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

Population and sampleTo find participants for the survey, this study drew from the 4,761 publicly listed members of the online group Awesome Assistants. As a result, the population was all young film/TV professionals, while the sample was the selected members of Awesome Assistants. On its Facebook page, Awesome Assistants allows film/TV professionals to post job openings and work-related questions. The author eliminated respondents younger than 18 or older than 35, and did not include moderators of Awesome Assistants. Members of the group work in the film/TV industry with either an assistant title or performing assistant duties, such as answering phones and running errands. The author randomly selected and contacted 500 individuals from this online group. The author also utilised two follow-up messages to improve the response rate.InstrumentationIn order to collect data from the sample, the author used the Career Decision Self-Efficacy Scale-Short Form (CDSES-SF).Data collection processTo distribute the survey to potential participants, the author sent a letter to the Awesome Assistants moderators to confirm their support. After, the author uploaded a message of informed consent and the survey to Qualtrics, then sent subjects a link to complete the study. Subjects received messages via Facebook Messenger, LinkedIn, or email, based on the contact information available. Once subjects completed the survey, a debriefing form invited them to enter a raffle for one of four $25 Amazon gift cards. After four weeks, the links expired. The author omitted surveys in which the subject did not answer at least one item from each subscale on the CDSES-SF. If respondents did not answer all items in a subscale, the author took the average of the completed questions. Additionally, the author eliminated subjects who fell outside of the target age range, as well as those who did not provide their age or number of contacts in the film/TV industry. Out of the 267 unique responses, the author analyzed 226 subjects as a result.Statistical Analysis ProceduresMuch like data collection, the author ensured that the statistical analysis process was legitimate and insightful. Next, the author entered data into IBM Statistical Product and Service Solutions (SPSS) Statistics 27 and determined what data should be coded as missing. Due to the assumption of linearity not being met by the data, the author used Spearman’s rho instead of a Pearson product-moment correlation. The author declared the results statistically significant if p < .05.

The study was approved by the Sichuan University Institutional Review Board. All participants read a statement that explained the purpose of the survey and written informed consents have been received before being involved in the investigation.This cross-sectional study was conducted in the Hezuo community, one of six sub-districts within a recently developed urban district in Chengdu, Sichuan from January to March in 2016. There were a total of 8884 older people (aged 60 and above) in this community as of December 2015. A sample of 670 older people was randomly drawn from the community older adult population using computer “random numbers” generator. MeasuresThe in-person interview questionnaire contains measures on socio-demographics, willingness to enter long-term care facilities, general wellbeing index, and social support. We collected the following information: age, gender, marital status, education, occupation, income (monthly household income per capita), insurance, living condition, being sick in the two weeks before the survey, number and type of chronic diseases, and any hospitalization in the prior year.The interviewer first defined long-term care facilities as institutions that integrate medical and social services in senior care facilities. Second, the interviewer asked whether the respondents are aware of the concept. For those who were not aware, explanations were made to inform them. Respondents were then asked: “Are you willing to enter into one of these facilities to receive integrated medical and social services in the future?” If the answer is “Yes”, additional questions were asked regarding: the most important aspect of choosing a long-term care facility, expectation of travel distance from one’s home to the facility, expectation of monthly costs of the services, expectations of the caregivers, medical staff, services, and quality provided, family support of such care arrangement. The WHO-5 items were used to describe the general wellbeing of the respondents about their rating of five statements considering the last 14 days.The Social Support Rating Scale (SSRS) assesses the level of overall social support that each subject received. All survey data were entered into EpiData 3.0. Statistical analyses were carried out using the IBM SPSS version 21.0. We used Pearson’s χ2 to examine differences in categorical variables. Multivariate logistic regression was used to examine the relationship between the independent variables.

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.

Abstract

The programme for the World Census of Agriculture 2000 is the eighth in the series for promoting a global approach to agricultural census taking. The first and second programmes were sponsored by the International Institute for Agriculture (IITA) in 1930 and 1940. Subsequent ones up to 1990 were promoted by the Food and Agriculture Organization of the United Nations(FAO). FAO recommends that each country should conduct at least one agricultural census in each census programme decade and its programme for the World Census of Agriculture 2000 for instance corresponds to agricultural census to be undertaken during the decade 1996 to 2005. Many countries do not have sufficient resources for conducting an agricultural census. It therefore became an acceptable practice since 1960 to conduct agricultural census on sample basis for those countries lacking the resources required for a complete enumeration.

