This replication package reproduces the results for the paper entitled "Adding measurement error to location data to protect subject confidentiality while allowing for consistent estimation of exposure effects," which is published in The Journal of the Royal Statistical Society: Series C (Applied Statistics), DOI: https://doi.org/10.1111/rssc.12439. This package contains 2 Stata Do-Files (.do) that produce the simulated dataset and run one replication of the simulation (the main paper runs 1,000 replications), and 2 Stata Data Files (.dta) that are used for the simulation in the main paper. (2020-02-29).

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.

Data for paper published in PLOS ONE 14.07.2023 These files were used for the statistical analysis of the hemparc feasibility trial using Stata software verson 17, and are as follows, both Stata format and .csv format as appropriate. The .do file is a simple text file. hepmarc_data minimum dataset: .csv, .dta: See doi:10.1136/bmjopen-2019-035596 for study protocol describing all data collected hepmarc Data dictionary .xls, .dta; description of each data fields in minimum dataset hepmarc AE listing: Adverse events listing .csv, .dta hepmarc SAP v1.0 240322_.xls .dta; description of each data fields in minimum dataset hepmarc data.do Stata .do file used to perform the analysis Notes: Each particpant's age has been altered by a random amount to preserve anonymity. There are two rows for two of the participants who each reported two adverse reactions. Abstract Objectives Maraviroc may reduce hepatic inflammation in people with HIV and non-alcoholic fatty liver disease (HIV-NAFLD) through CCR5-receptor antagonism, which warrants further exploration. Methods We performed an open-label 96-week randomised-controlled feasibility trial of maraviroc plus optimised background therapy (OBT) versus OBT alone, in a 1:1 ratio, for people with virologically-suppressed HIV-1 and NAFLD without cirrhosis. Dosing followed recommendations for HIV therapy in the Summary of Product Characteristics for maraviroc. The primary outcomes were safety, recruitment and retention rates, adherence and data completeness. Secondary outcomes included the change in Fibroscan-assessed liver stiffness measurements (LSM), controlled attenuation parameter (CAP) and Enhanced Liver Fibrosis (ELF) scores. Results Fifty-three participants (53/60, 88% of target) were recruited; 23 received maraviroc plus OBT; 89% were male; 19% had type 2 diabetes mellitus. The median baseline LSM, CAP & ELF scores were 6.2 (IQR 4.6-7.8) kPa, 325 (IQR 279-351) dB/m and 9.1 (IQR 8.6-9.6) respectively. Primary outcomes: all individuals eligible after screening were randomised; there was 92% (SD 6.6%) adherence to maraviroc [target >90%]; 83% (95%CI 70%-92%) participant retention [target >65%]; 5.5% of data were missing [target <20%]. There were noo Serious Adverse Reactions ; mild-moderate intensity Adverse Reactions were reported by five participants (5/23, 22% (95%CI 5%-49%)) [target <10%]. All Adverse Reactionss resolved. Secondary outcomes: no important differences were seen by treatment group for the change from baseline in LSM, CAP or ELF scores Conclusions This feasibility study provides preliminary evidence of maraviroc safety amongst people with HIV-NAFLD, and acceptable recruitment, retention, and adherence rates. These data support a definitive randomised-controlled trial assessing maraviroc impact on hepatic steatosis and fibrosis. Clinical trial registry: ISCRTN, registration number 31461655.

This replication package contains the raw data and code to replicate the findings reported in the paper. The data are licensed under a Creative Commons Attribution 4.0 International Public License. The code is licensed under a Modified BSD License. See LICENSE.txt for details.

Software requirements

All analysis were done in Stata version 16:

• Add-on packages are included in scripts/libraries/stata and do not need to be installed by user. The names, installation sources, and installation dates of these packages are available in scripts/libraries/stata/stata.trk.

