Abstract

The 2000 Republic of Palau Census of Population and Housing was the second census collected and processed entirely by the republic itself. This monograph provides analyses of data from the most recent census of Palau for decision makers in the United States and Palau to understand current socioeconomic conditions. The 2005 Census of Population and Housing collected a wide range of information on the characteristics of the population including demographics, educational attainments, employment status, fertility, housing characteristics, housing characteristics and many others.

Geographic coverage

National

Analysis unit

• Household;
• Individual.

Universe

The 1990, 1995 and 2000 censuses were all modified de jure censuses, counting people and recording selected characteristics of each individual according to his or her usual place of residence as of census day. Data were collected for each enumeration district - the households and population in each enumerator assignment - and these enumeration districts were then collected into hamlets in Koror, and the 16 States of Palau.

Kind of data

Census/enumeration data [cen]

Sampling procedure

No sampling - whole universe covered

Mode of data collection

Face-to-face [f2f]

Research instrument

The 2000 censuses of Palau employed a modified list-enumerate procedure, also known as door-to-door enumeration. Beginning in mid-April 2000, enumerators began visiting each housing unit and conducted personal interviews, recording the information collected on the single questionnaire that contained all census questions. Follow-up enumerators visited all addresses for which questionnaires were missing to obtain the information required for the census.

Cleaning operations

The completed questionnaires were checked for completeness and consistency of responses, and then brought to OPS for processing. After checking in the questionnaires, OPS staff coded write-in responses (e.g., ethnicity or race, relationship, language). Then data entry clerks keyed all the questionnaire responses. The OPS brought the keyed data to the U.S. Census Bureau headquarters near Washington, DC, where OPS and Bureau staff edited the data using the Consistency and Correction (CONCOR) software package prior to generating tabulations using the Census Tabulation System (CENTS) package. Both packages were developed at the Census Bureau's International Programs Center (IPC) as part of the Integrated Microcomputer Processing System (IMPS).

The goal of census data processing is to produce a set of data that described the population as clearly and accurately as possible. To meet this objective, crew leaders reviewed and edited questionnaires during field data collection to ensure consistency, completeness, and acceptability. Census clerks also reviewed questionnaires for omissions, certain inconsistencies, and population coverage. Census personnel conducted a telephone or personal visit follow-up to obtain missing information. The follow-ups considered potential coverage errors as well as questionnaires with omissions or inconsistencies beyond the completeness and quality tolerances specified in the review procedures.

Following field operations, census staff assigned remaining incomplete information and corrected inconsistent information on the questionnaires using imputation procedures during the final automated edit of the data. The use of allocations, or computer assignments of acceptable data, occurred most often when an entry for a given item was lacking or when the information reported for a person or housing unit on an item was inconsistent with other information for that same person or housing unit. In all of Palau’s censuses, the general procedure for changing unacceptable entries was to assign an entry for a person or housing unit that was consistent with entries for persons or housing units with similar characteristics. The assignment of acceptable data in place of blanks or unacceptable entries enhanced the usefulness of the data.

Sampling error estimates

Human and machine-related errors occur in any large-scale statistical operation. Researchers generally refer to these problems as non-sampling errors. These errors include the failure to enumerate every household or every person in a population, failure to obtain all required information from residents, collection of incorrect or inconsistent information, and incorrect recording of information. In addition, errors can occur during the field review of the enumerators' work, during clerical handling of the census questionnaires, or during the electronic processing of the questionnaires. To reduce various types of non-sampling errors, Census office personnel used several techniques during planning, data collection, and data processing activities. Quality assurance methods were used throughout the data collection and processing phases of the census to improve the quality of the data.

Census staff implemented several coverage improvement programs during the development of census enumeration and processing strategies to minimize under-coverage of the population and housing units. A quality assurance program improved coverage in each census. Telephone and personal visit follow-ups also helped improve coverage. Computer and clerical edits emphasized improving the quality and consistency of the data. Local officials participated in post-census local reviews. Census enumerators conducted additional re-canvassing where appropriate.

Abstract

The 2009 Population and Housing Census was implemented according to Prime Ministerial Decision No. 94/2008/QD-TTg dated 10 July, 2008. This was the fourth population census and the third housing census implemented in Vietnam since the nation was reunified in 1975. The Census aimed to collect basic data on the population and housing for the entire territory of the Socialist Republic of Vietnam, to provide data for research and analysis of population and housing developments nationally and for each locality. It responded to information needs for assessing implementation of socio-economic development plans covering the period 2001 to 2010, for developing the socio-economic development plans for 2011 to 2020 and for monitoring performance on Millennium Development Goals of the United Nations to which the Vietnamese Government is committed.

Geographic coverage

National

Analysis unit

Households Individuals Dwelling

Universe

The 2009 Population and Housing Census enumerated all Vietnamese regularly residing in the territory of the Socialist Republic of Vietnam at the reference point of 0:00 on 01 April, 2009; Vietnamese citizens given permission by the authorities to travel overseas and still within the authorized period; deaths (members of the household) that occurred between the first day of the Lunar Year of the Rat (07 February, 2008) to 31 March, 2009; and residential housing of the population.

Population and housing censuses were implemented simultaneously taking the household as the survey unit. The household could include one individual who eats and resides alone or a group of individuals who eat and reside together. For household with 2 persons and over, its members may or may not share a common budget; or be related by blood or not; or marital or adoptive relationship or not; or in combination of both. The household head was the main respondent. For information of which the head of household was unaware, the enumerator was required to directly interview the survey subject. For information on labour and employment, the enumerator was required to directly interview all respondents aged 15 and older; for questions on births, the enumerator was required to directly interview women in childbearing ages (from 15 to 49 years of age) to determine the responses. For information on housing, the enumerator was required to directly survey the household head and/or combine this with direct observation to determine the information to record in the forms.

Kind of data

Census/enumeration data [cen]

Sampling procedure

Sample size In the 2009 Population and Housing Census, besides a full enumeration, some indicators were collected in a sample survey. The census sample survey was designed to: (1) expand survey contents; (2) improve survey quality, especially for sensitive and complicated questions; and (3) save on survey costs. To improve the efficiency and reliability of the census sample data, the sample size was 15% of the total population of the country. The sample of the census is a single-stage cluster sample design with stratification and systematic sample selection. Sample selection is implemented in two steps: Step 1, select the strata to determine the sample size for each district. Step 2, independently and systematically select from the sample frame of enumeration areas in each district to determine the specific enumeration areas in the sample.

The sample size of the two census sample surveys in 1989 and 1999 was 5% and 3% respectively, only representative at the provincial level; sample survey indicators covered fertility history of women aged 15-49 years and deaths in the household in the previous 12 months. In the 2009 Census, besides the above two indicators, many other indicators were also included in the census sample survey. The census sample survey provides data representative at the district level. When determining sample size and allocation, the frequency of events was taken into account for various indicators including birth and deaths in the 12 months prior to the survey, and the number of people unemployed in urban areas, etc.; efforts were also made to ensure the ability to compare results between districts within the same province/municipality and between provinces/ municipalities.

Stratification and sample allocation across strata To ensure representativeness of the sample for each district throughout the country and because the population size is not uniform across districts or provinces, the Central Steering Committee decided to allocate the sample directly to 682 out of 684 districts (excluding 2 island districts) throughout the country in 2 steps:

Step 1: Determine the sampling rate f(r) for 3 regions including: - Region 1: including 132 urban districts; - Region 2: including 294 delta and coastal rural districts; - Region 3: including 256 mountainous and island districts.

Step 2: Allocate the sample across districts in each region based on the sampling rates for each region as determined in Step 1 using the inverse sampling allocation method. Through applying to this allocation method, the number of sampling units in each small district is increased adequately to ensure representativeness. The formula used to calculate the sample rate for each district in each region is provided on page 22 of the Census Report (Part1) provided as external resources.

Sampling unit and method The sampling unit is the enumeration area that was ascertained in the step to delimit enumeration areas. The sampling frame is the list of all enumeration areas that was made following the order of the list of administrative units at the commune level within each district. In this way, the whole country has 682 sample frames (682 strata).

The provincial steering committee was responsible for selecting sample enumeration areas using systematic random sampling as follows: Step 1: Take the total of all enumeration areas in the district, divide by the number of enumeration areas needed in the sampleto determine the skip (k), which is calculated with precision up to 1 decimal point. Step 2: Select the first enumeration area (b, with b = k), corresponding to the first enumeration area to be selected. Each successive enumeration area to be selected will correspond to the order number: bi = b + i x k ; here i = 1, 2, 3…. Stopping when the number of enumeration areas needed has been selected.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaires and survey materials were designed and tested three times before final approval.

Cleaning operations

The 2009 Population and Housing Census applied Intelligent Character Recognition technology/scanning technology for direct data entry from census forms to the computer to replace the traditional keyboard data entry that is commonly used in Vietnam at present. This is an advanced technology, and the first time it had been applied in a statistical survey in Vietnam. Preparatory work had to be done carefully and meticulously. Through organization of many workshops and 7 pilot applications with technical and financial assistance from the UNFPA, the new technology was mastered, and the Census Steering Committee Standing Committee approved use of this technology to process the entire results of the 2009 Population and Housing Census. The Government decided to allocate funds through the project on Modernization of the General Statistics Office using World Bank Loan funds to procure the scanning system equipment, software and technical assistance. The successful use of this technology will create a precedent for continued use of scanning technology in other statistical surveys

After checking and coding at the Provincial/municipal steering committee office, (both the complete census and the census sample survey), forms were checked and accepted then transferred for processing to one of three Statistical Computing Centres in Hanoi, Ho Chi Minh City and Da Nang. Data processing was implemented in only a few locations, following standard procedures and a fixed timeline. The steering committee at each level and processing centres fully implemented their assigned responsibilities, especially the checking, transmitting and maintenance of survey forms in good condition. The Central Steering Committee collaborated with the Statistical Computer Centres to set up a plan for processing and compiling results, setting up tabulation plans, interpreting and synthesizing output tables, and developing options for extrapolating from sample to population estimates.

The General Statistics Office completed the work of developing software applications and training using ReadSoft software (the one used in pilot testing), organized training on network management and training on systems and programs for logic checks and data editing, developed a data processing protocol, integrated these systems and completed data flow management programs. The General Statistics Office collaborated with the contractor, FPT, to develop software applications, train staff, testl the system and complete the programs using the new TIS and E-form software.

Compilation of results was implemented in 2 stages. In stage 1 data were compiled from the Census Sample Survey by the end of October, 2009, and in stage 2, data were compiled from the completed census forms, with work finalized in May 2010.

Sampling error estimates

Estimates from the Census sample survey were affected by two types of error: (1) non-sampling error, and (2) sampling error. Non-sampling error is the result of errors in implementation of data collection and processing such as visiting the

Abstract

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

The primary objective of the 1999 TRCHS was to collect data at the national level (with breakdowns by urban-rural and Mainland-Zanzibar residence wherever warranted) on fertility levels and preferences, family planning use, maternal and child health, breastfeeding practices, nutritional status of young children, childhood mortality levels, knowledge and behaviour regarding HIV/AIDS, and the availability of specific health services within the community.1 Related objectives were to produce these results in a timely manner and to ensure that the data were disseminated to a wide audience of potential users in governmental and nongovernmental organisations within and outside Tanzania. The ultimate intent is to use the information to evaluate current programmes and to design new strategies for improving health and family planning services for the people of Tanzania.

Geographic coverage

National. The sample was designed to provide estimates for the whole country, for urban and rural areas separately, and for Zanzibar and, in some cases, Unguja and Pemba separately.

Analysis unit

Households, individuals

Universe

Men and women 15-49, children under 5

Kind of data

Sample survey data

Sampling procedure

The TRCHS used a three-stage sample design. Overall, 176 census enumeration areas were selected (146 on the Mainland and 30 in Zanzibar) with probability proportional to size on an approximately self-weighting basis on the Mainland, but with oversampling of urban areas and Zanzibar. To reduce costs and maximise the ability to identify trends over time, these enumeration areas were selected from the 357 sample points that were used in the 1996 TDHS, which in turn were selected from the 1988 census frame of enumeration in a two-stage process (first wards/branches and then enumeration areas within wards/branches). Before the data collection, fieldwork teams visited the selected enumeration areas to list all the households. From these lists, households were selected to be interviewed. The sample was designed to provide estimates for the whole country, for urban and rural areas separately, and for Zanzibar and, in some cases, Unguja and Pemba separately. The health facilities component of the TRCHS involved visiting hospitals, health centres, and pharmacies located in areas around the households interviewed. In this way, the data from the two components can be linked and a richer dataset produced.

