In Sweden, 11 percent of adult men and seven percent of adult women were involved in the early stage of a start-up in 2023, meaning that they were either setting up or owning and running a new business. In Europe, Latvia had the highest share of men involved in a new business.

In Norway, nine percent of adult men and five percent of adult women were involved in the early stage of a start-up in 2023, meaning that they were either setting up or owning and running a new business. This was significantly lower than in many other countries in Europe.

Background Waist circumference (WC) adjusted for body mass index (BMI) is positively associated with mortality, but the association with changes in WC is less clear. We investigated the association between changes in WC and mortality in middle-aged men and women, and evaluated the influence from concurrent changes in BMI. Methodology/principal findings Data on 26,625 healthy men and women from the Danish Diet, Cancer and Health study was analyzed. WC and BMI were assessed in 1993-97 and in 1999-02. Information on mortality was obtained by linkage to the Danish central Person Register. Hazard ratios (HR) were estimated with Cox regression models. During 6.7 years of follow-up, 568 and 361 deaths occurred among men and women, respectively. Changes in WC were positively associated with mortality (HR per 5 cm for the sexes combined ?= 1.09 (1.02 : 1.16) with adjustment for covariates, baseline WC, BMI and changes in BMI), whereas changes in BMI were inversely associated with mortality (HR per kg/m2 for the sexes combined ?= 0.91 (0.86, 0.97) with adjustment for covariates, baseline WC, BMI and changes in WC). Associations between changes in WC and mortality were not notably different in sub-groups stratified according to changes in BMI, baseline WC or when smokers or deaths occurring within the first years of follow-up were excluded. Conclusions/significance Changes in WC were positively associated with mortality in healthy middle-aged men and women throughout the range of concurrent changes in BMI. These findings suggest a need for development of prevention and treatment strategies targeted against redistribution of fat mass towards the abdominal region.

As of January 2024, micro-blogging platform X (formerly Twitter) was more popular with men than women, with male audiences accounting for 60.9 percent of global users. Additionally, users between the ages of 25 and 34 were particularly active on X/Twitter, making up more than 38 percent of users worldwide. How many people use? Although X/Twitter holds its status as a mainstream social media site, it falls short in comparison to other well-known platforms in terms of user numbers. As of early 2022, X/Twitter had around 436 million monthly active users, whilst Meta’s Facebook reached almost three billion MAU. Overall, the United States is home to over 105 million X/Twitter users, making up Twitter’s largest audience base, followed by Japan, India, and the United Kingdom, respectively. How is Twitter used? X/Twitter is utilized by its audience for many different purposes. In May 2021, over 80 percent of high-volume X/Twitter users (defined as users who tweet around 20 times per month) in the United States reported using the platform for entertainment, whilst 78 percent said they used it as a way to stay informed. High-volume X/Twitter users were far more likely to use the service as a means of expressing their opinion. Furthermore, in 2022, over half of social media users in the U.S. used Twitter as a news resource.  

Abstract

Nepal Multiple indicator Cluster Survey (NMICS) was conducted in 2019 by Central Bureau of Statistics (CBS) with the primary objective of filling the data gap on children, women and men of Nepal. The NMICS 2019 was implemented as part of the sixth round of the global MICS household survey programme with technical and financial support of UNICEF, Nepal. NMICS 2019 has generated a wealth of information on children and women which is of immense importance to monitor and evaluate plan and programmes related to children and women of Nepal. These data will help to monitor towards goals and targets of international agreements such as Sustainable Development Goal. The NMICS 2019 covers topics related to child health, water and sanitation, reproductive health, child development, education and literacy, child protection, HIV and AIDS, mass media and use of information and communication technology, attitude towards domestic violence, tobacoo and alchohol use and life satisfaction. The 2019 Nepal MICS has as its primary objectives:

· To provide up-to-date information for assessing the situation of children, women and men in Nepal; · To generate data for the critical assessment of the progress made in various areas, and to put additional efforts in those areas that require more attention; · To furnish data needed for monitoring progress toward goals established in the Sustainable Development and other internationally agreed upon goals, as a basis for future action; · To collect disaggregated data for the identification of disparities, to allow for evidence-based policy-making aimed at social inclusion of the most vulnerable; · To contribute to the generation of baseline data for the post-2015 agenda; · To validate data from other sources and the results of focused interventions.

Geographic coverage

National, urban and rural, province

Analysis unit

Household, Women aged 15-49 years, Men aged 15-49 years, Children Under 5, Children Aged 5-17

Universe

The survey covered all de jure household members (usual residents), all women aged 15-49 years resident in the household, men aged 15-49 years resident in the alternative household, children aged 5-17 resident in the household, and all children aged 0-4 years (under age 5) resident in the household, Ecoli and arsenic test of resouce water & drinking water

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample for the Nepal MICS was designed to provide estimates for a large number of indicators on the situation of children, women and men at the national level, for urban and rural areas, and for the province.

The urban and rural areas within each region were identified as the main sampling strata and the sample was selected in two stages. Within each stratum, a specified number of census enumeration areas (i.e 512) 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 enumeratiion area from the 2011 Census frame.

The first stage of sampling was subsequently completed by selecting the required number of sample EAs specified from each of the seven provinces, separately for the urban and rural strata including Kathmandu valley urban. The households were then sequentially numbered from 1 to Mhi (the total number of households in each enumeration area) at the CBS, where the selection of 25 households in each enumeration area was carried out using random systematic selection procedures. The MICS6 spreadsheet template for systematic random selection of households was adapted for this purpose.

The survey also included a questionnaire for individual men that wasto be administered in half of the sampled households. The MICS household selection template includes an option to specify the proportion of households to be selected for administering the individual questionnaire for men, and the spreadsheet automatically selected the corresponding subsample of households.All men age 15 to 49 years in the selected households were eligible for interview.

The households listed in each sample cluster were divided into two strata for the second stage selection: households with and without children under 5. A separate sample of households was selected from each group, using a higher sampling rate for households with children under 5. This sampling strategy increased the number of children under 5 in the sample to increase the precision of the indicators based on under-5 children. Of the 25 households selected in each cluster, the target number of sample households with children under age 5 years was 13. Therefore, in sample clusters where more than 13 households with children under age 5 were listed, 13 of these households were selected using random systematic sampling; and 12 households without children under age 5 were selected from the other stratum. In sample clusters where 13 or less households with children under 5 were listed, all of these households were selected for the survey. In these clusters, the number of households without children under 5 to be selected was equal to 25 minus the number of households with children.

The Nepal MICS also included water quality testing for both E. coli and Arsenic for a subsample of households within each sample cluster. A subsample of 5 of the 25 selected households was selected in each sample cluster using random systematic sampling for conducting water quality testing, for both water in the household and at the source for E. coli, and only at the source for Arsenic. The MICS household selection template includes an option to specify the number of households to be selected for the water quality testing, and the spreadsheet automatically selected the corresponding subsample of households.

Mode of data collection

Face-to-face [f2f]

Research instrument

Six sets of questionnaires were used in the survey: 1. HH Questionnaire: a household questionnaire which was used to collect basic demographic information on all de jure household members (usual residents), the household, and the dwelling 2. Children Age 5-17 Questionnaire: an questionnaire was administered to mothers (or caretakers) for all children 5-17 years of age living in the household 3. Children Under 5 Questionnaire: an under-fives questionnaire, administered to mothers (or caretakers)for all children under five years of age living in the household 4. Individual Women Questionnaire: a questionnaire for individual women administered in each household to all women aged 15-49 years 5. Individual Men Questionnaire: a questionnaire for individual men administered in alternative household to all men aged 15-49 years 6. Water Quality Testing Questionnaire: a water quality testing questionnaire to test for bacteria and measure E. coli and arsenic content in household drinking water and source in a sub-sample of the households.

The quesitonnaires developed in English ' MICS6 Model Questionnaires' were modified somehow to Nepalese context where needed and were translated into Nepali version. After an initial review the questionnaires were translated back into English by an independent translator with no prior knowledge of the survey. The back translation version was independently reviewed and compared to the English original. Differences in translation were reviewed and resolved in collaboration with the original translators. The English and Nepali questionnaires were both piloted as part of the survey pretest.

The Household Questionnaire included the following modules:

• Household listing
• household characteristics
• education
• social transfer
• household energy use
• drinking water and sanitation
• handwashing facility

The Individual Women Questionnaire included the following modules:

• Woman's background
• access to mass media and use of information and/communication technology
• marriage/union
• fertility/birth history
• desire for last birth
• post-natal health checks
• maternal and newborn health
• illness symptoms
• contraception
• unmet need
• attitudes towards domestic violence
• human trafficking
• adult functioning
• HIV/AIDS knowledge
• tobacco and alcoholic use
• life satisfaction

The Individual Men Questionnaire included the following modules:

• Man's background
• access to mass media and use of information and/communication technology
• marriage/union
• fertility
• attitudes towards domestic violence
• human trafficking
• adult functioning
• HIV/AIDS knowledge
• tobacco and alcoholic use
• life satisfaction.

