BackgroundObesity causes a serious diet-related chronic disease, including type-2 diabetes, cardiovascular disease, hypertension, osteoarthritis, and certain forms of cancer. In Sub- Saharan Africa including Ethiopia, most nutritional interventions mainly focused on a child undernutrition and ignored the impacts of obesity among children. In Ethiopia, the magnitude and associated factors of obesity among school-age children were not clearly described. Therefore this study assesses the predictors of obesity among school- age children in Debre Berhan City, Ethiopia, 2022.MethodsA cross-sectional study design was conducted from June to July, 2022. Participants were selected by using multistage sampling method. Data were collected using pre-tested and structured questions. Data were coded and entered in Epi-data version 4.6 and exported and analyzed using SPSS version 25.ResultA total of 600 children were participating in the study. The prevalence of obesity was 10.7% (95% CI: 8.3, 13.2). In this study, attending at private school (AOR = 4.24, 95% CI: 1.58, 11.32), children aged between 10-12years (AOR = 2.67, 95% CI: 1.30, 5.48), soft drink available in home (AOR = 2.27, 95% CI: 1.25,18.13), Loneliness (AOR = 1.67 95% CI: 1.12, 3.15) and mothers with occupational status of daily labour (AOR = 8.54 95% CI: 1.12, 65.39) were significantly associated with childhood obesity.ConclusionIn this study, the overall magnitude of childhood obesity was (10.7%) which means one in eleven children and relatively high as compare to the EDHS survey. Therefore, more attention should be given to strengthening physical activities, providing nutritional education, and creating community awareness about healthy diets as well as other preventive measures.

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

Context The World Happiness Report is a landmark survey of the state of global happiness. The first report was published in 2012, the second in 2013, the third in 2015, and the fourth in the 2016 Update. The World Happiness 2017, which ranks 155 countries by their happiness levels, was released at the United Nations at an event celebrating International Day of Happiness on March 20th. The report continues to gain global recognition as governments, organizations and civil society increasingly use happiness indicators to inform their policy-making decisions. Leading experts across fields – economics, psychology, survey analysis, national statistics, health, public policy and more – describe how measurements of well-being can be used effectively to assess the progress of nations. The reports review the state of happiness in the world today and show how the new science of happiness explains personal and national variations in happiness.

Content The happiness scores and rankings use data from the Gallup World Poll. The scores are based on answers to the main life evaluation question asked in the poll. This question, known as the Cantril ladder, asks respondents to think of a ladder with the best possible life for them being a 10 and the worst possible life being a 0 and to rate their own current lives on that scale. The scores are from nationally representative samples for the years 2013-2016 and use the Gallup weights to make the estimates representative. The columns following the happiness score estimate the extent to which each of six factors – economic production, social support, life expectancy, freedom, absence of corruption, and generosity – contribute to making life evaluations higher in each country than they are in Dystopia, a hypothetical country that has values equal to the world’s lowest national averages for each of the six factors. They have no impact on the total score reported for each country, but they do explain why some countries rank higher than others.

Indicators/Factors Explain: 1. Rank, is the country ranking 2. Score, is the happiness score of the country 3. GDP, is the gross domestic product of the country 4. Family, is the indicator that shows family support to each citizen in the country 5. Life Expectancy, shows the healthiness level of the country 6. Freedom, is an indicator that shows the citizen freedom to choose their life path, job or etc 7. Trust, shows the level of trust from the citizen in the government (influenced by the corruption level and performance of the government) 8. Generosity, an indicator that shows the generosity level of the citizen of the country

Source: The World Happiness Report is a publication of the Sustainable Development Solutions Network, powered by the Gallup World Poll data.