In Nigeria's case, a combination of complete enumeration and sample enumeration is adopted whereby the rural (peasant) holdings are covered on sample basis while the modern holdings are covered on complete enumeration. The project named “National Agricultural Sample Census” derives from this practice. Nigeria through the National Agricultural Sample Census (NASC) participated in the 1970's, 1980's, 1990's programmes of the World Census of Agriculture. Nigeria failed to conduct the Agricultural Census in 2003/2004 because of lack of funding. The NBS regular annual agriculture surveys since 1996 had been epileptic and many years of backlog of data set are still unprocessed. The baseline agricultural data is yet to be updated while the annual regular surveys suffered set back. There is an urgent need by the governments (Federal, State, LGA), sector agencies, FAO and other International Organizations to come together to undertake the agricultural census exercise which is long overdue. The conduct of 2006/2008 National Agricultural Sample Census Survey is now on course with the pilot exercise carried out in the third quarter of 2007.

The National Agricultural Sample Census (NASC) 2006/08 is imperative to the strengthening of the weak agricultural data in Nigeria. The project is phased into three sub-projects for ease of implementation; the Pilot Survey, Modern Agricultural Holding and the Main Census. It commenced in the third quarter of 2006 and to terminate in the first quarter of 2008. The pilot survey was implemented collaboratively by National Bureau of Statistics.

The main objective of the pilot survey was to test the adequacy of the survey instruments, equipments and administration of questionnaires, data processing arrangement and report writing. The pilot survey conducted in July 2007 covered the two NBS survey system-the National Integrated Survey of Households (NISH) and National Integrated Survey of Establishment (NISE). The survey instruments were designed to be applied using the two survey systems while the use of Geographic Positioning System (GPS) was introduced as additional new tool for implementing the project.

The Stakeholders workshop held at Kaduna on 21st-23rd May 2007 was one of the initial bench marks for the take off of the pilot survey. The pilot survey implementation started with the first level training (training of trainers) at the NBS headquarters between 13th - 15th June 2007. The second level training for all levels of field personnels was implemented at headquarters of the twelve (12) concerned states between 2nd - 6th July 2007. The field work of the pilot survey commenced on the 9th July and ended on the 13th of July 07. The IMPS and SPSS were the statistical packages used to develop the data entry programme.

Geographic coverage

State

Analysis unit

Household crop farmers

Universe

Crop farming household

Kind of data

Census/enumeration data [cen]

Sampling procedure

The survey was carried out in 12 states falling under 6 geo-political zones. 2 states were covered in each geo-political zone. 2 local government areas per selected state were studied. 2 Rural enumeration areas per local government area were covered and
4 Crop farming housing units were systematically selected and canvassed .

Sampling deviation

No deviation

Mode of data collection

Face-to-face [f2f]

Research instrument

The NASC crop questionnaire was divided into the following sections: - Holding identification - Holding characteristics - Access to land - Access to credit and funds used - Production input utilization, quantity and cost - Sources of inputs/equipment - Area harvested - Agric machinery - Production - Farm expenditure - Processing facilities - Storage facilities - Employment in agric. - Farm expenditure - Sales - Consumption - Market channels - Livestock farming - Fish farming

Cleaning operations

The data processing and analysis plan involved five main stages: training of data processing staff; manual editing and coding; development of data entry programme; data entry and editing and tabulation. Census and Surveys Processing System (CSPro) software were used for data entry, Statistical Package for Social Sciences (SPSS) and CSPro for editing and a combination of SPSS, Statistical Analysis Software (SAS) and EXCEL for table generation. The subject-matter specialists and computer personnel from the NBS and CBN implemented the data processing work. Tabulation Plans were equally developed by these officers for their areas and topics covered in the three-survey system used for the exercise. The data editing is in 2 phases namely manual editing before the data entry were done. This involved using editors at the various zones to manually edit and ensure consistency in the information on the questionnaire. The second editing is the computer editing, this is the cleaning of the already entered data. The completed questionnaires were collected and edited manually (a) Office editing and coding were done by the editor using visual control of the questionnaire before data entry (b) Cspro was used to design the data entry template provided as external resource (c) Ten operator plus two suppervissor and two progammer were used (d) Ten machines were used for data entry (e) After data entry data entry supervisor runs fequency on each section to see that all the questionnaire were enterd