See detailed sample implementation in the APPENDIX A of the final report.

Mode of data collection

Face-to-face

Research instrument

The household survey component of the TRCHS involved three questionnaires: 1) a Household Questionnaire, 2) a Women’s Questionnaire for all individual women age 15-49 in the selected households, and 3) a Men’s Questionnaire for all men age 15-59.

The health facilities survey involved six questionnaires: 1) a Community Questionnaire administered to men and women in each selected enumeration area; 2) a Facility Questionnaire; 3) a Facility Inventory; 4) a Service Provider Questionnaire; 5) a Pharmacy Inventory Questionnaire; and 6) a questionnaire for the District Medical Officers.

All these instruments were based on model questionnaires developed for the MEASURE programme, as well as on the questionnaires used in the 1991-92 TDHS, the 1994 TKAP, and the 1996 TDHS. These model questionnaires were adapted for use in Tanzania during meetings with representatives from the Ministry of Health, the University of Dar es Salaam, the Tanzania Food and Nutrition Centre, USAID/Tanzania, UNICEF/Tanzania, UNFPA/Tanzania, and other potential data users. The questionnaires and manual were developed in English and then translated into and printed in Kiswahili.

The Household Questionnaire was used to list all the usual members and visitors in the selected households. Some basic information was collected on the characteristics of each person listed, including his/her age, sex, education, and relationship to the head of the household. The main purpose of the Household Questionnaire was to identify women and men who were eligible for individual interview and children under five who were to be weighed and measured. Information was also collected about the dwelling itself, such as the source of water, type of toilet facilities, materials used to construct the house, ownership of various consumer goods, and use of iodised salt. Finally, the Household Questionnaire was used to collect some rudimentary information about the extent of child labour.

The Women’s Questionnaire was used to collect information from women age 15-49. These women were asked questions on the following topics: · Background characteristics (age, education, religion, type of employment) · Birth history · Knowledge and use of family planning methods · Antenatal, delivery, and postnatal care · Breastfeeding and weaning practices · Vaccinations, birth registration, and health of children under age five · Marriage and recent sexual activity · Fertility preferences · Knowledge and behaviour concerning HIV/AIDS.

The Men’s Questionnaire covered most of these same issues, except that it omitted the sections on the detailed reproductive history, maternal health, and child health. The final versions of the English questionnaires are provided in Appendix E.

Before the questionnaires could be finalised, a pretest was done in July 1999 in Kibaha District to assess the viability of the questions, the flow and logical sequence of the skip pattern, and the field organisation. Modifications to the questionnaires, including wording and translations, were made based on lessons drawn from the exercise.

Response rate

In all, 3,826 households were selected for the sample, out of which 3,677 were occupied. Of the households found, 3,615 were interviewed, representing a response rate of 98 percent. The shortfall is primarily due to dwellings that were vacant or in which the inhabitants were not at home despite of several callbacks.

In the interviewed households, a total of 4,118 eligible women (i.e., women age 15-49) were identified for the individual interview, and 4,029 women were actually interviewed, yielding a response rate of 98 percent. A total of 3,792 eligible men (i.e., men age 15-59), were identified for the individual interview, of whom 3,542 were interviewed, representing a response rate of 93 percent. The principal reason for nonresponse among both eligible men and women was the failure to find them at home despite repeated visits to the household. The lower response rate among men than women was due to the more frequent and longer absences of men.

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

Sampling error estimates

The estimates from a sample survey are affected by two types of errors: (1) non-sampling errors, and (2) sampling errors. Non-sampling errors are the results of mistakes made in implementing data collection and data processing, such as failure to locate and interview the correct household, misunderstanding of the questions on the part of either the interviewer or the respondent, and data entry errors. Although numerous efforts were made during the implementation of the TRCHS to minimise this type of error, nonsampling errors are impossible to avoid and difficult to evaluate statistically.

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

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

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

Note: See detailed sampling error

Abstract

The Statistical and Forecasting Service has been entrusted with the production of the AC 2010. (SSP) which is the central statistical department of the Ministry in charge of agriculture, (MAAPRAT) the central department is in charge of the design of the operation, the drafting of the questionnaire and instructions, the training of regional services, the final quality control of the data collected and of the first publication of the results. The SSP has relied on its specialised decentralised levels, the services regional statistics (NUTS2) of statistical and economic information (SRISE). The threshold definition of agricultural holding applied has been the same since 1955, and corresponds exactly to the one proposed by the European regulation. The geographical area is the whole of France; for the DOM the territories of Saint-Martin and Saint-Barthélemy are now excluded, Mayotte is not yet included.

For statistical purposes, agricultural censuses in French territories (French Guyana, Guadeloupe, Reunion and Martinique) are recorded separately in the World Census of Agriculture Database. The census results are presented for all of France.

Geographic coverage

National coverage

Analysis unit

Households

Universe

The statistical unit in the AC 2010 was the agricultural holding, defined as an economic unit that participates in agricultural production and meets the following criteria: · it has an agricultural activity either of production, or of maintenance of the lands in good agricultural and environmental

Kind of data

Census/enumeration data [cen]

Sampling procedure

a. Frame The basic list of agricultural holdings was built using the SSP farm register, the SIRENE register (business register), the list of farmers who had applied for aid (area declarations),' and some additional sources for beekeeping, olive oil, aromatic plants. The holding lists were checked at local level by communal commissions.

b. Complete and/or sample enumeration method(s) The AC and SAPM were conducted using complete enumeration.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

Three questionnaires were used: one for France in Europe (including questions of regional interest) and two for France's overseas territories: one for Guadeloupe, Martinique and Reunion and another for Guyana. The census covered all 16 core items recommended in the WCA 2010. ie.

0001 Identification and location of agricultural holding 0002+ Legal status of agricultural holder 0003 Sex of agricultural holder 0004 Age of agricultural holder 0005 Household size 0006 Main purpose of production of the holding 0007 Area of holding according to land use types 0008 Total area of holding 0009 Land tenure types on the holding 0010 Presence of irrigation on the holding 0011 Types of temporary crops on the holding 0012 Types of permanent crops on the holding and whether in compact plantation 0013 Number of animals on the holding for each livestock type 0014 Presence of aquaculture on the holding 0015+ Presence of forest and other wooded land on the holding 0016 Other economic production activities of the holding's enterprise

Cleaning operations

a. DATA PROCESSING AND ARCHIVING The CAPI interface included controls to ensure that there were responses to all questions. In addition, interactive range and consistency checks were included for each variable so that corrections could be made by the enumerator during the interview. Further edits and imputations were completed at the central office where the census validation and tabulation was completed. To ensure that the list of holdings was complete, several tests were conducted at the end of collection. All available administrative sources were used to verify that existing holdings had been identified and included. The key databases and registers used included that for EU agriculture aid applications, the national database of bovine identification, the computerized vineyard register, organic producer records, and some local registers for small productions. The data, after validation, were archived on secured servers.

b. CENSUS DATA QUALITY To assess the quality of field data collection, completeness checks and feedback were performed at the end of field data collection operation, from March to June 2011. Data checking began during the collection phase on the farmer's premises. It then continued throughout the processing chain. A special effort was made to check the AC's coverage by using the administrative data available. The nonresponse rate was of only 0.96 percent, and the missing data were imputed using the hot deck method.

Data appraisal

The first provisional census results were disseminated in September 2011, ten months after the end of the reference period. The main final results were made available at the end of February 2012, 16 months after the end of the reference period. The AC 2010 results were disseminated online and are available on the SSP website.9 The "ADEL" tool allows web users to build their own tables.

The first table with main results shows the total number and area of holdings broken down by continental France, on one hand, and its overseas territories, on the other. See metadata review tables in external materials.

Abstract

The Statistical and Forecasting Service has been entrusted with the production of the AC 2010. (SSP) which is the central statistical department of the Ministry in charge of agriculture, (MAAPRAT) the central department is in charge of the design of the operation, the drafting of the questionnaire and instructions, the training of regional services, the final quality control of the data collected and of the first publication of the results. The SSP has relied on its specialised decentralised levels, the services regional statistics (NUTS2) of statistical and economic information (SRISE). The threshold definition of agricultural holding applied has been the same since 1955, and corresponds exactly to the one proposed by the European regulation. The geographical area is the whole of France; for the DOM the territories of Saint-Martin and Saint-Barthélemy are now excluded, Mayotte is not yet included.

For statistical purposes, agricultural censuses in French territories (French Guyana, Guadeloupe, Reunion and Martinique) are recorded separately in the World Census of Agriculture Database. The census results are presented for all of France.

Geographic coverage

National coverage

Analysis unit

Households

Universe

The statistical unit in the AC 2010 was the agricultural holding, defined as an economic unit that participates in agricultural production and meets the following criteria: · it has an agricultural activity either of production, or of maintenance of the lands in good agricultural and environmental

Kind of data

Census/enumeration data [cen]

Sampling procedure

a. Frame The basic list of agricultural holdings was built using the SSP farm register, the SIRENE register (business register), the list of farmers who had applied for aid (area declarations),' and some additional sources for beekeeping, olive oil, aromatic plants. The holding lists were checked at local level by communal commissions.

b. Complete and/or sample enumeration method(s) The AC and SAPM were conducted using complete enumeration.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

Three questionnaires were used: one for France in Europe (including questions of regional interest) and two for France's overseas territories: one for Guadeloupe, Martinique and Reunion and another for Guyana. The census covered all 16 core items recommended in the WCA 2010. ie.

0001 Identification and location of agricultural holding 0002+ Legal status of agricultural holder 0003 Sex of agricultural holder 0004 Age of agricultural holder 0005 Household size 0006 Main purpose of production of the holding 0007 Area of holding according to land use types 0008 Total area of holding 0009 Land tenure types on the holding 0010 Presence of irrigation on the holding 0011 Types of temporary crops on the holding 0012 Types of permanent crops on the holding and whether in compact plantation 0013 Number of animals on the holding for each livestock type 0014 Presence of aquaculture on the holding 0015+ Presence of forest and other wooded land on the holding 0016 Other economic production activities of the holding's enterprise

Cleaning operations

a. DATA PROCESSING AND ARCHIVING The CAPI interface included controls to ensure that there were responses to all questions. In addition, interactive range and consistency checks were included for each variable so that corrections could be made by the enumerator during the interview. Further edits and imputations were completed at the central office where the census validation and tabulation was completed. To ensure that the list of holdings was complete, several tests were conducted at the end of collection. All available administrative sources were used to verify that existing holdings had been identified and included. The key databases and registers used included that for EU agriculture aid applications, the national database of bovine identification, the computerized vineyard register, organic producer records, and some local registers for small productions. The data, after validation, were archived on secured servers.

b. CENSUS DATA QUALITY To assess the quality of field data collection, completeness checks and feedback were performed at the end of field data collection operation, from March to June 2011. Data checking began during the collection phase on the farmer's premises. It then continued throughout the processing chain. A special effort was made to check the AC's coverage by using the administrative data available. The nonresponse rate was of only 0.96 percent, and the missing data were imputed using the hot deck method.

Data appraisal

The first provisional census results were disseminated in September 2011, ten months after the end of the reference period. The main final results were made available at the end of February 2012, 16 months after the end of the reference period. The AC 2010 results were disseminated online and are available on the SSP website.9 The "ADEL" tool allows web users to build their own tables.

The first table with main results shows the total number and area of holdings broken down by continental France, on one hand, and its overseas territories, on the other. See metadata review tables in external materials.

Abstract

The energy statistics program has implemented fourteen rounds of the Household Energy Survey during 1999-2009.

Because of the importance of the household sector and due to its large contribution to energy consumption in the Palestinian Territory, PCBS decided to conduct a special Household Energy Survey to cover energy indicators in the household sector. To achieve this, a questionnaire was attached to the Labor Force Survey.

