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 demograohic 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 chracteristics 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

• Household/families
• Individuals

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 2008 Household Expenditure and Income Survey sample was designed using two-stage cluster stratified sampling method. In the first stage, the primary sampling units (PSUs), the blocks, were drawn using probability proportionate to the size, through considering the number of households in each block to be the block size. The second stage included drawing the household sample (8 households from each PSU) using the systematic sampling method. Fourth substitute households from each PSU were drawn, using the systematic sampling method, to be used on the first visit to the block in case that any of the main sample households was not visited for any reason.

To estimate the sample size, the coefficient of variation and design effect in each subdistrict were calculated for the expenditure variable from data of the 2006 Household Expenditure and Income Survey. This results was used to estimate the sample size at sub-district level, provided that the coefficient of variation of the expenditure variable at the sub-district level did not exceed 10%, with a minimum number of clusters that should not be less than 6 at the district level, that is to ensure good clusters representation in the administrative areas to enable drawing poverty pockets.

It is worth mentioning that the expected non-response in addition to areas where poor families are concentrated in the major cities were taken into consideration in designing the sample. Therefore, a larger sample size was taken from these areas compared to other ones, in order to help in reaching the poverty pockets and covering them.

Mode of data collection

Face-to-face [f2f]

Research instrument

List of survey questionnaires: (1) General Form (2) Expenditure on food commodities Form (3) Expenditure on non-food commodities Form

Cleaning operations

Raw Data The design and implementation of this survey procedures were: 1. Sample design and selection 2. Design of forms/questionnaires, guidelines to assist in filling out the questionnaires, and preparing instruction manuals 3. Design the tables template to be used for the dissemination of the survey results 4. Preparation of the fieldwork phase including printing forms/questionnaires, instruction manuals, data collection instructions, data checking instructions and codebooks 5. Selection and training of survey staff to collect data and run required data checkings 6. Preparation and implementation of the pretest phase for the survey designed to test and develop forms/questionnaires, instructions and software programs required for data processing and production of survey results 7. Data collection 8. Data checking and coding 9. Data entry 10. Data cleaning using data validation programs 11. Data accuracy and consistency checks 12. Data tabulation and preliminary results 13. Preparation of the final report and dissemination of final results

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 all 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

IntroductionThere is a need to develop harmonized procedures and a Minimum Data Set (MDS) for cross-border Multi Casualty Incidents (MCI) in medical emergency scenarios to ensure appropriate management of such incidents, regardless of place, language and internal processes of the institutions involved. That information should be capable of real-time communication to the command-and-control chain. It is crucial that the models adopted are interoperable between countries so that the rights of patients to cross-border healthcare are fully respected.ObjectiveTo optimize management of cross-border Multi Casualty Incidents through a Minimum Data Set collected and communicated in real time to the chain of command and control for each incident. To determine the degree of agreement among experts.MethodWe used the modified Delphi method supplemented with the Utstein technique to reach consensus among experts. In the first phase, the minimum requirements of the project, the profile of the experts who were to participate, the basic requirements of each variable chosen and the way of collecting the data were defined by providing bibliography on the subject. In the second phase, the preliminary variables were grouped into 6 clusters, the objectives, the characteristics of the variables and the logistics of the work were approved. Several meetings were held to reach a consensus to choose the MDS variables using a Modified Delphi technique. Each expert had to score each variable from 1 to 10. Non-voting variables were eliminated, and the round of voting ended. In the third phase, the Utstein Style was applied to discuss each group of variables and choose the ones with the highest consensus. After several rounds of discussion, it was agreed to eliminate the variables with a score of less than 5 points. In phase four, the researchers submitted the variables to the external experts for final assessment and validation before their use in the simulations. Data were analysed with SPSS Statistics (IBM, version 2) software.ResultsSix data entities with 31 sub-entities were defined, generating 127 items representing the final MDS regarded as essential for incident management. The level of consensus for the choice of items was very high and was highest for the category ‘Incident’ with an overall kappa of 0.7401 (95% CI 0.1265–0.5812, p 0.000), a good level of consensus in the Landis and Koch model. The items with the greatest degree of consensus at ten were those relating to location, type of incident, date, time and identification of the incident. All items met the criteria set, such as digital collection and real-time transmission to the chain of command and control.ConclusionsThis study documents the development of a MDS through consensus with a high degree of agreement among a group of experts of different nationalities working in different fields. All items in the MDS were digitally collected and forwarded in real time to the chain of command and control. This tool has demonstrated its validity in four large cross-border simulations involving more than eight countries and their emergency services.

General information: The data sets contain information on how often materials of studies available through GESIS: Data Archive for the Social Sciences were downloaded and/or ordered through one of the archive´s plattforms/services between 2004 and 2018.

Sources and plattforms: Study materials are accessible through various GESIS plattforms and services: Data Catalogue (DBK), histat, datorium, data service (and others).

Years available: - Data Catalogue: 2012-2018 - data service: 2006-2018 - datorium: 2014-2018 - histat: 2004-2018

Data sets: Data set ZA6899_Datasets_only_all_sources contains information on how often data files such as those with dta- (Stata) or sav- (SPSS) extension have been downloaded. Identification of data files is handled semi-automatically (depending on the plattform/serice). Multiple downloads of one file by the same user (identified through IP-address or username for registered users) on the same days are only counted as one download.

Data set ZA6899_Doc_and_Data_all_sources contains information on how often study materials have been downloaded. Multiple downloads of any file of the same study by the same user (identified through IP-address or username for registered users) on the same days are only counted as one download.

Both data sets are available in three formats: csv (quoted, semicolon-separated), dta (Stata v13, labeled) and sav (SPSS, labeled). All formats contain identical information.