Response rate

The response rate at EA level was 100 percent, while 98.44 percent was achieved at crop farming housing units level

Sampling error estimates

No computation of sampling error

Data appraisal

The Quality Control measures were carried out during the survey, essentially to ensure quality of data. There were two levels of supervision involving the supervisors at the first level, NBS State Officers and Zonal Controllers at second level and finally the NBS Headquarters staff constituting the second level supervision.

Data from: Doctoral dissertation; Preprint article entitled: Managers' and physicians’ perception of palm vein technology adoption in the healthcare industry. Formats of the files associated with dataset: CSV; SAV. SPSS setup files can be used to generate native SPSS file formats such as SPSS system files and SPSS portable files. SPSS setup files generally include the following SPSS sections: DATA LIST: Assigns the name, type, decimal specification (if any), and specifies the beginning and ending column locations for each variable in the data file. Users must replace the "physical-filename" with host computer-specific input file specifications. For example, users on Windows platforms should replace "physical-filename" with "C:\06512-0001-Data.txt" for the data file named "06512-0001-Data.txt" located on the root directory "C:\". VARIABLE LABELS: Assigns descriptive labels to all variables. Variable labels and variable names may be identical for some variables. VALUE LABELS: Assigns descriptive labels to codes in the data file. Not all variables necessarily have assigned value labels. MISSING VALUES: Declares user-defined missing values. Not all variables in the data file necessarily have user-defined missing values. These values can be treated specially in data transformations, statistical calculations, and case selection. MISSING VALUE RECODE: Sets user-defined numeric missing values to missing as interpreted by the SPSS system. Only variables with user-defined missing values are included in the statements. ABSTRACT: The purpose of the article is to examine the factors that influence the adoption of palm vein technology by considering the healthcare managers’ and physicians’ perception, using the Unified Theory of Acceptance and Use of Technology theoretical foundation. A quantitative approach was used for this study through which an exploratory research design was utilized. A cross-sectional questionnaire was distributed to responders who were managers and physicians in the healthcare industry and who had previous experience with palm vein technology. The perceived factors tested for correlation with adoption were perceived usefulness, complexity, security, peer influence, and relative advantage. A Pearson product-moment correlation coefficient was used to test the correlation between the perceived factors and palm vein technology. The results showed that perceived usefulness, security, and peer influence are important factors for adoption. Study limitations included purposive sampling from a single industry (healthcare) and limited literature was available with regard to managers’ and physicians’ perception of palm vein technology adoption in the healthcare industry. Researchers could focus on an examination of the impact of mediating variables on palm vein technology adoption in future studies. The study offers managers insight into the important factors that need to be considered in adopting palm vein technology. With biometric technology becoming pervasive, the study seeks to provide managers with the insight in managing the adoption of palm vein technology. KEYWORDS: biometrics, human identification, image recognition, palm vein authentication, technology adoption, user acceptance, palm vein technology

The data was obtained through a questionnaire survey. We distributed measurement questionnaires to 300 full-time employees in China and collected 258 valid questionnaires. For the collected data, we use Excel to input and analyze it in SPSS software. The data is all personal data, and the individuals providing the data are all Chinese citizens. The survey was conducted in the southeastern region of China from January 2023 to April 2023. The data consists of 258 rows, each representing the survey test results of one respondent. The data consists of 33 columns, with the first column representing the sample number, and each subsequent column representing the results of each question for the control and measurement variables.