Instructions

1. Save the folder ‘replication_PLOS’ to your local drive.
2. Open the master script ‘run.do’ and change the global pointing to the working direction (line 20) to the location where you save the folder on your local drive
3. Run the master script ‘run.do’ to replicate the analysis and generate all tables and figures reported in the paper and supplementary online materials

Datasets

• Wave 1 – Survey experiment: ‘wave1_survey_experiment_raw.dta’
• Wave 2 – Follow-up Survey: ‘wave2_follow_up_raw.dta'
• Map: shape-files ‘plz2stellig.shp’ ‘OSM_PLZ.shp’, area codes ‘Postleitzahlengebiete-_OSM.csv’_, (all links to the sources can be found in the script ‘04_figure2_germany_map.do’)
• Pretest: ‘pre-test_corona_raw.dta’
• For Appendix S7: ‘alter_geschlecht_zensus_det.xlsx’, ‘vaccination_landkreis_raw.dta’, ‘census2020_age_gender.csv’ (all links to the sources can be found in the script ‘06_AppendixS7.do’)
• For Appendix S10: ‘vaccination_landkreis_raw.dta’ (all links to the sources can be found in the script ‘07_AppendixS10.do’)

Descriptions of scripts

1_1_clean_wave1.do
This script processes the raw data from wave 1, the survey experiment
1_2_clean_wave2.do
This script processes the raw data from wave 2, the follow-up survey
1_3_merge_generate.do
This script creates the datasets used in the main analysis and for robustness checks by merging the cleaned data from wave 1 and 2, tests the exclusion criteria and creates additional variables
02_analysis.do
This script estimates regression models in Stata, creates figures and tables, saving them to results/figures and results/tables
03_robustness_checks_no_exclusion.do
This script runs the main analysis using the dataset without applying the exclusion criteria. Results are saved in results/tables
04_figure2_germany_map.do
This script creates Figure 2 in the main manuscript using publicly available data on vaccination numbers in Germany.
05_figureS1_dogmatism_scale.do
This script creates Figure S1 using data from a pretest to adjust the dogmatism scale.
06_AppendixS7.do
This script creates the figures and tables provided in Appendix S7 on the representativity of our sample compared to the German average using publicly available data about the age distribution in Germany.
07_AppendixS10.do
This script creates the figures and tables provided in Appendix S10 on the external validity of vaccination rates in our sample using publicly available data on vaccination numbers in Germany.

This includes the data and Stata code to replicate the results in the JAERE article. In order to replicate the results, create a folder on your computer. Then add each of the compressed folders that are listed as tar files (note: search for ".tar" in the list of files) into that folder. The folder dataRaw has two tar files associated with it that should be combined into a single folder dataRaw. Then create empty folders titled "figures", "logs", and "tables" as running the code will add files into these folders. Download the file "ReadMe.docx" and follow the instructions in the Word document to replicate the results.

Replication programs and data for the illustrative example in Mityakov and Mroz's "Unobserved Inputs in Household Production," with README file containing instructions and a list of all programs needed to be run to replicate the results using the enclosed data. The statistical software package STATA (and some STATA add-ons described in the README) is needed for the replication

Code and data to reproduce all results and graphs reported in Tannenbaum et al. (2022). This folder contains data files (.dta files) and a Stata do-file (code.do) that stitches together the different data files and executes all analyses and produces all figures reported in the paper. The do-file uses a number of user-written packages, which are listed below. Most of these can be installed using the ssc install command in Stata. Also, users will need to change the current directory path (at the start of the do-file) before executing the code. List of user written packages (descriptions): revrs (reverse-codes variable) ereplace (extends the egen command to permit replacing) grstyle (changes the settings for the overall look of graphs) spmap (used for graphing spatial data) qqvalue (used for obtaining Benjamini-Hochberg corrected p-values) parmby (creates a dataset by calling an estimation command for each by-group) domin (used to perform dominance analyses) coefplot (used for creating coefficient plots) grc1leg (combine graphs with a single common legend) xframeappend (append data frames to the end of the current data frame)

