Abstract

The work plan activities in Kiribati related to the updating of the listing of all households and institutions in Kiribati is to produce a sex and age disaggregated population count that forms the basis for a sampling frame for the upcoming Social Indicator Survey (SIS) and Household Income and Expenditure Survey (HIES). It also serves the purpose of digitalising and harmonising enumeration areas (EAs) to facilitate random sampling and census planning. To achieve this, SPC was engaged to conduct the following activities:

1. Planning and budgeting: prepare a comprehensive plan and budget for the household listing.
2. Mapping: prepare field maps to be used in the listing; digitalise EA boundaries and harmonisation of new EA framework; training and capacity building of the Ministry of Environment, Lands and Agricultural Development; prepare maps for the selected EAs in the SIS.
3. Listing questionnaire design, enumerator training and technology: develop a tablet-based household listing questionnaire and associated training resources, and set up of technology (e.g., server, tablet interviewer application, backup protocols); support Kiribati's National Statistics Office (KI-NSO) to conduct training of enumerators in all aspects of the collection; and administer South-South support to Kiribati for the duration of the listing.
4. Sample design: design the sample and field plan for the SIS; and build capacity of KI-NSO in sample design and field work planning.

Geographic coverage

National coverage (full coverage).

Analysis unit

Households/Institutions and Individuals.

Universe

Households, Institutions, de jure household members.

Kind of data

Census/enumeration data [cen]

Sampling procedure

Not Applicable.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The questionnaire, which is designed in English, is divided into three main sections:

1) Household ID and Building Type 2) Person Roster 3) Geographic Information and Photo

The questionnaire was generated by Survey Solutions and is provided as an external resource.

Cleaning operations

Data was processed using the software STATA. Corrections were made both automatically and by visual control: validation checks in the questionnaire as well as final editing of the raw data.

Abstract

The 1996 Papua New Guinea household survey is designed to measure the living standards of a random sample of PNG households. As well as looking at the purchases, own-production, gift giving/receiving and sales activities of households over a short period (usually 14 days), the survey also collects information on education, health, nutrition, housing conditions and agricultural activities. The survey also collects information on community level access to services for education, health, transport and communication, and on the price levels in each community so that the cost of living can be measured.

There are many uses of the data that the survey collects, but one main aim is for the results to help government, aid agencies and donors have a better picture of living conditions in all areas of PNG so that they can develop policies and projects that help to alleviate poverty. In addition, the survey will provide a socio-economic profile of Papua New Guinea, describing the access that the population has to agricultural, educational, health and transportation services, their participation in various economic activities, and household consumption patterns.

The survey is nationwide and the same questionnaire is being used in all parts of the country, including the urban areas. This fact can be pointed out if households find that some of the questions are irrelevant for their own living circumstances: there are at least some Papua New Guinean households for which the questions will be relevant and it is only by asking everyone the same questions that living standards can be compared.

Geographic coverage

The survey covers all provinces except Noth Solomons.

Analysis unit

• Household
• Individual
• Community

Kind of data

Sample survey data [ssd]

Sampling procedure

The Household Listing Form and Selection of the Sample Listing of households is the first job to be done after the team has settled in and completed the introductions to the community. Listing is best done by the whole team working together. This way they all get to know the community and its lay-out. However, if the census unit is too large this wastes too much time. So before beginning asks how many households there are, very roughly, in the census unit (noting that teams are supplied with the number of households that were there in the 1990 census). If the answer is 80 or more, divide the team into two and have each half-team work on one sector of the community/village. See the section below on what to do when the listing work is divided up.

If the census unit is a "line-up point" that does not correspond to any single village or community the number of households will often exceed 200 and frequently they are also quite dispersed. In this case it is not practical to attempt to list the whole census unit, so a decision is made in advance to split the census unit into smaller areas (perhaps groupings of clans). First, a local informant must communicate the boundaries of the census unit and for natural or administrative sub-units with the larger census unit (such as hamlets; or canyons/valleys). The sub-units should be big enough to allow for the selection of a set of households (about 30 or more), but should not be so large that excessive transport time will be needed each day just to find the household. Once the subunit is defined, its boundaries should be clearly described. Then one of the smaller units is randomly selected and the procedures outlined above are then followed to complete the listing. Note: only one of the sub-units are listed, sample chosen, and interviews undertaken.

The most important thing in the listing is to be sure that you list all the households and only the households belonging to the named village or census unit (or subset of the census unit if it is a line-up point). In rural areas, explain to village leaders at the beginning: "We have to write down all the households belonging to (Name) village." In case of doubt, always ask: "Does this household belong to (Name) village?" In the towns, the selected area is shown on a map. Check that the address where you are listing is within the same area shown.

Also explain: "We only write down the name of the head of household. When we have the list of all the households, we will select 12 by chance, for interview."

Procedure for Listing The listing team walks around in every part of the village, accompanied by a guide who is a member of the village. If possible, find a person who conducted the 1990 Census in this community or someone with similar knowledge of the community and ask them to be your guide. Make sure you go to all parts of the village, including outlying hamlets. In hamlets, on in any place far from the centre, always check: "Do these people belong to (Name) village?"

In every part of the village, ask the guide about every house: "Who lives in this house? What is the name of the household head?" Note that you do not have to visit every household. At best, you just need to see each house but you do not need to go inside it or talk to anyone who lives there. Even the rule of seeing each house may be relaxed if there are far away household for which good information can be provided by the guide.

Enter the names of household heads in the lines of the listing form. One line is used for each household. As the lines are numbered, the procedure gives a number to each household. When you come to the last house, check with the guide: "Are you sure we have seen all the houses in the village?"

NOTE: It does not matter in what order you list the households as long as they are all listed. After the listing is complete, check that all lines are numbered consecutively with no gaps, from start to finish. The number on the last line should be exactly the number of households listed.

Note: If the list is long (say more than 30 households) interviewer may encounter difficulties when looking for their selected household. One useful way to avoid this is to show the approximately the place in the list here certain landmarks come. This can be done by writing in the margin, CHURCH or STORE or whatever. You can also indicate where the lister started in a hamlet, for example.

Sample Selection The sampling work is done by the supervisor. The first steps are done at the foot of the first page of the listing form. The steps to be taken are as follows:

1. Fill in the numbers asked for at the foot of the last listing page, as follows:
2. M: enter the total number of households listed (same as last household number shown).
3. Interval L: calculate (M / 15) to the nearest whole number.
4. R: This is a random number with 3-digit decimals between 0.000 and 0.999.

5. MR: multiply M by R and round to the nearest whole number. (If decimal 0.5, round up).

6. MR gives the 1st selection. (Exception: If MR=0, L gives the first selection.) Enter S against this line in the selection column of the list.

7. Count down the list, beginning after the 1st selection, a distance of L lines to get the 2nd selection, then another L to get the 3rd, etc. When you come to the bottom of the list, jump back to the top as if the list were circular. Stop after the 15th selection. Mark the 13th, 14th, and 15th selections "RES" (for reserve). Mark the 1st - 12th selection "S" (for selection).

Mode of data collection

Face-to-face [f2f]

Research instrument

The 1996 Papua New Guinea Household Survey questionnaire consists of three basic parts:

Household questionnaire first visit: asks a series of questions about the household, discovering who lives there, what they do, their characteristics, where they live, and a little about what kinds of things they consume. This questionnaire consists of the following sections. - Section 1. Household Roster - Section 2. Education - Section 3. Income Sources - Section 4. Health - Section 5. Foods in the Diet - Section 6. Housing Conditions - Section 7. Agricultural Assets, Inputs and Services - Section 8. Anthropometrics - Section 9. Household Stocks

Consumption recall (second visit questionnaire): is focused primarily on assessing the household's expenditure, gift giving and recieving, production, and level of wealth. The information in the first and second visits will provide information that can determine the household's level of consumption, nutrition, degree of food security, and ways in which it organizes its income earning activities. This questionnaire consists of the following sections. - Section 1. Purchases of Food - Section 2. Other Frequent Purchases - Section 3. Own-production of Food - Section 4. Gifts Received: Food and Frequent Purchases (START) - Section 5. Annual Expenses and Gifts - Section 6. Inventory of Durable Goods - Section 7. Inward Transfers of Money - Section 8. Outward Transfers of Money - Section 9. Prices - Section 10. Repeat of Anthropometric Measurements - Section 11. Quality of Life

Community Questionnaire: which is completed by the interview team in consultation with community leaders. This questionnaire also includes market price surveys that are carried out by the team when they are working in the community. Associated with this is a listing of all households in the community, which has to be done prior to the selection of the 12 households. This questionnaire consists of the following sections. - Section A. Listing of Community Assets - Section B. Education - Section C. Health - Section D. Town or Government Station - Section E: Transport and Communications - Section F. Prices - Section G. Changes in Economic Activity, Infrastructure, and Services

Mode of data collection

Face-to-face [f2f]

Abstract

The Côte d'Ivoire Living Standards Survey (LSS) was the first LSMS Survey to have field tested the methodology and questionnaire developed by LSMS. It consists of three complementary surveys: the household survey, the community survey and the price survey. The household survey collected detailed information on expenditures, income, employment, assets, basic needs and other socio-economic characteristics of the households. The Community Survey collected information on economic and demographic characteristics of the rural communities to which each cluster of households belonged. This was designed to enable the linkage of community level with household level data. The price survey component of the CILSS collected data on prices at the nearest market to each cluster of households, so that regional price indices could be constructed for the household survey. The Côte d'Ivoire Living Standards Survey (LSS) was undertaken over a period of four years, 1985-88, by the Direction de la Statistique in Côte d'Ivoire, with financial and technical support from the World Bank during the first two years of the survey. It was the first year-round household survey to have been undertaken by the Ivorian Direction de la Statistique. The sample size each year was 1600 households and the sample design was a rotating panel. That is, half of the households were revisited the following year, while the other half were replaced with new households. The survey thus produced four cross-sectional data sets as well as three overlapping panels of 800 households each (1985-86, 1986-87, 1987-88).

Geographic coverage

National

Analysis unit

Households

Kind of data

Sample survey data [ssd]

Sampling procedure

(a) SAMPLE DESIGN The principal objective of the sample selection process for the LSS Household Survey was to obtain a nationally representative cross-section of African households, some of which could be interviewed in successive years as panel households. A two-stage sampling procedure was used. In the first stage, 100 Primary Sampling Units (PSUs) were selected across the country from a list of all PSUs available in the sampling frame. At the second stage, a cluster of 16 households was selected within each PSU. This led to a sample size of 1600 households a year, in 100 cluster s of 16 households each. Half of the households were replaced each year while the other half (the panel households in 1986, 1987 and 1988) were interviewed a second time. It is important to note that there was a change in the sampling procedures (the sampling frame, PSU selection process and listing procedures), used to select half of the clusters/households interviewed in 1987 (the other half were panel households retained from 1986), and all of the clusters/households interviewed in 1988. Households selected on the basis of the first set of sampling procedures will henceforth be referred to as Block 1 data while households based on the second set of sampling procedures will be referred to as Block 2 data.

(b) SAMPLE FRAME 1. Sampling Procedures for Block 1 Data The Sampling Frame. The sampling frame for the 1985, 1986, and half of the 1987 samples (except for Abidjan and Bouaké) was a list of localities constructed on the basis of the 1975 Census, updated to 1983 by the demographers of the Direction de la Statistique and based on a total population estimated at 9.4 million in 1983.The Block 1 frame for Abidjan and Bouaké was based on data from a 1979-80 electoral census of these two cities. The electoral census had produced detailed maps of the two cities that divided each sector of the city into smaller sub-sectors (îlots). Sub-sectors with similar types of housing were grouped together by statisticians in the Direction de la Statistique to form PSUs. From a list of all PSUs in each city, along with each PSU's population size, the required number of PSUs were selected using a systematic sampling procedure. The step size was equal to the city's population divided by the number of PSUs required in each city. One problem identified in the selection process for Abidjan arose from the fact that one sector of the city (Yopougon) which had been relatively small in 1980 at the time of the electoral census, had since become the largest agglomeration in Côte d'Ivoire. This problem was presumably unavoidable since accurate population data for Yopougon was not available at the time of the PSU selection process.

