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

Project Title: Add title here

Project Team: Add contact information for research project team members

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

Relevant publications/outputs: When available, add links to the related publications/outputs from this data.

Data availability statement: If your data is not linked on figshare directly, provide links to where it is being hosted here (i.e., Open Science Framework, Github, etc.). If your data is not going to be made publicly available, please provide details here as to the conditions under which interested individuals could gain access to the data and how to go about doing so.

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

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

Research Project Materials: What materials are necessary to fully reproduce your the contents of your dataset? Include a list of all relevant materials (e.g., surveys, interview questions) with a brief description of what is included in each file that should be uploaded alongside your datasets.

List of relevant datafile(s): If your project produces data that cannot be contained in a single file, list the names of each of the files here with a brief description of what parts of your research project each file is related to.

Data codebook: What is in each column of your dataset? Provide variable names as they are encoded in your data files, verbatim question associated with each response, response options, details of any post-collection coding that has been done on the raw-response (and whether that's encoded in a separate column).

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

CROSS_SECT_SAMPLE_PUB_PT: Cross-sectional surveys capture the shape of the stream channel at a specific location by measuring elevations at intervals across the channel. Cross-sections are used to determine bankfull width, mean bankfull depth, and entrenchment of a channel at a specific point. Cross-sections are usually installed and monitored to track geomorphic change in a stream before and after a physical alteration to the channel; these surveys can detect erosion and deposition of stream sediment as well as changes to the shape (profile) of stream bed and banks. The cross-section table defined in this data standard stores the summary measurements. Raw data can be stored in a spreadsheet or document and related to the record.

The General Social Surveys (GSS) have been conducted by the "https://www.norc.org/Pages/default.aspx" Target="_blank">National Opinion Research Center (NORC) annually since 1972, except for the years 1979, 1981, and 1992 (a supplement was added in 1992), and biennially beginning in 1994. The GSS are designed to be part of a program of social indicator research, replicating questionnaire items and wording in order to facilitate time-trend studies. The 2008 GSS featured special modules on attitudes toward science and technology, self-employment, terrorism preparation, global economics, sports and leisure, social inequality, sexual behaviors and religion. Items on religion covered denominational affiliation, church attendance, religious upbringing, personal beliefs, and religious experiences.

The GSS is in transition from a replicating cross-sectional design to a design that uses rotating panels. In 2008 there were two components: a new 2008 cross-section with 2,023 cases and the first re-interviews (panel) with 1,536 respondents from the 2006 GSS. The 2,023 cases in the cross-section have been previously released as a part of the 1972-2008 cumulative data. This new release includes those 1,536 re-interviewed panel cases along with the 2,023 cases. Please note that this is not a cumulative file - those cases and variables not surveyed in 2008 are excluded. Also note that, although those 1,536 cases were from the 2006 sample, this release does not include their responses in 2006. We plan to release a data file with the previous responses in the future. This release introduces new variables that were asked only of the panel cases of the 2008 GSS. The majority of variables introduced are related to the 2007 International Social Survey Program (ISSP) module on leisure time and sports.

To download syntax files for the GSS that reproduce well-known religious group recodes, including RELTRAD, please visit the "/research/syntax-repository-list" Target="_blank">ARDA's Syntax Repository.

BackgroundCurrently, the biggest issue facing the entire world is mental health. According to the Ethiopian Ministry of Health, nearly one-fourth of the community is experiencing any of the mental illness categories. Most of the cases were treated in religious and traditional institutions, which the community most liked to be treated. However, there were very limited studies conducted to show the level of mental health literacy among traditional healers.AimsThe study aimed to assess the level of mental health literacy and its associated factors among traditional healers toward mental illness found in Northeast, Ethiopia from September 1-30/2022.MethodA mixed approach cross-sectional study design was carried out on September 130, 2022, using simple random sampling with a total sample of 343. Pretested, structured questionnaires and face-to-face interviews were utilized for data collection. The level of Mental Health Literacy (MHL) was assessed using the 35 mental health literacy (35-MHLQ) scale. The semi-structured checklist was used for the in-depth interview and the FGD for the qualitative part. Data was entered using Epi-data version 4.6 and, then exported to SPSS version 26 for analysis. The association between outcome and independent variables was analyzed with bivariate and multivariable linear regression. P-values < 0.05 were considered statistically significant. Thematic analysis was used to analyze the qualitative data, and the findings were then referenced with the findings of the quantitative data.ResultsThe findings of this study showed that the sample of traditional healers found in Dessie City scored a total mean of mental health literacy of 91.81 ± 10:53. Age (β = -0.215, 95% CI (-0.233, -0.05), p = 0.003, informal educational status (β = -5.378, 95% CI (-6.505, -0.350), p = 0.029, presence of relative with a mental disorder (β = 6.030, 95% CI (0.073, 7.428),p = 0.046, getting information on mental illness (β = 6.565, 95% CI (3.432, 8.680), p =

This is the dataset containing simulated and real data used in the analyses for the paper "High-throughput phenotyping methods for quantifying hair fiber morphology" and is part of the Hair Phenotyping Methods Project run by Tina Lasisi.

The data can be analyzed with the fibermorph Python package available on PyPi and Github.

This repository has 2 datasets with 2 different types of data:

Simulated hair data

Cross-sectional data (simulated ellipses)

Curvature data (simulated arcs)

Real hair data

Cross-sectional data (micrographs of hair fiber cross-sections)

Curvature data (longitudinal images of hair fiber fragments)

Visit the Hair Phenotyping Methods Project website for the most up to date information about this project and any updates relevant to this dataset.

Details

Simulated data

Cross-sectional data

These ellipses were simulated with a python script developed as part of fibemorph. A version of that code that doesn't require the original Python package has been made available with the dataset (sim_ellipse.py).