Raw data, Mplus input files, and data documentation of the following research paper: Burk, C. L. & Wiese, B. S. (in press). How to alleviate the agony of providing negative feedback: emotion regulation strategies affect hormonal stress responses to a managerial task. Hormones and Behavior. Containing: • dataset with raw data in SPSS and *.dat formats • 10 Mplus input files concerning the comparison of latent growth models • four plus input files concerning the predictive models • a data supplement documentation including variable documentation, tables further describing the models and structural diagrams of the models

For a comprehensive guide to this data and other UCR data, please see my book at ucrbook.comVersion 15 release notes:Adds .parquet file formatVersion 14 release notes:Adds 2023 and 2024 dataVersion 13 release notes:Adds 2022 dataVersion 12 release notes:Adds 2021 data.Version 11 release notes:Adds 2020 data. Please note that the FBI has retired UCR data ending in 2020 data so this will (probably, I haven't seen confirmation either way) be the last LEOKA data they release. Changes .rda file to .rds.Version 10 release notes:Changes release notes description, does not change data.Version 9 release notes:Adds data for 2019.Version 8 release notes:Fix bug for years 1960-1971 where the number of months reported variable was incorrectly down by 1 month. I recommend caution when using these years as they only report either 0 or 12 months of the year, which differs from every other year in the data. Added the variable officers_killed_total which is the sum of officers_killed_by_felony and officers_killed_by_accident.Version 7 release notes:Adds data from 2018Version 6 release notes:Adds data in the following formats: SPSS and Excel.Changes project name to avoid confusing this data for the ones done by NACJD.Version 5 release notes: Adds data for 1960-1974 and 2017. Note: many columns (including number of female officers) will always have a value of 0 for years prior to 1971. This is because those variables weren't collected prior to 1971. These should be NA, not 0 but I'm keeping it as 0 to be consistent with the raw data. Removes support for .csv and .sav files.Adds a number_of_months_reported variable for each agency-year. A month is considered reported if the month_indicator column for that month has a value of "normal update" or "reported, not data."The formatting of the monthly data has changed from wide to long. This means that each agency-month has a single row. The old data had each agency being a single row with each month-category (e.g. jan_officers_killed_by_felony) being a column. Now there will just be a single column for each category (e.g. officers_killed_by_felony) and the month can be identified in the month column. This also results in most column names changing. As such, be careful when aggregating the monthly data since some variables are the same every month (e.g. number of officers employed is measured annually) so aggregating will be 12 times as high as the real value for those variables. Adds a date column. This date column is always set to the first of the month. It is NOT the date that a crime occurred or was reported. It is only there to make it easier to create time-series graphs that require a date input.All the data in this version was acquired from the FBI as text/DAT files and read into R using the package asciiSetupReader. The FBI also provided a PDF file explaining how to create the setup file to read the data. Both the FBI's PDF and the setup file I made are included in the zip files. Data is the same as from NACJD but using all FBI files makes cleaning easier as all column names are already identical. Version 4 release notes: Add data for 2016.Order rows by year (descending) and ORI.Version 3 release notes: Fix bug where Philadelphia Police Department had incorrect FIPS county code. The LEOKA data sets contain highly detailed data about the number of officers/civilians employed by an agency and how many officers were killed or assaulted. All the data was acquired from the FBI as text/DAT files and read into R using the package asciiSetupReader. The FBI also provided a PDF file explaining how to create the setup file to read the data. Both the FBI's PDF and the setup file I made are included in the zip files. About 7% of all agencies in the data report more officers or civilians than population. As such, I removed the officers/civilians per 1,000 population variables. You should exercise caution if deciding to generate and use these variables yourself. Several agency had impossible large (>15) officer deaths in a single month. For those months I changed the value to NA. The UCR Handbook (https://ucr.fbi.gov/additional-ucr-publications/ucr_handbook.pdf/view) describes the LEOKA data as follows:"The UCR Program collects data from all contributing agencies ... on officer line-of-duty deaths and assaults. Reporting agencies must submit data on ... their own duly sworn officers f

The SPSS data file containing the data information collected from the SCoA-IV questionnaire administered in 2006. The AMOS data file is the confirmatory factor analysis input for the SCoA-IV data set. Results were published in:Brown, G. T. L. (2006, September). Secondary school students’ conceptions of assessment: A Survey of Four Schools. Conceptions of Assessment and Feedback Project Report #5. Auckland, NZ: University of Auckland.DOI: 10.13140/RG.2.2.24963.71202

Field data is collected through a structured questionnaire. The questionniare included direct questions with options to answer and also statement based questions to be responded in Likert Scale. Mainly the statement based questions were used to assess the fire disaster coping capacity of the community of the study area. Others questions supported to understand limitations or strengths regarding the coping capacity. Data cleaning was performed before providing input in SPSS.

