Abstract

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

Geographic coverage

National coverage

Analysis unit

• Households
• Individuals
• Community

Universe

All Household members.

Individuals in hospitals, old age homes, hotels and hostels of educational institutions were not included in the sample. Migrant labour hostels were included. In addition to those that turned up in the selected ESDs, a sample of three hostels was chosen from a national list provided by the Human Sciences Research Council and within each of these hostels a representative sample was drawn on a similar basis as described above for the households in ESDs.

Kind of data

Sample survey data [ssd]

Sampling procedure

Sample size is 9,000 households

The sample design adopted for the study was a two-stage self-weightingdesign in which the first stage units were Census Enumerator Subdistricts (ESDs, or their equivalent) and the second stage were households.

The advantage of using such a design is that it provides a representative sample that need not be based on accurate census population distribution.in the case of South Africa, the sample will automatically include many poor people, without the need to go beyond this and oversample the poor. Proportionate sampling as in such a self-weighting sample design offers the simplest possible data files for further analysis, as weights do not have to be added. However, in the end this advantage could not be retained and weights had to be added.

The sampling frame was drawn up on the basis of small, clearly demarcated area units, each with a population estimate. The nature of the self-weighting procedure adopted ensured that this population estimate was not important for determining the final sample, however. For most of the country, census ESDs were used. Where some ESDs comprised relatively large populations as for instance in some black townships such as Soweto, aerial photographs were used to divide the areas into blocks of approximately equal population size. In other instances, particularly in some of the former homelands, the area units were not ESDs but villages or village groups.

In the sample design chosen, the area stage units (generally ESDs) were selected with probability proportional to size, based on the census population. Systematic sampling was used throughout that is, sampling at fixed interval in a list of ESDs, starting at a randomly selected starting point. Given that sampling was self-weighting, the impact of stratification was expected to be modest. The main objective was to ensure that the racial and geographic breakdown approximated the national population distribution. This was done by listing the area stage units (ESDs) by statistical region and then within the statistical region by urban or rural. Within these sub-statistical regions, the ESDs were then listed in order of percentage African. The sampling interval for the selection of the ESDs was obtained by dividing the 1991 census population of 38,120,853 by the 300 clusters to be selected. This yielded 105,800. Starting at a randomly selected point, every 105,800th person down the cluster list was selected. This ensured both geographic and racial diversity (ESDs were ordered by statistical sub-region and proportion of the population African). In three or four instances, the ESD chosen was judged inaccessible and replaced with a similar one.

In the second sampling stage the unit of analysis was the household. In each selected ESD a listing or enumeration of households was carried out by means of a field operation. From the households listed in an ESD a sample of households was selected by systematic sampling. Even though the ultimate enumeration unit was the household, in most cases "stands" were used as enumeration units. However, when a stand was chosen as the enumeration unit all households on that stand had to be interviewed.

Census population data, however, was available only for 1991. An assumption on population growth was thus made to obtain an approximation of the population size for 1993, the year of the survey. The sampling interval at the level of the household was determined in the following way: Based on the decision to have a take of 125 individuals on average per cluster (i.e. assuming 5 members per household to give an average cluster size of 25 households), the interval of households to be selected was determined as the census population divided by 118.1, i.e. allowing for population growth since the census. It was subsequently discovered that population growth was slightly over-estimated but this had little effect on the findings of the survey.

Individuals in hospitals, old age homes, hotels and hostels of educational institutions were not included in the sample. Migrant labour hostels were included. In addition to those that turned up in the selected ESDs, a sample of three hostels was chosen from a national list provided by the Human Sciences Research Council and within each of these hostels a representative sample was drawn on a similar basis as described abovefor the households in ESDs.

Mode of data collection

Face-to-face [f2f]

Research instrument

The main instrument used in the survey was a comprehensive household questionnaire. This questionnaire covered a wide range of topics but was not intended to provide exhaustive coverage of any single subject. In other words, it was an integrated questionnaire aimed at capturing different aspects of living standards. The topics covered included demography, household services, household expenditure, educational status and expenditure, remittances and marital maintenance, land access and use, employment and income, health status and expenditure and anthropometry (children under the age of six were weighed and their heights measured). This questionnaire was available to households in two languages, namely English and Afrikaans. In addition, interviewers had in their possession a translation in the dominant African language/s of the region.

In addition to the detailed household questionnaire referred to above, a community questionnaire was administered in each cluster of the sample. The purpose of this questionnaire was to elicit information on the facilities available to the community in each cluster. Questions related primarily to the provision of education, health and recreational facilities. Furthermore there was a detailed section for the prices of a range of commodities from two retail sources in or near the cluster: a formal source such as a supermarket and a less formal one such as the "corner cafe" or a "spaza". The purpose of this latter section was to obtain a measure of regional price variation both by region and by retail source. These prices were obtained by the interviewer. For the questions relating to the provision of facilities, respondents were "prominent" members of the community such as school principals, priests and chiefs.

Cleaning operations

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

These responses are coded in the data files with the following values: VALUE MEANING -1 : The data was not available on the questionnaire or form -2 : The field is not applicable -3 : Respondent refused to answer -4 : Respondent did not know answer to question

Data appraisal

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

N.B. This is not real data. Only here for an example for project templates.

Project Title: Add title here

Project Team: Add contact information for research project team members

Summary: Provide a descriptive summary of the nature of your research project and its aims/focal research questions.

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Data collection details: 1. When was your data collected? 2. How were your participants sampled/recruited?

Sample information: How many and who are your participants? Demographic summaries are helpful additions to this section.

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Examples available at: https://www.thearda.com/data-archive?fid=PEWMU17 https://www.thearda.com/data-archive?fid=RELLAND14

Abstract

The Community Survey (CS) is a nationally representative, large-scale household survey which was conducted from February to March 2007. The Community Survey is designed to provide information on the trends and levels of demographic and socio-economic data, such as population size and distribution; the extent of poor households; access to facilities and services, and the levels of employment/unemployment at national, provincial and municipality level. The data can be used to assist government and the private sector in the planning, evaluation and monitoring of programmes and policies. The information collected can also be used to assess the impact of socio-economic policies and provide an indication as to how far the country has gone in its strides to eradicate poverty.

Censuses 1996 and 2001 are the only all-inclusive censuses that Statistics South Africa has thus far conducted under the new democratic dispensation. Demographic and socio-economic data were collected and the results have enabled government and all other users of this information to make informed decisions. When cabinet took a decision that Stats SA should not conduct a census in 2006, it created a gap in information or data between Census 2001 and the next Census scheduled to be carried out in 2011. A decision was therefore taken to carry out the Community Survey in 2007.

The main objectives of the survey were: · To provide estimates at lower geographical levels than existing household surveys; · To build human, management and logistical capacities for Census 2011; and · To provide inputs into the preparation of the mid-year population projections.

The wider project strategic theme is to provide relevant statistical information that meets user needs and aspirations. Some of the main topics that are covered by the survey include demography, migration, disability and social grants, educational levels, employment and economic activities.

Geographic coverage

The survey covered the whole of South Africa, including all nine provinces as well as the four settlement types - urban-formal, urban-informal, rural-formal (commercial farms) and rural-informal (tribal areas).

Analysis unit

Households

Universe

The Community Survey covered all de jure household members (usual residents) in South Africa. The survey excluded collective living quarters (institutions) and some households in EAs classified as recreational areas or institutions. However, an approximation of the out-of-scope population was made from the 2001 Census and added to the final estimates of the CS 2007 results.

Kind of data

Sample survey data [ssd]

Sampling procedure

Sample Design

The sampling procedure that was adopted for the CS was a two-stage stratified random sampling process. Stage one involved the selection of enumeration areas, and stage tow was the selection of dwelling units.

Since the data are required for each local municipality, each municipality was considered as an explicit stratum. The stratification is done for those municipalities classified as category B municipalities (local municipalities) and category A municipalities (metropolitan areas) as proclaimed at the time of Census 2001. However, the newly proclaimed boundaries as well as any other higher level of geography such as province or district municipality, were considered as any other domain variable based on their link to the smallest geographic unit - the enumeration area.

The Frame

The Census 2001 enumeration areas were used because they give a full geographic coverage of the country without any overlap. Although changes in settlement type, growth or movement of people have occurred, the enumeration areas assisted in getting a spatial comparison over time. Out of 80 787 enumeration areas countrywide, 79 466 were considered in the frame. A total of 1 321 enumeration areas were excluded (919 covering institutions and 402 recreational areas).

On the second level, the listing exercise yielded the dwelling frame which facilitated the selection of dwellings to be visited. The dwelling unit is a structure or part of a structure or group of structures occupied or meant to be occupied by one or more households. Some of these structures may be vacant and/or under construction, but can be lived in at the time of the survey. A dwelling unit may also be within collective living quarters where applicable (examples of each are a house, a group of huts, a flat, hostels, etc.).

The Community Survey universe at the second-level frame is dependent on whether the different structures are classified as dwelling units (DUs) or not. Structures where people stay/live were listed and classified as dwelling units. However, there are special cases of collective living quarters that were also included in the CS frame. These are religious institutions such as convents or monasteries, and guesthouses where people stay for an extended period (more than a month). Student residences - based on how long people have stayed (more than a month) - and old-age homes not similar to hospitals (where people are living in a communal set-up) were treated the same as hostels, thereby listing either the bed or room. In addition, any other family staying in separate quarters within the premises of an institution (like wardens' quarters, military family quarters, teachers' quarters and medical staff quarters) were considered as part of the CS frame. The inclusion of such group quarters in the frame is based on the living circumstances within these structures. Members are independent of each other with the exception that they sleep under one roof.

The remaining group quarters were excluded from the CS frame because they are difficult to access and have no stable composition. Excluded dwelling types were prisons, hotels, hospitals, military barracks, etc. This is in addition to the exclusion on first level of the enumeration areas (EAs) classified as institutions (military bases) or recreational areas (national parks).

The Selection of Enumeration Areas (EAs)

The EAs within each municipality were ordered by geographic type and EA type. The selection was done by using systematic random sampling. The criteria used were as follows: In municipalities with fewer than 30 EAs, all EAs were automatically selected. In municipalities with 30 or more EAs, the sample selection used a fixed proportion of 19% of all sampled EAs. However, if the selected EAs in a municipality were less than 30 EAs, the sample in the municipality was increased to 30 EAs.