The script simulates a single cross-section per image.

Each image has a width of 5200px and a height of 3900 with a resolution set to 4.25 px/micron.

Curvature data

An R script used for curvature simulation, written by Arslan Zaidi, has also been made available with this dataset (sim_curvature.R).

The script generates 25 arcs per image. We used a set length of 1.57.

Each image has a resolution of 132 px/mm.

Please note that due to the use of random generations, it is not possible to recreate the exact same datasets that are saved here.

Real data

The real data images are very large files and have been split into multiple zip files. Please check the specific instructions for unzipping split zip files for your OS.

The images are from hair samples collected by the Shriver Lab at Penn State. There were a total of 192 samples, although not all images made it past quality control so certain IDs may have cross-section images but not curvature images or vice versa.

The images have been de-identified and the hair samples for these individuals were collected with informed consent and ethical approval by The Pennsylvania State University Institutional Review Board (#44929 and #45727).

Cross-sectional data

We developed and used this protocol to embed, section, and image the hairs.

We embedded 6 samples per person and took images of both sides of the sectioned sample (A and B). These should be mirror images of each other.

Curvature data

We developed and used this protocol to cut, wash, and image the hairs.

We used 3-5 hairs per person where available. A number of samples did not have enough hair for this, so the images contain fewer fragments. We have made these available for full transparency although we filtered them from our analyses downstream.

Please see the GitHub repository for additional related participant data we used in our analyses.

This file contains all of the cases and variables that are in the original 2012 General Social Survey, but is prepared for easier use in the classroom. Changes have been made in two areas. First, to avoid confusion when constructing tables or interpreting basic analysis, all missing data codes have been set to system missing. Second, many of the continuous variables have been categorized into fewer categories, and added as additional variables to the file.

The General Social Surveys (GSS) have been conducted by the National Opinion Research Center (NORC) annually since 1972, except for the years 1979, 1981, and 1992 (a supplement was added in 1992), and biennially beginning in 1994. The GSS are designed to be part of a program of social indicator research, replicating questionnaire items and wording in order to facilitate time-trend studies. This data file has all cases and variables asked on the 2012 GSS. There are a total of 4,820 cases in the data set but their initial sampling years vary because the GSS now contains panel cases. Sampling years can be identified with the variable SAMPTYPE.

The 2012 GSS featured special modules on religious scriptures, the environment, dance and theater performances, health care system, government involvement, health concerns, emotional health, financial independence and income inequality.

The GSS has switched from a repeating, cross-section design to a combined repeating cross-section and panel-component design. This file has a rolling panel design, with the 2008 GSS as the base year for the first panel. A sub-sample of 2,000 GSS cases from 2008 was selected for reinterview in 2010 and again in 2012 as part of the GSSs in those years. The 2010 GSS consisted of a new cross-section plus the reinterviews from 2008. The 2012 GSS consists of a new cross-section of 1,974, the first reinterview wave of the 2010 panel cases with 1,551 completed cases, and the second and final reinterview of the 2008 panel with 1,295 completed cases. Altogether, the 2012 GSS had 4,820 cases (1,974 in the new 2012 panel, 1,551 in the 2010 panel, and 1,295 in the 2008 panel).

To download syntax files for the GSS that reproduce well-known religious group recodes, including RELTRAD, please visit the "/research/syntax-repository-list" Target="_blank">ARDA's Syntax Repository.

Abstract

The Russia Longitudinal Monitoring Survey (RLMS) is a household-based survey designed to measure the effects of Russian reforms on the economic well-being of households and individuals. In particular, determining the impact of reforms on household consumption and individual health is essential, as most of the subsidies provided to protect food production and health care have been or will be reduced, eliminated, or at least dramatically changed. These effects are measured by a variety of means: detailed monitoring of individuals' health status and dietary intake, precise measurement of household-level expenditures and service utilization, and collection of relevant community-level data, including region-specific prices and community infrastructure data. Data have been collected since 1992.

As its name implies, the RLMS is a longitudinal study of populations of dwelling units. Rounds V-VII are designed to provide a repeated cross-section sampling. Barring the construction of major new housing structures, renewed contact with a fixed national probability sample of dwelling units provides high coverage cross-sectional representation. The repeat visit at each round to a static sample of dwelling units also introduces a correlation between successive samples that leads to improved efficiency in longitudinal analyses comparing aggregate statistics.

The repeated cross-section design is far and away the simplest alternative for the RLMS. The sampling is cost efficient, easy to maintain, and easy to update when needed. The design supports both efficient cross-sectional and aggregate longitudinal analyses of change in the Russian household population. Updates to the sample, including a full replenishment of the probability sample of dwelling units, will not seriously disrupt the longitudinal data series.

Geographic coverage

National

Analysis unit

Households and individuals.

Kind of data

Sample survey data [ssd]

Sampling procedure

The goal was to develop a sample of households (excluding institutionalized people) that would meet accepted scientific standards of a true probability sample to the greatest extent possible, while taking into account the severe operational constraints of Goskomstat. With the advice of William Kalsbeek [a sampling expert at the University of North Carolina at Chapel Hill (UNC-CH)] and later with help from Leslie Kish, the project developed a replicated three-stratified cluster sample of residential addresses, excluding military, penal, and other institutionalized populations. Replication was designated for Stage 1 of sampling so that the number of primary sampling units (PSUs) could be kept manageable, with the understanding that later they would be expanded. The sample size of each replicate was set at 20 PSUs. The quality of this sample was statistically analyzed.

Sample attrition due to nonresponse cannot be avoided. Table 1 summarizes RLMS Round V interview completion rates for the original sample of dwelling units in the eight regions that comprise the survey population. These are not response rates; each denominator includes dwelling units that were vacant or uninhabitable at the time of the Round V interviews. Overall, interviews were completed in 84.3% of the original national probability sample of n=4718 dwelling units.

