Calculation strategy for survey and population weighting of the data.

The "https://addhealth.cpc.unc.edu/" Target="_blank">National Longitudinal Study of Adolescent to Adult Health (Add Health) is a longitudinal study of a nationally representative sample of adolescents in grades 7-12 in the United States. The Add Health cohort has been followed into young adulthood with four in-home interviews, the most recent in 2008, when the sample was aged 24-32*. Add Health combines longitudinal survey data on respondents' social, economic, psychological and physical well-being with contextual data on the family, neighborhood, community, school, friendships, peer groups, and romantic relationships, providing unique opportunities to study how social environments and behaviors in adolescence are linked to health and achievement outcomes in young adulthood. The fourth wave of interviews expanded the collection of biological data in Add Health to understand the social, behavioral, and biological linkages in health trajectories as the Add Health cohort ages through adulthood. The fifth wave of data collection is planned to begin in 2016.

Initiated in 1994 and supported by three program project grants from the "https://www.nichd.nih.gov/" Target="_blank">Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) with co-funding from 23 other federal agencies and foundations, Add Health is the largest, most comprehensive longitudinal survey of adolescents ever undertaken. Beginning with an in-school questionnaire administered to a nationally representative sample of students in grades 7-12, the study followed up with a series of in-home interviews conducted in 1995, 1996, 2001-02, and 2008. Other sources of data include questionnaires for parents, siblings, fellow students, and school administrators and interviews with romantic partners. Preexisting databases provide information about neighborhoods and communities.

Add Health was developed in response to a mandate from the U.S. Congress to fund a study of adolescent health, and Waves I and II focus on the forces that may influence adolescents' health and risk behaviors, including personal traits, families, friendships, romantic relationships, peer groups, schools, neighborhoods, and communities. As participants have aged into adulthood, however, the scientific goals of the study have expanded and evolved. Wave III, conducted when respondents were between 18 and 26** years old, focuses on how adolescent experiences and behaviors are related to decisions, behavior, and health outcomes in the transition to adulthood. At Wave IV, respondents were ages 24-32* and assuming adult roles and responsibilities. Follow up at Wave IV has enabled researchers to study developmental and health trajectories across the life course of adolescence into adulthood using an integrative approach that combines the social, behavioral, and biomedical sciences in its research objectives, design, data collection, and analysis.

* 52 respondents were 33-34 years old at the time of the Wave IV interview.
** 24 respondents were 27-28 years old at the time of the Wave III interview.

Included here are weights to remove any differences between the composition of the sample and the estimated composition of the population. See the attached codebook for information regarding how these weights were calculated.

Stata module that implements Potter's (1990) weight distribution approach to trim extreme sampling weights. The basic idea is that the sampling weights are assumed to follow a beta distribution. The parameters of the distribution are estimated from the moments of the observed sampling weights and the resulting quantiles are used as cut-off points for extreme sampling weights. The process is repeated a specified number of times (10 by default) or until no sampling weights are more extreme than the specified quantiles.

Media use related to crime. Weighting of criminal offenses. Perception of safety.

A random sample of households were invited to participate in this survey. In the dataset, you will find the respondent level data in each row with the questions in each column. The numbers represent a scale option from the survey, such as 1=Excellent, 2=Good, 3=Fair, 4=Poor. The question stem, response option, and scale information for each field can be found in the var "variable labels" and "value labels" sheets. VERY IMPORTANT NOTE: The scientific survey data were weighted, meaning that the demographic profile of respondents was compared to the demographic profile of adults in Bloomington from US Census data. Statistical adjustments were made to bring the respondent profile into balance with the population profile. This means that some records were given more "weight" and some records were given less weight. The weights that were applied are found in the field "wt". If you do not apply these weights, you will not obtain the same results as can be found in the report delivered to the Bloomington. The easiest way to replicate these results is likely to create pivot tables, and use the sum of the "wt" field rather than a count of responses.

These data include the individual responses for the City of Tempe Annual Community Survey conducted by ETC Institute. This dataset has two layers and includes both the weighted data and unweighted data. Weighting data is a statistical method in which datasets are adjusted through calculations in order to more accurately represent the population being studied. The weighted data are used in the final published PDF report.These data help determine priorities for the community as part of the City's on-going strategic planning process. Averaged Community Survey results are used as indicators for several city performance measures. The summary data for each performance measure is provided as an open dataset for that measure (separate from this dataset). The performance measures with indicators from the survey include the following (as of 2023):1. Safe and Secure Communities1.04 Fire Services Satisfaction1.06 Crime Reporting1.07 Police Services Satisfaction1.09 Victim of Crime1.10 Worry About Being a Victim1.11 Feeling Safe in City Facilities1.23 Feeling of Safety in Parks2. Strong Community Connections2.02 Customer Service Satisfaction2.04 City Website Satisfaction2.05 Online Services Satisfaction Rate2.15 Feeling Invited to Participate in City Decisions2.21 Satisfaction with Availability of City Information3. Quality of Life3.16 City Recreation, Arts, and Cultural Centers3.17 Community Services Programs3.19 Value of Special Events3.23 Right of Way Landscape Maintenance3.36 Quality of City Services4. Sustainable Growth & DevelopmentNo Performance Measures in this category presently relate directly to the Community Survey5. Financial Stability & VitalityNo Performance Measures in this category presently relate directly to the Community SurveyMethods:The survey is mailed to a random sample of households in the City of Tempe. Follow up emails and texts are also sent to encourage participation. A link to the survey is provided with each communication. To prevent people who do not live in Tempe or who were not selected as part of the random sample from completing the survey, everyone who completed the survey was required to provide their address. These addresses were then matched to those used for the random representative sample. If the respondent’s address did not match, the response was not used. To better understand how services are being delivered across the city, individual results were mapped to determine overall distribution across the city. Additionally, demographic data were used to monitor the distribution of responses to ensure the responding population of each survey is representative of city population. Processing and Limitations:The location data in this dataset is generalized to the block level to protect privacy. This means that only the first two digits of an address are used to map the location. When they data are shared with the city only the latitude/longitude of the block level address points are provided. This results in points that overlap. In order to better visualize the data, overlapping points were randomly dispersed to remove overlap. The result of these two adjustments ensure that they are not related to a specific address, but are still close enough to allow insights about service delivery in different areas of the city. The weighted data are used by the ETC Institute, in the final published PDF report.The 2023 Annual Community Survey report is available on data.tempe.gov or by visiting https://www.tempe.gov/government/strategic-management-and-innovation/signature-surveys-research-and-dataThe individual survey questions as well as the definition of the response scale (for example, 1 means “very dissatisfied” and 5 means “very satisfied”) are provided in the data dictionary.Additional InformationSource: Community Attitude SurveyContact (author): Adam SamuelsContact E-Mail (author): Adam_Samuels@tempe.govContact (maintainer): Contact E-Mail (maintainer): Data Source Type: Excel tablePreparation Method: Data received from vendor after report is completedPublish Frequency: AnnualPublish Method: ManualData Dictionary

