The American Community Survey (ACS) is a nationwide survey designed to provide communities a fresh look at how they are changing. It will replace the decennial long form in future censuses and is a critical element in the Bureau of the Census' re-engineered 2010 census. The American Community Survey is conducted under the authority of Title 13, United States Code, Sections 141 and 193, and response is mandatory.

The American Community Survey (ACS) is a part of the Decennial Census Program and is designed to produce critical information about the characteristics of local communities. The ACS publishes social, housing, and economic characteristics for demographic groups covering a broad spectrum of geographic areas in the United States. Every year the ACS supports the release of single-year estimates for geographic areas with populations of 65,000 or more. Demographic variables include sex, age, relationship, households by type, race, and Hispanic origin. The American Community Survey is conducted under the authority of Title 13, United States Code, Sections 141 and 193, and response is mandatory.

This data collection contains data relating to the historical censuses of the United States that make up the Integrated Public Use Microdata Series (IPUMS) disseminated through the Minnesota Population Center at the University of Minnesota. Drawn from original census enumeration forms, the data collections in this series include samples of the American population taken from the American Community Survey (ACS) of 2002. The data contain information on housing, demographics, and economic characteristics of Americans previously included in the decennial census long-form sample.

Abstract

The JPFHS is part of the worldwide Demographic and Health Surveys Program, which is designed to collect data on fertility, family planning, and maternal and child health. The primary objective of the Jordan Population and Family Health Survey (JPFHS) is to provide reliable estimates of demographic parameters, such as fertility, mortality, family planning, fertility preferences, as well as maternal and child health and nutrition that can be used by program managers and policy makers to evaluate and improve existing programs. In addition, the JPFHS data will be useful to researchers and scholars interested in analyzing demographic trends in Jordan, as well as those conducting comparative, regional or crossnational studies.

The content of the 2002 JPFHS was significantly expanded from the 1997 survey to include additional questions on women’s status, reproductive health, and family planning. In addition, all women age 15-49 and children less than five years of age were tested for anemia.

Geographic coverage

National

Analysis unit

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

Kind of data

Sample survey data

Sampling procedure

The estimates from a sample survey are affected by two types of errors: 1) nonsampling errors and 2) sampling errors. Nonsampling errors are the result 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 2002 JPFHS to minimize this type of error, nonsampling errors are impossible to avoid and difficult to evaluate statistically.

Sampling errors, on the other hand, can be evaluated statistically. The sample of respondents selected in the 2002 JPFHS 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 2002 JPFHS sample is the result of a multistage stratified design and, consequently, it was necessary to use more complex formulas. The computer software used to calculate sampling errors for the 2002 JPFHS is the ISSA Sampling Error Module (ISSAS). This module used the Taylor linearization method of variance estimation for survey estimates that are means or proportions. The Jackknife repeated replication method is used for variance estimation of more complex statistics such as fertility and mortality rates.

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

Mode of data collection

Face-to-face

Research instrument

The 2002 JPFHS used two questionnaires – namely, the Household Questionnaire and the Individual Questionnaire. Both questionnaires were developed in English and translated into Arabic. The Household Questionnaire was used to list all usual members of the sampled households and to obtain information on each member’s age, sex, educational attainment, relationship to the head of household, and marital status. In addition, questions were included on the socioeconomic characteristics of the household, such as source of water, sanitation facilities, and the availability of durable goods. The Household Questionnaire was also used to identify women who are eligible for the individual interview: ever-married women age 15-49. In addition, all women age 15-49 and children under five years living in the household were measured to determine nutritional status and tested for anemia.

The household and women’s questionnaires were based on the DHS Model “A” Questionnaire, which is designed for use in countries with high contraceptive prevalence. Additions and modifications to the model questionnaire were made in order to provide detailed information specific to Jordan, using experience gained from the 1990 and 1997 Jordan Population and Family Health Surveys. For each evermarried woman age 15 to 49, information on the following topics was collected:

1. Respondent’s background
2. Birth history
3. Knowledge and practice of family planning
4. Maternal care, breastfeeding, immunization, and health of children under five years of age
5. Marriage
6. Fertility preferences
7. Husband’s background and respondent’s employment
8. Knowledge of AIDS and STIs

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

Cleaning operations

Fieldwork and data processing activities overlapped. After a week of data collection, and after field editing of questionnaires for completeness and consistency, the questionnaires for each cluster were packaged together and sent to the central office in Amman where they were registered and stored. Special teams were formed to carry out office editing and coding of the open-ended questions.

Data entry and verification started after one week of office data processing. The process of data entry, including one hundred percent re-entry, editing and cleaning, was done by using PCs and the CSPro (Census and Survey Processing) computer package, developed specially for such surveys. The CSPro program allows data to be edited while being entered. Data processing operations were completed by the end of October 2002. A data processing specialist from ORC Macro made a trip to Jordan in October and November 2002 to follow up data editing and cleaning and to work on the tabulation of results for the survey preliminary report. The tabulations for the present final report were completed in December 2002.

Response rate

A total of 7,968 households were selected for the survey from the sampling frame; among those selected households, 7,907 households were found. Of those households, 7,825 (99 percent) were successfully interviewed. In those households, 6,151 eligible women were identified, and complete interviews were obtained with 6,006 of them (98 percent of all eligible women). The overall response rate was 97 percent.

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

Sampling error estimates

The estimates from a sample survey are affected by two types of errors: 1) nonsampling errors and 2) sampling errors. Nonsampling errors are the result 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 2002 JPFHS to minimize this type of error, nonsampling errors are impossible to avoid and difficult to evaluate statistically.