This survey aimed to provide data on energy consumption in the household sector and to provide data on energy consumption behavior and patterns in the society by type of energy.

The survey presents data on energy indicators pertaining to households in the Palestinian Territory. This includes statistical data on electricity and other fuel consumption by households covering type of fuel for different activities (cooking, baking, heating, lighting, and water heating).

Geographic coverage

Palestinian Territory

Analysis unit

households

Universe

The target population was all Palestinian households living in the Palestinian Territory.

Kind of data

Sample survey data [ssd]

Sampling procedure

Sample Frame The sample is a two-stage stratified cluster random sample.

Target Population The target population was all Palestinian households living within the Palestinian Territory.

Sampling Frame The sampling frame is a master sample from the Population, Housing and Establishment Census 1997 for the households that were visited a second or third or fourth time, while the households to be visited for the first time were chosen from the general frame of Population, Housing and Establishment Census 2007. It consists of a list of enumeration areas used as PSU's in the first stage of selection, and the household frame was used in the enumerator areas to choose households in the second level. The frame of the households has been updated in the enumerator areas for the new general sample at the end of year 2003.

Sampling Design The sample of this survey is a sub-sample of the Labour Force Survey (LFS) sample, which is conducted every 13 weeks. The sample of LFS is distributed over 13 weeks. The sample of the Household Energy Survey occupies six weeks of the first quarter of 2009 of the LFS.

Sample allocation The sample allocation was according to the number of households in the stratum using three levels which are District, Locality type (urban,rural,refufugee camps), and the locality population size(Number of households within locality).

Sample Unit: In the first stage, the sampling units are the enumerator areas (clusters) in the master sample. In the second stage, the sampling units are households.

Analysis Unit: Analysis units are composed of households.

Sample Size: The sample size is of (3,850) Palestinian households in the West Bank and Gaza Strip, where this sample has been distributed according to the locality in urban areas, in rural areas and in refugee camps.

Mode of data collection

Face-to-face [f2f]

Research instrument

The design of the questionnaire for the Household Energy Survey was based on the experiences of similar countries as well as on international standards and recommendations for the most important indicators, taking into account the special situation of the Palestinian Territory.

Cleaning operations

The data processing stage consisted of the following operations: Editing and coding before data entry: All questionnaires were edited and coded in the office using the same instructions adopted for editing in the field.

Data entry: At this stage, data was entered into the computer using a data entry template developed in Access. The data entry program was prepared to satisfy a number of requirements such as: · To prevent the duplication of the questionnaires during data entry. · To apply integrity and consistency checks of entered data. · To handle errors in user friendly manner. · The ability to transfer captured data to another format for data analysis
using statistical analysis software such as SPSS.

Response rate

During fieldwork 3,850 Households were visited in the Palestinian Territory, the end results for the interview become as following: complete questioner (3,357)
traveling households (43)
housing unit not existed (32)
cases no body in the house (140)
objection cases (47)
housing unit abandoned (177)
household can't give data (26)
other cases (28)

Sampling error estimates

It includes many aspects of the survey, mainly statistical errors due to the sample, and not statistical errors referring to the workers and survey tools. It includes also the response rates in this survey and their effect on the assumptions. This section includes:

1. Sampling Errors These types of errors evolved as a result of studying a part of the society and not all of it. Because this survey is a sample, the data of this survey will be affected by sampling errors due to using a sample and not the whole frame of the society. Differences appear compared with the actual values that could be obtained through a census. For this survey, variance calculations were made for average household consumption and total consumption for the different types of energy in the Palestinian Territory.

The results of wood, charcoal and olive cake suffers from a high variance. This problem should be taken into consideration when dealing with the average household consumption of these types of fuel, keeping in mind that there are no problems in publishing the data for the geographical level (North of the West Bank, Middle of the West Bank, South of the West Bank and Gaza Strip). However, publishing data for the governorate level is not possible due to the high variance, especially for wood, charcoal and olive cake.

1. Non Sampling Errors These errors are due to non-response cases as well as the implementation of surveys. In this survey, these errors emerged because of (a) the special situation of the questionnaire itself, which depends on a type of estimation, (b) diversity of sources (e.g., the interviewers, respondents, editors, coders, data entry operator, etc).

The sources of these errors can be summarized as: Some of the households were not in their houses and the interviewers could not meet them. Some of the households did not give attention to the questionnaire.
Some errors occurred due to the way the questions were asked by interviewers. Misunderstanding of the questions by the respondents. Answering the questions related to consumption by making estimations.

It is important to mention that 5% from the sample of this survey was re-interviewed, and the results of this re-interview were reported by the supervisors. The re-interview shows the variance in estimation by interviewers for wood, charcoal and olive cake when the interviewee is different between the one who answers for the main survey questionnaire and the one who answers the re-interview questionnaire.

Data appraisal

The data of the Household Energy Survey is comparable geographically and over time by comparing the data between different geographical areas with the data of previous surveys and census 2007.

Abstract

Six agricultural censuses have been conducted in Italy in the years 1961, 1970, 1982, 1990, 2000 and the latest, to which data here refer, in 2010.

Its objective is:

a) to provide a statistical picture on the structure of the agricultural and livestock system at national, regional and local level.

b) to fulfil the Regulation (EC) n. 1166/2008 of the European Parliament and of the Council of 19 November 2008 on farm structure (FSS) and the survey on agricultural production methods (SAPM) and the Council Regulation (EEC) No 357/79 of 5 February 1979 on statistical surveys of areas under vines.

c) To update and validate the statistical register of the agricultural holdings built up by Istat through the integration of the administrative sources.

The Agricultural Census cover all agricultural holdings whoever is its management, with Utilized Agricultural Area (UAA) or livestock equal or higher than minimum thresholds stated by Istat.

Geographic coverage

National coverage

Analysis unit

Households

Universe

The statistical unit is the agricultural holding, defined as a single unit, both technically and economically, which has a single management and which undertakes the agricultural activities listed in Annex I to the European Parliament and Council Regulation (EC) No. 1166/2008 within the economic territory of the EU, as either its primary or secondary activity. Specific actions have been implemented to include all common lands with UAA2 in the AC 2010.

Kind of data

Census/enumeration data [cen]

Sampling procedure

Frame: The pre-census list of agricultural holdings was established based on the integration of administrative and statistical sources that contain information concerning the target population. The AC was conducted using complete enumeration.

Mode of data collection

Face-to-face [f2f]

Research instrument

There was one comprehensive census questionnaire, available either in print form or as and Internet-based electronic version that could be completed online. It was available in four languages (Italian, German, English and Slovenian). The questionnaire was used to collect both farm structure characteristics as well as items related to agricultural production methods. The questionnaire included all 16 core items recommended in the WCA 2010.

Questionnaire sections:

• General company news
• Information for companies with land
• Information for farms with livestock
• Location of land and farms
• Work and related activities (agricultural year 2009 - 2010)
• Other information (agricultural year 2009 - 2010)

Cleaning operations

1. DATA PROCESSING AND ARCHIVING Manual data entry was used (for paper questionnaires) along with direct data capture (when CAWI was used). Non-sampling errors were identified and treated by an Editing and Imputation System. For detecting outlier values, a special procedure based on the robust technique of Forward Search was implemented, in partnership with the University of Parma and centrally applied by ISTAT. The imputation process used was a combination of the following methodologies: (i) deductive imputation, if the values to impute are uniquely determined by the values assumed by other variables; (ii) rule-based imputation (based on deterministic "if then" rules); (iii) nearest neighbour imputation; (iv) model-based imputation (preferred for the imputation of continuous variables); and (v) interactive imputation. Administrative sources were used for the preparation of the pre-census list, for data control and correction.

2. CENSUS DATA QUALITY To evaluate the quality of the AC 2010, Istat implemented two PESs ("post-census surveys"): a Coverage Survey (CS) and a Re-Interview Survey (RIS). The CS was designed to obtain reliable estimates of under- or overcount, using another independent list of units existing in a sample of cadastral maps. The RS was carried out through a re-interview of a sample of agricultural holdings already interviewed in the AC to estimate response error due to respondents and/or enumerators. The survey was carried out from May 2011 to January 2012 on a sample of approximately 50 000 holdings, selected with one-stage stratified sample from the census frame. The survey was conducted using CATI.

Sampling error estimates

The non-sampling errors has been identified and treated by an Editing and Imputation System (E&IS), preserving as much as possible the collected information. The E&I activities could be grouped in three main stages. The first stage refers to the checks performed at the data gathering phase. The second stage concerns the activities carried out in order to provide the provisional figures. The last stage relates to the procedures aiming to release the final data.

Data appraisal

Preliminary results were disseminated in July 2011 through a press release and 23 tables were made available to users on the Istat website. The final results were released in July 2012. The main dissemination method was the Internet.

Abstract

The 2023-24 Lesotho Demographic and Health Survey (2023-24 LDHS) is designed to provide data for monitoring the population and health situation in Lesotho. The 2023-24 LDHS is the 4th Demographic and Health Survey conducted in Lesotho since 2004.

The primary objective of the 2023–24 LDHS is to provide up-to-date estimates of basic demographic and health indicators. Specifically, the LDHS collected information on fertility levels, marriage, sexual activity, fertility preferences, awareness and use of family planning methods, breastfeeding practices, nutrition, childhood and maternal mortality, maternal and child health, awareness and behaviour regarding HIV and AIDS and other sexually transmitted infections (STIs), other health issues (including tuberculosis) and chronic diseases, adult mortality (including maternal mortality), mental health and well-being, and gender-based violence. In addition, the 2023–24 LDHS provides estimates of anaemia prevalence among children age 6–59 months and adults as well as estimates of hypertension and diabetes among adults.

The information collected through the 2023–24 LDHS is intended to assist policymakers and programme managers in designing and evaluating programmes and strategies for improving the health of Lesotho’s population. The survey also provides indicators relevant to the Sustainable Development Goals (SDGs) for Lesotho.

Geographic coverage

National coverage

Analysis unit

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

Universe

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

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling frame used for the 2023–24 LDHS is based on the 2016 Population and Housing Census (2016 PHC), provided by the Lesotho Bureau of Statistics (BoS). The frame file is a complete list of all census enumeration areas (EAs) within Lesotho. An EA is a geographic area, usually a city block in an urban area or a village in a rural area, consisting of approximately 100 households. In rural areas, it may consist of one or more villages. Each EA serves as a counting unit for the population census and has a satellite map delineating its boundaries, with identification information and a measure of size, which is the number of residential households enumerated in the 2016 PHC. Lesotho is administratively divided into 10 districts; each district is subdivided into constituencies and each constituency into community councils.

The 2023–24 LDHS sample of households was stratified and selected independently in two stages. Each district was stratified into urban, peri-urban, and rural areas; this yielded 29 sampling strata because there are no peri-urban areas in Butha-Buthe. In the first sampling stage, 400 EAs were selected with probability proportional to EA size and with independent selection in each sampling stratum. A household listing operation was carried out in all of the selected sample EAs, and the resulting lists of households served as the sampling frame for the selection of households in the next stage.

In the second stage of selection, a fixed number of 25 households per cluster (EA) were selected with an equal probability systematic selection from the newly created household listing. All women age 15–49 who were usual members of the selected households or who spent the night before the survey in the selected households were eligible for the Woman’s Questionnaire. In every other household, all men age 15–59 who were usual members of the selected households or who spent the night before the survey in the selected households were eligible for the Man’s Questionnaire. All households in the men’s subsample were eligible for the Biomarker Questionnaire.

Fifteen listing teams, each consisting of three listers/mappers and a supervisor, were deployed in the field to complete the listing operation. Training of the household listers/mappers took place from 28 to 30 June 2024. The household listing operation was carried out in all of the selected EAs from 5 to 26 July 2024. For each household, Global Positioning System (GPS) data were collected at the time of listing and during interviews.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

Four questionnaires were used for the 2023–24 LDHS: the Household Questionnaire, the Woman’s Questionnaire, the Man’s Questionnaire, and the Biomarker Questionnaire. The questionnaires, based on The DHS Program’s model questionnaires, were adapted to reflect the population and health issues relevant to Lesotho and were translated into Sesotho. In addition, a self-administered Fieldworker Questionnaire collected information about the survey’s fieldworkers.