The City of Bloomington contracted with National Research Center, Inc. to conduct the 2019 Bloomington Community Survey. This was the second time a scientific citywide survey had been completed covering resident opinions on service delivery satisfaction by the City of Bloomington and quality of life issues. The first was in 2017. The survey captured the responses of 610 households from a representative sample of 3,000 residents of Bloomington who were randomly selected to complete the survey. VERY IMPORTANT NOTE: The scientific survey data were weighted, meaning that the demographic profile of respondents was compared to the demographic profile of adults in Bloomington from US Census data. Statistical adjustments were made to bring the respondent profile into balance with the population profile. This means that some records were given more "weight" and some records were given less weight. The weights that were applied are found in the field "wt". If you do not apply these weights, you will not obtain the same results as can be found in the report delivered to the City of Bloomington. The easiest way to replicate these results is likely to create pivot tables, and use the sum of the "wt" field rather than a count of responses.

Conventional survey tools such as weighting do not address non-ignorable nonresponse that occurs when nonresponse depends on the variable being measured. This paper describes non-ignorable nonresponse weighting and imputation models using randomized response instruments, which are variables that affect response but not the outcome of interest \citep{SunEtal2018}. The paper uses a doubly robust estimator that is valid if one, but not necessarily both, of the weighting and imputation models is correct. When applied to a national 2019 survey, these tools produce estimates that suggest there was non-trivial non-ignorable nonresponse related to turnout, and, for subgroups, Trump approval and policy questions. For example, the conventional MAR-based weighted estimates of Trump support in the Midwest were 10 percentage points lower than the MNAR-based estimates. Data to replicate estimation described in "Countering Non-Ignorable Nonresponse in Survey Models with Randomized Response Instruments and Doubly Robust Estimation"

The American Time Use Survey (ATUS) collects information on how people living in the United States spend their time. Data collected in this study measured the amount of time that people spent doing various activities in 2007, such as paid work, child care, religious activities, volunteering, and socializing. Respondents were interviewed only once about how they spent their time on the previous day, where they were, and whom they were with. The Eating and Health (EH) module includes questions related to eating, meal preparation, and health, all of which were asked after completion of the ATUS questions. Part 1, Respondent and Activity Summary File, contains demographic information about respondents and a summary of the total amount of time they spent doing each activity that day. Part 2, Roster File, contains information about household members and nonhousehold children under the age of 18. Part 3, Activity File, includes additional information on activities in which respondents participated, including the location of each activity and the total time spent on secondary child care. Part 4, Who File, includes data on who was present during each activity. Part 5, ATUS-CPS 2007 File, contains data on respondents and members of their household collected during their participation in the Current Population Survey (CPS). Parts 6-9 contain supplemental data files that can be used for further analysis of the data. Part 6, Case History File, contains information about the interview process. Part 7, Call History File, gives information about each call attempt. Part 8, Trips File, provides information about the number, duration, and purpose of overnight trips away from home for two or more nights in a row in a given reference month. Part 9, ATUS 2007 Replicate Weights File, contains base weights, replicate base weights, and replicate final weights for each case that was selected to be interviewed for the ATUS. Parts 10, 11, 12, and 13 correspond to the 2007 Eating and Health Module. Demographic variables include sex, age, race, ethnicity, education level, income, employment status, occupation, citizenship status, country of origin, and household composition.

Abstract

The 1998 Ghana Demographic and Health Survey (GDHS) is the latest in a series of national-level population and health surveys conducted in Ghana and it is part of the worldwide MEASURE DHS+ Project, designed to collect data on fertility, family planning, and maternal and child health.

The primary objective of the 1998 GDHS is to provide current and reliable data on fertility and family planning behaviour, child mortality, children’s nutritional status, and the utilisation of maternal and child health services in Ghana. Additional data on knowledge of HIV/AIDS are also provided. This information is essential for informed policy decisions, planning and monitoring and evaluation of programmes at both the national and local government levels.

The long-term objectives of the survey include strengthening the technical capacity of the Ghana Statistical Service (GSS) to plan, conduct, process, and analyse the results of complex national sample surveys. Moreover, the 1998 GDHS provides comparable data for long-term trend analyses within Ghana, since it is the third in a series of demographic and health surveys implemented by the same organisation, using similar data collection procedures. The GDHS also contributes to the ever-growing international database on demographic and health-related variables.

Geographic coverage

National

Analysis unit

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

Kind of data

Sample survey data

Sampling procedure

The major focus of the 1998 GDHS was to provide updated estimates of important population and health indicators including fertility and mortality rates for the country as a whole and for urban and rural areas separately. In addition, the sample was designed to provide estimates of key variables for the ten regions in the country.

The list of Enumeration Areas (EAs) with population and household information from the 1984 Population Census was used as the sampling frame for the survey. The 1998 GDHS is based on a two-stage stratified nationally representative sample of households. At the first stage of sampling, 400 EAs were selected using systematic sampling with probability proportional to size (PPS-Method). The selected EAs comprised 138 in the urban areas and 262 in the rural areas. A complete household listing operation was then carried out in all the selected EAs to provide a sampling frame for the second stage selection of households. At the second stage of sampling, a systematic sample of 15 households per EA was selected in all regions, except in the Northern, Upper West and Upper East Regions. In order to obtain adequate numbers of households to provide reliable estimates of key demographic and health variables in these three regions, the number of households in each selected EA in the Northern, Upper West and Upper East regions was increased to 20. The sample was weighted to adjust for over sampling in the three northern regions (Northern, Upper East and Upper West), in relation to the other regions. Sample weights were used to compensate for the unequal probability of selection between geographically defined strata.

