Percentage of total trial spend viewing each of the 9 locations.

The OECD Programme for International Student Assessment (PISA) surveys collected data on students’ performances in reading, mathematics and science, as well as contextual information on students’ background, home characteristics and school factors which could influence performance. This publication includes detailed information on how to analyse the PISA data, enabling researchers to both reproduce the initial results and to undertake further analyses. In addition to the inclusion of the necessary techniques, the manual also includes a detailed account of the PISA 2006 database and worked examples providing full syntax in SPSS.

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.

Vince Gray delivered an introduction to the basic parts of a SPSS syntax file to read in data, in addition to presenting tips and tricks for preparing syntax files, cleaning up blatant problems with the data, and held a short exercise in coding a SPSS syntax file.

No description is available. Visit https://dataone.org/datasets/doi%3A10.18739%2FA2KW3J for complete metadata about this dataset.

This is the SPSS syntax used to analyse the data on party lists for 2000 elections and MPs and Senators for 2000-2004.

Study setting We conducted this study in the paediatrics departments of the Ola During Children's Hospital (ODCH), Rokupa Government Hospital (RGH), and the King Harman Maternity and Children Hospital (KHMCH) located in Freetown, the Capital city of Sierra Leone. Ola During Children's hospital is a tertiary teaching hospital and the leading paediatric referral hospital in Sierra Leone. Rokupa Government and KHMCH are secondary hospitals that provide comprehensive emergency obstetric and newborn care inpatient and outpatient paediatric and maternity services.Study design and duration This study was conducted between April 2021 to July 2021 and had two phases. Phase 1 was a descriptive cross-sectional retrospective study of paediatrics prescriptions from the respective pharmacy departments from May 1 to May 31, 2021. In phase 2, we conducted a point prevalence descriptive inpatient chart review that lasted for one week to assess MEs and pDDIs among the paediatric patient population.Study populationThis data set is the SPSS file with both variable and data view. It contains the variables that were analysed for the two phases of the study namely: For phase 1 of the study, the population included paediatric prescriptions that came to the respective pharmacy departments in May 2021. Phase 2 included inpatients <16 years irrespective of their working diagnosis and gender and whose parents or guardians consented to participate in the study.Data collection procedure and tool For phase 1 of the study, the data collection tool was adapted from the Sierra Leone Pharmacy and Drugs Act 2001, the World Health Organization (WHO) guidelines for prescription writing, and a previous study [12, 32, 33]. Seventeen essential elements were selected for this study and compiled into a single data collection tool. We manually extracted all data through a review of prescriptions accessed from the pharmacies. The data collection tool for phase 2 was adapted from the WHO guide on reporting and learning systems for medication errors, American Society of Health System Pharmacists (ASHP) guidelines for preventing medication errors in hospitals, and previous studies [3, 4, 18, 34]. Data collection tools were piloted, and feedback was used to develop the final versions used in the study. The treatment charts were reviewed, and the following were extracted and entered into the data collection tool: wrong patient, wrong dose, wrong route, wrong medicine, wrong dosage form, wrong time of administration, contraindication including allergy, wrong duration, dose omitted or delay, wrong frequency, wrong indication, unnecessary medicine, and therapeutic duplication. In addition, nurses were accompanied during the medicine administration rounds and patients and caretakers were interviewed to gather information when necessary. Ethical consideration Clearance to conduct the study was obtained from the Research, Innovation and Publication Review Committee of the Faculty of Pharmaceutical Sciences, College of Medicine and Allied Health Sciences, University of Sierra Leone. The management of the hospitals permitted the study to be done in their facilities. Written informed consent was obtained from parents/caregivers after explaining the purpose and procedures of the study. Parents gave consent before data was collected, and they were not coerced to participate in the study. Patient information was coded and kept confidential. Data analyses The researchers evaluated the completion of the essential elements for each prescription, such as the use of the generic names, recommended abbreviations, and prescription legibility. We determined the accuracy score out of 34 total points. Each element was assessed, scoring 0, 1 or 2 for 'not completed', 'partially completed', or 'fully completed', respectively. Legibility was scored subjectively according to the prescription quality index (PQI) as 0, 1, or 2 for 'illegible', 'barely legible' or 'legible', respectively, by two or more persons [35]. The global accuracy score (GAS) for each prescription was determined by calculating the total percentage achieved out of 34 possible points for the 17 prescription elements considered. The GAS was then classified into one of four scores: 100%, 80% – 99%, 40% – 79%, and less than 40%. The desired prescription-writing accuracy score, or gold standard, is 100%. The definition and severity categorisation of the National Coordinating Council of Medication Error Reporting and Prevention (NCCMERP) was used [5]. Potential drug-drug interactions (pDDIs) were assessed by the Drug.com interaction checker and classified into no interaction, minor, moderate, and major [36]. The data obtained was cleaned and coded and then entered into Statistical Package for Social Sciences (SPSS) version 20 (IBM Statistics, Armonk, NY, USA) for analysis. Descriptive statistics were applied, and results were presented as frequency, percentages, mean, and standard deviation. Inferential statistics, including the Kruskal Wallis, Mann-Whitney U and Pearson correlation, were employed, and a p-value of < 0.05 was considered statistically significant.