The Children Under Five Questionnaire included the following modules:

• Under-Five Child Information
• Under-Five Children's background
• Birth registration
• Early Childhood Development
• Child Discipline
• Child Functioning
• Breastfeeding and Dietary intake
• Immunisation
• Care of illness and immunization
• Anthropometry

The Children age 5-17 included the following modules:

• Child Information
• Child's Background
• Child Discipline
• Child Functioning
• Parental involvement
• Foundational Learning Skills

The water quality test questionnaire included the following modules:

• Water quality testing- arsenic testing
• E-coli

Cleaning operations

Data were received at the Central Bureau of Statistics’ 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

As of February 2025, approximately 44.3 percent of TikTok global users were women. By comparison, male users on the popular social video platform were 55.7 percent of the total. TikTok generated 186 million downloads from global users during the last quarter of 2024. TikTok’s popularity TikTok's popularity continues to expand globally, with countries like Saudi Arabia, the United Arab Emirates, and Malaysia presenting a reach of virtually over 100 percent as of the end of 2023. In October 2023, the social video platform boasted over 1.2 billion active monthly users, of which United States users represented the largest audience worldwide. U.S. users were particularly active on the platform, spending 32 percent of their online time engaging with TikTok as of February 2023. TikTok’s global revenue TikTok rapidly increased its revenue in the past years. In the third quarter of 2023, TikTok generated 681 million U.S. dollars in revenues from users worldwide. In 2024, it is projected that the social video platform will reach global ad revenue close to 18.5 billion.

Abstract

The 2008-09 Kenya Demographic and Health Survey (KDHS) is a population and health survey that Kenya conducts every five years. It was designed to provide data to monitor the population and health situation in Kenya and also to be used as a follow-up to the previous KDHS surveys in 1989, 1993, 1998, and 2003.

From the current survey, information was collected on fertility levels; marriage; sexual activity; fertility preferences; awareness and use of family planning methods; breastfeeding practices; nutritional status of women and young children; childhood and maternal mortality; maternal and child health; and awareness and behaviour regarding HIV/AIDS and other sexually transmitted infections. The 2008-09 KDHS is the second survey to collect data on malaria and the use of mosquito nets, domestic violence, and HIV testing of adults.

The specific objectives of the 2008-09 KDHS were to: - Provide data, at the national and provincial levels, that allow the derivation of demographic rates, particularly fertility and childhood mortality rates, to be used to evaluate the achievements of the current national population policy for sustainable development - Measure changes in fertility and contraceptive prevalence use and study the factors that affect these changes, such as marriage patterns, desire for children, availability of contraception, breastfeeding habits, and other important social and economic factors - Examine the basic indicators of maternal and child health in Kenya, including nutritional status, use of antenatal and maternity services, treatment of recent episodes of childhood illness, use of immunisation services, use of mosquito nets, and treatment of children and pregnant women for malaria - Describe the patterns of knowledge and behaviour related to the transmission of HIV/AIDS and other sexually transmitted infections - Estimate adult and maternal mortality ratios at the national level - Ascertain the extent and pattern of domestic violence and female genital cutting in the country - Estimate the prevalence of HIV infection at the national and provincial levels and by urban-rural residence, and use the data to corroborate the rates from the sentinel surveillance system

The 2008-09 KDHS information provides data to assist policymakers and programme implementers as they monitor and evaluate existing programmes and design new strategies for demographic, social, and health policies in Kenya. The data will be useful in many ways, including the monitoring of the country’s achievement of the Millennium Development Goals.

As in 2003, the 2008-09 KDHS survey was designed to cover the entire country, including the arid and semi-arid districts, and especially those areas in the northern part of the country that were not covered in the earlier KDHS surveys. The survey collected information on demographic and health issues from a sample of women at the reproductive age of 15-49 and from a sample of men age 15-54 years in a one-in-two subsample of households.

Geographic coverage

National

Analysis unit

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

Kind of data

Sample survey data

Sampling procedure

The survey is household-based, and therefore the sample was drawn from the population residing in households in the country. A representative sample of 10,000 households was drawn for the 2008-09 KDHS. This sample was constructed to allow for separate estimates for key indicators for each of the eight provinces in Kenya, as well as for urban and rural areas separately. Compared with the other provinces, fewer households and clusters were surveyed in North Eastern province because of its sparse population. A deliberate attempt was made to oversample urban areas to get enough cases for analysis. As a result of these differing sample proportions, the KDHS sample is not self-weighting at the national level; consequently, all tables except those concerning response rates are based on weighted data.

The KNBS maintains master sampling frames for household-based surveys. The current one is the fourth National Sample Survey and Evaluation Programme (NASSEP IV), which was developed on the platform of a two-stage sample design. The 2008-09 KDHS adopted the same design, and the first stage involved selecting data collection points ('clusters') from the national master sample frame. A total of 400 clusters-133 urban and 267 rural-were selected from the master frame. The second stage of selection involved the systematic sampling of households from an updated list of households. The Bureau developed the NASSEP frame in 2002 from a list of enumeration areas covered in the 1999 population and housing census. A number of clusters were updated for various surveys to provide a more accurate selection of households. Included were some of the 2008-09 KDHS clusters that were updated prior to selection of households for the data collection.

All women age 15-49 years who were either usual residents or visitors present in sampled households on the night before the survey were eligible to be interviewed in the survey. In addition, in every second household selected for the survey, all men age 15-54 years were also eligible to be interviewed. All women and men living in the households selected for the Men's Questionnaire and eligible for the individual interview were asked to voluntarily give a few drops of blood for HIV testing.

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

Mode of data collection

Face-to-face

Research instrument

Three questionnaires were used to collect the survey data: the Household, Women’s, and Men’s Questionnaires. The contents of these questionnaires were based on model questionnaires developed by the MEASURE DHS programme that underwent only slight adjustments to reflect relevant issues in Kenya. Adjustment was done through a consultative process with all the relevant technical institutions, government agencies, and local and international organisations. The three questionnaires were then translated from English into Kiswahili and 10 other local languages (Kalenjin, Kamba, Kikuyu, Kisii, Luhya, Luo, Maasai, Meru, Mijikenda, and Somali). The questionnaires were further refined after the pretest and training of the field staff.

In each of the sampled households, the Household Questionnaire was the first to be administered and was used to list all the usual members and visitors. Basic information was collected on the characteristics of each person listed, including age, sex, education, and relationship to the head of the household. The main purpose of the Household Questionnaire was to identify women age 15-49 and men age 15-54 who were eligible for the individual interviews. The questionnaire also collected information on characteristics of the household’s dwelling unit, such as the source of water, type of toilet facilities, materials used for the floor, walls, and roof of the house, ownership of various durable goods, ownership of agricultural land, ownership of domestic animals, and ownership and use of mosquito nets. In addition, this questionnaire was used to capture information on height and weight measurements of women age 15-49 years and children age five years and below, and, in households eligible for collection of blood samples, to record the respondents’ consent to voluntarily give blood samples. A detailed description of HIV testing procedures is given in Section 1.10 below.

The Women’s Questionnaire was used to capture information from all women age 15-49 years and covered the following topics: - Respondent’s background characteristics (e.g., education, residential history, media exposure) - Reproductive history - Knowledge and use of family planning methods - Antenatal, delivery, and postnatal care - Breastfeeding - Immunisation, nutrition, and childhood illnesses - Fertility preferences - Husband’s background characteristics and woman’s work - Marriage and sexual activity - Infant and child feeding practices - Childhood mortality - Awareness and behaviour about HIV/AIDS and other sexually transmitted diseases - Knowledge of tuberculosis - Health insurance - Adult and maternal mortality - Domestic violence - Female genital cutting

The set of questions on domestic violence sought to obtain information on women’s experience of violence. The questions were administered to one woman per household. In households with more eligible women, special procedures (use of a ‘Kish grid’) were followed to ensure that the woman interviewed about domestic violence was randomly selected.

The Men’s Questionnaire was administered to all men age 15-54 years living in every second household in the sample. The Men’s Questionnaire collected information similar to that collected in the Women’s Questionnaire, but it was shorter because it did not contain questions on reproductive history, maternal and child health, nutrition, maternal mortality, and domestic violence.

Two pilot projects were conducted in 12 districts for the KDHS, the first from July 1-7, 2008, and the second from October 13-17, 2008, to test the questionnaires, which were written in English and then translated into eleven other languages. The pilot was repeated because the first pilot did not include the HIV blood testing component. Twelve teams (one for each language) were formed, each with one female interviewer, one male interviewer, and one health worker. A total of 260 households were covered in the pilots. The lessons learnt from the pilot surveys were used to finalise the survey instruments and set up strong, logistical arrangements to ensure the success of the survey.

Response

Abstract

Facebook’s Survey on Gender Equality at Home generates a global snapshot of women and men’s access to resources, their time spent on unpaid care work, and their attitudes about equality. This survey covers topics about gender dynamics and norms, unpaid caregiving, and life during the COVID-19 pandemic. Aggregated data is available publicly on Humanitarian Data Exchange (HDX). De-identified microdata is also available to eligible nonprofits and universities through Facebook’s Data for Good (DFG) program. For more information, please email dataforgood@fb.com.

Geographic coverage

This survey is fielded once a year in over 200 countries and 60 languages. The data can help researchers track trends in gender equality and progress on the Sustainable Development Goals.

Analysis unit

• Public Aggregate Data on HDX: country or regional levels
• De-identified Microdata through Facebook Data for Good program: Individual level

Universe

The survey was fielded to active Facebook users.