Cleaning operations

The survey data were collected using tablet computers running the Android operating system and Census and Survey Processing System (CSPro) software, jointly developed by the United States Census Bureau, ICF, and Serpro S.A. English and Sesotho questionnaires were used for collecting data via CAPI. The CAPI programmes accepted only valid responses, automatically performed checks on ranges of values, skipped to the appropriate question based on the responses given, and checked the consistency of the data collected. Answers to the survey questions were entered into the tablets by each interviewer. Supervisors downloaded interview data to their tablet, checked the data for completeness, and monitored fieldwork progress.

Each day, after completion of interviews, field supervisors submitted data to the central server. Data were sent to the central office via secure internet data transfer. The data processing managers monitored the quality of the data received and downloaded completed data files for completed clusters into the system. ICF provided the CSPro software for data processing and technical assistance in the preparation of the data capture, data management, and data editing programmes. Secondary editing was conducted simultaneously with data collection. All technical support for data processing and use of the tablets was provided by ICF.

Abstract

The conceptual framework used in this second labour force survey in Samoa aligns closely with the standards and guidelines set out in Resolutions of International Conferences of Labour Statistician

The 2017 Samoa Labour Force Survey was conducted as a joint exercise between the Samoa Bureau of Statistics and the Ministry of Commerce, Industry and Labour and was co-funded by the International Labour Organization and the Trade, Commerce and Manufacturing (TCM) Sector Coordinating Unit of MCIL. Furthermore, the 2017 Samoa Labour Force Survey was implemented simultaneously with the 2017 Samoa School to Work Transition Survey as the two surveys are closely inter-related.

This is the first time ever that the Samoa Bureau of Statistics has used CAPI (computer aided personal interview) where tablets were used to record answers out on the field

Geographic coverage

National

There are four statistical regions in SAMOA namely Apia urban area (AUA), North West Upolu (NWU), Rest of Upolu (RoU) and Savaii. AUA is the urban area while the other three regions are rural areas. Each region is subdivided into political districts, each district into villages and each village into census enumeration areas (EA). The sample for the 2017 Labour Force Survey (LFS) was designed to cover at least 3000 employed population aged 15years and over from all the four regions. This was made mainly to have sufficient cases to provide information on the employed population.

Analysis unit

Individual. Households were targeted during the actual field work where all those aged 15 years and above were interviewed.

Universe

Households were targeted during the actual field work where all those aged 15 years and above were interviewed therefore, information recorded were collected at the household level.

Kind of data

Sample survey data [ssd]

Sampling procedure

The 2017 Labour Force Survey (LFS) sample was drawn from the master sample frame of Household Listing from the most recent Population and Housing Census, 2016. In the 2017 LFS, a representative probability sample of households was selected in two stages. The first stage involved the selection of clusters or primary sampling units using probability proportional to size (PPS) resulting in a total of 259 clusters of which 67 clusters were selected from AUA, 95 in NWU, 49 in ROU and 48 in Savaii. In the second stage of selection, a fixed number of 10 households were selected systematically from the AUA clusters and a fixed number of 12 households were selected from the NWU region and 15 for the other two rural regions namely RoU and Savaii, due to the higher transportation costs in those regions. This resulted in a total of 670 selected households in AUA, 1140 in NWU, 735 in ROU and 720 in Savaii.

During the LFS, in each of the selected households, all persons in the household were interviewed hence the weighting was based on the responding households in the sample (household weights).

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The 2017 Samoa Labour Force Survey questionnaire was similar to the one used in the 2012 Labour Force Survey, with some changes to the questionnaire provided by the ILO. To maintain international comparability, most of the questions were retained such as current activities, characteristics of the main activity and hours of work. However, some questions were modified and altered so that they fit into the local context, such as the classification of education and the participation in the production of goods used by own household.

The twelve sections of the LFS questionnaire were divided into two parts where the first part was designed to obtain data on household characteristics and composition. The following ten sections were designed to collect data on those aged 15 years and above on literacy and education, training, employment, characteristics of the main job/ activity, hours of work, job search, previous work experience, occupational injuries, main activity and own use production. The last section was designed to obtain information on youth school-to-work transition, which was designed in a separate questionnaire in 2012.

The draft questionnaire was pre tested during the supervisors training and during the enumerators training and it was finally tested during the pilot test. The questionnaire was revised rigorously in accordance to the feedback received from each test. At the same time, a field operations manual for supervisors and enumerators was prepared and modified accordingly for field operators to use as a reference during the field work.

Abstract

The Integrated Household Survey is one of the primary instruments implemented by the Government of Malawi through the National Statistical Office (NSO) roughly every 3-5 years to monitor and evaluate the changing conditions of Malawian households. The IHS data have, among other insights, provided benchmark poverty and vulnerability indicators to foster evidence-based policy formulation and monitor the progress of meeting the Millennium Development Goals (MDGs), the goals listed as part of the Malawi Growth and Development Strategy (MGDS) and now the Sustainable Development Goals (SDGs).

Geographic coverage

National coverage

Analysis unit

• Households
• Individuals
• Consumption expenditure commodities/items
• Communities
• Agricultural household/ Holder/ Crop
• Market

Universe

Members of the following households are not eligible for inclusion in the survey: • All people who live outside the selected EAs, whether in urban or rural areas. • All residents of dwellings other than private dwellings, such as prisons, hospitals and army barracks. • Members of the Malawian armed forces who reside within a military base. (If such individuals reside in private dwellings off the base, however, they should be included among the households eligible for random selection for the survey.) • Non-Malawian diplomats, diplomatic staff, and members of their households. (However, note that non-Malawian residents who are not diplomats or diplomatic staff and are resident in private dwellings are eligible for inclusion in the survey. The survey is not restricted to Malawian citizens alone.) • Non-Malawian tourists and others on vacation in Malawi.

Kind of data

Sample survey data [ssd]

Sampling procedure

The IHS5 sampling frame is based on the listing information and cartography from the 2018 Malawi Population and Housing Census (PHC); includes the three major regions of Malawi, namely North, Center and South; and is stratified into rural and urban strata. The urban strata include the four major urban areas: Lilongwe City, Blantyre City, Mzuzu City, and the Municipality of Zomba. All other areas are considered as rural areas, and each of the 27 districts were considered as a separate sub-stratum as part of the main rural stratum. The sampling frame further excludes the population living in institutions, such as hospitals, prisons and military barracks. Hence, the IHS5 strata are composed of 32 districts in Malawi.

A stratified two-stage sample design was used for the IHS5.

Note: Detailed sample design information is presented in the "Fifth Integrated Household Survey 2019-2020, Basic Information Document" document.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

HOUSEHOLD QUESTIONNAIRE The Household Questionnaire is a multi-topic survey instrument and is near-identical to the content and organization of the IHS3 and IHS4 questionnaires. It encompasses economic activities, demographics, welfare and other sectoral information of households. It covers a wide range of topics, dealing with the dynamics of poverty (consumption, cash and non-cash income, savings, assets, food security, health and education, vulnerability and social protection). Although the IHS5 household questionnaire covers a wide variety of topics in detail it intentionally excludes in-depth information on topics covered in other surveys that are part of the NSO’s statistical plan (such as maternal and child health issues covered at length in the Malawi Demographic and Health Survey).

AGRICULTURE QUESTIONNAIRE All IHS5 households that are identified as being involved in agricultural or livestock activities were administered the agriculture questionnaire, which is primarily modelled after the IHS3 counterpart. The modules are expanding on the agricultural content of the IHS4, IHS3, IHS2, AISS, and other regional agricultural surveys, while remaining consistent with the NACAL topical coverage and methodology. The development of the agriculture questionnaire was done with input from the aforementioned stakeholders who provided input on the household questionnaire as well as outside researchers involved in research and policy discussions pertaining to the Malawian agriculture. The agriculture questionnaire allows, among other things, for extensive agricultural productivity analysis through the diligent estimation of land areas, both owned and cultivated, labor and non-labor input use and expenditures, and production figures for main crops, and livestock. Although one of the major foci of the agriculture data collection effort was to produce smallholder production estimates for major crops, it is also possible to disaggregate the data by gender and main geographical regions. The IHS5 cross-sectional households supply information on the last completed rainy season (2017/2018 or 2018/2019) and the last completed dry season (2018 or 2019) depending on the timing of their interview.

FISHERIES QUESTIONNAIRE The design of the IHS5 fishery questionnaire is identical to the questionnaire designed for IHS3. The IHS3 fisheries questionnaire was informed by the design and piloting of a fishery questionnaire by the World Fish Center (WFC), which was supported by the LSMS-ISA project for the purpose of assembling a fishery questionnaire that could be integrated into multi-topic household-surveys. The WFC piloted the draft instrument in November 2009 in the Lower Shire region, and the NSO team considered the revised draft in designing the IHS5 fishery questionnaire.

COMMUNITY QUESTIONNAIRE The content of the IHS5 Community Questionnaire follows the content of the IHS3 & IHS4 Community Questionnaires. A “community” is defined as the village or urban location surrounding the enumeration area selected for inclusion in the sample and which most residents recognize as being their community. The IHS5 community questionnaire was administered to each community associated with the cross-sectional EAs interviewed. Identical to the IHS3 and IHS4 approach, to a group of several knowledgeable residents such as the village headman, the headmaster of the local school, the agricultural field assistant, religious leaders, local merchants, health workers and long-term knowledgeable residents. The instrument gathers information on a range of community characteristics, including religious and ethnic background, physical infrastructure, access to public services, economic activities, communal resource management, organization and governance, investment projects, and local retail price information for essential goods and services.

MARKET QUESTIONNAIRE The Market Survey consisted of one questionnaire which is composed of four modules. Module A: Market Identification, Module B: Seasonal Main Crops, Module C: Permanents Crops, and Module D: Food Consumption.

Cleaning operations

DATA ENTRY PLATFORM To ensure data quality and timely availability of data, the IHS5 was implemented using the World Bank’s Survey Solutions CAPI software. To carry out IHS5, 1 laptop computer and a wireless internet router were assigned to each team supervisor, and each enumerator had an 8–inch GPS-enabled Lenovo tablet computer. The use of Survey Solutions allowed for the real-time availability of data as the completed data was completed, approved by the Supervisor and synced to the Headquarters server as frequently as possible. While administering the first module of the questionnaire the enumerator(s) also used their tablets to record the GPS coordinates of the dwelling units. In Survey Solutions, Headquarters can then see the location of the dwellings plotted on a map of Malawi to better enable supervision from afar – checking both the number of interviews performed and the fact that the sample households lie within EA boundaries. Geo-referenced household locations from that tablet complemented the GPS measurements taken by the Garmin eTrex 30 handheld devices and these were linked with publically available geospatial databases to enable the inclusion of a number of geospatial variables - extensive measures of distance (i.e. distance to the nearest market), climatology, soil and terrain, and other environmental factors - in the analysis.

The range and consistency checks built into the application was informed by the LSMS-ISA experience in previous IHS waves. Prior programming of the data entry application allowed for a wide variety of range and consistency checks to be conducted and reported and potential issues investigated and corrected before closing the assigned enumeration area. Headquarters (NSO management) assigned work to supervisors based on their regions of coverage. Supervisors then made assignments to the enumerators linked to their Supervisor account. The work assignments and syncing of completed interviews took place through a Wi-Fi connection to the IHS5 server. Because the data was available in real time it was monitored closely throughout the entire data collection period and upon receipt of the data at headquarters, data was exported to STATA for other consistency checks, data cleaning, and analysis.

DATA MANAGEMENT The IHS5 Survey Solutions CAPI based data entry application was designed to stream-line the data collection process from the field. IHS5 Interviews were collected in “sample” mode (assignments generated from headquarters) as opposed to “census” mode (new interviews created by interviewers from a template) for the NSO to have more control over the sample.