Selection of PSUs. Geographic stratification was not explicitly needed because the systematic sampling procedure that was used to select the PSUs ensured that the sample was balanced with respect to region and by site type, within each region. The main geographical regions defined were: East Forest, West Forest, and Savannah. Site types varied as follows: large cities, towns, large and small villages, surrounding towns, village centers, and villages attached to them. The 100 PSUs were selected, with probabilities proportional to the size of their population, from a list of PSUs sorted by region and within each region, by site type. Selection of households within each PSU. A pre-survey was conducted in June-July of 1984, to establish the second-stage sampling frame, i.e. a list of households for each PSU from which 16 households could be selected. The same listing exercise was to be used for both the 1985 and 1986 surveys, in order to avoid having to conduct another costly pre-survey in the second year. Thus, the 1984 pre-survey had to provide enough households so as to be able to select two clusters of households in each PSU and to allow for replacement households in the event that some in the sample could not be contacted or refused to participate. A listing of 64 households in each PSU met this requirement. In PSUs with 64 households or fewer, every household was listed. In selecting the households, the "step" used was equal to the estimated number of households in the PSU divided by 64. For example, if the PSU had an estimated 640 households, then every tenth household was included in the listing, counted from a random starting point in the PSU. For operational reasons, the maximum step allowable was a step of 30. In practice, it appears that enumerators used doors, instead of housing structures, in counting the step. Al though enumerators were supposed to start the listing process from a random point in the PSU, in rural areas and small towns, reportedly, the lister started from the center of the PSU.

1. Sampling Procedures for Block 2 Data

The Sampling Frame. The sampling frame for Block 2 data was established from a list of places from the results of the Census of inhabited sites (RSH) performed in preparation for the 1988 Population Census. Selection of PSUs. The PSUs were selected with probability proportional to size. However, in order to save what might have been exorbitant costs of listing every household in each selected PSU in a pre-survey, the Direction de la Statistique made a decision to enumerate a smaller unit within each PSU. The area within each PSU was divided into smaller blocks called `îlots'. Households were then selected from a randomly chosen îlot within each PSU. The sample îlot was selected with equal probability within each PSU, not on the basis of probability proportional to size. (These îlots are reportedly relatively small compared with the size of PSUs selected for the Block 1 frame, but no further information is available about their geographical position within the PSUs.) Selection of households within each PSU. All households in each îlot selected for the Block 2 sample were listed. Sixteen households were then randomly chosen from the list of households for each îlot.

Mode of data collection

Face-to-face [f2f]

Cleaning operations

The Household Questionnaire was almost entirely pre-coded, thus reducing errors involved in the coding process. Also, the decentralized data entry system allowed for immediate follow-up on inconsistencies that were detected by the data entry program. Household and personal identification codes were recorded in each section, facilitating merging data across sections

Sampling error estimates

(a) ACCURACY The general consensus is that the quality of the LSS household data is very good. An informal review of data quality conducted by Ainsworth and Mehra (1988) assessed the 1985 and 1986 LSS data in terms of their accuracy, completeness, and internal consistency. The LSS household data were found to score high marks on each of these three counts. One measure of data quality is the extent to which individuals in question respond for themselves during the interview, rather than having proxy responses provided for them by other household members. The investigation of CILSS household survey data for 1985 and 1986 showed that 93 percent of women responded for themselves to the fertility section and that 79 to 80 percent of all adult household members responded for themselves to the employment module. The percent of children responding for themselves to the employment module was far less, 43 to 45 percent. Nevertheless, these rates were found to be higher than for the Peru Living Standards Survey (29 percent).

(b) COMPLETENESS

Investigation of several variables and modules in the LSS (sex, age, parental characteristics, schooling, health, employment, migration, fertility, farming and family business), found that missing data in the household survey are rare. Rates for missing data were found to be close to 0 (0.01 to 0.05 percent) in many cases, but in any case, no higher than 0.76 percent.

Tsogolo la Thanzi (TLT) is a longitudinal study in Balaka, Malawi designed to examine how young people navigate reproduction in an AIDS epidemic. Tsogolo la Thanzi means "Healthy Futures" in Chichewa, Malawi's most widely spoken language. Data are being collected to develop better understandings of the reproductive goals and behavior of young adults in Malawi - the first cohort to never have experienced life without AIDS. To understand these patterns of family formation in a rapidly changing setting, TLT used the following approach: an intensive longitudinal design where respondents are interviewed every four months at TLT's centralized research center. Data collection began in May of 2009 and was completed in June of 2012. To assess changes on a longer time-horizon, a follow-up survey referred to as TLT-2 was fielded between June and August of 2015. The Household Listing Dataset are supplementary data related to the Tsogolo la Thanzi [Healthy Futures] longitudinal data series. The Household Listing includes data from the complete household census used to generate the sample for the TLT study. It includes data from all persons living within seven kilometers of the TLT research center.

Geographic coverage

Nationally representative sample

Analysis unit

• Household
• Individual

Kind of data

Sample survey data [ssd]

Sampling procedure

Sampling Frame

The 2002 population and housing census provided a frame for sample selection. The frame contains a list of all administrative units up to the lowest level called, 'Local Council 1', or LC1. This is usually, but not always consistent with a village in terms of area. The Enumeration Area (EA) may comprise of one village/LC1, or more than one village/LC1. The demarcation of EAs is based on total population within a given area and in many instances, may vary by locality. In addition the sampling frame also indicates the EA to which a particular LC belongs. The 2002 Uganda Sampling Frame has a total of 33,283 EAs.

Study population

The study population comprised of the entire population of Uganda. Based on the distribution of households in table 1 above, the sample was determined based on information from Uganda National Household survey 2005/06 conducted by the Uganda Bureau of Statistics. The proportion of internal migrants reported in the past 5 years has been used to estimate the required sample. Given the limited nature of the number of international migrants, the proportion of internal migrants is considered adequate to provide sufficient estimates of the indicators of interest.

Sample allocation by region

The above sample was proportionately allocated across the four statistical regions on the basis of the population in each of the regions. There was oversampling for urban population approximately by 5 times. To ease implementation, the regional sample was further disaggregated down to Enumeration Area level.

Selection of Enumeration Areas

The task was to undertake a nationally-representative survey of 2,000 households (urban and rural combined) in 2009 that would provide information on migration, remittances and their effects on development. The frame was be divided into two strata namely rural and urban. A two-stage stratified sample design was adopted. The first stage representing the primary sampling unit comprised of the selection of EAs from each of the strata while at the second and ultimate stage households were selected. EAs were selected from the list of Enumeration Areas developed after the 2002 Population and Housing Census and updated to include new districts.

The selection of EAs was proportionally done based on the number of households in the respective stratum according to the 2006 Uganda household survey. All the EAs in each domain were sorted by county, sub-county and parish. A random number was generated and an appropriate random start and sampling interval was systematically selected from the ordered list with probability proportionate to number of households. This was done separately for urban and rural areas, hence stratified sampling. The proportion of EAs sampled in urban areas is about 5 times that in rural.

Selection of households

At the second stage, a complete listing of households in each EA was done to classify the households into three groups: non migrants, internal migrants and international migrants. The number of households per EA varied from around 20 to about 1000. Most of the time, all households were listed even in the large EAs since it was difficult to establish lines of demarcation to segment the EA.

A total of 10 households were selected randomly from each of the 200 EAs. The goal was to select 4 households with an international migrant (emigrant), 3 with one or more internal migrants, and 3 with no migrant. This sampling was done from the three strata or listings of households according to migration status. Separate sampling was done from each stratum using systematic sampling. In case of a refusal or other reason for non-response, another household was selected from the same stratum to reach the desired quota. In case the number of households listed in any of the three strata was smaller than the numbers desired (4, 3, 3), then all those listed in that stratum were automatically sampled and the short fall selected from the next stratum.

For example, if there were, say, 150 households in an EA, with 3 with international migrants, 27 with internal migrants, and 120 with no migrants, the numbers selected would be, respectively, 3, 3 and 3. But to make up 10, priority would be given to the migrant stratum to add one more, randomly selected, from that stratum. As another example, suppose there were 0 international migrant households; then 7 would be selected from the internal migrant stratum and still only 3 from the list of non-migrant households.

The choice of 10 households per EA was based on experience from the various economic surveys conducted by UBOS, where 10 households provide adequate representation at EA level for most of the economic and social indicators.

The listing operation

The survey targeted household with in-migrants or former members who have migrated away, whether to another part of the country (urban or rural) or to another country. Since the census frame does not uniquely identify who is a migrant or non migrant, and owing to the lack of an up to date list of all households in Uganda from which to draw the sample, the survey team adopted a listing exercise as stop gap measure.

The exercise involved conducting a fresh listing of all households in each of the selected EAs. During the exercise, households with migrants were identified and the migrants clearly categorized as internal-within Uganda- or international where household members had moved to another country all together. The total number of listed household numbers was 24,618. Thereafter, a sample of 10 households was selected using systematic sampling procedure.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaire consisted of seven sections namely: A Cover Sheet requiring household identification particulars including district name and code, county name and code, parish name and code, EA name, stratum, household number, names of the household head and first spouse, number of household members and a description of the location of the household.

In addition, the page captured details of the interview including the interviewer name, date, duration and the outcome of the interview. It also provided for the team leaders remarks and signature.

Section 1: Household roster This section captured the socio-demographic characteristics of all household members.

Section 2: Households housing conditions In this section, information was sought on the type of dwelling, occupancy status, the physical characteristics of the dwelling, and access to basic utilities including water, electricity and sanitation.

Section 3 Household Assets and Expenditure The section collected information on the assets and expenditures of the household. This information was used to determine the welfare status of the household.

Section 4: Household Use of Financial Services: In this section, information relating to use of financial services by household members was collected.

Section 5: Internal and International Migration And Remittances From Former Household Members This section captured information on migration, both internal and international as well as remittances received by the household from former household member migrants.

Section 6: Internal and International Migration and Remittances From Former Household Members Like section 5 above, section 6 sought information on migration, both internal and international as well as remittances received by the household from non household member migrants.

Section 7: Return Migrants Here information on Return migrants was captured. A return migrant was defined as an adult member (over 18 years old) currently living in the household, who had lived in another country or another place in Uganda for at least 3 months in the 5 years preceding the survey. The information sought in this section related to the last migration episode for each return migrant.

Cleaning operations

Data Editing: Data editing was initially done by six editors from among the enumerators.

Prior to data entry, efforts were made to manually edit and ensure that inconsistent entries in the questionnaire were corrected. Data entry was initially done using the EPIDATA software after which it was exported to SPSS for further processing and analysis. This included the creation of variable and value labels for the data.

Response rate

Three categories of non response were encountered in the survey. These include: · Household not Visisted: In this category, the survey teams were unable to visit the households due to one reason or another. This happened in Karamoja, where 2 EAs could not be visited due to insecurity; Kalangala, an island EA where residents were reported to have vacated the EA on the advice of the National Environmental Management Authority (NEMA) in a bid to conserve the environment, four years prior to the visit by the survey team and in Kampala, where an EA could not be located. This led to a loss of 40 responses. · Incomplete Information: Here households were located but enumerators were not able to conduct or complete the interviews due to various reasons. Such reasons include respondents' hostility, interruption by an unforeseen event such as death of the respondent's close relative. The total number of responses lost in this category is 79. Overall, there were 1872 valid responses received representing a response rate of 94%. Of these, 49% reported having migrants.