Kind of data

Sample survey data [ssd]

Sampling procedure

Respondents were sampled across seven regions: - East Asia and Pacific; Europe and Central Asia - Latin America and Caribbean - Middle East and North Africa - North America - Sub-Saharan Africa - South Asia

For the purposes of this report, responses have been aggregated up to the regional level; these regional estimates form the basis of this report and its associated products (Regional Briefs). In order to ensure respondent confidentiality, these estimates are based on responses where a sufficient number of people responded to each question and thus where confidentiality can be assured. This results in a sample of 461,748 respondents.

The sampling frame for this survey is the global database of Facebook users who were active on the platform at least once over the past 28 days, which offers a number of advantages: It allows for the design, implementation, and launch of a survey in a timely manner. Large sample sizes allow for more questions to be asked through random assignment of modules, avoiding respondent fatigue. Samples may be drawn from diverse segments of the online population. Knowledge of the overall sampling frame allowed for more rigorous probabilistic sampling techniques and non-response adjustments than is typical for online and phone surveys

Mode of data collection

Internet [int]

Research instrument

The survey includes a total of 75 questions, split across into the following sections: - Basic demographics and gender norms - Decision making and resource allocation across household members - Unpaid caregiving - Additional household demographics and COVID-19 impact - Optional questions for special groups (e.g. students, business owners, the employed, and the unemployed)

Questions were developed collaboratively by a team of economists and gender experts from the World Bank, UN Women, Equal Measures 2030, and Ladysmith. Some of the questions have been borrowed from other surveys that employ alternative modes of administration (e.g., face-to-face, telephone surveys, etc.); this allows for comparability and identification of potential gaps and biases inherent to Facebook and other online survey platforms. As such, the survey also generates methodological insights that are useful to researchers undertaking alternative modes of data collection during the COVID-19 era.

In order to avoid “survey fatigue,” wherein respondents begin to disengage from the survey content and responses become less reliable, each respondent was only asked to answer a subset of questions. Specifically, each respondent saw a maximum of 30 questions, comprising demographics (asked of all respondents) and a set of additional questions randomly and purposely allocated to them.

Response rate

Response rates to online surveys vary widely depending on a number of factors including survey length, region, strength of the relationship with invitees, incentive mechanisms, invite copy, interest of respondents in the topic and survey design.

Sampling error estimates

Any survey data is prone to several forms of error and biases that need to be considered to understand how closely the results reflect the intended population. In particular, the following components of the total survey error are noteworthy:

Sampling error is a natural characteristic of every survey based on samples and reflects the uncertainty in any survey result that is attributable to the fact that not the whole population is surveyed.

Other factors beyond sampling error that contribute to such potential differences are frame or coverage error and nonresponse error.

Data appraisal

Survey Limitations The survey only captures respondents who: (1) have access to the Internet (2) are Facebook users (3) opt to take this survey through the Facebook platform. Knowledge of the overall demographics of the online population in each region allows for calibration such that estimates are representative at this level. However, this means the results only tell us something about the online population in each region, not the overall population. As such, the survey cannot generate global estimates or meaningful comparisons across countries and regions, given the heterogeneity in internet connectivity across countries. Estimates have only been generated for respondents who gave their gender as male or female. The survey included an “other” option but very few respondents selected it, making it impossible to generate meaningful estimates for non-binary populations. It is important to note that the survey was not designed to paint a comprehensive picture of household dynamics but rather to shed light on respondents’ reported experiences and roles within households

Abstract

The 2022 Kenya Demographic and Health Survey (2022 KDHS) was implemented by the Kenya National Bureau of Statistics (KNBS) in collaboration with the Ministry of Health (MoH) and other stakeholders. The survey is the 7th KDHS implemented in the country.

The primary objective of the 2022 KDHS is to provide up-to-date estimates of basic sociodemographic, nutrition and health indicators. Specifically, the 2022 KDHS collected information on: • Fertility levels and contraceptive prevalence • Childhood mortality • Maternal and child health • Early Childhood Development Index (ECDI) • Anthropometric measures for children, women, and men • Children’s nutrition • Woman’s dietary diversity • Knowledge and behaviour related to the transmission of HIV and other sexually transmitted diseases • Noncommunicable diseases and other health issues • Extent and pattern of gender-based violence • Female genital mutilation.

The information collected in the 2022 KDHS will assist policymakers and programme managers in monitoring, evaluating, and designing programmes and strategies for improving the health of Kenya’s population. The 2022 KDHS also provides indicators relevant to monitoring the Sustainable Development Goals (SDGs) for Kenya, as well as indicators relevant for monitoring national and subnational development agendas such as the Kenya Vision 2030, Medium Term Plans (MTPs), and County Integrated Development Plans (CIDPs).

Geographic coverage

National coverage

Analysis unit

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

Universe

The survey covered all de jure household members (usual residents), all women aged 15-49, men ageed 15-54, and all children aged 0-4 resident in the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample for the 2022 KDHS was drawn from the Kenya Household Master Sample Frame (K-HMSF). This is the frame that KNBS currently uses to conduct household-based sample surveys in Kenya. The frame is based on the 2019 Kenya Population and Housing Census (KPHC) data, in which a total of 129,067 enumeration areas (EAs) were developed. Of these EAs, 10,000 were selected with probability proportional to size to create the K-HMSF. The 10,000 EAs were randomised into four equal subsamples. A survey can utilise a subsample or a combination of subsamples based on the sample size requirements. The 2022 KDHS sample was drawn from subsample one of the K-HMSF. The EAs were developed into clusters through a process of household listing and geo-referencing. The Constitution of Kenya 2010 established a devolved system of government in which Kenya is divided into 47 counties. To design the frame, each of the 47 counties in Kenya was stratified into rural and urban strata, which resulted in 92 strata since Nairobi City and Mombasa counties are purely urban.

The 2022 KDHS was designed to provide estimates at the national level, for rural and urban areas separately, and, for some indicators, at the county level. The sample size was computed at 42,300 households, with 25 households selected per cluster, which resulted in 1,692 clusters spread across the country, 1,026 clusters in rural areas, and 666 in urban areas. The sample was allocated to the different sampling strata using power allocation to enable comparability of county estimates.

The 2022 KDHS employed a two-stage stratified sample design where in the first stage, 1,692 clusters were selected from the K-HMSF using the Equal Probability Selection Method (EPSEM). The clusters were selected independently in each sampling stratum. Household listing was carried out in all the selected clusters, and the resulting list of households served as a sampling frame for the second stage of selection, where 25 households were selected from each cluster. However, after the household listing procedure, it was found that some clusters had fewer than 25 households; therefore, all households from these clusters were selected into the sample. This resulted in 42,022 households being sampled for the 2022 KDHS. Interviews were conducted only in the pre-selected households and clusters; no replacement of the preselected units was allowed during the survey data collection stages.

For further details on sample design, see APPENDIX A of the survey report.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

Four questionnaires were used in the 2022 KDHS: Household Questionnaire, Woman’s Questionnaire, 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 Kenya. In addition, a self-administered Fieldworker Questionnaire was used to collect information about the survey’s fieldworkers.

Cleaning operations

CAPI was used during data collection. The devices used for CAPI were Android-based computer tablets programmed with a mobile version of CSPro. The CSPro software was developed jointly by the U.S. Census Bureau, Serpro S.A., and The DHS Program. Programming of questionnaires into the Android application was done by ICF, while configuration of tablets was completed by KNBS in collaboration with ICF. All fieldwork personnel were assigned usernames, and devices were password protected to ensure the integrity of the data.

Work was assigned by supervisors and shared via Bluetooth® to interviewers’ tablets. After completion, assigned work was shared with supervisors, who conducted initial data consistency checks and edits and then submitted data to the central servers hosted at KNBS via SyncCloud. Data were downloaded from the central servers and checked against the inventory of expected returns to account for all data collected in the field. SyncCloud was also used to generate field check tables to monitor progress and identify any errors, which were communicated back to the field teams for correction.

Secondary editing was done by members of the KNBS and ICF central office team, who resolved any errors that were not corrected by field teams during data collection. A CSPro batch editing tool was used for cleaning and tabulation during data analysis.

Response rate

A total of 42,022 households were selected for the survey, of which 38,731 (92%) were found to be occupied. Among the occupied households, 37,911 were successfully interviewed, yielding a response rate of 98%. The response rates for urban and rural households were 96% and 99%, respectively. In the interviewed households, 33,879 women age 15-49 were identified as eligible for individual interviews. Of these, 32,156 women were interviewed, yielding a response rate of 95%. The response rates among women selected for the full and short questionnaires were similar (95%). In the households selected for the men’s survey, 16,552 men age 15-54 were identified as eligible for individual interviews and 14,453 were successfully interviewed, yielding a response rate of 87%.

Sampling error estimates

The estimates from a sample survey are affected by two types of errors: (1) non-sampling errors, and (2) 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 2022 Kenya Demographic and Health Survey (2022 KDHS) 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 2022 KDHS is only one of many samples that could have been selected from the same population, using the same design and identical size. Each of these samples would yield results that differ somewhat from the results of the actual sample selected. Sampling errors are a measure of the variability between all possible samples. Although the degree of variability is not known exactly, it can be estimated from the survey results.