The survey was designed to obtain completed interviews of 4,500 women age 15-49. In addition, all males age 15-59 in every third selected household were interviewed, to obtain a target of 1,500 men. In order to take cognisance of non-response, a total of 6,375 households nation-wide were selected.

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

Mode of data collection

Face-to-face

Research instrument

Three types of questionnaires were used in the GDHS: the Household Questionnaire, the Women’s Questionnaire, and the Men’s Questionnaire. These questionnaires were based on model survey instruments developed for the international MEASURE DHS+ programme and were designed to provide information needed by health and family planning programme managers and policy makers. The questionnaires were adapted to the situation in Ghana and a number of questions pertaining to on-going health and family planning programmes were added. These questionnaires were developed in English and translated into five major local languages (Akan, Ga, Ewe, Hausa, and Dagbani).

The Household Questionnaire was used to enumerate all usual members and visitors in a selected household and to collect information on the socio-economic status of the household. The first part of the Household Questionnaire collected information on the relationship to the household head, residence, sex, age, marital status, and education of each usual resident or visitor. This information was used to identify women and men who were eligible for the individual interview. For this purpose, all women age 15-49, and all men age 15-59 in every third household, whether usual residents of a selected household or visitors who slept in a selected household the night before the interview, were deemed eligible and interviewed. The Household Questionnaire also provides basic demographic data for Ghanaian households. The second part of the Household Questionnaire contained questions on the dwelling unit, such as the number of rooms, the flooring material, the source of water and the type of toilet facilities, and on the ownership of a variety of consumer goods.

The Women’s Questionnaire was used to collect information on the following topics: respondent’s background characteristics, reproductive history, contraceptive knowledge and use, antenatal, delivery and postnatal care, infant feeding practices, child immunisation and health, marriage, fertility preferences and attitudes about family planning, husband’s background characteristics, women’s work, knowledge of HIV/AIDS and STDs, as well as anthropometric measurements of children and mothers.

The Men’s Questionnaire collected information on respondent’s background characteristics, reproduction, contraceptive knowledge and use, marriage, fertility preferences and attitudes about family planning, as well as knowledge of HIV/AIDS and STDs.

Response rate

A total of 6,375 households were selected for the GDHS sample. Of these, 6,055 were occupied. Interviews were completed for 6,003 households, which represent 99 percent of the occupied households. A total of 4,970 eligible women from these households and 1,596 eligible men from every third household were identified for the individual interviews. Interviews were successfully completed for 4,843 women or 97 percent and 1,546 men or 97 percent. The principal reason for nonresponse among individual women and men was the failure of interviewers to find them at home despite repeated callbacks.

Note: See summarized response rates by place of residence in Table 1.1 of the survey report.

Sampling error estimates

The estimates from a sample survey are affected by two types of errors: (1) nonsampling errors, and (2) sampling errors. Nonsampling errors are the results of shortfalls 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 1998 GDHS to minimize this type of error, nonsampling errors are impossible to avoid and difficult to evaluate statistically.

Sampling errors, on the other hand, can be evaluated statistically. The sample of respondents selected in the 1998 GDHS is only one of many samples that could have been selected from the same population, using the same design and expected size. Each of these samples would yield results that differ somewhat from the results of the actual sample selected. Sampling errors are a measure of the variability between all possible samples. Although the degree of variability is not known exactly, it can be estimated from the survey results.

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

If the sample of respondents had been selected as a simple random sample, it would have been possible to use straightforward formulas for calculating sampling errors. However, the 1998 GDHS sample is the result of a two-stage stratified design, and, consequently, it was necessary to use more complex formulae. The computer software used to calculate sampling errors for the 1998 GDHS is the ISSA Sampling Error Module. This module uses the Taylor linearization method of variance estimation for survey estimates that are means or proportions. The Jackknife repeated replication method is used for variance estimation of more complex statistics such as fertility and mortality rates.

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

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

Abstract

The STEP (Skills Toward Employment and Productivity) Measurement program is the first ever initiative to generate internationally comparable data on skills available in developing countries. The program implements standardized surveys to gather information on the supply and distribution of skills and the demand for skills in labor market of low-income countries.

The uniquely-designed Household Survey includes modules that measure the cognitive skills (reading, writing and numeracy), socio-emotional skills (personality, behavior and preferences) and job-specific skills (subset of transversal skills with direct job relevance) of a representative sample of adults aged 15 to 64 living in urban areas, whether they work or not. The cognitive skills module also incorporates a direct assessment of reading literacy based on the Survey of Adults Skills instruments. Modules also gather information about family, health and language.

Geographic coverage

The STEP target population is the urban population aged 15 to 64 included. Sri Lanka sampled both urban and rural areas. Areas are classified as rural or urban based on each country's official definition.

Analysis unit

The units of analysis are the individual respondents and households. A household roster is undertaken at the start of the survey and the individual respondent is randomly selected among all household members aged 15 to 64 included. The random selection process was designed by the STEP team and compliance with the procedure is carefully monitored during fieldwork.

Universe

The target population for the Sri Lanka STEP survey comprised all non-institutionalized persons 15 to 64 years of age (inclusive) living in private dwellings in urban and rural areas of Sri Lanka at the time of data collection. Exclusions The target population excludes: - Foreign diplomats and non-nationals working for international organizations; - People in institutions such as hospitals or prisons; - Collective dwellings or group quarters; - Persons living outside the country at the time of data collection, e.g., students at foreign universities; - Persons who are unable to complete the STEP assessment due to a physical or mental condition, e.g., visual impairment or paralysis.

The sample frame for the selection of first stage sample units was the Census 2011/12

Kind of data

Sample survey data [ssd]

Sampling procedure

The Sri Lanka sample size was 2,989 households. The sample design is a 5 stage stratified sample design. The stratification variable is Urban-Rural indicator.