Abstract

The CSES is a household survey with questions to households and the household members. In the household questionnaire there are a number of modules with questions relating to the living conditions, e.g. housing conditions, education, health, expenditure/income and labour force. It is designed to provide information on social and economic conditions of households for policy studies on poverty, household production and final consumption for the National Accounts and weights for the CPI.

The main objective of the survey is to collect statistical information about living standards of the population and the extent of poverty. Essential areas as household production and cash income, household level and structure of consumption including poverty and nutrition, education and access to schooling, health and access to medical care, transport and communication, housing and amenities and family and social relations. For recording expenditure, consumption and income the Diary Method was applied for the first time. The survey also included a Time Use Form detailing activities of household members during a 24-hour period.

Another main objective of the survey is also to collect accurate statistical information about living standards of the population and the extent of poverty as an essential instrument to assist the government in diagnosing the problems and designing effective policies for reducing poverty, and in evaluating the progress of poverty reduction which are the main priorities in the "Rectangular Strategy" of the Royal Government of Cambodia.

Geographic coverage

National Phnom Penh/Other Urban/Other Rural Provinces/Groups of provinces

Analysis unit

Households

Individuals

Universe

All resident households in Cambodia

Kind of data

Sample survey data [ssd]

Sampling procedure

In this section the sampling design and the sample selection for CSES 2009, is described. The sampling design for the 2009 survey is the same as that used for the CSES 2004. The sampling design for the 2004 CSES is described in for instance National Institute of Statistics (2005a).

The sampling frame for the 2009 survey is based on preliminary data from the General Population Census conducted in 2008. The sample is selected as a three stage cluster sample with villages in the first stage, enumeration areas in the second stage and households in the third.

The Sampling Frame

Preliminary data from the General Population Census 2008 was used to construct the sampling frame for the first stage sampling, i.e. sampling of villages. All villages except 'special settlements' were included in the frame. In all, the first stage sampling frame of villages consisted of 14,073 villages, see Appendix 1. Compared to previous years the frame used for the 2009 survey based on the census 2008 was more up to date than in previous surveys which were based on the population census 1998.

The following variables were used from the census; Province code, province name, district code, district name, commune code, commune name, village code, village name, urban-rural classification of villages, the number of households per village and, the number of enumeration areas in the village.

In the second-stage Enumeration Areas (EA) are selected in each selected village. In most villages only one EA was selected but in some large villages more than one was selected.

For the third stage, the sampling of households, a frame was constructed in field. For selected EAs the census map of the village, including EAs and residences, was given to enumerator who updated the map and listed the households in the selected EA. A sample of households was then selected from the list.

Stratification

The sampling frame of villages was stratified by province and urban and rural. There are 24 provinces and each village is classified as either urban or rural which means that in total we have 48 strata, see Appendix 1. Each stratum of villages was sorted by district, commune and village code.

Sampling

The sampling design in the CSES 2009 survey is a three-stage design. In stage one a sample of villages is selected, in stage two an Enumeration Area (EA) is selected from each village selected in stage one, and in stage three a sample of households is selected from each EA selected in stage two. The sampling designs used in the three stages were:

Stage 1. A systematic pps sample of villages, Primary Sampling Units (PSUs) was selected from each stratum,

i.e. without replacement systematic sampling with probabilities proportional to size. The size measure used was the number of households in the village according to the sampling frame.

Stage 2. One EA was selected by Simple Random Sampling (SRS), in each village selected in stage 1.

As mentioned above, in a few large villages more than one EA was selected.

Stage 3. In each selected EA a sample of households was selected by systematic sampling.

The selection of villages and EAs were done at NIS while the selection of households in stage three was done in field. As mentioned in section 1.1 all households in selected EAs were listed by the enumerator. The sample of households was then selected from the list.

Sample sizes and allocation

The sample size of PSUs, were, as in the 2004 survey, 720 villages (or EAs). In urban villages 10 households were selected and in rural 20 households. In all 12,000 households were selected.

Urban and rural villages were treated separately in the allocation. The allocation was done in two steps. First the sample sizes for urban and rural villages in the frame were determined and then sample sizes for the provinces within urban and rural areas were determined, i.e. the strata sample sizes.

The total sample size was divided into to two, one sample size for urban villages and the other for rural villages. The calculation of the sample sizes for urban and rural areas were done using the proportion of consumption in the two parts of the population. Data on consumption from the CSES 2007 survey was used. The resulting sample sizes for urban villages was 240 and for rural 480. (Some adjustments of the calculated sample sizes were done, resulting in the numbers 240 and 480).

Allocation of the total sample size on the strata within urban and rural areas respectively, was done in the following way. The sample size, i.e. the number of PSUs, villages, selected from stratum h, is proportional to the number of households in stratum h, i.e.

n(Ih)=n1(Mh/Sum of Mh) (1.1)

where,

is the sample size in stratum h, i.e. the number villages selected in stratum h,

is the total sample size of villages for urban or rural villages,

H is the number of strata in urban or rural areas,

is the number of households in stratum h according to the frame.

As mentioned above, the sample size calculations are done separately for urban and rural villages, i.e. for strata with urban villages (1.1) is used with nI = 240 and is the number of households in urban villages in the frame and for rural villages (1.1) is used with nI = 480 and is the

number of households in rural villages in the frame.

Monthly samples

In section 1.3 the selection of the annual sample was described. The annual sample was divided into 12 monthly samples of equal sizes. The monthly samples consisted of 20 urban and 40 rural villages. The division of the annual sample into monthly samples was done so that as far as possible each province would be represented in each monthly sample. Since the sample size of villages in some provinces is smaller than 12, all provinces were not included in all monthly samples. Also, the outline of the fieldwork with teams of 4 enumerators and one supervisor puts constraints on how to divide the annual sample into monthly samples. The supervisors must travel between the villages in a team and therefore the geographical distance between the villages surveyed by a team cannot be too large.

Estimation

Totals, ratios such as means or proportions were estimated for the population or for subgroups of population, i.e. domains of study. The domains were defined by e.g. region or sex. Means and proportions were estimated by first estimating totals and then calculating the ratio of two estimated totals. To estimate totals from a sample survey weights are needed.

Mode of data collection

Face-to-face [f2f]

Research instrument

Four different questionnaires or forms were used in the survey:

1. Household listing form

The Household listing and mapping were done prior to the sampling. During the household listing the enumerator recorded household information on e.g. location, number of members and principal economic activity.

1. Village questionnaire

The Village questionnaire was used to gather basic common information on:

    1. Demographic information

    2. Economy & Infrastructure

    3. Rainfall & Natural disasters

    4. Education

    5. Health

    6. Retail prices (food and non-food items)

    7. Employment & Wages

    8. Access to common property resources during the last 5 years

    9. Sale prices of agricultural land in the village

    10. Recruitment of children for work outside the village 

1. Household questionnaire

The following modules were included in the Household questionnaire:

1. Initial visit

01A. List of household member

01B. Food, beverages and tobacco consumption during the last 7 days

01C. Recall non-food expenditures

01D. Vulnerability

1. Education & Literacy

2. Information on migration (includes past and current migration)

   1. Housing

3. Household economic activities

05A.Land ownership

05B.Production of

Abstract

Institute for Strategic Studies and Prognoses (ISSP) in Montenegro has undertaken several household surveys in an effort to provide timely and relevant data that is useful for policy makers and analysts. While data constraints have limited the ability to evaluate poverty and living standards in recent years, new household surveys collected by ISSP in 2002, 2003 and 2004 allow baselines to be established in regards to the living standards of the Montenegrin population and against which we can monitor changes in the future. Furthermore, with these data on household living standards, analysis can evaluate the role of social policies in supporting the poor as well as the potential impact of major policy reforms.

The ISSP surveys drew attention, once again, to the need for accurately measuring household living conditions according to well accepted standards, and for monitoring these trends on a regular basis. These surveys have provided the country with an invaluable training ground towards the development of a permanent household survey system to support the government strategic planning in its fight against poverty.

Geographic coverage

National

Analysis unit

• Households
• Individuals
• Persons aged 15 and above
• Consumption Expenditure Commodities / Items

Kind of data

Sample survey data [ssd]

Sampling procedure

The 2004 Household Survey consists of a sample of about 1,000 households interviewed in all municipalities. Of these, 600 households are considered to be the Core Sample. In addition there are two booster samples (200 households each).

The Republic of Montenegro is divided geographically into 3 regions and into 21 municipalities which are, in turn, divided into settlements. Since the last census in Montenegro was undertaken in November 2003, the data were not fully available to be utilized for all stages of sample design. The preliminary results from the Census were used to compute the population share of each of the 21 municipalities in the total population. In turn, these population shares were used identify the target number of households for the Core sample.

In order to create a sample listing of households for each municipality and given the limited availability of the current Census data, the ISSP team had to look beyond the Census data. The research team identified two possible sources for developing the sample frame. The first is the Voting Registration list. The second source is the Mass Voucher Privatization (MVP) listing of all people compiled in order to distribute vouchers among the population of citizens over 18 years in the summer of 2001. Both lists exclude IDPs (which includes the Roma population in its definition). At the time when sampling was done, the MVP list was newer than the voting registration list. ISSP concluded that these two lists were fairly comparable. In addition, list of the households paying the bill to the Electricity Company was available as well, but with double entries included due to the almost 60,000 of weekend houses registered in Montenegro.

The MVP list was used to randomly list Core sample households such that the sample proportion in each municipality was equal to the overall population proportions. Households were interviewed based on this random sampling list for the municipality, with no clustering design in the sample within municipality, thereby reducing survey design effects which increase standard errors. The exception for this procedure was for Roma and displaced persons. The sample of Roma and displaced households in the Core sample were listed based on additional data sources (Roma NGOs and UNHCR list of displaced persons) since they are missing from the MVP. Roma and displaced persons in the Core sample listing are from Podgorica only since the largest share of these populations live in the central part of Montenegro (68% of Roma and 36% of displaced persons).

Of the Core sample of 600 households, 93% (559) are resident households, 3% (18) are Roma and 4% (23) are displaced households.

In addition to the Core sample, the 2004 Household Survey sample included two booster samples. A booster sample of 200 households was created in 3 municipalities defined as areas with certain ecological problems: Pljevlja (70), Mojkovac (60), and Zeta Valley (70). In order to have enough vulnerable and poor families for analytical purposes, the second booster sample of another 200 households was created from the listing of Family Material Support (FMS) program.

Mode of data collection

Face-to-face [f2f]

Research instrument

The 2004 Household Survey by ISSP consists of a detailed household questionnaire. The questionnaire is divided into several modules. These modules were aimed at matching as much as possible the specificity of Montenegro in terms of data needs, as driven by pressing policy questions. Their design (e.g. questions asked, their sequence, units and time-frames used) was adapted to fit the Montenegro reality. The questions covered in the 2004 survey were revised from the previous rounds with considerable input from policy-makers and analysts concerned with living standards measurement in Montenegro.

The questionnaire was divided in eight sections based on the topics covered, and was administered to households in one visit.

Cleaning operations

Data entry (DE) program was developed to facilitate the data entry process. The data entry program was developed using Microsoft Access software. Technical support of the World Bank was provided in order to develop ISSP capacities in this area. Among the useful features of the DE program which allowed for prompt and accurate entry were: a) The data entry form page was identical with the questionnaire page, which facilities data entry. b) Range checks for most variables where appropriate. c) Skip rules. The cursor of data entry jumps to the necessary box depending on the entered value of the previous variable.