A sampling error is usually measured in terms of the standard error for a particular statistic (mean, percentage, etc.), which is the square root of the variance. The standard error can be used to calculate confidence intervals within which the true value for the population can reasonably be assumed to fall. For example, for any given statistic calculated from a sample survey, the value of that statistic will fall within a range of plus or minus two times the standard error of that statistic in 95 percent of all possible samples of identical size and design.

If the sample of respondents had been selected as a simple random sample, it would have been possible to use straightforward formulas for calculating sampling errors. However, the 2022 KDHS sample is the result of a multi-stage stratified design, and, consequently, it was necessary to use more complex formulae. The computer software used to calculate sampling errors for the 2022 KDHS is a SAS program. This program used the Taylor linearisation method for variance estimation 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 report.

Data

Abstract

The Tanzania Demographic and Health Survey (TDHS) is part of the worldwide Demographic and Health Surveys (DHS) programme, which is designed to collect data on fertility, family planning, and maternal and child health.

The general objectives of the 1996 TDHS are to: - Provide national-level data that will allow the calculation of demographic rates, particularly fertility and childhood mortality rates - Analyze the direct and indirect factors which determine the level and trends of fertility - Measure the level of contraceptive knowledge and practice (of both women and men) by method, by urban-rural residence, and by region - Collect reliable data on maternal and child health indicators; immunization, prevalence, and treatment of diarrhea and other diseases among children under age five; antenatal visits; assistance at delivery; and breastfeeding - Assess the nutritional status of children under age five and their mothers by means of anthropometric measurements (weight and height), and child feeding practices - Assess among women and men the prevailing level of specific knowledge and attitudes regarding AIDS and evaluate patterns of recent behavior regarding condom use - Measure maternal mortality and collect data on female circumcision.

Geographic coverage

The survey was designed to provide estimates (based on the results of the Woman's Questionnaire) for the whole country, for urban and rural areas in the country, and groups of regions (zones). In addition, the sample provides certain estimates for each of the 20 regions in the mainland and 2 subgroups in Zanzibar: Pemba Island and Ungaja.

In most regions, one in every four households was selected for the men's survey, and in six regions (Dares Salaam, Dodoma, Iringa, Kilimanjaro, Morogoro, and Shinyanga), men in every second household were selected for the interview. The sample of men was designed to provide estimates for the country as a whole and for urban and rural areas.

Analysis unit

• Households
• Children under five years
• Women age 15-49
• Men age 15-59

Kind of data

Sample survey data

Sampling procedure

The TDHS sample was a three-stage design consisting of the same 357 enumeration areas (EAs) that were used in the 1991-92 TDHS (262 EAs in rural and 95 EAs in urban areas). The selection of EAs was made in two stages: first, wards/branches and then EAs within wards/branches were selected. Lists of all households were prepared for the selected EAs and, at the third sampling stage; households were selected from these lists. The TDHS was designed to provide estimates (based on the results of the Woman's Questionnaire) for the whole country, for urban and rural areas in the country, and groups of regions (zones). In addition, the sample will provide certain estimates for each of the 20 regions in the mainland and 2 subgroups in Zanzibar: Pemba Island and Ungaja. In most regions, one in every four households was selected for the men's survey, and in six regions (Dares Salaam, Dodoma, Iringa, Kilimanjaro, Morogoro, and Shinyanga), men in every second household were selected for the interview. The sample of men was designed to provide estimates for the country as a whole and for urban and rural areas.

Unlike most other DHS surveys, households in Tanzania were selected from the household listing for each ward (or branch) on the basis of contiguity, beginning with a randomly selected start number. This selection process was used to minimize the difficulty encountered in moving from one selected household to another given the scattered nature of households.

See detailed sample design information in the APPENDIX A of the final 1996 Tanzania Demographic and Health Survey report.

Mode of data collection

Face-to-face

Research instrument

Three types of questionnaires were used during the survey. The Household Questionnaire was used to list the names of the household members and certain individual characteristics of all usual members of the household and visitors who had spent the previous night in the household. Certain basic information was collected on characteristics of each person listed, including relationship, age, sex, education, and place of residence. Furthermore, the Household Questionnaire collected information on characteristics relating to the household. These included the source of water, type of toilet facilities, materials used for the floor of the house, and ownership of various durable goods. However, the main purpose of the Household Questionnaire was to identify women and men who were eligible for the individual interview.

The Female Questionnaire was used to collect information from eligible women age 15-49. The topics covered in this questionnaire included the following: - Background characteristics of the woman including age, education, residential history - Reproductive history - Knowledge and use of family planning methods - Fertility preferences and attitudes about family planning - Antenatal and delivery care - Breastfeeding and weaning practices - Vaccinations and health status of children under age five - Marriage and sexual activity - Husband's occupation and education - Woman's employment, occupation, and earnings - Awareness and behavior regarding AIDS and other sexually transmitted diseases - Maternal mortality - Female circumcision - Height and weight of children under five years and their mothers.

The Male Questionnaire was used to collect information from a subsample of men age 15-59, namely, those living in every fourth household except in Dares Salaam, Dodoma, Kilimanjaro, Morogoro, Shinyanga, and Iringa regions where every second household was selected for the male interview. The Male Questionnaire collected much of the same information found in the Women's Questionnaire, but was shorter because it did not contain questions on reproductive history and maternal and child health. All questionnaires were translated and printed in Kiswahili.

Before the design of the questionnaires could be finalized, a pretest was done in May-June, 1996 to assess the viability of the questions, the flow and logical sequence of the skip pattern, and the field organization. It covered an area outside Dares Salaam and took about a week to complete. Modifications to the questionnaires were then made based on lessons drawn from the exercise.

Response rate

In all, 8,900 households were selected, out of which 8,141 were occupied. Of the households found, 7,969 were interviewed, representing a response rate of 98 percent. The shortfall between the selected and the interviewed households was largely because many dwellings were either vacant or no competent respondents were present at the time of the visit.

In the interviewed households, 8,501 eligible women (i.e. women age 15- 49) were identified for the individual interview, and 8,120 women were actually interviewed, yielding a response rate of 96 percent. In the subsample of households selected for the male interview, 2,658 eligible men (i.e., men age 15-59) were identified, 2,256 were interviewed, representing a response rate of 85 percent. The principal reason for nonresponse among both eligible men and women was the failure to find them at home despite repeated visits to the household. The lower response rates among men than women were due to the more frequent and longer absences of men.

The response rates are lower in urban areas. One-member households are more common in urban areas and are more difficult to interview because they keep their houses locked up most of the time. In urban settings, neighbors often do not know the whereabouts of such people.

Sampling error estimates

The estimates from a sample survey are affected by two types of errors: non-sampling errors, and sampling errors. Non-sampling errors are the results of mistakes made in implementing data collection and data processing, such as failure to locate and interview the correct household, misunderstanding 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 1996 TDHS to minimize 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 TDHS is only one of many samples that could have been selected from the same population, using the same design and expected size. Each of these samples would yield results that differ somewhat from the results of the actual sample selected. Sampling errors are a measure of the variability between all possible samples. Although the degree of variability is not known exactly, it can be estimated from the survey results.

A sampling error is usually measured in terms of the standard error for a particular statistic (mean, percentage, etc.), which is the square root of the variance. The standard error can be used to calculate confidence intervals within which the true value for the population can reasonably be assumed to fall. For example, for any given statistic calculated from a sample survey, the value of that statistic will fall within a range of plus or minus two times the standard error of that statistic in 95 percent of all possible samples of identical size and design.

If the sample of respondents had been selected as a simple random sample, straightforward formulae for calculating sampling errors could have been used. However, the TDHS sample is the result of a two-stage stratified design, and, consequently, it was necessary to use more complex formulae. The computer software that calculated sampling errors

Abstract

The main objective of a demographic household survey (DHS) is to provide estimates of a number of basic demographic and health variables. This is done through interviews with a scientifically selected probability sample that is chosen from a well-defined population.

The 2007 Nauru Demographic and Health Survey (2007 NDHS) was one of four pilot demographic and health surveys conducted in the Pacific under an Asian Development Bank ADB/ Secretariat of the Pacific Community (SPC) Regional DHS Pilot Project. The primary objective of this survey was to provide up-to-date information for policy-makers, planners, researchers and programme managers, for use in planning, implementing, monitoring and evaluating population and health programmes within the country. The survey was intended to provide key estimates of Nauru's demographics and health situation. The findings of the 2007 NDHS are very important in measuring the achievements of family planning and other health programmes. To ensure better understanding and use of these data, the results of this survey should be widely disseminated at different planning levels. Different dissemination techniques will be used to reach different segments of society.

The primary purpose of the 2007 NDHS was to furnish policy-makers and planners with detailed information on fertility, family planning, infant and child mortality, maternal and child health, nutrition, and knowledge of HIV and AIDS and other sexually transmitted infections.

NOTE: The only dissemination used was wide distribution of the report. A planned data use workshop was not undertaken. Hence there is some misconceptions and lack of awareness on the results obtained from the survey. The report is provided on the NBOS website free for download.