Variables: Variables/columns in both data sets are identical. za_nr ´Archive study number´ version ´GESIS Archiv Version´ doi ´Digital Object Identifier´ StudyNo ´Study number of respective study´ Title ´English study title´ Title_DE ´German study title´ Access ´Access category (0, A, B, C, D, E)´ PubYear ´Publication year of last version of the study´ inZACAT ´Study is currently also available via ZACAT´ inHISTAT ´Study is currently also available via HISTAT´ inDownloads ´There are currently data files available for download for this study in DBK or datorium´ Total ´All downloads combined´ downloads_2004 ´downloads/orders from all sources combined in 2004´ [up to ...] downloads_2018 ´downloads/orders from all sources combined in 2018´ d_2004_dbk ´downloads from source dbk in 2004´ [up to ...] d_2018_dbk ´downloads from source dbk in 2018´ d_2004_histat ´downloads from source histat in 2004´ [up to ...] d_2018_histat ´downloads from source histat in 2018´ d_2004_dataservice ´downloads/orders from source dataservice in 2004´ [up to ...] d_2018_dataservice ´downloads/orders from source dataservice in 2018´

More information is available within the codebook.

Abstract copyright UK Data Service and data collection copyright owner.

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

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

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

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

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

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

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

Secure Access Opinions and Lifestyle Survey data

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

The objective of the project was to develop a light time budget instrument suitable for use as an add-on component to other surveys, without adding unduly to respondent burden. In the course of the activity, a development programme was undertaken, involving workshops, field-testing of alternative experimental instruments, evaluation and redesign of these, and a full-scale pilot study. The instrument is designed to be used in both self-response and interview completion modes.
Some 2005 Omnibus Survey respondents were asked to provide a retrospective diary-type account on a designated day. The pilot study has thus yielded useful statistical information, sufficient to make broad national estimates of adult time use patterns in the early summer of 1995. The sample is sufficient to make reliable contrasts between broad time use aggregates for subgroups at, for example, a full-time employed woman vs part-time employed woman level. It is too small to make reliable estimates for smaller time use categories and for smaller classificatory categories. Despite the presence of geographic classificatory variables (Standard Regions), the sample size is not sufficiently large to make reliable sub-national estimates of any of the time use categories.
Main Topics:
Each month's questionnaire consists of two elements: core questions, covering demographic information, are asked each month together with non-core questions that vary from month to month.
The non-core questions for this month were:

Time use (module 117): Each case records data for each of the 2005 people surveyed. There are around 100 classificatory variables which have SPSS data labels which are largely self-explanatory. These data were derived by interviewer or self-completion of a questionnaire.
The remaining 96 variables record activities in each of the 96 quarter hour periods throughout the designated day being measured. These data were derived from a self-completion diary, and again the data variables in the SPSS datasets are largely self-explanatory. Respondents were asked to code their major activity in each of the quarter hour periods, according to a coding frame specifying 30 separate activity codes.
Standard Measures: Prevailing Government Standard Socio-Economic Classificatory Variables were...

Abstract

The harmonized data set on health, created and published by the ERF, is a subset of Iraq Household Socio Economic Survey (IHSES) 2012. It was derived from the household, individual and health modules, collected in the context of the above mentioned survey. The sample was then used to create a harmonized health survey, comparable with the Iraq Household Socio Economic Survey (IHSES) 2007 micro data set.

----> Overview of the Iraq Household Socio Economic Survey (IHSES) 2012:

Iraq is considered a leader in household expenditure and income surveys where the first was conducted in 1946 followed by surveys in 1954 and 1961. After the establishment of Central Statistical Organization, household expenditure and income surveys were carried out every 3-5 years in (1971/ 1972, 1976, 1979, 1984/ 1985, 1988, 1993, 2002 / 2007). Implementing the cooperation between CSO and WB, Central Statistical Organization (CSO) and Kurdistan Region Statistics Office (KRSO) launched fieldwork on IHSES on 1/1/2012. The survey was carried out over a full year covering all governorates including those in Kurdistan Region.

The survey has six main objectives. These objectives are:

1. Provide data for poverty analysis and measurement and monitor, evaluate and update the implementation Poverty Reduction National Strategy issued in 2009.
2. Provide comprehensive data system to assess household social and economic conditions and prepare the indicators related to the human development.
3. Provide data that meet the needs and requirements of national accounts.
4. Provide detailed indicators on consumption expenditure that serve making decision related to production, consumption, export and import.
5. Provide detailed indicators on the sources of households and individuals income.
6. Provide data necessary for formulation of a new consumer price index number.

The raw survey data provided by the Statistical Office were then harmonized by the Economic Research Forum, to create a comparable version with the 2006/2007 Household Socio Economic Survey in Iraq. Harmonization at this stage only included unifying variables' names, labels and some definitions. See: Iraq 2007 & 2012- Variables Mapping & Availability Matrix.pdf provided in the external resources for further information on the mapping of the original variables on the harmonized ones, in addition to more indications on the variables' availability in both survey years and relevant comments.

Geographic coverage

National coverage: Covering a sample of urban, rural and metropolitan areas in all the governorates including those in Kurdistan Region.

Analysis unit

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

Universe

The survey was carried out over a full year covering all governorates including those in Kurdistan Region.

Kind of data

Sample survey data [ssd]

Sampling procedure

----> Design:

Sample size was (25488) household for the whole Iraq, 216 households for each district of 118 districts, 2832 clusters each of which includes 9 households distributed on districts and governorates for rural and urban.

----> Sample frame:

Listing and numbering results of 2009-2010 Population and Housing Survey were adopted in all the governorates including Kurdistan Region as a frame to select households, the sample was selected in two stages: Stage 1: Primary sampling unit (blocks) within each stratum (district) for urban and rural were systematically selected with probability proportional to size to reach 2832 units (cluster). Stage two: 9 households from each primary sampling unit were selected to create a cluster, thus the sample size of total survey clusters was 25488 households distributed on the governorates, 216 households in each district.

----> Sampling Stages:

In each district, the sample was selected in two stages: Stage 1: based on 2010 listing and numbering frame 24 sample points were selected within each stratum through systematic sampling with probability proportional to size, in addition to the implicit breakdown urban and rural and geographic breakdown (sub-district, quarter, street, county, village and block). Stage 2: Using households as secondary sampling units, 9 households were selected from each sample point using systematic equal probability sampling. Sampling frames of each stages can be developed based on 2010 building listing and numbering without updating household lists. In some small districts, random selection processes of primary sampling may lead to select less than 24 units therefore a sampling unit is selected more than once , the selection may reach two cluster or more from the same enumeration unit when it is necessary.