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

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

Data in SPSS formatMeasured language variables across the cultural groups, in SPSS data file format.Data.savData in CSV formatEquivalent data to the SPSS upload, in CSV format.Data.csvAnalysis syntax for SPSSSyntax used to generate the reported results using SPSS.Syntax.sps

Some surveys contain multiple units of observation, while others come in many parts. This workshop will give participants hands-on experience using both types of files. The General Social Survey, Cycle 8 and the Canadian Travel Surveys will be used as examples. (Note: Data associated with this presentation is available on the DLI FTP site under folder 1873-216.)

Data 1A

SPSS data file containing row data of all measuring points. (SAV)

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%).

With the rapid development of AIGC (Artificial Intelligence Generated Content) and its expanding role and scope in education and teaching. This study conducted a survey among 394 pre-service STEM teachers enrolled at a university located in Zhejiang Province. Data were collected and a structural model was constructed to examine interplay among psychological stress, anxiety self-efficacy, and learning burnout resulting from the utilization of AIGC. The findings indicate that pre-service STEM teachers may experience psychological stress when applying AIGC, which could exacerbate their anxiety towards artificial intelligence and potentially lead to academic burnout. In order to effectively integrate AIGC in the field of education and enhance the professional development of pre-service teachers, the key lies in the dissemination of artificial intelligence knowledge, enhancing pre-service teachers' understanding of artificial intelligence, and encouraging them to appropriately utilize AIGC as a learning auxiliary tool. Methods This study uses the internationally recognized five-point Likert scale as the main tool to quantitatively assess STEM teachers' psychological pressure, fear of artificial intelligence, self-efficacy and learning fatigue caused by the use of AIGC. The questionnaire design is based on proven and valid scales in published academic literature at home and abroad to ensure the reliability and validity of data collection. After collecting data using the questionnaire method, invalid data were eliminated and SPSS 29.0 was used for statistical analysis.

The main SPSS dataset of over 700 variables covers 11 sections on 12 developed capitalist electoral democracies. (see Outline file included here for an overview of both sections and countries included, with the names in order of every variable and label.) The second SPSS file is of 29 variables of Covid-related daily data from OWID website that covers the same 12 countries from the start of Covid-19 in January 2020 to Aug 2, 2023.

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

While some believe protecting the environment is a necessity to avoid the end of the world, others avoid the issue entirely due to the fear that arises from considering this issue. Our study examined the effects of death thoughts on peoples’ environmental attitudes and behaviors, accounting for their guiding values of life. College students (N = 120) reported their guiding values in life, then wrote about their own death (mortality salience manipulation) or about dental pain. Participants completed two filler tasks and then reported their environmental behavior, attitudes, and self-identity; they also answered demographic questions. Mortality salience positively affected peoples’ ecocentric attitudes regardless of baseline guiding values and other demographic differences. However, mortality salience did not influence perceived environmental behaviors, environmental self-identity, or anthropocentric and apathetic attitudes toward the environment. Women reported engaging in more environmentally-friendly behaviors than men. These results suggest that while provoking death thoughts can positively affect the pro-environmentalism of some persons, understanding of the situations under which this might occur requires further study.

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.