Geographic coverage

National Coverage - Districts

Analysis unit

• Households
• Children (0-14yrs)
• Individual women of reproductive age (15-49 yrs)
• Individual men of reproductive age (15yrs+)
• Facilities providing reproductive and child health services

Universe

The survey covered all household members (usual residents), - All children (aged 0-14 years) resident in the household - All women of reproductive age (15-49 years) resident in all household - All males (15yrs and above) in every second household (approx. 50%) resident in selected household

Results: The 2007 Nauru Demographic Health Survey (2007 NDHS) is a nationally representative survey of 655 eligible women (aged 15-49) and 392 eligible men (aged 15 and above).

Kind of data

Sample survey data [ssd]

Sampling procedure

IDG NOTES: Locate sampling documentation with SPC (Graeme Brown) and internal files. Add in this sections. Or second option dilute appendix A Sampling and extract key issues.

ESTIMATES OF SAMPLING ERRORS - Refer to Appendix A of final NDHS2007 report or; - External Resources - 2007 DHS- Appendix A and B Sampling (to be created separatedly by IDG progress ongoing)

Sampling deviation

IDG NOTES: Locate sampling documentation with Macro and internal files. Add in this section. Or second option dilute appendix B Sampling and extract key issues.

ESTIMATES OF SAMPLING ERRORS - Refer to Appendix B of final NDHS2007 report or;

• External Resources
  • 2007 DHS- Appendix A and B Sampling (to be created separatedly by IDG progress ongoing)

Extract:

In the 2007 NDHS Report of the survey results, sampling errors for selected variables have been presented in a tabular format. The sampling error tables should include:

.. Variable name

R: Value of the estimate; SE: Sampling error of the estimate; N: Unweighted number of cases on which the estimate is based; WN: Weighted number of cases; DEFT: Design effect value that compensates for the loss of precision that results from using cluster rather than simple random sampling; SE/R: Relative standard error (i.e. ratio of the sampling error to the value estimate); R-2SE: Lower limit of the 95% confidence interval; R+2SE: Upper limit of the 95% confidence interval (never >1.000 for a proportion).

Mode of data collection

Face-to-face [f2f]

Research instrument

DHS questionnaire for women cover the following sections:

• Background characteristics (age, education, religion, etc)
• Reproductive history
• Knowledge and use of contraception methods
• Antenatal care, delivery care and postnatal care
• Breastfeeding and infant feeding
• Immunization, child health and nutrition
• Marriage and recent sexual activity
• Fertility preferences
• Knowledge about HIV/AIDS and other sexually transmitted infections
• Husbands background and women's work

The men's questionnaire covers the same except for sections 4, 5, 6 which are not applicable to men.

It was also recognized that some countries have a need for special information that is not contained in the core questionnaire. Separate questionnaire modules were developed on a series of topics. These topics are optional and include:

• maternal mortality
• pill-taking behaviour
• sterilization experience
• children's education
• women's status
• domestic violence
• health expenditures
• consanguinity

The Papua New Guinea (PNG) questionnaire was proposed for Nauru to adapt as in comparison to the existing DHS model, this is not as lengthy and time-consuming. The PNG questionnaire also dealt with high incidence of alcohol and tobacco in Nauru. Questions on HIV/AIDS and STI knowledge were included in the men's questionnaire where it was not included in the PNG questionnaire.

Response rate

IDG NOTES: Locate response rate documentation with SPC (Graeme Brown) and internal files. Add in this sections.

The suicide rate among females in the United States is highest for those aged 45 to 64 years and lowest among girls aged 10 to 14 and elderly women 75 and over. Although the suicide rate among women remains over three times lower than that of men, rates of suicide among women have gradually increased over the past couple decades. Suicide among women in the United States In 2021, there were around six suicide deaths per 100,000 women in the United States. In comparison, the rate of suicide among women in the year 2000 was about four per 100,000. Suicide rates among women are by far the highest among American Indians or Alaska Natives and lowest among Hispanic and Black or African American women. Although firearms are involved in the highest share of suicide deaths among both men and women, they account for a much smaller share among women. In 2020, the firearm suicide rate among women was 1.8 per 100,000 population, while the rates of suicide for suffocation and poisoning were 1.7 and 1.5 per 100,000, respectively. Suicidal ideation among women Although not everyone who experiences suicidal ideation, or suicidal thoughts, will attempt suicide, suicidal thoughts are a risk factor for suicide. In 2022, just over five percent of women in the United States reported having serious thoughts of suicide in the past year. Suicidal thoughts are more common among women than men even though men have much higher rates of death from suicide than women. This is because men are more likely to use more lethal methods of suicide such as firearms. Women who suffer from substance use disorder are significantly more likely to have serious thoughts of suicide than women without substance use disorder.

Abstract

The main objective of the GTUS was to measure and analyze the time spent in a 24-hour period by different individuals aged 10 years and over - women, men, girls, and boys - on all activities including paid and unpaid work and leisure activities. solutions that address gender issues in macroeconomics and poverty reduction.

Geographic coverage

National coverage

Analysis unit

Household, individual

Universe

The survey covered all adult household members (usual residents) aged 15 years and older, and all chilrdren aged 3 years and above (usual residents) in the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

A representative sample of 4,800 households was drawn randomly from the list of Enumeration Areas (EAs) of the 2008 Ghana Demographic and Health Survey (GDHS), which served as a frame for the GTUS sample. In the selected households all individuals aged 10 years and older were interviewed. The sample frame was first stratified into the 10 administrative regions in the country, then into urban and rural EAs. GTUS used a two-stage stratified sample design. At the first stage of sampling, 300 EAs were selected. These are a sub-sample of the 412 EAs selected from the 2008 GDHS. The second stage involved selection of 16 households from the 2008 GDHS listing in each selected EA.

The Primary Sampling Unit (PSU) was the EA, while the Secondary Sampling Unit (SSU) was the household. In the selected households all individuals aged 10 years and older were interviewed for the 24-hour activity diary. The following factors were considered in the selection of EAs and households:

a) The regional population and average household size in the 2000 Population and Housing Census. The larger the average household size, the smaller the proportion of sampled households in the EA. b) A confidence interval of 95% with an error margin of 0.025. c) The number of EAs for each region in the 2008 GDHS. d) Allowance for a non-response rate of 20 percent for households. The rationale here was to eliminate the need for substitution of unfound or non-responding households during the fieldwork. Giving the option of substituting households to supervisors would have led to a biased sample and therefore field officers were not allowed to substitute. Furthermore, the selection of households considered the average household size of the regions and hence aimed at achieving an adequate sample of individual respondents who were the observation units. e) Increasing the number of selected households to compensate for the exclusion of the population under 10 years old in the households. f) As variations in the variables to be studied in the GTUS are expected to be higher in rural areas, it was decided to draw a larger sample (77% of EAs in GDHS 2008) for these areas than for urban areas (67% of EAs in GDHS).

The regional samples of EAs selected from the 2008 GDHS EAs were done using SPSS syntax that applies a systematic simple random sampling procedure. However, the sampling weights were calculated on the basis of the population size of the EAs and their totals in the region. The households were also selected using a systematic simple random sampling procedure in Microsoft Excel© using the 2008 DHS listing information. A sampling interval and a random starting number were determined. The random starting number served as the first household to be selected. The remaining 15 households were selected by adding multiples of the sampling interval to the random starting number until the desired number was achieved.

Mode of data collection

Face-to-face [f2f]

Research instrument

There were two types of questionnaires that were used in the GTUS: Household Questionnaire and individual Questionnaire. The household questionnaire collected information about demographic and socio-economic characteristics of the members of the household such as age, sex, level of education, household expenditures, housing and living conditions of the households. The household questionnaire permitted the interviewer to identify the eligible household members (10 years and older) for the individual interviews. The individual diary was used to record information on the individual's (10 years and older) activities, and the duration and the location of these activities within one-hour slots for a day (24 hours). All eligible household members were asked about their activities in the 24 hours beginning at 4am on the previous day. Each individual questionnaire was linked to a household questionnaire.

The Teleform automated data capturing software was used to design the questionnaires. They were then printed and tested to ensure that all the variables in the questionnaires were in the database. English language was used in published the questionnaires

Cleaning operations

Capturing of the data was automated through scanning to speed up data processing. A scanning technology called the Automated Teleform System was used to capture the data collected. This system combined Optical Mark Reader (OMR), Optical Character Reader (OCR) and Intelligent Character Recognition (ICR) for the processing. Before scanning, manual edits were performed on the questionnaires received from the field to check for completeness and accuracy of the questionnaires. After the scanning exercise, structural edits were done followed by consistency checks to further reduce errors.

Data were captured, cleaned and edited in Microsoft Access© format and transferred to SPSS. Further cleaning and imputations were done during analysis where the information was found to be inconsistent or incomplete. On the whole, scanning of the questionnaires, data cleaning and data validation were carried out from June 29 to July 31, 2009.

Response rate

The response rate for the 2009 GTUS was 99.5 percent at the household level and 86.5 percent at the individual level. As can be seen, the response rate at the individual level was higher in rural areas (87.2%) compared with urban areas (85.5%). It was also higher overall for females compared with males (88.1% against 84.8%). This can be explained by the fact that individuals are more likely to be absent from home in urban areas than in rural areas and females are more likely than males to be present in the household premises at the time of the interviewer's visit. It should also be noted that diary questionnaires that could not be linked to a fully completed household questionnaire have not been maintained in the sample for analyses.