The Selection of Dwelling Units

The second level of the frame required a full re-listing of dwelling units. The listing exercise was undertaken before the selection of DUs. The adopted listing methodology ensured that the listing route was determined by the lister. Thisapproach facilitated the serpentine selection of dwelling units. The listing exercise provided a complete list of dwelling units in the selected EAs. Only those structures that were classified as dwelling units were considered for selection, whether vacant or occupied. This exercise yielded a total of 2 511 314 dwelling units.

The selection of the dwelling units was also based on a fixed proportion of 10% of the total listed dwellings in an EA. A constraint was imposed on small-size EAs where, if the listed dwelling units were less than 10 dwellings, the selection was increased to 10 dwelling units. All households within the selected dwelling units were covered. There was no replacement of refusals, vacant dwellings or non-contacts owing to their impact on the probability of selection.

Mode of data collection

Face-to-face [f2f]

Research instrument

Consultation on Questionnaire Design Ten stakeholder workshops were held across the country during August and September 2004. Approximately 367 stakeholders, predominantly from national, provincial and local government departments, as well as from research and educational institutions, attended. The workshops aimed to achieve two objectives, namely to better understand the type of information stakeholders need to meet their objectives, and to consider the proposed data items to be included in future household surveys. The output from this process was a set of data items relating to a specific, defined focus area and outcomes that culminated with the data collection instrument (see Annexure B for all the data items).

Questionnaire Design The design of the CS questionnaire was household-based and intended to collect information on 10 people. It was developed in line with the household-based survey questionnaires conducted by Stats SA. The questions were based on the data items generated out of the consultation process described above. Both the design and questionnaire layout were pre-tested in October 2005 and adjustments were made for the pilot in February 2006. Further adjustments were done after the pilot results had been finalised.

Cleaning operations

Editing The automated cleaning was implemented based on an editing rules specification defined with reference to the approved questionnaire. Most of the editing rules were categorised into structural edits looking into the relationship between different record type, the minimum processability rules that removed false positive readings or noise, the logical editing that determine the inconsistency between fields of the same statistical unit, and the inferential editing that search similarities across the domain. The edit specifications document for the structural, population, mortality and housing edits was developed by a team of Stats SA subject-matter specialists, demographers, and programmers. The process was successfully

Abstract

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

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

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

Geographic coverage

National

Analysis unit

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

Kind of data

Sample survey data

Sampling procedure

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

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

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

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

Mode of data collection

Face-to-face

Research instrument

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

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

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

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

Response rate

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

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

Sampling error estimates

The estimates from a sample survey are affected by two types of errors: (1) nonsampling errors, and (2) sampling errors. Nonsampling errors are the results of shortfalls made in implementing data collection and data processing, such as failure to locate and interview the correct household, misunderstanding of the questions on the part of either the interviewer or the respondent, and data entry errors. Although numerous efforts were made during the implementation of the 1998 GDHS to minimize this type of error, nonsampling errors are impossible to avoid and difficult to evaluate statistically.

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

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

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

Data appraisal

Data Quality Tables - Household age distribution - Age distribution of eligible and interviewed women - Age distribution of eligible and interviewed men - Completeness of reporting - Births by calendar years - Reporting of age at death in days - Reporting of age at death in months

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

Abstract

The basic goal of this survey is to provide the necessary database for formulating national policies at various levels. It represents the contribution of the household sector to the Gross National Product (GNP). Household Surveys help as well in determining the incidence of poverty, and providing weighted data which reflects the relative importance of the consumption items to be employed in determining the benchmark for rates and prices of items and services. Generally, the Household Expenditure and Consumption Survey is a fundamental cornerstone in the process of studying the nutritional status in the Palestinian territory.

The raw survey data provided by the Statistical Office was cleaned and harmonized by the Economic Research Forum, in the context of a major research project to develop and expand knowledge on equity and inequality in the Arab region. The main focus of the project is to measure the magnitude and direction of change in inequality and to understand the complex contributing social, political and economic forces influencing its levels. However, the measurement and analysis of the magnitude and direction of change in this inequality cannot be consistently carried out without harmonized and comparable micro-level data on income and expenditures. Therefore, one important component of this research project is securing and harmonizing household surveys from as many countries in the region as possible, adhering to international statistics on household living standards distribution. Once the dataset has been compiled, the Economic Research Forum makes it available, subject to confidentiality agreements, to all researchers and institutions concerned with data collection and issues of inequality. Data is a public good, in the interest of the region, and it is consistent with the Economic Research Forum's mandate to make micro data available, aiding regional research on this important topic.

Geographic coverage

The survey data covers urban, rural and camp areas in West Bank and Gaza Strip.

Analysis unit

1- Household/families. 2- Individuals.

Universe

The survey covered all the Palestinian households who are a usual residence in the Palestinian Territory.

Kind of data

Sample survey data [ssd]

Sampling procedure

Sample and Frame:

The sampling frame consists of all enumeration areas which were enumerated in 1997; the enumeration area consists of buildings and housing units and is composed of an average of 120 households. The enumeration areas were used as Primary Sampling Units (PSUs) in the first stage of the sampling selection. The enumeration areas of the master sample were updated in 2003.

Sample Design:

The sample is a stratified cluster systematic random sample with two stages: First stage: selection of a systematic random sample of 299 enumeration areas. Second stage: selection of a systematic random sample of 12-18 households from each enumeration area selected in the first stage. A person (18 years and more) was selected from each household in the second stage.

Sample strata:

The population was divided by: 1- Governorate 2- Type of Locality (urban, rural, refugee camps)

Sample Size:

The calculated sample size is 3,781 households.

Target cluster size:

The target cluster size or "sample-take" is the average number of households to be selected per PSU. In this survey, the sample take is around 12 households.

Detailed information/formulas on the sampling design are available in the user manual.

Mode of data collection

Face-to-face [f2f]

Research instrument

The PECS questionnaire consists of two main sections:

First section: Certain articles / provisions of the form filled at the beginning of the month,and the remainder filled out at the end of the month. The questionnaire includes the following provisions:

Cover sheet: It contains detailed and particulars of the family, date of visit, particular of the field/office work team, number/sex of the family members.

Statement of the family members: Contains social, economic and demographic particulars of the selected family.

Statement of the long-lasting commodities and income generation activities: Includes a number of basic and indispensable items (i.e, Livestock, or agricultural lands).

Housing Characteristics: Includes information and data pertaining to the housing conditions, including type of shelter, number of rooms, ownership, rent, water, electricity supply, connection to the sewer system, source of cooking and heating fuel, and remoteness/proximity of the house to education and health facilities.

Monthly and Annual Income: Data pertaining to the income of the family is collected from different sources at the end of the registration / recording period.

Second section: The second section of the questionnaire includes a list of 54 consumption and expenditure groups itemized and serially numbered according to its importance to the family. Each of these groups contains important commodities. The number of commodities items in each for all groups stood at 667 commodities and services items. Groups 1-21 include food, drink, and cigarettes. Group 22 includes homemade commodities. Groups 23-45 include all items except for food, drink and cigarettes. Groups 50-54 include all of the long-lasting commodities. Data on each of these groups was collected over different intervals of time so as to reflect expenditure over a period of one full year.

Cleaning operations

Raw Data

Both data entry and tabulation were performed using the ACCESS and SPSS software programs. The data entry process was organized in 6 files, corresponding to the main parts of the questionnaire. A data entry template was designed to reflect an exact image of the questionnaire, and included various electronic checks: logical check, range checks, consistency checks and cross-validation. Complete manual inspection was made of results after data entry was performed, and questionnaires containing field-related errors were sent back to the field for corrections.

Harmonized Data

• The Statistical Package for Social Science (SPSS) is used to clean and harmonize the datasets.
• The harmonization process starts with cleaning all raw data files received from the Statistical Office.
• Cleaned data files are then all merged to produce one data file on the individual level containing all variables subject to harmonization.
• A country-specific program is generated for each dataset to generate/compute/recode/rename/format/label harmonized variables.
• A post-harmonization cleaning process is run on the data.
• Harmonized data is saved on the household as well as the individual level, in SPSS and converted to STATA format.

Response rate

The survey sample consists of about 3,781 households interviewed over a twelve-month period between January 2004 and January 2005. There were 3,098 households that completed the interview, of which 2,060 were in the West Bank and 1,038 households were in GazaStrip. The response rate was 82% in the Palestinian Territory.

Sampling error estimates

The calculations of standard errors for the main survey estimations enable the user to identify the accuracy of estimations and the survey reliability. Total errors of the survey can be divided into two kinds: statistical errors, and non-statistical errors. Non-statistical errors are related to the procedures of statistical work at different stages, such as the failure to explain questions in the questionnaire, unwillingness or inability to provide correct responses, bad statistical coverage, etc. These errors depend on the nature of the work, training, supervision, and conducting all various related activities. The work team spared no effort at different stages to minimize non-statistical errors; however, it is difficult to estimate numerically such errors due to absence of technical computation methods based on theoretical principles to tackle them. On the other hand, statistical errors can be measured. Frequently they are measured by the standard error, which is the positive square root of the variance. The variance of this survey has been computed by using the “programming package” CENVAR.

Abstract

The main objective of the HEIS survey is to obtain detailed data on household expenditure and income, linked to various demographic and socio-economic variables, to enable computation of poverty indices and determine the characteristics of the poor and prepare poverty maps. Therefore, to achieve these goals, the sample had to be representative on the sub-district level. The raw survey data provided by the Statistical Office was cleaned and harmonized by the Economic Research Forum, in the context of a major research project to develop and expand knowledge on equity and inequality in the Arab region. The main focus of the project is to measure the magnitude and direction of change in inequality and to understand the complex contributing social, political and economic forces influencing its levels. However, the measurement and analysis of the magnitude and direction of change in this inequality cannot be consistently carried out without harmonized and comparable micro-level data on income and expenditures. Therefore, one important component of this research project is securing and harmonizing household surveys from as many countries in the region as possible, adhering to international statistics on household living standards distribution. Once the dataset has been compiled, the Economic Research Forum makes it available, subject to confidentiality agreements, to all researchers and institutions concerned with data collection and issues of inequality.