This article presents a detailed notational analysis of women’s pickleball, focusing on the technical-tactical aspects that define performance in the sport. The study examines the court zones most utilized during points and the types of shots that determine point outcomes, such as forehands, backhands, and volleys. Through the analysis of 15 matches from five tournaments of the 2023 PPA Pro Tour, common gameplay patterns and significant differences in shot execution between women and men are identified. The results suggest that, while both genders display a combination of baseline and net strategies, women tend to focus more on baseline shots. Additionally, a high incidence of unforced errors is observed, emphasizing the importance of consistency and accuracy in play. Finally, the article provides practical applications to improve pickleball performance based on the observed findings.In the directory, three files are available. The SPSS subdirectory contains the database file for use with IBM's Statistical Package for the Social Sciences (SPSS). Additionally, the THEME6 subdirectory includes two files compatible with the Theme 6 Edu software for T-Pattern analysis. If using Theme 5, the :and & categories must be added to the "Start-End" criterion in the VVT file. Without this adjustment, the file will not function properly.--------------Este artículo presenta un análisis notacional detallado de la categoría femenina de pickleball, centrándose en los aspectos técnico-tácticos que definen el rendimiento en este deporte. Se examinan las zonas de la pista más utilizadas durante los puntos y los tipos de golpes que determinan el desenlace de las jugadas, como los golpes de derecha, revés y voleas. A través del análisis de 15 partidos de cinco torneos del PPA Pro Tour 2023, se identifican patrones de juego comunes y diferencias significativas en la ejecución de los golpes entre mujeres y hombres. Los resultados sugieren que, aunque ambos géneros muestran una combinación de tácticas de fondo y de red, las jugadoras tienden a centrarse más en los golpes de fondo. Además, se destaca la alta incidencia de errores no forzados, lo que subraya la importancia de la consistencia y precisión en el juego. Finalmente, el artículo ofrece aplicaciones prácticas para mejorar el rendimiento en pickleball, basadas en los hallazgos observados.En el directorio se encuentran tres archivos. En el subdirectorio SPSS se incluye el archivo de la base de datos diseñado para su uso con el software IBM SPSS (Statistical Package for the Social Sciences). Por otro lado, en el subdirectorio THEME6 se encuentran dos archivos compatibles con el programa Theme 6 Edu para la búsqueda de T-Patterns. Si se utiliza Theme 5, será necesario añadir al archivo VVT el criterio "Inicio-Fin" con las categorías : y &. De no realizar esta modificación, el archivo no funcionará correctamente.

ABSTRACT The study of the manner of execution (technique, and spatial aspects) can provide useful information to understand the game dynamics in beach volleyball and to obtain references values for the analysis of the game and the establishment of training goals. The aim of the study was to determine the influence of the manner of execution on serve and rally performance in elite women's beach volleyball players. A total of 3,009 serves from 44 women’s players were analyzed. The variables studied were: serve technique, serve zone, serve destination, serve performance, and rally performance. An observational punctual, nomothetic, multidimensional, and intragroup design was used. A descriptive and inferential analysis of the data (Chi-Square Test) was done using SPSS v.21.0 software. The level of significance was set at p < .05. The manner of execution influences the serve performance. The jump float serve was the most used. The most effective destination was the zone between players, probability due to the players' displacement and interference between them. An absence of association between serve technique and rally performance was found. These findings showed possible connections between the way of executing the serve with the following actions done by the players and the players' strategies to control their physical load. These values may be useful to guide to players training, or to evaluate players in competition.

ObjectiveTo establish a simple electrophysiological scale for patients with distal symmetric axonal polyneuropathy, in order to promote standardized and informative electrodiagnostic reporting, and understand the complex relationship between electrophysiological and clinical polyneuropathy severity.MethodsWe included 76 patients with distal symmetric axonal polyneuropathy, from a cohort of 151 patients with polyneuropathy prospectively recruited from November 2016 to May 2017. Patients underwent nerve conduction studies (NCS), were evaluated by the Toronto Clinical Neuropathy Score (TCNS), and additional tests. The number of abnormal NCS parameters was determined, within the range of 0–4, considering low amplitude or conduction velocity in the sural and peroneal nerve.ResultsHigher number of NCS abnormalities was associated with higher TCNS, indicating more severe polyneuropathy. Polyneuropathy severity per the TCNS was most frequently (63%-70%) mild in patients with a low (0–1) number of NCS abnormalities, and most frequently (57%-67%) severe in patients with a high number (3–4) of NCS abnormalities, while patients with an intermediate (2) number of NCS abnormalities showed mainly mild and moderate severity with equal distribution (40%).ConclusionsA simple NCS classification system can objectively grade polyneuropathy severity, although significant overlap exists especially at the intermediate range, underscoring the importance of clinical based scoring.

Overview

The Associated Press is sharing data from the COVID Impact Survey, which provides statistics about physical health, mental health, economic security and social dynamics related to the coronavirus pandemic in the United States.

Conducted by NORC at the University of Chicago for the Data Foundation, the probability-based survey provides estimates for the United States as a whole, as well as in 10 states (California, Colorado, Florida, Louisiana, Minnesota, Missouri, Montana, New York, Oregon and Texas) and eight metropolitan areas (Atlanta, Baltimore, Birmingham, Chicago, Cleveland, Columbus, Phoenix and Pittsburgh).

The survey is designed to allow for an ongoing gauge of public perception, health and economic status to see what is shifting during the pandemic. When multiple sets of data are available, it will allow for the tracking of how issues ranging from COVID-19 symptoms to economic status change over time.

The survey is focused on three core areas of research:

• Physical Health: Symptoms related to COVID-19, relevant existing conditions and health insurance coverage.
• Economic and Financial Health: Employment, food security, and government cash assistance.
• Social and Mental Health: Communication with friends and family, anxiety and volunteerism. (Questions based on those used on the U.S. Census Bureau’s Current Population Survey.) ## Using this Data - IMPORTANT This is survey data and must be properly weighted during analysis: DO NOT REPORT THIS DATA AS RAW OR AGGREGATE NUMBERS!!

