Analysis SPSS files used in the paper to analyze the experiment results. The tests we executed in the paper are as follows, in the SPSS syntax:** PreQuestionnaire.sav, leading to Table 2T-TEST GROUPS=form(1 2) /MISSING=ANALYSIS /VARIABLES=grade USLEC UCLEC /CRITERIA=CI(.95).NPAR TESTS /M-W= CDFAM UCFAM USFAM UCHW USHW CDHW BY form(1 2) /MISSING ANALYSIS.** Anova.sav, leading to the decision of analyzing the two case studies independentlyGLM EntRec EntPre RelRec RelPre TotRec TotPre AdjRelRec AdjRelPre AdjTotRec AdjTotPre BY Domain Form /METHOD=SSTYPE(3) /INTERCEPT=INCLUDE /POSTHOC=Domain Form(TUKEY) /PLOT=PROFILE(Domain*Form) TYPE=LINE ERRORBAR=NO MEANREFERENCE=NO YAXIS=AUTO /PRINT=DESCRIPTIVE ETASQ /CRITERIA=ALPHA(.05) /DESIGN= Domain Form Domain*Form.** DH.sav, leading to Table 3T-TEST GROUPS=Form(1 2) /MISSING=ANALYSIS /VARIABLES=EntRec EntPre RelRec RelPre TotRec TotPre AdjRelRec AdjRelPre AdjTotRec AdjTotPre /CRITERIA=CI(.95).** PH.sav, leading to Table 4T-TEST GROUPS=Form(1 2) /MISSING=ANALYSIS /VARIABLES=EntRec EntPre RelRec RelPre TotRec TotPre AdjRelRec AdjRelPre AdjTotRec AdjTotPre /CRITERIA=CI(.95).** Preferences.sav, leading to Table 5 and Table 6NPAR TESTS /M-W= UCCM USCM UCCDID USCDID UCRID USRID USSTRUCT UCSTRUCT UCOVER USOVER UCREQ USREQ BY Form(1 2) /MISSING ANALYSIS.EXAMINE VARIABLES=UCCM USCM UCCDID USCDID UCRID USRID USSTRUCT UCSTRUCT UCOVER USOVER UCREQ USREQ BY Form /PLOT HISTOGRAM NPPLOT /STATISTICS DESCRIPTIVES /CINTERVAL 95 /MISSING LISTWISE /NOTOTAL.NPAR TESTS /M-W= UCCM USCM UCCDID USCDID UCRID USRID USSTRUCT UCSTRUCT UCOVER USOVER UCREQ USREQ BY Form(1 2) /STATISTICS=DESCRIPTIVES /MISSING ANALYSIS.GLM EntRec EntPre RelRec RelPre TotRec TotPre AdjRelRec AdjRelPre AdjTotRec AdjTotPre BY Domain Form /METHOD=SSTYPE(3) /INTERCEPT=INCLUDE /POSTHOC=Domain Form(TUKEY) /PLOT=PROFILE(Domain*Form) TYPE=LINE ERRORBAR=NO MEANREFERENCE=NO YAXIS=AUTO /PRINT=DESCRIPTIVE ETASQ /CRITERIA=ALPHA(.05) /DESIGN= Domain Form Domain*Form.

GENERAL INFORMATION

Title of Dataset: A dataset from a survey investigating disciplinary differences in data citation

Date of data collection: January to March 2022

Collection instrument: SurveyMonkey

Funding: Alfred P. Sloan Foundation

SHARING/ACCESS INFORMATION

Licenses/restrictions placed on the data: These data are available under a CC BY 4.0 license

Links to publications that cite or use the data:

Gregory, K., Ninkov, A., Ripp, C., Peters, I., & Haustein, S. (2022). Surveying practices of data citation and reuse across disciplines. Proceedings of the 26th International Conference on Science and Technology Indicators. International Conference on Science and Technology Indicators, Granada, Spain. https://doi.org/10.5281/ZENODO.6951437

Gregory, K., Ninkov, A., Ripp, C., Roblin, E., Peters, I., & Haustein, S. (2023). Tracing data:
A survey investigating disciplinary differences in data citation. Zenodo. https://doi.org/10.5281/zenodo.7555266