Data collected through the survey helped in achieving the following objectives: 1. Provide data weights that reflect the relative importance of consumer expenditure items used in the preparation of the consumer price index 2. Study the consumer expenditure pattern prevailing in the society and the impact of demographic and socio-economic variables on those patterns 3. Calculate the average annual income of the household and the individual, and assess the relationship between income and different economic and social factors, such as profession and educational level of the head of the household and other indicators 4. Study the distribution of individuals and households by income and expenditure categories and analyze the factors associated with it 5. Provide the necessary data for the national accounts related to overall consumption and income of the household sector 6. Provide the necessary income data to serve in calculating poverty indices and identifying the poor characteristics as well as drawing poverty maps 7. Provide the data necessary for the formulation, follow-up and evaluation of economic and social development programs, including those addressed to eradicate poverty

Geographic coverage

National

Analysis unit

• Households
• Individuals

Kind of data

Sample survey data [ssd]

Sampling procedure

The Household Expenditure and Income survey sample for 2010, was designed to serve the basic objectives of the survey through providing a relatively large sample in each sub-district to enable drawing a poverty map in Jordan. The General Census of Population and Housing in 2004 provided a detailed framework for housing and households for different administrative levels in the country. Jordan is administratively divided into 12 governorates, each governorate is composed of a number of districts, each district (Liwa) includes one or more sub-district (Qada). In each sub-district, there are a number of communities (cities and villages). Each community was divided into a number of blocks. Where in each block, the number of houses ranged between 60 and 100 houses. Nomads, persons living in collective dwellings such as hotels, hospitals and prison were excluded from the survey framework.

A two stage stratified cluster sampling technique was used. In the first stage, a cluster sample proportional to the size was uniformly selected, where the number of households in each cluster was considered the weight of the cluster. At the second stage, a sample of 8 households was selected from each cluster, in addition to another 4 households selected as a backup for the basic sample, using a systematic sampling technique. Those 4 households were sampled to be used during the first visit to the block in case the visit to the original household selected is not possible for any reason. For the purposes of this survey, each sub-district was considered a separate stratum to ensure the possibility of producing results on the sub-district level. In this respect, the survey framework adopted that provided by the General Census of Population and Housing Census in dividing the sample strata. To estimate the sample size, the coefficient of variation and the design effect of the expenditure variable provided in the Household Expenditure and Income Survey for the year 2008 was calculated for each sub-district. These results were used to estimate the sample size on the sub-district level so that the coefficient of variation for the expenditure variable in each sub-district is less than 10%, at a minimum, of the number of clusters in the same sub-district (6 clusters). This is to ensure adequate presentation of clusters in different administrative areas to enable drawing an indicative poverty map.

It should be noted that in addition to the standard non response rate assumed, higher rates were expected in areas where poor households are concentrated in major cities. Therefore, those were taken into consideration during the sampling design phase, and a higher number of households were selected from those areas, aiming at well covering all regions where poverty spreads.

Mode of data collection

Face-to-face [f2f]

Research instrument

• General form
• Expenditure on food commodities form
• Expenditure on non-food commodities form

Cleaning operations

Raw Data: - Organizing forms/questionnaires: A compatible archive system was used to classify the forms according to different rounds throughout the year. A registry was prepared to indicate different stages of the process of data checking, coding and entry till forms were back to the archive system. - Data office checking: This phase was achieved concurrently with the data collection phase in the field where questionnaires completed in the field were immediately sent to data office checking phase. - Data coding: A team was trained to work on the data coding phase, which in this survey is only limited to education specialization, profession and economic activity. In this respect, international classifications were used, while for the rest of the questions, coding was predefined during the design phase. - Data entry/validation: A team consisting of system analysts, programmers and data entry personnel were working on the data at this stage. System analysts and programmers started by identifying the survey framework and questionnaire fields to help build computerized data entry forms. A set of validation rules were added to the entry form to ensure accuracy of data entered. A team was then trained to complete the data entry process. Forms prepared for data entry were provided by the archive department to ensure forms are correctly extracted and put back in the archive system. A data validation process was run on the data to ensure the data entered is free of errors. - Results tabulation and dissemination: After the completion of all data processing operations, ORACLE was used to tabulate the survey final results. Those results were further checked using similar outputs from SPSS to ensure that tabulations produced were correct. A check was also run on each table to guarantee consistency of figures presented, together with required editing for tables' titles and report formatting.

Harmonized Data: - The Statistical Package for Social Science (SPSS) was used to clean and harmonize the datasets. - The harmonization process started with cleaning all raw data files received from the Statistical Office. - Cleaned data files were then merged to produce one data file on the individual level containing all variables subject to harmonization. - A country-specific program was generated for each dataset to generate/compute/recode/rename/format/label harmonized variables. - A post-harmonization cleaning process was run on the data. - Harmonized data was saved on the household as well as the individual level, in SPSS and converted to STATA format.

This dataset was created and deposited onto the University of Sheffield Online Research Data repository (ORDA) on 23-Jun-2023 by Dr. Matthew S. Hanchard, Research Associate at the University of Sheffield iHuman Institute.

The dataset forms part of three outputs from a project titled ‘Fostering cultures of open qualitative research’ which ran from January 2023 to June 2023:

· Fostering cultures of open qualitative research: Dataset 1 – Survey Responses · Fostering cultures of open qualitative research: Dataset 2 – Interview Transcripts · Fostering cultures of open qualitative research: Dataset 3 – Coding Book

The project was funded with £13,913.85 Research England monies held internally by the University of Sheffield - as part of their ‘Enhancing Research Cultures’ scheme 2022-2023.

The dataset aligns with ethical approval granted by the University of Sheffield School of Sociological Studies Research Ethics Committee (ref: 051118) on 23-Jan-2021.This includes due concern for participant anonymity and data management.

ORDA has full permission to store this dataset and to make it open access for public re-use on the basis that no commercial gain will be made form reuse. It has been deposited under a CC-BY-NC license.

This dataset comprises one spreadsheet with N=91 anonymised survey responses .xslx format. It includes all responses to the project survey which used Google Forms between 06-Feb-2023 and 30-May-2023. The spreadsheet can be opened with Microsoft Excel, Google Sheet, or open-source equivalents.

The survey responses include a random sample of researchers worldwide undertaking qualitative, mixed-methods, or multi-modal research.

The recruitment of respondents was initially purposive, aiming to gather responses from qualitative researchers at research-intensive (targetted Russell Group) Universities. This involved speculative emails and a call for participant on the University of Sheffield ‘Qualitative Open Research Network’ mailing list. As result, the responses include a snowball sample of scholars from elsewhere.

The spreadsheet has two tabs/sheets: one labelled ‘SurveyResponses’ contains the anonymised and tidied set of survey responses; the other, labelled ‘VariableMapping’, sets out each field/column in the ‘SurveyResponses’ tab/sheet against the original survey questions and responses it relates to.

The survey responses tab/sheet includes a field/column labelled ‘RespondentID’ (using randomly generated 16-digit alphanumeric keys) which can be used to connect survey responses to interview participants in the accompanying ‘Fostering cultures of open qualitative research: Dataset 2 – Interview transcripts’ files.

A set of survey questions gathering eligibility criteria detail and consent are not listed with in this dataset, as below. All responses provide in the dataset gained a ‘Yes’ response to all the below questions (with the exception of one question, marked with an asterisk (*) below):

· I am aged 18 or over · I have read the information and consent statement and above. · I understand how to ask questions and/or raise a query or concern about the survey. · I agree to take part in the research and for my responses to be part of an open access dataset. These will be anonymised unless I specifically ask to be named. · I understand that my participation does not create a legally binding agreement or employment relationship with the University of Sheffield · I understand that I can withdraw from the research at any time. · I assign the copyright I hold in materials generated as part of this project to The University of Sheffield. · * I am happy to be contacted after the survey to take part in an interview.

The project was undertaken by two staff: Co-investigator: Dr. Itzel San Roman Pineda ORCiD ID: 0000-0002-3785-8057 i.sanromanpineda@sheffield.ac.uk

Postdoctoral Research Assistant Principal Investigator (corresponding dataset author): Dr. Matthew Hanchard ORCiD ID: 0000-0003-2460-8638 m.s.hanchard@sheffield.ac.uk Research Associate iHuman Institute, Social Research Institutes, Faculty of Social Science

This data set contains the replication data and supplements for the article "Knowing, Doing, and Feeling: A three-year, mixed-methods study of undergraduates’ information literacy development." The survey data is from two samples: - cross-sectional sample (different students at the same point in time) - longitudinal sample (the same students and different points in time)Surveys were distributed via Qualtrics during the students' first and sixth semesters. Quantitative and qualitative data were collected and used to describe students' IL development over 3 years. Statistics from the quantitative data were analyzed in SPSS. The qualitative data was coded and analyzed thematically in NVivo. The qualitative, textual data is from semi-structured interviews with sixth-semester students in psychology at UiT, both focus groups and individual interviews. All data were collected as part of the contact author's PhD research on information literacy (IL) at UiT. The following files are included in this data set: 1. A README file which explains the quantitative data files. (2 file formats: .txt, .pdf)2. The consent form for participants (in Norwegian). (2 file formats: .txt, .pdf)3. Six data files with survey results from UiT psychology undergraduate students for the cross-sectional (n=209) and longitudinal (n=56) samples, in 3 formats (.dat, .csv, .sav). The data was collected in Qualtrics from fall 2019 to fall 2022. 4. Interview guide for 3 focus group interviews. File format: .txt5. Interview guides for 7 individual interviews - first round (n=4) and second round (n=3). File format: .txt 6. The 21-item IL test (Tromsø Information Literacy Test = TILT), in English and Norwegian. TILT is used for assessing students' knowledge of three aspects of IL: evaluating sources, using sources, and seeking information. The test is multiple choice, with four alternative answers for each item. This test is a "KNOW-measure," intended to measure what students know about information literacy. (2 file formats: .txt, .pdf)7. Survey questions related to interest - specifically students' interest in being or becoming information literate - in 3 parts (all in English and Norwegian): a) information and questions about the 4 phases of interest; b) interest questionnaire with 26 items in 7 subscales (Tromsø Interest Questionnaire - TRIQ); c) Survey questions about IL and interest, need, and intent. (2 file formats: .txt, .pdf)8. Information about the assignment-based measures used to measure what students do in practice when evaluating and using sources. Students were evaluated with these measures in their first and sixth semesters. (2 file formats: .txt, .pdf)9. The Norwegain Centre for Research Data's (NSD) 2019 assessment of the notification form for personal data for the PhD research project. In Norwegian. (Format: .pdf)

Abstract

The Multiple Indicator Cluster Survey (MICS) is a household survey programme developed by UNICEF to assist countries in filling data gaps for monitoring human development in general and the situation of children and women in particular. MICS is capable of producing statistically sound, internationally comparable estimates of social indicators. The current round of MICS is focused on providing a monitoring tool for the Millennium Development Goals (MDGs), the World Fit for Children (WFFC), as well as for other major international commitments, such as the United Nations General Assembly Special Session (UNGASS) on HIV/AIDS and the Abuja targets for malaria.