Mode of data collection

Face-to-face [f2f]

Research instrument

----> Preparation:

The questionnaire of 2006 survey was adopted in designing the questionnaire of 2012 survey on which many revisions were made. Two rounds of pre-test were carried out. Revision were made based on the feedback of field work team, World Bank consultants and others, other revisions were made before final version was implemented in a pilot survey in September 2011. After the pilot survey implemented, other revisions were made in based on the challenges and feedbacks emerged during the implementation to implement the final version in the actual survey.

----> Questionnaire Parts:

The questionnaire consists of four parts each with several sections: Part 1: Socio – Economic Data: - Section 1: Household Roster - Section 2: Emigration - Section 3: Food Rations - Section 4: housing - Section 5: education - Section 6: health - Section 7: Physical measurements - Section 8: job seeking and previous job

Part 2: Monthly, Quarterly and Annual Expenditures: - Section 9: Expenditures on Non – Food Commodities and Services (past 30 days). - Section 10 : Expenditures on Non – Food Commodities and Services (past 90 days). - Section 11: Expenditures on Non – Food Commodities and Services (past 12 months). - Section 12: Expenditures on Non-food Frequent Food Stuff and Commodities (7 days). - Section 12, Table 1: Meals Had Within the Residential Unit. - Section 12, table 2: Number of Persons Participate in the Meals within Household Expenditure Other Than its Members.

Part 3: Income and Other Data: - Section 13: Job - Section 14: paid jobs - Section 15: Agriculture, forestry and fishing - Section 16: Household non – agricultural projects - Section 17: Income from ownership and transfers - Section 18: Durable goods - Section 19: Loans, advances and subsidies - Section 20: Shocks and strategy of dealing in the households - Section 21: Time use - Section 22: Justice - Section 23: Satisfaction in life - Section 24: Food consumption during past 7 days

Part 4: Diary of Daily Expenditures: Diary of expenditure is an essential component of this survey. It is left at the household to record all the daily purchases such as expenditures on food and frequent non-food items such as gasoline, newspapers…etc. during 7 days. Two pages were allocated for recording the expenditures of each day, thus the roster will be consists of 14 pages.

Cleaning operations

----> Raw Data:

Data Editing and Processing: To ensure accuracy and consistency, the data were edited at the following stages: 1. Interviewer: Checks all answers on the household questionnaire, confirming that they are clear and correct. 2. Local Supervisor: Checks to make sure that questions has been correctly completed. 3. Statistical analysis: After exporting data files from excel to SPSS, the Statistical Analysis Unit uses program commands to identify irregular or non-logical values in addition to auditing some variables. 4. World Bank consultants in coordination with the CSO data management team: the World Bank technical consultants use additional programs in SPSS and STAT to examine and correct remaining inconsistencies within the data files. The software detects errors by analyzing questionnaire items according to the expected parameter for each variable.

----> Harmonized Data:

• The SPSS package is used to harmonize the Iraq Household Socio Economic Survey (IHSES) 2007 with Iraq Household Socio Economic Survey (IHSES) 2012.
• The harmonization process starts with raw data files received from the Statistical Office.
• A program is generated for each dataset to create harmonized variables.
• Data is saved on the household and individual level, in SPSS and then converted to STATA, to be disseminated.

Response rate

Iraq Household Socio Economic Survey (IHSES) reached a total of 25488 households. Number of households refused to response was 305, response rate was 98.6%. The highest interview rates were in Ninevah and Muthanna (100%) while the lowest rates were in Sulaimaniya (92%).

As the UK went into the first lockdown of the COVID-19 pandemic, the team behind the biggest social survey in the UK, Understanding Society (UKHLS), developed a way to capture these experiences. From April 2020, participants from this Study were asked to take part in the Understanding Society COVID-19 survey, henceforth referred to as the COVID-19 survey or the COVID-19 study.

The COVID-19 survey regularly asked people about their situation and experiences. The resulting data gives a unique insight into the impact of the pandemic on individuals, families, and communities. The COVID-19 Teaching Dataset contains data from the main COVID-19 survey in a simplified form. It covers topics such as

• Socio-demographics
• Whether working at home and home-schooling
• COVID symptoms
• Health and well-being
• Social contact and neighbourhood cohesion
• Volunteering

The resource contains two data files:

• Cross-sectional: contains data collected in Wave 4 in July 2020 (with some additional variables from other waves);
• Longitudinal: Contains mainly data from Waves 1, 4 and 9 with key variables measured at three time points.

Key features of the dataset

• Missing values: in the web survey, participants clicking "Next" but not answering a question were given further options such as "Don't know" and "Prefer not to say". Missing observations like these are recorded using negative values such as -1 for "Don't know". In many instances, users of the data will need to set these values as missing. The User Guide includes Stata and SPSS code for setting negative missing values to system missing.

• The Longitudinal file is a balanced panel and is in wide format. A balanced panel means it only includes participants that took part in every wave. In wide format, each participant has one row of information, and each measurement of the same variable is a different variable.

• Weights: both the cross-sectional and longitudinal files include survey weights that adjust the sample to represent the UK adult population. The cross-sectional weight (betaindin_xw) adjusts for unequal selection probabilities in the sample design and for non-response. The longitudinal weight (ci_betaindin_lw) adjusts for the sample design and also for the fact that not all those invited to participate in the survey, do participate in all waves.

• Both the cross-sectional and longitudinal datasets include the survey design variables (psu and strata).

A full list of variables in both files can be found in the User Guide appendix.

Who is in the sample?

