SPSS datasets are available with German and French labelling. STATA datasets with English labels are also available. Codebooks and questionnaires document all three survey languages (German, French and Italian). All generic and explanatory information on the data is available in English, German and French.

Note: This version supersedes version 1: https://doi.org/10.15482/USDA.ADC/1522654. In Fall of 2019 the USDA Food and Nutrition Service (FNS) conducted the third Farm to School Census. The 2019 Census was sent via email to 18,832 school food authorities (SFAs) including all public, private, and charter SFAs, as well as residential care institutions, participating in the National School Lunch Program. The questionnaire collected data on local food purchasing, edible school gardens, other farm to school activities and policies, and evidence of economic and nutritional impacts of participating in farm to school activities. A total of 12,634 SFAs completed usable responses to the 2019 Census. Version 2 adds the weight variable, “nrweight”, which is the Non-response weight. Processing methods and equipment used The 2019 Census was administered solely via the web. The study team cleaned the raw data to ensure the data were as correct, complete, and consistent as possible. This process involved examining the data for logical errors, contacting SFAs and consulting official records to update some implausible values, and setting the remaining implausible values to missing. The study team linked the 2019 Census data to information from the National Center of Education Statistics (NCES) Common Core of Data (CCD). Records from the CCD were used to construct a measure of urbanicity, which classifies the area in which schools are located. Study date(s) and duration Data collection occurred from September 9 to December 31, 2019. Questions asked about activities prior to, during and after SY 2018-19. The 2019 Census asked SFAs whether they currently participated in, had ever participated in or planned to participate in any of 30 farm to school activities. An SFA that participated in any of the defined activities in the 2018-19 school year received further questions. Study spatial scale (size of replicates and spatial scale of study area) Respondents to the survey included SFAs from all 50 States as well as American Samoa, Guam, the Northern Mariana Islands, Puerto Rico, the U.S. Virgin Islands, and Washington, DC. Level of true replication Unknown Sampling precision (within-replicate sampling or pseudoreplication) No sampling was involved in the collection of this data. Level of subsampling (number and repeat or within-replicate sampling) No sampling was involved in the collection of this data. Study design (before–after, control–impacts, time series, before–after-control–impacts) None – Non-experimental Description of any data manipulation, modeling, or statistical analysis undertaken Each entry in the dataset contains SFA-level responses to the Census questionnaire for SFAs that responded. This file includes information from only SFAs that clicked “Submit” on the questionnaire. (The dataset used to create the 2019 Farm to School Census Report includes additional SFAs that answered enough questions for their response to be considered usable.) In addition, the file contains constructed variables used for analytic purposes. The file does not include weights created to produce national estimates for the 2019 Farm to School Census Report. The dataset identified SFAs, but to protect individual privacy the file does not include any information for the individual who completed the questionnaire. Description of any gaps in the data or other limiting factors See the full 2019 Farm to School Census Report [https://www.fns.usda.gov/cfs/farm-school-census-and-comprehensive-review] for a detailed explanation of the study’s limitations. Outcome measurement methods and equipment used None Resources in this dataset:Resource Title: 2019 Farm to School Codebook with Weights. File Name: Codebook_Update_02SEP21.xlsxResource Description: 2019 Farm to School Codebook with WeightsResource Title: 2019 Farm to School Data with Weights CSV. File Name: census2019_public_use_with_weight.csvResource Description: 2019 Farm to School Data with Weights CSVResource Title: 2019 Farm to School Data with Weights SAS R Stata and SPSS Datasets. File Name: Farm_to_School_Data_AgDataCommons_SAS_SPSS_R_STATA_with_weight.zipResource Description: 2019 Farm to School Data with Weights SAS R Stata and SPSS Datasets

Here you can find the datasets and Stata codes to replicate the tables and figures in "Gender quotas and politicians' education", authored by Francesca Passarelli and David Boto-García.

The dataset contains modified Living Standards Measurment Survey (LSMS) data from eight African countries: Cote D' Ivoire, Ghana, Guinea, Malawi, Madagascar, Niger, Nigeria and Uganda that is used for the analysis in the thesis. The dataset is saved in Stata format (.dta) and can be opened using Stata software. The information contained in the country level data sets includes individual and parental education (converted into years of schooling), the average education of the parental ethnic group (converted into years of schooling), the ethnicity, age and sex of the individuals, and average sizes of the household. The pooled dataset further contains the variable country which is the country identifier.