Sampling errors, on the other hand, can be evaluated statistically. The sample of respondents selected in the 2002 JPFHS 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 2002 JPFHS sample is the result of a multistage stratified design and, consequently, it was necessary to use more complex formulas. The computer software used to calculate sampling errors for the 2002 JPFHS is the ISSA Sampling Error Module (ISSAS). This module used the Taylor linearization method of variance estimation for survey estimates that are means or proportions. The Jackknife repeated replication method is used for variance estimation of more complex statistics such as fertility and mortality rates.

Note: See detailed

The metropolitan survey is conducted in even-numbered years, cycling through a set of 41 metropolitan areas, surveying each one about once every six years. This data collection provides information on the characteristics of a metropolitan sample of housing units including apartments, single-family homes, mobile homes, and vacant housing units. The data are presented in eight separate parts: Part 1, Work Done Record (Replacement or Addition to the House), Part 2, Worker Record, Part 3, Mortgages (Owners Only), Part 4, Housing Unit Record (Main Record), Recodes (One Record per Housing Unit), and Weights, Part 5, Manager and Owner Record (Renters Only), Part 6, Person Record, Part 7, Ratio Verification, and Part 8, Mover Group Record. Data include year the structure was built, type and number of living quarters, occupancy status, access, number of rooms, presence of commercial establishments on the property, and property value. Additional data focus on kitchen and plumbing facilities, types of heating fuel used, source of water, sewage disposal, heating and air-conditioning equipment, and major additions, alterations, or repairs to the property. Information provided on housing expenses includes monthly mortgage or rent payments, cost of services such as utilities, garbage collection, and property insurance, and amount of real estate taxes paid in the previous year. Also included is information on whether the household received government assistance to help pay heating or cooling costs or for other energy-related services. Similar data are provided for housing units previously occupied by respondents who had recently moved. Additionally, indicators of housing and neighborhood quality are supplied. Housing quality variables include privacy of bedrooms, condition of kitchen facilities, basement or roof leakage, breakdowns of plumbing facilities and equipment, and overall opinion of the structure. For quality of neighborhood, variables include use of exterminator services, existence of boarded-up buildings, and overall quality of the neighborhood. In addition to housing characteristics, some demographic data are provided on household members, such as age, sex, race, marital status, income, and relationship to householder. Additional data provided on the householder include years of school completed, Spanish origin, length of residence, and length of occupancy.

Abstract

The study included four separate surveys:

1. The LSMS survey of general population of Serbia in 2002

2. The survey of Family Income Support (MOP in Serbian) recipients in 2002 These two datasets are published together.

3. The LSMS survey of general population of Serbia in 2003 (panel survey)

4. The survey of Roma from Roma settlements in 2003 These two datasets are published together separately from the 2002 datasets.

Objectives

LSMS represents multi-topical study of household living standard and is based on international experience in designing and conducting this type of research. The basic survey was carried out in 2002 on a representative sample of households in Serbia (without Kosovo and Metohija). Its goal was to establish a poverty profile according to the comprehensive data on welfare of households and to identify vulnerable groups. Also its aim was to assess the targeting of safety net programs by collecting detailed information from individuals on participation in specific government social programs. This study was used as the basic document in developing Poverty Reduction Strategy (PRS) in Serbia which was adopted by the Government of the Republic of Serbia in October 2003.

The survey was repeated in 2003 on a panel sample (the households which participated in 2002 survey were re-interviewed).

Analysis of the take-up and profile of the population in 2003 was the first step towards formulating the system of monitoring in the Poverty Reduction Strategy (PRS). The survey was conducted in accordance with the same methodological principles used in 2002 survey, with necessary changes referring only to the content of certain modules and the reduction in sample size. The aim of the repeated survey was to obtain panel data to enable monitoring of the change in the living standard within a period of one year, thus indicating whether there had been a decrease or increase in poverty in Serbia in the course of 2003. [Note: Panel data are the data obtained on the sample of households which participated in the both surveys. These data made possible tracking of living standard of the same persons in the period of one year.]

Along with these two comprehensive surveys, conducted on national and regional representative samples which were to give a picture of the general population, there were also two surveys with particular emphasis on vulnerable groups. In 2002, it was the survey of living standard of Family Income Support recipients with an aim to validate this state supported program of social welfare. In 2003 the survey of Roma from Roma settlements was conducted. Since all present experiences indicated that this was one of the most vulnerable groups on the territory of Serbia and Montenegro, but with no ample research of poverty of Roma population made, the aim of the survey was to compare poverty of this group with poverty of basic population and to establish which categories of Roma population were at the greatest risk of poverty in 2003. However, it is necessary to stress that the LSMS of the Roma population comprised potentially most imperilled Roma, while the Roma integrated in the main population were not included in this study.

Geographic coverage

The surveys were conducted on the whole territory of Serbia (without Kosovo and Metohija).

Kind of data

Sample survey data [ssd]

Sampling procedure

Sample frame for both surveys of general population (LSMS) in 2002 and 2003 consisted of all permanent residents of Serbia, without the population of Kosovo and Metohija, according to definition of permanently resident population contained in UN Recommendations for Population Censuses, which were applied in 2002 Census of Population in the Republic of Serbia. Therefore, permanent residents were all persons living in the territory Serbia longer than one year, with the exception of diplomatic and consular staff.

The sample frame for the survey of Family Income Support recipients included all current recipients of this program on the territory of Serbia based on the official list of recipients given by Ministry of Social affairs.