analyze the survey of consumer finances (scf) with r the survey of consumer finances (scf) tracks the wealth of american families. every three years, more than five thousand households answer a battery of questions about income, net worth, credit card debt, pensions, mortgages, even the lease on their cars. plenty of surveys collect annual income, only the survey of consumer finances captures such detailed asset data. responses are at the primary economic unit-level (peu) - the economically dominant, financially interdependent family members within a sampled household. norc at the university of chicago administers the data collection, but the board of governors of the federal reserve pay the bills and therefore call the shots. if you were so brazen as to open up the microdata and run a simple weighted median, you'd get the wrong answer. the five to six thousand respondents actually gobble up twenty-five to thirty thousand records in the final pub lic use files. why oh why? well, those tables contain not one, not two, but five records for each peu. wherever missing, these data are multiply-imputed, meaning answers to the same question for the same household might vary across implicates. each analysis must account for all that, lest your confidence intervals be too tight. to calculate the correct statistics, you'll need to break the single file into five, necessarily complicating your life. this can be accomplished with the meanit sas macro buried in the 2004 scf codebook (search for meanit - you'll need the sas iml add-on). or you might blow the dust off this website referred to in the 2010 codebook as the home of an alternative multiple imputation technique, but all i found were broken links. perhaps it's time for plan c, and by c, i mean free. read the imputation section of the latest codebook (search for imputation), then give these scripts a whirl. they've got that new r smell. the lion's share of the respondents in the survey of consumer finances get drawn from a pretty standard sample of american dwellings - no nursing homes, no active-duty military. then there's this secondary sample of richer households to even out the statistical noise at the higher end of the i ncome and assets spectrum. you can read more if you like, but at the end of the day the weights just generalize to civilian, non-institutional american households. one last thing before you start your engine: read everything you always wanted to know about the scf. my favorite part of that title is the word always. this new github repository contains t hree scripts: 1989-2010 download all microdata.R initiate a function to download and import any survey of consumer finances zipped stata file (.dta) loop through each year specified by the user (starting at the 1989 re-vamp) to download the main, extract, and replicate weight files, then import each into r break the main file into five implicates (each containing one record per peu) and merge the appropriate extract data onto each implicate save the five implicates and replicate weights to an r data file (.rda) for rapid future loading 2010 analysis examples.R prepare two survey of consumer finances-flavored multiply-imputed survey analysis functions load the r data files (.rda) necessary to create a multiply-imputed, replicate-weighted survey design demonstrate how to access the properties of a multiply-imput ed survey design object cook up some descriptive statistics and export examples, calculated with scf-centric variance quirks run a quick t-test and regression, but only because you asked nicely replicate FRB SAS output.R reproduce each and every statistic pr ovided by the friendly folks at the federal reserve create a multiply-imputed, replicate-weighted survey design object re-reproduce (and yes, i said/meant what i meant/said) each of those statistics, now using the multiply-imputed survey design object to highlight the statistically-theoretically-irrelevant differences click here to view these three scripts for more detail about the survey of consumer finances (scf), visit: the federal reserve board of governors' survey of consumer finances homepage the latest scf chartbook, to browse what's possible. (spoiler alert: everything.) the survey of consumer finances wikipedia entry the official frequently asked questions notes: nationally-representative statistics on the financial health, wealth, and assets of american hous eholds might not be monopolized by the survey of consumer finances, but there isn't much competition aside from the assets topical module of the survey of income and program participation (sipp). on one hand, the scf interview questions contain more detail than sipp. on the other hand, scf's smaller sample precludes analyses of acute subpopulations. and for any three-handed martians in the audience, ther e's also a few biases between these two data sources that you ought to consider. the survey methodologists at the federal reserve take their job...

This dataset contains the data of the survey waves from age 11 to 20 (K4-K8) of the target population. The following documents help to understand the content of the data (partially restricted access):

• Handbook K4-K8 in two versions: Short version (public access) containing general information such as descriptions of questionnaire themes, source, derived constructs, and key publications ("K4-8_Handbook_short") and a long version providing a detailed overview of each scale, and item wordings ("K4-8_Handbook_long”). • Overview of standard variables (e.g., sex, SES, treatment allocation) that are part of every data package on SWISSUbase • Codebooks (questionnaires with question/variable names) in English • Original questionnaires in German • Scale syntaxes (SPSS) for each data collection wave • File info including all variable/value labels and dataset structure • Description of the z-proso project, containing general information on the project, methods and data ("z-proso_ProjectOverview", public access) • Tabular overview on all z-proso project phases, data collections, and questionnaires including information on scales/domains, and page numbers in the original German questionnaires ("z-proso_DataCollectionsInstruments_W1-9", public access) • A publication list with selected z-proso methods publications (public access)

The datafile is available in the CSV, SAV (SPSS), and DTA (STATA) formats.

