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

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

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

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

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

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

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

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

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

Data Dictionary:

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

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

Data Location:

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

The 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.

Quantitative data from community observations are stored and managed using SPSS social survey software. The sampling unit used is a harvest event, typically a hunting or fishing event in a particular season. As of 5 September, 2008 we have received and encoded data for 56 harvest events as follows: Harvest type: Mammal (10), Fish (45), Shellfish (1) Community: Gambell (10), Kanchalan (22), Nikolskoye (6), Sandpoint (18) Preliminary SPSS Data structure: Name, Label, Type, Width ID Respondent s Identification Number String 10 INTERNO Interview Number String 2 DATE Date On Which the Interview Took Place Date 8 SEX Gender Numeric 1 YEARBO Year of Birth Numeric 11 VILLAGE Village Where Respndent Resides String 6 LOCATI Respondent Resides in Russia or Alaska Numeric 8 LIVED How Long Respondent Lived in the Area String 100 LANGUAG Language in Which Interiew Conducted Numeric 7 HARVEST Level of Harvester Numeric 4 YEARHU How Many Years Respondent Has Hunted/Fished in the Area Numeric 8 EMPLOY Is the Respondent Employed in a Non-Harvesting Field Numeric 3 TIMEWOR Time Per Week/Month Is Spent in Non-Harvest Work Numeric 8 YEARWOR How Many Years Spent in Non-Harvest Work CATEGORIES Numeric 8 Q1FISHM Is Respondent Hunting Fish or Mammals On Next Trip Numeric 4 SPECIES Species of Fish/Mammal Being Hunted/Fished Numeric 8 Q2RECA Does Respondent Recall When Last Hunt/Fish Trip Occurre Numeric 3 Q2WHEN Date of Last Hunt/Fish Trip String 50 Q2AAGO How Long Ago Was Last Hunt/Fish Trip Numeric 16 Q3FAR How Far Respondent Travelled On Last Hunt/Fish Trip Numeric Q4OFTEN How Often Respondent Hunted/Fished in the Location of Last Trip Numeric 6 Q5AGE Age When Respondent First Went to Location of Last Trip Numeric 18 Q6PROX Prefers Loc. of Last Trip Due to Proximity to Village Numeric 11 Q6ACCES Prefers Location of Last Trip Due to Ease of Access Numeric 11 Q6CATCH Prefers Location of Last Trip Due to Ease of Catching Numeric 11 Q6OTHER Prefers Location of Last Trip Due to Some Other Reason Numeric 11 Q6SPECI Other Reason Prefers Locatin of Last Trip String 200 Q6DONT Respondent Does Not Like Location of Last Trip Numeric 11 Q7RELY Is Location of Last Trip Reliable for Fishing/Hunting Numeric 3 Q8NOTIC In Previous 5-10 Years Has Respondent Noticed Changes at Last Hunt/Fish Location Numeric 3 Q9OTHER Do Others From the Village Also Hunt/Fish at Location of Last Trip Numeric 3 Q10GETA On Last Trip, Was it Easier or More Difficult to Get to Location Numeric 3 Q10GETR On Last Trip Did Respondent Encounter Difficulties Getting to Hunt/Fish Location Numeric 8 Q10ATRA More Difficult to Get to Location of Last Trip Due to Lack of Transportation Numeric 11 Q10AROA More Difficult to Get to Location of Last Trip Due to Poor Road Conditions Numeric 11 Q10AENV More Difficult to Get to Location of Last Trip Due to Poor Environ Conditions Numeric 11 Q10AECO More Diff. to Get to Location of Last Trip Due to Economics Numeric 11 Q10AHEA More Difficult to Get to Location of Last Trip Due to Personal Health Condition Numeric 11 Q10AOTHE More Difficult to Get to Location of Last Trip Due to Other Reasons Numeric 23 Q11TRAD Last Harvest Used for Traditional/Personal Use Numeric 11 Q11CASH Last Harvest Used for Generating Cash or Bartering Numeric 11 Q11REC Last Harvest Used for Recreational Hunting/Fishing Numeric 11 Q11COM Last Harvest Used for Commercial or Business Activity Numeric 11 Q11DOG Last Harvest Used for Feeding Dogs Numeric 11 Q11SHAR Last Harvest Used for Sharing with Friends/Family Numeric 11 Q11OTHE Last Harvest Used for Something Else Numeric 20 Q12QUAN Quantity of XXX Caught on Last Hunt/Fish Trip Numeric 21

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.

