This is the raw data file with which the analysis of the paper entitled ' Gratefully received, gratefully repaid: the role of perceived fairness in cooperative interactions' was carried out. Please refer to the dataset explanatory document (http://figshare.com/articles/Explanatory_Document_for_the_SPSS_Data_File_of_the_Study/1243735) for details.

SPSS Data sets for study 1 to 3

A SPSS file with data used in the statistical analysis. Covariates were excluded in the file due to restrictions of the ethical permission. However a complete file is provided for researchers after request at publication@ventorp.com. (SAV)

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

Data is based on factor analysis of newly developed tool for professionalism assessment

Data was collected from two different studies conducted for the undergraduate student E-learning fall semester 2020 experience at the University of Science and Technology Fujairah (USTF) in the United Arab Emirates. The two studies were conducted using an online questionnaire via Google Forms. Students were invited to participate voluntarily by email. They were asked to answer questions regarding their distance learning experience during their 2020 academic year.

General information: The data sets contain information on how often materials of studies available through GESIS: Data Archive for the Social Sciences were downloaded and/or ordered through one of the archive´s plattforms/services between 2004 and 2017.

Sources and plattforms: Study materials are accessible through various GESIS plattforms and services: Data Catalogue (DBK), histat, datorium, data service (and others).

Years available: - Data Catalogue: 2012-2017 - data service: 2006-2017 - datorium: 2014-2017 - histat: 2004-2017

Data sets: Data set ZA6899_Datasets_only_all_sources contains information on how often data files such as those with dta- (Stata) or sav- (SPSS) extension have been downloaded. Identification of data files is handled semi-automatically (depending on the plattform/serice). Multiple downloads of one file by the same user (identified through IP-address or username for registered users) on the same days are only counted as one download.

Data set ZA6899_Doc_and_Data_all_sources contains information on how often study materials have been downloaded. Multiple downloads of any file of the same study by the same user (identified through IP-address or username for registered users) on the same days are only counted as one download.

Both data sets are available in three formats: csv (quoted, semicolon-separated), dta (Stata v13, labeled) and sav (SPSS, labeled). All formats contain identical information.

Variables: Variables/columns in both data sets are identical. za_nr ´Archive study number´ version ´GESIS Archiv Version´ doi ´Digital Object Identifier´ StudyNo ´Study number of respective study´ Title ´English study title´ Title_DE ´German study title´ Access ´Access category (0, A, B, C, D, E)´ PubYear ´Publication year of last version of the study´ inZACAT ´Study is currently also available via ZACAT´ inHISTAT ´Study is currently also available via HISTAT´ inDownloads ´There are currently data files available for download for this study in DBK or datorium´ Total ´All downloads combined´ downloads_2004 ´downloads/orders from all sources combined in 2004´ [up to ...] downloads_2017 ´downloads/orders from all sources combined in 2017´ d_2004_dbk ´downloads from source dbk in 2004´ [up to ...] d_2017_dbk ´downloads from source dbk in 2017´ d_2004_histat ´downloads from source histat in 2004´ [up to ...] d_2017_histat ´downloads from source histat in 2017´ d_2004_dataservice ´downloads/orders from source dataservice in 2004´ [up to ...] d_2017_dataservice ´downloads/orders from source dataservice in 2017´

More information is available within the codebook.

The SPSS data file and output file for the study "Relationship Between Big Five Personality Traits and Attitudes Towards Artificial Intelligence".

These two syntax files were used to convert the SPSS data output from the Qualtrics survey tool into the 17 cleansed and anonymised RAAAP-2 datasets form the 2019 international survey of research managers and administrators. The first creates and interim cleansed and anonymised datafile, the latter splits these into separate datasets to ensure anonymisation. Errata (16/6/23): v13 of the main Data Cleansing file has an error (two variables were missing value labels). This file has now been replaced with v14, and the Main Dataset has also been updated with the new data.

SPSS DATA. This file contains the data imported wiyh the Software IBM SPSS Statistics, versión 30.

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

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.

This is a subset of data extracted from a larger data set on studnent wellness and wellbeing collected in November 2018 at a small liberal arts university in Northern California. The variables used in our manuscript submitted for publication are included in this file.

This SPPS document has been processed from an Excel spreadsheet questionnaire about the frequency, form of procrastination, and influences on behaviour when trying to undertake stages of a design process that was completed by 146 students and 9 staff within a UK design and creative arts school.

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

Data from an online questionnaire study with 145 participants self-identified as dancers who had experienced a significant dance-related injury.

Data files to be used in teaching Research Methods (MAPSYK302) at the University of Bergen - Spring 2020

This is a translation of a workshop that describes the procedures for manipulating microdata files using SPSS software. It also points out a few things to keep in mind when opening a file or data extraction created by Statistics Canada. Translation of: "Un pas de plus avec SPSS".

This data file contains raw data from questionnaires measuring attachment, pain, disability and depression in two different pain conditions. All regression analyses are based on these data. The file includes pain condition (pain_condition), age (Age), sex (Sex), living in partnership (Partnerschaft_kat), college entrance (Education), employment (Employment), sick leave (EM_Rente_jn), pain intensity last seven days (Schmerz_1_t1), raw data from HADS, FFbH, ECR-R, RQ, global attachment dimensions (RQ_t1_modelself, RQ_t1_modelother), global attachment styles (RQ_4Bindungsstile). (SAV)

This data file supports the journal article written by Tanja Sobko and Gavin Brown and which appears in 2021 in the journal Challenges. The paper is entitled The influence of connectedness to nature on psychological well-being: Evidence from the randomized controlled trial Play&Grow. Dr Sobko developed the Play&Grow intervention and collected all data. Dr Brown conducted the statistical analyses.