The range and consistency checks built into the application was informed by the LSMS-ISA experience in previous IHS waves. Prior programming of the data

Abstract

The Statistical and Forecasting Service has been entrusted with the production of the AC 2010. (SSP) which is the central statistical department of the Ministry in charge of agriculture, (MAAPRAT) the central department is in charge of the design of the operation, the drafting of the questionnaire and instructions, the training of regional services, the final quality control of the data collected and of the first publication of the results. The SSP has relied on its specialised decentralised levels, the services regional statistics (NUTS2) of statistical and economic information (SRISE). The threshold definition of agricultural holding applied has been the same since 1955, and corresponds exactly to the one proposed by the European regulation. The geographical area is the whole of France; for the DOM the territories of Saint-Martin and Saint-Barthélemy are now excluded, Mayotte is not yet included.

For statistical purposes, agricultural censuses in French territories (French Guyana, Guadeloupe, Reunion and Martinique) are recorded separately in the World Census of Agriculture Database. The census results are presented for all of France.

Geographic coverage

National coverage

Analysis unit

Households

Universe

The statistical unit in the AC 2010 was the agricultural holding, defined as an economic unit that participates in agricultural production and meets the following criteria: · it has an agricultural activity either of production, or of maintenance of the lands in good agricultural and environmental

Kind of data

Census/enumeration data [cen]

Sampling procedure

a. Frame The basic list of agricultural holdings was built using the SSP farm register, the SIRENE register (business register), the list of farmers who had applied for aid (area declarations),' and some additional sources for beekeeping, olive oil, aromatic plants. The holding lists were checked at local level by communal commissions.

b. Complete and/or sample enumeration method(s) The AC and SAPM were conducted using complete enumeration.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

Three questionnaires were used: one for France in Europe (including questions of regional interest) and two for France's overseas territories: one for Guadeloupe, Martinique and Reunion and another for Guyana. The census covered all 16 core items recommended in the WCA 2010. ie.

0001 Identification and location of agricultural holding 0002+ Legal status of agricultural holder 0003 Sex of agricultural holder 0004 Age of agricultural holder 0005 Household size 0006 Main purpose of production of the holding 0007 Area of holding according to land use types 0008 Total area of holding 0009 Land tenure types on the holding 0010 Presence of irrigation on the holding 0011 Types of temporary crops on the holding 0012 Types of permanent crops on the holding and whether in compact plantation 0013 Number of animals on the holding for each livestock type 0014 Presence of aquaculture on the holding 0015+ Presence of forest and other wooded land on the holding 0016 Other economic production activities of the holding's enterprise

Cleaning operations

a. DATA PROCESSING AND ARCHIVING The CAPI interface included controls to ensure that there were responses to all questions. In addition, interactive range and consistency checks were included for each variable so that corrections could be made by the enumerator during the interview. Further edits and imputations were completed at the central office where the census validation and tabulation was completed. To ensure that the list of holdings was complete, several tests were conducted at the end of collection. All available administrative sources were used to verify that existing holdings had been identified and included. The key databases and registers used included that for EU agriculture aid applications, the national database of bovine identification, the computerized vineyard register, organic producer records, and some local registers for small productions. The data, after validation, were archived on secured servers.

b. CENSUS DATA QUALITY To assess the quality of field data collection, completeness checks and feedback were performed at the end of field data collection operation, from March to June 2011. Data checking began during the collection phase on the farmer's premises. It then continued throughout the processing chain. A special effort was made to check the AC's coverage by using the administrative data available. The nonresponse rate was of only 0.96 percent, and the missing data were imputed using the hot deck method.

Data appraisal

The first provisional census results were disseminated in September 2011, ten months after the end of the reference period. The main final results were made available at the end of February 2012, 16 months after the end of the reference period. The AC 2010 results were disseminated online and are available on the SSP website.9 The "ADEL" tool allows web users to build their own tables.

The first table with main results shows the total number and area of holdings broken down by continental France, on one hand, and its overseas territories, on the other. See metadata review tables in external materials.

Abstract

Censuses are principal means of collecting basic population and housing statistics required for social and economic development, policy interventions, their implementation and evaluation. The Post-Apartheid South African government has conducted four Censuses, in 1996, 2001, 2011 and 2022.

Geographic coverage

The South African Census 2022 has national coverage.

Analysis unit

Households and individuals

Universe

The South African Census 2022 covered every person present in South Africa on the Census reference night, midnight of 2-3 February 2022 including all de jure household members and residents of institutions.

Kind of data

Census/enumeration data

Sampling procedure

Census 2022 micro-data were sampled from the latest census data based on prescribed business requirements to ensure the generated estimates match the census population counts at local municipal level by age, sex, and population group. The Census 2022 Household record file and Person record file were used as a basis for the creation of the household and person sampling frame respectively. Households and household members in the in-scope households (records of the household questionnaire) formed part of the 10% sample. The municipal sample sizes for households were determined by taking 10% of the respective municipal measure of sizes. The household frame was implicitly stratified within each local municipality using household characteristics. Systematic samples of households were selected in each local municipality from the implicitly sorted household frame. The procedure used the allocated sample within each local municipality to produce the sample of households as well as the sampling weights, which are the inverse of the inclusion probabilities.

A Post-enumeration Survey (PES) is an independent sample survey that is conducted immediately after the completion of census enumeration to evaluate the coverage and content errors of the census. A sample of 840 sub-enumeration areas was selected across South Africa's nine provinces for the PES sampling frame. A mixed-mode data collection methodology was implemented to counteract the effects of the COVID19 pandemic. This was made possible by having integrated, digitally enabled survey processes with a geo-spatial information frame as a base.

Mode of data collection

Face-to-Face and Computer Assisted Personal and Telephone Interview

Research instrument

One questionnaire was used to capture the Census 2022 which included information on: 1. Households - particulars of the household (cover page of the household questionnaire) and household information sections of the household questionnaire. 2. Persons - particulars of the household member and person information sections of the household questionnaire. 3. Geography - geographic information associated with households and persons

A Post-Enumeration Survey was carried out after the census, which used a PES questionnaire.

Sampling error estimates

Coverage errors are a measure of how many persons or households were missed or counted more than once in the census. The final net coverage error rate relative to the final true population of 61,4 million persons is thus 31,1%. The final net coverage error rate relative to the final true population of 19,3 million households is 30,5%.

Content errors indicate the quality of key characteristics in the census. With respect to content errors, six variables were tested for consistency in terms of the responses that were recorded in the Census and the PES. The aggregated index of inconsistency was 7,5% for population group, 8,2% for sex, and 13,6% for age group, indicating a high level of agreement. The aggregated index of inconsistency for marital status was 23,0%, relationship to head of household was 34,8%, and country of birth was 42,3%, indicating moderate rates of agreement.

Abstract

Census of Population and Housing refers to the entire process of collecting, compiling, evaluating, analyzing, and publishing data about the population and the living quarters in a country. It entails the listing and recording of the characteristics of each individual and each living quarter as of a specified time and within a specified territory.

Census 2000 is designed to take an inventory of the total population and housing units in the Philippines and to collect information about their characteristics. The census of population is the source of information on the size and distribution of the population as well as information about the demographic, social, economic and cultural characteristics. The census of housing, on the other hand, provides information on the supply of housing units, their structural characteristics and facilities which have bearing on the maintenance of privacy, health and the development of normal family living conditions. These information are vital for making rational plans and programs for national and local development.

The Census 2000 aims to provide government planners, policy makers and administrators with data on which to base their social and economic development plans and programs.

May 1, 2000 has been designated as Census Day for the 2000 Census of Population and Housing or Census 2000, on which date the enumeration of the population and the collection of all pertinent data on housing in the Philippines shall refer.

Geographic coverage

National Coverage Regions Provinces Cities and Municipalities Barangays

Analysis unit

Individuals Households Housing units

Universe

The Census 2000 covered all persons who were alive as of 12:01 a.m. of May 1, 2000 and who are: - Filipino nationals permanently residing in the Philippines; - Filipino nationals who are temporarily at sea or are temporarily abroad as of census date; - Filipino overseas workers as of census date, even though expected to be away for more than a year; - Philippine government officials, both military and civilian, including Philippine diplomatic personnel and their families, assigned abroad; and - Civilian citizens of foreign countries having their usual residence in the Philippines or foreign visitors who have stayed or are expected to stay for at least a year from the time of their arrival in this country.

Kind of data

Census/enumeration data [cen]

Sampling procedure

In the Census 2000, there are basically two types of questionnaires to be used for the enumeration of hosueholds memmbers. These are CPH Form 2 or the Common Household Questionnaire and the CPH Form 3 or the Sample Household Questionnaire. There are procedures for selecting those households to whom CPH Form 3 will be administered. All enumerators are required to strictly follow these procedures.

The sampling rate, or the proportion of households to be selected as samples within each EA, varies from one EA to another. It can be either 100%, 20% or 10%. If the sampling rate applied to an EA is 100%, it means that all households in that EA will use CPH Form 3. IF it is 20% or 10%, it means that one-fifth or one-tenth, respectively, of all households will use CPH Form 3 while the rest will use CPH Form 2.

The scheme for the selection of sample households is known as systematic sampling with clusters as the sampling units. Under this scheme, the households in an EA are grouped in clusters of size 5. Clusters are formed by grouping together households that have been assigned consecutive serial numbers as they are listed in the Listing Page.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaires for 2000 Census of Population and Housing were basically patterned from previous censuses except that it should be in Intelligent Character Recognition (ICR) format. The basic questionnaires designed for this undertaking were as follows:

CPH Form 1 - Listing Page This is a sheet wherein all buildings, housing units, households and institutional living quarters within an enumeration area (EA) will be listed. Other information pertaining to the population of households and institutional living quarters will also be recorded in this form.

CPH Form 2 - Common Household Questionnaire This is the basic census questionnaire, which will be used for interview and for recording information about the common or non-sample households. This questionnaire gathers information on the following demographic and social characteristics of the population: relationship to household head, family nucleus, date of birth, age, birth registration, sex, marital status, religious affiliation, disability, ethnicity, residence five years ago and highest educational attainment. This also gathers information on building and housing unit characteristics.

CPH Form 3 - Sample Household Questionnaire This is the basic census questionnaire, which will be used for interview and for recording information about the sample households. This questionnaire contains the same question as in CPH Form 2 and additional questions, namely: citizenship, language, literacy, school attendance, type of school, place of school, usual activity/occupation, kind of business/industry, place of work and some items on fertility. It also asks additional questions on household characteristics and amenities and residence five years ago.

CPH Form 4 - Institutional Population Questionnaire This questionnaire records information about persons considered part of the institutional population. It contains questions on residence status, date of birth, age, sex, marital status, religious affiliation, disability, ethnicity and highest educational attainment.

CPH Form 5 - Barangay Schedule This questionnaire will gather indicators to update the characteristics of all barangays which will determine its urbanity.

CPH Form 6 - Notice of Listing/Enumeration This is the sticker that will be posted in a very conspicuous place, preferably in front of the house or gate of the building after listing and interviewing. This sticker indicates that the Building/Housing Unit/Household has already been enumerated.

CPH Form 7 - Common Household Questionnaire Self Administered Questionnaire (SAQ) Instructions This form contains the detailed instructions on how to fill up/answer CPH Form 2. It will accompany CPH Form 2 to be distributed to households who will answer the form themselves, such as those in designated SAQ areas or those where three callbacks or four visits have been made.

CPH Form 8 - Institutional Population Questionnaire SAQ Instructions This form describes the instructions on how to accomplish CPH Form 4 - Institutional Population Questionnaire. It will accompany CPH Form 4 to be distributed to head of institutions who will accomplish the form.

CPH Form 9 - Appointment Slip This form will be used to set an appointment with the household head or any responsible member of the household in case you were unable to interview any one during your first visit or second visit. You will indicate in this form the date and time of your next visit.

Blank Barangay Map This form will be used to enlarge map of each block of an enumeration area/barangay especially if congested areas are being enumerated.

The main questionnaires were developed in English and were translated to major dialects: Bicol, Cebuano, Hiligaynon, Ifugao, Ilocano, Kapampangan, Tagalog, and Waray.

Abstract

This survey intends to: - · Measure the labour force or economically active population size in relation to the general population in the country. · Identify and analyse the factors leading to the emergence and growth of Labour Force in the country. · Monitor the labour force participation. · Identify and measure the informal sector from within the labour force. · Monitor other Key Indicators of the Labour Market such as employment rates,unemployment rates, hours of work, average income and/or wages etc.