Interview completion rates outside St. Petersburg, Moscow City, and Moscow Oblast range from 84.8% in the combined Central/Central Black Earth region to 92.6% in Western Siberia. Rates in the highly urban Moscow/St. Petersburg region are much lower. In part, these rates may reflect higher vacancy rates in metropolitan areas, but clearly lower household contact and response rates also come into play. Lower rates in Moscow and St. Petersburg were anticipated at the design stage, and initial allocations to these strata were increased to offset expected losses from refusal and noncontact. This is one form of what we might call "designing for nonresponse." The over-sampling strategy is beneficial in that it means reduced variability in the final analysis weights (due to the offset in the product of higher sample selection probability and lower response propensity); however, over-sampling eliminates the potential for bias only if attrition is occurring at random within the final weighting adjustment cells.

If independent samples were developed for each round of the repeated cross-section design, attrition in one round would be independent of (although possibly similar in nature to) that in other rounds. However, since the RLMS uses a static sample of dwellings across multiple rounds, the impact of nonresponse and attrition is the net effect of several factors. Round V attrition bias can arise only from differential nonresponse and noncontact for subclasses of households that occupy the original sample of dwelling units. The potential for nonresponse bias in cross-sectional analysis or contrasts involving the Rounds VI and VII data is a complex function of: (1) initial nonresponse in Round V; (2) net difference in characteristics of households and individuals who move out of or into sample dwellings; (3) nonresponse on the part of old households continuing to reside in sample dwelling units; and (4) nonresponse on the part of new households currently living in sample dwelling units.

Time did not permit analysis of each of these factors. Instead, I performed several simple analyses of the net effect of household turnover and nonresponse on the marginal sample distributions (unweighted) of population characteristics that should not change significantly over time.

The general observation is that the combined influence of nonresponse attrition and household turnover does not seriously distort the geographic distribution of the sample or its size or household-head characteristics. The distributions for the geographic variables indicate that, between Round V and Round VII, there is a decline in the nominal representation of households in the Moscow/St. Petersburg region, reflected in a decline in the proportion of sample households from the urban domain. Households with a male head aged 18-59 may be subject to slightly higher than average attrition/net loss in replacement. If we focus only on these characteristics, the problem is not serious.

In summary, the net effect of nonresponse attrition and change in dwelling unit occupants across rounds on the marginal characteristics of the observed cross-sectional samples is modest. Loss in nominal "sample share" between Rounds V and VII is greatest for residents of Moscow/St. Petersburg--a loss in representation that is readily corrected with the combined sample selection/nonresponse adjustment factors that have been computed for each round. It is important to note that the simple analysis described here cannot demonstrate that no uncorrected attrition bias remains. The potential for uncorrected nonresponse bias can be specific to the dependent variable under study. Nevertheless, it appears that, with the nonresponse and post-stratification adjustments developed by Michael Swafford, the potential for serious attrition bias in repeated cross-section analysis is small.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaire are English-language translations of the original Russian questionnaires. The English versions have been translated as literally as possible. The order of the questions and the layout of the pages have been preserved in the English versions.

The questionnaires are also designed to function as codebooks. The variable names, as they appear in the data sets, are usually listed below or to the left of the questions. If the abbreviation (char) appears with a variable name, then the responses to that question are stored in a character variable. If there is no variable name associated with a particular question, then the responses to that question do not appear in the data set. Some questions in the questionnaires are color coded. Pink means that the question was added. Green indicates changes from the previous round (e.g., year). Gray means that the questions were asked, but the data are not available for public use - the questions were added at the request of the Pension Office and are for their use only.

Cleaning operations

In Phase II (Rounds V - XX), when questionnaires were returned to local supervisors, those supervisors were required to examine them to locate problems that could best be remedied in the field, e.g., by returning to get key demographic information or cleaning ID numbers so that the roster of individuals located in the household questionnaire matched those on the individual questionnaires from that household. The questionnaires were then transported to Moscow, where yet another ID check was performed.

In Moscow, coders looked through all questionnaires to code so-called "other: specify" responses. However, open-ended questions (e.g., occupation questions) were not coded at this time. Instead, their texts were fully entered as long string variables. Entering the open-ended answers as character variables offered several advantages. First, it allowed data entry to begin immediately, with no delay for coding. Second, it permited the use of computer programs to assist in coding the string variables. Third, the method allowed any user of the original data sets to recode the character variables to suit his or her purposes without going back to the paper copies of the questionnaires.

All data entry was handled in-house using the SPSS data entry program on PCs.

This dataset contains 42 individual images that are part of a single sample. The individual images can be combined into a single image via image stitching. These data are provided as an example so that different image stitching methods may be tested. Some of the features of this data that make the image stitching challenging are: the color gradient across the screen due to the differential interference contrast technique, the deep scratches that exist on some of the images but may appear to change position due to the slight changes in lighting conditions between different images depending on position of the scratch on the image.