Training for the data entry operators ran from May 25 to May 30, 2004.

Abstract

The Jordan Population and Family Health Survey (JPFHS) is part of the worldwide Demographic and Health Surveys Program, which is designed to collect data on fertility, family planning, and maternal and child health.

The primary objective of the 2012 Jordan Population and Family Health Survey (JPFHS) is to provide reliable estimates of demographic parameters, such as fertility, mortality, family planning, and fertility preferences, as well as maternal and child health and nutrition, that can be used by program managers and policymakers to evaluate and improve existing programs. The JPFHS data will be useful to researchers and scholars interested in analyzing demographic trends in Jordan, as well as those conducting comparative, regional, or cross-national studies.

Geographic coverage

National coverage

Analysis unit

• Household
• Women age 15-49

Kind of data

Sample survey data [ssd]

Sampling procedure

Sample Design The 2012 JPFHS sample was designed to produce reliable estimates of major survey variables for the country as a whole, urban and rural areas, each of the 12 governorates, and for the two special domains: the Badia areas and people living in refugee camps. To facilitate comparisons with previous surveys, the sample was also designed to produce estimates for the three regions (North, Central, and South). The grouping of the governorates into regions is as follows: the North consists of Irbid, Jarash, Ajloun, and Mafraq governorates; the Central region consists of Amman, Madaba, Balqa, and Zarqa governorates; and the South region consists of Karak, Tafiela, Ma'an, and Aqaba governorates.

The 2012 JPFHS sample was selected from the 2004 Jordan Population and Housing Census sampling frame. The frame excludes the population living in remote areas (most of whom are nomads), as well as those living in collective housing units such as hotels, hospitals, work camps, prisons, and the like. For the 2004 census, the country was subdivided into convenient area units called census blocks. For the purposes of the household surveys, the census blocks were regrouped to form a general statistical unit of moderate size (30 households or more), called a "cluster", which is widely used in surveys as a primary sampling unit (PSU).

Stratification was achieved by first separating each governorate into urban and rural areas and then, within each urban and rural area, by Badia areas, refugee camps, and other. A two-stage sampling procedure was employed. In the first stage, 806 clusters were selected with probability proportional to the cluster size, that is, the number of residential households counted in the 2004 census. A household listing operation was then carried out in all of the selected clusters, and the resulting lists of households served as the sampling frame for the selection of households in the second stage. In the second stage of selection, a fixed number of 20 households was selected in each cluster with an equal probability systematic selection. A subsample of two-thirds of the selected households was identified for anthropometry measurements.

Refer to Appendix A in the final report (Jordan Population and Family Health Survey 2012) for details of sampling weights calculation.

Mode of data collection

Face-to-face [f2f]

Research instrument

The 2012 JPFHS used two questionnaires, namely the Household Questionnaire and the Woman’s Questionnaire (see Appendix D). The Household Questionnaire was used to list all usual members of the sampled households, and visitors who slept in the household the night before the interview, and to obtain information on each household member’s age, sex, educational attainment, relationship to the head of the household, and marital status. In addition, questions were included on the socioeconomic characteristics of the household, such as source of water, sanitation facilities, and the availability of durable goods. Moreover, the questionnaire included questions about child discipline. The Household Questionnaire was also used to identify women who were eligible for the individual interview (ever-married women age 15-49 years). In addition, all women age 15-49 and children under age 5 living in the subsample of households were eligible for height and weight measurement and anemia testing.

The Woman’s Questionnaire was administered to ever-married women age 15-49 and collected information on the following topics: • Respondent’s background characteristics • Birth history • Knowledge, attitudes, and practice of family planning and exposure to family planning messages • Maternal health (antenatal, delivery, and postnatal care) • Immunization and health of children under age 5 • Breastfeeding and infant feeding practices • Marriage and husband’s background characteristics • Fertility preferences • Respondent’s employment • Knowledge of AIDS and sexually transmitted infections (STIs) • Other health issues specific to women • Early childhood development • Domestic violence

In addition, information on births, pregnancies, and contraceptive use and discontinuation during the five years prior to the survey was collected using a monthly calendar.

The Household and Woman’s Questionnaires were based on the model questionnaires developed by the MEASURE DHS program. Additions and modifications to the model questionnaires were made in order to provide detailed information specific to Jordan. The questionnaires were then translated into Arabic.

Anthropometric data were collected during the 2012 JPFHS in a subsample of two-thirds of the selected households in each cluster. All women age 15-49 and children age 0-4 in these households were measured for height using Shorr height boards and for weight using electronic Seca scales. In addition, a drop of capillary blood was taken from these women and children in the field to measure their hemoglobin level using the HemoCue system. Hemoglobin testing was used to estimate the prevalence of anemia.

Cleaning operations

Fieldwork and data processing activities overlapped. Data processing began two weeks after the start of the fieldwork. After field editing of questionnaires for completeness and consistency, the questionnaires for each cluster were packaged together and sent to the central office in Amman, where they were registered and stored. Special teams were formed to carry out office editing and coding of the openended questions.

Data entry and verification started after two weeks of office data processing. The process of data entry, including 100 percent reentry, editing, and cleaning, was done by using PCs and the CSPro (Census and Survey Processing) computer package, developed specially for such surveys. The CSPro program allows data to be edited while being entered. Data processing operations were completed by early January 2013. A data processing specialist from ICF International made a trip to Jordan in February 2013 to follow up on data editing and cleaning and to work on the tabulation of results for the survey preliminary report, which was published in March 2013. The tabulations for this report were completed in April 2013.

Response rate

In all, 16,120 households were selected for the survey and, of these, 15,722 were found to be occupied households. Of these households, 15,190 (97 percent) were successfully interviewed.

In the households interviewed, 11,673 ever-married women age 15-49 were identified and interviews were completed with 11,352 women, or 97 percent of all eligible women.

Sampling error estimates

The estimates from a sample survey are affected by two types of errors: (1) nonsampling errors and (2) sampling errors. Nonsampling errors are the results of mistakes made in implementing data collection and data processing, such as failure to locate and interview the correct household, misunderstanding of the questions on the part of either the interviewer or the respondent, and data entry errors. Although numerous efforts were made during the implementation of the 2012 Jordan Population and Family Health Survey (JPFHS) 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 2012 JPFHS is only one of many samples that could have been selected from the same population, using the same design and identical size. Each of these samples would yield results that differ somewhat from the results of the actual sample selected. Sampling error is a measure of the variability 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 2012 JPFHS sample is the result of a multistage stratified design, and, consequently, it was necessary to use more complex formulae. The computer

Abstract

The Sudan Household Health Survey 2nd round (SHHS2) 2010 provides up-to-date information on the situation of children and women and measures of key indicators that allow countries to monitor progress towards the Millennium Development Goals (MDGs) and other internationally agreed upon commitments.

The raw survey data provided by the Statistical Office were then harmonized by the Economic Research Forum, to create a comparable version with the 2006 Household Health Survey in Sudan. Harmonization at this stage only included unifying variables' names, labels and some definitions. See: Sudan 2006 & 2010- Variables Mapping & Availability Matrix.pdf provided in the external resources for further information on the mapping of the original variables on the harmonized ones, in addition to more indications on the variables' availability in both survey years and relevant comments.

The sample harmonized and disseminated by the Economic research represents Northern Sudan only.

Geographic coverage

The Sudan Household Health Survey (SHHS) 2010 dataset covers the states of Northern Sudan only (Northern, River Nile, Red Sea, Kassala, Gedarif, Khartoum, Gezira, White Nile, Sinnar, Blue Nile, North Kordofan, South Kordofan, North Darfur, West Darfur and South Darfur).

Analysis unit

1- Household/family. 2- Individual/person. 3- Woman. 4- Child.

Universe

The target universe for the SHHS includes the households and members of individual households, including nomadic households camping at a location/place at the time of the survey. The population living in institutions and group quarters such as hospitals, military bases and prisons, were excluded from the sampling frame.

Kind of data

Sample survey data [ssd]

Mode of data collection

Face-to-face [f2f]

Research instrument

Five sets of questionnaires were used in the Sudan Household Health Survey. The first three questionnaires are based on the MICS3 and PAPFAM model questionnaires. Those three were subject to harmonization.

1) Household questionnaire which was used to collect information on all de jure household members and the household. It included the following modules: - Household information panel - Household listing - Education - Female Genital Mutilation - Chronic diseases & injuries (Northern States only) - Tobacco use (Northern States only) - Child disability - Water and sanitation - Household characteristics - Insecticide treated nets - Salt iodization

2) Women's questionnaire administered to all women aged 15-49 years in each household. It included the following modules: - Women's information panel - Women's background - Child mortality - Desire for last birth - Maternal and newborn health - Illness symptoms - Contraception - Unmet need - Marriage and union - HIV/AIDS
- Birth history - Female Genital Mutilation - Attitudes towards domestic violence - Sexual behavior STIs (Southern States only)

3) Under-five questionnaire administered to mothers. In case the mother was not listed in the household list/roster, a primary caretaker for the child was identified and interviewed. The Questionnaire for Children under Five included the following modules: - Under-five children information panel - Birth registration - Vitamin A supplementation - Breastfeeding - Care of illness - Immunization - Malaria - Anthropometry

4) Men's questionnaire administered to all men aged 15-49 years in each household. It included the following modules: - Men information panel - Men's background Marriage - Circumcision - Condom - Sexual behavior STIs - HIV/AIDS

5) Food Security Questionnaire which included the following modules: - Food security information panel - Income sources - Expenditures - Food consumption and dietary diversity

In addition to the administration of questionnaires, fieldwork teams tested the salt used for cooking in the households for iodine content, and measured the weights and heights of children under five years of age.

Cleaning operations

---> Harmonized Data:

• The SPSS package is used to harmonize the SHHS 2010 with SHHS 2006.
• The harmonization process starts with raw data files received from the Statistical Office.
• A program is generated for each dataset to create harmonized variables.
• Data is saved on the household, individual, women, as well as the children level, in SPSS and then converted to STATA, to be disseminated.

Response rate

Of the 15,000 households selected for the sample, 14,778 were successfully interviewed, yielding a response rate of 99 percent. Of the 18,614 women (age 15-49 years) identified in the selected households, 17,174 were successfully interviewed, yielding a response rate of 91.4 percent. Of the 13,587 children under age five listed in the households, questionnaires were completed for 13,282 children, which correspond to a response rate of 96.8 percent.

Abstract

The main purpose of the KPMS surveys is to provide data for the study of multiple aspects of household welfare and behavior, analysis of poverty, and understanding the effect of government policies on households.

Geographic coverage

National coverage

Analysis unit

• Households
• Individuals
• Community

Kind of data

Sample survey data [ssd]

Sampling procedure

In order to expedite the survey process, NATSTATCOM used much of the same sample design and survey instruments as those used for the 1993 Baseline Survey. However, the Fall 1996-1998 KPMS surveys used a new sampling frame based on the Kyrgyz Household Registration System. This system was taken from the Census Posts intended for use by the first National Census of the Kyrgyz Republic. Using this system, NATSTATCOM updated the central household registration files effective January 1, 1996, and the information that was used for the sampling frame was as up to date as possible. The procedures followed in the stratification and identification of Primary Sampling Units (PSUs) were similar for all rounds of the KPMS as discussed below.

Formation of Strata

Initially the country was divided into seven (7) strata defined by oblasts (Oblasts are administrative divisions of the country which in turn are sub-divided in to Rayons) and by residence location (i.e. urban vs. rural) within oblasts. The rural portion of Bishkek oblast was combined with the rural portion of neighboring Chui oblast for stratification purposes as Bishkek has practically no rural population.

