Qualtrics Export Import Data. Follow the Eximpedia platform for HS code, importer-exporter records, and customs shipment details.

a MOCK dataset used to show how to import Qualtrics metadata into the codebook R package

Table of variables

This table contains variable names, labels, and number of missing values. See the complete codebook for more.

Note

This dataset was automatically described using the codebook R package (version 0.9.5).

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

Food insecurity among college students is a serious problem that can impact student performance in the classroom and ultimately effect student success. The Center for Healthy Communities (CHC) developed the Basic Needs Student Success Survey (BNS3) and administered it to undergraduate students participating in the Educational Opportunity Program (EOP) at three California State Universities between November 2020 and March 2021.

The purpose of this second cross-sectional pilot study was to revise the BNS3 tool and validate student perception of the following:

The impact of receiving CalFresh assistance.

The impact of utilization of the campus food pantry on their health, nutrition, cooking confidence, time management and academic performance.

This entry contains

The anonymized and cleaned data set

A codebook (data dictionary)

The survey tool as a Qualtrics export to Word file

This dataset consists of 3 files:

File 1: Qualitative data enclosed in Microsoft Excel file. Data collection includes one to one interviews with Western Australian migrants.

Content analysis: interview utterances were analysed for content, classified into categories, and coded and entered in Microsoft Excel file columns.

File 2: Qualitative data enclosed in Microsoft Word file. Data collection includes one to one interviews with Western Australian migrants.

Content analysis: interview utterances were analysed for content, classified into categories, and coded and entered in Microsoft Word file columns.

File 3: Quantitative data enclosed in Qualtrics software which includes public librarians’ responses to a Web survey. SPSS Tables and Figures imported from the Qualtrics Software raw data into a Word file.

SMaRteN, in partnership with Vitae, conducated research into the impact of COVID-19 on the working lives of doctoral researchers and research staff. This is the Time 2 data set. Data was collected at the end of September and start of October 2020. Please see link at bottom of page for the first data set.SMaRteN www.smarten.org.ukThe UK Research and Innovation (UKRI) funded Student Mental Health Research Network (SMaRteN) is working to support and encourage better research into student mental health. SMaRteN is based at Institute of Psychiatry, Psychology and Neurosciences at King’s College London.Vitae is a non-profit programme supporting the professional and career development of researchers. www.vitae.ac.uk @vitae_newsCovid-19 and the associated lock down has caused substantive disruption to the study and work of doctoral students and researchers in universities. The response to the pandemic has varied across universities and research funders.SMaRteN and Vitae aim to develop a national picture for how doctoral researchers and research staff have been affected by the pandemic.​The survey includes questions relating to the impact of COVID-19 on research work, mental wellbeing, social connection. We further address the impact of COVID-19 on changes to employment outside of academia, living arrangements and caring arrangements and the consequent effect of these changes on research work. The survey considers the support provided by supervisors / line managers and by universities.Data available here as either an SPSS or Excel download:SPSS file contains labelsExcel file contains labels and brief notes about codingRecoding data for CV19 impact - SPSS Syntax file describes steps taken to code dataCV19_impact_on_researchers - word document, export from Qualtrics of the survey.Please note, data has been removed from this data set to ensure participant anonymity.For further information, please contact Dr Nicola Byrom - nicola.byrom@kcl.ac.uk

This is the dataset for Kalis et al (forthcoming). It is in long format, with three rows for each participant. Repeated measures are indicated in the "gesture" variable. Between subjects attention condition is indicated in the AttCond variable. Reyimmediate and Reydelay are the RCFT immediate and delayed scores for each participant. Dependent variables are GestCorrect, MisGestMisled and TotalCorrect.

Data were collected from 94 participants as part of an Honours project in 2022. Qualtrics was used to collect demographic data. Reyimmediate and Reydelay data were collected on paper hard copies, and then manually coded and entered electronically to Stata. GestCorrect, MisGestMisled and TotalCorrect were verbal recall scores that were collected through individual interviews with participants which were audio and video recorded.

Recordings were then coded in ELAN (https://archive.mpi.nl/tla/elan). Codes were exported as .csv files, collated in Microsoft Excel and imported to Stata for analysis.