DATA & FILE OVERVIEW

File List

• Filename: MDCDatacitationReuse2021Codebook.pdf
  Codebook
• Filename: MDCDataCitationReuse2021surveydata.csv
  Dataset format in csv
• Filename: MDCDataCitationReuse2021surveydata.sav
  Dataset format in SPSS
• Filename: MDCDataCitationReuseSurvey2021QNR.pdf
  Questionnaire

Additional related data collected that was not included in the current data package: Open ended questions asked to respondents

METHODOLOGICAL INFORMATION

Description of methods used for collection/generation of data:

The development of the questionnaire (Gregory et al., 2022) was centered around the creation of two main branches of questions for the primary groups of interest in our study: researchers that reuse data (33 questions in total) and researchers that do not reuse data (16 questions in total). The population of interest for this survey consists of researchers from all disciplines and countries, sampled from the corresponding authors of papers indexed in the Web of Science (WoS) between 2016 and 2020.

Received 3,632 responses, 2,509 of which were completed, representing a completion rate of 68.6%. Incomplete responses were excluded from the dataset. The final total contains 2,492 complete responses and an uncorrected response rate of 1.57%. Controlling for invalid emails, bounced emails and opt-outs (n=5,201) produced a response rate of 1.62%, similar to surveys using comparable recruitment methods (Gregory et al., 2020).

Methods for processing the data:

Results were downloaded from SurveyMonkey in CSV format and were prepared for analysis using Excel and SPSS by recoding ordinal and multiple choice questions and by removing missing values.

Instrument- or software-specific information needed to interpret the data:

The dataset is provided in SPSS format, which requires IBM SPSS Statistics. The dataset is also available in a coded format in CSV. The Codebook is required to interpret to values.

DATA-SPECIFIC INFORMATION FOR: MDCDataCitationReuse2021surveydata

Number of variables: 94

Number of cases/rows: 2,492

Missing data codes: 999 Not asked

Refer to MDCDatacitationReuse2021Codebook.pdf for detailed variable information.

The SPSS file includes the raw data as well as the generated variables. The word file explains the SPSS file and provides information on the data analyses. The data is NOT available for public use.

The survey dataset for identifying Shiraz old silo’s new use which includes four components: 1. The survey instrument used to collect the data “SurveyInstrument_table.pdf”. The survey instrument contains 18 main closed-ended questions in a table format. Two of these, concern information on Silo’s decision-makers and proposed new use followed up after a short introduction of the questionnaire, and others 16 (each can identify 3 variables) are related to the level of appropriate opinions for ideal intervention in Façade, Openings, Materials and Floor heights of the building in four values: Feasibility, Reversibility, Compatibility and Social Benefits. 2. The raw survey data “SurveyData.rar”. This file contains an Excel.xlsx and a SPSS.sav file. The survey data file contains 50 variables (12 for each of the four values separated by colour) and data from each of the 632 respondents. Answering each question in the survey was mandatory, therefor there are no blanks or non-responses in the dataset. In the .sav file, all variables were assigned with numeric type and nominal measurement level. More details about each variable can be found in the Variable View tab of this file. Additional variables were created by grouping or consolidating categories within each survey question for simpler analysis. These variables are listed in the last columns of the .xlsx file. 3. The analysed survey data “AnalysedData.rar”. This file contains 6 “SPSS Statistics Output Documents” which demonstrate statistical tests and analysis such as mean, correlation, automatic linear regression, reliability, frequencies, and descriptives. 4. The codebook “Codebook.rar”. The detailed SPSS “Codebook.pdf” alongside the simplified codebook as “VariableInformation_table.pdf” provides a comprehensive guide to all 50 variables in the survey data, including numerical codes for survey questions and response options. They serve as valuable resources for understanding the dataset, presenting dictionary information, and providing descriptive statistics, such as counts and percentages for categorical variables.