These Stata files—a .dta dataset and an accompanying .do script—replicate all analyses reported in “Attitudes toward education spending when facing a fiscal trade-off: An analysis of stakeholders,” by Özel, Parrado, and Yildirim, published in European Sociological Review.

To all,

This dataset was generated in order to fullfill a requirement for a graduate class in applied econometrics. I originally wanted to collect data on the effect of COVID-19 on student performance from a school district, but was unable to given that our local district was already conducting their own research.

The set contains a panel dataset, meant to emulate 6 semesters/trimesters with the first three taking place before the COVID-19 lockdowns, and the final three coming after the lockdowns. It also contains a cross-sectional dataset that is meant to be a single semester/trimester after the COVID-19 lockdowns. Variables were included and manipulated to model real world trends, or local demographics in Portland Oregon. There is a full list of variables at the end of this markdown.

It should be noted that student performance has greatly been diminished as a result of online education.

Feel free to reach out about the Stata code. It ended up being about 1500 lines to generate and manipulate, but I'm happy to share it with the same Public Domain license.

// VARIABLES used in the program // // NAME DATATYPE PURPOSE // // PERSONAL INFORMATION // // studentID into Number assigned to student. // school dummy 0/1, bool 1=school B (poor), 0= school A (wealthy) // gradelevel int Determine grade level of child. // gender dummy 0/1, bool 1=male, 0=female // covidpos dummy 0/1 1=child had Covid, 0=null // freelunch dummy 0/1 1=takes free and reduced lunch, 0=null // timeperiod categorical {0,1,2}=in-person learning, {3,4,5}=online learning // numcomputers into Defines number of computers in child's home. // familysize int Defines size of family, parents and siblings // householdincome float Household income for child. // fathereduc categorical System of values for highest level of father education // /* // no HS diploma 0 -- // High School diploma 1 Highest level of education is High School. // Bachelor degre 2 " " bachelors degree. // Master's Degree 3 " " masters degree. // Doctoral Degree 4 " " PhD. // \ // \ // then, if fathereduc=0, father did not finish High School. // / // mothereduc categorical System of values for highest level of mother education // / // no HS diploma 0 -- // High School diploma 1 Highest level of education is High School. // Bachelor degre 2 " " bachelors degree. // Master's Degree 3 " " masters degree. // Doctoral Degree 4 " " PhD. // \ // \ // then, if mothereduc=0, mother did not finish High School. // */ // // SCHOOL PERFORMANCE INFORMATION // // readingscore float Score for "reading" test in school. // writingscore float Score for "writing" test in school. // mathscore float Score for "math" test in school.
// // STATE PERFORMANCE INFORMATION // // readingscoreSL float Score for "reading" test at state level. // writingscoreSL float Score for "writing" test at state level. // mathscoreSL float Score for "math" test at state level. */

This dataset supports the publication: Woods-Townsend, Kathryn et al. (2021). A cluster-randomised controlled trial of the LifeLab education intervention to improve health literacy in adolescents. PLOS ONE The dataset contains the answers to the original questions asked of the adolescents in this trial and derived variables needed for the analysis. A few variables have been removed from the original dataset, such as date of birth, to preserve confidentiality. Three versions of the dataset are available: an SPSS portable file (.por); a Stata data file (.dta); and an SPSS data file (.sav).

This repository contains datasets and analysis code accompanying the paper "An Empirical Evaluation of Chinese College Admissions Reforms Through A Natural Experiment" by Chen, Jiang, and Kesten. The datasets contain the college admission data for a county in China's Sichuan Province for year 2008 and 2009. These include students' submitted rank-ordered lists of colleges and admission results. All variables are recoded to remove any identifiable information (including college and high school code). The analysis code can be used to replicate the tables and figures in the paper.