All adults (16 years old and over as of April 2020), in households who had participated in at least one of the last two waves of the main study Understanding Society, were invited to participate in this survey. From the September 2020 (Wave 5) survey onwards, only sample members who had completed at least one partial interview in any of the first four web surveys were invited to participate. From the November 2020 (Wave 6) survey onwards, those who had only completed the initial survey in April 2020 and none since, were no longer invited to participate

The User guide accompanying the data adds to the information here and includes a full variable list with details of measurement levels and links to the relevant questionnaire.

Abstract copyright UK Data Service and data collection copyright owner.

The General Household Survey (GHS), ran from 1971-2011 (the UKDS holds data from 1972-2011). It was a continuous annual national survey of people living in private households, conducted by the Office for National Statistics (ONS). The main aim of the survey was to collect data on a range of core topics, covering household, family and individual information. This information was used by government departments and other organisations for planning, policy and monitoring purposes, and to present a picture of households, families and people in Great Britain. In 2008, the GHS became a module of the Integrated Household Survey (IHS). In recognition, the survey was renamed the General Lifestyle Survey (GLF). The GLF closed in January 2012. The 2011 GLF is therefore the last in the series. A limited number of questions previously run on the GLF were subsequently included in the Opinions and Lifestyle Survey (OPN).

Secure Access GHS/GLF
The UKDS holds standard access End User Licence (EUL) data for 1972-2006. A Secure Access version is available, covering the years 2000-2011 - see SN 6716 General Lifestyle Survey, 2000-2011: Secure Access.

History
The GHS was conducted annually until 2011, except for breaks in 1997-1998 when the survey was reviewed, and 1999-2000 when the survey was redeveloped. Further information may be found in the ONS document An overview of 40 years of data (General Lifestyle Survey Overview - a report on the 2011 General Lifestyle Survey) (PDF). Details of changes each year may be found in the individual study documentation.

EU-SILC
In 2005, the European Union (EU) made a legal obligation (EU-SILC) for member states to collect additional statistics on income and living conditions. In addition, the EU-SILC data cover poverty and social exclusion. These statistics are used to help plan and monitor European social policy by comparing poverty indicators and changes over time across the EU. The EU-SILC requirement was integrated into the GHS/GLF in 2005. After the closure of the GLF, EU-SILC was collected via the Family Resources Survey (FRS) until the UK left the EU in 2020.

Reformatted GHS data 1973-1982 - Surrey SPSS Files
SPSS files were created by the University of Surrey for all GHS years from 1973 to 1982 inclusive. The early files were restructured and the case changed from the household to the individual with all of the household information duplicated for each individual. The Surrey SPSS files contain all the original variables as well as some extra derived variables (a few variables were omitted from the data files for 1973-76). In 1973 only, the section on leisure was not included in the Surrey SPSS files. This has subsequently been made available, however, and is now held in a separate study, General Household Survey, 1973: Leisure Questions (SN 3982). Records for the original GHS 1973-1982 ASCII files have been removed from the UK Data Archive catalogue, but the data are still preserved and available upon request.

Changes to the 2006 data
The GHS methodology has changed to longitudinal data collection. The design changed in 2005 but the 2006 dataset is the first wave where a proportion (68%) of the sample are people who were also interviewed the year before. It should be noted however that the dataset is still cross-sectional as it contains data only from 2006.

For the third edition (February 2009), amendments were made to variables LGLSTAT, CHNBORN and CHEXCM in the individual file. A minor error had been discovered by the depositor with the LGLSTAT variable, where 188 cases had been assigned as being in a cohabiting couple, when they should have been classified as either single, widowed or divorced. The subsequent derived variables concerning the number of children in cohabiting relationships (variables CHNBORN and CHEXCM) should also have been set to 'not applicable' for these cases. This error has now been corrected, but it had a minor impact on the breakdown between non-married categories in tables 5.3, 5.4, 5.5, 5.6, 5.8, 5.10 and 5.11 of the 2006 GHS report. Original and correct versions of the tables are included in the documentation for reference (also available from the GHS website). For a full edition history, see READ file (link below).
Main Topics:

The main GHS consisted of a household questionnaire, completed by the Household Reference Person (HRP), and an individual questionnaire, completed by all adults aged 16 and over resident in the household. A number of different trailers each year covering extra topics were included in later (post-review) surveys in the series from 2000.

• The household questionnaire covered the following topics: household information, accommodation type, housing tenure/costs, and consumer...

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

These data are part of NACJD's Fast Track Release and are distributed as they were received from the data depositor. The files have been zipped by NACJD for release, but not checked or processed except for the removal of direct identifiers. Users should refer to the accompanying readme file for a brief description of the files available with this collection and consult the investigator(s) if further information is needed. The Empirical Assessment of Domestic Radicalization (EADR) project seeks to provide practitioners, researchers, and the public with an empirical foundation for understanding the radicalization processes of United States-based extremists. Project researchers utilized a mixed-method, nested approach to explore a number of key research questions related to radicalization, including: what are the demographic, background, and radicalization differences between and within the different ideological milieus? are there important contextual, personal, ideological, or experiential differences between radicals who commit violent acts and those who do not? is it possible to identify sufficient pathways to violent extremism? are the causal mechanisms highlighted by extant theories of radicalization supported by empirical evidence? To address these questions, EADR researchers built the largest known database on individual radicalization in the United States: Profiles of Individual Radicalization in the United States (PIRUS). The database includes 147 variables covering demographic, background, group affiliation, and ideological information for a sample of 1,473 violent and non-violent extremists who radicalized in the United States from 1948-2014. The database is not limited to a particular ideological milieu, but instead contains information on individuals who adhere(d) to far right, far left, Islamist, and single-issue ideologies The collection includes 5 SPSS datasets and 2 SPSS syntax files: PIRUS_full_dataset_ICPSR_archive.sav (n=1,473; 113 variables) PIRUS_expected_maximization_version.sav (n=16,203; 27 variables) PIRUS_fixed_value_imputation_version.sav (n=1,473; 27 variables) PIRUS_regression_based_imputation_version.sav (n=16,203; 28 variables) PIRUS_subgroup_mean_substitution_version.sav (n=1,473; 27 variables) quantitative_analysis_syntax.sps variable_prep_syntax.sps

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.