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 =

IntroductionCervical cancer is a significant public health problem for women worldwide. It is the fourth most frequent cancer in women globally. While early detection of cancerous lesions through screening tests leads to a better prognosis and a better chance of being cured, the number of people who go for screening is still low, especially for groups that are marginalized, like immigrant women.ObjectiveThe purpose of this study was to identify cervical cancer screening practices and factors influencing screening status among Yemeni immigrant women living in the Klang Valley, Malaysia.MethodA cross-sectional study among 355 randomly selected respondents between the ages of 20 and 65 was conducted through an online survey. A questionnaire was sent directly to the participants via WhatsApp. The analysis was conducted using SPSS 25 with a significance level of 0.05. It included descriptive analysis, chi-square and multiple logistic regression.ResultsThe response rate was 59%, with the majority of the respondents being married and between the ages of 35 and 49. Screening was reported at 23.1% in the previous three years. The final model revealed that age group 50–65 years (AOR = 5.39, 95% CI: 1.53–18.93), insurance status (AOR 2.22, 95% CI = 1.15–4.3), knowledge (AOR = 6.67, 95% CI = 3.45–12.9), access to health care facilities (AOR = 4.64, 95% CI = 1.29–16.65), and perceived barriers (AOR = 2.5, 95% CI = 1.3–4.83) were significant predictors of cervical screening uptake among Yemeni immigrant women in Malaysia (p<0.05).ConclusionAccording to the results, cervical cancer screening was found to be low among Yemeni immigrant women. The predictors were age group 50–65 years, insurance status, knowledge, access to health care facilities and perceived barriers. Efforts to enhance immigrant women’s participation in cervical cancer screening must tackle barriers to access to healthcare services as well as expand cervical cancer screening education programs.

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.

This dataset supports research on how engagement with social media (Instagram and TikTok) was related to problematic social media use (PSMU) and mental well-being. There are three different files. The SPSS and Excel spreadsheet files include the same dataset but in a different format. The SPSS output presents the data analysis in regard to the difference between Instagram and TikTok users.

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%).

From the massive set of V-Dem v 12 variables, we have inserted 27 in the main CPEDB dataset. Here is the entire set, organized in an excel file to match their country/year rows in the main SPSS file. This precise correspondence makes it easy to insert other variables from the V-Dem dataset into the main file where they can be statistically combined with a wide variety of variables from other sources.

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.

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

Database of the nation''s substance abuse and mental health research data providing public use data files, file documentation, and access to restricted-use data files to support a better understanding of this critical area of public health. The goal is to increase the use of the data to most accurately understand and assess substance abuse and mental health problems and the impact of related treatment systems. The data include the U.S. general and special populations, annual series, and designs that produce nationally representative estimates. Some of the data acquired and archived have never before been publicly distributed. Each collection includes survey instruments (when provided), a bibliography of related literature, and related Web site links. All data may be downloaded free of charge in SPSS, SAS, STATA, and ASCII formats and most studies are available for use with the online data analysis system. This system allows users to conduct analyses ranging from cross-tabulation to regression without downloading data or relying on other software. Another feature, Quick Tables, provides the ability to select variables from drop down menus to produce cross-tabulations and graphs that may be customized and cut and pasted into documents. Documentation files, such as codebooks and questionnaires, can be downloaded and viewed online.

The shared dataset includes study variables and covariates in the study entitled 'Substance use, psychiatric sypmtoms, personal mastery, and social suport among COVID-19 long haulers: A compensatory model'.