Continuous data were indicated with mean±SD (standard deviation) while categorical data were indicated with number and percentage (%). For comparisons of means between groups, Mann-Whitney U test or student’s independent t-test was used depends on normality assumption. Categorical data were tested using Chi-square test or Fisher’s exact text (if expected value ≤ 5 was found). Spearman’s correlation coefficient was used to observe the relation among independent variables. Further, univariate and multivariate logistic regression models were used to analyze the association between independent variables and survival results.The independent variables which were significant in univariate were entered into a multivariate model. Two kinds of multivariate models were used, including the enter method and forward (Wald test) method. In the enter method, significant variables were recognized as associated factors. In the forward method with Wald test, the combination of independent variables with best explained variation were reported. The estimated odds ratio (OR) and its 95% confidence interval (CI) were reported in all logistic regression results. The probabilities generated from the final multivariate logistic regression model was further validated by ROC analysis. The AUC and its 95% confidence interval (CI) were reported. All above analyses were performed using IBM SPSS Version 25 (SPSS Statistics V25, IBM Corporation, Somers, New York).

This dataset consists of three data folders for the eye-tracking experiment conducted within the NESP 3.2.3 project (Tropical Water Quality Hub): Folder (1) The folder of Eye-tracking videos contains 66 Tobii recordings of participants’ eye movements on screen, Folder (2) The Heatmaps folder includes 21 heatmaps created by Tobii eye-tracking software on the basis of 66 participants’ data and Folder (3) The input folder has 21 original pictures used in eye-tracking experiment. Moreover, The dataset also includes 1 excel file representing eye-tracking data extracted from Tobii software and participant interview results, 1 SPV. file as the input of SPSS data analysis process and 1 SPV. file as the output of data analysis process.

Methods: This dataset resulted from both input and output data of eye-tracking experiments. The input includes 21 underwater pictures of the Great Barrier Reef, selected from online searching with the keyword “Great Barrier Reef”. These pictures are imported to Tobii eye-tracking software to design the eye-tracking experiments. 66 participants were recruited using convenience sampling in this study. They were asked to sit in front of a screen-based eye-tracking equipment (i.e. Tobii T60 eye-tracker) after providing informed consent. Participants were free to look at each picture on screen as long as they wanted during which their eye movements were recorded. They also rated each picture on a 10-point beauty scale (1-Not beautiful at all, 10-Very beautiful) and a 10-point expectation scale (1-Not at all, 10-Very much). After the experiment, 40 subjects were also interviewed to identify the areas of interest (AOI) in each picture and to rate the beauty of these AOIs. Eye-tracking data was then extracted from Tobii eye-tracking device including participants’ eye-tracking recordings, heatmaps (i.e. images showing viewers’ attention focus) and raw eye-tracking measures (i.e. picture beauty, time to first fixation, fixation count, fixation duration and total visit time) using XLSX. download format. Raw eye-tracking data was then imported to IBM SPSS using SAV. format for data analysis which results in a SPV. output file.

Further information can be found in the following publication: Scott, N., Le, D, Becken, S., and Connelly, R. (2018 Submitted) Measuring perceived beauty of the Great Barrier Reef using eye tracking. Journal of Sustainable Tourism. 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 project dataset includes 132 eye-tracking videos of AVI. format, 21 heatmaps of PNG. format, 21 pictures of JPEG. format, 1 XLSX. format document representing raw eye-tracking measures and interview data, 1 SAV. format document as the input of data analysis and 1 SPV. format file showing data analysis results.

Data Dictionary:

Q1, Q2, Q3, Q4, Q5, Q6, Q7, Q8, Q9, Q10: Names of pictures used in the eye-tracking experiment 2. 3Q1, 3Q2, 3Q3, 3Q4, 3Q5, 3Q6, 3Q7, 3Q8, 3Q9, 3Q10, 3Q11: Names of pictures used in the eye-tracking experiment 3.