The SPSS data file (RES-062-23-1831 FBS data for ESRC archive.sav) contains 215 variables entered either directly from Farm Management Survey (FMS) Field Books or derived from calculations using field book data and supplementary information (such as price indices). The file ‘RES-062-23-1831 SPSS data handbook.xlsx’ lists all of the variables (both in alphabetical order and the order they appear in in the SPSS file) and includes additional explanatory notes for each variable. Data cleaning was undertaken by looking for logically inconsistent relationships between various variables, querying and checking of anomalous results during data analysis and double checking a number of entries with the original field books. The data file contains information on 168 farm holdings in Devon, Dorset and Cornwall from 1939 to 1984. The file contains 4,987 cases. Each case in the SPSS file relates to a specific field book for a specific year for a particular farm. The 168 farms selected for inclusion in the SPSS dataset represent a proportion of all of the farms in the University of Exeter FMS archive. Farms were purposively selected, initially on grounds of longevity in the FMS sample and then to achieve coverage of a cross-section of farming situations in the counties of Devon, Dorset and Cornwall.

The objectives of this project were to produce a detailed survey of agricultural change, and technical change in particular, over the period 1935 – 1985, and to shed light on how and when changes on individual farms were brought about. These objectives were realised, as detailed in the project end of award report. We should note that there was no requirement at the time of the awarding of the grant to produce a pathways to impact plan, and impact beyond these objectives was not the central focus of the project. As an historical project its impact beyond its contribution to the field of knowledge in this area was always bound to be limited. We did, however, identify groups of beneficiaries and we have worked to engage with these audiences to discuss our findings and to broaden knowledge and cultural understanding, and this work is outlined below. In particular we were keen to discuss our findings with rural historians, focusing on but not restricting ourselves to individuals and groups in the area studied, and to this end we undertook engagement with publics including relevant societies and other organisations, and this engagement conintues. Crucially, the PI and Co-Is lead numerous other funded research projects and the findings and knowledge gained from this project help to set the context for and feed into each of those. The policy work of the PI in particular is informed by broad historical contexts and knowledge about the implementation of and response to technological change provided by work on this project is vital in this regard.limited. We did, however, identify groups of beneficiaries and we have worked to engage with these audiences to discuss our findings and to broaden knowledge and cultural understanding, and this work is outlined below. In particular we were keen to discuss our findings with rural historians, focusing on but not restricting ourselves to individuals and groups in the area studied, and to this end we undertook engagement with publics including relevant societies and other organisations, and this engagement conintues. Crucially, the PI and Co-Is lead numerous other funded research projects and the findings and knowledge gained from this project help to set the context for and feed into each of those. The policy work of the PI in particular is informed by broad historical contexts and knowledge about the implementation of and response to technological change provided by work on this project is vital in this regard.

Abstract copyright UK Data Service and data collection copyright owner.The Opinions and Lifestyle Survey (formerly known as the ONS Opinions Survey or Omnibus) is an omnibus survey that began in 1990, collecting data on a range of subjects commissioned by both the ONS internally and external clients (limited to other government departments, charities, non-profit organisations and academia).Data are collected from one individual aged 16 or over, selected from each sampled private household. Personal data include data on the individual, their family, address, household, income and education, plus responses and opinions on a variety of subjects within commissioned modules. The questionnaire collects timely data for research and policy analysis evaluation on the social impacts of recent topics of national importance, such as the coronavirus (COVID-19) pandemic and the cost of living, on individuals and households in Great Britain. From April 2018 to November 2019, the design of the OPN changed from face-to-face to a mixed-mode design (online first with telephone interviewing where necessary). Mixed-mode collection allows respondents to complete the survey more flexibly and provides a more cost-effective service for customers. In March 2020, the OPN was adapted to become a weekly survey used to collect data on the social impacts of the coronavirus (COVID-19) pandemic on the lives of people of Great Britain. These data are held in the Secure Access study, SN 8635, ONS Opinions and Lifestyle Survey, Covid-19 Module, 2020-2022: Secure Access. From August 2021, as coronavirus (COVID-19) restrictions were lifting across Great Britain, the OPN moved to fortnightly data collection, sampling around 5,000 households in each survey wave to ensure the survey remains sustainable. The OPN has since expanded to include questions on other topics of national importance, such as health and the cost of living. For more information about the survey and its methodology, see the ONS OPN Quality and Methodology Information webpage.Secure Access Opinions and Lifestyle Survey dataOther Secure Access OPN data cover modules run at various points from 1997-2019, on Census religion (SN 8078), cervical cancer screening (SN 8080), contact after separation (SN 8089), contraception (SN 8095), disability (SNs 8680 and 8096), general lifestyle (SN 8092), illness and activity (SN 8094), and non-resident parental contact (SN 8093). See Opinions and Lifestyle Survey: Secure Access for details. The objective of the project was to develop a light time budget instrument suitable for use as an add-on component to other surveys, without adding unduly to respondent burden. In the course of the activity, a development programme was undertaken, involving workshops, field-testing of alternative experimental instruments, evaluation and redesign of these, and a full-scale pilot study. The instrument is designed to be used in both self-response and interview completion modes. Some 2005 Omnibus Survey respondents were asked to provide a retrospective diary-type account on a designated day. The pilot study has thus yielded useful statistical information, sufficient to make broad national estimates of adult time use patterns in the early summer of 1995. The sample is sufficient to make reliable contrasts between broad time use aggregates for subgroups at, for example, a full-time employed woman vs part-time employed woman level. It is too small to make reliable estimates for smaller time use categories and for smaller classificatory categories. Despite the presence of geographic classificatory variables (Standard Regions), the sample size is not sufficiently large to make reliable sub-national estimates of any of the time use categories. Main Topics:Each month's questionnaire consists of two elements: core questions, covering demographic information, are asked each month together with non-core questions that vary from month to month. The non-core questions for this month were: Time use (module 117): Each case records data for each of the 2005 people surveyed. There are around 100 classificatory variables which have SPSS data labels which are largely self-explanatory. These data were derived by interviewer or self-completion of a questionnaire. The remaining 96 variables record activities in each of the 96 quarter hour periods throughout the designated day being measured. These data were derived from a self-completion diary, and again the data variables in the SPSS datasets are largely self-explanatory. Respondents were asked to code their major activity in each of the quarter hour periods, according to a coding frame specifying 30 separate activity codes. Standard Measures: Prevailing Government Standard Socio-Economic Classificatory Variables were used. Multi-stage stratified random sample Self-completion Diaries Face-to-face interview