The definition of the Roma population from Roma settlements was faced with obstacles since precise data on the total number of Roma population in Serbia are not available. According to the last population Census from 2002 there were 108,000 Roma citizens, but the data from the Census are thought to significantly underestimate the total number of the Roma population. However, since no other more precise data were available, this number was taken as the basis for estimate on Roma population from Roma settlements. According to the 2002 Census, settlements with at least 7% of the total population who declared itself as belonging to Roma nationality were selected. A total of 83% or 90,000 self-declared Roma lived in the settlements that were defined in this way and this number was taken as the sample frame for Roma from Roma settlements.

Planned sample: In 2002 the planned size of the sample of general population included 6.500 households. The sample was both nationally and regionally representative (representative on each individual stratum). In 2003 the planned panel sample size was 3.000 households. In order to preserve the representative quality of the sample, we kept every other census block unit of the large sample realized in 2002. This way we kept the identical allocation by strata. In selected census block unit, the same households were interviewed as in the basic survey in 2002. The planned sample of Family Income Support recipients in 2002 and Roma from Roma settlements in 2003 was 500 households for each group.

Sample type: In both national surveys the implemented sample was a two-stage stratified sample. Units of the first stage were enumeration districts, and units of the second stage were the households. In the basic 2002 survey, enumeration districts were selected with probability proportional to number of households, so that the enumeration districts with bigger number of households have a higher probability of selection. In the repeated survey in 2003, first-stage units (census block units) were selected from the basic sample obtained in 2002 by including only even numbered census block units. In practice this meant that every second census block unit from the previous survey was included in the sample. In each selected enumeration district the same households interviewed in the previous round were included and interviewed. On finishing the survey in 2003 the cases were merged both on the level of households and members.

Stratification: Municipalities are stratified into the following six territorial strata: Vojvodina, Belgrade, Western Serbia, Central Serbia (Šumadija and Pomoravlje), Eastern Serbia and South-east Serbia. Primary units of selection are further stratified into enumeration districts which belong to urban type of settlements and enumeration districts which belong to rural type of settlement.

The sample of Family Income Support recipients represented the cases chosen randomly from the official list of recipients provided by Ministry of Social Affairs. The sample of Roma from Roma settlements was,as in the national survey, a two-staged stratified sample, but the units in the first stage were settlements where Roma population was represented in the percentage over 7%, and the units of the second stage were Roma households. Settlements are stratified in three territorial strata: Vojvodina, Beograd and Central Serbia.

Mode of data collection

Face-to-face [f2f]

Research instrument

In all surveys the same questionnaire with minimal changes was used. It included different modules, topically separate areas which had an aim of perceiving the living standard of households from different angles. Topic areas were the following: 1. Roster with demography. 2. Housing conditions and durables module with information on the age of durables owned by a household with a special block focused on collecting information on energy billing, payments, and usage. 3. Diary of food expenditures (weekly), including home production, gifts and transfers in kind. 4. Questionnaire of main expenditure-based recall periods sufficient to enable construction of annual consumption at the household level, including home production, gifts and transfers in kind. 5. Agricultural production for all households which cultivate 10+ acres of land or who breed cattle. 6. Participation and social transfers module with detailed breakdown by programs 7. Labour Market module in line with a simplified version of the Labour Force Survey (LFS), with special additional questions to capture various informal sector activities, and providing information on earnings 8. Health with a focus on utilization of services and expenditures (including informal payments) 9. Education module, which incorporated pre-school, compulsory primary education, secondary education and university education. 10. Special income block, focusing on sources of income not covered in other parts (with a focus on remittances).

Response rate

During field work, interviewers kept a precise diary of interviews, recording both successful and unsuccessful visits. Particular attention was paid to reasons why some households were not interviewed. Separate marks were given for households which were not interviewed due to refusal and for cases when a given household could not be found on the territory of the chosen census block.

In 2002 a total of 7,491 households were contacted. Of this number a total of 6,386 households in 621 census rounds were interviewed. Interviewers did not manage to collect the data for 1,106 or 14.8% of selected households. Out of this number 634 households or

Abstract

The key objective of every census is to count every person (man, woman, child) resident in the country on census night, and also collect information on assorted demographic (sex, age, marital status, citizenship) and socio-economic (education/qualifications; labour force and economic activity) information, as well as data pertinent to household and housing characteristics. This count provides a complete picture of the population make-up in each village and town, of each island and region, thus allowing for an assessment of demographic change over time.

The need for a national census became obvious to the Census Office (Bureau of Statistics) during 1997 when a memo was submitted to government officials proposing the need for a national census in an attempt to update old socio-economic figures. The then Acting Director of the Bureau of Statistics and his predecessor shared a similar view: that the 'heydays' and 'prosperity' were nearing their end. This may not have been apparent, as it took until almost mid-2001 for the current Acting Government Statistician to receive instructions to prepare planning for a national census targeted for 2002. It has been repeatedly said that for adequate planning at the national level, information about the characteristics of the society is required. With such information, potential impacts can be forecast and policies can be designed for the improvement and benefit of society. Without it, the people, national planners and leaders will inevitably face uncertainties.

Geographic coverage

National coverage as the Population Census covers the whole of Nauru.

Analysis unit

• Household
• Individual (in a private household dwelling, institutions and non-private dwelling).

Universe

The Census covers all individuals living in private and non-private dwellings and institutions.