Raw Eye tracking Measurements excel spreadsheets:

Tab - Picture: INDEX: the 10-point scale showed to participants VALUE: meaning of the 10-point scale Q1.1: Beauty score Q1.2: Expectation score

Tab - Area of Interest (AOI)" TIME TO FIRST FIXATION_Q1: Time to first fixation in the picture Q1 (i.e. i.e. the average time from the beginning of the recording until the respective picture was first fixated upon) TOTAL FIXATION DURATION_Q1: Fixation duration in the picture Q1 (i.e. the average length of all fixations during all recordings in the whole picture). A longer fixation means that the object is more engaging in some way. FIXATION COUNT_Q1: Fixation count in the picture Q1 (i.e. the average number of fixations in the picture). TOTAL VISIT DURATION_Q1: Total time visit for the picture Q1 (i.e. the average time participants spent looking at a picture). TIME TO FIRST FIXATION_AOI1: Time to first fixation in the AOI identified in the picture Q1 (i.e. i.e. the average time from the beginning of the recording until the respective picture was first fixated upon) TOTAL FIXATION DURATION_AOI1: Fixation duration in the AOI identified in the picture Q1 (i.e. the average length of all fixations during all recordings in the whole picture). A longer fixation means that the object is more engaging in some way. FIXATION COUNT_AOI1: Fixation count in the AOI identified in the picture Q1 (i.e. the average number of fixations in the picture). TOTAL VISIT DURATION_AOI1: Total time visit for the AOI identified in the picture Q1 (i.e. the average time participants spent looking at a picture).

Tab - AOI interview: AOI IDENTIFIED: The AOI that is the most mentioned by participants NUMBER OF PARTICIPANTS: the number of participants who mentioned the AOI in the previous column. BEAUTY MEAN: The average beauty score of the correspondent AOI rated by 40 participants. AOI-1: The AOI identified by the correspondent participant. RATING: the beauty score associated to the AOI identified by the correspondent participant.

Tab - Analysis: REC: Recording PICTURE: Picture number BEAUTY: The average beauty score of the correspondent picture by 66 participants EXPECTATION: The average expectation score of the correspondent picture by 66 participants AOI BEAUTY: The average beauty score of the AOI identified in the correspondent picture by interviewed participants. PICTURE 1st TIME: The average time to first fixation in the correspondent picture (i.e. i.e. the average time from the beginning of the recording until the respective picture was first fixated upon) by 66 participants PFDURATION: The average fixation duration in the correspondent picture (i.e. the average length of all fixations during all recordings in the whole picture) by 66 participants PFCOUNT: The average fixation count in the correspondent picture (i.e. the average number of fixations in the picture) by 66 participants PTING VISIT: The average of total time visit for the correspondent picture (i.e. the average time participants spent looking at a picture) by 66 participants AOI 1stTIME: The average time to first fixation in the AOI identified in the correspondent picture (i.e. i.e. the average time from the beginning of the recording until the respective picture was first fixated upon) by 66 participants AOIFDURATION: The average fixation duration in the AOI identified in the correspondent picture (i.e. the average length of all fixations during all recordings in the whole picture) by 66 participants AOIFCOUNT: The average fixation count in the the AOI identified in correspondent picture (i.e. the average number of fixations in the picture) by 66 participants AOITIMEVISIT: The average of total time visit for the AOI identified in the correspondent picture (i.e. the average time participants spent looking at a picture) by 66 participants

References:

Scott, N., Le, D, Becken, S., and Connelly, R. (2018 Submitted) Measuring perceived beauty of the Great Barrier Reef using eye tracking. Journal of Sustainable Tourism.

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 SPSS data file containing the data information collected from the SCoA-V questionnaire administered in 2007. The AMOS data file is the confirmatory factor analysis input for the SCoA-V data set. Results were published in:Brown, G. T., Irving, S. E., Peterson, E. R., & Hirschfeld, G. H. (2009). Use of interactive–informal assessment practices: New Zealand secondary students' conceptions of assessment. Learning and Instruction, 19(2), 97-111. doi:10.1016/j.learninstruc.2008.02.003Weekers, A. M., Brown, G. T. L., & Veldkamp, B. P. (2009). Analyzing the dimensionality of the Students' Conceptions of Assessment (SCoA) inventory. In D. M. McInerney, G. T. L. Brown & G. A. D. Liem (Eds.), Student perspectives on assessment: What students can tell us about assessment for learning. (pp. 133-157). Charlotte, NC US: Information Age Publishing.

