A presentation aimed at providing ACCOLEDS 2011 participants with the skills in using the SPSS Text Import Wizard to create SPSS syntax (.sps) files, which will, in turn, allow them to open .dat or .txt data files in the DLI collection.

Part 1 of the course will offer an introduction to SPSS and teach how to work with data saved in SPSS format. Part 2 will demonstrate how to work with SPSS syntax, how to create your own SPSS data files, and how to convert data in other formats to SPSS. Part 3 will teach how to append and merge SPSS files, demonstrate basic analytical procedures, and show how to work with SPSS graphics.

Tasks, data files, and SPSS analysis scripts for the paper "People in pain make poorer decisions".

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

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

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 enumerated in 1997 and the numeration area consists of buildings and housing units and has in average about 150 households in it. We use the enumeration areas 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 stratified cluster systematic random sample with two stages: The calculated sample size is 1,616 households, the completed households were 1,281 (847 in the west bank and 434 in the Gaza strip). First stage: selection a systematic random sample of 120 enumeration areas. Second stage: selection a systematic random sample of 12-18 households from each enumeration area selected in the first stage.

Sample strata:

We divided the population by: 1- Region (North West Bank, Middle West Bank, South West Bank, Gaza Strip) 2- Type of Locality (urban, rural, refugee camps)

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.

Sample Size:

The calculated sample size is 1,616 households, the completed households were 1,281 (847 in the west bank and 434 in the Gaza strip).

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 house, 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.

Assistance and poverty: includes questions about household conditions and assistances that got through the the past month.

Second section: The second section of the questionnaire includes a list of 55 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 50-55 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, except the cars group the data of which was collected for three previous years. These data was abotained from the recording book which is covered a period of month for each household.

Cleaning operations

Raw Data

Data editing took place though a number of stages, including: 1. Office editing and coding 2. Data entry 3. Structure checking and completeness 4. Structural checking of SPSS data files

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 1,616 households interviewed over a twelve months period between (January 2006-January 2007), 1,281 households completed interview, of which 847 in the West Bank and 434 household in Gaza Strip, the response rate was 79.3% in the Palestinian Territory.

Sampling error estimates

Generally, surveys samples are exposed to two types of errors. The statistical errors, being the first type, result from studying a part of a certain society and not including all its sections. And since the Household Expenditure and Consumption Surveys are conducted using a sample method, statistical errors are then unavoidable. Therefore, a potential sample using a suitable design has been employed whereby each unit of the society has a high chance of selection. Upon calculating the rate of bias in this survey, it appeared that the data is of high quality. The second type of errors is the non-statistical errors that relate to the design of the survey, mechanisms of data collection, and management and analysis of data. Members of the work commission were trained on all possible mechanisms to tackle such potential problems, as well as on how to address cases in which there were no responses (representing 9.6%).

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. This study was designed to understand the economic and social structure of the market for stolen data on-line. This data provides information on the costs of various forms of personal information and cybercrime services, the payment systems used, social organization and structure of the market, and interactions between buyers, sellers, and forum operators. The PIs used this data to assess the economy of stolen data markets, the social organization of participants, and the payment methods and services used. The study utilized a sample of approximately 1,900 threads generated from 13 web forums, 10 of which used Russian as their primary language and three which used English. These forums were hosted around the world, and acted as online advertising spaces for individuals to sell and buy a range of products. The content of these forums were downloaded and translated from Russian to English to create a purposive, yet convenient sample of threads from each forum. The collection contains 1 SPSS data file (ICPSR Submission Economic File SPSS.sav) with 39 variables and 13,735 cases and 1 Access data file (Social Network Analysis File Revised 04-11-14.mdb) with a total of 16 data tables and 199 variables. Qualitative data used to examine the associations and working relationships present between participants at the micro and macro-level are not available at this time.

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%).