Survey Objectives The 2006 Thailand Multiple Indicator Cluster Survey has as its primary objectives: - To provide up-to-date information for assessing the situation of children and women in Thailand; - To furnish data needed for monitoring progress toward goals established by the Millennium Development Goals (MDG), the goals of A World Fit for Children (WFFC) and other internationally agreed upon goals, as a basis for future action at national and provincial level; and - To contribute to the improvement of data and monitoring systems on the situation of children and women in Thailand and strengthening technical expertise for the design, implementation, and analysis of such systems.

Survey Content MICS questionnaires are designed in a modular fashion that can be easily customized to the needs of a country. They consist of a household questionnaire, a questionnaire for women aged 15-49 and a questionnaire for children under the age of five (to be administered to the mother or caretaker). Other than a set of core modules, countries can select which modules they want to include in each questionnaire.

Survey Implementation The survey was implemented by the National Statistical Office of Thailand, with the support and assistance of UNICEF and other partners. Technical assistance and training for the surveys is provided through a series of regional workshops, covering questionnaire content, sampling and survey implementation; data processing; data quality and data analysis; report writing and dissemination.

Geographic coverage

The survey was designed to produce estimates for indicators at the national level, by urban and rural disaggregation, for each of the 4 regions of Thailand (North, Northeast, Central, and South) and by individual province for 26 (out of 76 total) targeted provinces (note: additional data collections were performed for the targeted provinces during March-May 2006; separate results publications for each province are pending).

Analysis unit

• Households
• Women aged 15-49
• Children under 5 years of age

Universe

The survey covered all de jure household members (usual residents), all women aged 15-49 years resident in the household, and all children aged 0-4 years (under age 5) resident in the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

The Thailand Multiple Indicator Cluster Survey (MICS) was carried out by a sample survey method that used a stratified two stage sampling plan. The primary sample units (PSUs) consisted of blocks (in municipal areas) or villages (in non-municipal areas). The secondary sample units consisted of collective households systematically drawn from a household listing. The plan is designed to provide estimates of situation indicators for children and women at the national level, for municipal and non-municipal areas, and for four regions: Central (including Bangkok), North, Northeast and South. The household listing is obtained from The Basic Household Information Survey conducted every two years by the National Statistical Office (NSO). In the survey, members of each household located in the block/village samples are counted.

Data on basic household information from the survey are to be used as the sample frame in various survey projects of the NSO. Data from the 2006 Basic Household Information Survey were used as the frame for household samples in the Thailand MICS. Thirty collective household samples per block/village sample were selected in both municipal and non-municipal areas. Field staff then created a Listing of Household Samples by adding together all the names of household heads and the addresses. After a household listing was carried out within the selected 30 households in each block/village, a systematic sample of households was drawn. For national-level results, sample data were weighted in accordance with sampling plan.

A block is an operational boundary in a municipal area that is made up of approximately 100 to 200 households. Blocks are established on a map so that field staff know the exact area they are to cover in the survey.

A village is an administrative unit, a community, in a non-municipal area governed by a village head (Phuyaiban) or a district head (Kamnan).

The MICS national-level report included 1,449 block/village samples. Thirty collective household samples per block/village samples were selected and a total of 43,470 household samples were obtained.

For MICS provincial-level reports, 1,032 block/village samples were selected and 30,960 household samples were included.

More detailed information on the sample design is available in Appendix A of the Survey Final Report.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaires for the Thailand MICS were structured questionnaires based on the MICS3 Model Questionnaire with some modifications and additions. A household questionnaire was administered in each household, which collected various information on household members including sex, age, relationship, and orphanhood status.

In addition to a household questionnaire, questionnaires were administered in each household for women age 15-49 and children under age five. For children, the questionnaire was administered to the mother or caretaker of the child.

The questionnaires were translated into Thai by the NSO MICS coordinators in September 2005.

In addition to the administration of questionnaires, fieldwork teams tested salt used for cooking in the households surveyed for presence of iodine, and measured the weight and height of children under 5 years of age.

Cleaning operations

After the fieldwork, the team supervisor checked the data collected during the interview for completeness. Then the Provincial Statistical Officer in each province and the Director of the Data Management Division of the Bangkok Metropolitan Administration randomly rechecked the data before sending all the questionnaires to the National Statistical Office (NSO) for processing.

Upon receiving the questionnaires from the 76 provinces, the collected data were entered on 30 microcomputers by data entry operators and data entry supervisors at the Thai NSO, using CSPro software. In order to ensure quality control, editing and structural checks, all questionnaires were double entered for verification and internal consistency checks were performed, followed by secondary editing. The data entry and verification used CSPro programme applications that were developed under the global Multiple Indicator Cluster Survey (MICS) project by UNICEF to be used as standard processing procedures worldwide. In Thailand, the standard CSPro programme was modified appropriately to the Thai version questionnaires. The modification was done by NSO staff that had been trained on data processing by MICS experts from UNICEF.

Data entry and data verification for the national level report began in February 2006 and was completed in April 2006. For the provincial reports, the process was completed in June 2006. Data were analysed using the Statistical Package for Social Sciences (SPSS) software programme, Version 14, and the model syntax and tabulation plans developed by UNICEF for this purpose.

Data processing used the CSPro programme applications developed under the global Multiple Indicator Cluster Survey project by UNICEF.

Data were processed in clusters, with each cluster being processed as a complete unit through each stage of data processing. Each cluster goes through the following steps: 1) Questionnaire reception 2) Office editing and coding 3) Data entry 4) Structure and completeness checking 5) Verification entry 6) Comparison of verification data 7) Back up of raw data 8) Secondary editing 9) Edited data back up After all clusters are processed, all data is concatenated together and then the following steps are completed for all data files: 10) Export to SPSS in 4 files (hh - household, hl - household members, wm - women, ch - children under 5) 11) Recoding of variables needed for analysis 12) Adding of sample weights 13) Calculation of wealth quintiles and merging into data 14) Structural checking of SPSS files 15) Data quality tabulations 16) Production of analysis tabulations

For data entry, CSPro version 2.6.007 was used with a highly structured data entry program, using system controlled approach, that controlled entry of each variable. All range checks and skips were controlled by the program and operators could not override these. A limited set of consistency checks were also included inthe data entry program. In addition, the calculation of anthropometric Z-scores was also included in the data entry programs for use during analysis. Open-ended responses ("Other" answers) were not entered or coded, except in rare circumstances where the response matched an existing code in the questionnaire.

Structure and completeness checking ensured that all questionnaires for the cluster had been entered, were structurally sound, and that

Abstract

In 1992, Bosnia-Herzegovina, one of the six republics in former Yugoslavia, became an independent nation. A civil war started soon thereafter, lasting until 1995 and causing widespread destruction and losses of lives. Following the Dayton accord, BosniaHerzegovina (BiH) emerged as an independent state comprised of two entities, namely, the Federation of Bosnia-Herzegovina (FBiH) and the Republika Srpska (RS), and the district of Brcko. In addition to the destruction caused to the physical infrastructure, there was considerable social disruption and decline in living standards for a large section of the population. Along side these events, a period of economic transition to a market economy was occurring. The distributive impacts of this transition, both positive and negative, are unknown. In short, while it is clear that welfare levels have changed, there is very little information on poverty and social indicators on which to base policies and programs.

In the post-war process of rebuilding the economic and social base of the country, the government has faced the problems created by having little relevant data at the household level. The three statistical organizations in the country (State Agency for Statistics for BiH –BHAS, the RS Institute of Statistics-RSIS, and the FBiH Institute of Statistics-FIS) have been active in working to improve the data available to policy makers: both at the macro and the household level. One facet of their activities is to design and implement a series of household series. The first of these surveys is the Living Standards Measurement Study survey (LSMS). Later surveys will include the Household Budget Survey (an Income and Expenditure Survey) and a Labor Force Survey. A subset of the LSMS households will be re-interviewed in the two years following the LSMS to create a panel data set.

The three statistical organizations began work on the design of the Living Standards Measurement Study Survey (LSMS) in 1999. The purpose of the survey was to collect data needed for assessing the living standards of the population and for providing the key indicators needed for social and economic policy formulation. The survey was to provide data at the country and the entity level and to allow valid comparisons between entities to be made.

The LSMS survey was carried out in the Fall of 2001 by the three statistical organizations with financial and technical support from the Department for International Development of the British Government (DfID), United Nations Development Program (UNDP), the Japanese Government, and the World Bank (WB). The creation of a Master Sample for the survey was supported by the Swedish Government through SIDA, the European Commission, the Department for International Development of the British Government and the World Bank.

The overall management of the project was carried out by the Steering Board, comprised of the Directors of the RS and FBiH Statistical Institutes, the Management Board of the State Agency for Statistics and representatives from DfID, UNDP and the WB. The day-to-day project activities were carried out by the Survey Mangement Team, made up of two professionals from each of the three statistical organizations.

The Living Standard Measurement Survey LSMS, in addition to collecting the information necessary to obtain a comprehensive as possible measure of the basic dimensions of household living standards, has three basic objectives, as follows:

1. To provide the public sector, government, the business community, scientific institutions, international donor organizations and social organizations with information on different indicators of the population’s living conditions, as well as on available resources for satisfying basic needs.