This dataset originates from a series of experimental studies titled “Tough on People, Tolerant to AI? Differential Effects of Human vs. AI Unfairness on Trust” The project investigates how individuals respond to unfair behavior (distributive, procedural, and interactional unfairness) enacted by artificial intelligence versus human agents, and how such behavior affects cognitive and affective trust.1 Experiment 1a: The Impact of AI vs. Human Distributive Unfairness on TrustOverview: This dataset comes from an experimental study aimed at examining how individuals respond in terms of cognitive and affective trust when distributive unfairness is enacted by either an artificial intelligence (AI) agent or a human decision-maker. Experiment 1a specifically focuses on the main effect of the “type of decision-maker” on trust.Data Generation and Processing: The data were collected through Credamo, an online survey platform. Initially, 98 responses were gathered from students at a university in China. Additional student participants were recruited via Credamo to supplement the sample. Attention check items were embedded in the questionnaire, and participants who failed were automatically excluded in real-time. Data collection continued until 202 valid responses were obtained. SPSS software was used for data cleaning and analysis.Data Structure and Format: The data file is named “Experiment1a.sav” and is in SPSS format. It contains 28 columns and 202 rows, where each row corresponds to one participant. Columns represent measured variables, including: grouping and randomization variables, one manipulation check item, four items measuring distributive fairness perception, six items on cognitive trust, five items on affective trust, three items for honesty checks, and four demographic variables (gender, age, education, and grade level). The final three columns contain computed means for distributive fairness, cognitive trust, and affective trust.Additional Information: No missing data are present. All variable names are labeled in English abbreviations to facilitate further analysis. The dataset can be directly opened in SPSS or exported to other formats.2 Experiment 1b: The Mediating Role of Perceived Ability and Benevolence (Distributive Unfairness)Overview: This dataset originates from an experimental study designed to replicate the findings of Experiment 1a and further examine the potential mediating role of perceived ability and perceived benevolence.Data Generation and Processing: Participants were recruited via the Credamo online platform. Attention check items were embedded in the survey to ensure data quality. Data were collected using a rolling recruitment method, with invalid responses removed in real time. A total of 228 valid responses were obtained.Data Structure and Format: The dataset is stored in a file named Experiment1b.sav in SPSS format and can be directly opened in SPSS software. It consists of 228 rows and 40 columns. Each row represents one participant’s data record, and each column corresponds to a different measured variable. Specifically, the dataset includes: random assignment and grouping variables; one manipulation check item; four items measuring perceived distributive fairness; six items on perceived ability; five items on perceived benevolence; six items on cognitive trust; five items on affective trust; three items for attention check; and three demographic variables (gender, age, and education). The last five columns contain the computed mean scores for perceived distributive fairness, ability, benevolence, cognitive trust, and affective trust.Additional Notes: There are no missing values in the dataset. All variables are labeled using standardized English abbreviations to facilitate reuse and secondary analysis. The file can be analyzed directly in SPSS or exported to other formats as needed.3 Experiment 2a: Differential Effects of AI vs. Human Procedural Unfairness on TrustOverview: This dataset originates from an experimental study aimed at examining whether individuals respond differently in terms of cognitive and affective trust when procedural unfairness is enacted by artificial intelligence versus human decision-makers. Experiment 2a focuses on the main effect of the decision agent on trust outcomes.Data Generation and Processing: Participants were recruited via the Credamo online survey platform from two universities located in different regions of China. A total of 227 responses were collected. After excluding those who failed the attention check items, 204 valid responses were retained for analysis. Data were processed and analyzed using SPSS software.Data Structure and Format: The dataset is stored in a file named Experiment2a.sav in SPSS format and can be directly opened in SPSS software. It contains 204 rows and 30 columns. Each row represents one participant’s response record, while each column corresponds to a specific variable. Variables include: random assignment and grouping; one manipulation check item; seven items measuring perceived procedural fairness; six items on cognitive trust; five items on affective trust; three attention check items; and three demographic variables (gender, age, and education). The final three columns contain computed average scores for procedural fairness, cognitive trust, and affective trust.Additional Notes: The dataset contains no missing values. All variables are labeled using standardized English abbreviations to facilitate reuse and secondary analysis. The file can be directly analyzed in SPSS or exported to other formats as needed.4 Experiment 2b: Mediating Role of Perceived Ability and Benevolence (Procedural Unfairness)Overview: This dataset comes from an experimental study designed to replicate the findings of Experiment 2a and to further examine the potential mediating roles of perceived ability and perceived benevolence in shaping trust responses under procedural unfairness.Data Generation and Processing: Participants were working adults recruited through the Credamo online platform. A rolling data collection strategy was used, where responses failing attention checks were excluded in real time. The final dataset includes 235 valid responses. All data were processed and analyzed using SPSS software.Data Structure and Format: The dataset is stored in a file named Experiment2b.sav, which is in SPSS format and can be directly opened using SPSS software. It contains 235 rows and 43 columns. Each row corresponds to a single participant, and each column represents a specific measured variable. These include: random assignment and group labels; one manipulation check item; seven items measuring procedural fairness; six items for perceived ability; five items for perceived benevolence; six items for cognitive trust; five items for affective trust; three attention check items; and three demographic variables (gender, age, education). The final five columns contain the computed average scores for procedural fairness, perceived ability, perceived benevolence, cognitive trust, and affective trust.Additional Notes: There are no missing values in the dataset. All variables are labeled using standardized English abbreviations to support future reuse and secondary analysis. The dataset can be directly analyzed in SPSS and easily converted into other formats if needed.5 Experiment 3a: Effects of AI vs. Human Interactional Unfairness on TrustOverview: This dataset comes from an experimental study that investigates how interactional unfairness, when enacted by either artificial intelligence or human decision-makers, influences individuals’ cognitive and affective trust. Experiment 3a focuses on the main effect of the “decision-maker type” under interactional unfairness conditions.Data Generation and Processing: Participants were college students recruited from two universities in different regions of China through the Credamo survey platform. After excluding responses that failed attention checks, a total of 203 valid cases were retained from an initial pool of 223 responses. All data were processed and analyzed using SPSS software.Data Structure and Format: The dataset is stored in the file named Experiment3a.sav, in SPSS format and compatible with SPSS software. It contains 203 rows and 27 columns. Each row represents a single participant, while each column corresponds to a specific measured variable. These include: random assignment and condition labels; one manipulation check item; four items measuring interactional fairness perception; six items for cognitive trust; five items for affective trust; three attention check items; and three demographic variables (gender, age, education). The final three columns contain computed average scores for interactional fairness, cognitive trust, and affective trust.Additional Notes: There are no missing values in the dataset. All variable names are provided using standardized English abbreviations to facilitate secondary analysis. The data can be directly analyzed using SPSS and exported to other formats as needed.6 Experiment 3b: The Mediating Role of Perceived Ability and Benevolence (Interactional Unfairness)Overview: This dataset comes from an experimental study designed to replicate the findings of Experiment 3a and further examine the potential mediating roles of perceived ability and perceived benevolence under conditions of interactional unfairness.Data Generation and Processing: Participants were working adults recruited via the Credamo platform. Attention check questions were embedded in the survey, and responses that failed these checks were excluded in real time. Data collection proceeded in a rolling manner until a total of 227 valid responses were obtained. All data were processed and analyzed using SPSS software.Data Structure and Format: The dataset is stored in the file named Experiment3b.sav, in SPSS format and compatible with SPSS software. It includes 227 rows and