Kind of data

Census/enumeration data [cen]

Sampling deviation

There is no sampling for the population census, full coverage.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaire was based on the Pacific Islands Model Population and Housing Census Form and the 1992 census, and comprised two parts: a set of household questions, asked only of the head of household, and an individual questionnaire, administered to each household member. Unlike the previous census, which consisted of a separate household form plus two separate individual forms for Nauruans and non-Nauruans, the 2 002 questionnaire consisted of only one form separated into different parts and sections. Instructions (and skips) were desi

The questionnaire cover recorded various identifiers: district name, enumeration area, house number, number of households (family units) residing, total number of residents, gender, and whether siblings of the head of the house were also recorded. The second page, representing a summary page, listed every individual residing within the house. This list was taken by the enumerator on the first visit, on the eve of census night. The first part of the census questionnaire focused on housing-related questions. It was administered only once in each household, with questions usually asked of the household head. The household form asked the same range of questions as those covered in the 1992 census, relating to type of housing, structure of outer walls, water supply sources and storage, toilet and cooking facilities, lighting, construction materials and subsistence-type activities. The second part of the census questionnaire focused on individual questions covering all household members. This section was based on the 1992 questions, with notable differences being the exclusion of income-level questions and the expansion of fertility and mortality questions. As in 1992, a problem emerged during questionnaire design regarding the question of who or what should determine a ‘Nauruan’. Unlike the 1992 census, where the emphasis was on blood ties, the issue of naturalisation and citizenship through the sale of passports seriously complicated matters in 2 002. To resolve this issue, it was decided to apply two filtering processes: Stage 1 identified persons with tribal heritage through manual editing, and Stage 2 identified persons of Nauruan nationality and citizenship through designed skips in the questionnaire that were incorporated in the data-processing programming.

The topics of questions for each of the parts include: - Person Particulars: - name - relationship - sex - ethnicity - religion - educational attainment - Economic Activity (to all persons 15 years and above): - economic activity - economic inactive - employment status - Fertility: - Fertility - Mortality - Labour Force Activity: - production of cash crops - fishing - own account businesses - handicrafts. - Disability: - type of disability - nature of disability - Household and housing: - electricity - water - tenure - lighting - cooking - sanitation - wealth ownerships

Cleaning operations

Coding, data entry and editing Coding took longer than expected when the Census Office found that more quality-control checks were required before coding could take place and that a large number of forms still required attention. While these quality-control checks were supposed to have been done by the supervisors in the field, the Census Office decided to review all census forms before commencing the coding. This process took approximately three months, before actual data processing could begin. The amount of additional time required to recheck the quality of every census form meant that data processing fell behind schedule. The Census Office had to improvise, with a little pressure from external stakeholders, and coding, in conjunction with data entry, began after recruiting two additional data entry personnel. All four Census Office staff became actively involved with coding, with one staff member alternating between coding and data entry, depending on which process was dropping behind schedule. In the end, the whole process took almost two months to complete. Prior to commencing data entry, the Census Office had to familiarise itself with the data entry processing system. For this purpose, SPC’s Demography/Population Programme was invited to lend assistance. Two office staff were appointed to work with Mr Arthur Jorari, SPC Population Specialist, who began by revising their skills for the data processing software that had been introduced by Dr McMurray. This training attachment took two weeks to complete. Data entry was undertaken using the 2 .3 version of the US Census Bureau’s census and surveying processing software, or CSPro2.3. This version was later updated to CSPro2.4, and all data were transferred accordingly. Technical assistance for data editing was provided by Mr Jorari over a two-week period. While most edits were completed during this period, it was discovered that some batches of questionnaires had not been entered during the initial data capturing. Therefore, batch-edit application had to be regenerated. This process was frequently interrupted by power outages prevailing at the time, which delayed data processing considerably and also required much longer periods of technical support to the two Nauru data processing staff via phone or email (when available).

Data appraisal

Data was compared with Administrative records after the Census to review the quality and reliability of the data.

The Annual Social and Economic Supplement or March CPS supplement is the primary source of detailed information on income and work experience in the United States. Numerous publications based on this survey are issued each year by the Bureaus of Labor Statistics and Census. A public-use microdata file is available for private researchers, who also produce many academic and policy-related documents based on these data. The Annual Social and Economic Supplement is used to generate the annual Population Profile of the United States, reports on geographical mobility and educational attainment, and detailed analysis of money income and poverty status. The labor force and work experience data from this survey are used to profile the U.S. labor market and to make employment projections. To allow for the same type of in-depth analysis of hispanics, additional hispanic sample units are added to the basic CPS sample in March each year. Additional weighting is also performed so that estimates can be made for households and families, in addition to persons.

Analysis unit

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

Universe

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

Kind of data

Demographic and Household Survey [hh/dhs]

Sampling procedure

MICRODATA SOURCE: Department of Statistics [Jordan] and ORS Macro.

SAMPLE UNIT: Woman SAMPLE SIZE: 6006

SAMPLE UNIT: Birth SAMPLE SIZE: 25296

SAMPLE UNIT: Child SAMPLE SIZE: 6073

SAMPLE UNIT: Member SAMPLE SIZE: 46755

Mode of data collection

Face-to-face [f2f]

Abstract

The 2002 Marshall Islands (RMI) Household Income and Expenditure Survey was the first complete HIES to be conducted in the RMI. The primary purpose of the survey was to gather more accurate and detailed information on income and expenditure levels and flows in the RMI and to update and revise the RMI Consumer Price Index (a separate series of publications document the CPI revision efforts). The survey was made possible through funding assistance from the US Department of the Interior’s Office of Insular Affairs (OIA) and technical assistance from the OIA’s Insular Areas Statistical Enhancement Program.

Geographic coverage

National

Analysis unit

• Households
• Individuals

Kind of data

Sample survey data [ssd]

Mode of data collection

Face-to-face [f2f]

Abstract

General objective: To design and carry out the National Forest Inventory (NFI) of Guatemala and create a system for the periodic gathering of forest information at the national level.