Instead, use our queries linked below or statistical software such as R or SPSS to weight the data.

Queries

If you'd like to create a table to see how people nationally or in your state or city feel about a topic in the survey, use the survey questionnaire and codebook to match a question (the variable label) to a variable name. For instance, "How often have you felt lonely in the past 7 days?" is variable "soc5c".

Nationally: Go to this query and enter soc5c as the variable. Hit the blue Run Query button in the upper right hand corner.

Local or State: To find figures for that response in a specific state, go to this query and type in a state name and soc5c as the variable, and then hit the blue Run Query button in the upper right hand corner.

The resulting sentence you could write out of these queries is: "People in some states are less likely to report loneliness than others. For example, 66% of Louisianans report feeling lonely on none of the last seven days, compared with 52% of Californians. Nationally, 60% of people said they hadn't felt lonely."

Margin of Error

The margin of error for the national and regional surveys is found in the attached methods statement. You will need the margin of error to determine if the comparisons are statistically significant. If the difference is:

• At least twice the margin of error, you can report there is a clear difference.
• At least as large as the margin of error, you can report there is a slight or apparent difference.
• Less than or equal to the margin of error, you can report that the respondents are divided or there is no difference. ## A Note on Timing Survey results will generally be posted under embargo on Tuesday evenings. The data is available for release at 1 p.m. ET Thursdays.

About the Data

The survey data will be provided under embargo in both comma-delimited and statistical formats.

Each set of survey data will be numbered and have the date the embargo lifts in front of it in the format of: 01_April_30_covid_impact_survey. The survey has been organized by the Data Foundation, a non-profit non-partisan think tank, and is sponsored by the Federal Reserve Bank of Minneapolis and the Packard Foundation. It is conducted by NORC at the University of Chicago, a non-partisan research organization. (NORC is not an abbreviation, it part of the organization's formal name.)

Data for the national estimates are collected using the AmeriSpeak Panel, NORC’s probability-based panel designed to be representative of the U.S. household population. Interviews are conducted with adults age 18 and over representing the 50 states and the District of Columbia. Panel members are randomly drawn from AmeriSpeak with a target of achieving 2,000 interviews in each survey. Invited panel members may complete the survey online or by telephone with an NORC telephone interviewer.

Once all the study data have been made final, an iterative raking process is used to adjust for any survey nonresponse as well as any noncoverage or under and oversampling resulting from the study specific sample design. Raking variables include age, gender, census division, race/ethnicity, education, and county groupings based on county level counts of the number of COVID-19 deaths. Demographic weighting variables were obtained from the 2020 Current Population Survey. The count of COVID-19 deaths by county was obtained from USA Facts. The weighted data reflect the U.S. population of adults age 18 and over.

Data for the regional estimates are collected using a multi-mode address-based (ABS) approach that allows residents of each area to complete the interview via web or with an NORC telephone interviewer. All sampled households are mailed a postcard inviting them to complete the survey either online using a unique PIN or via telephone by calling a toll-free number. Interviews are conducted with adults age 18 and over with a target of achieving 400 interviews in each region in each survey.Additional details on the survey methodology and the survey questionnaire are attached below or can be found at https://www.covid-impact.org.

Attribution

Results should be credited to the COVID Impact Survey, conducted by NORC at the University of Chicago for the Data Foundation.

AP Data Distributions

​To learn more about AP's data journalism capabilities for publishers, corporations and financial institutions, go here or email kromano@ap.org.

Mortality in patients with COVID-19 versus non-COVID-19- related acute respiratory distress syndrome: A single center retrospective observational cohort study.

1. SPSS Raw data before Propensity score matching. Including baseline and outcome
2. SPSS Raw data after Propensity score mathcing. Including baseline and outcome

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%).