2. To provide information for the evaluation of the results of different forms of government policy and programs developed with the aim to improve the population’s living standard. The survey will enable the analysis of the relations between and among different aspects of living standards (housing, consumption, education, health, labor) at a given time, as well as within a household.

3. To provide key contributions for development of government’s Poverty Reduction Strategy Paper, based on analyzed data.

Geographic coverage

National coverage. Domains: Urban/rural/mixed; Federation; Republic

Kind of data

Sample survey data [ssd]

Sampling procedure

A total sample of 5,400 households was determined to be adequate for the needs of the survey: with 2,400 in the Republika Srpska and 3,000 in the Federation of BiH. The difficulty was in selecting a probability sample that would be representative of the country's population. The sample design for any survey depends upon the availability of information on the universe of households and individuals in the country. Usually this comes from a census or administrative records. In the case of BiH the most recent census was done in 1991. The data from this census were rendered obsolete due to both the simple passage of time but, more importantly, due to the massive population displacements that occurred during the war.

At the initial stages of this project it was decided that a master sample should be constructed. Experts from Statistics Sweden developed the plan for the master sample and provided the procedures for its construction. From this master sample, the households for the LSMS were selected.

Master Sample [This section is based on Peter Lynn's note "LSMS Sample Design and Weighting - Summary". April, 2002. Essex University, commissioned by DfID.]

The master sample is based on a selection of municipalities and a full enumeration of the selected municipalities. Optimally, one would prefer smaller units (geographic or administrative) than municipalities. However, while it was considered that the population estimates of municipalities were reasonably accurate, this was not the case for smaller geographic or administrative areas. To avoid the error involved in sampling smaller areas with very uncertain population estimates, municipalities were used as the base unit for the master sample.

The Statistics Sweden team proposed two options based on this same method, with the only difference being in the number of municipalities included and enumerated. For reasons of funding, the smaller option proposed by the team was used, or Option B.

Stratification of Municipalities

The first step in creating the Master Sample was to group the 146 municipalities in the country into three strata- Urban, Rural and Mixed - within each of the two entities. Urban municipalities are those where 65 percent or more of the households are considered to be urban, and rural municipalities are those where the proportion of urban households is below 35 percent. The remaining municipalities were classified as Mixed (Urban and Rural) Municipalities. Brcko was excluded from the sampling frame.

Urban, Rural and Mixed Municipalities: It is worth noting that the urban-rural definitions used in BiH are unusual with such large administrative units as municipalities classified as if they were completely homogeneous. Their classification into urban, rural, mixed comes from the 1991 Census which used the predominant type of income of households in the municipality to define the municipality. This definition is imperfect in two ways. First, the distribution of income sources may have changed dramatically from the pre-war times: populations have shifted, large industries have closed and much agricultural land remains unusable due to the presence of land mines. Second, the definition is not comparable to other countries' where villages, towns and cities are classified by population size into rural or urban or by types of services and infrastructure available. Clearly, the types of communities within a municipality vary substantially in terms of both population and infrastructure.

However, these imperfections are not detrimental to the sample design (the urban/rural definition may not be very useful for analysis purposes, but that is a separate issue). [Note: It may be noted that the percent of LSMS households in each stratum reporting using agricultural land or having livestock is highest in the "rural" municipalities and lowest in the "urban" municipalities. However, the concentration of agricultural households is higher in RS, so the municipality types are not comparable across entities. The percent reporting no land or livestock in RS was 74.7% in "urban" municipalities, 43.4% in "mixed" municipalities and 31.2% in "rural" municipalities. Respective figures for FbiH were 88.7%, 60.4% and 40.0%.]

The classification is used simply for stratification. The stratification is likely to have some small impact on the variance of survey estimates, but it does not introduce any bias.

Selection of Municipalities

Option B of the Master Sample involved sampling municipalities independently from each of the six strata described in the previous section. Municipalities were selected with probability proportional to estimated population size (PPES) within each stratum, so as to select approximately 50% of the mostly urban municipalities, 20% of the mixed and 10% of the mostly rural ones. Overall, 25 municipalities were selected (out of 146) with 14 in the FbiH and 11 in the RS. The distribution of selected municipalities over the sampling strata is shown below.

Stratum / Total municipalities Mi / Sampled municipalities mi 1. Federation, mostly urban / 10 / 5 2. Federation, mostly mixed / 26 / 4 3. Federation, mostly rural / 48 / 5 4. RS, mostly urban /4 / 2 5. RS, mostly mixed /29 / 5 6. RS, mostly rural / 29 / 4

Note: Mi is the total number of municipalities in stratum i (i=1, … , 6); mi is the number of municipalities selected from stratum

Abstract

THE CLEANED AND HARMONIZED VERSION OF THE SURVEY DATA PRODUCED AND PUBLISHED BY THE ECONOMIC RESEARCH FORUM REPRESENTS 100% OF THE ORIGINAL SURVEY DATA COLLECTED BY THE DEPARTMENT OF STATISTICS OF THE HASHEMITE KINGDOM OF JORDAN

The Department of Statistics (DOS) carried out four rounds of the 2016 Employment and Unemployment Survey (EUS). The survey rounds covered a sample of about fourty nine thousand households Nation-wide. The sampled households were selected using a stratified multi-stage cluster sampling design.

It is worthy to mention that the DOS employed new technology in data collection and data processing. Data was collected using electronic questionnaire instead of a hard copy, namely a hand held device (PDA).

The survey main objectives are: - To identify the demographic, social and economic characteristics of the population and manpower. - To identify the occupational structure and economic activity of the employed persons, as well as their employment status. - To identify the reasons behind the desire of the employed persons to search for a new or additional job. - To measure the economic activity participation rates (the number of economically active population divided by the population of 15+ years old). - To identify the different characteristics of the unemployed persons. - To measure unemployment rates (the number of unemployed persons divided by the number of economically active population of 15+ years old) according to the various characteristics of the unemployed, and the changes that might take place in this regard. - To identify the most important ways and means used by the unemployed persons to get a job, in addition to measuring durations of unemployment for such persons. - To identify the changes overtime that might take place regarding the above-mentioned variables.

The raw survey data provided by the Statistical Agency were cleaned and harmonized by the Economic Research Forum, in the context of a major project that started in 2009. During which extensive efforts have been exerted to acquire, clean, harmonize, preserve and disseminate micro data of existing labor force surveys in several Arab countries.

Geographic coverage

Covering a sample representative on the national level (Kingdom), governorates, and the three Regions (Central, North and South).

Analysis unit

1- Household/family. 2- Individual/person.

Universe

The survey covered a national sample of households and all individuals permanently residing in surveyed households.

Kind of data

Sample survey data [ssd]

Sampling procedure

THE CLEANED AND HARMONIZED VERSION OF THE SURVEY DATA PRODUCED AND PUBLISHED BY THE ECONOMIC RESEARCH FORUM REPRESENTS 100% OF THE ORIGINAL SURVEY DATA COLLECTED BY THE DEPARTMENT OF STATISTICS OF THE HASHEMITE KINGDOM OF JORDAN

Mode of data collection

Computer Assisted Personal Interview [capi]

Cleaning operations

----> Raw Data

A tabulation results plan has been set based on the previous Employment and Unemployment Surveys while the required programs were prepared and tested. When all prior data processing steps were completed, the actual survey results were tabulated using an ORACLE package. The tabulations were then thoroughly checked for consistency of data. The final report was then prepared, containing detailed tabulations as well as the methodology of the survey.

----> Harmonized Data

• The SPSS package is used to clean and harmonize the datasets.
• The harmonization process starts with a cleaning process for all raw data files received from the Statistical Agency.
• All cleaned data files are then merged to produce one data file on the individual level containing all variables subject to harmonization.
• A country-specific program is generated for each dataset to generate/ compute/ recode/ rename/ format/ label harmonized variables.
• A post-harmonization cleaning process is then conducted on the data.
• Harmonized data is saved on the household as well as the individual level, in SPSS and then converted to STATA, to be disseminated.

This spreadsheet is provided as an example of the format that should to be used prior to referencing it within the python code. The attachment is an example of the fields and formats of information required to be in a spreadsheet before using the python code to format it into a Survey 123 survey. The field names need to stay the same and in the same order in the spreadsheet (don't capitalize names, don't move columns, etc).

Abstract

IFAD's Coastal Community Development Project (CCDP) in Indonesia, a US$43.2 million project, had the overall goal of reducing poverty through enhanced, sustainable and replicable economic growth among the active poor in coastal and small island communities. This was to be achieved through investments in fishery, aquaculture, processing and marketing, in addition to provision of related support structures. To this end, the project aimed at addressing constraints on small-scale fishery communities by increasing fish catch, fish productivity and income through improvements in fishing gears (technology) used and fishing practices as well as increasing household participation in high-potential marine and aquaculture value chains. CCDP also aimed at rehabilitating coastal and natural resources to ensure sustainability of the environment, fish stock and economic livelihoods. The project was implemented in 181 villages within 12 districts throughout eastern Indonesia.

The CCDP was selected for rigorous ex-post impact assessment (IA) to analyze the effects of CCDP on a number of impact and outcome indicators, including economic mobility, food security and nutrition, resilience, women's empowerment and natural resources rehabilitation. For more information, please, click on the following link https://www.ifad.org/en/web/knowledge/-/publication/impact-assessment-the-coastal-community-development-ccdp-.

Geographic coverage

The project was implemented in 181 villages within 12 districts throughout eastern Indonesia.