The data is available with prior agreement of project co-directors (Manuel Eisner, Denis Ribeaud, Lilly Shanahan) only. The project direction will grant access to the data based on a research proposal. The research proposal needs to be in the form of a project description with the following components: research questions and hypotheses, operationalisation, planned publications, linking with other project or other data (if planned). If you have questions or need more detailed information or additional documentation, do not hesitate to contact the project direction (z-proso@jacobscenter.uzh.ch). The research proposal is part of the application form.

If you, as a data user, are or were a z-proso participant yourself (focal participant, primary caregiver, or teacher), you are required to contact us before submitting a proposal.

If you require further data from earlier data collections, or from other informants (parent, teacher), or from add-on data collections that are not (yet) available on SWISSUbase, please provide a brief outline of your research questions along with a rationale for your specific data requirements.

This series of code and corresponding data files are intended for use in cognitive decline and Alzheimer’s disease and related dementias (ADRD) research. The files include twelve years of cleaned datasets derived from the 2007-2018 years of the National Health Interview Survey (NHIS). NHIS is a nationally representative study aimed at monitoring the health of the non-institutionalized United States population. The provided datasets include sociodemographic information on respondents’ age, sex, race, and marital status from the Sample Adult Files, cognition variables from the Sample Adult files and, in applicable years, merged cognition data from the Adult Functioning and Disability (AFD) supplement. The files were constructed to allow for users to append multiple years of data for longitudinal analysis. Brief and detailed summaries of the variables available in these datasets along with more detailed descriptions of performed calculations can be found in the provided data dictionaries. Users may also refer to the provided “Overview of variables across years” document to see which variables are available each year. SAS, Stata, and CSV data file formats are provided as are the full coding scripts used in Stata.

This data set contains the information necessary to reproduce our article "Depenbusch L, Klasen S. The effect of bigger human bodies on the future global calorie requirements. PLoS ONE. 2019. Forthcoming" Abstract: Existing studies show how population growth and rising incomes will cause a massive increase in the future global demand for food. We add to the literature by estimating the potential effect of increases in human weight, caused by rising BMI and height, on future calorie requirements. Instead of using a market based approach, the estimations are solely based on human energy requirements for maintenance of weight. We develop four different scenarios to show the effect of increases in human height and BMI. In a world where the weight per age-sex group would stay stable, we project calorie requirements to increases by 61.05 percent between 2010 and 2100. Increases in BMI and height could add another 18.73 percentage points to this. This additional increase amounts to more than the combined calorie requirements of India and Nigeria in 2010. These increases would particularly affect Sub-Saharan African countries, which will already face massively rising calorie requirements due to the high population growth. The stark regional differences call for policies that increase food access in currently economically weak regions. Such policies should shift consumption away from energy dense foods that promote overweight and obesity, to avoid the direct burden associated with these conditions and reduce the increases in required calories. Supplying insufficient calories would not solve the problem but cause malnutrition in populations with weak access to food. As malnutrition is not reducing but promoting rises in BMI levels, this might even aggravate the situation. An earlier version appeared as GlobalFood Discussion Papers, No. 109. The data is stored as Stata Version 13 .dta file, and in Excel .xlsx format. In the Excel file the first row contains variable names, the second row contains variable labels. Age specifications in the label of the type "<=x" describe that the variable aggregates from the next smaller age group over all ages up to age "x".