Abstract (en): The purpose of this data collection is to provide an official public record of the business of the federal courts. The data originate from 94 district and 12 appellate court offices throughout the United States. Information was obtained at two points in the life of a case: filing and termination. The termination data contain information on both filing and terminations, while the pending data contain only filing information. For the appellate and civil data, the unit of analysis is a single case. The unit of analysis for the criminal data is a single defendant. 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: Performed consistency checks.; Standardized missing values.; Checked for undocumented or out-of-range codes.. All federal court cases, 1970-2000. 2012-05-22 All parts are being moved to restricted access and will be available only using the restricted access procedures.2005-04-29 The codebook files in Parts 57, 94, and 95 have undergone minor edits and been incorporated with their respective datasets. The SAS files in Parts 90, 91, 227, and 229-231 have undergone minor edits and been incorporated with their respective datasets. The SPSS files in Parts 92, 93, 226, and 228 have undergone minor edits and been incorporated with their respective datasets. Parts 15-28, 34-56, 61-66, 70-75, 82-89, 96-105, 107, 108, and 115-121 have had identifying information removed from the public use file and restricted data files that still include that information have been created. These parts have had their SPSS, SAS, and PDF codebook files updated to reflect the change. The data, SPSS, and SAS files for Parts 34-37 have been updated from OSIRIS to LRECL format. The codebook files for Parts 109-113 have been updated. The case counts for Parts 61-66 and 71-75 have been corrected in the study description. The LRECL for Parts 82, 100-102, and 105 have been corrected in the study description.2003-04-03 A codebook was created for Part 105, Civil Pending, 1997. Parts 232-233, SAS and SPSS setup files for Civil Data, 1996-1997, were removed from the collection since the civil data files for those years have corresponding SAS and SPSS setup files.2002-04-25 Criminal data files for Parts 109-113 have all been replaced with updated files. The updated files contain Criminal Terminations and Criminal Pending data in one file for the years 1996-2000. Part 114, originally Criminal Pending 2000, has been removed from the study and the 2000 pending data are now included in Part 113.2001-08-13 The following data files were revised to include plaintiff and defendant information: Appellate Terminations, 2000 (Part 107), Appellate Pending, 2000 (Part 108), Civil Terminations, 1996-2000 (Parts 103, 104, 115-117), and Civil Pending, 2000 (Part 118). The corresponding SAS and SPSS setup files and PDF codebooks have also been edited.2001-04-12 Criminal Terminations (Parts 109-113) data for 1996-2000 and Criminal Pending (Part 114) data for 2000 have been added to the data collection, along with corresponding SAS and SPSS setup files and PDF codebooks.2001-03-26 Appellate Terminations (Part 107) and Appellate Pending (Part 108) data for 2000 have been added to the data collection, along with corresponding SAS and SPSS setup files and PDF codebooks.1997-07-16 The data for 18 of the Criminal Data files were matched to the wrong part numbers and names, and now have been corrected. Funding insitution(s): United States Department of Justice. Office of Justice Programs. Bureau of Justice Statistics. (1) Several, but not all, of these record counts include a final blank record. Researchers may want to detect this occurrence and eliminate this record before analysis. (2) In July 1984, a major change in the recording and disposition of an appeal occurred, and several data fields dealing with disposition were restructured or replaced. The new structure more clearly delineates mutually exclusive dispositions. Researchers must exercise care in using these fields for comparisons. (3) In 1992, the Administrative Office of the United States Courts changed the reporting period for statistical data. Up to 1992, the reporting period...

These data are part of NACJD's Fast Track Release and are distributed as they there received from the data depositor. The files have been zipped by NACJD for release, but not checked or processed except of the removal of direct identifiers. Users should refer to the accompany readme file for a brief description of the files available with this collections and consult the investigator(s) if further information is needed.The study used a multi-method approach including 1. a process evaluation in all eight sites involving yearly site visits from 2012 to 2014 with key stakeholder interviews, observations, and participant focus groups; 2. a prospective impact evaluation (in four sites) including interviews at release from jail or prison and at 12 months after release (as well as oral swab drug tests) with reentry court participants and a matched comparison group; 3. a recidivism impact evaluation (in seven sites) with a matched comparison group tracking recidivism for 2 years post reentry court entry and 4. a cost-benefit evaluation (in seven sites) involving a transactional and institutional cost analysis (TICA) approach. Final administrative data were collected through the end of 2016.This collection includes four SPSS data files: "interview_archive2.sav" with 746 variables and 412 cases, "NESCCARC_Archive_File_3.sav" with 518 variables and 3,710 cases, "Interview Data1.sav" with 1,356 variables and 412 cases, "NESCCARC Admin Data File.sav" with 517 variables and 3,710 cases, and three SPSS syntax files: "Interview Syntax.sps", "archive_2-17.sps", and "NESCCARC Admin Data Syntax.sps".

The project aimed to understand whether young adults who take care of a loved-one (young adult caregivers; YACs) differ in their perceived life balance and psychosocial functioning as compared to young adults without care responsibilities (non-YACs). In addition, this project aimed to understand how YACs evaluated a tool to support informal careg

ivers. This tool (“Caregiver Balance”; https://balans.mantelzorg.nl) is specifically designed to support informal caregivers taking care of a loved-one in the palliative phase and could potentially be adapted to meet the needs of YACs.

In this project, we collected data of 74 YACs and 246 non-YACs. Both groups completed questionnaires, and the YACs engaged in a usability test. The questionnaire data was used to compare the perceived life balance and psychological functioning between YACs and non-YACs, aged 18-25 years, and studying in the Netherlands (study 1). Furthermore, we examined the relationship between positive aspects of caregiving and relational factors, in particular, relationship quality and collaborative coping among YACs (study 2). Finally, we conducted a usability study where we interviewed YACs to understand the needs and preferences towards a supportive web-based solution (study 3).

