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.

Integrated Postsecondary Education Data System (IPEDS) Complete Data Files from 1980 to 2023. Includes data file, STATA data file, SPSS program, SAS program, STATA program, and dictionary. All years compressed into one .zip file due to storage limitations.Updated on 2/14/2025 to add Microsoft Access Database files.From IPEDS Complete Data File Help Page (https://nces.ed.gov/Ipeds/help/complete-data-files):Choose the file to download by reading the description in the available titles. Then, click on the link in that row corresponding to the column header of the type of file/information desired to download.To download and view the survey files in basic CSV format use the main download link in the Data File column.For files compatible with the Stata statistical software package, use the alternate download link in the Stata Data File column.To download files with the SPSS, SAS, or STATA (.do) file extension for use with statistical software packages, use the download link in the Programs column.To download the data Dictionary for the selected file, click on the corresponding link in the far right column of the screen. The data dictionary serves as a reference for using and interpreting the data within a particular survey file. This includes the names, definitions, and formatting conventions for each table, field, and data element within the file, important business rules, and information on any relationships to other IPEDS data.For statistical read programs to work properly, both the data file and the corresponding read program file must be downloaded to the same subdirectory on the computer’s hard drive. Download the data file first; then click on the corresponding link in the Programs column to download the desired read program file to the same subdirectory.When viewing downloaded survey files, categorical variables are identified using codes instead of labels. Labels for these variables are available in both the data read program files and data dictionary for each file; however, for files that automatically incorporate this information you will need to select the Custom Data Files option.

This dataset consists of three data folders including all related documents of the online survey conducted within the NESP 3.2.3 project (Tropical Water Quality Hub) and a survey format document representing how the survey was designed. Apart from participants’ demographic information, the survey consists of three sections: conjoint analysis, picture rating and open question. Correspondent outcome of these three sections are downloaded from Qualtrics website and used for three different data analysis processes.

Related data to the first section “conjoint analysis” is saved in the Conjoint analysis folder which contains two sub-folders. The first one includes a plan file of SAV. Format representing the design suggestion by SPSS orthogonal analysis for testing beauty factors and 9 photoshoped pictures used in the survey. The second (i.e. Final results) contains 1 SAV. file named “data1” which is the imported results of conjoint analysis section in SPSS, 1 SPS. file named “Syntax1” representing the code used to run conjoint analysis, 2 SAV. files as the output of conjoint analysis by SPSS, and 1 SPV file named “Final output” showing results of further data analysis by SPSS on the basis of utility and importance data.

Related data to the second section “Picture rating” is saved into Picture rating folder including two subfolders. One subfolder contains 2500 pictures of Great Barrier Reef used in the rating survey section. These pictures are organised by named and stored in two folders named as “Survey Part 1” and “Survey Part 2” which are correspondent with two parts of the rating survey sections. The other subfolder “Rating results” consist of one XLSX. file representing survey results downloaded from Qualtric website.

Finally, related data to the open question is saved in “Open question” folder. It contains one csv. file and one PDF. file recording participants’ answers to the open question as well as one PNG. file representing a screenshot of Leximancer analysis outcome.

Methods: This dataset resulted from the input and output of an online survey regarding how people assess the beauty of Great Barrier Reef. This survey was designed for multiple purposes including three main sections: (1) conjoint analysis (ranking 9 photoshopped pictures to determine the relative importance weights of beauty attributes), (2) picture rating (2500 pictures to be rated) and (3) open question on the factors that makes a picture of the Great Barrier Reef beautiful in participants’ opinion (determining beauty factors from tourist perspective). Pictures used in this survey were downloaded from public sources such as websites of the Tourism and Events Queensland and Tropical Tourism North Queensland as well as tourist sharing sources (i.e. Flickr). Flickr pictures were downloaded using the key words “Great Barrier Reef”. About 10,000 pictures were downloaded in August and September 2017. 2,500 pictures were then selected based on several research criteria: (1) underwater pictures of GBR, (2) without humans, (3) viewed from 1-2 metres from objects and (4) of high resolution.