First Stage Sample The primary sample unit (PSU) is a Grama Niladari (GN) division. The sampling objective was to conduct interviews in 200 GNs, consisting of 80 urban GNs and 120 rural GNs. Because there was some concern that it might not be possible to conduct any interviews in some initially selected GNs (e.g. due to war, conflict, or inaccessibility, for some other reason), the sampling strategy also called for the selection of 60 extra GNs (i.e., 24 urban GNs and 36 rural GNs) to be held in reserve for such eventualities. Hence, a total of 260 GNs were selected, consisting of 200 'initial' GNs and 60 'reserve' GNs. Two GNS from the initial sample of GNs were not accessible and reserve sampled GNs were used instead. Thus a total of 202 GNs were activated for data collection, and interviews were conducted in 200 GNs. The sample frame for the selection of first stage sample units was the list of GNs from the Census 2011/12. Note: The sample of first stage sample units was selected by the Sri Lanka Department of Census & Statistics (DCS) and provided to the World Bank. The DCS selected the GNs with probability proportional to size (PPS), where the measure of size was the number of dwellings in a GN.

Second Stage Sample The second stage sample unit (SSU) is a GN segment, i.e., GN BLOCK. One GN Block was selected from each activated PSU (i.e., GN). According to the Sri Lanka survey firm, each sampled GN was divided into a number of segments, i.e., GN Blocks, with approximately the same number of households, and one GN Block was selected from each sampled GN.

Third Stage Sample The third stage sample unit is a dwelling. The sampling objective was to obtain interviews at 15 dwellings within each selected SSU.

Fourth Stage Sample The fourth stage sample unit is a household. The sampling objective was to select one household within each selected third stage dwelling.

Fifth Stage Sample The fourth stage sample unit is an individual aged 15-64 (inclusive). The sampling objective was to select one individual with equal probability from each selected household.

Please refer to the Sri Lanka STEP Survey Weighting Procedures Summary for additional information on sampling.

Mode of data collection

Face-to-face [f2f]

Research instrument

The STEP survey instruments include: (i) A Background Questionnaire developed by the WB STEP team. (ii) A Reading Literacy Assessment developed by Educational Testing Services (ETS).

All countries adapted and translated both instruments following the STEP Technical Standards: 2 independent translators adapted and translated the Background Questionnaire and Reading Literacy Assessment, while reconciliation was carried out by a third translator. - The survey instruments were both piloted as part of the survey pretest. - The adapted Background Questionnaires are provided in English as external resources. The Reading Literacy Assessment is protected by copyright and will not be published.

Cleaning operations

STEP Data Management Process 1. Raw data is sent by the survey firm 2. The WB STEP team runs data checks on the Background Questionnaire data. - ETS runs data checks on the Reading Literacy Assessment data. - Comments and questions are sent back to the survey firm. 3. The survey firm reviews comments and questions. When a data entry error is identified, the survey firm corrects the data. 4. The WB STEP team and ETS check the data files are clean. This might require additional iterations with the survey firm. 5. Once the data has been checked and cleaned, the WB STEP team computes the weights. Weights are computed by the STEP team to ensure consistency across sampling methodologies. 6. ETS scales the Reading Literacy Assessment data. 7. The WB STEP team merges the Background Questionnaire data with the Reading Literacy Assessment data and computes derived variables.

Detailed information data processing in STEP surveys is provided in the 'Guidelines for STEP Data Entry Programs' document provided as an external resource. The template do-file used by the STEP team to check the raw background questionnaire data is provided as an external resource.

Response rate

The response rate for Sri Lanka (urban and rural) was 63%. (See STEP Methodology Note Table 4).

Sampling error estimates

A weighting documentation was prepared for each participating country and provides some information on sampling errors. Weighting documentation is provided as an external resource.

In the November 2016 U.S. presidential election, many state level public opinion polls, particularly in the Upper Midwest, incorrectly predicted the winning candidate. One leading explanation for this polling miss is that the precipitous decline in traditional polling response rates led to greater reliance on statistical methods to adjust for the corresponding bias---and that these methods failed to adjust for important interactions between key variables like educational attainment, race, and geographic region. Finding calibration weights that account for important interactions remains challenging with traditional survey methods: raking typically balances the margins alone, while post-stratification, which exactly balances all interactions, is only feasible for a small number of variables. In this paper, we propose multilevel calibration weighting, which enforces tight balance constraints for marginal balance and looser constraints for higher-order interactions. This incorporates some of the benefits of post-stratification while retaining the guarantees of raking. We then correct for the bias due to the relaxed constraints via a flexible outcome model; we call this approach Double Regression with Post-stratification (DRP). We use these tools to to re-assess a large-scale survey of voter intention in the 2016 U.S. presidential election, finding meaningful gains from the proposed methods. The approach is available in the multical R package. Contains replication materials for "Multilevel calibration weighting for survey data", including raw data, scripts to clean the raw data, scripts to replicate the analysis, and scripts to replicate the simulation study.

Abstract

Since its inception in the mid-1990s, the Multiple Indicator Cluster Surveys programme, known as MICS, has become the largest source of statistically sound and internationally comparable data on children and women worldwide. In countries as diverse as Bangladesh, Thailand, Fiji, Qatar, Cote d’Ivoire, Turkmenistan and Argentina, trained fieldwork teams conduct face-to-face interviews with household members on a variety of topics – focusing mainly on those issues that directly affect the lives of children and women. MICS is an integral part of plans and policies of many governments around the world, and a major data source for more than 30 Sustainable Development Goals (SDGs) indicators. The MICS programme continues to evolve with new methodologies and initiatives, including MICS Plus, MICS Link, MICS GIS and the MICS Tabulator.

Geographic coverage

Thailand The majority of MICS surveys are designed to be representative at the national level. Sample sizes are sufficient to generate robust data at the regional or provincial levels, and for urban and rural areas. Subnational surveys, covering specific population groups (such as Palestinians in Lebanon) or specific geographical areas (such as selected regions of East in Afghanistan) within countries are also conducted.

Analysis unit

Household, Individual

Sampling procedure

Sample sizes vary greatly from one survey to the other, currently averaging around 12,000 households (for national surveys).

The sample for the Multiple Indicator Cluster Survey (MICS) was designed to provide estimates on a large number of indicators on the situation of children and women at the national level, for areas of residence, and for geographical locations, such as regions, governorates, or districts. A multi-stage, stratified cluster sampling approach was typickly used for the selection of the survey sample. MICS6 surveys are not self-weighting. For reporting national level results, sample weights were used. A more detailed description of the sample design can be found in Appendix A of Final Report.