Table: Study details and associated files

    Number
    Study Name
    Study Aim
    Study Type
    Type of data
    Associated Files


    1
    Perceived life balance among young adult students: a comparison between caregivers and non-caregivers
    Compare the perceived life balance and psychological functions among student young adult caregivers aged 18-25 years (YACs) with young adult without care responsibilities
    Survey study
    Quantitative

ENTWINE_YACs_nonYACsSurvey_RawData

ENTWINE_PerceivedLifeBalanceSurvey_YACs_nonYACs_CleanedData

ENTWINE_ PerceivedLifeBalanceSurvey _Syntax

ENTWINE_YACs_nonYACsSurvey_codebook

    2
    Examining the relationship of positive aspects of caregiving with relational factors among young adult caregivers
    Examine the relationship of positive aspects of caregiving with relational factors, in particular, relationship quality and collaborative coping among a particular group of ICGs, young adult caregivers (YACs), aged 18-25 years.
    Survey study
    Quantitative

ENTWINE_YACs_nonYACsSurvey_RawData

ENTWINE_PositiveAspectsCaregiving_Survey_YACs_cleanedData

ENTWINE_PositiveAspectsCaregiving_Survey_Syntax

ENTWINE_YACs_nonYACsSurvey_codebook

    3
    Exploring the support needs of young adult caregivers, their issues, and preferences towards a web-based tool
    Explore (i) challenges and support needs of YACs in caregiving, (ii) their needs towards the content of the ‘MantelzorgBalans’ tool, and (iii) issues they encountered in using the tool and their preferences for adaptation of the tool.
    Usability study

Qualitative and Quantitative

ENTWINE_Needs_Web-basedTools_YACs_Interview_Usability_RawData [to be determined whether data can be shared]

ENTWINE_Needs_Web-basedTools_YACs_Questionnaires_RawData

Description of the files to be uploaded

Study 1: Perceived life balance among young adult students: a comparison between caregivers and non-caregivers

ENTWINE_YACs_nonYACsSurvey_RawData: SPSS file with the complete, raw, pseudonomyzed survey data. The following cleaned dataset ‘ENTWINE_PerceivedLifeBalanceSurvey_YACs_nonYACs_CleanedData’ was generated from this raw data.

ENTWINE_PerceivedLifeBalanceSurvey_YACs_nonYACs_CleanedData: SPSS file with the cleaned dataset having the following metadata -

Population: young adult caregivers and young adult non-caregivers aged 18-25 years studying in the Netherlands;

Number of participants: 320 participants in total (74 young adult caregivers and 246 young adult non-caregivers)

Time point of measurement: Data was collected from December 2020 till March 2022

Type of data: quantitative

Measurements included, topics covered: perceived life balance (based on the Occupational balance questionnaire [1]), burnout (Burnout Assessment Tool [2]), negative and positive affect (Positive and Negative Affect Schedule [3]), and life satisfaction (Satisfaction with Life Scale [4])

Short procedure conducted to receive data: online survey on Qualtrics platform

SPSS syntax file ‘ENTWINE_ PerceivedLifeBalanceSurvey _Syntax’ was used to clean and analyse ENTWINE_PerceivedLifeBalanceSurvey_YACs_nonYACs_CleanedData dataset

ENTWINE_YACs_nonYACsSurvey_codebook: Codebook having the variable names, variable labels, and the associated code values and code labels for ENTWINE_PerceivedLifeBalanceSurvey_YACs_nonYACs_CleanedData dataset

Study 2: Examining the relationship of positive aspects of caregiving with relational factors among young adult caregivers

ENTWINE_YACs_nonYACsSurvey_RawData: SPSS file with the complete, raw survey data. The following cleaned dataset ‘ENTWINE_PositiveAspectsCaregiving_Survey_YACs_cleanedData’ was generated from this raw data.

ENTWINE_PositiveAspectsCaregiving_Survey_YACs_cleanedData: SPSS file with the cleaned dataset having the following metadata -

Population: young adult caregivers aged 18-25 years studying in the Netherlands;

Number of participants: 74 young adult caregivers

Time point of measurement: Data was collected from December 2020 till March 2022

Type of data: quantitative

Measurements included, topics covered: positive aspects of caregiving (positive aspects of caregiving scale [5]), relationship quality (Relationship Assessment Scale [6]), collaborative coping (Perception of Collaboration Questionnaire [7] )

Short procedure conducted to receive data: online survey on Qualtrics platform.

SPSS syntax file ‘ENTWINE_PositiveAspectsCaregiving_Survey_Syntax’ was used to clean and analyse ‘ENTWINE_PositiveAspectsCaregiving_Survey_YACs_cleanedData’ dataset.

ENTWINE_YACs_nonYACsSurvey_codebook: Codebook having the variable names, variable labels, and the associated code values and code labels for ENTWINE_PositiveAspectsCaregiving_Survey_YACs_cleanedData dataset.