Understanding Society, (UK Household Longitudinal Study), which began in 2009, is conducted by the Institute for Social and Economic Research (ISER) at the University of Essex and the survey research organisations Verian Group (formerly Kantar Public) and NatCen. It builds on and incorporates, the British Household Panel Survey (BHPS), which began in 1991. The Understanding Society: Longitudinal Teaching Dataset, Waves 1-9, 2009-2018 is a teaching resource using data from Understanding Society, the UK Household Longitudinal Study, which interviews individuals in the sampled households every year. There are two target audiences – 1) lecturers who would like to use the data file provided for longitudinal methods teaching purposes, and 2) data users who are new to using longitudinal data and can get a better understanding of using longitudinal data by using the supplied analysis guidance which utilizes the data file. The statistical software used to construct the dataset is Stata and the analysis guidance provided is accompanied by Stata syntax only. The datafile is also available to download in SPSS and tab-delimited text formats. The User Guide includes guidance on how to convert the datafile in Stata format to R. A second teaching resource using the Understanding Society survey is also available, see SN 8465, Understanding Society: Ethnicity and Health Teaching Dataset. For information on the main Understanding Society study, see SN 6614, Understanding Society and Harmonised BHPS.

This study covers topics such as socio-demographic characteristics, education and labour market information, residential information, income, health and wellbeing, political behaviour and opinions, environmental attitudes and behaviours.

BackgroundBangladesh is one of the most densely populated countries in the world, with more than one-third of its people living in cities, and its air quality is among the worst in the world. The present study aimed to measure knowledge, attitudes and practice (KAP) towards air pollution and health effects among the general population living in the large cities in Bangladesh.MethodsA cross-sectional e-survey was conducted between May and July 2022 among eight divisions in Bangladesh. A convenience sampling technique was utilized to recruit a total of 1,603 participants (55.58% males; mean age: 23.84 ± 5.93 years). A semi-structured questionnaire including informed consent, socio-demographic information, as well as questions regarding knowledge (11-item), attitudes (7-item) and practice (11-item) towards air pollution, was used to conduct the survey. All analyses (descriptive statistics and regression analyses) were performed using STATA (Version 15.0) and SPSS (Version 26.0).ResultsThe mean scores of the knowledge, attitudes, and practice were 8.51 ± 2.01 (out of 11), 19.24 ± 1.56 (out of 21), and 12.65 ±5.93 (out of 22), respectively. The higher scores of knowledge, attitudes, and practice were significantly associated with several socio-demographic factors, including educational qualification, family type, residential division, cooking fuel type, etc.ConclusionsThe present study found a fair level of knowledge and attitudes towards air pollution; however, the level of practice is not particularly noteworthy. The finding suggests the need to create more awareness among the general population to increase healthy practice to reduce the health effects of air pollution.

These data are part of NACJD's Fast Track Release and are distributed as they were received from the data depositor. The files have been zipped by NACJD for release, but not checked or processed except for the removal of direct identifiers. Users should refer to the accompanying readme file for a brief description of the files available with this collection and consult the investigator(s) if further information is needed. This study evaluated a school-based, wraparound intervention for police- and court-involved youth in four high schools in Florida's School District of Palm Beach County. The intervention involved a collaboration between the schools, school police, the juvenile court, and several service providers. The collection contains 1 Stata data file (Data.dta (n=863; 118 variables)) and 1 Stata program file (Syntax.do).

The file "Material Overview" provides an overview of the data and documentation for PICE; including information in which languages the respective documentation is available.

The Zip with the data contains the interviews of youngsters and parents. Youngsters have been interviewed once and their parents twice. This Zip also contains a Stata file "Closed questions PICE Parents".

A detailed description of the dataset can be found in the Technical Report.

Administrative data used in regression analyses to test for interactions among student gender, teacher gender, and performance on a state civics and economics exam. Data were provided by the North Carolina Education Research Data Center. To maintain privacy of the individuals involved in the study, all of whom were public high school students, we are not permitted to share our dataset. We have instead posted a copy of the data use agreement and a Stata do file and codebook for the main regression analysis used in the paper.