The General Household Survey (GHS) is a continuous national survey of people living in private households conducted on an annual basis, by the Social Survey Division of the Office for National Statistics (ONS). The main aim of the survey is to collect data on a range of core topics, covering household, family and individual information. This information is used by government departments and other organisations for planning, policy and monitoring purposes, and to present a picture of house holds, family and people in Great Britain. From 2008, the General Household Survey became a module of the Integrated Household Survey (IHS). In recognition, the survey was renamed the General Lifestyle Survey (GLF/GLS). The GHS started in 1971 and has been carried out continuously since then, except for breaks in 1997-1998 when the survey was reviewed, and 1999-2000 when the survey was redeveloped. Following the 1997 review, the survey was relaunched from April 2000 with a different design. The relevant development work and the changes made are fully described in the Living in Britain report for the 2000-2001 survey. Following its review, the GHS was changed to comprise two elements: the continuous survey and extra modules, or 'trailers'. The continuous survey remained unchanged from 2000 to 2004, apart from essential adjustments to take account of, for example, changes in benefits and pensions. The GHS retained its modular structure and this allowed a number of different trailers to be included for each of those years, to a plan agreed by sponsoring government departments. Further changes to the GHS methodology from 2005: From April 1994 to 2005, the GHS was conducted on a financial year basis, with fieldwork spread evenly from April of one year to March the following year. However, in 2005 the survey period reverted to a calendar year and the whole of the annual sample was surveyed in the nine months from April to December 2005. Future surveys will run from January to December each year, hence the title date change to single year from 2005 onwards. Since the 2005 GHS (held under SN 5640) does not cover the January-March quarter, this affects annual estimates for topics which are subject to seasonal variation. To rectify this, where the questions were the same in 2005 as in 2004-2005, the final quarter of the latter survey was added (weighted in the correct proportion) to the nine months of the 2005 survey. Furthermore, 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 to this 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 has been integrated into the GHS, leading to large-scale changes in the 2005 survey questionnaire. The trailers on 'Views of your Local Area' and 'Dental Health' have been removed. Other changes have been made to many of the standard questionnaire sections, details of which may be found in the GHS 2005 documentation. Further changes to the GLF/GHS methodology from 2008 As noted above, the General Household Survey (GHS) was renamed the General Lifestyle Survey (GLF/GLS) in 2008. The sample design of the GLF/GLS is the same as the GHS before, and the questionnaire remains largely the same. The main change is that the GLF now includes the IHS core questions, which are common to all of the separate modules that together comprise the IHS. Some of these core questions are simpl y questions that were previously asked in the same or a similar format on all of the IHS component surveys (including the GLF/GLS). The core questions cover employment, smoking prevalence, general health, ethnicity, citizenship and national identity. These questions are asked by proxy if an interview is not possible with the selected respondent (that is a member of the household can answer on behalf of other respondents in the household). This is a departure from the GHS which did not ask smoking prevalence and general health questions by proxy, whereas the GLF/GLS does from 2008. For details on other changes to the GLF/GLS questionnaire, please see the GLF/GLS 2008: Special Licence Access documentation held with SN 6414. Currently, the UK Data Archive holds only the SL (and not the EUL) version of the GLF/GLS for 2008. Changes to the drinking section There have been a number of revisions to the methodology that is used to produce the alcohol consumption estimates. In 2006, the average number of units assigned to the different drink types and the assumption around the average size of a wine glass was updated, resulting in significantly increased consumption estimates. In addition to the revised method, a new question about wine glass size was included in the survey in 2008. Respondents were asked whether they have consumed small (125 ml), standard (175 ml) or large (250 ml) glasses of wine. The data from this question are used when calculating the number of units of alcohol consumed by the respondent. It is assumed that a small glass contains 1.5 units, a standard glass contains 2 units and a large glass contains 3 units. (In 2006 and 2007 it was assumed that all respondents drank from a standard 175 ml glass containing 2 units.) The datasets contain the original set of variables based on the original methodology, as well as those based on the revised and (for 2008 onwards) updated methodologies. Further details on these changes are provided in the Guidelines documents held in SN 5804 - GHS 2006; and SN 6414 - GLF/GLS 2008: Special Licence Access. Special Licence GHS/GLF/GLS Special Licence (SL) versions of the GHS/GLF/GLS are available from 1998-1999 onwards. The SL versions include all variables held in the standard 'End User Licence' (EUL) version, plus extra variables covering cigarette codes and descriptions, and some birthdate information for respondents and household members. Prospective SL users will need to complete an extra application form and demonstrate to the data owners exactly why they need access to t he extra variables, in order to get permission to use the SL version. Therefore, most users should order the EUL version of the data. In order to help users choose the correct dataset, 'Special Licence Access' has been added to the dataset titles for the SL versions of the data. A list of all GHS/GLF/GLS studies available from the UK Data Archive may be found on the GHS/GLF/GLS major studies web page. See below for details of SL datasets for the corresponding GHS/GLF/GLS year (1998-1999 onwards only). UK Data Archive data holdings and formats The UK Data Archive GHS/GLF/GLS holdings begin with the 1971 study for EUL data, and from 1998-1999 for SL versions (see above). Users should note that data for the 1971 study are currently only available as ASCII files without accompanying SPSS set-up files. SPSS files for the 1972 study were created by John Simister, and redeposited at the Archive in 2000. Currently, the UK Data Archive holds only the SL versions of the GHS/GLF/GLS for 2007 and 2008. Reformatted Data 1973 to 1982 - Surrey SPSS Files SPSS files have been created by the University of Surrey for all study years from 1973 to 1982 inclusive. These 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 variabl es 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 (held under 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. Users should note that GHS/GLF/GLS data are also available in formats other than SPSS.