The survey was created on Qualtrics website and launched on 4th October 2017 using Qualtrics survey service. Each participant rated 50 pictures randomly selected from the pool of 2500 survey pictures. 772 survey completions were recorded and 705 questionnaires were eligible for data analysis after filtering unqualified questionnaires. Conjoint analysis data was imported to IBM SPSS using SAV. format and the output was saved using SPV. format. Automatic aesthetic rating of 2500 Great Barrier Reef pictures –all these pictures are rated (1 – 10 scale) by at least 10 participants and this dataset was saved in a XLSX. file which is used to train and test an Artificial Intelligence (AI)-based system recognising and assessing the beauty of natural scenes. Answers of the open-question were saved in a XLSX. file and a PDF. file to be employed for theme analysis by Leximancer software.

Further information can be found in the following publication: Becken, S., Connolly R., Stantic B., Scott N., Mandal R., Le D., (2018), Monitoring aesthetic value of the Great Barrier Reef by using innovative technologies and artificial intelligence, Griffith Institute for Tourism Research Report No 15.

Format: The Online survey dataset includes one PDF file representing the survey format with all sections and questions. It also contains three subfolders, each has multiple files. The subfolder of Conjoint analysis contains an image of the 9 JPG. Pictures, 1 SAV. format file for the Orthoplan subroutine outcome and 5 outcome documents (i.e. 3 SAV. files, 1 SPS. file, 1 SPV. file). The subfolder of Picture rating contains a capture of the 2500 pictures used in the survey, 1 excel file for rating results. The subfolder of Open question includes 1 CSV. file, 1 PDF. file representing participants’ answers and one PNG. file for the analysis outcome.

Data Dictionary:

Card 1: Picture design option number 1 suggested by SPSS orthogonal analysis. Importance value: The relative importance weight of each beauty attribute calculated by SPSS conjoint analysis. Utility: Score reflecting influential valence and degree of each beauty attribute on beauty score. Syntax: Code used to run conjoint analysis by SPSS Leximancer: Specialised software for qualitative data analysis. Concept map: A map showing the relationship between concepts identified Q1_1: Beauty score of the picture Q1_1 by the correspondent participant (i.e. survey part 1) Q2.1_1: Beauty score of the picture Q2.1_1 by the correspondent participant (i.e. survey part 2) Conjoint _1: Ranking of the picture 1 designed for conjoint analysis by the correspondent participant

References: Becken, S., Connolly R., Stantic B., Scott N., Mandal R., Le D., (2018), Monitoring aesthetic value of the Great Barrier Reef by using innovative technologies and artificial intelligence, Griffith Institute for Tourism Research Report No 15.

Data Location:

This dataset is filed in the eAtlas enduring data repository at: data esp3\3.2.3_Aesthetic-value-GBR

Dataset with parents' quantitative responses collected within the impact study of the project Parental Engagement and Relationships (PEAR) in Early Childhood (EC).

The file (.sav) can be opened using IBM SPSS Software. The file is named using the following naming convention: Project acronym_Date (YYYYMMDD)_Study_Type of data_Type of participant_Version number of the file.

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 890925.

Raw data file.xlsx and.sav format of raw data of the study that is available by Excel and SPSS software.

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

Este artículo analiza los patrones ofensivos después de los tiempos muertos (ATOs) en momentos críticos de los partidos de la temporada 2022/23 de la EuroLeague masculina. Utilizando metodología observacional y herramientas estadísticas avanzadas, se evaluaron 365 ATOs de 169 partidos cerrados (diferencia final de 10 puntos o menos). Los hallazgos destacan que los equipos líderes finalizan las jugadas con mayor éxito a través de tiros libres tras faltas, mientras que los equipos perdedores tienden a emplear estrategias ofensivas más rápidas, como bandejas y triples. Estos resultados ofrecen a entrenadores y personal técnico información clave para optimizar decisiones tácticas en momentos de alta presión. Además, el estudio subraya la importancia de entrenar estas jugadas en condiciones que simulen la intensidad física y psicológica de la competición real.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.---------------------------This article examines offensive patterns after timeouts (ATOs) during critical moments of the 2022/23 men's EuroLeague season. Using observational methodology and advanced statistical tools, 365 ATOs from 169 close-score games (final point difference of 10 or fewer) were analyzed. Findings highlight that leading teams successfully conclude plays through free throws following fouls, while trailing teams often rely on quicker offensive strategies like layups and three-pointers. These insights provide coaches and technical staff with critical information to optimize tactical decisions under high-pressure conditions. The study also emphasizes the importance of training these plays in scenarios that replicate the physical and psychological intensity of real competition.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.