Specific objectives: A. To adapt the methodology provided by FRA to carry out the National Forest Inventory, adequate to the needs of the country. The methodology shall be statistically reliable and allow periodic surveys of information related to forest resources. B. To carry out the first data collection of the variables that respond to the needs of the country's forestry sector, with emphasis on: forest cover, total and commercial volume of timber species, biomass based on stem volume, non-timber products, biophysical data, and socioeconomic data on the use and management of forest products and services. C. To design a database to archive and manage the field inventory information, which may be part of the National Forest Information System.

Geographic coverage

National Coverage

Analysis unit

Forest types and land use classes

Universe

Tree and stump population > 10 cm diameter at breast height across the nation, in and out of forest. The socioeconomic surveys focused on users of forest products across the nation.

Kind of data

Sample survey data [ssd]

Sampling procedure

The design of the NFI was based on the aforementioned objectives and the methodological design proposed by FAO. It had a low sample intensity, but was statistically reliable. It was designed to cover the total area of the country (108,889 km2). The sampling did not only contemplate forest areas, because it was aiming to carry out periodic surveys in the same plots to know land use dynamics throughout the country. In addition, it aimed to evaluate forest resources outside of forest areas, to expand the forest information towards other land uses where these resources are also managed.

The sampling design is systematic stratified. It has three defined strata based on the map of natural divisions of Guatemala ("Mapa de Divisiones Naturales de Guatemala" in the original document), because it was sought that the strata are stable over time to ensure that the area they occupied was the same in future measurements). The strata are named: Zona Norte, Centro and Sur (North, Central and South), according to the geographical area of the country they represent. The systematic design is predetermined by a grid of geographic coordinates (latitude-longitude).

The sampling intensity is relatively low, compared to larger-scale inventories, such as those carried out on farms where forest harvesting or forest concessions. This low intensity only affects the sampling error, but the data are statistically valid, since they will be developed under a strict statistical design and must be interpreted on a national scale. The number of sampling units (SUs) vary according to the defined strata. The largest number of SUs was collected in the Central Zone (70 SUs - with 15min x 15min grid, approximately 26.8 x 26.8 km) because it is the area with the greatest diversity of ecosystems and socioeconomic activities. In the North and South Zones, 30 and 8 SUs were built, respectively (with SUs every 15 minutes in latitude and 30 minutes in longitude - approximately every 26.8 x 53.6 km).

A specific land use and forest type classification was developed, based on the global FAO classes (Forest, Other Forest Lands, Other Lands and Inland Waters) and the classes used in the country's forest cover map. The global classes are located in the upper hierarchical level and in the next levels the national categories are specified. The definitions of each class are described in the adjunct document "Inventario Forestal Nacional de Guatemala: Manual de Campo". Plots were positioned around the selected center point of the point grid. The SU consists of a square conglomerate, with 4 rectangular plots, whose starting point is located at each corner of the square (Figure 2 in "Inventario Forestal Nacional de Guatemala: Manual de Campo"). The first plot was located in the southwest corner of the square and had a northward direction, the second plot was located in the northwest corner and had an eastward direction, the third one was in the northeast corner with a southward direction and the fourth one in the southeast corner facing west.

The plots, following FAO's NFMA design, had a rectangular shape and a size of 250 x 20 m (0.5 ha). They had a nested structure, according to the size and type of resources measured. There were also measurement points for the soil and topographic variables. Each plot has three groups of nested plots and three measurement points, systematically distributed. The nested structure is described below: - The SU is a cluster of 500 x 500 m composed by four rectangular plots, depicted below. - At plot level (250 x 20 m - 0.5 ha) all trees with diameter at breast height DBH=20 cm were measured. - 3 nested plots below (PAN1, 20 x 10 m - 0.02 ha), all trees 10=DBH<20 cm were measured. - One PAN2 plot nested per PAN1 plot (3 per main plot). Circle 3.99 m radius (0.005 ha), enumerating all trees DBH<10 cm and height=1.3 m plus regeneration abundance by species. - One PAN3 plot per PAN1 plot (3 per main plot). 10 x 10 m (0.01 ha) square measuring presence and abundance of bayal and mimbre. - One PAN4 plot per PAN2 plot (3 per main plot). Northwest quadrant from PAN2 circle (0.00125 ha) measuring presence and abundance of xate. - Finally in the center of each PAN2, topographic and soil characteristics were recorded.

Besides, data were collected about the villages, which benefitted from the area occupied by the SU. These had to be obtained in the municipalities or auxiliary townships.

Mode of data collection

Face-to-face paper [f2f]

Cleaning operations

The databases of each sampling unit were entered into the general NFI database by the field crews, after the approval of the reports and field forms. Subsequently the last control filter was performed by the technical unit, based on a protocol of review of the database: scientific names of species were normalized, development of outlier analysis, data gaps, discussions and decisions of data management. The review criteria for each registered attribute were reported. The data processing rout map was performed for the estimation and reporting with the support of national and international specialist.

The processing and analysis was carried out in Microsoft Excel. This program has certain advantages, although it is not the most suitable for all processing, however, since it was the most accessible tool at the beginning of the project, it was decided to use it. However, the importance of building a more adequate database was discussed, and that is how FAO-FRA created a Microsoft Access Data Management System for all the projects they have worldwide, so the data was migrated from Excel. Certain adaptations were made to each of the countries, according to the information requirements.

The structure of the Excel and Access databases are quite compatible, since from the design of the forms, easy links were sought between all the information of the NFI. In the documentation (“Evaluación Nacional Forestal: Inventario Nacional Forestal de Guatemala 2002-2003”) the field forms can be found. For each field form, there is an Excel sheet and an Access form.

Response rate

98% of projected primary sampling units were finally enumerated. Hence, 2% were inaccessible, mostly due to topography and denied access permissions.

Sampling error estimates

All the estimates were made with the estimation error, which is the limit of the estimator with a confidence level of 95% (alpha/2) expressed as a percentage of the mean.