Hypertension poses a significant public health challenge in sub-Saharan Africa due to various risk factors. Community-based intervention for prevention and control of hypertension is an effective strategy to minimize the negative health outcomes. However, comprehensive systematic review evidence to inform effective community-based interventions for prevention and control of hypertension in low resource settings is lacking. This study aimed to synthesize the effectiveness of community-based interventions on prevention and control of hypertension in sub-Saharan Africa. A comprehensive search for studies was carried out on PubMed, CINAHL, Web of Science Core Collection, Embase, Scopus, and Google scholar databases. The result of the review was reported according to PRISMA guidelines. Studies published in English language were included. Two independent reviewers conducted critical appraisal of included studies and extracted the data using predefined excel sheet. Experimental, quasi experimental, cohort and analytical cross-sectional studies conducted on adults who have received community-based interventions for prevention and controls of hypertension in sub-Saharan Africa were included. In this systematic review, a total of eight studies were included, comprising of two interventional studies, two quasi-experimental studies, three cohort studies, and one comparative cross-sectional study. The interventions included health education, health promotion, home-based screening and diagnosis, as well as referral and treatment of hypertensive patients. The sample sizes ranged from 236 to 13,412 in the intervention group and 346 to 6,398 in the control group. This systematic review shows the effect of community-based interventions on reduction of systolic and diastolic blood pressure. However, the existing evidence is inconsistence and not strong enough to synthesize the effect of community-based interventions for the prevention and control of hypertension in sub-Saharan Africa. Hence, further primary studies need on the effect of community-based interventions for the prevention and control of hypertension in sub-Saharan Africa.Systematic review registration number: PROSPERO CRD42022342823.

Abstract

In 2009, the EU-SILC instrument covered all EU Member States plus Iceland, Turkey, Norway and Switzerland. EU-SILC has become the EU reference source for comparative statistics on income distribution and social exclusion at European level, particularly in the context of the "Program of Community action to encourage cooperation between Member States to combat social exclusion" and for producing structural indicators on social cohesion for the annual spring report to the European Council. The first priority is to be given to the delivery of comparable, timely and high quality cross-sectional data.

There are two types of datasets: 1) Cross-sectional data pertaining to fixed time periods, with variables on income, poverty, social exclusion and living conditions. 2) Longitudinal data pertaining to individual-level changes over time, observed periodically - usually over four years.

Social exclusion and housing-condition information is collected at household level. Income at a detailed component level is collected at personal level, with some components included in the "Household" section. Labour, education and health observations only apply to persons 16 and older. EU-SILC was established to provide data on structural indicators of social cohesion (at-risk-of-poverty rate, S80/S20 and gender pay gap) and to provide relevant data for the two 'open methods of coordination' in the field of social inclusion and pensions in Europe.

The 7th version of the 2009 Cross-Sectional User Database (UDB) as released in July 2015 is documented here.

Geographic coverage

The survey covers following countries: Austria, Belgium, Bulgaria, Czech Republic, Denmark, Germany, Estonia, Greece, Spain, France, Ireland, Italy, Cyprus, Latvia, Lithuania, Luxembourg, Hungary, Malta, Netherlands, Poland, Portugal, Romania, Slovenia, Slovakia, Finland, Sweden, United Kingdom, Iceland, Norway.

Small parts of the national territory amounting to no more than 2% of the national population and the national territories listed below may be excluded from EU-SILC: France - French Overseas Departments and territories; Netherlands - The West Frisian Islands with the exception of Texel; Ireland - All offshore islands with the exception of Achill, Bull, Cruit, Gorumna, Inishnee, Lettermore, Lettermullan and Valentia; United kingdom - Scotland north of the Caledonian Canal, the Scilly Islands.

Analysis unit

• Households;
• Individuals 16 years and older.

Universe

The survey covered all household members over 16 years old. Persons living in collective households and in institutions are generally excluded from the target population.

Kind of data

Sample survey data [ssd]

Sampling procedure

On the basis of various statistical and practical considerations and the precision requirements for the most critical variables, the minimum effective sample sizes to be achieved were defined. Sample size for the longitudinal component refers, for any pair of consecutive years, to the number of households successfully interviewed in the first year in which all or at least a majority of the household members aged 16 or over are successfully interviewed in both the years.

For the cross-sectional component, the plans are to achieve the minimum effective sample size of around 131.000 households in the EU as a whole (137.000 including Iceland and Norway). The allocation of the EU sample among countries represents a compromise between two objectives: the production of results at the level of individual countries, and production for the EU as a whole. Requirements for the longitudinal data will be less important. For this component, an effective sample size of around 98.000 households (103.000 including Iceland and Norway) is planned.

Member States using registers for income and other data may use a sample of persons (selected respondents) rather than a sample of complete households in the interview survey. The minimum effective sample size in terms of the number of persons aged 16 or over to be interviewed in detail is in this case taken as 75 % of the figures shown in columns 3 and 4 of the table I, for the cross-sectional and longitudinal components respectively.

The reference is to the effective sample size, which is the size required if the survey were based on simple random sampling (design effect in relation to the 'risk of poverty rate' variable = 1.0). The actual sample sizes will have to be larger to the extent that the design effects exceed 1.0 and to compensate for all kinds of non-response. Furthermore, the sample size refers to the number of valid households which are households for which, and for all members of which, all or nearly all the required information has been obtained. For countries with a sample of persons design, information on income and other data shall be collected for the household of each selected respondent and for all its members.

At the beginning, a cross-sectional representative sample of households is selected. It is divided into say 4 sub-samples, each by itself representative of the whole population and similar in structure to the whole sample. One sub-sample is purely cross-sectional and is not followed up after the first round. Respondents in the second sub-sample are requested to participate in the panel for 2 years, in the third sub-sample for 3 years, and in the fourth for 4 years. From year 2 onwards, one new panel is introduced each year, with request for participation for 4 years. In any one year, the sample consists of 4 sub-samples, which together constitute the cross-sectional sample. In year 1 they are all new samples; in all subsequent years, only one is new sample. In year 2, three are panels in the second year; in year 3, one is a panel in the second year and two in the third year; in subsequent years, one is a panel for the second year, one for the third year, and one for the fourth (final) year.