Mode of data collection

Face-to-face [f2f]

Abstract copyright UK Data Service and data collection copyright owner.

The Food and You Surveys (2010-2018), and Food and You 2 (2020- ) are bi-annual, cross-sectional surveys of adults aged 16 years and over living in England, Wales and Northern Ireland, commissioned by the Food Standards Agency. Food and You provides data about the prevalence of different attitudes, reported behaviour and knowledge about ways in which food is purchased, stored, prepared and eaten.

Food and You, 2010-2018
From 2010, Food and You became the FSA’s flagship social survey. In addition, the FSA conducted regular tracking surveys including the bi-annual Public Attitudes Tracker and annual Food Hygiene Rating Scheme (FHRS) Consumer Attitudes Tracker (these studies are not held at UKDS. From Wave 4, Food and You included new questions to cover affordability of food, choice, security and sustainability.

Food and You 2, 2020-
In 2018, the FSA’s Advisory Committee for Social Science (ACSS) recommended that Food and You and the Public Attitudes Tracker were replaced with a new ‘push-to-web’ survey, Food and You 2, which was commissioned in 2019 with data collection commencing in July 2020. Due to differences in the survey methodologies, comparisons cannot be made between the earlier Food and You surveys, or the Public Attitudes Tracker, and Food and You 2. Therefore Food and You 2, 2020 is the start of a new data time series. Data will be collected through Food and You 2 on a bi-annual basis. Unlike the previous surveys, as well as the standard End User Licence (EUL) study for Food and You 2 the UKDS also holds additional variables under Special Licence (see SN 8815).

Further information may be found on the FSA's Food and You Survey webpage.

The Food and You Waves 1 and 2 combined data file contains the original data from the individual surveys plus the derived variables used for these two secondary analysis projects.
Main Topics:

The 2010-2012 data covered the following topics:

• information about household members
• eating habits (including eating out)
• shopping habits
• food safety attitudes and behaviour
• attitudes towards food production
• self-reported health
• healthy eating (Scotland and Northern Ireland only)
• demographics

Abstract copyright UK Data Service and data collection copyright owner.

The Opinions and Lifestyle Survey (formerly known as the ONS Opinions Survey or Omnibus) is an omnibus survey that began in 1990, collecting data on a range of subjects commissioned by both the ONS internally and external clients (limited to other government departments, charities, non-profit organisations and academia).

Data are collected from one individual aged 16 or over, selected from each sampled private household. Personal data include data on the individual, their family, address, household, income and education, plus responses and opinions on a variety of subjects within commissioned modules.

The questionnaire collects timely data for research and policy analysis evaluation on the social impacts of recent topics of national importance, such as the coronavirus (COVID-19) pandemic and the cost of living, on individuals and households in Great Britain.

From April 2018 to November 2019, the design of the OPN changed from face-to-face to a mixed-mode design (online first with telephone interviewing where necessary). Mixed-mode collection allows respondents to complete the survey more flexibly and provides a more cost-effective service for customers.

In March 2020, the OPN was adapted to become a weekly survey used to collect data on the social impacts of the coronavirus (COVID-19) pandemic on the lives of people of Great Britain. These data are held in the Secure Access study, SN 8635, ONS Opinions and Lifestyle Survey, Covid-19 Module, 2020-2022: Secure Access.

From August 2021, as coronavirus (COVID-19) restrictions were lifting across Great Britain, the OPN moved to fortnightly data collection, sampling around 5,000 households in each survey wave to ensure the survey remains sustainable.

The OPN has since expanded to include questions on other topics of national importance, such as health and the cost of living. For more information about the survey and its methodology, see the ONS OPN Quality and Methodology Information webpage.

Secure Access Opinions and Lifestyle Survey data

Other Secure Access OPN data cover modules run at various points from 1997-2019, on Census religion (SN 8078), cervical cancer screening (SN 8080), contact after separation (SN 8089), contraception (SN 8095), disability (SNs 8680 and 8096), general lifestyle (SN 8092), illness and activity (SN 8094), and non-resident parental contact (SN 8093). See Opinions and Lifestyle Survey: Secure Access for details.

Main Topics:
Each month's questionnaire consists of two elements: core questions, covering demographic information, are asked each month together with non-core questions that vary from month to month.
The non-core questions for this month were:
Company Cars (Module 1a): questions about the number of petrol-fuelled and diesel- fuelled company cars as well as total mileage and total business mileage. Also questions are asked on age, engine size and value of car when new.
Second Homes (Module 4): ownership of a second home by any member of the household and reasons for having the second home.
Education and Training (Module 48): questions to employees about job related education and training, either undertaken, offered or discussed in the last 12 months.
Registration with GP's (Module 43): to explore whether people are registered with a GP and reasons for not registering.
Sensible Drinking (Module 40): finding out how aware people are of units for different alcoholic drinks. Also whether they know the recommended limits, in terms of units, per week for men and women.
Heights and Weights (Module 42): designed to find out whether people know their own height and weight (to nearest inch/2 pounds).
Investment Income (Module 7a): ownership of shares and income from shares and bank accounts. Also question about investments in PEPs and TESSAs.