The NFI 2002-03 design has a multidimensional approach, that is, it includes information on various topics related to forest resources and areas outside the forest. That is why there are several target populations from which various measurements were obtained according to the variables that were initially proposed. On the other hand, a design was sought that is practical and economical that provides information at the strategic level for the country, and not at the specific planning level of management units. Under these considerations, it is necessary to interpret the results of the estimates obtained and their respective sampling errors, where each user decides their use depending on the level of risk that this error can determine. There is no scientific way to decide which error is acceptable, because it is an administrative, pragmatic and even political decision. The estimation error is a function of the variability of the data for each variable. In addition, they are also affected by the number of samples that we have of each variable in the sample. The greater the number of samples, the more precise and potentially more accurate the data.

Forest inventories are designed depending on the geographical distribution of the elements to be measured. The largest elements of IFN 2002-03 are forests and the smallest were the leaves, roots and stems of non-timber forest products. Thus, the design tried to focus on the range of intermediate elements, obviously the trees being the most important according to the objectives and information needs. Currently a stratified systematic design was used, which had a direct effect on the size of the units to be measured, which is why in general better precision was achieved in the elements that occupy more area than in those scarce. However, high errors should not totally disqualify the data, since they only indicate that the probability of not being

Abstract

This pooled database is the result of a recent stock-taking exercise, which has assembled data from all the Living Standards Measurement Study containing financial access data.

Kind of data

Sample survey data [ssd]

Mode of data collection

Other [oth]

Abstract

At the micro level, the People's Security Survey (PSS), a household survey that seeks to track the seven forms of work-related security comprising decent work, as well as highlighting people's aspirations and sense of social justice. This survey instrument is the most experimental of the three major sources of information collected by the IFP|SES Programme. Between 2000 and 2003, 15 surveys were conducted and four are in process [Namibia, Mozambique, Sri lanka and Morocco]. Because of the fact that the instrument was being developed, and for budgetary reasons, the samples and survey design varied. In some countries, a national representative survey was conducted; in others, representative samples were drawn only from selected regions or from urban areas only. In Gujarat, India, a disproportionately large sample of women workers was chosen. And in Pakistan the sample was very specific, focusing only on workers in the transport sector in Karachi City.

Geographic coverage

3 regions. Rural and Urban.

Kind of data

Sample survey data [ssd]

Sampling procedure

3000 Individuals

Mode of data collection

Face-to-face

Microdata for the Time Employment Survey 2002-2003 and 2009-2010 The files are distributed in ASCII format and are accompanied by the registration design in excel or word format

The Housing Affordability Data System (HADS), 2002, is a housing-unit level dataset that measures the affordability of housing units and the housing cost burdens of households, relative to area median incomes, poverty level incomes, and Fair Market Rents. The dataset contains selected variables from the AMERICAN HOUSING SURVEY, 2002: METROPOLITAN MICRODATA (ICPSR 4589), as well as custom, derived variables measuring monthly housing costs, housing cost burdens, assisted housing, and total salary income. Housing-level variables include information on the number of rooms in the housing unit, the year the unit was built, whether it was occupied or vacant, whether the unit was rented or owned, whether it was a single family or multi-unit structure, the number of units in the building, the current market value of the unit, and measures of relative housing costs. The dataset also includes variables describing the number of people living in the household, household income, and the type of residential area (e.g., urban or suburban).

Abstract

This research was conducted in Moldova from June 19 to July 31, 2002, as part of the second round of the Business Environment and Enterprise Performance Survey. The objective of the survey is to obtain feedback from enterprises on the state of the private sector as well as to help in building a panel of enterprise data that will make it possible to track changes in the business environment over time, thus allowing, for example, impact assessments of reforms. Through face-to-face interviews with firms in the manufacturing and services sectors, the survey assesses the constraints to private sector growth and creates statistically significant business environment indicators that are comparable across countries.

The survey topics include company's characteristics, information about sales and suppliers, competition, infrastructure services, judiciary and law enforcement, security, government policies and regulations, bribery, sources of financing, overall business environment, performance and investment activities, and workforce composition.

Geographic coverage

National

Analysis unit

The primary sampling unit of the study is the establishment.

Universe

The manufacturing and services sectors are the primary business sectors of interest.

Kind of data

Sample survey data [ssd]

Sampling procedure

The information below is taken from "The Business Environment and Enterprise Performance Survey - 2002. A brief report on observations, experiences and methodology from the survey" prepared by MEMRB Custom Research Worldwide (now part of Synovate), a research company that implemented BEEPS II instrument.

The general targeted distributional criteria of the sample in BEEPS II countries were to be as follows:

1) Coverage of countries: The BEEPS II instrument was to be administered to approximately 6,500 enterprises in 28 transition economies: 16 from CEE (Albania, Bosnia and Herzegovina, Bulgaria, Croatia, Czech Republic, Estonia, FR Yugoslavia, FYROM, Hungary, Latvia, Lithuania, Poland, Romania, Slovak Republic, Slovenia and Turkey) and 12 from the CIS (Armenia, Azerbaijan, Belarus, Georgia, Kazakhstan, Kyrgyzstan, Moldova, Russia, Tajikistan, Turkmenistan, Ukraine and Uzbekistan).

2) In each country, the sector composition of the total sample in terms of manufacturing versus services (including commerce) was to be determined by the relative contribution of GDP, subject to a 15% minimum for each category. Firms that operated in sectors subject to government price regulations and prudential supervision, such as banking, electric power, rail transport, and water and wastewater were excluded.