According to the Commission Regulation on sampling and tracing rules, the selection of the sample will be drawn according to the following requirements:

1. For all components of EU-SILC (whether survey or register based), the crosssectional and longitudinal (initial sample) data shall be based on a nationally representative probability sample of the population residing in private households within the country, irrespective of language, nationality or legal residence status. All private households and all persons aged 16 and over within the household are eligible for the operation.
2. Representative probability samples shall be achieved both for households, which form the basic units of sampling, data collection and data analysis, and for individual persons in the target population.
3. The sampling frame and methods of sample selection shall ensure that every individual and household in the target population is assigned a known and non-zero probability of selection.
4. By way of exception, paragraphs 1 to 3 shall apply in Germany exclusively to the part of the sample based on probability sampling according to Article 8 of the Regulation of the European Parliament and of the Council (EC) No 1177/2003 concerning

Community Statistics on Income and Living Conditions. Article 8 of the EU-SILC Regulation of the European Parliament and of the Council mentions: 1. The cross-sectional and longitudinal data shall be based on nationally representative probability samples. 2. By way of exception to paragraph 1, Germany shall supply cross-sectional data based on a nationally representative probability sample for the first time for the year 2008. For the year 2005, Germany shall supply data for one fourth based on probability sampling and for three fourths based on quota samples, the latter to be progressively replaced by random selection so as to achieve fully representative probability sampling by 2008. For the longitudinal component, Germany shall supply for the year 2006 one third of longitudinal data (data for year 2005 and 2006) based on probability sampling and two thirds based on quota samples. For the year 2007, half of the longitudinal data relating to years 2005, 2006 and 2007 shall be based on probability sampling and half on quota sample. After 2007 all of the longitudinal data shall be based on probability sampling.

Detailed information about sampling is available in Quality Reports in Related Materials.

Mode of data collection

Mixed

This data is the 165Ho neutron capture cross-section data measured using the total energy detection system C6D6 in the CSNS Back-n .The measurement time is May 2022, with a total beam measurement time of 100 hours. The measurement time for different samples varies, for example, the measurement time for sample 165Ho is 52 hours and 56 minutesThe data in this dataset is mainly generated and calculated using the Cern root 5.34/34 program.

The excel file contains one day's data on one stock and shows how to construct risk-targeting option portfolios and estimate the market price of risk for each risk dimension. The Internet Appendix describes the operations in the excel file.

CERN-LHC. Measurements of cross sections for the production of jets in association with a Z0 boson in proton-proton collisions at a centre of mass energy of 7 TeV. The 2011 data sample used has an integrated luminosity of 4.6 fb^{-1}. Inclusive cross sections for Z0 decaying into either electron or muon pairs with jets of transverse momentum above 30 GeV and |rapidity| less than 4.4 are presented along with a detailed breakdown of the systematic errors in the measurements. Data are given here for the individual electron and muon channels as well as for the combined data as shown in the paper. The combined cross section has been extrapolated to the common phase space |eta|<2.5 and pT>20 GeV, similar to the 2010 analysis. Note added (25 JUN 2014): The cross section values reported in Tables 1,3,5,7,9-13,15-28 below should be multiplied by a factor of 1.0141 to take into account the updated value of the integrated luminosity for the ATLAS 2011 data taking period. The uncertainty on the global normalisation ("Lumi") remains at 1.8%. See Eur.Phys.J. C73 (2013) 2518 for more details Note added (8 MAY 2015): The entries for Z->mumu final state in table 2, 4, 6 were shifted by one row. This mistake has been corrected.

The General Social Surveys (GSS) have been conducted by the National Opinion Research Center (NORC) annually since 1972, except for the years 1979, 1981, and 1992 (a supplement was added in 1992), and biennially beginning in 1994. The GSS are designed to be part of a program of social indicator research, replicating questionnaire items and wording in order to facilitate time-trend studies. This data file has all cases and variables asked on the 2012 GSS. There are a total of 4,820 cases in the data set but their initial sampling years vary because the GSS now contains panel cases. Sampling years can be identified with the variable SAMPTYPE.

The 2012 GSS featured special modules on religious scriptures, the environment, dance and theater performances, health care system, government involvement, health concerns, emotional health, financial independence and income inequality.

The GSS has switched from a repeating, cross-section design to a combined repeating cross-section and panel-component design. This file has a rolling panel design, with the 2008 GSS as the base year for the first panel. A sub-sample of 2,000 GSS cases from 2008 was selected for reinterview in 2010 and again in 2012 as part of the GSSs in those years. The 2010 GSS consisted of a new cross-section plus the reinterviews from 2008. The 2012 GSS consists of a new cross-section of 1,974, the first reinterview wave of the 2010 panel cases with 1,551 completed cases, and the second and final reinterview of the 2008 panel with 1,295 completed cases. Altogether, the 2012 GSS had 4,820 cases (1,974 in the new 2012 panel, 1,551 in the 2010 panel, and 1,295 in the 2008 panel).

To download syntax files for the GSS that reproduce well-known religious group recodes, including RELTRAD, please visit the "/research/syntax-repository-list" Target="_blank">ARDA's Syntax Repository.

Background Investigating the validity of the self-reported values of weight allows for the proper assessment of studies using questionnaire-derived data. The study examined the accuracy of gender-specific self-reported weight in a sample of adults. The effects of age, education, race and ethnicity, income, general health and medical status on the degree of discrepancy (the difference between self-reported weight and measured weight) are similarly considered. Methods The analysis used data from the US Third National Health and Nutrition Examination Survey. Self-reported and measured weights were abstracted and analyzed according to sex, age, measured weight, self-reported weight, and body mass index (BMI). A proportional odds model was applied. Results The weight discrepancy was positively associated with age, and negatively associated with measured weight and BMI. Ordered logistic regression modeling showed age, race-ethnicity, education, and BMI to be associated with the degree of discrepancy in both sexes. In men, additional predictors were consumption of more than 100 cigarettes and the desire to change weight. In women, marital status, income, activity level, and the number of months since the last doctor's visit were important. Conclusions Predictors of the degree of weight discrepancy are gender-specific, and require careful consideration when examined.