Furthermore, the survey seeks to examine the relationships of socio-economic factors such as education, health, social security, employment within the labour force, and more importantly to measure the causes and effects of children’s involvements in economic activities with special focus on the conditions and environment under which affected children operate.

The main objective of the 2012 LFS was to collect data on the social and economic activities of the population, including detailed information on employment, unemployment, underemployment, wages, informal sector, general characteristics of the labour force and economically inactive population. The survey was designed to specifically measure and monitor Key Indicators of the Labour Market (KILM) such as employment levels, unemployment, income and child labour in Zambia. The measurement of the KILM was with a view to informing users and policy-makers for decision-making. The methodology used in carrying out the survey and the design of questionnaire conform to internationally acceptable standards.

Geographic coverage

The 2012 Labour Force Survey (LFS) was a nation-wide survey covering household population in all the ten provinces and, in both rural and urban areas.

The survey covered a representative sample of 11, 520 households, which were selected at two stages. In the first stage, 576 Standard Enumeration Areas (SEAs) were selected from a sampling frame developed from the 2010 Census of Population and Housing. In the second stage, households in each of the selected SEA were first listed/updated and then 20 households for enumeration were selected. The total sample of 11,520 households was first allocated between rural, urban and the provincial domains in proportion to the population of each domain according to the 2010 Census results.

Analysis unit

The unit of analysis was Households and Individuals (men and women of 5 years and older).

Universe

The survey covered all de jure household members (usual residents) in non-institutionalised housing units, all women and men aged 5 years and older.

The survey excluded institutional populations such as those in hospitals, barracks, prisons or refugee camps. This is because the survey was intended only for usual members of the households, i.e. members who lived together as a household for at least six months or who intended to live together as a household for more than six months - who constituted a household.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample was designed to allow separate estimates at national level for rural and urban areas. Further, it also allowed for provincial estimates. A cluster, which is equivalent to a Standard Enumeration Area (SEA), was the primary sampling unit in the first stage. In the second stage, a household was a sampling unit for enumeration purposes.

Zambia is administratively divided into ten provinces. Each province is in turn subdivided into districts. For statistical purposes each district is subdivided into Census Supervisory Areas (CSAs) and these are in turn demarcated into Standard Enumeration Areas (SEAs). The Census mapping exercise of 2006-2010 in preparation for the 2010 Census of Population and Housing, demarcated the CSAs within wards, wards within constituencies and constituencies within districts. As at the time of the survey, Zambia had 74 districts, 150 constituencies, 1,430 wards and about 25,000 SEAs. Information borne on the list of SEAs from the sampling frame also includes number of households and the population size as at the last update of the SEA. The number of households determined the selection of primary sampling units (PSU). The SEAs are stratified as urban and rural.

The total sample of 11,520 households was first allocated between rural, urban and the provincial domains in proportion to the population of each domain according to the 2010 Census results. The proportional allocation does not however allow for reliable estimates for lower domains like district, ward or constituency. Adjustments to the proportional allocation of the sample were made to allow for reasonable comparison to be achieved between strata or domains. Therefore, disproportionate allocation was adopted, for the purpose of maximizing the precision of survey estimates. The disproportionate allocation is based on the optimal square root allocation method designed by Leslie Kish. The sample was then selected using a stratified two-stage cluster design.

Sampling deviation

There was no deviation from sample design.

Mode of data collection

Face-to-face [f2f]

Research instrument

Two types of questionnaires (Form A and Forma B) were used to collect data from the household members. Form A was used in the first stage for listing purposes while Form B was used in the second stage for collecting detailed data from the selected households. It was a requirement for each household member to provide responses during the face-to-face interview to the questions that were asked.

The main questionnaire has ten sections namely: a. Demographic Characteristics b. Education, Literacy and Skills Training c. Economic Activity d. Employment e. Hours of Work and Underemployment f. Income g. Unemployment/Job Search h. Previous Work Experience i. Household Chores j. Working Conditions (i.e. Forced labour)

Cleaning operations

Data editing took place at a number of stages throughout the processing. These included: 1. Field editing 2. Office editing and coding 3. During data entry 4. Structure checking and completeness 5. Secondary editing 6. Strucural checking of SAS data files

Response rate

At the end of the field work and editing in the provinces, a total of at least 11,000 of completed questionnaires, representing a 99.8 percent response rate were sent to Head Office for data processing.

Data appraisal

A series of data quality tables and graphs are available to review the quality of the data and in addition to this, external resources such as the 2012 Labour Force Survey report has been attached.

Abstract

This is the fourth Labor Force Survey of Tonga. The first one was conducted in 1990. Earlier surveys were conducted in 1990, 1993/94, and 2003 and the results of those surveys were published by the Statistics Department.

The objective of the LFS survey is providing information on not only well-known employment and unemployment as well as providing comprehensive information on other standard indicators characterizing the country labour market. It covers those age 10 and over in the whole Kingdom. Information includes age, sex, activity, current and usual employment status, hours worked and wages and in addition included a seperate Food Insecurity Experiences Survey (FIES) questionniare module at the Household Level.

The conceptual framework used in this labour force survey in Tonga aligns closely with the standards and guidelines set out in Resolutions of International Conferences of Labour Statistician.

Geographic coverage

National coverage.

There are six statistical regions known as Division's in Tonga namely Tongatapu urban area, Tongatapu rural area, Vava'u, Ha'pai, Eua and the Niuas.Tongatapu Urban refers to the capital Nuku'alofa is the urban area while the other five divisions are rural areas. Each Division is subdivided into political districts, each district into villages and each village into census enumeration areas known as Census Blocks. The sample for the 2018 Labour Force Survey (LFS) was designed to cover at least 2500 employed population aged 10 years and over from all the regions. This was made mainly to have sufficient cases to provide information on the employed population.

Analysis unit

• Households (for food insecurity module questionnaire)
• Individuals.

Universe

Population living in private households in Tonga. The labour force questionnaire is directed to the population aged 10 and above. Disability short set of questions is directed to all individuals age 2 and above and the food insecurity experience scale is directed to the head of household.

Kind of data

Sample survey data [ssd]

Sampling procedure

2018 Tonga Labour force survey aimed at estimating all the main ILO indicators at the island group level (geographical stratas). The sampling strategy is based on a two stages stratified random survey.

1. Computation of the survey parameters: Total sample size per strata, number of households to interview in each Primary Sampling Unit (PSU = census block) and number of PSUs to select The stratification of the survey is the geographical breakdown by island group (6 stratas Tongatapu urban, Tongatapu rural, Vava'u, Ha'apai, 'Eua, Niuas)
2. The selection strategy is a 2 stages random survey where: Random selection of census blocks within each
3. Census blocks are randomly selected in first place, using probability proportional to size

4. 15 households per block are randomly selected using uniform probability

5. The sampling frame used to select PSUs (census blocks) and household is the 2016 Tonga population census.

The computation of sample size required the use of: - Tonga 2015 HIES dataset (labour force section) - Tonga 2016 population census (distribution of households across the stratas) The resource variable used to compute the sample size is the labour force participation rate from the 2015 HIES. The use of the 2015 labour force section of the Tonga HIES allows the computation of the design effect of the labour force participation rate within each strata. The design effect and sampling errors of the labour force participation rate estimated from the 2015 HIES in combination with the 2016 household population distribution allow to predict the minimum sample size required (per strata) to get a robust estimate from the 2018 LFS.

Total sample size: 2685 households Geographical stratification: 6 island groups Selection process: 2 stages random survey where census blocks are selected using Probability Proportional to Size (Primary Sampling Unit) in the first place and households are randomly selected within each selected blocks (15 households per block) Non response: a 10% increase of the sample happened in all stratas to account for non-response Sampling frame: the household listing from the 2016 population census was used as a sampling frame and the 2015 labour force section of the HIES was used to compute the sample size (using labour force participation rate.

Sampling deviation

No major deviation from the original sample has taken place.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The 2018 Tonga Labour Force Survey questionnaire included 15 sections:

IDENTIFICATION SECTION B: INDIVIDUAL CHARACTERISTICS SECTION C: EDUCATION (AGE 3+) SECTIONS B & C: EMPLOYMENT IDENTIFICATION AND TEMPORARY ABSENCE (AGE 10+) SECTION D: AGRICULTURE WORK AND MARKET DESTINATION SECTION E1: MAIN EMPLOYMENT CHARACTERISTICS SECTION E2: SECOND PAID JOB/ BUSINESS ACTIVITY CHARACTERISTICS SECTION F: INCOME FROM EMPLOYMENT SECTION G: WORKING TIME SECTION H: JOB SEARCH SECTION I: PREVIOUS WORK EXPERIENCE SECTION J: MAIN ACTIVITY SECTION K: OWN USE PRODUCTION WORK FOOD INSECURITY EXPERIENCES GPS + PHOTO

The questionniares were developed and administered in English and were translated into Tongan language. The questionnaire is provided as external resources.

The draft questionnaire was pre-tested during the supervisors training and during the enumerators training and it was finally tested during the pilot test. The pilot testing was undertaken on the 27th of May to the 1st of June 2018 in Tongatapu Urban and Rural areas. The questionnaire was revised rigorously in accordance to the feedback received from each test. At the same time, a field operations manual for supervisors and enumerators was prepared and modified accordingly for field operators to use as a reference during the field work.

Cleaning operations

The World Bank Survey Solutions software was used for Data Processing, STATA software was used for data cleaning, tabulation tabulation and analysis.

Editing and tabulation of the data will be undertaken in February/March 2019 in collaboration with SPC and ILO.

Response rate

A total, 2,685 households were selected for the sample. Of these existing households, 2,584 were successfully interviewed, giving a household response rate of 96.2%.

Response rates were higher in urban areas than in the rural area of Tongatapu.

-1 Tongatapu urban: 97.30%
-2 Tongatapu rural: 93.00%
-3 Vava'u: 100.00% -4 Ha'pai: 100.00% -5 Eua: 95.20% -6 Niuas: 80.00% -Total: 96.20%.

Sampling error estimates

Sampling errors were computed and are presented in the final report.

The sampling error were computed using the survey set package in Stata. The Finite Population Correction was included in the sample design (optional in svy set Stata command) as follow: - Fpc 1: total number of census blocks within the strata (variable toteas) - Fpc 2: Here is a list of some LF indicators presented with sampling error

-RSE: Labour force population: 2.2% Employment - population in employment: 2.2% Labour force participation rate (%): 1.7% Unemployment rate (%): 13.5% Composite rate of labour underutilization (%): 7.3% Youth unemployment rate (%): 18.2% Informal employment rate (%): 2.7% Average monthly wages - employees (TOP): 12%.

-95% Interval: Labour force population: 28,203 => 30,804 Employment - population in employment: 27,341 => 29,855 Labour force participation rate (%): 45.2% => 48.2% Unemployment rate (%): 2.2% => 3.9% Composite rate of labour underutilization (%): 16% => 21.4% Youth unemployment rate (%): 5.7% => 12.1% Informal employment rate (%): 44.3% => 49.4% Average monthly wages - employees (TOP): 1,174 => 1,904.

Abstract

Cambodia Living Standards Measurement Study – Plus (LSMS+) Survey 2019- 2020 was implemented by the National Institute of Statistics, with support from the World Bank LSMS+ program (www.worldbank.org/lsmsplus). The survey attempted to conduct private interviews with all the adult household members (aged 18 and older) in each sampled household as part of a nationally-representative survey sample. The individual disaggregated data collection had a focus on (i) ownership of and rights to physical and financial assets, (ii) work and employment, and (iii) non-farm enterprises, and was anchored in the latest international recommendations for survey data collection on these topics.

Geographic coverage

National

Analysis unit

• Household
• Individual

Kind of data

Sample survey data [ssd]

Sampling procedure

The Primary Sampling Units (PSUs) of this survey were the subsamples of the selected PSUs of the Cambodia Socio-Economic Survey (CSES) 2019/20. The PSU in this case can either be a village (if the village is small) or an Enumeration Area (EA) from the mapping operation of 2019 General Population Census of Cambodia (if the village is large, exceeding 120 households). The Cambodia LSMS+ sample covered all the CSES’ s sample villages in three months (those selected for interviews during the October - December period of fieldwork) out of its twelve-month sample.