Analysis unit

Households

Kind of data

Sample survey data [ssd]

Sampling procedure

The households that directly participated in the CCDP were termed beneficiaries or the "treatment" group, while households residing in the same villages as CCDP beneficiaries but did not directly participate in the CCDP were termed "spillover" group, as they were likely to indirectly benefit from some of the CCDP interventions, in one way or another. A separate "control" or comparison group of households was drawn from separate districts and villages, which had similar characteristics at baseline as those where CCDP was implemented. More detail on the sampling procedure is available in the IA plan and report attached in the documentation section.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

A detailed household survey questionnaire was developed to collect primary data on the livelihood activities of the CCDP beneficiaries as well as the spillover and comparison group households. The questionnaire primarily captured data on fisheries and aquaculture activities of the households, characteristics of their fishing gears, fishing boats and technologies used by the fishers as well as the kinds of fish and quantities caught during the peak and low seasons. For aquaculture fishers, the questionnaire collected data on the aquaculture infrastructure used such as cages, rafts and nets, in addition to the types of inputs used such as fingerlings and fish feed. Data on labor use and how fishers organized their fishing/aquaculture activities, including whether they fished in groups or not and whether they sold their fish catch in groups or as individuals and where they sold their fish (whether fresh or after processing), etc. were all captured by the questionnaire.

Additional variables captured by the questionnaire include household-level variables such as income sources (including non-fishing activities), diet composition and food insecurity experiences. Variables on household assets, including productive assets (fishing assets, farming assets, etc.), housing assets, durable assets, savings, and access to credit were also collected through the questionnaire. As is standard with most household surveys, the questionnaire captured household demographic variables, including the ages, sex, education levels, ethnicity and religion of the individuals in the households interviewed. Variables designed to measure resilience to a variety of shocks as well as measure women's empowerment were also captured through the household survey questionnaire. In addition to the household survey questionnaire, a community-level survey questionnaire was designed and used to collect data on a number of community-level variables. This questionnaire captured variables such as the types of infrastructure and public services available in the communities, the various development projects implemented in the community, as well as variables on shocks that the communities experienced and the development and social groups operating in the communities. The community-level survey questionnaire allowed for the collection of important community-level variables useful for matching as well as for controlling for as part of data analysis.

Note: some variables may have missing labels. Please, refer to the questionnaire for more details.

The underlying data is from Stack Overflow's 2019 Developer Survey Responses and can be found: https://stackoverflow.blog/2019/04/09/the-2019-stack-overflow-developer-survey-results-are-in/ Please note my intent with uploading this is to showcase my experience working with the datasets. My goal is to build a centralized portfolio.

Please note that we are using a randomized sample of 1/10th the original data set. Conclusions may not reflect real world.

The goal of this project was to explore, analyze, and visualize.

Follow this link to see the Cognos Dashboard I created: https://dataplatform.cloud.ibm.com/dashboards/ee7bf962-3882-4145-a41c-ecdda9323484/view/4427dc2d63b71c921ee1e6e4079c29002c362d5fe4bb860ad18c7b495d607297f3614099c82f4d5bde135661a7e8400f9d

Feel free to filter and play with the dashboard as you want.

Abstract

THE CLEANED AND HARMONIZED VERSION OF THE SURVEY DATA PRODUCED AND PUBLISHED BY THE ECONOMIC RESEARCH FORUM REPRESENTS 100% OF THE ORIGINAL SURVEY DATA COLLECTED BY THE PALESTINIAN CENTRAL BUREAU OF STATISTICS

The Palestinian Central Bureau of Statistics (PCBS) carried out four rounds of the Labor Force Survey 2021 (LFS). The survey rounds covered a total sample of about 25,179 households (about 6,300 households per quarter).

The main objective of collecting data on the labour force and its components, including employment, unemployment and underemployment, is to provide basic information on the size and structure of the Palestinian labour force. Data collected at different points in time provide a basis for monitoring current trends and changes in the labour market and in the employment situation. These data, supported with information on other aspects of the economy, provide a basis for the evaluation and analysis of macro-economic policies.

The raw survey data provided by the Statistical Agency were cleaned and harmonized by the Economic Research Forum, in the context of a major project that started in 2009. During which extensive efforts have been exerted to acquire, clean, harmonize, preserve and disseminate micro data of existing labor force surveys in several Arab countries.

Geographic coverage

Covering a representative sample on the region level (West Bank, Gaza Strip), the locality type (urban, rural, camp) and the governorates.

Analysis unit

1- Household/family. 2- Individual/person.

Universe

The survey covered all Palestinian households who are a usual residence of the Palestinian Territory.

Kind of data

Sample survey data [ssd]

Sampling procedure

THE CLEANED AND HARMONIZED VERSION OF THE SURVEY DATA PRODUCED AND PUBLISHED BY THE ECONOMIC RESEARCH FORUM REPRESENTS 100% OF THE ORIGINAL SURVEY DATA COLLECTED BY THE PALESTINIAN CENTRAL BUREAU OF STATISTICS

The methodology was designed according to the context of the survey, international standards, data processing requirements and comparability of outputs with other related surveys.

---> Target Population: It consists of all individuals aged 10 years and Above and there are staying normally with their households in the state of Palestine during 2020.

---> Sampling Frame: The sampling frame consists of a comprehensive sample selected from the Population, Housing and Establishments Census 2017: This comprehensive sample consists of geographical areas with an average of 150 households, and these are considered as enumeration areas used in the census and these units were used as primary sampling units (PSUs).

---> Sampling Size: The estimated sample size is 8,040 households in each quarter of 2021.

---> Sample Design The sample is two stage stratified cluster sample with two stages : First stage: we select a systematic random sample of 536 enumeration areas for the whole round. Second stage: we select a systematic random sample of 15 households from each enumeration area selected in the first stage.

---> Sample strata: The population was divided by: 1- Governorate (17 governorates, where Jerusalem was considered as two statistical areas) 2- Type of Locality (urban, rural, refugee camps).

---> Sample Rotation: Each round of the Labor Force Survey covers all of the 536 master sample enumeration areas. Basically, the areas remain fixed over time, but households in 50% of the EAs were replaced in each round. The same households remain in the sample for two consecutive rounds, left for the next two rounds, then selected for the sample for another two consecutive rounds before being dropped from the sample. An overlap of 50% is then achieved between both consecutive rounds and between consecutive years (making the sample efficient for monitoring purposes).

Mode of data collection

Face-to-face [f2f]

Research instrument

The survey questionnaire was designed according to the International Labour Organization (ILO) recommendations. The questionnaire includes four main parts:

---> 1. Identification Data: The main objective for this part is to record the necessary information to identify the household, such as, cluster code, sector, type of locality, cell, housing number and the cell code.

---> 2. Quality Control: This part involves groups of controlling standards to monitor the field and office operation, to keep in order the sequence of questionnaire stages (data collection, field and office coding, data entry, editing after entry and store the data.

---> 3. Household Roster: This part involves demographic characteristics about the household, like number of persons in the household, date of birth, sex, educational level…etc.

---> 4. Employment Part: This part involves the major research indicators, where one questionnaire had been answered by every 15 years and over household member, to be able to explore their labour force status and recognize their major characteristics toward employment status, economic activity, occupation, place of work, and other employment indicators.

Cleaning operations

---> Raw Data PCBS started collecting data since 1st quarter 2020 using the hand held devices in Palestine excluding Jerusalem in side boarders (J1) and Gaza Strip, the program used in HHD called Sql Server and Microsoft. Net which was developed by General Directorate of Information Systems. From the beginning of March 2020, with the spread of the COVID-19 pandemic and the home quarantine imposed by the government, the personal (face to face) interview was replaced by the phone interview for households who had phone numbers from previous rounds, and for those households that did not have phone numbers, they were referred to and interviewed in person (face to face interview). Using HHD reduced the data processing stages, the fieldworkers collect data and sending data directly to server then the project manager can withdrawal the data at any time he needs. In order to work in parallel with Gaza Strip and Jerusalem in side boarders (J1), an office program was developed using the same techniques by using the same database for the HHD.

---> Harmonized Data - The SPSS package is used to clean and harmonize the datasets. - The harmonization process starts with a cleaning process for all raw data files received from the Statistical Agency. - All cleaned data files are then merged to produce one data file on the individual level containing all variables subject to harmonization. - A country-specific program is generated for each dataset to generate/ compute/ recode/ rename/ format/ label harmonized variables. - A post-harmonization cleaning process is then conducted on the data. - Harmonized data is saved on the household as well as the individual level, in SPSS and then converted to STATA, to be disseminated.

Response rate

The survey sample consists of about 32,160 households of which 25,179 households completed the interview; whereas 16,355 households from the West Bank and 8,824 households in Gaza Strip. Weights were modified to account for non-response rate. The response rate in the West Bank reached 79.8% while in the Gaza Strip it reached 90.5%.

Sampling error estimates

---> Sampling Errors Data of this survey may be affected by sampling errors due to use of a sample and not a complete enumeration. Therefore, certain differences can be expected in comparison with the real values obtained through censuses. Variances were calculated for the most important indicators: the variance table is attached with the final report. There is no problem in disseminating results at national or governorate level for the West Bank and Gaza Strip.

---> Non-Sampling Errors Non-statistical errors are probable in all stages of the project, during data collection or processing. This is referred to as non-response errors, response errors, interviewing errors, and data entry errors. To avoid errors and reduce their effects, great efforts were made to train the fieldworkers intensively. They were trained on how to carry out the interview, what to discuss and what to avoid, carrying out a pilot survey, as well as practical and theoretical training during the training course. Also data entry staff were trained on the data entry program that was examined before starting the data entry process. To stay in contact with progress of fieldwork activities and to limit obstacles, there was continuous contact with the fieldwork team through regular visits to the field and regular meetings with them during the different field visits. Problems faced by fieldworkers were discussed to clarify any issues. Non-sampling errors can occur at the various stages of survey implementation whether in data collection or in data processing. They are generally difficult to be evaluated statistically.

They cover a wide range of errors, including errors resulting from non-response, sampling frame coverage, coding and classification, data processing, and survey response (both respondent and interviewer-related). The use of effective training and supervision and the careful design of questions have direct bearing on limiting the magnitude of non-sampling errors, and hence enhancing the quality of the resulting data. The implementation of the survey encountered non-response where the case ( household was not present at home ) during the fieldwork visit and the case ( housing unit is vacant) become the high percentage of the non response cases. The total non-response rate reached 16.7% which is very low once compared to the

Since the beginning of the 1960s, Statistics Sweden, in collaboration with various research institutions, has carried out follow-up surveys in the school system. These surveys have taken place within the framework of the IS project (Individual Statistics Project) at the University of Gothenburg and the UGU project (Evaluation through follow-up of students) at the University of Teacher Education in Stockholm, which since 1990 have been merged into a research project called 'Evaluation through Follow-up'. The follow-up surveys are part of the central evaluation of the school and are based on large nationally representative samples from different cohorts of students.