For a comprehensive guide to this data and other UCR data, please see my book at ucrbook.comVersion 12 release notes:Adds 2022 dataVersion 11 release notes:Adds 2021 data.Version 10 release notes:Adds 2020 data. Please note that the FBI has retired UCR data ending in 2020 data so this will be the last arson data they release. Changes .rda file to .rds.Version 9 release notes:Changes release notes description, does not change data.Version 8 release notes:Adds 2019 data.Note that the number of months missing variable sharply changes starting in 2018. This is probably due to changes in UCR reporting of the column_2_type variable which is used to generate the months missing county (the code I used does not change). So pre-2018 and 2018+ years may not be comparable for this variable. Version 7 release notes:Adds a last_month_reported column which says which month was reported last. This is actually how the FBI defines number_of_months_reported so is a more accurate representation of that. Removes the number_of_months_reported variable as the name is misleading. You should use the last_month_reported or the number_of_months_missing (see below) variable instead.Adds a number_of_months_missing in the annual data which is the sum of the number of times that the agency reports "missing" data (i.e. did not report that month) that month in the card_2_type variable or reports NA in that variable. Please note that this variable is not perfect and sometimes an agency does not report data but this variable does not say it is missing. Therefore, this variable will not be perfectly accurate.Version 6 release notes:Adds 2018 dataVersion 5 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 4 release notes: Adds 1979-2000, 2006, and 2017 dataAdds agencies that reported 0 months.Adds monthly data.All data now from FBI, not NACJD. Changes some column names so all columns are <=32 characters to be usable in Stata.Version 3 release notes: Add data for 2016.Order rows by year (descending) and ORI.Removed data from Chattahoochee Hills (ORI = "GA06059") from 2016 data. In 2016, that agency reported about 28 times as many vehicle arsons as their population (Total mobile arsons = 77762, population = 2754.Version 2 release notes: Fix bug where Philadelphia Police Department had incorrect FIPS county code. This Arson data set is an FBI data set that is part of the annual Uniform Crime Reporting (UCR) Program data. This data contains information about arsons reported in the United States. The information is the number of arsons reported, to have actually occurred, to not have occurred ("unfounded"), cleared by arrest of at least one arsoning, cleared by arrest where all offenders are under the age of 18, and the cost of the arson. This is done for a number of different arson location categories such as community building, residence, vehicle, and industrial/manufacturing structure. The yearly data sets here combine data from the years 1979-2018 into a single file for each group of crimes. Each monthly file is only a single year as my laptop can't handle combining all the years together. These files are quite large and may take some time to load. I also added state, county, and place FIPS code from the LEAIC (crosswalk).A small number of agencies had some months with clearly incorrect data. I changed the incorrect columns to NA and left the other columns unchanged for that agency. The following are data problems that I fixed - there are still likely issues remaining in the data so make sure to check yourself before running analyses. Oneida, New York (ORI = NY03200) had multiple years that reported single arsons costing over $700 million. I deleted this agency from all years of data.In January 1989 Union, North Carolina (ORI = NC09000) reported 30,000 arsons in uninhabited single occupancy buildings and none any other months. In December 1991 Gadsden, Florida (ORI = FL02000) reported that a single arson at a community/public building caused $99,999,999 in damages (the maximum possible).In April 2017 St. Paul, Minnesota (ORI = MN06209) reported 73,400 arsons in uninhabited storage buildings and 10,000 arsons in uninhabited community/public buildings and one or fewer every other month.When an arson is determined to be unfound