Selection of PSUs and Households

For the 1998 KPMS, a total of 255 PSUs (of which 178 were urban and 77 rural) were identified. The estimated total population was around 1.1 million of which about 421,000 was classified as urban. A minimum of 384 households per oblast was targeted in order to get a representative data at the oblast level11. This translated in to a targeted sample size of 2,688 households for the whole of the Kyrgyz Republic (i.e. 384*7 oblasts=2,688). These households were divided into urban (887 households) and rural (1,801 households). The overall projected response rate for the 1998 KPMS was also set at somewhat above 0.90. With an overall sampling rate of 1/336, this resulted in to a sample close to a target size of 3,000 households for the whole survey.

Once the strata and PSUs were formed and identified, selection of sample PSUs and households was then carried out in the following order:

1) Selection of large and small towns12 [Note: For the 1998 KPMS, large towns were defined as those with a population size of 41,125 or larger. Small towns are those with population less than 41,125. This number, according to a NATSTATCOM document was calculated as follows: n=4.7*350*25. This calculation was based on an estimated household size of 4.7, an estimated interval rate of 350 and an average work load per interviewer of 25 households. No further information is available regarding the bases of such an assumption. At the moment, we do not have information about the cut off number that separates large towns from small ones for the other two KPMS.]

2) Selection of Census Posts in urban areas

3) Selection of Ayil Kenshes (village authorities) and population points in rural areas, and

4) Selection of households from selected Census Posts and Ayil Kenshes. In the rural stratum of each oblast, villages were used as the listing units and within these listing units, equal probability sampling methods were used to select the ultimate sampling units (households). In urban areas, the centralized computer listings from various sources of household registration were used for the selection of households. These lists are categorized into four: Type 1 - Private house resident households listed by BTIs Type 2 - Public house residents listed with other organizations with dormitories only Type 3 - Public and private households listed by JSKs Type 4 - Public and private households listed by all other organizations. In some cases, private households were included in the last three public categories (Types 2, 3 and 4). However, only public households were selected from these types since it was believed that any private households listed in these category types were also included in the Type 1 category. The counts for Type 2, 3, and 4 lists were then adjusted based on the oblast estimates of all urban households.13 Prior to actual household sample selection, lists from types 2 to 4 were updated and adjusted to remove private households, so that any potential double eligibility was eliminated. Urban strata were then formed within each oblast based on type of household listing. In most cases, types had to be combined to form strata of a reasonable size.

Within the limits of rounding and requiring at least one sampling unit per stratum, the allocation of sampling units to urban strata was proportional to the number of households projected for that stratum after allowing for removal of duplicates (private households appearing on a BTI and other lists).

As for rural households, selection of urban households was done using systematic random sampling within each stratum except that more subdividing of urban lists was required before selecting the final list sample that defines each sampling unit.

Even though the list sources were identified and sampled using data as of January 1, 1996 (and using projections of unduplicated counts in some cases), the final listings were updated in the field just prior to the survey period. Therefore, the sample households in selected areas were drawn from the most current available listings.

Mode of data collection

Face-to-face [f2f]

Research instrument

The KPMS surveys were carried out using a household questionnaire and a community (population point) questionnaire. The household questionnaires were used to collect demographic information on the composition of the household, housing, household consumption including home production, as well as economic activities in agricultural and non-agricultural sectors. For each household member, individual level data on health, education, migration and labor was collected using the household questionnaires. Community questionnaires were used to collect price data and the presence of social services and infrastructure in the community (population point) where the sampled household is located.

The household questionnaire was extensive and required several hours of intense interviewing to gather all that was needed from each household and its embers. The household questionnaire was split into two parts. The first part was used to collect data through a face to face interview on household roster, dwelling, education, health, migration, etc. At the end of the first part, members who shop for food for the whole household and those who know most about income, expenditure and savings of other household members were identified and designated as respondents for the next part (second round). The second round of interview was administered two weeks after the first half and collected data on crops, food and animal products produced by the household, food expenditure and home produced food consumption.

Some sections of the household questionnaire such as those that deal with dwelling and expenditure information were administered to the person most knowledgeable of the family's overall expenditures, income and other finances as well as about the family's business activities and employment. In other sections, each adult in each sample household was interviewed individually. The information gathered from each household included extensive data on education, health, employment, migration, reproduction and reproductive health (for women aged 15 to 49), land use, expenditure, revenue and other financial matters, as well as anthropometric measurements (for children 5 years and younger). Information about children under 14 years of age was collected by asking the relevant questions to the adult household member who is primarily responsible for each child's care.

The community (Population Point) questionnaires were administered to each sample cluster. They were used to collect data on prices of goods and services, distance to schools, shopping and medical facilities, types of housing, commercial and private land use and availability of infrastructure.

HOUSEHOLD QUESTIONNAIRE

The KPMS household questionnaires generally contain 15 major sections, and each of these sections covers a separate aspect of household activity. In some cases, the section has sub-sections. These household questionnaires were designed to better assess the changing environment brought about by the advent of a market economy and to enable a more in depth analysis of topics such as housing, health, and education. The various sections of the KPMS household questionnaire are described below.The household questionnaires administered in the KPMS surveys are more or less similar with minor modifications and additions in the successive rounds of the KPMS.

POPULATION POINT QUESTIONNAIRE

The community (population point) questionnaire was used to collect information and data that are relevant to the community/population point where the household is located. The questionnaire was designed to be administered in the geographical area of each sample cluster. It was used to collect data regarding prices of goods and services in the local area and data on community infrastructure. Respondents to these questionnaires are those believed to be well informed members of the community that the interviewers identified by going to the rayon, city, oblast administration or other governmental agency located in the population point6. The

Abstract

The District Level Household and facility Survey (DLHS) is a household survey at the district level and in DLHS-3, the survey covered 611 districts in India. The total number of households representing a district varies from 1000 to 1500 households. The DLHS-3 is designed to provide information on family planning, maternal and child health, reproductive health of ever married women and adolescent girls, utilization of maternal and child healthcare services at the district level for India. In addition, DLHS-3 also provides information on new-born care, post-natal care within 48 hours, role of ASHA in enhancing the reproductive and child health care and coverage of Janani Suraksha Yojana (JSY). An important component of DLHS-3 is the integration of Facility Survey of health institution (Sub centre, Primary Health Centre, Community Health Centre and District Hospital) accessible to the sampled villages. The focus of DLHS-3 is to provide health care and utilization indicators at the district level for the enhancement of the activities under National Rural Health Mission (NRHM).

Documentation

You can access the data at the International Institute for Population Sciences.\

Methodology

Survey design and sample size

The survey as well as the preparation of reports was carried out in two separate phases. Approximately 50 percent of the districts from each state and union territory were covered in each phase. The survey for phase I was carried out from May to November, 1998 and for phase II it was carried out from to October, 1999. In the first phase of the RHS, 50 percent of the total districts in India as existing in 1995 were selected for the survey. Systematic random sampling was adopted for the selection of the districts for phase1. For selection purposes, districts within the state were arranged alphabetically, and starting at random from either first or second district, alternative districts were selected. The second phase covered all the remaining districts of the country.

In each of the selected districts, 50 Primary Sampling Units (PSUs), i.e. either villages or urban wards were selected adopting probability proportional to size (PPS) sampling. The village/ ward level population as per the 1991 census was used for this purpose. The sample size for DLHS-DLHS was fixed at 1000 households with 20 households from each PSU. In order to take care of non-response due to various reasons, 10 percent over sampling was done. In other words, 22 households from each PSU were selected. The selection of the households in a PSU was done after listing of all the households in the PSUs. For the selection of households circular systematic random sampling was adopted. In the first phase the work of drawing sample of PSUs was entrusted to the Institute of Research in Medical Statistics (IRMS), New Delhi and in the second phase IIPS did the sampling of PSUs in all the districts.

House listing

House listing involved the preparation of a location map of each PSU and layout sketch of the structures and recording details of the households in the village/census enumeration block. An independent team comprising of one lister and one mapper carried out the houselisting exercise.

Complete listing was carried out in villages with population up to 1500. In the case of larger villages, with more than 1500 population, the village was divided into two or more segments of equal size, one segment was selected at random for listing and in the selected segment complete listing was carried out. In the urban wards with population exceeding 1500 one census enumeration block was selected at random.

** ****Questionnaires**

Two types of questionnaires were used in the survey: the household questionnaire and the woman’s questionnaire. IIPS in consultation with MoHFW and World Bank decided the overall contents of the questionnaires. These questionnaires were discussed and finalized in training-cum-workshop organized at IIPS during the third week of May 1998. Representatives of Regional Agencies, MoHFW, IIPS and World Bank participated in this workshop. IIPS carried out pre-testing of these questionnaires in Maharashtra. Questionnaires were also pre-tested in different languages by regional agencies. Though the overall contents of questionnaire for both the phases were the same, there were some changes in the second phase. The changes were mainly regarding ordering and phrasing of the questions. The household questionnaire was used to list all the eligible women in the selected households (de jure) and to collect information on marriages and births among the usual residents. In the first phase the reference period for the recording of marriages and births was from 1st January 1995 to survey date and in the second phase it was from 1st January 1996 to survey date. For all the marriages reported in the survey, age at marriage of boy/ girl of that household who got marri

Abstract

The Project for Statistics on Living standards and Development was a countrywide World Bank Living Standards Measurement Survey. It covered approximately 9000 households, drawn from a representative sample of South African households. The fieldwork was undertaken during the nine months leading up to the country's first democratic elections at the end of April 1994. The purpose of the survey was to collect statistical information about the conditions under which South Africans live in order to provide policymakers with the data necessary for planning strategies. This data would aid the implementation of goals such as those outlined in the Government of National Unity's Reconstruction and Development Programme.

Geographic coverage

National

Analysis unit

Households

Kind of data

Sample survey data [ssd]

Sampling procedure

(a) SAMPLE SIZE

Sample size is 9,000 households. The sample design adopted for the study was a two-stage self-weighting design in which the first stage units were Census Enumerator Subdistricts (ESDs, or their equivalent) and the second stage were households. The advantage of using such a design is that it provides a representative sample that need not be based on accurate census population distribution.in the case of South Africa, the sample will automatically include many poor people, without the need to go beyond this and oversample the poor. Proportionate sampling as in such a self-weighting sample design offers the simplest possible data files for further analysis, as weights do not have to be added. However, in the end this advantage could not be retained, and weights had to be added. The sampling frame was drawn up on the basis of small, clearly demarcated area units, each with a population estimate. The nature of the self-weighting procedure adopted ensured that this population estimate was not important for determining the final sample, however. For most of the country, census ESDs were used. Where some ESDs comprised relatively large populations as for instance in some black townships such as Soweto, aerial photographs were used to divide the areas into blocks of approximately equal population size. In other instances, particularly in some of the former homelands, the area units were not ESDs but villages or village groups. In the sample design chosen, the area stage units (generally ESDs) were selected with probability proportional to size, based on the census population.