Evaluation through follow-up (UGU) is one of the country's largest research databases in the field of education. UGU is part of the central evaluation of the school and is based on large nationally representative samples from different cohorts of students. The longitudinal database contains information on nationally representative samples of school pupils from ten cohorts, born between 1948 and 2004. The sampling process was based on the student's birthday for the first two and on the school class for the other cohorts.

For each cohort, data of mainly two types are collected. School administrative data is collected annually by Statistics Sweden during the time that pupils are in the general school system (primary and secondary school), for most cohorts starting in compulsory school year 3. This information is provided by the school offices and, among other things, includes characteristics of school, class, special support, study choices and grades. Information obtained has varied somewhat, e.g. due to changes in curricula. A more detailed description of this data collection can be found in reports published by Statistics Sweden and linked to datasets for each cohort.

Survey data from the pupils is collected for the first time in compulsory school year 6 (for most cohorts). Questionnaire in survey in year 6 includes questions related to self-perception and interest in learning, attitudes to school, hobbies, school motivation and future plans. For some cohorts, questionnaire data are also collected in year 3 and year 9 in compulsory school and in upper secondary school.

Furthermore, results from various intelligence tests and standartized knowledge tests are included in the data collection year 6. The intelligence tests have been identical for all cohorts (except cohort born in 1987 from which questionnaire data were first collected in year 9). The intelligence test consists of a verbal, a spatial and an inductive test, each containing 40 tasks and specially designed for the UGU project. The verbal test is a vocabulary test of the opposite type. The spatial test is a so-called ‘sheet metal folding test’ and the inductive test are made up of series of numbers. The reliability of the test, intercorrelations and connection with school grades are reported by Svensson (1971).

For the first three cohorts (1948, 1953 and 1967), the standartized knowledge tests in year 6 consist of the standard tests in Swedish, mathematics and English that up to and including the beginning of the 1980s were offered to all pupils in compulsory school year 6. For the cohort 1972, specially prepared tests in reading and mathematics were used. The test in reading consists of 27 tasks and aimed to identify students with reading difficulties. The mathematics test, which was also offered for the fifth cohort, (1977) includes 19 assignments. After a changed version of the test, caused by the previously used test being judged to be somewhat too simple, has been used for the cohort born in 1982. Results on the mathematics test are not available for the 1987 cohort. The mathematics test was not offered to the students in the cohort in 1992, as the test did not seem to fully correspond with current curriculum intentions in mathematics. For further information, see the description of the dataset for each cohort.

For several of the samples, questionnaires were also collected from the students 'parents and teachers in year 6. The teacher questionnaire contains questions about the teacher, class size and composition, the teacher's assessments of the class' knowledge level, etc., school resources, working methods and parental involvement and questions about the existence of evaluations. The questionnaire for the guardians includes questions about the child's upbringing conditions, ambitions and wishes regarding the child's education, views on the school's objectives and the parents' own educational and professional situation.

The students are followed up even after they have left primary school. Among other things, data collection is done during the time they are in high school. Then school administrative data such as e.g. choice of upper secondary school line / program and grades after completing studies. For some of the cohorts, in addition to school administrative data, questionnaire data were also collected from the students.

he sample consisted of students born on the 5th, 15th and 25th of any month in 1953, a total of 10,723 students.

The data obtained in 1966 were: 1. School administrative data (school form, class type, year and grades). 2. Information about the parents' profession and education, number of siblings, the distance between home and school, etc.

This information was collected for 93% of all born on the current days. The reason for this is reduced resources for Statistics Sweden for follow-up work - reminders etc. Annual data for cohorts in 1953 were collected by Statistics Sweden up to and including academic year 1972/73.

1. Answers to certain questions that shed light on students' school motivation, leisure activities and study and career plans. Some of the questions changed significantly compared to the cohort in 1948 due to the fact that they did not function satisfactorily from a metrological point of view.
2. Results on three aptitude tests, one verbal, one spatial and one inductive.
3. Standard test results in reading, writing, mathematics and English, which were offered to the students who belonged to year 6.

Response rate for test and questionnaire data is 88% Standard test results were received for just over 85% of those who took the tests.

The sample included a total of 9955 students, for whom some form of information was obtained.

Part of the "Individual Statistics Project" together with cohort 1953.

The Project Information Literacy (PIL) lifelong learning survey dataset was produced as part of a two-year federally funded study on relatively recent US college graduates and their information-seeking behavior for continued learning. The goal of the survey was to collect quantitative data about the information-seeking behavior of a sample of recent graduates—the strategies, techniques, information support systems, and best practices—used to support lifelong learning in post-college life. The dataset contains responses from 1,651 respondents to a 21-item questionnaire administered between October 9, 2014 and December 15, 2014. The voluntary sample of respondents consisted of relatively recent graduates, who had completed their degrees between 2007 and 2012, from one of 10 US colleges and universities in the institutional sample. Quantitative data are included in the dataset about the learning needs of relatively recent graduates as well as the information sources they used in three arenas of their post-college lives (i.e., personal life, workplace, and the communities in which they resided). Demographic information—including age, gender, major, GPA, employment status, graduate school attendance, and geographic proximity of current residence to their alma mater—is also included in the dataset for the respondents. "Staying Smart: How Today's Graduates Continue to Learn Once They Complete College," Alison J. Head, Project Information Literacy Research Report, Seattle: University of Washington Information School (January 5, 2016), 112 pages, 6.9 MB.

The 2015 American Housing Survey marks the first release of a newly integrated national sample and independent metropolitan area samples. The 2015 release features many variable name revisions, as well as the integration of an AHS Codebook Interactive Tool available on the U.S. Census Bureau We site. This data collection provides information on the characteristics of a national sample of housing units in 2015, including apartments, single-family homes, mobile homes, and vacant housing units. Data from the 15 largest metropolitan areas in the United States are included in the national sample survey (the AHS 2015 Metropolitan Data are also available as ICPSR 36805). The data are presented in three separate parts: Part 1, Household Record (Main Record), Part 2, Person Record, and Part 3, Project Record. Household Record data includes questions about household occupancy and tenure, household exterior and interior structural features, household equipment and appliances, housing problems, housing costs, home improvement, neighborhood features, recent moving information, income, and basic demographic information. The household record data also features four rotating topical modules: Arts and Culture, Food Security, Housing Counseling, and Healthy Homes. Person Record data includes questions about personal disabilities, income, and basic demographic information. Finally, the Project Record data includes questions about home improvement projects. Specific questions were asked about the types of projects, costs, funding sources, and year of completion.

Abstract

The National Survey of Household Income and Expenditure (ENIGH) aims to provide a statistical overview of the behavior of household income and expenditure in terms of its amount, origin and distribution. In addition, it offers information on the occupational and sociodemographic characteristics of the members of the household, as well as the characteristics of the housing infrastructure and household equipment.

The ENIGH is part of the Information System of National Interest (IIN), which means that the results obtained from this project are mandatory for the Federation, the states and the municipalities, in order to contribute to national development.

In 1984, a trend began to broaden the objectives and homogenize the methodology, taking into account international recommendations and the information requirements of the different users, taking care of historical comparability.

Periodicity: Since 1992 it has been carried out biennially (every two years) with the exception of 2005 when an extraordinary survey was carried out.

Target population: It is made up of the households of nationals or foreigners, who usually reside in private homes within the national territory.

Selection Unit: Private home. The dwellings are chosen through a meticulous statistical process that guarantees that the results obtained from only a part of the population (sample) can be generalized to the total.

Sampling Frame: INEGI's multi-purpose framework is made up of demographic and cartographic information obtained from the 2010 Population and Housing Census.

Observation unit: The home.

Unit of analysis: The household, the dwelling and the members of the household.

Thematic coverage:

Characteristics of the house. Residents and identification of households in the dwelling. Sociodemographic characteristics of the residents of the dwelling. Home equipment, services. Activity condition and occupational characteristics of household members aged 12 and over. Total current income (monetary and non-monetary) of households. Financial and capital perceptions of households and their members. Current monetary expenditure of households. Financial and capital expenditures of households.

The different concepts of the ENIGH are governed by recommendations agreed upon in international conventions, for example:

The resolutions and reports of the 18 International Conferences on Labour Statistics, of the International Labour Organization (ILO).

The final report and recommendations of the Canberra Group, an expert group on "Household Income Statistics".

Manual of Household Surveys. Department of International Economic and Social Affairs, Bureau of Statistics. United Nations, New York, 1987.

They are also articulated with the CNational Accounts and with the Household Surveys carried out by the INEGI.

Sample size: At the national level, including the ten-one, there are 93,186 private homes.

Survey period: The collection of information will take place between August 11 and November 18 of this year. Throughout this period, ten cuts are made, each organized in ten days; Therefore, each of these cuts will be known as tens (see calendar in the annex).

Workload: According to the meticulousness in the recording of information in this project, a load of six interviews in private homes per dozen has been defined for each interviewer. The number of interviews may decrease or increase according to several factors: non-response, recovery from non-response, or additional households.

Geographic coverage

National and at the state level - Urban: localities with 2,500 or more inhabitants - Rural: localities with less than 2,500 inhabitants

Analysis unit

The household, the dwelling and the members of the household.

Universe

The survey is aimed at households in the national territory.

Kind of data

Probabilistic household survey

Sampling procedure

The design of the exhibition for ENIGH-2018 is characterized by being probabilistic; consequently, the results obtained from the survey are generalized to the entire population of the study domain; in turn, it is two-stage, stratified and by clusters, where the ultimate unit of selection is the dwelling and the unit of observation is the household.

The ENIGH-2018 subsample was selected from the 2012 INEGI master sample, this master sample was designed and selected from the 2012 Master Sampling Framework (Marco Maestro de Muestreo (MMM)) which was made up of housing clusters called Primary Sampling Units (PSU), built from the cartographic and demographic information obtained from the 2010 Population and Housing Census. The master sample allows the selection of subsamples for all housing surveys carried out by INEGI; Its design is probabilistic, stratified, single-stage and by clusters, since it is in them that the dwellings that make up the subsamples of the different surveys were selected in a second stage. The design of the MMM was built as follows:

Formation of the primary sampling units (PSU)

First, the set of PSUs that will cover the national territory is built.