The Secondary Sampling Units (SSUs) in this survey constitute sample households. In this stage, 6 households were selected in each selected PSU. The selections of these households were carried out in the field by the field enumerators. The selection was done under the Circular Systematic Random Sampling (CSRS) scheme using the PSU frame of household from the household listing conducted by the CSES field enumerator in the selected PSU. More details can be found in the Basic Information Document.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The Cambodia LSMS+ covered the following topics:

Household Questionnaire: - Household Roster - Children Living Elsewhere - Housing - Food Consumption - Non-food Consumption - Household Enterprises - Land Roster - Livestock Roster - Durables Roster

Individual-level Questionnaire: - Education - Health - Internal and International Migration - Labor - Time Use - Land Ownership and Rights - Livestock Ownership - Durables Ownership - Mobile Phone Ownership - Financial Accounts

Cleaning operations

Data Entry Platform

The Cambodia LSMS+ was conducted using Computer Assisted Personal Interview (CAPI) techniques. The questionnaire was implemented using the CAPI software, Survey Solutions. The Survey Solutions software was developed and maintained by the Data Analytics and Tools Unit within the Development Economics Data Group (DECDG) at the World Bank. Each interviewer was given one tablet, which they used to conduct the interviews. Overall, implementation of survey using Survey Solutions CAPI was highly successful, as it allowed for timely availability of the data from completed interviews.

Data Management

The data communication system used in the Cambodia LSMS+ was highly automated. Field teams were provided with routers to carry with them in the field so they could connect to internet as frequently as possible to sync their questionnaires and this ensured access to the data in real-time.

Data Cleaning The data cleaning process was done in two main stages. The first stage was to ensure proper quality control during the fieldwork. This was achieved in part by incorporating validation and consistency checks into the Survey Solutions application used for the data collection and designed to highlight many of the errors that occurred during the fieldwork.

The second stage of cleaning involved a comprehensive review of the final raw data following the first stage of cleaning. Every variable was examined individually for (1) consistency with other sections and variables, (2) out of range responses, and (3) formatting.

Abstract

The National Sample Census of Agriculture (NCA) of Nepal 2001/02, conducted by the Central Bureau of Statistics is the most recent census of agriculture in His Majestic Government, Nepal. The first census in this country was conducted in 1961/62. Since then, the Census of Agriculture has been conducted decennially: 1971/72, 1981/82 and 1991/92.

The 2001/02 NCA was undertaken in two phases. The first phase involved the complete enumeration of all agricultural holdings in the country including the area of the holding and livestock number. The enumeration of all the holdings was an integral activity of the first phase of the Census of Population 2001, which undertook the listing operation from May 14-28, 2001. Some questions on agricultural activities were asked to identify the agricultural holding. The second phase of the 2001/02 NCA was the selection and enumeration of sample holdings to widen the scope of the census from January to June 2002. Data on agricultural crops gathered refer to calendar year 2001 while the livestock and poultry population refers at the time of enumeration, from January to June 2002.

The main objective of the census of agriculture of Nepal is to publish data at district level on the following: 1. Structure and characteristics of the holding such as size, agricultural land use, land tenure, land fragmentation, area planted to crops, number of livestock, and others; 2. To provide benchmark data for improving the reliability of estimates from current agricultural survey; and, 3. To provide basic data for national, ecological belts and development regions levels for national as well as sub-national policy, planning and decision making purposes.

Geographic coverage

National Coverage

Analysis unit

Agricultural holdings

Universe

All agricultural households having a minimum specified agricultural land area operated by holding (for hill and mountain region 4 anna and 8 dhure in terai) or having a specified minimum number of livestock or poultry.

Agricultural activities undertaken by government organizations, businesses like corporations and other juridical persons were not covered by the NCA.

Kind of data

Census/enumeration data [cen]

Sampling procedure

A two-stage stratified sampling was employed in the selection of the samples for enumeration to obtain the characteristics of the holdings for the 2001/02 NCA. This design is similar to that of the 1991/92 sampling design, which is a self-weighting sample.

The listing of the wards in each district with the summarized data of the number of holdings and area was used to form enumeration areas (EA's). However, wards containing less than 30 holdings were combined to form one EA. The EAs in each district were stratified according to the number of holdings enumerated, arranged from the highest to the lowest.

There are some VDCs (Village Development Committees) that were not covered by the listing operations or census enumeration during the Census of Population 2001 involving 12 districts. However, some estimates of the number of households and population were prepared by the Population Division based on the census listing or some independent sources of information, in the absence of the listing of households. The 12 districts are: Jhapa and Siraha districts both in Eastern Tarai; Surkhet and Salyan districts in Midwestern Hill; Sindhupalchok and Dolakha districts in Central Mountain; Sinduli district in Central Hill; Dolpa, Jumla, Kalitkot, and Mugu districts all in Midwestern Mountain; and Bajura district in Far-Western Mountain.

The first stage of sample selection involved the primary sampling units (PSUs), where sample enumeration areas (EAs) were selected with probability proportional to size (PPS). The measure of size is the number of holdings enumerated in the EAs during the Census of Population 2001 listing operations.

To measure the importance of each district, the total area under 8 major crops was determined (paddy, wheat, maize, millet, barley, sugarcane, oilseed and potato). Districts were stratified into four groups according to this criterion. Group I represented the 10 least important districts; Group II, the next 15 important districts and Group III, the next 25 important districts and Group IV, the 25 most important districts. The number of selected EAs per district in each group follows: Group I - 50 EAs per district (total of 450 EAs because one district, specifically Manang district was taken as a certainty sample district) Group II - 60 EAs per district (total of 900 EAs) Group III - 70 EAs per district (total of 1,750 EAs) Group IV- 80 EAs per district (total of 2000 EAs).

The second stage of sample selection involved the selection of sample holdings systematically in each sample EA. Before the sample selection was done, a listing of holdings in each sample PSU was conducted to update the listing during the Population Census. The target number of holdings for enumeration in each sample EA was 25.

The Census of Agriculture sample was designed to be self-weighting within each district, i.e. all holdings within a district have the same chance of being included in the sample. Approximately 5,100 enumeration areas were selected in the 74 districts and about 125,000 agricultural holdings were selected for enumeration. One district was completely covered in the second phase of the census of agriculture because of the few number of enumeration areas and holdings. This is the district of Manang. The detailed stratification scheme done on the districts, the sampling procedures and the estimation of parameters for each district are found in the technical report, which is one of the series of reports prepared for the 2001/02 NCA.

Mode of data collection

Face-to-face paper [f2f]

Cleaning operations

The data were subjected to the following editing processes: 1. Manual editing and coding were done at the head office after collecting the filled questionnaires. 2. Completeness check after data entry done by a completeness checking computer program. 3. Machine editing by machine editing program.

Data appraisal

A pilot survey was conducted one year ago i.e. in 2000. Training for district officers and supervisors was held in the centre and training for the field supervisors and enumerators was held in districts at the beginning of the field work. On the average, one hour is taken for filling the questionnaire i.e. schedule. Nepali national language was used for conducting the interviews.

Abstract

The 2022 Cameroon Malaria Indicator Survey (2022 MIS) was implemented by the National Institute of Statistics (NIS). Data collection took place from August 22 to December 1, 2022. The survey is a national sample survey designed to provide information on topics such as availability and use of insecticide-treated nets (ITNs), prophylactic and therapeutic use of antimalarials, diagnostic testing for malaria in children presenting with fever, and the prevalence of malaria among children under age 5 (based on a rapid diagnostic test carried out at home).

The primary objective of the 2022 CMIS is to provide up-to-date estimates of basic demographic and health indicators related to malaria. Specifically, the survey collected information on vector control interventions (such as mosquito nets), intermittent preventive treatment of malaria among pregnant women, and care seeking for and treatment of fever among children. In addition, young children were tested for anemia and for malaria. Community knowledge, perceptions, and practices regarding malaria prevention and control were also assessed.

The information collected through the 2022 CMIS is intended to help policymakers and program managers in evaluating and implementing programs and strategies for improving the health of the country’s population.

Geographic coverage

National coverage

Analysis unit

• Household
• Individual
• Woman age 15-49

Kind of data

Sample survey data [ssd]

Sampling procedure

The 2022 CMIS targeted individuals in households throughout the country. A national sample of 6,580 households (3,598 in 257 urban clusters and 2,982 in 213 rural clusters) was planned for the survey. The sample was distributed to ensure adequate representation of urban and rural areas as well as the following 12 regions: Adamawa, Centre (excluding Yaoundé), Douala, East, Far North, Littoral (excluding Douala), North, North-West, West, South, South-West, and Yaoundé. In each of the regions (excluding Yaoundé and Douala, which are considered as having no rural sections), two layers were created: the urban layer and the rural layer.

A stratified, two-stage survey was implemented. In the first stage, 470 enumeration areas (EAs) or clusters were selected systematically with probability proportional to household size. The EAs were derived from the mapping work of the fourth General Census of Population and Housing (GRPH), carried out in 2017–18 by the Central Bureau of Population Censuses and Studies (BUCREP). A mapping exercise and enumeration of households in the clusters selected were implemented on tablet PCs by NIS from May 11 to August 14, 2022, to establish an updated list of households in each EA to serve as the basis for the second-degree draw. In the second stage, a sample of 14 households per cluster was selected using a systematic draw with equal probability.

All women age 15–49 who were residents of selected households or visitors who spent the night preceding the interview in the household were eligible to be interviewed. In addition, all children age 6–59 months were eligible for malaria and anemia tests.

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

Mode of data collection

Face-to-face [f2f]

Research instrument

Three questionnaires were used in the 2022 CMIS: the Household Questionnaire, the Woman’s Questionnaire, and the Biomarker Questionnaire. The questionnaires were based on standard DHS Program templates and adapted to reflect Cameroon’s specific population and malaria control needs. Information on survey data collectors was also gathered via a self-administered Fieldworker Questionnaire. All questionnaires were prepared in French and English.

Cleaning operations

In the interviews, responses were recorded directly on tablets using the appropriate computer application, developed using CSPro software. This application has several menus and includes internal controls and interview guides. Then data collected in the field were sent to the central server via the Internet using a quality control program, allowing almost instantaneous detection of the main collection errors for each team and each fieldworker. This information was immediately sent to the field teams to improve data quality, including returning to households for necessary checks. Regular activities of the chief supervisor focused mainly on teams for which there were specific concerns regarding data quality tables.

Once all of the field data were sent to the server, the survey data file was checked and cleaned and the weighting coefficients applied. All original identifiers were deleted from the data file. After checking that the data file was in its final format, the findings shown here were produced. All cover pages of the paper questionnaires containing identifiers were wiped out.

Response rate

Of the 6,580 households initially scheduled to be surveyed, 6,290 were actually selected. Of these 6,290 households, 6,080 were occupied at the time of the survey. Of the occupied households, 6,031 were successfully surveyed, for a response rate of 99%. In the surveyed households, 6,647 women age 15–49 were eligible for the individual women’s survey and 6,532 were successfully interviewed, for a response rate of 98%.

Sampling error estimates

The estimates from a sample survey are affected by two types of errors: nonsampling errors and sampling errors. Nonsampling errors are the results of mistakes made in implementing data collection and in 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, or incorrect data entry. Although numerous efforts were made during the implementation of the 2022 Cameroon Malaria Indicator Survey (2022 CMIS) 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 2022 CMIS is only one of many samples that could have been selected from the same population, using the same design and expected sample size. Each of these samples would yield results that differ somewhat from the results of the selected sample. Sampling errors are a measure of the variability among all possible samples. Although the exact degree of variability is unknown, it can be estimated from the survey results.

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

If the sample of respondents had been selected as a simple random sample, it would have been possible to use straightforward formulas for calculating sampling errors. However, the 2022 CMIS sample was the result of a multistage stratified design, and, consequently, it was necessary to use more complex formulas. Sampling errors are computed via SAS programs developed by ICF. These programs use the Taylor linearization method to estimate variances for estimated means, proportions, and ratios. The Jackknife repeated replication method is used for variance estimation of more complex statistics such as fertility and mortality rates.

