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

In this folder you will find the raw data from the experiment that was conducted in August and September 2018 separately for each of the four universities in Colombia where the experiments were conducted. Data were gathered using the psytoolkit software. The final dataset is called “final.dta”. For the analysis we use the software Stata. Data were prepared and cleaned (see do-files on preparation and data cleaning). Variables are labelled. Additionally, a definition of each variable can be found in the appendix of the manuscript. All tables and figures can be replicated using the do-file “figures” and “tables”. For additional questions please contact: Kerstin Unfried: Kerstin.unfried@uni-goettingen.de

This upload conains two Stata do-files that process the raw data and reproduce tables and figures in the paper and in the online appendix. The readme.txt file discusses the data.

Stata do-file for the study called: "Post-trial follow-up after a randomized clinical trial of COVID-19 convalescent plasma"

This upload contains all replication material for "Global evidence for ultraviolet radiation decreasing COVID-19 growth rates" (PNAS, 2020). Please note that previous versions of this upload provided data and code for the pre-print version of the article, which changed somewhat through the peer review process.

Authors: Tamma Carleton, Jules Cornetet, Peter Huybers, Kyle C. Meng, Jonathan Proctor.

Code is located within CCHMP_covid_climate_code_release.zip, and is written in R, Stata, and Matlab. The working directory should be set to the repository folder at the top of each script (all other filepaths are relative).

Please find the code needed to replicate the main findings of the paper described below:

Plots of data: R and Stata scripts to make figures 1B, 2A/B/C, S1, S2, and S3, can be found within “code/analysis/data_plots/”.

Regression analysis: Stata scripts to run the distributed lag regressions and plot the results in figures 2, 3C, S5, S6, S7, S8, S10, and S14, as well as Table S1, can be found within “code/analysis/regressions/”. R scripts for data analysis and plotting for figures 3A/B and S9 are also within "code/analysis/regressions/".

Seasonal simulations: R and Stata scripts to replicate the seasonal simulation shown in figures 4, S4 and S11 can be found within “code/analysis/seasonal_sim/”.

SEIR simulations: Matlab scripts to replicate the SEIR simulations shown in figures S12 and S13 can be found within “code/analysis/SEIR/”.

Data are located within CCHMP_covid_climate_data_release.zip.

Abstract (en): This special topic poll, conducted February 20-24, 1997, solicited responses from parents and their teenage children, aged 12-17, on the topic of illegal drug use among America's youth. One parent and one child from each household were asked a series of questions covering illegal drugs, violence in school, underage drinking, academic challenges, and parent-child communication. Respondents were asked to assess their understanding of the presence of drugs and drug users in their local schools, throughout the community, across the nation, among the teen's peer group, and within their own family. A series of topics covered the availability and effectiveness of school-sponsored anti-drug programs. Parents were asked how their possible past and present use and/or experimentation with marijuana and other illegal drugs, alcohol, and tobacco products influenced the manner in which they approached drug use with their own children. Teenage respondents were asked for their reaction to the use of drugs and alcohol by their friends, the seriousness of the contemporary drug problem, and whether they believed that their parents had used or experimented with illegal drugs. Other questions asked about teenage respondents' plans after high school and whether they attended a public or private school. Demographic variables for parental respondents included age, race, sex, education level, household income, political party affiliation, and type of residential area (e.g., urban or rural). Demographic variables for teenage respondents included age, race, sex, residential area, and grade level in school. The data contain a weight variable (WEIGHT) that should be used in analyzing the data. This poll consists of "standard" national representative samples of the adult population with sample balancing of sex, race, age, and education. The weight variable contains two implied decimal places, and applies only to the parental respondents. 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: Created online analysis version with question text.. Persons aged 18 and over living in households with telephones in the contiguous 48 United States. Households were selected by random-digit dialing. Within households, the respondent selected was the adult living in the household who last had a birthday and who was home at the time of the interview. 2007-02-27 SAS, SPSS, and Stata setup files, and SAS and Stata supplemental files have been added to this data collection. Respondent names were removed from the data file and the CASEID variable was created for use with online analysis.2006-11-10 SAS, SPSS, and Stata setup files have been added to this data collection. telephone interview (1) The data available for download are not weighted and users will need to weight the data prior to analysis. (2) Original reports using these data may be found via the ABC News Web site. (3) According to the data collection instrument, code 3 in the variable P_EDUC also included respondents who answered that they had attended a technical college. (4) The CASEID variable was created for use with online analysis.