(b) SAMPLE DESIGN

Systematic sampling was used throughout that is, sampling at fixed interval in a list of ESDs, starting at a randomly selected starting point. Given that sampling was self-weighting, the impact of stratification was expected to be modest. The main objective was to ensure that the racial and geographic breakdown approximated the national population distribution. This was done by listing the area stage units (ESDs) by statistical region and then within the statistical region by urban or rural. Within these sub-statistical regions, the ESDs were then listed in order of percentage African. The sampling interval for the selection of the ESDs was obtained by dividing the 1991 census population of 38,120,853 by the 300 clusters to be selected. This yielded 105,800. Starting at a randomly selected point, every 105,800th person down the cluster list was selected. This ensured both geographic and racial diversity (ESDs were ordered by statistical sub-region and proportion of the population African). In three or four instances, the ESD chosen was judged inaccessible and replaced with a similar one. In the second sampling stage the unit of analysis was the household. In each selected ESD a listing or enumeration of households was carried out by means of a field operation. From the households listed in an ESD a sample of households was selected by systematic sampling. Even though the ultimate enumeration unit was the household, in most cases "stands" were used as enumeration units. However, when a stand was chosen as the enumeration unit all households on that stand had to be interviewed. Census population data, however, was available only for 1991. An assumption on population growth was thus made to obtain an approximation of the population size for 1993, the year of the survey. The sampling interval at the level of the household was determined in the following way: Based on the decision to have a take of 125 individuals on average per cluster (i.e. assuming 5 members per household to give an average cluster size of 25 households), the interval of households to be selected was determined as the census population divided by 118.1, i.e. allowing for population growth since the census. It was subsequently discovered that population growth was slightly over-estimated, but this had little effect on the findings of the survey. Individuals in hospitals, old age homes, hotels and hostels of educational institutions were not included in the sample. Migrant labour hostels were included. In addition to those that turned up in the selected ESDs, a sample of three hostels was chosen from a national list provided by the Human Sciences Research Council and within each of these hostels a representative sample was drawn on a similar basis as described above for the households in ESDs.

Mode of data collection

Face-to-face [f2f]

Cleaning operations

All the questionnaires were checked when received. Where information was incomplete or appeared contradictory, the questionnaire was sent back to the relevant survey organization. As soon as the data was available, it was captured using local development platform ADE. This was completed in February 1994. Following this, a series of exploratory programs were written to highlight inconsistencies and outlier. For example, all person level files were linked together to ensure that the same person code reported in different sections of the questionnaire corresponded to the same person. The error reports from these programs were compared to the questionnaires and the necessary alterations made. This was a lengthy process, as several files were checked more than once, and completed at the beginning of August 1994. In some cases, questionnaires would contain missing values, or comments that the respondent did not know, or refused to answer a question. These responses are coded in the data files with the following values:

VALUE MEANING -1 : The data was not available on the questionnaire or form -2 : The field is not applicable -3 : Respondent refused to answer -4 : Respondent did not know answer to question

Data appraisal

The data collected in clusters 217 and 218 should be viewed as highly unreliable and therefore removed from the data set. The data currently available on the web site has been revised to remove the data from these clusters. Researchers who have downloaded the data in the past should revise their data sets. For information on the data in those clusters, contact SALDRU http://www.saldru.uct.ac.za/.

Abstract

The National Family Health Survey (NFHS) was carried out as the principal activity of a collaborative project to strengthen the research capabilities of the Population Reasearch Centres (PRCs) in India, initiated by the Ministry of Health and Family Welfare (MOHFW), Government of India, and coordinated by the International Institute for Population Sciences (IIPS), Bombay. Interviews were conducted with a nationally representative sample of 89,777 ever-married women in the age group 13-49, from 24 states and the National Capital Territoty of Delhi. The main objective of the survey was to collect reliable and up-to-date information on fertility, family planning, mortality, and maternal and child health. Data collection was carried out in three phases from April 1992 to September 1993. THe NFHS is one of the most complete surveys of its kind ever conducted in India.

The households covered in the survey included 500,492 residents. The young age structure of the population highlights the momentum of the future population growth of the country; 38 percent of household residents are under age 15, with their reproductive years still in the future. Persons age 60 or older constitute 8 percent of the population. The population sex ratio of the de jure residents is 944 females per 1,000 males, which is slightly higher than sex ratio of 927 observed in the 1991 Census.

The primary objective of the NFHS is to provide national-level and state-level data on fertility, nuptiality, family size preferences, knowledge and practice of family planning, the potentiel demand for contraception, the level of unwanted fertility, utilization of antenatal services, breastfeeding and food supplemation practises, child nutrition and health, immunizations, and infant and child mortality. The NFHS is also designed to explore the demographic and socioeconomic determinants of fertility, family planning, and maternal and child health. This information is intended to assist policymakers, adminitrators and researchers in assessing and evaluating population and family welfare programmes and strategies. The NFHS used uniform questionnaires and uniform methods of sampling, data collection and analysis with the primary objective of providing a source of demographic and health data for interstate comparisons. The data collected in the NFHS are also comparable with those of the Demographic and Health Surveys (DHS) conducted in many other countries.

Geographic coverage

National

Analysis unit

• Household
• Data collected for women 13-49, indicators calculated for women 15-49

Universe

The population covered by the 1992-93 DHS is defined as the universe of all women age 13-49 who were either permanent residents of the households in the NDHS sample or visitors present in the households on the night before the survey were eligible to be interviewed.

Kind of data

Sample survey data

Sampling procedure

SAMPLE DESIGN

The sample design for the NFHS was discussed during a Sample Design Workshop held in Madurai in Octber, 1991. The workshop was attended by representative from the PRCs; the COs; the Office of the Registrar General, India; IIPS and the East-West Center/Macro International. A uniform sample design was adopted in all the NFHS states. The Sample design adopted in each state is a systematic, stratified sample of households, with two stages in rural areas and three stages in urban areas.

SAMPLE SIZE AND ALLOCATION

The sample size for each state was specified in terms of a target number of completed interviews with eligible women. The target sample size was set considering the size of the state, the time and ressources available for the survey and the need for separate estimates for urban and rural areas of the stat. The initial target sample size was 3,000 completed interviews with eligible women for states having a population of 25 million or less in 1991; 4,000 completed interviews for large states with more than 25 million population; 8,000 for Uttar Pradesh, the largest state; and 1,000 each for the six small northeastern states. In States with a substantial number of backward districts, the initial target samples were increased so as to allow separate estimates to be made for groups of backward districts.

The urban and rural samples within states were drawn separetly and , to the extent possible, sample allocation was proportional to the size of the urban-rural populations (to facilitate the selection of a self-weighting sample for each state). In states where the urban population was not sufficiently large to provide a sample of at least 1,000 completed interviews with eligible women, the urban areas were appropriately oversampled (except in the six small northeastern states).

THE RURAL SAMPLE: THE FRAME, STRATIFICATION AND SELECTION

A two-stage stratified sampling was adopted for the rural areas: selection of villages followed by selection of households. Because the 1991 Census data were not available at the time of sample selection in most states, the 1981 Census list of villages served as the sampling frame in all the states with the exception of Assam, Delhi and Punjab. In these three states the 1991 Census data were used as the sampling frame.

Villages were stratified prior to selection on the basis of a number of variables. The firts level of stratification in all the states was geographic, with districts subdivided into regions according to their geophysical characteristics. Within each of these regions, villages were further stratified using some of the following variables : village size, distance from the nearest town, proportion of nonagricultural workers, proportion of the population belonging to scheduled castes/scheduled tribes, and female literacy. However, not all variables were used in every state. Each state was examined individually and two or three variables were selected for stratification, with the aim of creating not more than 12 strata for small states and not more than 15 strata for large states. Females literacy was often used for implicit stratification (i.e., the villages were ordered prior to selection according to the proportion of females who were literate). Primary sampling Units (PSUs) were selected systematically, with probaility proportional to size (PPS). In some cases, adjacent villages with small population sizes were combined into a single PSU for the purpose of sample selection. On average, 30 households were selected for interviewing in each selected PSU.

In every state, all the households in the selected PSUs were listed about two weeks prior to the survey. This listing provided the necessary frame for selecting households at the second sampling stage. The household listing operation consisted of preparing up-to-date notional and layout sketch maps of each selected PSU, assigning numbers to structures, recording addresses (or locations) of these structures, identifying the residential structures, and listing the names of the heads of all the households in the residentiak structures in the selected PSU. Each household listing team consisted of a lister and a mapper. The listing operation was supervised by the senior field staff of the concerned CO and the PRC in each state. Special efforts were made not to miss any household in the selected PSU during the listing operation. In PSUs with fewer than 500 households, a complete household listing was done. In PSUs with 500 or more households, segmentation of the PSU was done on the basis of existing wards in the PSU, and two segments were selected using either systematic sampling or PPS sampling. The household listing in such PSUs was carried out in the selected segments. The households to be interviewed were selected from provided with the original household listing, layout sketch map and the household sample selected for each PSU. All the selected households were approached during the data collection, and no substitution of a household was allowed under any circumstances.

THE RURAL URBAN SAMPLE: THE FRAME, STRATIFICATION AND SELECTION

A three-stage sample design was adopted for the urban areas in each state: selection of cities/towns, followed by urban blocks, and finally households. Cities and towns were selected using the 1991 population figures while urban blocks were selected using the 1991 list of census enumeration blocks in all the states with the exception of the firts phase states. For the first phase states, the list of urban blocks provided by the National Sample Survey Organization (NSSSO) served as the sampling frame.

All cities and towns were subdivided into three strata: (1) self-selecting cities (i.e., cities with a population large enough to be selected with certainty), (2) towns that are district headquaters, and (3) other towns. Within each stratum, the cities/towns were arranged according to the same kind of geographic stratification used in the rural areas. In self-selecting cities, the sample was selected according to a two-stage sample design: selection of the required number of urban blocks, followed by selection of households in each of selected blocks. For district headquarters and other towns, a three stage sample design was used: selection of towns with PPS, followed by selection of two census blocks per selected town, followed by selection of households from each selected block. As in rural areas, a household listing was carried out in the selected blocks, and an average of 20 households per block was selected systematically.

Mode of data collection

Face-to-face

Research instrument

Three types of questionnaires were used in the NFHS: the Household Questionnaire, the Women's Questionnaire, and the Village Questionnaire. The overall content

Abstract

The main purpose of the Household Income Expenditure Survey (HIES) 2016 was to offer high quality and nationwide representative household data that provided information on incomes and expenditure in order to update the Consumer Price Index (CPI), improve National Accounts statistics, provide agricultural data and measure poverty as well as other socio-economic indicators. These statistics were urgently required for evidence-based policy making and monitoring of implementation results supported by the Poverty Reduction Strategy (I & II), the AfT and the Liberia National Vision 2030. The survey was implemented by the Liberia Institute of Statistics and Geo-Information Services (LISGIS) over a 12-month period, starting from January 2016 and was completed in January 2017. LISGIS completed a total of 8,350 interviews, thus providing sufficient observations to make the data statistically significant at the county level. The data captured the effects of seasonality, making it the first of its kind in Liberia. Support for the survey was offered by the Government of Liberia, the World Bank, the European Union, the Swedish International Development Corporation Agency, the United States Agency for International Development and the African Development Bank. The objectives of the 2016 HIES were:

1. Update the Consumer Price Index (CPI): To obtain a new set of weights for the basket of goods and services that upgrade the Monrovia Consumer Price Index (MCPI) and the National Consumer Price Index (NCPI) and to revise the CPI basket of goods and services in Liberia to reflect the current consumption pattern of residence.
2. Improve National Accounts Statistics: To get information on annual household expenditure patterns in order to update the household component of the National Accounts.
3. Measure Poverty: To prepare robust poverty indices that enable the understanding of poverty dynamics across the country and of the factors influencing them.
4. Improve Agricultural Statistics: To obtain nationally representative and policy relevant agricultural statistics in order to undertake in-depth analysis of agricultural households.
5. Capture Socio-economic Impact of Ebola Virus Disease (EVD): To obtain a post-EVD dataset which allows for an in-depth analysis of the socioeconomic impact of EVD on households.
6. Benchmark Agenda for Transformation Indicators: To provide an update on selected socioeconomic indicators used to benchmark the government’s policies embedded within the Agenda for Transformation.
7. Develop Statistical Capacity: Emphasize capacity building and development of sustainable statistical systems through every stage of the project to produce accurate and timely information about Liberia.