Study 3: Exploring the support needs of young adult caregivers, their issues, and preferences towards a web-based tool

ENTWINE_Needs_Web-basedTools_YACs_Interview_Usability_RawData: Pseudonymized word file including 13 transcripts having the qualitative data from interview and usability testing with the following metadata –

Population: young adult caregivers aged 18-25 years studying in the Netherlands; 13 participants in total

Time point of measurement: data was collected from October 2021 till February 2022

Type of data: qualitative and quantitative

Measurements included, topics covered: Caregiving challenges, support needs and barriers, usability needs, preferences and issues towards eHealth tool

Short procedure conducted to receive data: Online interviews

ENTWINE_Needs_Web-basedTools_YACs_Questionnaires_RawData: Excel sheet having the quantitative questionnaire raw data with the following metadata

Population: young adult caregivers aged 18-25 years studying in the Netherlands; 13 participants in total

Time point of measurement: data was collected from October 2021 till February 2022

Type of data: qualitative and quantitative

Measurements included, topics covered: User experience (user experience questionnaire [8]), satisfaction of using the web-based tool (After scenario questionnaire [9]), Intention of use and persuasive potential of the eHealth tool (persuasive potential questionnaire [10])

Short procedure conducted to receive data: Online questionnaire

Data collection details

All data was collected, processed, and archived in accordance with the General Data Protection Regulation (GDPR) and the FAIR (Findable, Accessible, Interoperable, Reusable) principles under the supervision of the Principal Investigator.

The principal researcher and a team of experts (supervisors) in the field of health psychology and eHealth (University of Twente, The Netherlands) reviewed the scientific quality of the research. The studies were piloted and tested before starting the collection of the data. For the survey study, the researchers monitored the data collection weekly to ensure it was running smoothly.

The ethical review board, Centrale Ethische Toetsingscommissie of the University Medical Center Groningen, The Netherlands (CTc), granted approval for this research (Registration number: 202000623).

Participants digitally signed informed consent for participating in the study.

Terms of use

Interested persons can send a data request by contacting the principal investigator (Prof. dr. Mariët Hagedoorn, University Medical Center Groningen, the Netherlands mariet.hageboorn@umcg.nl).

Interested persons must provide the research plan (including the research question, methodology, and analysis plan) when requesting for the data.

The principal investigator reviews the research plan on its quality and fit with the data and informs the interested person(s).

(Pseudo)anonymous data of those participants who agreed on the reuse of their data is available on request for 15 years from the time of completion of the PhD project.

Data will be available in Excel or SPSS format alongside the variable codebook after the completion of this PhD project and publication of the study results.

References

1. Wagman P, Håkansson C. Introducing the Occupational Balance Questionnaire (OBQ). Scand J Occup Ther 2014;21(3):227–231. PMID:24649971

2. Schaufeli WB, Desart S, De Witte H. Burnout assessment tool (Bat)—development, validity, and reliability. Int J Environ Res Public Health 2020;17(24):1–21. PMID:33352940

3. Watson D, Clark LA, Tellegen A. Development and Validation of Brief Measures of Positive and Negative Affect: The

Research Hypothesis H1: There would be a lack of IT employees' knowledge about the importance of good neck posture, isometric and stretching exercises. H2: Isometric and stretching exercises would be a good effect on improving neck pain

Abstract Background: Neck pain is one of the most common musculoskeletal complaints among men and women specifically those working on a computer. Aim to evaluate the effect of an educational program about isometric and stretching exercises on neck pain among Information Technology employees at new Assuit city. Method and material: Quasi-experimental research design and a single population proportion formula to calculate sample size required for the study through using Open Epi, Version 3. Total final size 118 employee and the program was implemented on (73) employees having neck pain according exclusion criteria. Period of collecting data was from the mid of April 2021 to mid of December 2021. Three tools were used, a tool I: A structured questionnaire which consisted of three parts: 1st part: socio-demographic data, 2nd part: assessment of the nature of the work, and 3rd part: assessment of knowledge of employees. Tool (II): Neck Pain Questionnaire(NPQ) was used to evaluate the degree of neck pain and functional disability, tool (III): observational checklist. Data entry and data analysis were done using SPSS version 22 (Statistical Package for Social Science) (SPSS Inc., Chicago, II., USA). Data were presented as number, percentage, mean, standard deviation, median and range. Chi-square test was used to compare qualitative variables. In case of parametric data, Paired samples t-test was done to compare quantitative data between pre-test and post-test. Pearson correlation was done to measure correlation between quantitative variables. While in case of non-parametric data, Wilcoxon Signed Rank Test was done to compare quantitative variables between pre-test and post-test. P-value considered statistically significant when P < 0.05.

Abstract

The Vietnam Multiple Indicator Cluster Survey (MICS 2011) was conducted from December 2010 to January 2011 by the General Statistics Office of Vietnam, in collaboration with the Ministry of Health (MOH) and the Ministry of Labour, Invalids and Social Affairs (MOLISA). Financial and technical support for the survey was provided by the United Nations Children's Fund (UNICEF). Financial support was also provided by the United Nations Population Fund (UNFPA) in Vietnam.