The SPSS data file containing the data information collected from the SCoA-I questionnaire administered in 2003. The AMOS data files are the confirmatory factor analysis input for the SCoA-I data set. Results were published in:Brown, G. T., & Hirschfield, G. H. (2007). Students' Conceptions of Assessment and Mathematics: Self-Regulation Raises Achievement. Australian Journal of Educational & Developmental Psychology, 7, 63-74.

Background:

These studies aim to investigate the effects of pandemics of varying severity on death thought accessibility in two studies while controlling for health anxiety. Study 1 (n = 203) examined the effect of standard MS, severe pandemic, mild pandemic, and dental conditions on death thought accessibility as assessed by the death word fragment task (DWFT). Study 2 (n = 163) was conducted with more sensitive death thought accessibility measures such as the lexical decision task and dot probe task.

Study 1 hypothesized that severe pandemic condition and standard MS condition will yield significantly higher levels of death cognitions than the mild pandemic and dental conditions after a time delay, and continued to do so when we control for health anxiety.

Information pertaining to Study 2 is located at: https://doi.org/10.25903/cn68-9963

This data record contains:

1 x SPSS (.sav) file containing input data and calculation of number of death words completed used in analysis. File is also available in Open Document (.ods) format.

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Software/equipment used to collect the data: Qualtrics

Software/equipment used to analyse the data: SPSS

survey input into SPSS

The SPSS and AMOS data files containing all data information collected from the SCoA-II questionnaire administered in 2004. The SPSS data file containing the data information collected from the SCoA-II questionnaire administered in 2004. The AMOS data file is the confirmatory factor analysis input for the SCoA-II data set. Results were published in:Brown, G. T. L., & Hirschfeld, G. H. F. (2008). Students’ conceptions of assessment: Links to outcomes. Assessment in Education: Principles, Policy and Practice, 15(1), 3-17. doi:10.1080/09695940701876003Hirschfeld, G. H. F., & Brown, G. T. L. (2009). Students’ conceptions of assessment: Factorial and structural invariance of the SCoA across sex, age, and ethnicity. European Journal of Psychological Assessment, 25(1), 30-38. doi:10.1027/1015-5759.25.1.30Walton, K. F. (2009). Secondary students’ conceptions of assessment mediated by self-motivational attitudes: Effects on academic performance. (Unpublished M.Ed. thesis), University of Auckland, Auckland, NZ.

I attach data and code to reproduce analyses for manuscript - Personality and Team Identification Predict Violent Intentions Among Soccer Supporters. I have attached the following data files: - Soccer_supporters_raw.sav - Soccer_data_raw.csv - Soccer_data.xlsx - Soccerpathmodel.txt

Codebook: - CodeBook_soccersupportersdata.csv*Note that this codebook applies to the raw data.

And code: Syntax_soccer_supporters.sps (to be opened in SPSS)*Note that this code is also available in non-proprietary .txt format: Syntax_soccer_supporters.txt

Soccerpathmodel.inp (to be opened in MPLUS (Muthén & Muthén, 2012, see also https://www.statmodel.com/ ).

@font-face {font-family:"Cambria Math"; panose-1:2 4 5 3 5 4 6 3 2 4; mso-font-charset:0; mso-generic-font-family:roman; mso-font-pitch:variable; mso-font-signature:3 0 0 0 1 0;}@font-face {font-family:Calibri; panose-1:2 15 5 2 2 2 4 3 2 4; mso-font-charset:0; mso-generic-font-family:swiss; mso-font-pitch:variable; mso-font-signature:-536859905 -1073732485 9 0 511 0;}p.MsoNormal, li.MsoNormal, div.MsoNormal {mso-style-unhide:no; mso-style-qformat:yes; mso-style-parent:""; margin-top:6.0pt; margin-right:0cm; margin-bottom:12.0pt; margin-left:0cm; mso-pagination:widow-orphan; font-size:12.0pt; mso-bidi-font-size:11.0pt; font-family:"Times New Roman",serif; mso-fareast-font-family:Calibri; mso-fareast-theme-font:minor-latin; mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:minor-bidi; mso-ansi-language:EN-US; mso-fareast-language:EN-US;}.MsoChpDefault {mso-style-type:export-only; mso-default-props:yes; font-size:11.0pt; mso-ansi-font-size:11.0pt; mso-bidi-font-size:11.0pt; font-family:"Cambria",serif; mso-ascii-font-family:Cambria; mso-ascii-theme-font:major-latin; mso-fareast-font-family:Calibri; mso-fareast-theme-font:minor-latin; mso-hansi-font-family:Cambria; mso-hansi-theme-font:major-latin; mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:minor-bidi; mso-ansi-language:EN-US; mso-fareast-language:EN-US;}.MsoPapDefault {mso-style-type:export-only; margin-bottom:10.0pt; line-height:115%;}div.WordSection1 {page:WordSection1;} *Note that this code is also available in non-proprietal .txt format: soccerpathmodelcode.txt