Sampling errors tables are presented in Appendix B of the final report.

Data appraisal

Data Quality Tables

• Household age distribution
• Age distribution of eligible and interviewed women
• Age displacement at ages 14/15
• Age displacement at ages 49/50
• Live births by years preceding the survey
• Completeness of reporting
• Observation of mosquito nets
• Number of enumeration areas completed by month and region
• Positive rapid diagnostic test (RDT) results by month and region

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

Abstract

The 2016 Integrated Household Panel Survey (IHPS) was launched in April 2016 as part of the Malawi Fourth Integrated Household Survey fieldwork operation. The IHPS 2016 targeted 1,989 households that were interviewed in the IHPS 2013 and that could be traced back to half of the 204 enumeration areas that were originally sampled as part of the Third Integrated Household Survey (IHS3) 2010/11. The 2019 IHPS was launched in April 2019 as part of the Malawi Fifth Integrated Household Survey fieldwork operations targeting the 2,508 households that were interviewed in 2016. The panel sample expanded each wave through the tracking of split-off individuals and the new households that they formed. Available as part of this project is the IHPS 2019 data, the IHPS 2016 data as well as the rereleased IHPS 2010 & 2013 data including only the subsample of 102 EAs with updated panel weights. Additionally, the IHPS 2016 was the first survey that received complementary financial and technical support from the Living Standards Measurement Study – Plus (LSMS+) initiative, which has been established with grants from the Umbrella Facility for Gender Equality Trust Fund, the World Bank Trust Fund for Statistical Capacity Building, and the International Fund for Agricultural Development, and is implemented by the World Bank Living Standards Measurement Study (LSMS) team, in collaboration with the World Bank Gender Group and partner national statistical offices. The LSMS+ aims to improve the availability and quality of individual-disaggregated household survey data, and is, at start, a direct response to the World Bank IDA18 commitment to support 6 IDA countries in collecting intra-household, sex-disaggregated household survey data on 1) ownership of and rights to selected physical and financial assets, 2) work and employment, and 3) entrepreneurship – following international best practices in questionnaire design and minimizing the use of proxy respondents while collecting personal information. This dataset is included here.

Geographic coverage

National coverage

Analysis unit

• Households
• Individuals
• Children under 5 years
• Consumption expenditure commodities/items
• Communities
• Agricultural household/ Holder/ Crop

Universe

The IHPS 2016 and 2019 attempted to track all IHPS 2013 households stemming from 102 of the original 204 baseline panel enumeration areas as well as individuals that moved away from the 2013 dwellings between 2013 and 2016 as long as they were neither servants nor guests at the time of the IHPS 2013; were projected to be at least 12 years of age and were known to be residing in mainland Malawi but excluding those in Likoma Island and in institutions, including prisons, police compounds, and army barracks.

Kind of data

Sample survey data [ssd]

Sampling procedure

A sub-sample of IHS3 2010 sample enumeration areas (EAs) (i.e. 204 EAs out of 768 EAs) was selected prior to the start of the IHS3 field work with the intention to (i) to track and resurvey these households in 2013 in accordance with the IHS3 fieldwork timeline and as part of the Integrated Household Panel Survey (IHPS 2013) and (ii) visit a total of 3,246 households in these EAs twice to reduce recall associated with different aspects of agricultural data collection. At baseline, the IHPS sample was selected to be representative at the national, regional, urban/rural levels and for each of the following 6 strata: (i) Northern Region - Rural, (ii) Northern Region - Urban, (iii) Central Region - Rural, (iv) Central Region - Urban, (v) Southern Region - Rural, and (vi) Southern Region - Urban. The IHPS 2013 main fieldwork took place during the period of April-October 2013, with residual tracking operations in November-December 2013.

Given budget and resource constraints, for the IHPS 2016 the number of sample EAs in the panel was reduced to 102 out of the 204 EAs. As a result, the domains of analysis are limited to the national, urban and rural areas. Although the results of the IHPS 2016 cannot be tabulated by region, the stratification of the IHPS by region, urban and rural strata was maintained. The IHPS 2019 tracked all individuals 12 years or older from the 2016 households.

Mode of data collection

Computer Assisted Personal Interview [capi]

Cleaning operations

Data Entry Platform To ensure data quality and timely availability of data, the IHPS 2019 was implemented using the World Bank’s Survey Solutions CAPI software. To carry out IHPS 2019, 1 laptop computer and a wireless internet router were assigned to each team supervisor, and each enumerator had an 8–inch GPS-enabled Lenovo tablet computer that the NSO provided. The use of Survey Solutions allowed for the real-time availability of data as the completed data was completed, approved by the Supervisor and synced to the Headquarters server as frequently as possible. While administering the first module of the questionnaire the enumerator(s) also used their tablets to record the GPS coordinates of the dwelling units. Geo-referenced household locations from that tablet complemented the GPS measurements taken by the Garmin eTrex 30 handheld devices and these were linked with publically available geospatial databases to enable the inclusion of a number of geospatial variables - extensive measures of distance (i.e. distance to the nearest market), climatology, soil and terrain, and other environmental factors - in the analysis.

Data Management The IHPS 2019 Survey Solutions CAPI based data entry application was designed to stream-line the data collection process from the field. IHPS 2019 Interviews were mainly collected in “sample” mode (assignments generated from headquarters) and a few in “census” mode (new interviews created by interviewers from a template) for the NSO to have more control over the sample. This hybrid approach was necessary to aid the tracking operations whereby an enumerator could quickly create a tracking assignment considering that they were mostly working in areas with poor network connection and hence could not quickly receive tracking cases from Headquarters.

The range and consistency checks built into the application was informed by the LSMS-ISA experience with the IHS3 2010/11, IHPS 2013 and IHPS 2016. Prior programming of the data entry application allowed for a wide variety of range and consistency checks to be conducted and reported and potential issues investigated and corrected before closing the assigned enumeration area. Headquarters (the NSO management) assigned work to the supervisors based on their regions of coverage. The supervisors then made assignments to the enumerators linked to their supervisor account. The work assignments and syncing of completed interviews took place through a Wi-Fi connection to the IHPS 2019 server. Because the data was available in real time it was monitored closely throughout the entire data collection period and upon receipt of the data at headquarters, data was exported to Stata for other consistency checks, data cleaning, and analysis.

Data Cleaning 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 in the field by the field-based field teams utilizing error messages generated by the Survey Solutions 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 supervisors were expected to sync the enumerator tablets as frequently as possible to avoid having many questionnaires on the tablet, and to enable daily checks of questionnaires. Some supervisors preferred to review completed interviews on the tablets so they would review prior to syncing but still record the notes in the supervisor account and reject questionnaires accordingly. The second stage of data cleaning was also done in the field, and this resulted from the additional error reports generated in Stata, which were in turn sent to the field teams via email or DropBox. The field supervisors collected reports for their assignments and in coordination with the enumerators reviewed, investigated, and collected errors. Due to the quick turn-around in error reporting, it was possible to conduct call-backs while the team was still operating in the EA when required. Corrections to the data were entered in the rejected questionnaires and sent back to headquarters.

The data cleaning process was done in several stages over the course of the fieldwork and through preliminary analyses. The first stage was during the interview itself. Because CAPI software was used, as enumerators asked the questions and recorded information, error messages were provided immediately when the information recorded did not match previously defined rules for that variable. For example, if the education level for a 12 year old respondent was given as post graduate. The second stage occurred during the review of the questionnaire by the Field Supervisor. The Survey Solutions software allows errors to remain in the data if the enumerator does not make a correction. The enumerator can write a comment to explain why the data appears to be incorrect. For example, if the previously mentioned 12 year old was, in fact, a genius who had completed graduate studies. The next stage occurred when the data were transferred to headquarters where the NSO staff would again review the data for errors and verify the comments from the

Abstract

The 2011 Mauritius Housing & Population Census will be carried out by the Central Statistics Office in two distinct rounds: the Housing Census from 31 January 2011 to June 2011 followed by the Population Census from 20 June to 31 July 2011 in respect of all persons alive on the night of 3 - 4 July 2011. The main objective of the Housing and Population census is to provide up-to-date and disaggregated data on the housing conditions, the spatial distribution, and the demographic and socio-economic characteristics of the Mauritian population.

Geographic coverage

National

Analysis unit

• Household;
• Indivudual.

Universe

The Housing Census will enumerate all buildings, housing units, households, commercial and industrial establishments, hotels and boarding houses as well as fruit trees of bearing age on residential premises.

The Population Census will enumerate all persons present on census night in all households and communal establishments, as well as usual residents who are away on census night.

Housing and population enumerations will be conducted in the islands of Mauritius, Rodrigues and Agalega.

Kind of data

Census/enumeration data [cen]

Sampling procedure

Census 2011, like the four previous ones, was taken in two distinct rounds: the Housing Census followed by the Population Census four months later. This enumeration procedure was adopted in order to obtain at the Housing Census a list of names and addresses of heads of households which served as frame for the Population Census.

Mode of data collection

Face-to-face [f2f]

Research instrument

4.1 Questionnaire design The questionnaire type, format and contents were determined on the basis of the following factors:

Data to be collected Data collected were in line with UN recommendations and, in addition, catered for local data needs.

Method of enumeration For Census 2011, the questionnaires were completed by enumerators who carried out field interviews.

Data capture and processing techniques The office used scanning and recognition technology for census data capture directly from the questionnaires.

4.2 Contents of questionnaire The questionnaire contents were determined as follows: (i) The data needs of main stakeholders from Government Ministries and Departments were considered. As from 2008, heads of Government Ministries and Departments were invited via a circular letter to submit their requirements for demographic, social and economic data considered essential for administration, planning and policy-making and which could be collected at the census. Topics were retained after considering: - their usefulness to the country; - the cost for data collection and processing - where it is possible by other means to obtain satisfactory information more cheaply, the topic was not selected; and - their suitability for data collection at a Census - sensitive and controversial issues as well as questions that are too complicated or difficult for the average respondent to answer were avoided. (ii) The concepts and questions used for the previous census were examined for relevance and only those found relevant were kept. (iii) The latest “Principles and Recommendations for Population and Housing Censuses” were reviewed to determine whether to add questions or to modify existing questions. (iv) The questions thus arrived at were tested during a pilot census conducted in September 2010. In the light of observations made on the field, some changes were made to the wording and sequence of the questions and a final set of questions adopted. 4.2.1 The Housing Census questionnaire The Housing Census questionnaire covered all topics and items covered at Census 2000; some new items were added for the reasons given in the column “Remarks”.

The questionnaire was designed to cover 1 housing unit, up to two households, up to three planters and 1 commercial/industrial establishment, guest house or tourist residence. More than one questionnaire was used in other cases.

4.2.2 The Population Census questionnaire The 2011 Population Census questionnaire included the topics covered at the 2000 Population Census except that on income. Questions were added on National Identity number of each person as well as on residence for the reasons mentioned in the column “Remarks”.

4.3 Questionnaire layout and size The layout and design of response areas was done to ensure optimum conditions for data capture through scanning and recognition technology. The layout was also influenced by the cost (the number of pages had to be kept to a minimum to cut down on paper, printing and scanning costs) while at the same time ensuring ease of recording the answers on the field.

Cleaning operations

The quality of information collected depends not only on the training of field workers, but also on the day-to-day control and supervision of the fieldwork. Supervisors had to accompany each of their Enumerators in the first visits to ensure that interviews were done according to instructions given and that all concepts were clearly understood. Surprise and pre-arranged field checks as well as re-interviews also helped to increase the reliability of the information collected. Furthermore, Supervisors had to check all completed questionnaires at the early stage of enumeration and later a sample of the completed questionnaires to ensure that the quality of work was satisfactory. Meetings were held regularly to take stock of the field situation and to solve problems met on the field.

All supervisory staff had to record their field activities in provided diaries. The day-today record outlined the activities carried out, the dates and the places at which the activities were carried out, problems encountered and remedial actions taken. The day-to-day recording of activities allowed supervisory staff to follow the progress of work and to assess the performance of each and every staff working under their supervision. Furthermore, it ensured that supervisory control prevailed all along the fieldwork.