Abstract

The Lao Social Indicator Survey (LSIS) II provides a set of single national figure on social indicators. It combines the Multiple Indicator Cluster Survey (MICS) and the Demographic and Health Survey modules to maximize government resources for a nationally representative sample survey. LSIS II follows the first LSIS I survey which was carried out in 2011-12 jointly by the Ministry of Health (MoH) and the Lao Statistics Bureau (LSB) of the Ministry of Planning and Investment in collaboration with other line ministries. The LSIS I provided baseline data for the 7th National Socio-Economic Development Plan (NSEDP) and the Millennium Development Goals.

The LSISII 2017 of Lao PDR has as its primary objectives:

• To provide up-to-date information that will assist with the selection of data on key social development indicators to support the monitoring of the Sustainable Development Goals (SDGs);

• To establish a baseline for national development plans and priorities including the 8th National Socio- Economic Development Plan (NSEDP), provincial core social development indicators data, as well as supporting the data for Least Developed Country Graduation;

• To produce a range of population and social indicators that are statistically sound and based on internationally comparable methodology and best practices; and

• To continue reinforcing coordination mechanisms on supporting and strengthening social statistics in Lao PDR and making use of its findings to formulate and advocate for policies, programme formulation and monitoring.

Geographic coverage

The sample for the Lao Social Indicator Survey 2017 was designed to provide estimates at the national level, for urban and rural areas, including rural with roads and rural without roads, for three regions including: North, Central and South and 18 provinces including: Vientiane Capital, Phongsaly, Luangnamtha, Oudomxay, Bokeo, Luangprabang, Huaphanh, Xayabury, Xiengkhuang, Vientinae, Borikhamxay, Khammuane, Savannakhet, Saravane, Sekong, Champasack, Attapeu and Xaysomboun.

Analysis unit

• Individuals

• Households

Universe

The survey covered all de jure household members (usual residents), all women age 15-49 years, all men age 15-49 years and all children under 5 living in the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

The major features of the sample design are described in this appendix. Sample design features include defining the sampling frame, target sample size, sample allocation, listing in sample clusters, choice of domains, sampling stages, stratification, and the calculation of sample weights.

The primary objective of the sample design for the 2017 Lao Social Indicator Survey (LSIS 2017) was to produce statistically reliable estimates of most indicators, at the national level, for urban and rural areas, and for the 18 provinces of the country.

A multi-stage, stratified cluster sampling approach was used for the selection of the survey sample. The primary sampling units (PSUs) selected at the first stage were villages (PSU and Village are used interchangeably in this Chapter). A listing of households was conducted in each sample village, and a sample of households was selected at the second stage.

SAMPLING FRAME AND STRATIFICATION

The sampling frame for this survey consisted of a list of all villages in the country, arranged by province, with appropriate size estimates (number of households) and other relevant information about each village. The village register is maintained by Lao Statistics Bureau (LSB). It is updated in December each year. The version used as sampling frame was the village register of December 2015.

The 18 provinces were defined as the sampling strata. Within provinces a further, implicit, stratification - on village category - was achieved by systematic sampling from a list of villages ordered by village category.

SAMPLE SIZE AND SAMPLE ALLOCATION

The overall sample size for the 2017 Lao Social Indicator Survey was calculated as 23,400 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 provincial level, it was necessary to determine the minimum sample size for each province.

The number of households selected per cluster for the survey was determined as 20 households, based on a number of considerations, including the design effect, the budget available, and the time that would be needed per team to complete one cluster. Dividing the total number of households by the number of sample households per cluster, it was calculated that 1,170 sample clusters would need to be selected for the survey.

The sample allocation over provinces was determined by a procedure where the sample at first was allocated proportionally to the square root of the number of households in each province. This allocation was further adjusted so that provinces getting less than 1,100 households in the preliminary allocation were given additional households up to 1,100. These additional households were taken from the three provinces that had the largest samples according to the preliminary allocation. The sample sizes for provinces vary between 1,100 and 1,680 households. The justification for using different sample sizes is that the standard errors for national estimates will be lower than the standard errors that would have been achieved with equal sample sizes over the provinces.

Within province the sample was allocated over implicit strata defined by village category. This was achieved by systematic sampling from a list of villages ordered by village category. This way of sampling resulted in approximately proportional allocation of the province sample over the implicit strata urban villages, rural villages with road and rural villages without road.

SELECTION OF VILLAGES (CLUSTERS)

Villages were selected from each of the sampling strata (provinces) by using systematic probability proportional to size (PPS) sampling procedures. The measure of size was the number of households in the village; the number was obtained from the LBS village register. Altogether 32 villages were so large in size so they had the probability equal to one to be selected to the sample. These large villages were thus selected to the sample with certainty.

LISTING ACTIVITIES

A new listing of households was conducted in all the sample villages prior to the selection of households. For this purpose, listing teams were trained to visit all the sampled villages and list all households in the village. The listing operation took place from December 2016 to February 2017 with 70 listing team members. In each Province, there were two teams each consisting of a lister and a mapper, except in Champasack, where three teams were assigned.

Listing could not be done in four villages. In two of the villages the area had been completely cleared of dwellings due to preparations for dam construction. One village was not accessible by car or motorcycle due to poor roads and one village could not be properly identified due to village mergers.

Large villages, where the number of households exceeded 300 households, were divided into two or more segments, and one segment was picked randomly before listing. Segmentation was done in 216 villages.

SELECTION OF HOUSEHOLDS

Lists of households were prepared by the listing teams in the field for each village. The households were then sequentially numbered from 1 to Mhi (the total number of households in each village or segment) at the Lao Bureau of Statistics, where the selection of 20 households in each village was carried out using random systematic selection procedures. The MICS6 spreadsheet template for systematic random selection of households was adapted for this purpose.

The survey also included a questionnaire for individual men that was to be administered in half of the sample of households. The MICS household selection template includes an option to specify the proportion of households to be selected for administering the individual questionnaire for men, and the spreadsheet automatically selected the corresponding subsample of households. All men age 15 to 49 years in the selected households were eligible for interview.

LSIS 2017 also included water quality testing for a subsample of households within each sample cluster. A subsample of 3 of the 20 selected households was selected in each sample cluster using random systematic sampling for conducting water quality testing, for both water in the household and at the source. The MICS household selection template includes an option to specify the number of households to be selected for the water quality testing, and the spreadsheet automatically selected the corresponding subsample of households.

Mode of data collection

Face-to-face [f2f]

Research instrument

Six questionnaires were used in the survey: 1) a household questionnaire which was used to collect basic demographic information, the household, and the dwelling; 2) a water quality testing questionnaire administered in three households in each cluster of the sample; 3) a questionnaire for individual women; 4) a questionnaire for individual men; 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.

Questionnaires to capture anthropometry measurements among children under 5 years and to record anaemia test results for children under 5 years and women age 15-19

Mental health treatment facilities are instrumental in helping those suffering from acute or chronic mental health issues get care in a safe and secure environment. As of 2023, there were 12,012 mental health treatment facilities in the U.S., of which 9,856 completed the N-SUMHSS* survey. Within those, 8,270 were outpatient facilities while 1,184 facilities were hospital inpatient facilities. U.S. Mental health facilities Inpatient mental health treatment may be needed for those that are a danger to themselves or others, those using drugs, those that need to be stabilized or those that are experiencing psychosis. The top hospitals in the U.S. for adult psychiatry include McLean Hospital in Massachusetts and Massachusetts General Hospital. Few mental health treatment facilities offered treatment programs specific client groups, with just a third offering such to LGBTQ clients. Mental health in the U.S. Mental illness can affect anyone of any age; however, some groups experience more mental illness than others. It is estimated that up to one quarter of the U.S. adult population face some mental illness, with women suffering more than men. A recent survey also demonstrated that Utah, Oregon, and District of Columbia had the highest percentage of people that described their mental health as poor. Other mental health variables can compound one another. For example, mental illness and substance use can be especially difficult to diagnose and treat.

Stalking experienced by women and men, including numbers, type and personal characteristics, based upon annual findings from the Crime Survey for England and Wales.

Abstract

The 2015 Zimbabwe Demographic and Health Survey (2015 ZDHS) is the sixth in a series of Demographic and Health Surveys conducted in Zimbabwe. As with prior surveys, the main objective of the 2015 ZDHS is to provide up-to-date information on fertility and child mortality levels; maternal mortality; fertility preferences and contraceptive use; utilization of maternal and child health services; women’s and children’s nutrition status; knowledge, attitudes and behaviours related to HIV/AIDS and other sexually transmitted diseases; and domestic violence. All women age 15-49 and all men age 15-54 who are usual members of the selected households and those who spent the night before the survey in the selected households were eligible to be interviewed and for anaemia and HIV testing. All children age 6-59 months were eligible for anaemia testing, and children age 0-14 for HIV testing. In all households, height and weight measurements were recorded for children age 0-59 months, women age 15-49, and men age 15-54. The domestic violence module was administered to one selected woman selected in each of surveyed households.

The 2015 ZDHS sample is designed to yield representative information for most indicators for the country as a whole, for urban and rural areas, and for each of Zimbabwe’s ten provinces (Manicaland, Mashonaland Central, Mashonaland East, Mashonaland West, Matabeleland North, Matebeleland South, Midlands, Masvingo, Harare, and Bulawayo).