The Cohort Hip & Cohort Knee (CHECK) is a population-based observational multicenter cohort study of 1002 individuals with early symptomatic osteoarthritis (OA) of knee and/or hip in the Netherlands. The participants were followed for 10 years. The study evaluated clinical, radiographic and biochemical variables in order to establish the course, prognosis and underlying mechanisms of early symptomatic osteoarthritis. The Dutch Artritis Foundation initiated and funded this inception cohort.This dataset covers the data collection of baseline (T0) without the variable 'Subject identification number'. Included is a Kellgren-Lawrence radiographic classification covering T0,T2,T5, T8 and T10. Also X-rays of hips and knees of baseline are available. More information on the variables can be found in the documentation. In the description file you can find an overview of the data belonging to this dataset and more information about the format and kind of view of the X rays.The complete data are available via three separate datasets, each containing again the baseline T0 data of this current dataset. All SPSS data files of these three datasets include the variable 'Subject identification number'.The X-ray data are not included in the dataset, they are stored outside of EASY. If you wish to use this data, please contact DANS via info@dans.knaw.nl. Or consult the X-ray_data_request.pdf document for more information.If you wish to make use of the complete CHECK data, please see the see relations for the other CHECK datasets and for the overview 'Thematic collection: CHECK (Cohort Hip & Cohort Knee)'. Date Submitted: 2015-12-09 2019-12-20: a new data file on X-Ray data 'Rontgen_opT10_20191118' was added to the dataset.2017-09-19: A data file on X-Ray ratings has been added and the variable guide is replaced by a new version (6) with information on this data file. Please note the variable names start with 'RontgT10_' in the data file.2017-07-12: Due to an error a data file has been replaced.CHECK_T0_DANS_nsinENG_20151211.sav is now replaced by CHECK_T0_DANS_nsin_ENG_20161128.sav---The informed consent statements of the participants are stored at the participating hospitals.The .dta (STATA) and .por (SPSS) files are conversions of the original .sav (SPSS) files.

This data set was acquired using a survey which intends to measure: • Participants previous experience of cybersecurity training • Participants perception of ideal cybersecurity training • Participants perception of a specific cybersecurity training type called ContextBased MicroTraining • What usability aspects the participants find most important for security features Data was acquired from Sweden, UK and Italy to allow for comparative analysis. Demographic data was collected to allow for further analysis based on those. The files included in this data set are: • Completesurvey: This document includes the full survey presented to the participants. • Dataset: This file contains the variables and data for the different questions (available as .sav (SPSS and .csv)). • Var_info: contains information about the variables in the dataset • Overview: Contains frequency tables for the survey question (for the complete data set) • Sweden, UK, and Italy: Contains frequency tables for the survey questions divided by national sample groups.

Se attahed description

Abstract copyright UK Data Service and data collection copyright owner.

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

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

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

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


Main Topics:

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

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

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

Abstract copyright UK Data Service and data collection copyright owner.

The Active Lives Children and Young People Survey, which was established in September 2017, provides a world-leading approach to gathering data on how children engage with sport and physical activity. This school-based survey is the first and largest established physical activity survey with children and young people in England. It gives anyone working with children aged 5-16 key insight to help understand children's attitudes and behaviours around sport and physical activity. The results will shape and influence local decision-making as well as inform government policy on the PE and Sport Premium, Childhood Obesity Plan and other cross-departmental programmes. More general information about the study can be found on the Sport England Active Lives Survey webpage and the Active Lives Online website, including reports and data tables.

The Active Lives Children and Young People survey is a school-based survey (i.e., historically always completed at school as part of lessons). Academic years 2020-2021 and 2019-20 have both been disrupted by the coronavirus pandemic, resulting in school sites being closed to many pupils for some of the year (e.g., during national lockdown periods, and during summer term for 2019-20). Due to the closure of school sites, the Active Lives Children and Young People Survey, 2020-2021 was adapted to allow at-home completion. Despite the disruption, the survey has still received a sufficient volume of responses for analysis.

The adaptions involved minor questionnaire changes (e.g., to ensure the wording was appropriate for those not attending school and to enable completion at home), and communication changes. For further details on the survey changes, please see the accompanying User Guide document. Academic year 2020-21 saw a more even split of responses by term across the year, compared to 2019-20 which had a reduced proportion of summer term responses due to the disruption caused by Covid-19. It is recommended to analyse the data within term, as well as at an overall level, because of the changes in termly distribution.

The survey identifies how participation varies across different activities and sports, by regions of England, between school types and terms, and between different demographic groups in the population. The survey measures levels of activity (active, fairly active and less active), attitudes towards sport and physical activity, swimming capability, the proportion of children and young people that volunteer in sport, sports spectating, and wellbeing measures such as happiness and life satisfaction. The questionnaire was designed to enable analysis of the findings by a broad range of variables, such as gender, family affluence and school year.

The following datasets have been provided:

1) Main dataset – this file includes responses from children and young people from school years 3 to 11, as well as responses from parents of children in years 1-2. The parents of children in years 1-2 provide behavioural answers about their child’s activity levels, they do not provide attitudinal information. Using this main dataset, full analyses can be carried out into sports and physical activity participation, levels of activity, volunteering (years 5 to 11), etc. Weighting is required when using this dataset (wt_gross / wt_gross.csplan files are available for SPSS users who can utilise them).