The present data were used to through the LPM the effect of CLI in the acquisition of L3 English past perfect, present progressive, and present perfect tenses by L1 Kirundi-L2 French bilinguals. The subtractive language groups design was used: One trilingual (L1 Kirundi-L2 French-L3 English learners) group was compared to two bilingual (L1 Kirundi-L2 English and L1 French-L2 English learners) groups in order to derive which previously acquired language was driving CLI among L3 learners. Each language group had 30 learners distributed in four proficiency groups, namely the pre-intermediate group (6 participants), lower-intermediate group (7 participants), upper-intermediate group (11 participants), and advanced group (6 participants). Therefore, there were two independent variables (language group and proficiency group) and three continuous dependent variables which were the participants' scores on the three target structures, namely the past perfect, present progressive, and present perfect tenses.

The data were used to test the following predictions:

1. With regard to the past perfect tense (L1=L2=L3), learners of L3 English with a background knowledge in L1 Kirundi and L2 French are likely to have no difficulty in the acquisition of the said tense in English regardless of their English proficiency level; i.e. even lower proficiency learners will perform well on that tense. However, higher proficiency learners may make most correct use of this tense.

2. With regard to the present progressive tense (L1≠L3≠L2), we can predict that all the three language groups, i.e. L1 Kirundi, L1 French and L3 groups, will face difficulties in their performance on this tense. In other words, none of the previously acquired languages (neither L1 Kirundi, nor L2 French) is expected to significantly affect the performance of L3ers on the said tense. Lower proficiency learners are predicted to face most difficulty on the tense.

3. With regard to the present perfect tense (L3=L2≠L1), we can predict that the L3 group will perform similarly as the L1 French group, while the two groups are likely to outperform the L1 Kirundi group. This implies that facilitative CLI is expected from L2 French in the L3 group.

4. Considering the present research scenarios for the past perfect (L1=L2=L3), present perfect (L1=L3≠L2), and present progressive (L1≠L2≠L3) tenses, we predict CLI where L3 learners are expected to acquire the past perfect earlier than the present perfect, and the present perfect earlier than the present progressive. In other words, their performance on the past perfect tense should be significantly higher than that on the present perfect while their score on the present perfect is expected to be significantly higher than that on the present progressive.

Data were elicited through the grammaticality judgment task, and the raw data analyzed in the SPSS software using descriptive statistics, MANOVA, post-hoc comparisons, and independent samples t-tests.

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.