Geographic coverage

National

Analysis unit

• Households
• Individuals

Kind of data

Sample survey data [ssd]

Sampling procedure

The original sample design for the HIES exploited two-phased clustered sampling methods, encompassing a nationally representative sample of households in every quarter and was obtained using the 2008 National Housing and Population Census sampling frame. The procedures used for each sampling stage are as follows:
i. First stage
Selection of sample EAs. The sample EAs for the 2016 HIES were selected within each stratum systematically with Probability Proportional to Size from the ordered list of EAs in the sampling frame. They are selected separately for each county by urban/rural stratum. The measure of size for each EA was based on the number of households from the sampling frame of EAs based on the 2008 Liberia Census. Within each stratum the EAs were ordered geographically by district, clan and EA codes. This provided implicit geographic stratification of the sampling frame.

ii. Second stage
Selection of sample households within a sample EA. A random systematic sample of 10 households were selected from the listing for each sample EA. Using this type of table, the supervisor only has to look up the total number of households listed, and a specific systematic sample of households is identified in the corresponding row of the table.

Mode of data collection

Face-to-face [f2f]

Research instrument

There were three questionnaires administered for this survey: 1. Household and Individual Questionnaire 2. Market Price Questionnaire 3. Agricultural Recall Questionnaire

Cleaning operations

The data entry clerk for each team, using data entry software called CSPro, entered data for each household in the field. For each household, an error report was generated on-site, which identified key problems with the data collected (outliers, incorrect entries, inconsistencies with skip patterns, basic filters for age and gender specific questions etc.). The Supervisor along with the Data Entry Clerk and the Enumerator that collected the data reviewed these errors. Callbacks were made to households if necessary to verify information and rectify the errors while in that EA.

Once the data were collected in each EA, they were sent to LISGIS headquarters for further processing along with EA reports for each area visited. The HIES Technical committee converted the data into STATA and ran several consistency checks to manage overall data quality and prepared reports to identify key problems with the data set and called the field teams to update them about the same. Monthly reports were prepared by summarizing observations from data received from the field alongside statistics on data collection status to share with the field teams and LISGIS Management.

Abstract

The GHS is a cross-sectional survey of 22,000 households throughout the country. The panel component (GHS-Panel) is now being applied to 5,000 households of the GHS and covers multiple agricultural activities. The focus of this panel component is to improve data from the agriculture sector and link this to other facets of household behaviour and characteristics. The GHS-Panel drew heavily on the HNLSS and the NASS to create a new survey instrument and method to shed light on the role of agriculture in households' economic wellbeing. The NBS implemented the first stage (Post Planting) of the first wave of the GHS-Panel in 2010. This panel is a subset of the full GHS (or GHS-Cross Section) that will be finished in 2011.) It is envisaged that the GHS-Panel will be carried out every two years while the GHS-Cross Section will be carried out annually.

The specific outputs and outcomes of the revised GHS with panel component are:

• Development of an innovative model for collecting agricultural data in conjunction with household data;
• Development of a model of inter-institutional collaboration between NBS and the FMA&RD and NFRA, inter alia, to ensure the relevance and use of the new GHS;
• Building the capacity to generate a sustainable system for the production of accurate and timely information on agricultural households in Nigeria.
• Comprehensive analysis of poverty indictors and socio-economic characteristics.

Geographic coverage

National, the survey covered all the 36 states and Federal Capital Territory (FCT).

Analysis unit

Households, Individuals, Agricutural plots

Kind of data

Sample survey data [ssd]

Sampling procedure

Sample Design The GHS-Panel (Post Planting 2010), like all household surveys in the country, is based on the Master Sample Frame, This Frame is based on the 2006 Housing and Population Census conducted by the National Population Commission (NpopC). The census includes approximately 662,000 enumeration areas (EAs) throughout the country. From the census, the Master Frame was constructed at the local government area (LGA). In 668 LGAs, 30 EAs were scientifically selected. The remaining six LGAs are found in FCT, Abuja. Forty EAs were scientifically selected in each of these 6 LGAs. This gives a total of 23,280 EAs selected nationally. This is the Master Frame.

From the Master Frame a master sample frame, called the National Integrated Survey of Households 2007/2012 Master Sample Frame (NISH-MSF) was developed. The NISHMSF was constructed by pooling the LGAs in the Master Frame by state. Thereafter, a systematic sample of 200 EAs was selected with equal probability across all LGAs within the state. Furthermore, the NISH EAs in each state were divided into 20 replicates of 10 EAs each. However, the sample EAs for most national household surveys such as the GHS are based on a sub-sample of the NISH-MSF, selected as a combination of replicates from NISH-MSF frame. For the GHS-Panel, the sample is a subset of the EAs selected for the GHS.

Sample Framework The sample frame includes all thirty-six (36) states of the federation and Federal Capital Territory (FCT), Abuja. Both urban and rural areas were covered and in all, 500 clusters/EAs were canvassed and 5,000 households were interviewed. These samples were proportionally selected in the states such that different states have different samples.

Sample Selection The GHS Panel Survey used a two stage stratified sample selection process.

First Stage The Primary Sampling Units (PSUs) were the Enumeration Areas (EAs). These were selected based on probability proportional to size (PPS) of the total EAs in each state and FCT, Abuja and the total households listed in those EAs.

Second Stage The second stage involved the systematic selection of ten (10) households per EA. This involved obtaining the total number of households listed in a particular EA, and then calculating a Sampling Interval (S.I) by dividing the total households listed by ten (10). The next step is to generate a random start 'r' from the table of random numbers which stands as the 1st selection. The second selection is obtained by adding the sampling interval to the random start. For each of the next selections, the sampling interval was added to the value of the previous selection until the 10th selection is obtained. Determination of the sample size at the household level was based on the experience gained from previous rounds of the GHS, in which 10 HHs per EA are usually selected and give robust estimates.

Mode of data collection

Face-to-face [f2f]

Cleaning operations

This survey used concurrent data entry approach. In this method, the fieldwork and data entry were handled by each team assigned to the state. Each team consisted of a field supervisor, 2-4 interviewers and a data entry operator. Immediately after the data were collected in the field by the interviewers and supervisors (the supervisors administered the community questionnaires and collected data on prices), the questionnaires were handed over to the supervisor to be checked and documented. At the end of each day of fieldwork, the questionnaires were then passed to the data entry operator for entry. After the questionnaires were entered, the data entry operator generated an error report which reported issues including out of range values and inconsistencies in the data. The supervisor then checked the report, determined what should be corrected, and decided if the field team needed to revisit the household to obtain additional information. The benefits of this method are that it allows one to: - Capture errors that might have been overlooked by a visual inspection only, - Identify errors early during the field work so that if any correction required a revisit to the household, it could be done while the team was still in the EA

The CSPro software was used to design the specialized data entry program that was used for the data entry of the questionnaires.

Abstract

In recognition of the critical role the agricultural sector plays in national development, Sierra Leone joined the 50x2030 Initiative in early 2023. This partnership focuses on establishing a sustainable annual agricultural survey program. The program's primary aim is to generate high-quality, timely, and relevant agricultural data that directly addresses the country's needs. The implementation of the 50x2030 activities in Sierra Leone relies on a variety of statistical undertakings, one of which is the Sierra Leone Listing Survey (SLLIST) conducted in 2023. This survey plays a vital role in monitoring and achieving the goals of the 50x2030 initiative. It serves as a foundational element for subsequent surveys, establishing a comprehensive sampling frame that will be utilized in future data collection efforts. By meticulously gathering data on various aspects, SLLIST lays the groundwork for further analysis and ensures the accuracy of subsequent surveys.

The other specific objectives for the design of the Listing Survey were to: - Accurately enumerate and document all dwelling units/structures and households within the selected enumeration areas (EAs) in preparation for agricultural holdings/households' selection - Record detailed description of every structure and identify the heads of agricultural holdings/households - Collect information in each of the 520 selected EAs, ensuring effective supervision and monitoring of the agricultural holdings/households to be selected for data collection.

Geographic coverage

National coverage, with the exception of the Western Urban district

Analysis unit

Households

Kind of data

Sample survey data [ssd]

Sampling procedure

The survey employed a stratified random sampling technique to ensure a representative sample of agricultural households across all five regions and fifteen districts of Sierra Leone with the exception of the Western Urban district. From 514 Enumeration Areas, a total of 42990 agricultural households were interviewed.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The questionnaire was administered in each household, preferably to the head of household. It includes questions on household demographic characteristics and agricultural activities practiced. The questionnaire is provided as external resource.

Cleaning operations

The listing questionnaire was implemented on CSPRO as CAPI tool. During data collection, some validation controls were integrated into the app to minimize mistakes when typing households’ answers. After data collection, a processing program designed under SPSS software permitted to clean both cases and variables. Duplicated cases were deleted and then the sampling weights adjusted to take the two non-covered EAs into account. Missing, illegal, unlike and incoherent values were detected and then locally imputed objectively in respecting filters. Finally, the necessary tabulation variables were created and then tables were produced according to the tabulation plan designed earlier.

Response rate

The response rate was 99.9%.

Sampling error estimates

To appreciate the data quality, some tables were supported by sampling errors estimates. Especially, coefficients of variations and standard errors were estimated for a set of indicators in open data publishing purposes.

Geographic coverage

The NHFS is focused on forest proximate households. Therefore, the sample is limited to enumeration areas which fall within 2.5km of the nearest forest, as defined using Metria and Geoville (2019) land cover data. The final sample includes enumeration areas from all 15 of Liberia's counties, but excludes urban areas of Montserrado.

Analysis unit

Household; Community

Universe

All EAs within 2.5 kilometers of forests except for the EAs from the urban part of the Montserrado county.

Kind of data

Sample survey data [ssd]

Sampling procedure

Given the focus of the NHFS on the population living in close proximity to forests4, a first step was to clearly define forest for the purposes of the survey. Building on the national definition of forest used in Liberia, and modifying it in order to minimize the impact of small urban forests and facilitate survey operations, the NHFS employed the following definition:

Forest = area with at least 30 percent tree canopy cover, with trees higher than 5 meters and at least 50 hectares in size

The forest cover was determined using high-resolution forest cover data produced in 2019 based on satellite information on forest cover in Liberia for 2015.6 All EAs within 2.5 kilometers of forests identified with this definition were deemed eligible for inclusion in the NHFS.7 EAs from the Montserrado county (part of Greater Monrovia) were excluded from the sample universe due to the high rate of urbanization. However, rural parts of Montserrado county were included in the sample universe.

Based on the forest definition defined above, the distance from each EA in the country (except urban Montserrado) to the nearest forest was computed. That distance was subsequently used to assign each EA to one of the following strata: S1 (less than 2km from forest); S2 (two to 7 km from forest); S3 (7 to 15 km from forest).

Following strata classification, a total of 250 EAs were selected through a Probability Proportional to Size (PPS) sampling approach within each stratum, with the following purposeful allocation across strata: 90 EAs in S1; 90 EAs in S2; 70 EAs in S3.8 The measure of size for each EA was based on the total number of households listed in the 2008 PHC.

Following the selection of the 250 sample EAs, a listing of households was conducted in each sample EA to provide the sampling frame for the second stage selection of households. Random sampling was used to select 12 households from the household listing for each sample EA.

The original sample design provided a total household sample size of 3,000 (250 EAs with 12 households sampled per EA), data from 14 households are missing or unusable, representing 0.05 percent of the sample and resulting in a final sample of 2,986 households. Similarly, data from 5 of the community questionnaires were missing or unusable, resulting in a total sample of 245 community questionnaires. The final sample of 2,986 households is distributed across counties.