Estimating the distributional impacts of energy subsidy removal and compensation schemes in Ecuador based on input-output and household data.

Import files: Dictionary Categories.csv, Dictionary ENI-IOT.csv, and Dictionary Subcategories.csv based on [1] Dictionary IOT.csv and IOT_2012.csv (cannot be redistruted) based on [2] Dictionary Taxes.csv and Dictionary Transfers.csv based on [3] ENIGHUR11_GASTOS_V.csv, ENIGHUR11_HOGARES_AGREGADOS.csv, and ENIGHUR11_PERSONAS_INGRESOS.csv based on [4] Price increase scenarios.csv based on [5]

Further basic files and documents: [1] 4_M&D_Mapping ENIGHUR expenditures to IOT_180605.xlsm [2] Input-output table 2012 (https://contenido.bce.fin.ec/documentos/PublicacionesNotas/Catalogo/CuentasNacionales/Anuales/Dolares/MIP2012Ampliada.xls). Save the sheet with the IOT 2012 (Matriz simétrica) as IOT_2012.csv and edit the format: first column and row: IOT labels [3] 4_M&D_ENIGHUR income_180606.xlsx [4] ENIGHUR data can be retrieved from http://www.ecuadorencifras.gob.ec/encuesta-nacional-de-ingresos-y-gastos-de-los-hogares-urbanos-y-rurales/ Household datasets are only available in SPSS file format and the free software PSPP is used to convert .sav- to .csv-files, as this format can be read directly and efficiently into a Python Pandas DataFrame. See PSPP syntax below: save translate /outfile = filename /type = CSV /textoptions decimal = DOT /textoptions delimiter = ';' /fieldnames /cells=values /replace. [5] 3_Ecuador_Energy subsidies and 4_M&D_Price scenarios_180610.xlsx

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

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

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/ ).

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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.

IntroductionA required step for presenting results of clinical studies is the declaration of participants demographic and baseline characteristics as claimed by the FDAAA 801. The common workflow to accomplish this task is to export the clinical data from the used electronic data capture system and import it into statistical software like SAS software or IBM SPSS. This software requires trained users, who have to implement the analysis individually for each item. These expenditures may become an obstacle for small studies. Objective of this work is to design, implement and evaluate an open source application, called ODM Data Analysis, for the semi-automatic analysis of clinical study data.MethodsThe system requires clinical data in the CDISC Operational Data Model format. After uploading the file, its syntax and data type conformity of the collected data is validated. The completeness of the study data is determined and basic statistics, including illustrative charts for each item, are generated. Datasets from four clinical studies have been used to evaluate the application’s performance and functionality.ResultsThe system is implemented as an open source web application (available at https://odmanalysis.uni-muenster.de) and also provided as Docker image which enables an easy distribution and installation on local systems. Study data is only stored in the application as long as the calculations are performed which is compliant with data protection endeavors. Analysis times are below half an hour, even for larger studies with over 6000 subjects.DiscussionMedical experts have ensured the usefulness of this application to grant an overview of their collected study data for monitoring purposes and to generate descriptive statistics without further user interaction. The semi-automatic analysis has its limitations and cannot replace the complex analysis of statisticians, but it can be used as a starting point for their examination and reporting.

This folders contains three types of files. The first one is in excel format. The excel files consist of the raw data, descriptive data that describes the background of the participants, correlation and confirmatory Factor analysis outputs, and survey questionnaire in Malay and English. The second file type contains raw data in SPSS and the output in SPSS. The third file type contains the survey questionnaire; the items are bilingual.