Eligible enterprise activities were as follows (ISIC sections): - Mining and quarrying (Section C: 10-14), Construction (Section F: 45), Manufacturing (Section D: 15-37) - Transportation, storage and communications (Section I: 60-64), Wholesale, retail, repairs (Section G: 50-52), Real estate, business services (Section K: 70-74), Hotels and restaurants (Section H: 55), Other community, social and personal activities (Section O: selected groups).

3) Size: At least 10% of the sample was to be in the small and 10% in the large size categories. A small firm was defined as an establishment with 2-49 employees, medium - with 50-249 workers, and large - with 250 - 9,999 employees. Companies with only one employee or more than 10,000 employees were excluded.

4) Ownership: At least 10% of the firms were to have foreign control (more than 50% shareholding) and 10% of companies - state control.

5) Exporters: At least 10% of the firms were to be exporters. A firm should be regarded as an exporter if it exported 20% or more of its total sales.

6) Location: At least 10% of firms were to be in the category "small city/countryside" (population under 50,000).

7) Year of establishment: Enterprises which were established later than 2000 should be excluded.

The sample structure for BEEPS II was designed to be as representative (self-weighted) as possible to the population of firms within the industry and service sectors subject to the various minimum quotas for the total sample. This approach ensured that there was sufficient weight in the tails of the distribution of firms by the various relevant controlled parameters (sector, size, location and ownership).

As pertinent data on the actual population or data which would have allowed the estimation of the population of foreign-owned and exporting enterprises were not available, it was not feasible to build these two parameters into the design of the sample guidelines from the onset. The primary parameters used for the design of the sample were: - Total population of enterprises; - Ownership: private and state; - Size of enterprise: Small, medium and large; - Geographic location: Capital, over 1 million, 1 million-250,000, 250-50,000 and under 50,000; - Sub-sectors (e.g. mining, construction, wholesale, etc).

For certain parameters where statistical information was not available, enterprise populations and distributions were estimated from other accessible demographic (e.g. human population concentrations in rural and urban areas) and socio-economic (e.g. employment levels) data.

Sampling deviation

The survey was discontinued in Turkmenistan due to concerns about Turkmen government interference with implementation of the study.

Mode of data collection

Face-to-face [f2f]

Research instrument

The current survey instruments are available: - Screener and Main Questionnaires.

The survey topics include company's characteristics, information about sales and suppliers, competition, infrastructure services, judiciary and law enforcement, security, government policies and regulations, bribery, sources of financing, overall business environment, performance and investment activities, and workforce composition.

Cleaning operations

Data entry and first checking and validation of the results were undertaken locally. Final checking and validation of the results were made at MEMRB Custom Research Worldwide headquarters.

Response rate

Overall, in all BEEPS II countries, the implementing agency contacted 18,052 enterprises and achieved an interview completion rate of 36.93%.

Respondents who either refused outright (i.e. not interested) or were unavailable to be interviewed (i.e. on holiday, etc) accounted for 38.34% of all contacts. Enterprises which were contacted but were non-eligible (i.e. business activity, year of establishment, etc) or quotas were already met (i.e. size, ownership etc) or to which “blind calls” were made to meet quotas (i.e. foreign ownership, exporters, etc) accounted for 24.73% of the total number of enterprises contacted.

Abstract

The Consumer Expenditure Survey (CE) program provides a continuous and comprehensive flow of data on the buying habits of American consumers. These data are used widely in economic research and analysis, and in support of revisions of the Consumer Price Index. To meet the needs of users, the Bureau of Labor Statistics (BLS) produces population estimates for consumer units (CUs) of average expenditures in news releases, reports, issues, and articles in the Monthly Labor Review. Tabulated CE data are also available on the Internet and by facsimile transmission (See Section XV. APPENDIX 4). The microdata are available online at http://www/bls.gov/cex/pumdhome.htm. These microdata files present detailed expenditure and income data for the Diary component of the CE for 2002. They include weekly expenditure (EXPD) and annual income (DTBD) files. The data in EXPD and DTBD files are categorized by a Universal Classification Code (UCC). The advantage of the EXPD and DTBD files is that with the data classified in a standardized format, the user may perform comparative expenditure (income) analysis with relative ease. The FMLD and MEMD files present data on the characteristics and demographics of CUs and CU members. The summary level expenditure and income information on the FMLD files permits the data user to link consumer spending, by general expenditure category, and household characteristics and demographics on one set of files. Estimates of average expenditures in 2002 from the Diary survey, integrated with data from the Interview survey, are published in Consumer Expenditures in 2002. A list of recent publications containing data from the CE appears at the end of this documentation. The microdata files are in the public domain and with appropriate credit, may be reproduced without permission. A suggested citation is: "U.S. Department of Labor, Bureau of Labor Statistics, Consumer Expenditure Survey, Diary Survey, 2002".

Analysis unit

Consumer Units

Kind of data

Sample survey data [ssd]

Sampling procedure

Samples for the CE are national probability samples of households designed to be representative of the total U. S. civilian population. Eligible population includes all civilian noninstitutional persons. The first step in sampling is the selection of primary sampling units (PSUs), which consist of counties (or parts thereof) or groups of counties. The set of sample PSUs used for the 2002 sample is composed of 105 areas. The design classifies the PSUs into four categories: • 31 "A" certainty PSUs are Metropolitan Statistical Areas (MSA's) with a population greater than 1.5 million. • 46 "B" PSUs, are medium-sized MSA's. • 10 "C" PSUs are nonmetropolitan areas that are included in the CPI. • 18 "D" PSUs are nonmetropolitan areas where only the urban population data will be included in the CPI.