Upon post-data collection analysis, it was discovered that the initial variable that was used to stratify EAs by distance to forest was incorrectly computed. Despite thorough attempts to understand the nature and source of the error, it was determined that a mechanical error must have occurred during the process of the distance calculations. This error rendered the stratification incorrect. Therefore, the stratification by distance to forest has been abandoned and the sample weighted to reflect only geographic clusters, not distance to forest. This was determined to be the most appropriate way forward following consultation with sampling experts.

The resulting sample, therefore, is weighted to reflect all EAs in Liberia (with the exception of urban Montserrado) that fall within 2.5 km of the nearest forest, which was the upper bound of the distances for the selected EAs.

Sampling deviation

Please refer to the Basic Information Document found in the External Resources section.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The NHFS survey consisted of: 1. A HH questionnaire, administered to 12 selected HHs in each enumeration area, and 2. A community questionnaire, administered to a group of members from the EA.

Each questionnaire was administered using computer-assisted personal interviewing (CAPI) with CSPro3 software.

Cleaning operations

The data cleaning process was done in several stages over the course of fieldwork and through preliminary analysis. The first stage of data cleaning was conducted by the field-based teams during the interview itself utilizing error messages generated by the CSPro application when a response did not fit the rules for a particular question. For questions that flagged an error, the enumerators were expected to record a comment within the questionnaire to explain to their supervisor the reason for the error and confirming that they double checked the response with the respondent.

The second stage occurred during the review of the questionnaire by the supervisors. Prior to sharing data with LISGIS HQ, the supervisor was to review the interviewers. Depending on the outcome, the supervisors can either approve or reject the case. If rejected, the case goes back to the respective enumerator and a re-visit to the household may be necessary. Additional errors were compiled into error reports by the World Bank and LISGIS HQ that were regularly sent to the teams and then corrected based on re-visits to the household.

The last stage involved a comprehensive review of the final raw data following the first and second stage cleaning, after data collection completion. Every variable was examined individually for (1) consistency with other sections and variables, (2) out of range responses, and (3) outliers. However, special care was taken to avoid making strong assumptions when resolving potential errors. Some minor errors remain in the data where the diagnosis and/or solution were unclear to the data cleaning team.

The first and the second stage of the cleaning activities were led by LISGIS and the World Bank provided technical assistance. The third stage of data cleaning was performed by the World Bank team exclusively.

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 survey covers the urban area of two largest cities of Vietnam, Ha Noi and HCMCT.

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 STEP target population is the population aged 15 to 64 included, living in urban areas, as defined by each country's statistical office. In Vietnam, the target population comprised all people from 15-64 years old living in urban areas in Ha Noi and Ho Chi Minh City (HCM).

The reasons for selection of these two cities include :

(i) They are two biggest cities of Vietnam, so they would have all urban characteristics needed for STEP study, and (ii) It is less costly to conduct STEP survey in these to cities, compared to all urban areas of Vietnam, given limitation of survey budget.

• The target population is not representative for the national urban population.

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

Kind of data

Sample survey data [ssd]

Sampling procedure

• The sample of 3405 households was selected from 227 urban Enumeration Areas (EAs) in Ha Noi (107 EAs) and Ho Chi Minh City (120 EAs). From each EA 15 households were selected, so the number of households selected in Ha Noi was 1245 HHs, and in HCM, 2160 HHs.
• The 2009 Population and Housing Census was used as a sample frame.
• Regarding PSUs (EAs), the sampling frame is the list of 15% of total EAs of the 2009 Population Census. Data items on the frame for PSU include provincecode, districtcode, commune code, and EA code; address of EA, number of households.
• Regarding ultimate sampling units (households), sampling frame is a list of (100) households in each EA. Data items on the frame for ultimate sampling units (households) include names of heads of households.

The sample frame includes the list of urban EAs and the count of households for each EA. Changes of the EAs list and household list would impact on coverage of sample frame. In a recent review of Ha Noi, there were only 3 EAs either new or destroyed from 140 randomly selected Eas (2%). GSO would increase the coverage of sample frame (>95% as standard) by updating the household list of the selected Eas before selecting households for STEP.

A detailed description of the sample design is available in section 4 of the NSDPR provided with the metadata. On completion of the household listing operation, GSO will deliver to the World Bank a copy of the lists, and an Excel spreadsheet with the total number of households listed in each of the 227 visited PSUs.

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 WB STEP team and ETS collaborated closely with the survey firms during the process and reviewed the adaptation and translation to Vietnamese (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

The response rate for Vietnam (urban) was 62%. (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. All country weighting documentations are provided as an external resource.

Abstract

The Multiple Indicator Cluster Survey (MICS) is an international household survey programme developed and supported by UNICEF. MICS is designed to collect estimates of key indicators that are used to assess the situation of children and women. Over the past 20 years MICS has evolved to respond to changing data needs, expanding from 28 indicators in the first round to 200 indicators in the current sixth round, and becoming a key source of data on child protection, early childhood education, and a major source of data on child health and nutrition. In addition to being a data collection tool to generate data for monitoring the progress towards national goals and global commitments aimed at promoting the welfare of children, MICS has provided valuable data for MDG monitoring being a major source of data for the UN Secretary General's Final Millennium Development Goals Progress Report.

MICS was already covering some of the SDG indicators that are household-based. After undergoing rigorous methodological and validation work to broaden the scope of the tools and include new topics that reflect SDG indicators and emerging issues in the 2030 Agenda for Sustainable Development context.

Geographic coverage

The survey is nationally representative and covers the whole of Palestine and The Data are representative at region level (West Bank, Gaza Strip), locality type (urban, rural, camp) and governorates.

Analysis unit

Households (defined as a group of persons who usually live and eat together).

Household members (defined as members of the household who usually live in the household, which may include persons who did not sleep in the household the previous night; it does not include visitors who slept in the household the previous night but who do not usually live in the household).

Women aged 15-49 years

Children aged 0-4 years

Children aged 5-17 years

Universe

The survey covered a household questionnaire to collect basic demographic information on all household members (usual residents), the household, and the dwelling; a water quality testing questionnaire administered in 5 households selected; 4 for testing and 1 for blank testing, in each cluster of the sample; a questionnaire for individual women administered in each household to all women age 15-49 years; an under-5 questionnaire, administered to mothers (or caretakers) of all children under 5 living in the household; and 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.

Kind of data

Sample survey data [ssd]

Sampling procedure

After determining the sample size which equals 10,080 households, we selected a probability sample, which is multi-stage stratified cluster sample as following:

First stage: selecting sample of clusters (enumeration areas), using PPS without replacement method to get 420 enumeration areas from the total EAs frame

Second stage: selecting 24 households from each EA selected in the first stage.

Third stage: Select the targeting person.

• Sample Strata The population was divided by: 1- Governorate: 16 governorates, 11 in the West Bank, and 5 in the Gaza Strip. The process of publishing at the level of Jerusalem J1 has been taken into consideration. 2- locality type (urban, rural, refugee camps).

Sampling deviation

No major deviations from the original sample design were made. All sample enumeration areas were accessed and successfully interviewed with good response rates.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The Palestinian Multiple Indicator Cluster Survey included the following modules in the questionnaires:

HOUSEHOLD QUESTIONNAIRE : Household listing, education, household characteristics, social transfers, household energy use, water and sanitation, handwashing, and salt iodization.

WATER QUALITY TESTING QUESTIONNAIRE : water quality test and results.

WOMEN'S QUESTIONNAIRE 15-49 YEARS: Woman's background, mass media and ICT, marriage, fertility/birth history, desire for last birth, maternal and newborn health, post-natel health checks, contraception, unmet need, attitudes toward domestic violence, victimization, adult function, HIV/AIDS knowledge, tobacco, and life satisfaction.

QUESTIONNAIRE FOR CHILDREN UNDER FIVE : under five background, birth registration, early childhood development, child discipline, child functioning , breastfeeding and dietary intake, immunization, care of illness and anthropometry.

QUESTIONNAIRE FOR CHILDREN AGE 5-17 years: child's background, child labour, child discipline, child functioning, parental involvement, foundational learning skills.

The questionnaires were based on the MICS6 standard questionnaires5 . From the standard MICS6 English version, the questionnaires were customised and translated into Arabic and were pre-tested in May 2019. Based on the results of the pre-test, modifications were made to the wording and translation of the questionnaires

Cleaning operations

During the fieldwork, field-testing the use of tables that examine the distribution and collection of questionnaires depending on the difference, sex ratio, age heaping, target groups, and other relevant tests

Completing the questionnaire was done through the use of a software package, where all the examination rules were placed on the application, which enabled the researcher to review any errors while she was in the household. In Jerusalem J1, there was an office audit of the questionnaire , then it were entered.

Response rate

10,080 households selected for the sample, 9,751 were found occupied. Of these, 9,326 were successfully interviewed for a household response rate of 95.6 percent. The Water Quality Testing Questionnaire was administered to 1,909 randomly selected households in each cluster. Of these, 1,848 were successfully tested for household drinking water yielding a response rate of 96.8 percent. Also 1,819 were successfully tested for source drinking water quality yielding a response rate of 95.3 percent. In the interviewed households, 11,464 women (age 15-49 years) were identified. Of these, 11,135 were successfully interviewed, yielding a response rate of 97.1 percent within the interviewed households. There were 6,394 children under age five listed in the household questionnaires. Questionnaires were completed for 6,328 of these children, which corresponds to a response rate of 99.0 percent within interviewed households. A sub-sample of children age 5-17 years was used to administer the questionnaire for children age 5-17. Only one child has been selected randomly in each household interviewed, and there were 14,329 children age 5-17 years listed in the household questionnaires. Of these, 5,456 children were selected, and questionnaires were completed for 5,360 which correspond to a response rate of 98.2 percent within the interviewed households. Overall response rates of 92.9, 94.7, and 94.0 percent are calculated for the individual interviews of women, under-5s, and children age 5-17 years, respectively.

Sampling error estimates

Accuracy of data comprises different aspects of the survey, mainly statistical errors due to the use of a statistical sample, as well as non-statistical errors due to staff and survey tools, in addition to response rates in the survey and its effect on estimates.

Statistical Errors Since the data reported in this survey are based on a sample survey and not on a complete enumeration, there may be sampling errors as well as non-sampling errors.

Data from this survey may be affected by statistical errors due to use of the sample. Therefore, the emergence of certain differences from the real values obtained through censuses is possible.

Non-Statistical Errors Procedures were developed to ensure that non-statistical errors were minimized as much as possible. Fieldworkers were selected based on strict criteria with adequate qualifications and experience in data collection. All fieldworkers underwent training on data collection best practices, topics of the questionnaires, and how to interview and obtain accurate answers from respondents. In order to reduce the percentage of errors that can occur during the completion of the questionnaire on the tablet, the software package (the application) has been designed very carefully so as not to allow any consistency errors that may occur during the entry process.

In addition, office editors were also trained on editing guidance to ensure data was consistent and complete. Data entry programs were also designed to resemble the structure of the questionnaire itself to ensure consistency within the data in each record and cross-records. All entered data were verified by different data entry clerks to ensure that all data were entered correctly.

Data appraisal

Different methods were applied in the assessment of the survey data, including: Occurrences of missing values and answers like "other" and "do not know". Examining inconsistencies between the various sections of the questionnaire, including within record and cross-record consistencies. comprarability of data with previous surveys 2010, 2014 and showed logical homogeneity in the results.

The results of these assessment procedures show that the data are of high quality and consistency.