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

DesignA cross-sectional, web-based survey design was employed, consisting of validated self-report measures designed to capture demographic information, insulin use, diabetes-related distress, disordered eating, and body shape perception.Inclusion/Exclusion criteria. Participants were eligible to participate if they self-described as being aged 18 or over, with a diagnosis of Type 1 diabetes and on a prescribed insulin regimen. They were required to be at least one-year post-diagnosis, as people who have been prescribed insulin for less than one year may not have settled into a routine with insulin management and may mismanage their insulin unintentionally. Additionally, participants were required to reside within the UK, as this removed a potential confound of cost or resources as a barrier to accessing insulin. People with a diagnosis of type 2 diabetes were excluded from the study, as the pathophysiology and treatment of the two illnesses are quite different. For example, as those with type 2 diabetes still produce some degree of insulin naturally, non-adherence to an insulin regimen is likely to have less of an immediate impact than for those with type 1 diabetes, who produce no insulin naturally (Peyrot et al., 2010). Potential participants were provided with a link to the study which provided detailed information about the study, details of informed consent and their right to withdraw. When the survey was completed, or participants chose to exit, a debrief page was presented with signposts towards various supports and resources. Participants were offered the opportunity to receive a brief summary of findings from the study and given the chance to win a £25 Amazon gift voucher, both of which required an email address to be supplied through separate surveys, so as to protect the confidentiality of responses. Ethical approval for this study was granted by the chair of the relevant Ethics Committee.Statistical AnalysisPrior to beginning the study, an estimate of the minimum number of participants required was calculated using statistical power tables (Clark-Carter, 2010) and G*Power version 3.1. Based on previous research (Ames, 2017), a medium effect size (.5) was used to calculate sample sizes with a power of .8 (Clark-Carter, 2010), which generated a necessary sample size of 208. All analyses were adequately powered.Data were analysed using IBM SPSS Statistics for Mac version 25. MeasuresDemographic Information. This section collected basic demographic information, including age; gender; country of residence; and current or historical diagnosis of an eating disorder. The data were screened to ensure participants met the inclusion criteria.Insulin Measure. A 16-item questionnaire has been designed to assess rates and reasons for insulin non-adherence (Ames, 2017). Eating Disorder Psychopathology. The Eating Disorder Examination-Questionnaire (EDE-Q) assesses eating disorder psychopathology, and data from this measure was key to informing the primary research questions. It was designed as a self-report version of the interview-based Eating Disorders Examination (EDE; 32), which is considered to be the gold standard measure (Fairburn, Wilson, & Schleimer, 1993). The EDE-Q assesses four subscales: Restraint, Eating Concern, Shape Concern, and Weight Concern. It was found to be an adequate alternative to the EDE (Fairburn & Beglin, 1994). Body Shape Questionnaire (BSQ). The Body Shape Questionnaire is a 34-item self-report measure, designed to assess concerns regarding body shape and the phenomenological experience of “feeling fat” (Cooper, Taylor, Cooper, & Fairbum, 1987). The BSQ targets body image as a central feature of both AN and BN and thus is a useful supplementary measure of eating disorder psychopathology. Diabetes Distress. The Diabetes Distress Scale (Polonsky et al., 2005) is a 17-item scale designed to measure diabetes-related emotional distress via four domains: emotional burden, physician distress, interpersonal distress and regimenn distress. This measure was included on the basis of results from Ames (Ames, 2017), which identified diabetes-related emotional distress as a key reason for insulin non-adherence in type 1 diabetes. Inclusion in this study allowed for further investigation of its role.

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

Abstract

The Viet Nam Multiple Indicator Cluster Survey (MICS) was carried by General Statistics Office of Viet Nam (GSO) in collaboration with Viet Nam Committee for Population, Family and Children (VCPFC). Financial and technical support by the United Nations Children's Fund (UNICEF).

In the World Summit for children held in New York in 1990, the Government of Vietnam committed itself to the implementation of the World Declaration and Plan of Action for children.