Description of the experiment setting: location, influential climatic conditions, controlled conditions (e.g. temperature, light cycle) In 1986, the Congress enacted Public Laws 99-500 and 99-591, requiring a biennial report on the Special Supplemental Nutrition Program for Women, Infants, and Children (WIC). In response to these requirements, FNS developed a prototype system that allowed for the routine acquisition of information on WIC participants from WIC State Agencies. Since 1992, State Agencies have provided electronic copies of these data to FNS on a biennial basis. FNS and the National WIC Association (formerly National Association of WIC Directors) agreed on a set of data elements for the transfer of information. In addition, FNS established a minimum standard dataset for reporting participation data. For each biennial reporting cycle, each State Agency is required to submit a participant-level dataset containing standardized information on persons enrolled at local agencies for the reference month of April. The 2016 Participant and Program Characteristics (PC2016) is the thirteenth data submission to be completed using the WIC PC reporting system. In April 2016, there were 90 State agencies: the 50 States, American Samoa, the District of Columbia, Guam, the Northern Mariana Islands, Puerto Rico, the American Virgin Islands, and 34 Indian tribal organizations. Processing methods and equipment used Specifications on formats (“Guidance for States Providing Participant Data”) were provided to all State agencies in January 2016. This guide specified 20 minimum dataset (MDS) elements and 11 supplemental dataset (SDS) elements to be reported on each WIC participant. Each State Agency was required to submit all 20 MDS items and any SDS items collected by the State agency.   Study date(s) and duration The information for each participant was from the participants’ most current WIC certification as of April 2016. Due to management information constraints, Connecticut provided data for a month other than April 2016, specifically August 16 – September 15, 2016. Study spatial scale (size of replicates and spatial scale of study area) In April 2016, there were 90 State agencies: the 50 States, American Samoa, the District of Columbia, Guam, the Northern Mariana Islands, Puerto Rico, the American Virgin Islands, and 34 Indian tribal organizations. Level of true replication Unknown Sampling precision (within-replicate sampling or pseudoreplication) State Agency Data Submissions. PC2016 is a participant dataset consisting of 8,815,472 active records. The records, submitted to USDA by the State Agencies, comprise a census of all WIC enrollees, so there is no sampling involved in the collection of this data. PII Analytic Datasets. State agency files were combined to create a national census participant file of approximately 8.8 million records. The census dataset contains potentially personally identifiable information (PII) and is therefore not made available to the public. National Sample Dataset. The public use SAS analytic dataset made available to the public has been constructed from a nationally representative sample drawn from the census of WIC participants, selected by participant category. The nationally representative sample is composed of 60,003 records. The distribution by category is 5,449 pregnant women, 4,661 breastfeeding women, 3,904 postpartum women, 13,999 infants, and 31,990 children. Level of subsampling (number and repeat or within-replicate sampling) The proportionate (or self-weighting) sample was drawn by WIC participant category: pregnant women, breastfeeding women, postpartum women, infants, and children. In this type of sample design, each WIC participant has the same probability of selection across all strata. Sampling weights are not needed when the data are analyzed. In a proportionate stratified sample, the largest stratum accounts for the highest percentage of the analytic sample. 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 all MDS and SDS information submitted by the State agency on the sampled WIC participant. In addition, the file contains constructed variables used for analytic purposes. To protect individual privacy, the public use file does not include State agency, local agency, or case identification numbers. Description of any gaps in the data or other limiting factors Due to management information constraints, Connecticut provided data for a month other than April 2016, specifically August 16 – September 15, 2016.   Outcome measurement methods and equipment used None Resources in this dataset:Resource Title: WIC Participant and Program Characteristics 2016. File Name: wicpc_2016_public.csvResource Description: The 2016 Participant and Program Characteristics (PC2016) is the thirteenth data submission to be completed using the WIC PC reporting system. In April 2016, there were 90 State agencies: the 50 States, American Samoa, the District of Columbia, Guam, the Northern Mariana Islands, Puerto Rico, the American Virgin Islands, and 34 Indian tribal organizations.Resource Software Recommended: SAS, version 9.4,url: https://www.sas.com/en_us/software/sas9.html Resource Title: WIC Participant and Program Characteristics 2016 Codebook. File Name: WICPC2016_PUBLIC_CODEBOOK.xlsxResource Software Recommended: SAS, version 9.4,url: https://www.sas.com/en_us/software/sas9.html Resource Title: WIC Participant and Program Characteristics 2016 - Zip File with SAS, SPSS and STATA data. File Name: WIC_PC_2016_SAS_SPSS_STATA_Files.zipResource Description: WIC Participant and Program Characteristics 2016 - Zip File with SAS, SPSS and STATA data