Geographic coverage

National coverage

Analysis unit

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

Universe

The survey covered all de jure household members resident in the household, all women age 15-49 years, men age 15-54 years and their young children.

Kind of data

Sample survey data [ssd]

Sampling procedure

The 2015 ZDHS sample was designed to yield representative information for most indicators for the country as a whole, for urban and rural areas, and for each of Zimbabwe’s ten provinces: Manicaland, Mashonaland Central, Mashonaland East, Mashonaland West, Matabeleland North, Matabeleland South, Midlands, Masvingo, Harare, and Bulawayo. The 2012 Zimbabwe Population Census was used as the sampling frame for the 2015 ZDHS.

Administratively, each province in Zimbabwe is divided into districts, and each district is divided into smaller administrative units called wards. During the 2012 Zimbabwe Population Census, each ward was subdivided into convenient areas, which are called census enumeration areas (EAs). The 2015 ZDHS sample was selected with a stratified, two-stage cluster design, with EAs as the sampling units for the first stage. The 2015 ZDHS sample included 400 EAs-166 in urban areas and 234 in rural areas.

The second stage of sampling included the listing exercises for all households in the survey sample. A complete listing of households was conducted for each of the 400 selected EAs in March 2015. Maps were drawn for each of the clusters and all private households were listed. The listing excluded institutional living arrangements such as army barracks, hospitals, police camps, and boarding schools. A representative sample of 11,196 households was selected for the 2015 ZDHS.

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 for the 2015 ZDHS: - Household Questionnaire, - Woman’s Questionnaire, - Man’s Questionnaire, and - Biomarker Questionnaire.

These questionnaires were adapted from model survey instruments developed for The DHS Program to reflect the population and health issues relevant to Zimbabwe. Issues were identified at a series of meetings with various stakeholders from government ministries and agencies, research and training institutions, non-governmental organisations (NGOs), and development partners. In addition to English, the questionnaires were translated into two major languages, Shona and Ndebele. All four questionnaires were programmed into tablet computers to facilitate computer assisted personal interviewing (CAPI) for data collection, with the option to choose English, Shona, or Ndebele for each questionnaire.

Cleaning operations

CSPro was used for data editing, weighting, cleaning, and tabulation. In ZIMSTAT’s central office, data received from the supervisor’s tablets were registered and checked for inconsistencies and outliers. Data editing and cleaning included structure and internal consistency checks to ensure the completeness of work in the field. Any anomalies were communicated to the respective team through the technical team and the team supervisor. The corrected results were then re-sent to the central office.

Response rate

A total of 11,196 households were selected for inclusion in the 2015 ZDHS and of these, 10,657 were found to be occupied. A total of 10,534 households were successfully interviewed, yielding a response rate of 99 percent.

In the interviewed households, 10,351 women were identified as eligible for the individual interview, and 96 percent of them were successfully interviewed. For men, 9,132 were identified as eligible for interview, with 92 percent successfully interviewed.

Sampling error estimates

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 2015 Zimbabwe DHS (ZDHS) to minimize 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 2015 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 between 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 percent of all possible samples of identical size and design.

If the sample of respondents had been selected as a simple random sample, it would have been possible to use straightforward formulas for calculating sampling errors. However, the 2015 ZDHS sample is the result of a multi-stage stratified design, and, consequently, it was necessary to use more complex formulae. Sampling errors are computed in either ISSA or SAS, using programs developed by ICF International. These programs use the Taylor linearization method of variance estimation 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.

The Taylor linearization method treats any percentage or average as a ratio estimate, r = y x , where y represents the total sample value for variable y, and x represents the total number of cases in the group or subgroup under consideration.

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 - Nutritional status of children based on the NCHS/CDC/WHO International Reference Population - Completeness of information on siblings - Sibship size and sex ratio of siblings

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

A survey conducted in the second quarter of 2023 revealed that globally, women purchased beauty products online more than men. An average of 38 percent of women worldwide purchased beauty products online on a monthly basis from April to June of 2023. In comparison, around a quarter of men did the same.

Leading beauty markets

Worldwide, the United States ranks as the leading e-commerce market for beauty, with an industry worth around 14 billion U.S. dollars as of 2022. China follows closely with a market generating roughly 12 billion dollars in revenue. Cosmetics are the leading online segment of all beauty categories, worth around 40 billion U.S. dollars worldwide in 2023. Fragrances are the second-largest beauty segment, valued at around 19 billion U.S. dollars.

A hub of beauty

Global health, personal, and beauty care shoppers seem to favor online marketplaces for their shopping needs, with Alibaba leading the ranking. In 2022, Alibaba’s beauty segment achieved net sales of roughly 58 billion U.S. dollars, followed by Amazon, at 44 billion dollars. On online marketplaces such as Alibaba and Amazon, consumers have the option to choose from countless different brands and third-party vendors. On these sites, prices tend to be on the affordable side. In recent years, Amazon has focused on expanding their catalog to include more skincare and cosmetics items, which has proved to be a successful strategy. In fact, beauty sales make up 6.4 percent of total sales, a share expected to grow in the coming years.

Abstract

One of the major challenges facing Ghana is the need for a more comprehensive, reliable and up-to-date statistics and indicators to monitor and evaluate the effects of development polices and programmes on living standards. The Ghana Living Standards Survey was initiated to address this need.

The Ghana Living Standards Survey (GLSS) has emerged as one of the important tools in the welfare monitoring system and together with other surveys like the Core Welfare Indicators Questionnaire (CWIQ) and the Ghana Demographic and Health Survey (GDHS) has provided a wealth of information for understanding living conditions in Ghana.

The objectives of the Ghana Living Standards Survey- Round Five are to: - - Provide insight into living standards in Ghana by providing data to facilitate in-depth analysis of the living conditions of households. - Provide information on patterns of household consumption and expenditure at a sub-regional levels of disaggregation. - Provide data on total earnings, hours of work, etc., for in-depth study of differentials among branches of industry, sectors of employment, occupations at geographic areas, levels and between women and men. - Provide the basis for the construction of the consumer price index. - Up-date the National Accounts and provide databases for national and regional level planning and poverty monitoring.

Geographic coverage

National coverage

Analysis unit

Households, Individuals, Community, Commodities

Universe

The survey covered all de jure household members (usual residents) who have not been away from their usual residents for more than 6 months. This excludes heads of households.

Kind of data

Sample survey data [ssd]

Sampling procedure

Sampling Frame and Units As in all probability sample surveys, it is important that each sampling unit in the surveyed population has a known, non-zero probability of selection. To achieve this, there has to be an appropriate list, or sampling frame of the primary sampling units (PSUs).The universe defined for the GLSS 5 is the population living within private households in Ghana. The institutional population (such as schools, hospitals etc), which represents a very small percentage in the 2000 Population and Housing Census (PHC), is excluded from the frame for the GLSS 5.

The Ghana Statistical Service (GSS) maintains a complete list of census EAs, together with their respective population and number of households as well as maps, with well defined boundaries, of the EAs. . This information was used as the sampling frame for the GLSS 5. Specifically, the EAs were defined as the primary sampling units (PSUs), while the households within each EA constituted the secondary sampling units (SSUs).

Stratification In order to take advantage of possible gains in precision and reliability of the survey estimates from stratification, the EAs were first stratified into the ten administrative regions. Within each region, the EAs were further sub-divided according to their rural and urban areas of location. The EAs were also classified according to ecological zones and inclusion of Accra (GAMA) so that the survey results could be presented according to the three ecological zones, namely 1) Coastal, 2) Forest, and 3) Northern Savannah, and for Accra.

Sample size and allocation The number and allocation of sample EAs for the GLSS 5 depend on the type of estimates to be obtained from the survey and the corresponding precision required. It was decided to select a total sample of around 8000 households nationwide.

To ensure adequate numbers of complete interviews that will allow for reliable estimates at the various domains of interest, the GLSS 5 sample was designed to ensure that at least 400 households were selected from each region.

A two-stage stratified random sampling design was adopted. Initially, a total sample of 550 EAs was considered at the first stage of sampling, followed by a fixed take of 15 households per EA. The distribution of the selected EAs into the ten regions or strata was based on proportionate allocation using the population.

For example, the number of selected EAs allocated to the Western Region was obtained as: 1924577/18912079*550 = 56

Under this sampling scheme, it was observed that the 400 households minimum requirement per region could be achieved in all the regions but not the Upper West Region. The proportionate allocation formula assigned only 17 EAs out of the 550 EAs nationwide and selecting 15 households per EA would have yielded only 255 households for the region. In order to surmount this problem, two options were considered: retaining the 17 EAs in the Upper West Region and increasing the number of selected households per EA from 15 to about 25, or increasing the number of selected EAs in the region from 17 to 27 and retaining the second stage sample of 15 households per EA.

The second option was adopted in view of the fact that it was more likely to provide smaller sampling errors for the separate domains of analysis. Based on this, the number of EAs in Upper East and the Upper West were adjusted from 27 and 17 to 40 and 34 respectively, bringing the total number of EAs to 580 and the number of households to 8,700.

A complete household listing exercise was carried out between May and June 2005 in all the selected EAs to provide the sampling frame for the second stage selection of households. At the second stage of sampling, a fixed number of 15 households per EA was selected in all the regions. In addition, five households per EA were selected as replacement samples.The overall sample size therefore came to 8,700 households nationwide.