analyze the current population survey (cps) annual social and economic supplement (asec) with r the annual march cps-asec has been supplying the statistics for the census bureau's report on income, poverty, and health insurance coverage since 1948. wow. the us census bureau and the bureau of labor statistics ( bls) tag-team on this one. until the american community survey (acs) hit the scene in the early aughts (2000s), the current population survey had the largest sample size of all the annual general demographic data sets outside of the decennial census - about two hundred thousand respondents. this provides enough sample to conduct state- and a few large metro area-level analyses. your sample size will vanish if you start investigating subgroups b y state - consider pooling multiple years. county-level is a no-no. despite the american community survey's larger size, the cps-asec contains many more variables related to employment, sources of income, and insurance - and can be trended back to harry truman's presidency. aside from questions specifically asked about an annual experience (like income), many of the questions in this march data set should be t reated as point-in-time statistics. cps-asec generalizes to the united states non-institutional, non-active duty military population. the national bureau of economic research (nber) provides sas, spss, and stata importation scripts to create a rectangular file (rectangular data means only person-level records; household- and family-level information gets attached to each person). to import these files into r, the parse.SAScii function uses nber's sas code to determine how to import the fixed-width file, then RSQLite to put everything into a schnazzy database. you can try reading through the nber march 2012 sas importation code yourself, but it's a bit of a proc freak show. this new github repository contains three scripts: 2005-2012 asec - download all microdata.R down load the fixed-width file containing household, family, and person records import by separating this file into three tables, then merge 'em together at the person-level download the fixed-width file containing the person-level replicate weights merge the rectangular person-level file with the replicate weights, then store it in a sql database create a new variable - one - in the data table 2012 asec - analysis examples.R connect to the sql database created by the 'download all microdata' progr am create the complex sample survey object, using the replicate weights perform a boatload of analysis examples replicate census estimates - 2011.R connect to the sql database created by the 'download all microdata' program create the complex sample survey object, using the replicate weights match the sas output shown in the png file below 2011 asec replicate weight sas output.png statistic and standard error generated from the replicate-weighted example sas script contained in this census-provided person replicate weights usage instructions document. click here to view these three scripts for more detail about the current population survey - annual social and economic supplement (cps-asec), visit: the census bureau's current population survey page the bureau of labor statistics' current population survey page the current population survey's wikipedia article notes: interviews are conducted in march about experiences during the previous year. the file labeled 2012 includes information (income, work experience, health insurance) pertaining to 2011. when you use the current populat ion survey to talk about america, subract a year from the data file name. as of the 2010 file (the interview focusing on america during 2009), the cps-asec contains exciting new medical out-of-pocket spending variables most useful for supplemental (medical spending-adjusted) poverty research. confidential to sas, spss, stata, sudaan users: why are you still rubbing two sticks together after we've invented the butane lighter? time to transition to r. :D

Abstract copyright UK Data Service and data collection copyright owner.

The Skills Survey is a series of nationally representative sample surveys of individuals in employment aged 20-60 years old (since 2006, the surveys have additionally sampled those aged 61-65). The surveys aim to investigate the employed workforce in Great Britain. Although they were not originally planned as part of a series and had different funding sources and objectives, continuity in questionnaire design has meant the surveys now provide a unique, national representative picture of change in British workplaces as reported by individual job holders. This allows analysts to examine how various aspects of job quality and skill levels have changed over 30 years.The first surveys in the series were carried out in 1986 and 1992. These surveys also form part of this integrated data series, and are known as the Social Change and Economic Life Initiative (SCELI) and Employment in Britain (EIB) studies respectively.

The 1997 survey was the first to collect primarily data on skills using the job requirements approach. This focused on collecting data on objective indicators of job skill as reported by respondents. The 2001 survey assessed how much had changed between the two surveys and a third survey in 2006 enhanced the time series data, while providing a resource for analysing skill and job requirements in the British economy at that time. The 2012 survey aimed to again add to the time series data and, coinciding as it did with a period of economic recession, to provide insight into whether workers in Britain felt under additional pressure/demand from employers as a result of redundancies and cut backs. In addition, a series dataset, covering 1986, 1992, 1997, 2001, 2006 and 2012 is also available . A follow-up to the 2012 survey was conducted in 2014, revisiting respondents who had agreed to be interviewed again. The 2017 survey was the seventh in the series, designed to examine to what extent pressures had continued as a result of austerity and economic uncertainties triggered, for example, by Brexit as well as examining additional issues such as productivity, fairness at work and the retirement intentions of older workers.

Each survey comprises a large number of respondents: 4,047 in the 1986 survey; 3,855 in 1992; 2,467 in 1997; 4,470 in 2001; 7,787 in 2006; 3,200 in 2012; and 3,306 in 2017.

The Skills and Employment Surveys Series Dataset, 1986, 1992, 1997, 2001, 2006, 2012 and 2017 combines data from all seven surveys in the series, where common survey questions were asked. For each survey, weights are computed to take into account the differential probabilities of sample selection, the over-sampling of certain areas and some small response rate variations between groups (defined by sex, age and occupation). All surveys cover Great Britain except the Skills Survey, 2006 which covers the United Kingdom.

The surveys are all available separately from the UK Data Archive:

• Social Change and Economic Life Initiative Surveys, 1986-1987 (SN 2798)
• Employment in Britain 1992 (SN 5368)
• Skills Survey 1997 (SN 3993)
• Skills Survey 2001 (SN 4972)
• Skills Survey 2006 (SN 6004)
• Skills and Employment Survey 2012 (SNs 7465 and 7466)
• Skills and Employment Survey 2017 (SNs 8580 and 8581)

A Special Licence access version of this combined dataset including finer detailed geographical variables including (Travel To Work Area, or TTWA) is also available under Special Licence (SN 8590).

An earlier Skills and Employment Surveys Series Dataset, covering 1986, 1992, 1997, 2001, 2006 and 2012 is available under SN 7467.

Main Topics:

The main topics include:

• skills at work
• job quality
• training and skills development
• terms and conditions of employment

Abstract

The STEP (Skills Toward Employment and Productivity) Measurement program is the first ever initiative to generate internationally comparable data on skills available in developing countries. The program implements standardized surveys to gather information on the supply and distribution of skills and the demand for skills in labor market of low-income countries.

The uniquely-designed Household Survey includes modules that measure the cognitive skills (reading, writing and numeracy), socio-emotional skills (personality, behavior and preferences) and job-specific skills (subset of transversal skills with direct job relevance) of a representative sample of adults aged 15 to 64 living in urban areas, whether they work or not. The cognitive skills module also incorporates a direct assessment of reading literacy based on the Survey of Adults Skills instruments. Modules also gather information about family, health and language.

Geographic coverage

The STEP target population is the urban population aged 15 to 64 (inclusive). Areas are classified as urban based on Armenia's official definition.

Analysis unit

The units of analysis are the individual respondents and households. A household roster is undertaken at the start of the survey and the individual respondent is randomly selected among all household members aged 15 to 64 included. The random selection process was designed by the STEP team and compliance with the procedure is carefully monitored during fieldwork.