2) Year 1-2 dataset – this file include responses from children in school years 1-2 directly, providing their attitudinal responses (e.g. whether they like playing sport and find it easy). Analysis can be carried out into feelings towards swimming, enjoyment for being active, happiness etc. Weighting is required when using this dataset (wt_gross / wt_gross.csplan files are available for SPSS users who can utilise them).

3) Teacher dataset – this file includes response from the teachers at schools selected for the survey. Analysis can be carried out into school facilities available, length of PE lessons, whether swimming lessons are offered, etc. Weighting was formerly not available, however, as Sport England have started to publish the Teacher data, from December 2023 we decide to apply weighting to the data. The Teacher dataset now includes weighting by applying the ‘wt_teacher’ weighting variable.

For further information about the variables available for analysis, and the relevant school years asked survey questions, please see the supporting documentation. Please read the documentation before using the datasets. More general information about the study can be found on the Sport England Active Lives Survey webpages.

Latest edition information

For the second edition (January 2024), the Teacher dataset now includes a weighting variable (‘wt_teacher’). Previously, weighting was not available for these...

"This study is an SPSS file created at the University of Iowa taking information from three other studies. Two of those studies were conducted by Survey Research Center, University of Michigan in 1960 and 1964. The other study was conducted by Gabriel Almond and Sidney Verba for the report entitled The Civic Culture. Approximately 1,500 respondents were interviewed for each study. Only a part of the data from each of the studies is used in this study, but there are sufficient data for examini ng a number of different societal structures. Topics covered in this study are upward and downward mobility in society, changing relationships between parents and children; relationship between social class and political participation; social forces and attitudes on integration; and relationship of religion to voting in presidential elections of 1960 and 1964. 171 variables have been collected in all. However, there are three SPSS files taken from this data. One file contains all variables and all 4495 cases. A second file contains all variables and 40% sample of cases. The third file contains all cases but only 45 variables."

Note: This version supersedes version 1: https://doi.org/10.15482/USDA.ADC/1522654. In Fall of 2019 the USDA Food and Nutrition Service (FNS) conducted the third Farm to School Census. The 2019 Census was sent via email to 18,832 school food authorities (SFAs) including all public, private, and charter SFAs, as well as residential care institutions, participating in the National School Lunch Program. The questionnaire collected data on local food purchasing, edible school gardens, other farm to school activities and policies, and evidence of economic and nutritional impacts of participating in farm to school activities. A total of 12,634 SFAs completed usable responses to the 2019 Census. Version 2 adds the weight variable, “nrweight”, which is the Non-response weight. Processing methods and equipment used The 2019 Census was administered solely via the web. The study team cleaned the raw data to ensure the data were as correct, complete, and consistent as possible. This process involved examining the data for logical errors, contacting SFAs and consulting official records to update some implausible values, and setting the remaining implausible values to missing. The study team linked the 2019 Census data to information from the National Center of Education Statistics (NCES) Common Core of Data (CCD). Records from the CCD were used to construct a measure of urbanicity, which classifies the area in which schools are located. Study date(s) and duration Data collection occurred from September 9 to December 31, 2019. Questions asked about activities prior to, during and after SY 2018-19. The 2019 Census asked SFAs whether they currently participated in, had ever participated in or planned to participate in any of 30 farm to school activities. An SFA that participated in any of the defined activities in the 2018-19 school year received further questions. Study spatial scale (size of replicates and spatial scale of study area) Respondents to the survey included SFAs from all 50 States as well as American Samoa, Guam, the Northern Mariana Islands, Puerto Rico, the U.S. Virgin Islands, and Washington, DC. Level of true replication Unknown Sampling precision (within-replicate sampling or pseudoreplication) No sampling was involved in the collection of this data. Level of subsampling (number and repeat or within-replicate sampling) No sampling was involved in the collection of this data. Study design (before–after, control–impacts, time series, before–after-control–impacts) None – Non-experimental Description of any data manipulation, modeling, or statistical analysis undertaken Each entry in the dataset contains SFA-level responses to the Census questionnaire for SFAs that responded. This file includes information from only SFAs that clicked “Submit” on the questionnaire. (The dataset used to create the 2019 Farm to School Census Report includes additional SFAs that answered enough questions for their response to be considered usable.) In addition, the file contains constructed variables used for analytic purposes. The file does not include weights created to produce national estimates for the 2019 Farm to School Census Report. The dataset identified SFAs, but to protect individual privacy the file does not include any information for the individual who completed the questionnaire. Description of any gaps in the data or other limiting factors See the full 2019 Farm to School Census Report [https://www.fns.usda.gov/cfs/farm-school-census-and-comprehensive-review] for a detailed explanation of the study’s limitations. Outcome measurement methods and equipment used None Resources in this dataset:Resource Title: 2019 Farm to School Codebook with Weights. File Name: Codebook_Update_02SEP21.xlsxResource Description: 2019 Farm to School Codebook with WeightsResource Title: 2019 Farm to School Data with Weights CSV. File Name: census2019_public_use_with_weight.csvResource Description: 2019 Farm to School Data with Weights CSVResource Title: 2019 Farm to School Data with Weights SAS R Stata and SPSS Datasets. File Name: Farm_to_School_Data_AgDataCommons_SAS_SPSS_R_STATA_with_weight.zipResource Description: 2019 Farm to School Data with Weights SAS R Stata and SPSS Datasets