This is raw and analysis data for empirical study, entitled entitled “Integrating Multidimensional Dependability with the Technology Acceptance Model: A Study of Adoption of Cloud Computing at the Organizational Level”. This study investigated how perceived dependability affects user acceptance by integrating perceived dependability with the technology acceptance model in the context of cloud computing. In this study, perceived dependability was treated as a multi-dimensional variable and conceptualized as a second-order construct. A total of 216 samples (organizational managers) were analyzed using the structural equation modeling. IBM SPSS AMOS 23 tool was used for data analysis. For file 1 (1. Survey in Korean), this is a Korean version. If researchers want a English version, they can use "Appendix A." in our original article. For file 2 (2. DATASET (216)), researchers can use this for their own analysis contexts. This spss file also contains the values obtained from item parceling technique this study used. For other files (file 3, 4, 5, 6), they are the results of using Excel to calculate CR and AVE values. This data is valuable because no other research have empirically considered the multidimensional approach to dependability. These empirical data can provide academic researchers and businesses with insights on organizational level adoption of cloud computing.

Mental Health Services for Children

Monthly Statistics from England

By data.world's Admin [source]

About this dataset

This dataset provides insight into the mental health services available to children and young people in England. The data includes all primary and secondary levels of care, as well as breakdowns by age group. Information is provided on the number of people in contact with mental health services; open ward stays; open referrals; referrals starting in reporting period; attended contacts; indirect activity; discharged from referral; missed care contacts by DNA reasons and more. With these statistics, analysts may be able to better understand the scope of mental health service usage across different age groups in England and make valuable conclusions about best practices for helping children & young people receive proper care

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How to use the dataset

This guide provides information on how to use this dataset effectively.

Understanding the Columns:

Each row represents data from a specific month within a reporting period. The first thing to do is to find out what each column represents - this is explained by their titles and descriptions included at the beginning of this dataset. Note that there are primary level columns (e.g., Reporting Period, Breakdown) which provide overall context while secondary level columns (e.g., CYP01 People in contact with children and young peoples' mentally health service…) provide more detail on specific indicators of interest related to that primary level column value pair (i.e., Reporting Period X).

Exploring Data Variables:

The next step is exploring which data variables could potentially be helpful when analyzing initiatives/programs related to mental health care for children & youth in England or developing policies related to them – look through all columns included here for ones you think would be most helpful such as ‘CYP21 – Open ward stays...’ or ‘MHS07a - People with an open hospital spell…’ and note down those that have been considered necessary/relevant based on your particular situation/needs before further analyzing using software packages like Excel or SPSS etc..

Analyzing Data Values:

Now comes the time for analyzing individual values provided under each respective column – take one single numerical data element such as ‘CYP02 – People… CPA end RP’ & run through it all looking at trends over time, averages across different sections by performing calculations via software packages available like tables provided above based upon sorted hierarchies needed.. Then you can then start looking into making meaningful correlations between different pieces of information given herein by cross-referencing contexts against each other resulting if any noticeable patterns found significant enough will make informative decisions towards policy implementations & program improvement opportunities both directly concerned

Research Ideas

• Using this dataset to identify key trends in mental health services usage among children and young people in England, such as the number of open ward stays and referrals received.
• Using the information to develop targeted solutions on areas of need identified from the data by geographical area or age group, i.e creating campaigns or programs specifically targeting specific groups at a higher risk of experiencing mental health difficulties or engaging with specialist services.
• Tracking how well these initiatives are working over time by monitoring relevant metrics such as attendance at appointments, open referrals etc to evaluate their effectiveness in improving access and engagement with mental health services for those most in need

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - ...

The 2008 National Survey of Drinking and Driving Attitudes and Behaviors was composed of a single questionnaire administered to a sample of randomly selected individuals 16 and older, with ages 16 through 24 over-sampled. The respondents were asked about their drinking behavior, their drinking and driving behavior, use of designated drivers, their hosting events in which drinking occurred, risks they perceive associated with drinking and driving, experience with anti-DWI enforcement activity, and their attitudes concerning major intervention strategies.The survey was administered from September 10, 2008 to December 22, 2008. A total of 6,999 respondents completed the survey, including 5,392 landline interviews and 1,607 cell phone interviews. The total number of completed interviews for each of the four Census regions (Northeast, Midwest, South, and West) was 1,409, 1,654, 2,390, and 1,546, respectively.