This study tests the efficacy of an intervention--Safe Public Spaces (SPS) -- focused on improving the safety of public spaces in schools, such as hallways, cafeterias, and stairwells. Twenty-four schools with middle grades in a large urban area were recruited for participation and were pair-matched and then assigned to either treatment or control. The study comprises four components: an implementation evaluation, a cost study, an impact study, and a community crime study. Community-crime-study: The community crime study used the arrest of juveniles from the NYPD (New York Police Department) data. The data can be found at (https://data.cityofnewyork.us/Public-Safety/NYPD-Arrests-Data-Historic-/8h9b-rp9u). Data include all arrest for the juvenile crime during the life of the intervention. The 12 matched schools were identified and geo-mapped using Quantum GIS (QGIS) 3.8 software. Block groups in the 2010 US Census in which the schools reside and neighboring block groups were mapped into micro-areas. This resulted in twelve experimental school blocks and 11 control blocks which the schools reside (two of the control schools existed in the same census block group). Additionally, neighboring blocks using were geo-mapped into 70 experimental and 77 control adjacent block groups (see map). Finally, juvenile arrests were mapped into experimental and control areas. Using the ARIMA time-series method in Stata 15 statistical software package, arrest data were analyzed to compare the change in juvenile arrests in the experimental and control sites. Cost-study: For the cost study, information from the implementing organization (Engaging Schools) was combined with data from phone conversations and follow-up communications with staff in school sites to populate a Resource Cost Model. The Resource Cost Model Excel file will be provided for archiving. This file contains details on the staff time and materials allocated to the intervention, as well as the NYC prices in 2018 US dollars associated with each element. Prices were gathered from multiple sources, including actual NYC DOE data on salaries for position types for which these data were available and district salary schedules for the other staff types. Census data were used to calculate benefits. Impact-evaluation: The impact evaluation was conducted using data from the Research Alliance for New York City Schools. Among the core functions of the Research Alliance is maintaining a unique archive of longitudinal data on NYC schools to support ongoing research. The Research Alliance builds and maintains an archive of longitudinal data about NYC schools. Their agreement with the New York City Department of Education (NYC DOE) outlines the data they receive, the process they use to obtain it, and the security measures to keep it safe. Implementation-study: The implementation study comprises the baseline survey and observation data. Interview transcripts are not archived.

Stata dataset and syntax file to replicate article findings.

The Health Survey for England, 2003-2005: Multilevel Modelling Teaching Dataset has been prepared as a resource for those interested in learning multilevel modelling techniques. It was first presented as part of a workshop entitled 'Introducing multilevel models and applying them to the Health Survey for England using MLwiN'. The HSE teaching dataset is available in both Stata and MLwIN formats and is accompanied by a practical guide that includes the multilevel modelling practical exercises. A separate document provides information on the teaching dataset and materials.

The main dataset is an edited version of the Health Survey for England (HSE) data from 2003, 2004 and 2005 (the full HSEs are at the UK Data Archive under SNs 5098, 5439 and 5675). Details of the recoding of HSE variables for the teaching dataset and how the aggregate data were produced can be found in the documentation.

WARNING – Users should note that this dataset is intended as a learning resource and should not be used for research purposes. In particular the dataset uses adult measures of Body Mass Index (BMI) for children and so the results from the data should not be reported in research contexts.

Stata dataset and do-files. The main do-file is "WBER replication.do", which calls up the other two do-files.

While this special pandemic period has been seeing an increasing use of blended learning, few studies have meta-analytically reviewed the effectiveness of blended learning in different countries. This meta-analysis summarizes previous studies on blended learning effectiveness in different countries in terms of students' performance, students' attitudes toward blended learning, learning achievement, and student engagement in different countries. Through the meta-analysis via Stata/MP 14.0, it is concluded that blended learning can improve performance, attitude, and achievement in most countries. However, in both China and the USA, blended learning cannot significantly improve student engagement in academic activities. No significant differences were revealed in student performance in the USA between blended and non-blended learning. Future research can extend the research into blended learning to more countries and areas across the world.

Replication data for: "Lead and Juvenile Delinquency: New Evidence from Linked Birth, School and Juvenile Detention Records". These files include a readme file, stata do files that convert the raw data into the the analysis dataset, stata do files that perform the analysis. This file also includes the publicly available data used for the analysis and instructions for obtaining the non-public data (the individual level data on child blood, school, natality and crime).

This study contains script files to create teaching versions of Understanding Society: Waves 1-3, the new UK household panel survey. Specifically, the user can focus on individual waves, or can create a panel survey dataset for use in teaching undergraduates and postgraduates. Core areas of focus are attitudes to voting and political parties, to the environment, and to ethnicity and migration. Script files are available for SPSS, STATA and R. Individuals wishing to make use of this resource will need to apply separately to the UK data archive for access to the original datasets: http://discover.ukdataservice.ac.uk/catalogue/?sn=6614 &type=Data%20catalogue