Sampling deviation

Under this sampling scheme, it was observed that the 400 households minimum requirement per region could be achieved in all the regions but not the Upper West Region. The proportionate allocation formula assigned only 17 EAs out of the 550 EAs nationwide and selecting 15 households per EA would have yielded only 255 households for the region. In order to surmount this problem, two options were considered: retaining the 17 EAs in the Upper West Region and increasing the number of selected households per EA from 15 to about 25, or increasing the number of selected EAs in the region from 17 to 27 and retaining the second stage sample of 15 households per EA.

The second option was adopted in view of the fact that it was more likely to provide smaller sampling errors for the separate domains of analysis. Based on this, the number of EAs in Upper East and the Upper West were adjusted from 27 and 17 to 40 and 34 respectively, bringing the total number of EAs to 580 and the number of households to 8,700.

Mode of data collection

Face-to-face [f2f]

Cleaning operations

Data editing at the Statistical Service occurs at 3 levels 1. Field editing by interviewers and supervisors 2. Office editing 3. Data cleaning and imputation (including structural checking and completeness)

Response rate

At the end of the survey, 8,687 households were successfully interviewed representing a 99.85 percent response rate.

Sampling error estimates

The CENVAR software of IMPS was used for estimating the sampling errors, the coefficient of variation (CV), the confidence limits and the design effect for the GLSS 5 data. A design effect of 1.0 indicates that the sample design is as efficient as a simple random sample, whereas a value greater than 1.0 indicates the increase in the sampling error due to the use of a more complex and less statistically efficient design (see Table A1.2 in main report).

Data appraisal

A series of data quality tables and graphs are available to review the quality of the data in the main 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. Among these global commitments are those emanating from the World Fit for Children Declaration and Plan of Action, the goals of the United Nations General Assembly Special Session on HIV/AIDS, the Education for All Declaration and the Sustainable Development Goals (SDGs).

The Sierra Leone MICS results will be critically important because it forms the baselines for nearly half of Sierra Leone survey-based SGD indicators. In addition, it will also track progress on the many indicators not measured since the country’s last MICS in 2010.

Sierra Leone MICS is expected to contribute to the evidence base of several other important initiatives, including in filling data gaps for national post-MDG reporting, providing a measure of the socio-economic impact of the Ebola virus disease (EVD), as well as developing a monitoring and evaluation system for Sierra Leone’s National Programme for Food Security, Job Creation and Good Governance, the third-generation Poverty Reduction Strategy Paper (PRSP3), dubbed “Agenda for Prosperity” developed in 2012.

The 2017 Sierra Leone MICS has as its primary objectives:

• To provide up-to-date information for assessing the situation of children and women in Sierra Leone;

• To provide a measure of the socio-economic impact of the Ebola virus disease (EVD) in Sierra Leone;

• To provide additional data needed for preparing a country progress report on achieving the goals of World fit for children (WFFC), and the reporting requirements of other international development declarations and agendas;

• To contribute to the development of the national statistical system, data and monitoring systems, and strengthen national capacity in the design, implementation, and analysis of such monitoring systems.

• To obtain a nationally-representative view of the quality of water that people drink in their home and the quality of their drinking water source.;

• To contribute to the generation of baseline data for the 2030 Agenda for Sustainable Development

Geographic coverage

The national level, for urban and rural areas, four regions of the country (Northern Province, Eastern Province, Southern Province, and the West), and for the 14 districts of the country: (1) Kailahun, (2) Kenema; (3) Kono; (4) Bombali; (5) Kambia; (6) Koinadugu; (7) Port Loko; (8) Tonkolili; (9) Bo; (10) Bonthe; (11) Moyamba; (12) Pujehun; (13) Western Rural; and (14) Western Urban.

Analysis unit

• Individuals

• Households

Universe

The survey covered all de jure household members (usual residents), all women age 15-49 years, all men age 15-49 years and all children under 5 living in the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

A multi-stage, stratified cluster sampling approach was used for the selection of the survey sample. The sampling frame for the Sierra Leone MICS 2017 was based on the 2015 Sierra Leone Population and Housing Census. The primary sampling units (PSUs) selected at the first stage were census enumeration areas (EAs). A new listing of households was conducted in each sample EA, and the sample households were selected at the second stage.

A unique feature of the sampling plan for the Sierra Leone MICS 2017 is that it was coordinated with the sample design for the Sierra Leone Integrated Household Survey (SLIHS) 2017. Although the sample size and allocation for the SLIHS 2017 was different from that of the MICS 2017, the sample enumeration areas (EAs) for the MICS 2017 were selected in such a way that provided a maximum overlap between the sample EAs selected for the two surveys. In the overlapping sample EAs the two surveys shared the same listing of households, and a subsample of the MICS sample households was selected for the SLIHS so that it would be possible to have an integrated database from the two surveys for the common sample households.

SAMPLE SIZE AND SAMPLE ALLOCATION

In developing the sampling plans for the Sierra Leone MICS 2017 the sample design and results from the Sierra Leone MICS 2010, which had similar objectives, was first examined. The MICS 2010 was based on an overall sample of 480 sample clusters and 12,000 households, with 25 sample households selected per cluster. A minimum of 30 sample clusters and 750 sample households were selected for the smaller districts, and a maximum of 66 clusters and 1,650 households were selected for the Western Area Urban. In studying the sampling errors for key indicators for children under 5 at the district level it was found that the 95% confidence intervals for some of the estimates were relatively wide, so for the Sierra Leone MICS 2017 it was decided to increase the sample size to have a minimum of 936 sample households for the smaller districts. The overall sample size was increased to 15,360 households.

Based on the experience of the Sierra Leone MICS 2010, it was decided to select 26 sample households per cluster (EA) for the MICS 2017. Although this very small increase of one sample household per cluster compared to MICS 2010 would result in a very minor increase in the design effects, it would still slightly improve the level of precision. Given that a 50% subsample of the MICS sample households are selected for the men’s questionnaire, it is best to select an even number of households in each sample cluster. If less households were selected per cluster for the MICS 2017 it would be necessary to select more clusters, thus increasing the survey costs for listing and transportation. Therefore, at the national level, a sample of 600 sample EAs were selected at the first stage and 15,360 households were selected at the second stage.

In allocating the sample clusters by district it was decided to have a minimum of 36 sample clusters for the smallest districts and 64 for the largest district of Western Area Urban. This resulted in a sample of 936 to 1,664 households per district. In between this range, the sample clusters were allocated to the districts approximately in proportion to the square root of the number of households in the Census frame. This approach increased the sample for smaller districts and decreased the sample for larger districts compared to a proportional allocation. Within each district the sample clusters were allocated to the rural and urban strata in proportion to the number of households in the frame.

SELECTION OF ENUMERATION AREAS (CLUSTERS)

At the first sampling stage the EAs in each stratum (district, rural and urban) were selected from the 2015 Sierra Leone Census frame systematically with probability proportional to size (PPS), where the measure of size for each EA was based on the number of households in the Census frame.

A total of 685 EAs were selected for the Sierra Leone Integrated Household Survey (SLIHS) 2017. This sample was also stratified by district, urban and rural areas, but the allocation of the sample clusters by stratum was different from that for the Sierra Leone MICS 2017. The selection procedures were designed to provide a maximum overlap of the sample EAs between the two surveys. A total of 505 sample EAs are included in both surveys, so that the listing could be shared. In these sample EAs the SLIHS sample households were selected as a subsample of the MICS 2017 sample households.

LISTING ACTIVITIES

Since the sampling frame (the 2015 Sierra Leone Census) was not up-to-date, a new listing of households was conducted in all the sample EAs prior to the selection of households. For this purpose, listing teams were formed who visited all of the selected enumeration areas and listed all households in each sample EA. In the case of large EAs (for example, with more than 300 households), the EA was divided into smaller segments. Following a quick count of the households in each segment, one segment was selected randomly with PPS in the EA for the listing. The mapping and household listing operations consisted of training of mapping and listing field staff, fieldwork (mapping and listing of households), and household selection. The training of listing staff took place from 29th November - 3rd December 2016 while the fieldwork commenced on 5th December 2016 and was completed on 12th January 2017. The household listing fieldwork was carried out by 15 teams: each team consisted of a supervisor, one mapper and one lister.

SELECTION OF HOUSEHOLDS

Lists of households were prepared by the listing teams in the field for each enumeration area. The households were then sequentially numbered from 1 to Mhi (the total number of households in each enumeration area) at the Statistics Sierra Leone (SSL) central office, where the selection of 26 households in each EA was carried out using random systematic selection procedures.

The survey also included a questionnaire for individual men that was to be administered in one-half of the sample of households. A random number of 1 or 2 specified whether the sample households with odd or even serial numbers would be selected for the men’s questionnaire in each sample cluster. All men between the ages of 15 and 49 years in the selected households were interviewed.

The Sierra Leone MICS 2017 also included water quality tests for a subsample of households within each sample EA. A subsample of 3 of the 26 households was selected in each cluster using random systematic sampling for conducting water quality tests, for both water in the household and at the source. The MICS household selection template includes an option to specify the number of households to be selected for the water quality tests,