CERN-LHC. Measurement of the differential cross sections for inclusive production of di-muon and di-lepton pairs in proton proton collisions at a centre-of-mass energy of 7 TeV from a data sample of total integrated luminosity of 36 pb-1. The differential cross section which is normalized to the measured cross section in the Z0 region (60 to 120 GeV) covers the di-lepton invariant mass range from 15 to 600 GeV. Cross sections are given both for full phase space and within the detector acceptance.

Abstract

The EICV5 survey (Enquête Intégrale sur les Conditions de Vie des ménages) was conducted over a 12-month cycle from October 2016 to October 2017. Data collection was divided into 10 cycles in order to represent seasonality in the income and consumption data. A main cross-sectional sample survey, a panel survey and a VUP sample survey were conducted simultaneously.

The objectives of the EICV5 Panel Survey are to measure the trends in key socioeconomic indicators over time for a nationally representative panel of households. EICV5 aims to provide timely and updated statistics to facilitate monitoring progress on poverty reduction programmes and evaluation of different policies as stipulated in the First National Strategy for Transformation (NST1), the 2030 Sustainable Development Goals (SDGs), as well as the Vision 2020 and Vision 2050. The survey data are also very important for national accounts and updating the consumer price index (CPI).

Geographic coverage

National coverage.

Analysis unit

Households

Universe

All household members

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling frame for the EICV5 cross-sectional survey is based on the NISR master sample data. More recently, the NISR used the 2012 Census frame to select a large master sample of villages 3,960 that can be used for the different national household surveys in Rwanda. The primary sampling units (PSUs) for the Master Sample are individual villages, or a combination of small villages, with the number of households tabulated from the 2012 Census data. A new listing of households was conducted in order to update the frame for the EICV5 cross-sectional survey. The sample households in the EICV5 sample villages were selected from the new listing.

1) The EICV5 Cross-sectional survey sample size

The sample size for the EICV5 cross-sectional survey depends on the level of precision that is required for key indicators at the district level, as well as on resource constraints and logistical considerations. It is very important to ensure good quality control in order to minimize the non-sampling errors. The estimates of the sampling errors for the poverty rate by district from the EICV4 data were examined in order to determine whether it would be necessary to adjust the sample size. For EIVC4 the number of households selected per cluster was 9 for Kigali Province, which is mostly urban, and 12 for the remaining provinces, which are mostly rural. This sampling strategy has been consistent for all the EICV surveys because it is statistically efficient and is also effective for the EICV logistics of the fieldwork and the workload of the team of enumerators each cycle. The urban areas generally have a higher intraclass correlation for socioeconomic characteristics between households within a cluster compared to rural areas. There is also a different interviewing schedule for the sample households in Kigali Province, so only 9 households are interviewed in each cluster. In terms of the number of sample clusters allocated to each district, it should be a multiple of 10 so that the sample can be evenly distributed to the 10 cycles. In the case of EICV4 the districts in Kigali Province were assigned 5 sample clusters each month, and in the other provinces each district was assigned 4 sample clusters each month.

In EICV5, the sample was increased for the districts in Kigali Province because the estimates of the poverty rate for those districts had higher coefficients of variation (CVs) or relative standard errors (RSEs) compared to the other districts. However, one reason why the RSEs for the districts of Kigali Province were higher is that the value of the poverty rate is lower for these districts. It was pointed out that in the case of estimates of percentages or proportions, it is more effective to use the margin of error to study the sample size. The margin of error is equal to half of the width of the 95% confidence interval, or 1.96 times the standard error. Therefore, the margins of error for the estimates of the poverty rate by district were also examined. In this case the margins of error were also higher for the districts of Kigali Province, given the relatively higher design effects (especially for Gasabo District), and considering that the number of sample households for these districts in EICV4 was only 450, compared to 480 sample households in the districts of the other provinces. For these reasons, it was decided to increase the number of sample PSUs for each district in Kigali Province from 50 to 60, for a total increase of 30 sample clusters and 270 sample households. For the districts in the other provinces it was decided to have the same sample size of 40 clusters and 480 households each cycle, since the level of precision of the EICV4 results for these districts was considered satisfactory.

The sample PSUs in each district were allocated to the urban and rural strata proportionately to the number of households in the 2012 Census frame. In the case of districts where the proportional number of sample PSUs was only 1 for the urban stratum, the number of sample PSUs was increased to 2. For the selection of sample villages for EICV5, it was assumed that the Master Sample villages for each district were explicitly stratified by urban and rural areas. A separate subsample of villages was selected within each stratum from the Master Sample.

At the national level, there are 1,260 sample villages and 14,580 sample households. In the urban strata there are 245 sample villages and 2,526 sample households, and in the rural strata there are 1,015 sample villages and 12,054 sample households. The sample size for the EICV5 cross-sectional survey has 30 more sample PSUs and 270 more sample households than the corresponding sample for EICV4.

In the case of EICV4 the national sample of 177 villages selected from EICV3 for the Panel Survey were also used as part of the EICV4 cross-sectional survey. However, for EICV5 it was decided to select a completely separate sample of villages for the cross-sectional survey.

2) Assignment of sample villages to cycles and sub-cycles

Similar to the EICV4 methodology, a nationally-representative sample of clusters will be assigned for the EICV5 data collection each cycle, so that the sample is geographically representative over time. A subsample serial number from 1 to 10 can be assigned systematically to the geographically ordered list of all sample clusters in each district. In order to assign the cycles to the EICV5 cross-sectional sample villages, random cycle numbers from 1 to 10 were generated to identify the selection sequence. For the 27 districts outside of Kigali Province, the sub-cycle numbers of 1 or 2 were assigned systematically with a random start. This process ensured that the final distribution of the sample clusters to cycles and sub-cycles was geographically representative within each district.