This workshop takes you on a quick tour of Stata, SPSS, and SAS. It examines a data file using each package. Is one more user friendly than the others? Are there significant differences in the codebooks created? This workshop also looks at creating a frequency and cross-tabulation table in each. Which output screen is easiest to read and interpret? The goal of this workshop is to give you an overview of these products and provide you with the information you need to determine whick package fits the requirements of you and your user.

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%).

Risk-taking behaviour refers to how one decides to act with the possibility of negative outcomes which is often pursued in the hope of achieving a lucrative reward, and it can happen in various circumstances, which makes it important to study and understand the key factors behind this cognitive process. The present study focused on understanding these dynamics with emotional intelligence and cognitive flexibility. The present study incorporated a healthy sample (n=121) whose emotional intelligence was assessed with MSREIS-R, risk-taking with GDT and cognitive flexibility with WCST. The findings suggested that cognitive flexibility and sub-scale of emotional intelligence did impact risk-taking behaviour, while no moderating effect was found between them. However, the sub-scales of emotional intelligence did predict cognitive flexibility hinting at their interactive relationship which does not erase the possibility of them not affecting risk-taking behaviour completely.

Emotional intelligence questionnaire and manuals, an Excel file consisting of Emotional Intelligence Score, SPSS file, Software file (Inquisit Program file), the link to the task script and other important links, Game of dice task(raw and summary data), Wisconsin Card Sorting Test (raw and summary data).

These data are part of NACJD's Fast Track Release and are distributed as they were received from the data depositor. The files have been zipped by NACJD for release, but not checked or processed except for the removal of direct identifiers. Users should refer to the accompanying readme file for a brief description of the files available with this collection and consult the investigator(s) if further information is needed. The purpose of this study was to produce knowledge about how to prevent at-risk youth from joining gangs and reduce delinquency among active gang members. The study evaluated a modification of Functional Family Therapy, a model program from the Blueprints for Healthy Youth Development initiative, to assess its effectiveness for reducing gang membership and delinquency in a gang-involved population. The collection contains 5 SPSS data files and 4 SPSS syntax files: adolpre_archive.sav (129 cases, 190 variables), adolpost_archive.sav (119 cases, 301 variables), Fidelity.archive.sav (66 cases, 25 variables), parentpre_archive.sav (129 cases, 157 variables), and parentpost_archive.sav {116 cases, 220 variables).

Dataset survey methods document and report. There is a dataset in R format plus an SPSS .sav file and an accompanying .sps syntax codefile. Running the syntax file on the .sav file should provide labels etc for the .sav file.

NATIONAL FACE - TO - FACE SURVEYS OF REPRESENTATIVE SAMPLES OF PARENTS OF ELEMENTARY SCHOOL CHILDREN IN 10 SOUTH EAST EUROPEAN COUNTRIES Center for Educational Policy Studies (CEPS) in cooperation with Open Society Institute Education Support Program

MOSES Study data definition programs for SPSS to read-in data files

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

Statistic anxiety is the feeling of worrying and tension that students experience when taking statistics courses, especially in social sciences programs. Studying statistic anxiety and the related variables is crucial because this anxiety negatively and significantly affects students’ achievement and learning.

Data was collected for a PhD research project. The project investigated factors related to academic help-seeking behaviour in higher education using quantitative and qualitative methods. Quantitative data was collected via an online survey. Qualitative data was collected via semi-structured interviews. These were conducted via video calls.

It was found that reducing stigma, increasing positive attitudes and subjective norm, ensuring satisfaction, and providing timely and targeted promotion increase engagement with academic support. Universities can use these findings to improve academic support and ultimately student success.

The data methods are available in the Open Access publications from the Related publications link below.

The de-identified quantitative dataset is stored as an SPSS file (.sav). The SPSS files have also been exported in MS Excel (.xlxs) and CSV formats with the value labels. These files are available via conditional access i.e. negotiation with the Data Manager. The SPSS variable information and labels (codebook) are saved as a PDF file and can be downloaded and viewed (for context) via the link below.

The interview recordings (.m4a) and transcripts (MS Word and PDF), SPSS Amos files (.amw) and Nvivo project (.nvp) have been archived in secure storage. Access to these files is restricted.