Abstract (en): This study is part of a time-series collection of national surveys fielded continuously since 1952. The election studies are designed to present data on Americans' social backgrounds, enduring political predispositions, social and political values, perceptions and evaluations of groups and candidates, opinions on questions of public policy, and participation in political life. In addition to core items, new content includes questions on values, political knowledge, and attitudes on racial policy, as well as more general attitudes conceptualized as antecedent to these opinions on racial issues. The Main Data File also contains vote validation data that were expanded to include information from the appropriate election office and were attached to the records of each of the respondents in the post-election survey. The expanded data consist of the respondent's post case ID, vote validation ID, and two variables to clarify the distinction between the office of registration and the office associated with the respondent's sample address. The second data file, Bias Nonresponse Data File, contains respondent-level field administration variables. Of 3,833 lines of sample that were originally issued for the 1990 Study, 2,176 resulted in completed interviews, others were nonsample, and others were noninterviews for a variety of reasons. For each line of sample, the Bias Nonresponse Data File includes sampling data, result codes, control variables, and interviewer variables. Detailed geocode data are blanked but available under conditions of confidential access (contact the American National Election Studies at the Center for Political Studies, University of Michigan, for further details). This is a specialized file, of particular interest to those who are interested in survey nonresponse. Demographic variables include age, party affiliation, marital status, education, employment status, occupation, religious preference, and ethnicity. ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Performed consistency checks.; Standardized missing values.; Checked for undocumented or out-of-range codes.. Response Rates: The response rate for this study is 67.7 percent. The study was in the field until January 31, although 67 percent of the interviews were taken by November 25, 80 percent by December 7, and 93 percent by December 31. All United States households in the 50 states. National multistage area probability sample. 2015-11-10 The study metadata was updated.2009-01-09 YYYY-MM-DD Part 1, the Main Data File, incorporates errata that were posted separately under the Fourth ICPSR Edition. Part 2, the Bias Nonresponse Data File, has been added to the data collection, along with corresponding SAS, SPSS, and Stata setup files and documentation. The codebook has been updated by adding a technical memorandum on the sampling design of the study previously missing from the codebook. The nonresponse file contains respondent-level field administration variables for those interested in survey nonresponse. The collection now includes files in ASCII, SPSS portable, SAS transport (CPORT), and Stata system formats.2000-02-21 The data for this study are now available in SAS transport and SPSS export formats in addition to the ASCII data file. Variables in the dataset have been renumbered to the following format: 2-digit (or 2-character) year prefix + 4 digits + [optional] 1-character suffix. Dataset ID and version variables have also been added. Additionally, the Voter Validation Office Administration Interview File (Expanded Version) has been merged with the main data file, and the codebook and SPSS setup files have been replaced. Also, SAS setup files have been added to the collection, and the data collection instrument is now provided as a PDF file. Two files are no longer being released with this collection: the Voter Validation Office Administration Interview File (Unexpanded Version) and the Results of First Contact With Respondent file. Funding insitution(s): National Science Foundation (SOC77-08885 and SES-8341310). face-to-face interviewThere was significantly more content in this post-election survey than ...

We add the data set as a stata file. This 'raw' data set is not the actual data set we have used for our article which has been accepted for publication in the British Journal of Political Science. Thus we supply a 'clean' data set (please note that we have used ESS 2008 version 4.0. in the article).

The Feed the Future Innovation Lab for Nutrition, with funding from the USAID, conducted a series of four annual surveys – The Policy and Science for Health, Agriculture and Nutrition (PoSHAN) Surveys – in Nepal from 2013-2016. The aim of the surveys was to assess nutritional status, diet and morbidity of preschool aged children and their mothers, and recently married, nulliparous women, and household food security, agricultural practices, participation in services and programs, among other factors, and examine associations between agricultural practices and food security, nutritional status, diet and health. The broader goal was guide policy and program interventions that may influence household food security, poverty, and the diets, health and nutrition of pre-school children and their mothers. The PoSHAN surveys were conducted in a total of 21 sub-district units or Village Development Committees (VDCs), each in a unique district. The VDCs in PoSHAN form a nationally representative sample, stratified by the agroecological zones of Mountains, Hills and Terai (plains) with each zone containing 7 VDCs. Further, each VDC consisted of a set of 3 wards, adding up to a total of 63 wards in the full sample. The PoSHAN surveys were conducted in the same season and site every year, with seasonal (sentinel) surveys conducted every 3 months during the first two years of the survey. Eligible households included in the survey consisted of households that either had at least one child under 5 years or a newly-married woman (married within the past 2 years). The annual surveys were conducted in all 21 VDCs with data collected using a total of 10 types of forms that collected information at the community, market, household and individual levels. The seasonal surveys were conducted in 3 of the 21 VDCs, one from each zone, that collected data on up to 7 forms with the focus of collecting data on household’s seasonal agricultural production. The datasets thus generated from these surveys make up a total of 4 annual surveys and 4 seasonal surveys. This collection contains the cross-sectional dataset for the annual surveys conducted from 2013 through 2016. For more information visit: http://pubs.sciepub.com/jfs/6/2/5/. (Link to Seasonal Surveys.) Data files are formatted for STATA, and include MS Excel and CSV formats. Data specificity is limited to the regional district level to maintain participants' anonymity for public access. Restricted access to data at the VDC level may be requested by contacting the study team. Additional content details are available in the file set Metadata folder.