MICS 2011 gives valuable information and the latest evidence on the situation of children and women in Vietnam, updating information from the previous 2006 Vietnam MICS survey as well as earlier data collected in the first two MICS rounds carried out in 1996 and 2000.

The survey presents data from an equity perspective by indicating disparities by sex, region, area, ethnicity, living standards and other characteristics. MICS 2011 is based on a sample of 11,614 households interviewed and provides a comprehensive picture of children and women in Vietnam's six regions.

Geographic coverage

National

Analysis unit

• individuals,
• households.

Universe

The survey covered all de jure household members (usual residents), all women aged between 15-49 years, all children under 5 living in the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

The primary objective of the sample design for the Vietnam MICS 2011 was to produce statistically reliable estimates of most indicators, at the national level, for urban and rural areas, and for the six regions of Vietnam: Red River Delta, Northern Midlands and Mountainous areas, North Central area and Central Coastal area, Central Highlands, South East and Mekong River Delta. Urban and rural areas in each of the six regions were designated as the sampling strata.

A multi-stage, stratified cluster sampling approach was used for the selection of the survey sample.

The target sample size for the Vietnam MICS 2011 was calculated as 12,000 households. For the calculation of the sample size, the key indicator used was the underweight prevalence among children aged 0-4 years.

The resulting number of households from this exercise was 2,050 households which is the sample size needed in each region - thus yielding about 12,000 in total. The average number of households selected per cluster for the Vietnam MICS 2011 was determined as 20 households, based on a number of considerations, including the design effect, the budget available, and the time that would be needed per team to complete one cluster. Dividing the total number of households by the number of sample households per cluster, it was calculated that 100 sample clusters would need to be selected in each region.

Equal allocation of the total sample size to the six regions was used. Therefore, 100 clusters were allocated to each region, with the final sample size calculated at 12,000 households (100 clusters * 6 regions * 20 sample households per cluster). In each region, the clusters (primary sampling units) were distributed to urban and rural domains, proportional to the size of urban and rural populations in that region.

The sampling procedures are more fully described in "Multiple Indicator Cluster Survey 2011 - Final Report" pp.215-218.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaires for the Generic MICS were structured questionnaires based on the MICS4 model questionnaire with some modifications and additions. Household questionnaires were administered to a knowledgeable adult living in the household. The household questionnaire includes household listing form, education, water and sanitation, household characteristics, insecticide treated bednets, indoor residual spraying, child labour, child discipline, handwashing and salt Iodisation.

In addition to a household questionnaire, questionnaires were administered in each household for women age 15-49 and children under age five. The questionnaire for children under 5 years of age was administered to mothers or caregivers of all children under 5 years of age living in the households.

The women's questionnaire includes woman's background, child mortality, desire for last birth, maternal and newborn health, illness symptoms, contraception, unmet need, attitudes toward domestic violence, marriage/union, sexual behavior and HIV/AIDS.

The children's questionnaire includes child's age, birth registration, early childhood development, breastfeeding, care of illness, malaria, immunization and anthropometry.

Cleaning operations

Data were entered using CSPro software on eight small computers. Ten operators working in shifts performed data entry under supervision of two data entry supervisors. In order to ensure quality control, all questionnaires were double entered and internal consistency checks were performed. Procedures and standard programs developed under the global MICS 4 programme and adapted to the Viet Nam questionnaire were used throughout. Data processing began on 27 December 2010 and was completed on 21 March 2011. Data were analysed using the Statistical Package for Social Sciences (SPSS) software program, Version 19. The model syntax and tabulation plans developed by UNICEF were used for this purpose.

Response rate

Of the 12,000 households selected for the sample, 11,642 were present at the time of the survey. Of these, 11,614 successfully completed the interview, resulting in a household response rate of 99.8 percent. In the interviewed households, 12,115 women (aged 15-49 years) were identified. Of these, 11,663 completed the interview, yielding a response rate of 96.3 percent compared to eligible respondents in interviewed households. In addition, 3,729 children under 5 years were listed in the household questionnaire. Questionnaires were completed for 3,678 of these children, which corresponds to a response rate of 98.6 percent within interviewed households. The overall response rates (household response rate times the woman and child response rates within households) were 96 and 98.4 percent for the survey of women and of children under 5 years of age, respectively.

Sampling error estimates

Sampling errors are a measure of the variability between the estimates from all possible samples. The extent of variability is not known exactly, but can be estimated statistically from the survey data.