In implementation of directive 34/1999/CT-TTg on 27 December 1999 on promoting the implementation of the end-decade goals for children, reviewing the National Plan of Action for children, 1991-2000 and designing the National Plan of Action for children, 2001-2010, in the framework of the “Development of Social Indicators” project, the General Statistical Office (GSO) has chaired and coordinated with the Viet Nam Committee for the Protection and Care for Children (CPCC) to conduct the survey evaluating the end- decade goals for children, 1991-2000 (MICS). MICS has covered a sample size of 7628 households in 240 communes and wards representing the whole country, the urban area, the rural area and the 8 geographical areas in 61 towns/provinces. Field activities to collect data lasted 2 months, May- June/2000. The survey was technically supported by statisticians from EAPRO, UNICEF regional offices, UNICEF Hanoi on sample and questionnaire designing, data input software, not least the software analyzing and calculating the estimates generalizing the results of survey.

Survey Objectives: The end-decade survey on children is aimed at. · Providing up-to-date and reliable data to analyse the situation of children and women in 2000. · Providing data to assess the implementation of the World summit goals for children and of the National Plan of Action for Vietnamese Children, 1991-2000. · Serving as a basis (with baseline data and information) for development of the National Plan of Action for Children, 2001-2010. · Building professional capacity in monitoring, managing and evaluating all the goals of child protection, care and education at all levels.

Geographic coverage

The 2000 MICS of Vietnam was a nationally representative sample survey.

Analysis unit

Households, Women, Child.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample for the Viet Nam Multiple Indicator Cluster Survey (MICSII) was designed to provide reliable estimates on a large number of indicators on the situation of children and women at the national level, for urban and rural areas, and for 8 regions: Red River Delta, North West, North East, North Central Coast, South Central Coast, Central Highlands, South East, and Mekong River Delta. Regions were identified as the main sampling domains and the sample was selected in two stages: At the first stage, 240 EAs are sellected. After a household listing was carried out within the selected enumeration areas, a systematic sample of 1/3 of households in each EA was drawn. The survey managed to visit all of 240 selected EAs during the fieldwork period. The sample was stratified by region and is not self-weighting. For reporting national level results, sample weights are used.

Sampling deviation

No major deviations from the original sample design were made. All sample enumeration areas were accessed and successfully interviewed with good response rates.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaires for MICS in Vietnam are based on the New York UNICEF module questionnaires with some modifications and additions to fit in with Vietnam's context and to evaluate the goals set out in the National Plan of Action. The questionnaires have been arranged in such a way as to prevent the loss of questionnaire sheets and to facilitate the logic control between the items in the modules. Questionnaires include 3 sections. Section 1: general questions to be administered to families and family members. Section 2: questions for child bearing-age women (aged 15-49). Section 3: for children under 5.

Section 1: Household questionnaire Part A: Household information panel Part B: Household listing form Part C: Education Part D: Child labour Part E: Maternal mortality Part F: Water and sanitation Part G: Salt iodization

Section 2: Questionnaire for child bearing-age women Part A: Child mortality Part B: Tetanus toxoid (TT) Part C: Maternal and newborn health Part D: Contraceptive use Part E: HIV/AIDS

Section 3: Questionnaire for children under five Part A:Birth registration and early learning Part B: Vitamin A Part C: Breastfeeding Part D: Care of illness Part E: Malaria Part F: Immunization Part G: Anthropometry

Apart from the questionnaires to collect information at family level, questionnaires are also designed to gather information at community level supplementary to some indicators that can not have data collected at family level. The information garnered includes local population, socio-economic and physical conditions, education, health and progress of projects/plans of actions for children.

Cleaning operations

To minimize the errors made by data entry staff members, all the records were double- entered by two different members. Any error detected between the two entries was re-checked to find out which one is wrong. Data cleaning started in to early September. This process was closely observed to ensure the accuracy, quality and practicality of all the data collected.

To minimize the errors due to wrong statements of respondents or wrong registration by interviewers, a cleaning programme was used to check the consistency and logic in the items of questionnaires and between the questionnaires. The cleaning programme printed out all the errors, then questionnaires were checked by qualified officials.

Response rate

8356 households were selected for the sample. Of these all were found to be occupied households and 8355 were successfully interviewed for a response rate of 100%. Within these households, 10063 eligible women aged 15-49 were identified for interview, of which 9473 were successfully interviewed (response rate 94.1%), and 2707 children aged 0-4 were identified for whom the mother or caretaker was successfully interviewed for 2680 children (response rate 99%).