Mode of data collection

Face-to-face paper [f2f]

Research instrument

The same questionnaire was used for cross-sectional, panel and VUP samples. Part A of the questionnaire contains modules on household and individual information. Part B is on agriculture and consumption. The questionnaire was developed in English, and translated into Kinyarwanda.

Questionnaire design took into account the requests raised by major data users and stakeholders, as well as consistency with the previous EICV questionnaires. In addition to methodological improvements, some simplifications were made:

-The major changes introduced in this survey were changes to Section 6, the Economic Activity. Further questioning was added on unemployment and underemployment in response to questions from users, and also to comply with international standards. The section was simplified to enable the analysis to be undertaken by local analysts.

-The Section on the VUP participation was expanded to provide more information, better classification of beneficiaries and to provide greater consistency within the questionnaire. The same questionnaire is to be used on the separate VUP sample which runs in parallel with the EICV5

Questionnaire was tested in pilot surveys and amended in time prior to the fieldwork starting in October 2016. The complete questionnaire is provided as external resources.

Cleaning operations

A day before the interview started, the enumerator, accompanied by a controller, did an introduction to household, explaining how often they will come in that household and delivering a letter indicating that the HH has been selected.

During the field work, after each cycle, the data processing team produced tables and reports of inconsistencies, which were checked by the field supervisor. The data entry system also contained consistency checks that alerted the data entry operators. In case of an alert, the questionnaire was sent back to the supervisor of data entry for correction.

Response rate

The response rate for EICV5 (cross-sectional) is 100%. All households sampled(14,580) were interviewed with no refusal.

Abstract

The EICV4 survey (Enquête Intégrale sur les Conditions de Vie des ménages) was conducted over a 12-month cycle from October 2013 to October 2014. Data collection was divided into 10 cycles in order to represent seasonality in the income and consumption data. A main cross-sectional sample survey, a panel survey and a VUP sample survey were conducted simultaneously.

The EICV4 provides information on poverty and living conditions in Rwanda and measures changes over time as part of the on-going monitoring of the Poverty Reduction Strategy and other Government policies. The survey data are also very important for national accounts and updating the consumer price index (CPI).

Geographic coverage

National coverage.

Analysis unit

Households

Universe

All household members (variable s1q15 identifies household membership).

Kind of data

Sample survey data [ssd]

Sampling procedure

The EICV4 cross sectional (CS) sample includes two independent subsets selected using different sampling frames: 1) A new EICV4 sample of households in enumeration areas (EAs) selected using the 2012 Rwanda Population and Housing Census frame and 2) a panel of households selected from 177 EICV3 villages. A new listing of households was conducted in both the panel and new sample clusters in order to update the frame for the CS Survey. The sample households in the new CS sample EAs were selected from the new listing.

1) The new EICV4 sample The main sampling frame for the EICV4 is based on the 2012 Rwanda Census. The primary sampling units (PSUs) are the 2012 census Enumeration Areas (EAs). In the Census, each EA was classified as urban, semi-urban, peri-urban or rural. The urban areas include Kigali-Ville and the district capitals. The semi-urban areas generally correspond to smaller towns that have service facilities and markets. The peri-urban areas currently have the characteristics of rural areas, but they are located on the periphery of urban areas and are designated for future development. For the EICV4 sampling frame, the semi-urban areas were grouped with the urban strata, and the peri-urban areas with the rural strata. This results in a final distribution of 17.2% urban households and 82.8% rural households in the sampling frame. EAs in the 177 EICV3 sample villages selected for the panel study were excluded from the sampling frame, in order to avoid any overlap between the two samples.

The new EICV4 sample of 12,312 households was selected using a stratified two-stage design. At the first stage, sample EAs were selected within each stratum (district) with probability proportional to size (PPS) from the ordered list of EAs in the sampling frame. The EAs are implicitly stratified by urban and rural strata within each district, ordered first by urban, semi-urban, peri-urban and rural areas, and then geographically by sector, cellule, village and EA codes. This first stage sampling procedure provides a proportional allocation of the sample to the urban and rural areas of each district. At the second stage, households in each sample EA are selected from the listing. For the three districts in Kigali Province, 9 households were selected in each sample EA as original households; for the remaining 27 districts, 12 households were selected in each sample EA as original households. In addition, a reserve sample of 3 replacement households were selected for each sample EA in Kigali Province and 4 replacement households for each sample EA in the remaining provinces.

This new EICV4 sample contains 12,312 households, including 12,233 original households and 79 replacement households.

2) Households from 177 EICV3 villages used for panel study The second component of the EICV4 cross sectional sample consists of all the sample households interviewed inside the 177 EICV3 villages selected for the panel study (including any replacements households and panel split households inside the clusters).

Within each of the 177 villages, all households that were interviewed during EICV3 were included in the cross-sectional sample. When an EICV3 sample household moved and a new household occupied the same house in the cluster, it was interviewed for the Cross-Sectional Survey, and assigned a PID (dependency) code of 94. If an EICV3 household was empty or not found, a random replacement household was selected for the EICV4 Cross-Sectional Survey from the new listing of the sample cluster, and assigned a PID code of 95. The sample households with PID codes 94 and 95 are only used for the cross-sectional study, not the panel study.

This second component of the cross-sectional sample includes 2108 households drawn from the 177 EICV3 villages sampled for the panel study. These include 1604 original EICV3 households, 181 dependent household splitting from the original household in the same cluster, along with 243 households living in the dwelling formerly occupied by a panel household and 80 replacement households in the cluster in order to have 9/12 households per cluster.