Software/equipment used to create/collect the data: Qualtrics, Zoom

Software/equipment used to manipulate/analyse the data: SPSS, SPSS Amos, NVivo

This includes an SPSS command file (.sps) that will execute, with comments, a series of descriptive statistics against the data file (.sav) that correspond to tables 1-5 in the manuscript *McCluskey, John and Craig Uchida (2023) "Video Data Analysis and Police Body-Worn Camera Footage" Sociological Methods and Research. The data are based on observers' assessments of BWC audio and video channels of LAPD officers' footage from encounters and events.

The syntax file should be run against the .sav file in the archive. The syntax is saved in the .txt file and should offer a guidance in how the results might be obtained in Excel or another program such as R or STATA using the CSV file stored here.

Abstract (en): This data collection provides comparable measures of state appellate and trial court caseloads by type of case for the 50 states, the District of Columbia, and Puerto Rico. Court caseloads are tabulated according to generic reporting categories developed by the Court Statistics Project Committee of the Conference of State Court Administrators. These categories describe differences in the unit of count and the point of count when compiling each court's caseload. Major areas of investigation include (1) case filings in state appellate and trial courts, (2) case processing and dispositions in state appellate and trial courts, and (3) appellate opinions. Within each of these areas of state government investigation, cases are separated by main case type, including civil cases, capital punishment cases, other criminal cases, juvenile cases, and administrative agency appeals. 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.; Checked for undocumented or out-of-range codes.. State appellate and trial court cases in the United States. 2005-11-04 On 2005-03-14 new files were added to one or more datasets. These files included additional setup files as well as one or more of the following: SAS program, SAS transport, SPSS portable, and Stata system files. The metadata record was revised 2005-11-04 to reflect these additions.2003-08-27 Part 45, Appellate Court Data, 2001, and Part 46, Trial Court Data, 2001, have been added to the data collection, along with corresponding SAS and SPSS data definition statements and PDF codebooks.2002-08-13 Part 43, Appellate Court Data, 2000, and Part 44, Trial Court Data, 2000, have been added to the data collection, along with corresponding SAS and SPSS data definition statements and PDF codebooks.2001-10-31 Part 41, Appellate Court Data, 1999, and Part 42, Trial Court Data, 1999, have been added to the data collection, along with corresponding SAS and SPSS data definition statements and PDF codebooks.2000-03-23 Part 39, Appellate Court Data, 1998, and Part 40, Trial Court Data, 1998, have been added to the data collection, along with corresponding SAS and SPSS data definition statements and PDF codebooks.1999-07-16 Part 37, Appellate Court Data, 1997, and Part 38, Trial Court Data, 1997, have been added to the data collection, along with corresponding SAS and SPSS data definition statements and PDF codebooks. Funding insitution(s): State Justice Institute (SJI-91-N-007-001-1). United States Department of Justice. Office of Justice Programs. Bureau of Justice Statistics. The Court Statistics Project Web page is: http://www.ncsconline.org/D_Research/csp/CSP_Main_Page.html.A user guide containing court codes and variable descriptions for the 1987 data and the codebooks for the 1995-2001 data are provided as Portable Document Format (PDF) files, and the codebooks for the 1988-1992 data are available in both ASCII text and PDF versions.

These data are part of NACJD's Fast Track Release and are distributed as they were received from the data depositor. The files have been zipped by NACJD for release but not checked or processed except for the removal of direct identifiers. Users should refer to the accompanying readme file for a brief description of the files available with this collection and consult the investigator(s) if further information is needed. This study identified risk and protective factors for dating violence (DV) among young adults (ages 18-22) with a history of maltreatment and placement in foster care, and who had enrolled in Fostering Healthy Futures (FHF) during 2002-2009. FHF is a Colorado-based positive youth program for maltreated youth. This study focused on factors that ameliorated the effects of risk to reduce DV perpetration and victimization in young adulthood. The participants were interviewed at three different points during the FHF time frame. That data provided a basis for determining risk and mediating factors which in turn were compared to the current study's DV outcomes. The risk and protective factors included: Mental health Substance abuse Social support Gender Stereotypes Attitudes about Teen DV Communication Skills Perpetration and victimization outcomes were then examined in relation to the risk and protective factors. The collection includes 1 SPSS file: NIJ-2013-VA-CX-0002---2nd-revision---5-17-18.sav (215 cases / 2023 variables).