Sampling error estimates

Estimates from a sample survey are affected by two types of errors: 1) non-sampling errors and 2) sampling errors. Non-sampling errors are the results of mistakes made in the implementation of data collection and data processing. Numerous efforts were made during implementation of the MICS - 3 to minimize this type of error, however, non-sampling errors are impossible to avoid and difficult to evaluate statistically.

Sampling errors can be evaluated statistically. The sample of respondents to the MICS - 3 is only one of many possible samples that could have been selected from the same population, using the same design and expected size. Each of these samples would yield results that different somewhat from the results of the actual sample selected. Sampling errors are a measure of the variability in the results of the survey between all possible samples, and, although, the degree of variability is not known exactly, it can be estimated from the survey results. The sampling errors are measured in terms of the standard error for a particular statistic (mean or percentage), which is the square root of the variance. Confidence intervals are calculated for each statistic within which the true value for the population can be assumed to fall. Plus or minus two standard errors of the statistic is used for key statistics presented in MICS, equivalent to a 95 percent confidence interval.

If the sample of respondents had been a simple random sample, it would have been possible to use straightforward formulae for calculating sampling errors. However, the MICS - 3 sample is the result of a two-stage stratified design, and consequently needs to use more complex formulae. The SPSS complex samples module has been used to calculate sampling errors for the MICS - 3. This module uses the Taylor linearization method of variance estimation for survey estimates that are means or proportions. This method is documented in the SPSS file CSDescriptives.pdf found under the Help, Algorithms options in SPSS.

Sampling errors have been calculated for a select set of statistics (all of which are proportions due to the limitations of the Taylor linearization method) for the national sample, urban and rural areas, and for each of the five regions. For each statistic, the estimate, its standard error, the coefficient of variation (or relative error -- the ratio between the standard error and the estimate), the design effect, and the square root design effect (DEFT -- the ratio between the standard error using the given sample design and the standard error that would result if a simple random sample had been used), as well as the 95 percent confidence intervals (+/-2 standard errors).

Data appraisal

A series of data quality tables and graphs are available to review the quality of the data and include the following:

Age distribution of the household population Age distribution of eligible women and interviewed women Age distribution of eligible children and children for whom the mother or caretaker was interviewed Age distribution of children under age 5 by 3 month groups Age and period ratios at boundaries of eligibility Percent of observations with missing information on selected variables Presence of mother in