The following sampling error measures are presented in this appendix for each of the selected indicators: - Standard error (se): Sampling errors are usually measured in terms of standard errors for particular indicators (means, proportions etc). Standard error is the square root of the variance of the estimate. The Taylor linearization method is used for the estimation of standard errors. - Coefficient of variation (se/r) is the ratio of the standard error to the value of the indicator, and is a measure of the relative sampling error. - Design effect (deff) is the ratio of the actual variance of an indicator, under the sampling method used in the survey, to the variance calculated under the assumption of simple random sampling. The square root of the design effect (deft) is used to show the efficiency of the sample design in relation to the precision. A deft value of 1.0 indicates that the sample design is as efficient as a simple random sample, while a deft value above 1.0 indicates the increase in the standard error due to the use of a more complex sample design. - Confidence limits are calculated to show the interval within which the true value for the population can be reasonably assumed to fall, with a specified level of confidence. For any given statistic calculated from the survey, the value of that statistic will fall within a range of plus or minus two times the standard error (r + 2.se or r – 2.se) of the statistic in 95 percent of all possible samples of identical size and design.

For the calculation of sampling errors from MICS data, SPSS Version 18 Complex Samples module has been used. The results are shown in the tables that follow. In addition to the sampling error measures described above, the tables also include weighted and unweighted counts of denominators for each indicator.

Sampling errors are calculated for indicators of primary interest, for the national level, for the regions, and for urban and rural areas. Three of the selected indicators are based on households, 8 are based on household members, 13 are based on women, and 15 are based on children under 5. All indicators presented here are in the form of proportions.

Data appraisal

A series of data quality tables are available to review the quality of the data and include the following:

• Age distribution of the household population
• Age distribution of eligible and interviewed women
• Age distribution of children under 5 in household and children under 5 questionnaires
• Completeness of reporting
• Completeness of information for anthropometric indicators
• Heaping in anthropometric measurements
• Observation of bednets places for hand washing
• Observation of women's health cards
• Observation of children under 5 birth certificates
• Observation of vaccination cards
• Presence of mother in the household and the person interviewed for the under-5 questionnaire
• Selection of children age 2-14 years for the child discipline module
• School attendance by single age
• Sex ratio at birth among children ever born and living

The results of each of these data quality tables are shown in appendix D in document "Multiple Indicator Cluster Survey 2011 - Final Report"

SPSS data bankThe de-identified data presented here has been used for the analyses reported in the manuscript "NIGHTTIME INTENSIVE CARE UNIT DISCHARGE AND OUTCOMES: A PROPENSITY MATCHED RETROSPECTIVE COHORT STUDY", including baseline characteristics of study participants and the main clinical outcomes before propensity score matching.Data_bank.sav

Data of three studies reported in the above manuscript. All datafiles are provided in MLWIN/WSZ-format and in SPSS-file (Dataverse does not support MLWIN/WSZ-files but the data were analyzed in this program rather than in SPSS). For each study (1, 2, 3) two datafiles are uploaded, one for the accuracy scores and one for the reaction times. An Excel-file with information about the stimulus materials (e.g., word frequency of the manipulated words) has also been uploaded.

SPSS raw data of our matched pair anaysis

Data analysis for study1.sav and study2.sav were analyzed using SPSS software.Study 1 used the self-mother perceptual matching task, and measured whether there was a self- (mother-) prioritization effect in abstinent heroin misusers and healthy controls. The data contained participants' performance in three conditions: self-matched, mother-matched, and stranger-matched. The dependent variables were accuracy and response time.Study 2 used the self-referential memory task, and measured whether there was a self- (mother-) reference effect in abstinent heroin misusers and healthy controls. The data contained participants' performance in three conditions: self-reference, mother-reference, and stranger-reference. The dependent variable was recognition accuracy.

SPSS-dataset of the gaze data of the 4.5-month-old German and Swedish infants in our cross-cultural eye-tracking study

The objective of this research was to examine the Love-Hate and Identification Relationship of Individuals Participating in Euroleague Match for Recreational Purposes. The study was conducted using a relational survey methodology. The study’s population comprises persons who watching recreational purpose part in the Euroleague match held in Istanbul in 2023–2024 season, while the sample consists of 178 voluntary participants selected through convenience sampling. The participants completed the Fan Love-Hate Scale and Fan Identification Scale, in addition to being asked about their gender, marital status, age, educational status, and frequency of attending football matches per week. The data collected from the personal information form and scales was entered into the IBM SPSS 24.0 software package for analysis. Statistical analyses were conducted using the Independent Sample T test and One-way Anova methods. The LSD test was employed to ascertain the dissimilarity between the groups. The Pearson correlation analysis was utilized to ascertain the association between the variables of love-hate and identity. In summary, it is evident that demographic factors, including gender and age, significantly influence fan perceptions and sports identification. In contrast, there is no substantial correlation observed between attributes such as level of education achieved and the frequency of engaging in sports activities, and the aforementioned outcomes. The significant associations identified between the Fan Love-Hate Scale and the Sports Fan Identification Scale underscore the complex relationship between fans’ emotional experiences and their connection to sports. Further investigations could be conducted to go deeper into the underlying causes that contribute to these relationships and inequalities, so resulting in a more thorough understanding of fan psychology.