The sampling frame (that is, the list from which housing units were chosen) for the 2002 survey is generated from the 1990 Population Census 100-percent-detail file. The sampling frame is augmented by new construction permits and by techniques used to eliminate recognized deficiencies in census coverage. All Enumeration Districts (ED's) from the Census that fail to meet the criterion for good addresses for new construction, and all ED's in nonpermit-issuing areas are grouped into the area segment frame. To the extent possible, an unclustered sample of units is selected within each PSU. This lack of clustering is desirable because the sample size of the Diary Survey is small relative to other surveys, while the intraclass correlations for expenditure characteristics are relatively large. This suggests that any clustering of the sample units could result in an unacceptable increase in the within-PSU variance and, as a result, the total variance. Each selected sample unit is requested to keep two 1-week diaries of expenditures over consecutive weeks. The earliest possible day for placing a diary with a household is predesignated with each day of the week having an equal chance to be the first of the reference week. The diaries are evenly spaced throughout the year. During the last 6 weeks of the year, however, the Diary Survey sample is supplemented to twice its normal size to increase the reporting of types of expenditures unique to the holidays.

STATE IDENTIFIER Since the CE is not designed to produce state-level estimates, summing the consumer unit weights by state will not yield state population totals. A CU's basic weight reflects its probability of selection among a group of primary sampling units of similar characteristics. For example, sample units in an urban nonmetropolitan area in California may represent similar areas in Wyoming and Nevada. Among other adjustments, CUs are post-stratified nationally by sex-age-race. For example, the weights of consumer units containing a black male, age 16-24 in Alabama, Colorado, or New York, are all adjusted equivalently. Therefore, weighted population state totals will not match population totals calculated from other surveys that are designed to represent state data. To summarize, the CE sample was not designed to produce precise estimates for individual states. Although state-level estimates that are unbiased in a repeated sampling sense can be calculated for various statistical measures, such as means and aggregates, their estimates will generally be subject to large variances. Additionally, a particular state-population estimate from the CE sample may be far from the true state-population estimate.

INTERPRETING THE DATA Several factors should be considered when interpreting the expenditure data. The average expenditure for an item may be considerably lower than the expenditure by those CUs that purchased the item. The less frequently an item is purchased, the greater the difference between the average for all consumer units and the average of those purchasing. (See Section V.B. for ESTIMATION OF TOTAL AND MEAN EXPENDITURES). Also, an individual CU may spend more or less than the average, depending on its particular characteristics. Factors such as income, age of family members, geographic location, taste and personal preference also influence expenditures. Furthermore, even within groups with similar characteristics, the distribution of expenditures varies substantially. Expenditures reported are the direct out-of-pocket expenditures. Indirect expenditures, which may be significant, may be reflected elsewhere. For example, rental contracts often include utilities. Renters with such contracts would record no direct expense for utilities, and therefore, appear to have no utility expenses. Employers or insurance companies frequently pay other costs. CUs with members whose employers pay for all or part of their health insurance or life insurance would have lower direct expenses for these items than those who pay the entire amount themselves. These points should be considered when relating reported averages to individual circumstances.

Mode of data collection

Computer Assisted Personal Interview [capi]

Abstract

The 2012 Population and Housing census of Tuvalu is the second census conducted by the Central Statistics Division since Tuvalu gained political independence in 1978. This census provides the population and housing information on areas covering general health, education, labour force, employment, disability, children, youth, aging-population, gender, communication, technology, urbanization, home appliances and many others.

Geographic coverage

National Coverage.

Analysis unit

Individuals and Household-levels.

Universe

The Census covered the whole de-facto population of Tuvalu, which is the population enumerated at a particular place at census night. It includes visitors, but excludes people temporarily absent from Tuvalu.

Kind of data

Census/enumeration data [cen]

Sampling procedure

This is a Census of the Population which covers 100% of the Tuvalu population which does not have any sampling procedures.

Mode of data collection

Face-to-face [f2f]

Research instrument

The 2002 Tuvalu Census questionnaires is divided into 2 sections:

1) A household questionnaire which was used to collect information on all household characteristics (dwelling). 2) A personal questionnaire administered in each household to all household members currently residing or away on temporary basis during census night.

The Household section of the questionnaire covers areas of household characteristics such as the type of living quaters, house ownership, construction of the house, source of drinking water, source of cooking energy, source of lighting, electrical appliances, etc.

The second section of the Questionnaire module or the Personal (Individual) section covers all household members demographic characteristics, social and economical backgrounds.

IPUMS-International is an effort to inventory, preserve, harmonize, and disseminate census microdata from around the world. The project has collected the world's largest archive of publicly available census samples. The data are coded and documented consistently across countries and over time to facillitate comparative research. IPUMS-International makes these data available to qualified researchers free of charge through a web dissemination system. The IPUMS project is a collaboration of the Minnesota Population Center, National Statistical Offices, and international data archives. Major funding is provided by the U.S. National Science Foundation and the Demographic and Behavioral Sciences Branch of the National Institute of Child Health and Human Development. Additional support is provided by the University of Minnesota Office of the Vice President for Research, the Minnesota Population Center, and Sun Microsystems.

IPUMS-International is an effort to inventory, preserve, harmonize, and disseminate census microdata from around the world. The project has collected the world's largest archive of publicly available census samples. The data are coded and documented consistently across countries and over time to facillitate comparative research. IPUMS-International makes these data available to qualified researchers free of charge through a web dissemination system. The IPUMS project is a collaboration of the Minnesota Population Center, National Statistical Offices, and international data archives. Major funding is provided by the U.S. National Science Foundation and the Demographic and Behavioral Sciences Branch of the National Institute of Child Health and Human Development. Additional support is provided by the University of Minnesota Office of the Vice President for Research, the Minnesota Population Center, and Sun Microsystems.