The Cohort Hip & Cohort Knee (CHECK) is a population-based observational multicenter cohort study of 1002 individuals with early symptomatic osteoarthritis (OA) of knee and/or hip in the Netherlands. The participants were followed for 10 years. The study evaluated clinical, radiographic and biochemical variables in order to establish the course, prognosis and underlying mechanisms of early symptomatic osteoarthritis. The Dutch Artritis Foundation initiated and funded this inception cohort.This dataset covers the data collection of baseline (T0) without the variable 'Subject identification number'. Included is a Kellgren-Lawrence radiographic classification covering T0,T2,T5, T8 and T10. Also X-rays of hips and knees of baseline are available. More information on the variables can be found in the documentation. In the description file you can find an overview of the data belonging to this dataset and more information about the format and kind of view of the X rays.The complete data are available via three separate datasets, each containing again the baseline T0 data of this current dataset. All SPSS data files of these three datasets include the variable 'Subject identification number'.The X-ray data are not included in the dataset, they are stored outside of EASY. If you wish to use this data, please contact DANS via info@dans.knaw.nl. Or consult the X-ray_data_request.pdf document for more information.If you wish to make use of the complete CHECK data, please see the see relations for the other CHECK datasets and for the overview 'Thematic collection: CHECK (Cohort Hip & Cohort Knee)'. Date Submitted: 2015-12-09 2019-12-20: a new data file on X-Ray data 'Rontgen_opT10_20191118' was added to the dataset.2017-09-19: A data file on X-Ray ratings has been added and the variable guide is replaced by a new version (6) with information on this data file. Please note the variable names start with 'RontgT10_' in the data file.2017-07-12: Due to an error a data file has been replaced.CHECK_T0_DANS_nsinENG_20151211.sav is now replaced by CHECK_T0_DANS_nsin_ENG_20161128.sav---The informed consent statements of the participants are stored at the participating hospitals.The .dta (STATA) and .por (SPSS) files are conversions of the original .sav (SPSS) files.

Tämä englanninkielinen yhdistelmäaineisto koostuu vuosien 2003, 2007, 2011, 2015 ja 2019 eduskuntavaalien jälkeen kerättyjen aineistojen harmonisoiduista muuttujista. Yhdistetyt aineistot FSD1260, FSD2269, FSD2653, FSD3067 ja FSD3467 ovat osa Kansalliset eduskuntavaalitutkimukset -sarjaa ja niiden käyntikyselyosuudet muodostavat Suomen osuuden kansainvälisestä CSES-hankkeesta. Yhdistelmäaineiston harmonisoidut muuttujat on nimetty fnes-alkuisiksi muuttujiksi (Finnish National Election Studies). Fnes-muuttujat koostuvat yhdestä tai useammasta eri kyselyaallon eli sarjan yksittäisen kyselyn muuttujasta. Kyselyaaltojen muuttujat on koodattu Tietoarkistossa uudelleen siten, että ne ovat keskenään vertailukelpoisia, ja yhdistetty tämän jälkeen fnes-muuttujaksi. Yhdistelmäaineiston mukana toimitetaan taulukko (bgF2556_eng.csv), josta käy ilmi, mitkä yksittäisten kyselyaaltojen muuttujat vastaavat mitäkin fnes-muuttujaa. Taulukko sisältää myös tiedot muuttujien aihealueista ja sisällöstä, muuttujien vertailtavuuteen liittyvät kommentit sekä aineiston harmonisoinnissa käytetyt SPSS-syntaksikomennot kommentteineen. Yhdistettyihin aineistoihin liittyvät muuttujatiedot ovat myös saatavilla Tietoarkiston englanninkielisessä Loda-pitkittäisaineistoportaalissa (https://loda.fsd.tuni.fi/). Yhdistelmäaineiston muuttujista noin 130 on ollut mukana jokaisella kyselyaallolla. Lisäksi yli 200 muuttujaa on sisällytetty useampaan kuin yhteen kyselyyn. Loput muuttujat ovat yksittäisiä. Usein toistuvia muuttujia ovat muun muassa vastaajan kiinnostus politiikkaan, äänestäminen ja äänestämättä jättäminen, demokratiaa koskevat kysymykset sekä poliittisia asenteita mittaavat kysymykset. Taustatietoina vastaajista on kerätty sarjan aikana kaiken kaikkiaan noin 70 eri muuttujaa. Kerätyistä taustatiedoista osa toistuu kaikissa sarjan kyselyissä, osa vain joissakin ja osa vain yhdessä kyselyssä. Kaikissa aineistoissa toistuvat taustamuuttujat ovat vastaajan sukupuoli, ammatti, asuinkunta ja asuinalueen tyyppi sekä vaalipiiri, koulutus, kotitalouden yhteenlasketut bruttotulot, kotitalouden lasten lukumäärä, kaikkien kotitaloudessa asuvien henkilöiden lukumäärä, vastaajan kuuluminen kirkkoon tai uskonnolliseen yhteisöön, uskonnollisuus, syntymävuosi, siviilisääty, työllisyystilanne nyt ja viimeisen kahdentoista kuukauden aikana, työttömyysjaksot, ammatti (ISCO-08), äidinkieli ja kotitaloudessa käytetty kieli. This combined dataset contains the harmonised variables of datasets FSD1260, FSD2269, FSD2653, FSD3067, and FSD3467, which were collected after the Finnish parliamentary elections of 2003, 2007, 2011, 2015, and 2019. The studies belong to the Finnish National Election Studies data series, and their survey data contain Finland's contribution to the international Comparative Study of Electoral Systems (CSES). The harmonised variables, labelled "fnes", comprise responses from one or more collection rounds of the Finnish National Election Studies. Variables from different collection rounds were recoded and combined into the fnes variables at FSD to enable easier comparability between study waves. The data package includes a concordance table (bgF2556.csv) that details which variables in a collection round correspond to each fnes variable. The table also includes a topic and keyword description for the harmonised variables, comparability notes on the variables, as well as SPSS syntax commands and comments relating to the harmonisation of the data. The variables can also be browsed and compared via FSD's Loda portal (https://loda.fsd.tuni.fi/). Out of all the variables in the combined data, approximately 130 were included in all surveys of the series. Additionally, over 200 variables were included in more than one survey. The rest of the variables were specific to individual surveys. Recurring variables in the FNES data included, for example, interest in politics and elections as well as questions about voting and abstaining from voting. The variables also included respondents' political attitudes and views on democracy. A total of 70 different background variables were used over the course of the study series. Some background variables were included in all of the studies, some in fewer and some only in one study. Background variables that were included in all of the studies include gender, economic activity and occupational status, electoral district, level of education, household gross annual income, type of neighbourhood, number of children in the household, number of all persons living in the household, religious affiliation, year of birth, marital status, employment status, mother tongue and language spoken at home.