Universe

The target population for the Armenia STEP survey comprises all non-institutionalized persons 15 to 64 years of age (inclusive) living in private dwellings in urban areas of the country at the time of data collection. This includes all residents except foreign diplomats and non-nationals working for international organizations.

The following are excluded from the sample: - Residents of institutions (prisons, hospitals, etc) - Residents of senior homes and hospices - Residents of other group dwellings such as college dormitories, halfway homes, workers' quarters, etc - Persons living outside the country at the time of data collection

In some countries, extremely remote villages or conflict-ridden regions could not be surveyed.

Kind of data

Sample survey data [ssd]

Sampling procedure

The Armenia sample design is a 3 stage sample design. There was no explicit stratification but the sample is implicitly stratified by Region. Implicit stratification was achieved by sorting the PSUs by Region and selecting a systematic sample of PSUs.

First Stage Sample The primary sample unit (PSU) is a cluster of households that are users of Electricity Networks of Armenia (ENA). The first stage units were selected by the World Bank Survey Methodologist. Each PSU is uniquely defined by the sample frame variable 'Cluster_ID'. The sampling objective was to conduct interviews in 200 PSUs. In addition, 25 extra PSUs were selected for use in case it was impossible to conduct any interviews in one or more initially selected PSUs. (N.B. None of the 25 extra PSUs were required to be activated.)

Second Stage Sample The second stage sample unit (SSU) is a household. The sampling objective was to obtain interviews at 15 households within each selected PSU. The households were selected in each PSU using a systematic random method. There was an expectation of high non-response for the Armenia STEP. Therefore, it was decided to select 60 households in each PSU; in each PSU, 2 replicates of 30 households each were selected. The sample of 60 households was divided randomly into an initial sample of 15 households and a reserve sample of 45 households which was activated as necessary in the order in which the sample was selected. During the data collection operation, one PSU (i.e., PSU #183) required additional sample due to exceptionally high non-response. A 3rd replicate of 30 households was selected to accommodate this requirement. Thus, a sample of 90 households was selected in this PSU.

Third Stage Sample The third stage sample unit was an individual aged 15-64 (inclusive). The sampling objective was to select one individual with equal probability from each selected household.

Mode of data collection

Face-to-face [f2f]

Research instrument

The STEP survey instruments include: - The background questionnaire developed by the WB STEP team - Reading Literacy Assessment developed by Educational Testing Services (ETS).

All countries adapted and translated both instruments following the STEP Technical Standards: 2 independent translators adapted and translated the Background Questionnaire and Reading Literacy Assessment, while reconciliation was carried out by a third translator.

The WB STEP team and ETS collaborated closely with the Armenian survey firm during the process and reviewed the adaptation and translation to Armenian (using a back translation).

The survey instruments were both piloted as part of the survey pretest.

The adapted Background Questionnaires are provided in English as external resources. The Reading Literacy Assessment is protected by copyright and will not be published.

Cleaning operations

STEP Data Management Process:

1) Raw data is sent by the survey firm 2) The WB STEP team runs data checks on the Background Questionnaire data. - ETS runs data checks on the Reading Literacy Assessment data. - Comments and questions are sent back to the survey firm. 3) The survey firm reviews comments and questions. When a data entry error is identified, the survey firm corrects the data. 4) The WB STEP team and ETS check the data files are clean. This might require additional iterations with the survey firm. 5) Once the data has been checked and cleaned, the WB STEP team computes the weights. Weights are computed by the STEP team to ensure consistency across sampling methodologies. 6) ETS scales the Reading Literacy Assessment data. 7) The WB STEP team merges the Background Questionnaire data with the Reading Literacy Assessment data and computes derived variables.

Detailed information data processing in STEP surveys is provided in the 'Guidelines for STEP Data Entry Programs' document provided as an external resource. The template do-file used by the STEP team to check the raw background questionnaire data is provided as an external resource.

Response rate

An overall response rate of 50.3% was achieved in the Armenia STEP Survey. Table 18 of the STEP Survey Weighting Procedures Summary provides the detailed percentage distribution by final status code.

Sampling error estimates

A weighting documentation was prepared for each participating country and provides some information on sampling errors. Please refer to the STEP Survey Weighting Procedures Summary provided as an external resource.

Abstract copyright UK Data Service and data collection copyright owner.

The Health Survey for England (HSE), 2002: Teaching Dataset has been prepared solely for the purpose of teaching and student use. The dataset will help class tutors to incorporate empirical data into their courses and thus to develop students’ skills in quantitative methods of analysis.

All the variables and value labels are those used in the original HSE files, with one exception (New-wt) which is a new weighting variable.

Users may be interested in the Guide to using SPSS for Windows available from Online statistical guides and which explores this dataset.

The original HSE 2002 dataset is held at the UK Data Archive under SN 4912.


Main Topics:

The HSE, 2002 : Teaching Dataset includes 60 variables, and only the 9,281 cases from the general population sample; the boost sample cases of young people aged 0-24 and mothers of children aged under one year are excluded. Most of the variables contained within the dataset are individual ones, and require individual based analysis. However, there are a number of household-level variables included such as ‘TenureB’ and ‘Hhsize’. The dataset contains a mix of discrete and continuous variables and all, apart from the weighting variable 'New_wt', are taken directly from the HSE 2002 dataset deposited at UKDA. The variable names on the Teaching Dataset correspond directly to those on the 2002 HSE dataset.

Topics covered include demographic characteristics, illness and general health, recent periods of sickness, medication used, contraception, smoking, alcohol use, consumption of fruit and vegetables, General Health Questionnaire (GHQ12) score, height, weight, body mass index (BMI), waist-hip ratio and blood pressure measurement.

Standard Measures
The General Health Questionnaire (GHQ12), which has 12 items, is used widely to screen for psycho-social disorders. It asks questions about general level of happiness, depression, anxiety and self-confidence. A score of four or more has been used to identify potential psychological disorder.

Abstract

The Government of the Kyrgyz Republic, with support from UNICEF finalized and launched a Multiple Indicator Cluster Survey (MICS 6) in 2018. 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. Data and information from MICS6 provides credible and reliable evidence for the Government of Kyrgyz Republic draw a comprehensive picture of the lives of children and women in Kyrgyzstan and monitor progress towards Sustainable Development Goals (SGDs). It helps the government and its stakeholders to understand disparities and the wider development challenges in the country.

