducted in local languages. A facilitator and note-taker trained on how to use the focus group guide, how to facilitate the group to elicit the information sought, and how to take detailed notes. All focus group discussions took place in the local language and were tape-recorded, and the consent process included permission to tape-record the session. Participants were identified only by their first names and participants were asked not to share what was discussed outside of the focus group. Participants were read an informed consent form and asked to give written consent. In-depth interviews were conducted with purposively selected sample of consenting adolescent girls who participated in the adolescent survey. We conducted a total of 45 In-depth interviews with adolescent girls (20 in Homa Bay County and 25 in Narok County respectively). In addition, 8 FGDs (4 each per county) were conducted with mothers of adolescent girls who are usual residents of the villages which had been identified for the interviews and another 4 FGDs (2 each per county) with CHVs.

Sampling deviation

N/A

Mode of data collection

Face-to-face [f2f] for quantitative data collection and Focus Group Discussions and In Depth Interviews for qualitative data collection

Research instrument

The questionnaire covered; socio-demographic and household information, SRH knowledge and sources of information, sexual activity and relationships, family planning knowledge, access, choice and use when needed, exposure to family planning messages and voice and decision making autonomy and quality of care for those who visited health facilities in the 12 months before the survey. The questionnaire was piloted before the data collection and the questions reviewed for appropriateness, comprehension and flow. The questionnaire was piloted among a sample of 42 adolescent girls (two each per field interviewer) 15-19 from a community outside the study counties.

The questionnaire was originally developed in English and later translated into Kiswahili. The questionnaire was programmed using ODK-based Survey CTO platform for data collection and management and was administered through face-to-face interview.

Cleaning operations

The survey tools were programmed using the ODK-based SurveyCTO platform for data collection and management. During programming, consistency checks were in-built into the data capture software which ensured that there were no cases of missing or implausible information/values entered into the database by the field interviewers. For example, the application included controls for variables ranges, skip patterns, duplicated individuals, and intra- and inter-module consistency checks. This reduced or eliminated errors usually introduced at the data capture stage. Once programmed, the survey tools were tested by the programming team who in conjunction with the project team conducted further testing on the application's usability, in-built consistency checks (skips, variable ranges, duplicating individuals etc.), and inter-module consistency checks. Any issues raised were documented and tracked on the Issue Tracker and followed up to full and timely resolution. After internal testing was done, the tools were availed to the project and field teams to perform user acceptance testing (UAT) so as to verify and validate that the electronic platform worked exactly as expected, in terms of usability, questions design, checks and skips etc.

Data cleaning was performed to ensure that data were free of errors and that indicators generated from these data were accurate and consistent. This process begun on the first day of data collection as the first records were uploaded into the database. The data manager used data collected during pilot testing to begin writing scripts in Stata 14 to check the variables in the data in 'real-time'. This ensured the resolutions of any inconsistencies that could be addressed by the data collection teams during the fieldwork activities. The Stata 14 scripts that perform real-time checks and clean data also wrote to a .rtf file that detailed every check performed against each variable, any inconsistencies encountered, and all steps that were taken to address these inconsistencies. The .rtf files also reported when a variable was