This study selected the relevant literature related to the adverse drug reactions of metformin from 1991 to 2020 as the data source, divided the time segment with a period of 3 years, and obtained the title information (see the title collection of the literature included in the study. zip), and then extracted the subject words through the bicomb2021 software to construct the co-occurrence matrix, and a total of 10 co-occurrence matrices were obtained (see the subject word co-occurrence matrix collection included in the study. zip). Import the 10 co-occurrence matrices into the self-designed python code and r code (see opportunity code. zip; trust code. zip; open triangle and closed triangle code. zip) to obtain the opportunity, trust value and the number of edge triangles of each node pair in the 10 networks. Use GePhi0.9.7 software to calculate the motivation value of the node pair, use Excel to calculate the global clustering coefficient of each network, and the edge clustering coefficient of each node pair, The number of edge triangles of each node pair is built by using excel software to construct the scatter diagram of node pair opportunity, trust, motivation value and node pair edge clustering coefficient, and the correlation between node pair opportunity value and edge clustering coefficient is calculated by using spss software, as well as the correlation between node pair trust, motivation value and edge clustering coefficient, and the number of closed triangles of node pair (see code operation and software calculation result set. zip).Select the literature bibliography data from 2000 to 2009 to build the panel data (see the literature bibliography collection included in the study. zip), and also use the self-designed python code and r code (see opportunity code. zip; trust code. zip; open triangle and closed triangle code. zip) to get the opportunity, trust value and the number of edge triangles of each node pair in 10 networks, and use GePhi0.9.7 software to calculate the motivation value of node pairs Proximity centrality, intermediary centrality, feature vector centrality and average path length of node pairs are imported into Stata/MP 17.0 software to obtain the correlation between node attributes and network characteristics (see code operation and software calculation result set. zip).The data contained in each data name is described in detail:1. Collection of bibliographies included in the studyThe data collection contains two folders, named the literature collection from 1991 to 2020 and the literature collection from 2000 to 2009. The literature collection from 1991 to 2020 stores the bibliographic data of 10 time periods from 1991 to 2020, and the literature collection from 2000 to 2009 stores the bibliographic data of 10 overlapping windows from 2000 to 2009.2. Co-occurrence matrix set of subject words included in the studyThe data set contains two folders, named the 1991-2020 subject word co-occurrence matrix set and the 2000-2009 subject word co-occurrence matrix set. The subject word co-occurrence matrix of 1991-2020 contains the subject word co-occurrence matrix of 10 time segments from 1991-2020. The first row and first column of each co-occurrence matrix are subject words, and the number represents the number of co-occurrence times of the subject word pair. The subject word co-occurrence matrix set in 2000-2009 stores the subject word co-occurrence matrix of 10 time windows in 2000-2009.3. Opportunity Code.zipThis code is used to calculate the opportunity value of node pair. The input data is co-occurrence matrix, and the input format is. csv format.4. Trust Code.zipThis code is used to calculate the opportunity value of node pair. The input data is co-occurrence matrix, and the input format is. csv format.5. Code of open triangle and closed triangle.zipThis code is used to calculate the number of closed triangles and open triangles on the side of the node pair. The input data is the co-occurrence matrix, and the input format is. csv format.6. Code run and software calculation result set.zipThe data set contains two folders, named 1991-2020 calculation results and 2000-2009 calculation results. The 1991-2020 calculation results store the calculation results and scatter diagrams of 10 time segments in 1991-2020. Take 1991-1993 as an example, the first row of each table is marked with the opportunity, comprehensive trust, motivation, edge clustering coefficient, and the number of closed triangles. At the end of each table, the mean value of opportunity, trust, motivation and Pearson correlation coefficient with edge clustering coefficient and the number of closed triangles are calculated.The 2000-2009 folder stores the panel data and the opportunity, trust, motivation of the stata software calculation, and the correlation between the node attributes and the network characteristics of the node.

For a comprehensive guide to this data and other UCR data, please see my book at ucrbook.comVersion 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 feloniously or accidentally killed or assaulted in the line of duty. The purpose of this data collect

survey input into SPSS

For any questions about this data please email me at jacob@crimedatatool.com. If you use this data, please cite it.Version 7 release notes:Add 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.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. See the R code for a complete list. For the R code used to clean this data, see here. https://github.com/jacobkap/crime_data.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 feloniously or accidentally killed or assaulted in the line of duty. The purpose of this data collection is to identify situations in which officers are killed or assaulted, describe the incidents statistically, and publish the data to aid agencies in developing policies to improve officer safety."... agencies must record assaults on sworn officers. Reporting agencies must count all assaults that resulted in serious injury or assaults in which a weapon was used that could have caused serious injury or death. They must include other assaults not causing injury if the assault involved more than mere verbal abuse or minor resistance to an arrest. In other words, agencies must include in this section all assaults on officers, whether or not the officers sustained injuries."

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.

Data collection for Study 2 was conducted between December 2021 to February 2022, during which COVID-19 continued to ravage the world. The study design is the same as Study 1, except for the removal of the mild pandemic condition and a change of dependent variables (DVs) to assess DTA. The mild pandemic condition was removed as it did not trigger significant perception of threats in our pilot study and there were no significant effects of mild pandemic condition on DTA in Study 1. The DWFT was replaced with the Death Anxiety Scale (DAS), lexical-decision task (LDT), and the dot-probe task (DPT).

Similar to Study 1, Study 2 hypothesizes that the pandemic condition and the standard MS condition will yield significantly higher levels of death cognitions than the control conditions after a time delay, even when we control for health anxiety after a time delay. Study 2 was pre-registered with Open Science Framework (OSF) Registries (Registration DOI: 10.17605/OSF.IO/4SD2J).

Information pertaining to Study 1 can be found: https://doi.org/10.25903/sx6c-re28

This data record contains:

1 x SPSS (.sav) file containing input data and calculation of death anxiety, response time on lexical decision task and attention bias index from dot probe task used in analysis. File 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