The 2018 Kyrgyzstan MICS has as its primary objectives:

• To provide high quality data for assessing the situation of children, adolescents, women and households in Kyrgyzstan;

• To furnish data needed for monitoring progress toward national goals, as a basis for future action;

• To collect disaggregated data for the identification of disparities, to inform policies aimed at social inclusion of the most vulnerable;

• To validate data from other sources and the results of focused interventions;

• To generate data on national and global SDG indicators;

• To generate internationally comparable data for the assessment of the progress made in various areas, and to put additional efforts in those areas that require more attention;

• To generate behavioural and attitudinal data not available in other data sources.

Geographic coverage

The sample for the Kyrgyz Republic MICS 2018 was designed to provide estimates at the national/area/sub-population level, for urban and rural areas. Specifically, the sample for the Kyrgyz Republic MICS 2018 survey included 7 regions and two cities of the country: Batken, Jalal-abad, Issyk-kul, Naryn, Talas, Chui region and Bishkek, Osh cities.

Analysis unit

• Individuals

• Households

Universe

The survey covered all de jure household members (usual residents), all women age 15-49 years, and mothers (or caretakers) of children 0 to 17 years living in the houshold. Additionally a basic skills assessment was administered to children age 7 - 14 years.

Kind of data

Sample survey data [ssd]

Sampling procedure

SAMPLING FRAME

A two-stage, stratified cluster sampling approach was used for the selection of the survey sample. The sampling frame was based on the 2009 Country Census of Population and Housing. The primary sampling units (PSUs) selected at the first stage were the enumeration areas (EAs) defined for the census enumeration. After conducting the listing of households in the sample enumeration areas, in a random systematic sample of 20 households was selected in each EA.

SAMPLE SIZE AND SAMPLE ALLOCATION

The overall sample size for the 2018 Kyrgyz Republic MICS was calculated as 7,200 households. For the calculation of the sample size, the key indicator used was the underweight prevalence among children age 0-4 years. Since the survey results are tabulated at the regional level, it was necessary to determine the minimum sample size for each region. Variables considered to determine the minimum sample size for the region: underweight prevalence, design effect, and mean household size (more details are provided in Appendix A in the report available in related materials.

The estimated sample size requirements for obtaining a relative margin of error of 10% for stunting prevalence of children under-five (with a 2014 estimate of 13%, and calculated sample size of 6,858 households). It is also necessary to determine the sample size for each region, although sometimes the requirements for the level of precision are relaxed for sub-national domains. So, all regional level sample size estimates were also done for regions of the Kyrgyz Republic for stunting children (calculated sample size of 7,466 households).

It was also desired to have about minimum of 70 and max 110 "Children age 12-23 months" in every region (only 60 reserved for Osh city). Based on a review of the 2014 results, and above requirements, it was decided to have a minimum of sample size of 400 households and a maximum sample size of 1,300 HHs for Bishkek. These calculations resulted a final sample size of 7,200 households within 360 clusters.

Within each region, the sample EAs are allocated to the 30% urban and 70% rural strata proportionately to the number of households in each stratum, except for two urban strata Bishkek and Osh city since they do not have any rural strata. The purpose of this disproportionate allocation is to have more cases in urban domains of such regions since their actual proportion of rural is very high already. This allocation of the sample results in an urban sample of 174 sample EAs and 3,480 households, and a rural sample of 186 EAs and 3,720 households, which should be sufficient for providing reliable estimates for the urban and rural domain at the national level.

SELECTION OF ENUMERATION AREAS (CLUSTERS)

Census enumeration areas were selected from each of the sampling strata by using systematic probability proportional to size (pps) sampling procedures, based on the number of households in each enumeration area from the 2009 Census frame. The first stage of sampling was thus completed by selecting the required number of sample EAs from each of the nine regions, separately for the urban and rural strata.

LISTING ACTIVITIES

Given that there had been many changes in the households enumerated in the 2009 Census, a new listing of households was conducted in all the sample enumeration areas prior to the selection of households. For this purpose, listing teams were trained to visit all the selected enumeration areas and list all households in each enumeration area. Listing of households and enumeration areas was done by the National Statistical Committee from May to July 2018. One team was trained in each area. The segmentation procedures were applied in only two of the enumeration areas with large size in the city of Bishkek. EAs were divided in almost three equal size segments and one of them was selected randomly in which full listing and selection procedures were implemented.

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 National Statistical Committee, where the selection of 20 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.

Mode of data collection

Face-to-face [f2f]

Research instrument

Four questionnaires were used in the survey: 1) a household questionnaire to collect basic demographic information on all de jure household members (usual residents), the household, and the dwelling; 2) a questionnaire for individual women administered in each household to all women age 15-49 years; 3) an under-5 questionnaire, administered to mothers (or caretakers) of all children under 5 living in the household; and 4) 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.

Additionally, for all children age 0-2 years with a completed Questionnaire for Children Under Five, the Questionnaire Form for Vaccination Records, was used to record vaccinations from medical vaccinations card (form No 63).

In addition to the administration of questionnaires, fieldwork teams tested the salt used for cooking in the households for iodine content, observed the place for handwashing, availability of water and soup, measured the weights and heights of children age under 5 years. Details and findings of these observations and measurements are provided in the respective sections of the report. Further, the questionnaire for children age 5-17 years included basic skills that are necessary for learning (reading and mathematics assessment) administered to children age 7-14 years.

The questionnaires were based on the MICS6 standard questionnaires.2 From the MICS6 model Russian version, the questionnaires were customised and translated into the Kyrgyz language and were pre-tested in the Chui region and Bishkek during May, 2018. Based on the results of the pre-test, modifications were made to the wording and translation of the questionnaires.

Cleaning operations

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

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

Data were analysed using the Statistical Package for Social Sciences (SPSS) software, Version 24. Model syntax and tabulation plan developed by UNICEF were customised and used for this purpose.

Response rate

Of 7,200 households selected for the sample, 7,065 were found occupied. Of these, 6,968 were successfully interviewed for a household response rate of 98.6% percent.

In the interviewed households, 5,826 women age 15-49 years were identified. Of these, 5,742 women