BackgroundCoinfection with HIV, hepatitis B virus (HBV) and syphilis increases the risk of vertical transmission. Hence, affecting overall maternal health and child health outcomes. The Tanzanian government is planning to add HBV screening to the existing Prevention of Mother to Child Transmission (PMTCT) of HIV and syphilis program; however, the burden of coinfections in the country is unknown. Therefore, this study aimed to determine the prevalence of HIV, HBV and syphilis coinfections and their associated factors among pregnant women receiving antenatal care in Tanzania.MethodsA facility-based cross-sectional study design was conducted, utilizing data from the national feasibility study of triple testing for HIV, syphilis and HBV among pregnant women. The data were analysed via STATA version 16.1, and bivariate and multivariate logistic regressions were used to check for associations. Variables with a P value of < 0.05 were considered statistically significant.ResultsA total of 7,828 pregnant women were enrolled, 0.4% (95% CI 0.3–0.6) of whom were coinfected. The prevalence rates for HIV/HBV, HIV/syphilis, HBV/syphilis and HIV/HBV/syphilis coinfections were 0.1% (95% CI 0.1–0.2), 0.2% (95% CI 0.1–0.4), 0.1% (95% CI 0.0–0.2) and 0.0% (95% CI 0.0–0.1), respectively. History of multiple sexual partners (AOR 6.1; 95% CI: 1.3–29.7, P = 0.025) was associated with HIV/HBV coinfection. Age 25–49 years (AOR 13.5; 95% CI 1.8–103.8, P = 0.012) and marital status (AOR 0.2; 95% CI 0.1–0.8, P = 0.018) were associated with HIV/syphilis coinfection. For HBV/syphilis coinfection, marital status (AOR 0.1; 95% CI 0.0–0.9, P = 0.036) and history of multiple sexual partners (AOR 16.8; 95% CI 2.5–114.9, P = 0.004) were independently associated.ConclusionCoinfections are present among pregnant women in Tanzania; therefore, it is important to include hepatitis B screening in the existing PMTCT of HIV and syphilis program. Interventions should focus on single, child-bearing women with multiple sexual partners.

This is the replication dataset for this paper: Busemeyer, Marius/Iversen, Torben, 2018: The Welfare State with Private Alternatives, Journal of Politics. It contains: (1) a modified ISSP dataset (modified in the sense that we add macro-level variables) (2) the Stata Do file to reconstruct the tables and figures in the paper (3) an additional dataset that describes which macro-level variables were added to the original ISSP 2006 RoG dataset (2018-11-23)

This log file shows all the regressions and tests conducted on Stata by using a Tobit model with 95% stadium capacity cut-off points. (LOG)

Abstract

The high-frequency phone survey of refugees monitors the economic and social impact of and responses to the COVID-19 pandemic on refugees and nationals, by calling a sample of households every four weeks. The main objective is to inform timely and adequate policy and program responses. Since the outbreak of the COVID-19 pandemic in Ethiopia, two rounds of data collection of refugees were completed between September and November 2020. The first round of the joint national and refugee HFPS was implemented between the 24 September and 17 October 2020 and the second round between 20 October and 20 November 2020.

Analysis unit

Household

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample was drawn using a simple random sample without replacement. Expecting a high non-response rate based on experience from the HFPS-HH, we drew a stratified sample of 3,300 refugee households for the first round. More details on sampling methodology are provided in the Survey Methodology Document available for download as Related Materials.

Mode of data collection

Computer Assisted Telephone Interview [cati]

Research instrument

The Ethiopia COVID-19 High Frequency Phone Survey of Refugee questionnaire consists of the following sections:

• Interview Information
• Household Roster
• Camp Information
• Knowledge Regarding the Spread of COVID-19
• Behaviour and Social Distancing - Access to Basic Services
• Employment
• Income Loss
• Coping/Shocks
• Social Relations
• Food Security
• Aid and Support/ Social Safety Nets.

A more detailed description of the questionnaire is provided in Table 1 of the Survey Methodology Document that is provided as Related Materials. Round 1 and 2 questionnaires available for download.

Cleaning operations

DATA CLEANING At the end of data collection, the raw dataset was cleaned by the Research team. This included formatting, and correcting results based on monitoring issues, enumerator feedback and survey changes. Data cleaning carried out is detailed below.