The article uses a dataset, which cannot be deposited online, but is freely available to registered users. The data of the British Household Panel Study can be requested via https://discover.ukdataservice.ac.uk/catalogue/?sn=5151. Here we provide a STATA do-file that will create the working file, recode the original data and run some robustness tests. The data was prepared in Stata and then saved as SPSS files .sav using Stattrans. This was necessary, as the main Markov Chain models of the paper were estimated using LatentGOLD, which only reads .sav files. Here we also provide the syntax files that were used for estimating these models.

BACKGROUND: The level of public support for restrictions on tobacco use is an important element in a nation’s response to the need for tobacco control. However, no data have been available from Georgia on public opinion regarding smoking restrictions in public places, and on bans on tobacco advertisement, sponsorship and promotion. METHODS: 1,588 respondents aged 13-70 were interviewed at home about their level of agreement with eight possible smoking restrictions, used to calculate a dichotomous scale indicating high versus low level of agreement with restrictions. RESULTS: In all demographic segments, and among tobacco users, a majority of respondents indicated a high level of agreement with restrictions, ranging from a low of 51% in the 13-25 age group to a high of 98% in the 56-70 age group. Logistic regression with all demographic variables entered showed that agreement with restrictions rose significantly with age and was significantly higher among never smokers compared to daily ...

Abstract (en): This collection presents in computer-readable form the data items used to produce the corresponding printed volume of the COUNTY AND CITY DATA BOOK, 1988. Included is a broad range of statistical information, made available by federal agencies and national associations, for counties, cities, and places. Information also is provided for the 50 states, the District of Columbia, and for the United States as a whole. The dataset is comprised of seven files: a county file, a city file, and a place file, with footnote files and data dictionaries for both the county and the city files. The county data file contains information on areas such as age, agriculture, banking, construction, crime, education, federal expenditures, personal income, population, and vital statistics. The city data file includes variables such as city government, climate, crime, housing, labor force and employment, manufactures, retail trade, and service industries. Included in the place data file are items on population and money income. ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Created variable labels and/or value labels.. The universe varies from item to item within the files, e.g., all persons, all housing units, all local governments, etc. 2009-05-26 SAS, SPSS, and Stata setups have been added to this data collection.2006-03-30 File CB9251.ALL.PDF was removed from any previous datasets and flagged as a study-level file, so that it will accompany all downloads. Users are advised that the codebook that the Census Bureau has issued for use with this dataset is a preliminary one and does not include codes and definitions for states, counties, and cities. The codes and definitions may be listed off the tape or users may refer to other sources such as the printed version of the COUNTY AND CITY DATA BOOK, 1988. For each case in the Counties Data file, there are two 1,239-character records.

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

CapillaryRarefactionFinaloriginal source of the information: collected in the field software used to create the data file: spss statistics 21 description of any abbreviations: see labeling of variables, HDL - high density lipids, LDL - low density lipids

The article uses a dataset, which cannot be deposited online, but is freely available to registered users. The data of the British Household Panel Study can be requested via https://discover.ukdataservice.ac.uk/catalogue/?sn=5151. Here we provide a STATA do-file that will create the working file, recode the original data and run some robustness tests. The data was prepared in Stata and then saved as SPSS files .sav using Stattrans. This was necessary, as the main cross-lagged latent class models of the paper were estimated using LatentGOLD, which only reads .sav files. Here we also provide the syntax files that were used for estimating these models.