The primary sampling units are made up of groups of dwellings with differentiated characteristics depending on the area to which they belong, as specified below:

a) In high urban areas

The minimum size of a PSU is 80 inhabited dwellings and the maximum is 160. They can be made up of:

• A block. • The union of two or more contiguous blocks of the same AGEB. • The union of two or more contiguous blocks of different AGEBs in the same locality. • The union of two or more contiguous blocks from different localities, which belong to the same size of locality.

b) In urban complement: The minimum size of a PSU is 160 inhabited dwellings and the maximum is 300. They can be made up of:

• A block. • The union of two or more contiguous blocks of the same AGEB. • The union of two or more contiguous blocks of different AGEBs in the same locality. • The union of two or more contiguous blocks from different AGEBs and localities, but from the same municipality.

c) In rural areas: The minimum size of a PSU is 160 inhabited dwellings and the maximum is 300. They can be made up of:

• An AGEB. • Part of an AGEB. • The union of two or more adjoining AGEBs in the same municipality. • The union of an AGEB with a part of another adjoining AGEB in the same municipality.

The total number of PSUs formed was 240,912.

Stratification

Once the set of PSUs has been constructed, those with similar characteristics are grouped, that is, they are stratified.

The political division of the country and the formation of localities differentiated by their size, naturally form a geographical stratification.

In each federal entity there are three areas, divided into zones.

High urban, Zone 01 to 09, Cities with 100,000 or more inhabitants.

Urban complement, Zone 25, 35, 45 and 55, From 50,000 to 99,999 inhabitants, 15,000 to 49,999 inhabitants, 5,000 to 14,999 inhabitants, 2,500 to 4,999 inhabitants.

Rural, Zone 60, Localities with less than 2,500 inhabitants.

At the same time, four sociodemographic strata were formed in which all the PSUs in the country were grouped, this stratification considers the sociodemographic characteristics of the inhabitants of the dwellings, as well as the physical characteristics and equipment of the same, expressed through 34 indicators built with information from the 2010 Population and Housing Census*, for which multivariate statistical methods were used.

In this way, each PSU was classified into a single geographical and a sociodemographic stratum.

As a result, there are a total of 683 strata throughout the country.

Selection of the PSUs of the master sample The PSUs of the master sample were selected by means of a sampling with probability proportional to the size.

Sample size For the calculation of the sample size of the ENIGH-2018, the average total current income per household was considered as a reference variable.

Sampling deviation

As a result of the sum of the 87,826 homes selected and 1,312 additional homes that were found in those homes, the total amounted to 89,138 households.

Mode of data collection

Face-to-face [f2f]

Research instrument

Six collection instruments will be used to collect information in each household, four of which concentrate information on the household as a whole.

These are:

• Household and housing questionnaire
• Household expenditure questionnaires
• Daily expenditure booklet

In the other three, individual information is recorded for people:

• Questionnaire for people aged 12 or over
• Questionnaire for people under 12 years of age
• Questionnaire for household businesses

Cleaning operations

Capture activities

The capture consisted of transferring the information from the questionnaires that were fully answered to electronic means through IKTAN, in accordance with the procedures established for the capture process of the ENIGH 2018.

The Person in Charge of Capture and Validation, together with his work team, began the capture of the questionnaires collected by each Interviewer, organized by packages of questionnaires of each page with the result of a complete interview, following the established order:

• Household and housing questionnaire. • Questionnaires for people under 12 years of age. • Questionnaires for people aged 12 and over. • Questionnaires for home businesses. • Household expenditure questionnaire. • Daily expenses booklet.

In addition, the IKTAN made it possible to record and know the progress or conclusion of workloads.

Validation activities

In parallel to the capture, the state coordination

Abstract

The Jerusalem Household Social Survey 2005 is one of the most important statistical activities that have been conducted by PCBS. It is the most detailed and comprehensive statistical activity that PCBS has conducted in Jerusalem. The main objective of the Jerusalem household social survey, 2005 is to provide basic information about: Demographic and social characteristics for the Palestinian society in Jerusalem governorate including age-sex structure, Illiteracy rate, enrollment and drop-out rates by background characteristics, Labor force status, unemployment rate, occupation, economic activity, employment status, place of work and wage levels, Housing and housing conditions, Living levels and impact of Israeli measures on nutrition behavior during Al-Aqsa intifada, Criminal offence, its victims, and injuries caused.

Geographic coverage

Social survey data covering the province of Jerusalem only, the type locality (urban, rural, refugee camps) and Governorate

Analysis unit

households, Individual

Universe

The target population was all Palestinian households living in Jerusalem Governorate.

Kind of data

Sample survey data [ssd]

Sampling procedure

The Sample Frame Were estimated sample size of Jerusalem by 3,300 family, including 2,240 families in the region J1, and 1,060 families in the region of J2 has been the establishment of Sample Frame to Jerusalem (J2) of the General Census of Population and Housing, and Establishment, which was carried out by the PCBS at the end of 1997, was create Sample Frame to Jerusalem (J1) of project data that has been exclusively in 2004. And the frame is a list of counting areas, and these areas are used as units an initial preview (PSUs) in the first stage of the process of selecting the sample. Stratified cluster random sample of regular two phases: Phase I: was selected a stratified random sample of enumeration areas from Jerusalem (J1) and Jerusalem (J2). The number of enumeration areas that have been chosen counting area 123 divided into two regions: 70 the count of Jerusalem (J1), 53 the count of Jerusalem (J2). Phase II: A random sample was withdrawn systematically with size of 20 families from each enumeration area that was selected in the first stage of the Jerusalem J2, and 32 families from each enumeration area that was selected in the first stage of the Jerusalem J1.

Mode of data collection

Face-to-face [f2f]

Research instrument

A survey questionnaire the main tool for gathering information, so do not need to check the technical specifications for the phase of field work, as required to achieve the requirements of data processing and analysis, has been designed form the survey after examining the experience of other countries on the subject of social surveys, covering the form as much as possible the most important social indicators as recommended by the United Nations, taking into account the specificity of the Palestinian community in this aspect.

Cleaning operations

Phase included a set of data processing Activities and operations that have been made to the Forms to prepare her for the analysis phase, This phase included the following operations: Before the introduction of audit data: at this stage was Check all the forms using the instructions To check to make sure the field of logical data and re- Incomplete, including a second field. Data Entry: The data entry Central to the central headquarters in Al-Bireh, was organized The data entry process using the BLAISE Program Where the form has been programmed through this program. Was marked by the program that was developed in the Device properties and features the following: The possibility of dealing with an exact copy of the form The computer screen. The ability to conduct all tests and possibilities Possible and logical sequence of data in the form. Maintain a minimum of errors Portal Digital data or errors of field work. Ease of use and deal with the software and data (User-Friendly). The possibility of converting the data to the other formula can be Use and analysis of the statistical systems Analysis such as SPSS.

Response rate

during the field work we visit 3,300 family in Jerusalem Governorate, 2,240 in Area J1 and1,060 in Area J2 where the final results of the interviews were as follows: The number of families who were interviewed (2,485) in Jerusalem Governorate, complete questioner 75.3% (1,773) in J1 79.2% (712) in J2 67.2%

Sampling error estimates

Data were collected in a manner that the survey sample and not Balhsr destruction, so she is exposed to two main types of errors. The first sampling errors (statistical errors), and the second non-statistical errors. It is intended that sampling errors of the errors resulting from sample design, so it is easy to measure, the contrast has been calculated and the effect of sample design.

The non-statistical errors are possible to occur in every stage of project implementation, through data collection, inserting, and mistakes can be summarized by the non-response, and response errors (surveyed), and the mistakes of the interview (the researcher) and data-entry errors. To avoid errors and reduce the impact it has made significant efforts through the training of researchers extensive training, and the presence of a group of experts in the concepts and terminology, medical / health, and training on how to conduct interviews, and the things that must be followed during the interview, and the things that should be avoided.

Have been trained on the data entry program entry, program, and were examined in order to see the picture of the situation and reduce any problems, there was constant contact between supervisors and checkers through ongoing visits and periodic meetings. In addition, has been drafting a set of circulars and instructions reminder to the team. Also been circulated answers to questions and problems faced by the researchers during the field work.

As for office work have been trained crew to check the special forms and field detection of errors, which greatly reduces the rates of errors that can occur during field work. In order to reduce the proportion of errors that can occur during entry form to the computer, the software is designed to entry so as not to allow any errors Tnasagah can get during the process of input and contains many of the conditions Logical, where they were loading the program the input of many tests on private answers each question in addition to the relations between the different questions and testing the other logical. This process has led to the disclosure of most of the errors that are not found in previous phases of work, where they were correct all errors that have been discovered.

Data were evaluated according to the following areas: 1. Definition of family members and how to register. 2. Demographic characteristics that have a relationship on Christmas. 3. Breakdown of the profession and activity.

Methods of assessment vary according to the data subject in this survey include the following: 1. Occurrences of missing values and Answers "other" and "Do not know" and examine inconsistencies between different sections or between the date of birth and other sections. Add to examine the internal consistency of the data as part of a logical data and completeness. 2. Compared to survey data with the results of surveys of the relationship and by the Central Bureau of Statistics Palestinian implementation.

Can be summarized as sources of some non-statistical errors that have emerged during the implementation of the survey including the following: Inability to meet the data in some cases the forms because of the lack of a home or be in the housing unit does not exist or are uninhabited and there are families not able to provide some data or refused to do so. Some families did not take the form subject very seriously affecting the quality of the data provided. Errors resulting from the method of asking the question by the researcher in the field. Category understand the question and answer based on his understanding of it. The inability of the technical team overseeing the project from the field visit on a regular basis for all duty stations in order to see the workflow and meet researchers and directing them, especially in the area J1. There was difficulty in reaching the families because of the construction of the wall, especially in the Ram Area and also in the area of Bir Nabala where the switch was a full count area due to additional incompleteness caused by the absence of the families in the region because of the separation wall. It was not easy to follow and adjust the time researchers because of the prevailing security conditions.