Variable naming and labeling: • Variable names were changed to reflect the lowercase question name in the paper survey copy, and a word or two related to the question. • Variables were labeled with longer descriptions of their contents and the full question text was stored in Notes for each variable. • “Other, specify” variables were named similarly to their related question, with “_other” appended to the name. • Value labels were assigned where relevant, with options shown in English for all variables, unless preloaded from the roster in Amharic.

Variable formatting: • Variables were formatted as their object type (string, integer, decimal, time, date, or datetime). • Multi-select variables were saved both in space-separated single-variables and as multiple binary variables showing the yes/no value of each possible response. • Time and date variables were stored as POSIX timestamp values and formatted to show Gregorian dates. • Location information was left in separate ID and Name variables, following the format of the incoming roster. IDs were formatted to include only the variable level digits, and not the higher-level prefixes (2-3 digits only.)
• Only consented surveys were kept in the dataset, and all personal information and internal survey variables were dropped from the clean dataset. • Roster data is separated from the main data set and kept in long-form but can be merged on the key variable (key can also be used to merge with the raw data). • The variables were arranged in the same order as the paper instrument, with observations arranged according to their submission time.

Backcheck data review: Results of the backcheck survey are compared against the originally captured survey results using the bcstats command in Stata. This function delivers a comparison of variables and identifies any discrepancies. Any discrepancies identified are then examined individually to determine if they are within reason.

Data appraisal

The following data quality checks were completed: • Daily SurveyCTO monitoring: This included outlier checks, skipped questions, a review of “Other, specify”, other text responses, and enumerator comments. Enumerator comments were used to suggest new response options or to highlight situations where existing options should be used instead. Monitoring also included a review of variable relationship logic checks and checks of the logic of answers. Finally, outliers in phone variables such as survey duration or the percentage of time audio was at a conversational level were monitored. A survey duration of close to 15 minutes and a conversation-level audio percentage of around 40% was considered normal. • Dashboard review: This included monitoring individual enumerator performance, such as the number of calls logged, duration of calls, percentage of calls responded to and percentage of non-consents. Non-consent reason rates and attempts per household were monitored as well. Duration analysis using R was used to monitor each module's duration and estimate the time required for subsequent rounds. The dashboard was also used to track overall survey completion and preview the results of key questions. • Daily Data Team reporting: The Field Supervisors and the Data Manager reported daily feedback on call progress, enumerator feedback on the survey, and any suggestions to improve the instrument, such as adding options to multiple choice questions or adjusting translations. • Audio audits: Audio recordings were captured during the consent portion of the interview for all completed interviews, for the enumerators' side of the conversation only. The recordings were reviewed for any surveys flagged by enumerators as having data quality concerns and for an additional random sample of 2% of respondents. A range of lengths were selected to observe edge cases. Most consent readings took around one minute, with some longer recordings due to questions on the survey or holding for the respondent. All reviewed audio recordings were completed satisfactorily. • Back-check survey: Field Supervisors made back-check calls to a random sample of 5% of the households that completed a survey in Round 1. Field Supervisors called these households and administered a short survey, including (i) identifying the same respondent; (ii) determining the respondent's position within the household; (iii) confirming that a member of the the data collection team had completed the interview; and (iv) a few questions from the original survey.

Data and Stata code for replication. Abstract: To explore the evolution of political science research on race, Walton, Miller, and McCormick (1995), and Walton (1997, Chapter 4) do a systematic review of more than a century of publications appearing in the discipline’s oldest and most prestigious journals: Political Science Quarterly (PSQ) and the American Political Science Review (APSR), respectively. Walton and his colleagues uncover “dual traditions” of race scholarship: an African American Politics (AAP) paradigm, emphasizing empowerment and Blacks’ cultural distinctiveness, and a Race Relations Politics (RRP) approach that focuses on Blacks’ socio-political status vis-à-vis Whites. Using computer-assisted text analyses, we introduce a measure of racial dialogue that is informed by theory and has suitable empirical properties. We replicate and extend Walton’s research by adding a third periodical (the Journal of Politics [JOP]) and demonstrating that while race conversations are becoming more frequent over time, the dialogues taking place in mainstream journals typically fit Walton’s RRP (rather than AAP) tradition. Following our analyses, we offer guidelines for researchers seeking to apply our measure to alternative contexts.