To reproduce the results for this manuscript, please first open the file “Soccer_supporters_raw.sav” in SPSS (ideally version 25, with PROCESS add-on), and run the accompanying syntax: “Syntax_soccer_supporters.sps”. I also attach a non-proprietary version of this raw data - Soccer_data_raw.csv

Note that the code/syntax to run mediation analyses with PROCESS, is not available, since PROCESS does not allow for the pasting of syntax. So this part of the analyses needs to be completed manually through the point-and-click interface.

The remaining analyses were conducted in MPLUS. To do so, the original raw SPSS file was saved (after recoding and computing index variables), as a text file. We have also included this data in .xlsx format - see file Soccer data.xlsx

To reproduce the path model tested in MPLUS, run the input file “soccerpathmodel.inp” ensuring that the accompanying file - Soccerpathmodel.txt is located in the same folder.

Social Influence on Shopping

Social Survey Data from 300,000 Millennials and Gen Z Members

By Adam Halper [source]

About this dataset

This dataset offers a comprehensive look into the shopping habits of millennials and Gen Z members, including valuable insights about how their choices are influenced by social media. By exploring the responses given to survey questions related to this topic, we can gain an understanding of how these generations' interests, beliefs and desires shape their decisions when it comes to retail experiences. With 150 million survey responses from our 300,000+ millennial and Gen Z participants, we can uncover powerful insights that could help influencers, businesses and marketers more accurately target this demographic. Our data includes important information such as questions asked during the survey, segment types targeted by those questions and corresponding answers gathered with detailed counts/percentages - making this dataset incredibly useful for anyone wanting an in-depth understanding of what drives the purchasing behavior of today's youth

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For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

The first step in using this dataset is to take a look at each column: Question, Segment Type, Segment Description, Answer, Count & Percentage. The Question column will provide background on what exactly each survey question was asking - allowing you to get an overall view of what kind of topics were being surveyed in relation to millennials' shopping habits & social media influence. You will then be able to follow up with analysis based on the respective Segment Types & Descriptions given (such as income levels), which leads us into analyzing answers from both Count & Percentage columns combined - providing absolute numbers vs relative ones for further analysis (such as percentages).

Afterwards you'll need an advanced data analysis program such as SPSS or R-Studio - depending on your technical ability - though all most basic spreadsheet programs should suffice, excluding Matlab supported ones due its excessive complexity for something simple like this.. After selecting your preferred program inputting our file with all 150 million survey responses may take some time based on your computers processing capabilities but once loaded you'll be ready for endless possibilities! Now it's time get running with pulling out key insights you require utilizing various different tools found within these platforms whether it be linear regression or guided ANOVA testing which ever technique fits best should help lead navigate through uncovering deeper meaning in your ultra specific question!

As a final precaution while diving through waters filled surprises also keep note any adjustments needed potentially due overfitting or multicollinearity otherwise could cause major issues skew end results unfit requiring start whole process anew! Good luck delving deep discovering millennial behavior related digital world!

Research Ideas

• Identifying which type of segment is most responsive to engaging shopping experiences, such as influencer marketing, social media discounts and campaigns, etc.
• Analyzing the answers given to survey questions in order to understand millennial and Gen Z's opinion about social influence on their shopping habits - what do they view positively or negatively?
• Using the survey responses to uncover any interesting trends or correlations between different segments - is there a particular demographic that values or uses certain types of social influence on their shopping habits more than others?

Acknowledgements

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

License

License: Attribution-ShareAlike 4.0 International (CC BY-SA 4.0) - 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.

Columns

File: WhatsgoodlyData-6.csv | Column name | Description ...