The reason why we combine the EICV4 data from the new and panel clusters for the CS analysis is to obtain the most accurate CS estimates. In the case of the CS estimates from the combined samples, the additional data from the 177 sample panel clusters will result in a significant reduction in the variance component of the MSE. Although the bias of the CS data from the sample panel clusters may slightly increase the bias component, this bias is very small compared to the corresponding reduction in the variance component. Therefore the CS results from the EICV4 data for the combined new and panel clusters can be considered more accurate than the corresponding results using only the EICV4 data for the new sample clusters.

In total, the final EICV4 cross-sectional sample contains 14,419 households.

3) Assignment of EAs to cycles and sub-cycles Data collection covering a period of 12 month is divided into 10 cycles to represent seasonality in consumption, income, employment and agricultural activity patterns. For rural enumeration, each cycle is further divided into two sub-cycles. For the 177 EICV3 villages, the cycle and sub-cycle were pre-determined. Households were re-interviewed in the same cycle, correponding to the same time of the year as they were in EICV3. To assign cycles to the new EICV4 sample EAs, random cycle numbers from 1 to 10 were generated to identify the selection sequence. For the 27 districts outside Kigali, sub-cycle numbers of 1 or 2 were assigned systematically with a random start. This process ensured that the final distribution of the sample EAs to cycles and sub-cycles was geographically representative within each district.

Mode of data collection

Face-to-face paper [f2f]

Research instrument

The same questionnaire was used for cross-sectional, panel and VUP samples. Part A of the questionnaire contains modules on household and individual information. Part B is on agriculture and consumption. The questionnaire was developed in English, and translated into Kinyarwanda.

Questionnaire design took into account the requests raised by major data users and stakeholders, as well as consistency with the previous EICV questionnaires. In addition to methodological improvements, some simplifications were made:

-The major changes introduced in this survey were changes to Section 6, the Economic Activity. Further questioning was added on unemployment and underemployment in response to questions from users, and also to comply with international standards. The section was simplified to enable the analysis to be undertaken by local analysts.

-The Section on the VUP participation was expanded to provide more information, better classification of beneficiaries and to provide greater consistency within the questionnaire. The same questionnaire is to be used on the separate VUP sample which runs in parallel with the EICV4.

-The health section was reduced to try to cut respondent burden, as health-related information is being collected by Demographic and Health Surveys (DHS).

-The expenditure section was changed in minor ways to provide better information for national accounts (housing investment) and for CPI weights (retail outlets).

Questionnaire was tested in pilot surveys and amended in time prior to the fieldwork starting in October 2013. The complete questionnaire is provided as external resources.

Cleaning operations

A day before the interview started, the enumerator, accompanied by a controller, did an introduction to household, explaining how often they will come in that household and delivering a letter indicating that the HH has been selected.

During the field work, after each cycle, the data processing team produced tables and reports of inconsistencies, which were checked by the field supervisor. The data entry system also contained consistency checks that alerted the data entry operators. In case of an alert, the questionnaire was sent back to the supervisor of data entry for correction.

Response rate

Out of the 12,312 sample households selected in the new sample clusters for EICV4, only 79 were non-interviews, for a response rate of 99.4% for this sample. All of the 79 non-interviews were replaced. There were only 12 refusals, and there were few cases of houses that were empty or not found, given that the listing was conducted very close to the interviewing period.

The power of standard panel cointegration statistics may be affected by misspecification errors if structural breaks in the parameters generating the process are not considered. In addition, the presence of cross-section dependence among the panel units can distort the empirical size of the statistics. We therefore design a testing procedure that allows for both structural breaks and cross-section dependence when testing the null hypothesis of no cointegration. The paper proposes test statistics that can be used when one or both features are present. We illustrate our proposal by analysing the pass-through of import prices on a sample of European countries.

The General Social Surveys (GSS) have been conducted by the National Opinion Research Center (NORC) annually since 1972, except for the years 1979, 1981, and 1992 (a supplement was added in 1992), and biennially beginning in 1994. The GSS are designed to be part of a program of social indicator research, replicating questionnaire items and wording in order to facilitate time-trend studies. This data file has all cases and variables asked on the 2010 GSS. There are a total of 4,901 cases in the data set but their initial sampling years vary because the GSS now contains panel cases. Sampling years can be identified with the variable SAMPTYPE.

The 2010 GSS featured special modules on aging, the Internet, shared capitalism, gender roles, intergroup relations, immigration, meeting spouse, knowledge about and attitudes toward science, religious identity, religious trends, genetics, veterans, crime and victimization, social networks and group membership, and sexual behavior (continuing the series started in 1988).

The GSS has switched from a repeating, cross-section design to a combined repeating cross-section and panel-component design. The 2006 GSS was the base year for the first panel. A sub-sample of 2,000 GSS cases from 2006 was selected for reinterview in 2008 and again in 2010 as part of the GSSs in those years. The 2008 GSS consists of a new cross-section plus the reinterviews from 2006. The 2010 GSS consists of a new cross-section of 2,044, the first reinterview wave of the 2,023 2008 panel cases with 1,581 completed cases, and the second and final reinterview of the 2006 panel with 1,276 completed cases. Altogether, the 2010 GSS had 4,901 cases (2,044 in the new 2010 panel, 1,581 in the 2008 panel, and 1,276 in the 2006 panel). The 2010 GSS is the first round to fully implement the new, rolling panel design. In 2012 and later GSSs, there will likewise be a fresh cross-section (wave one of a new panel), wave two panel cases from the immediately preceding GSS, and wave three panel cases from the next earlier GSS.

To download syntax files for the GSS that reproduce well-known religious group recodes, including RELTRAD, please visit the "/research/syntax-repository-list" Target="_blank">ARDA's Syntax Repository.