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. This study used secondary analysis of data from several different sources to examine the impact of increased oil development on domestic violence, dating violence, sexual assault, and stalking (DVDVSAS) in the Bakken region of Montana and North Dakota. Distributed here are the code used for the secondary analysis data; the data are not available through other public means. Please refer to the User Guide distributed with this study for a list of instructions on how to obtain all other data used in this study. This collection contains a secondary analysis of the Uniform Crime Reports (UCR). UCR data serve as periodic nationwide assessments of reported crimes not available elsewhere in the criminal justice system. Each year, participating law enforcement agencies contribute reports to the FBI either directly or through their state reporting programs. Distributed here are the codes used to create the datasets and preform the secondary analysis. Please refer to the User Guide, distributed with this study, for more information. This collection contains a secondary analysis of the National Incident Based Reporting System (NIBRS), a component part of the Uniform Crime Reporting Program (UCR) and an incident-based reporting system for crimes known to the police. For each crime incident coming to the attention of law enforcement, a variety of data were collected about the incident. These data included the nature and types of specific offenses in the incident, characteristics of the victim(s) and offender(s), types and value of property stolen and recovered, and characteristics of persons arrested in connection with a crime incident. NIBRS collects data on each single incident and arrest within 22 offense categories, made up of 46 specific crimes called Group A offenses. In addition, there are 11 Group B offense categories for which only arrest data were reported. NIBRS data on different aspects of crime incidents such as offenses, victims, offenders, arrestees, etc., can be examined as different units of analysis. Distributed here are the codes used to create the datasets and preform the secondary analysis. Please refer to the User Guide, distributed with this study, for more information. The collection includes 17 SPSS syntax files. Qualitative data collected for this study are not available as part of the data collection at this time.

This collection contains anonymized survey data collected from 1350 responding Social Enterprises in all Canadian provinces and territories, with the exception of Quebec. The surveys were conducted in 2014 and 2015. For the purposes of these surveys, social enterprises were defined as follows: "A social enterprise is a business venture owned or operated by a non-profit organization that sells goods or provides services in the market for the purpose of creating a blended return on investment, both financial and social/environmental/cultural". The purpose of the surveys, and of making the data available, is to support the development of the social enterprise sector in Canada by highlighting the size, scope and impact of social enterprises. Funding for the surveys has included the Social Sciences and Humanities Research Council, the Institute for Community Prosperity, Mount Royal University, Enterprising Non-Profits Canada, the TRICO Foundation of Calgary, and Employment and Social Development Canada, and generous local sponsors and supporters. These surveys have been undertaken with the tremendous support, dedication and enthusiasm of provincial umbrella groups that want to see social enterprises develop and flourish in Canada. Without these organizations this initiative would not have been possible. The collections consists of 11 SPSS-format Data Files, 11 excel-format Variable Keys and 10 pdf-format Questionnaires. Geographical information for each individual file can be found in item_metadata.csv. The researchers who created this dataset would be pleased to hear from you and how you have used this data (pvhall@sfu.ca and pelson@uvic.ca). Software used was SPSS. Confidentiality declaration: Use of this anonymized survey microdata has been approved by the SFU Research Ethics Board. Survey respondents were assured that their names would be kept confidential, as would the individual answers they provide. Hence all identifying variables, as well as open-response text fields and almost all financial data (except total revenue and expense) have been deleted. This dataset was originally deposited in the Simon Fraser University institutional repository.

v5 (3rd Aug 2023) now replaces v4. The change was for RAAAP-1 data to include Puerto Rico as part of the USA rather than as a separate country.The SPSS (v26) syntax file [v4 used to create the 6th June 2023 version of the datasets] that takes the RAAAP-1, RAAAP-2, and RAAAP-3 main datasets, harmonising the common fields, and concatenates the data into a single dataset. A new variable (RAAAPSurvey) is added to indicate which of the 3 datasets each record is from.

This dataset contains a SPSS (v23) syntax file (.sps) and the relating graph template (.sgt) that can be used to create anonymized graphs of spinal motion in the transversal plane relative to phases of gait. All sections are commented within the script. The algebraic sign of the pelvis gets corrected. It required correction since the pelvis is in alignment of surface rotation, and in order to be in parallel with the vertebral body’s rotation it has to be reversed. So, if the surface above the spinous process runs to the right, this implies a rotation to left of the vertebral body beneath.