Four anonymized datasets used in our study published in Palgrave Communications. Analyses, psychometrics and formation of dependent and independent variables have been described in the manuscript. Data for Study 3 is in Stata-13 format and contains 303 participants whom were analyzed. For the retained sample of 303 participants few missing values were imputed, as described in the manuscript. For full details regarding the non imputed data-set (not anonymized), please contact the authors if necessary. Stata 13.1 has a bug in its programming and it does not exporting .csv files correctly. Variables related to geolocation, participant numbers and time stamps have been removed. Also personal questions related to possible psychological problems. sexual orientation, occupational details have been removed. Many of the variables have been translated to English. We also collected political party affiliations and voting choices, which have been removed. Finland is a multi-party system and voting behavior, combined with age, gender, timestamps and location can be used to identify people. For full description of the complete collected data the interested parties are encouraged to contact the authors.Other exploratory variables not pertinent to the main study and not reported in the manuscript have also been removed, since this data will be used to explore other research questions and ideas to be published later. Once published, they will be made available as well.

This dataset includes responses to two survey experiments testing the effects of officer diversity, which were conducted with a national sample (N = 1,100). The survey was fielded by YouGov in the spring of 2022 (between April 21 and May 2). For our experiments, YouGov constructed two synthetic sampling frames (SSF) via stratified sampling from the 2019 American Community Survey, which were used to select two matched (on gender, age, and education) samples of opt-in panelists: a general population sample (N = 650) and a large oversample of Black Americans (N = 450). (The general population sample was also matched on race.) Using propensity scoring based on region and the matching variables, both samples were then weighted to their respective SSFs, after which the weights were post-stratified on 2016 and 2020 Presidential vote choice. The purpose of the oversample was to yield (after combining Black respondents in the oversample with those in the general population sample) similarly sized analytic samples of Black and non-Black Americans. Per this sampling design, we estimated the models for the experiments separately for Black and non-Black respondents. For the main analysis, we applied the provided sampling weights. (NOTE: The original files were uploaded in Stata-12 version.)

analyze the national health and nutrition examination survey (nhanes) with r nhanes is this fascinating survey where doctors and dentists accompany survey interviewers in a little mobile medical center that drives around the country. while the survey folks are interviewing people, the medical professionals administer laboratory tests and conduct a real doctor's examination. the b lood work and medical exam allow researchers like you and me to answer tough questions like, "how many people have diabetes but don't know they have diabetes?" conducting the lab tests and the physical isn't cheap, so a new nhanes data set becomes available once every two years and only includes about twelve thousand respondents. since the number of respondents is so small, analysts often pool multiple years of data together. the replication scripts below give a few different examples of how multiple years of data can be pooled with r. the survey gets conducted by the centers for disease control and prevention (cdc), and generalizes to the united states non-institutional, non-active duty military population. most of the data tables produced by the cdc include only a small number of variables, so importation with the foreign package's read.xport function is pretty straightforward. but that makes merging the appropriate data sets trickier, since it might not be clear what to pull for which variables. for every analysis, start with the table with 'demo' in the name -- this file includes basic demographics, weighting, and complex sample survey design variables. since it's quick to download the files directly from the cdc's ftp site, there's no massive ftp download automation script. this new github repository co ntains five scripts: 2009-2010 interview only - download and analyze.R download, import, save the demographics and health insurance files onto your local computer load both files, limit them to the variables needed for the analysis, merge them together perform a few example variable recodes create the complex sample survey object, using the interview weights run a series of pretty generic analyses on the health insurance ques tions 2009-2010 interview plus laboratory - download and analyze.R download, import, save the demographics and cholesterol files onto your local computer load both files, limit them to the variables needed for the analysis, merge them together perform a few example variable recodes create the complex sample survey object, using the mobile examination component (mec) weights perform a direct-method age-adjustment and matc h figure 1 of this cdc cholesterol brief replicate 2005-2008 pooled cdc oral examination figure.R download, import, save, pool, recode, create a survey object, run some basic analyses replicate figure 3 from this cdc oral health databrief - the whole barplot replicate cdc publications.R download, import, save, pool, merge, and recode the demographics file plus cholesterol laboratory, blood pressure questionnaire, and blood pressure laboratory files match the cdc's example sas and sudaan syntax file's output for descriptive means match the cdc's example sas and sudaan synta x file's output for descriptive proportions match the cdc's example sas and sudaan syntax file's output for descriptive percentiles replicate human exposure to chemicals report.R (user-contributed) download, import, save, pool, merge, and recode the demographics file plus urinary bisphenol a (bpa) laboratory files log-transform some of the columns to calculate the geometric means and quantiles match the 2007-2008 statistics shown on pdf page 21 of the cdc's fourth edition of the report click here to view these five scripts for more detail about the national health and nutrition examination survey (nhanes), visit: the cdc's nhanes homepage the national cancer institute's page of nhanes web tutorials notes: nhanes includes interview-only weights and interview + mobile examination component (mec) weights. if you o nly use questions from the basic interview in your analysis, use the interview-only weights (the sample size is a bit larger). i haven't really figured out a use for the interview-only weights -- nhanes draws most of its power from the combination of the interview and the mobile examination component variables. if you're only using variables from the interview, see if you can use a data set with a larger sample size like the current population (cps), national health interview survey (nhis), or medical expenditure panel survey (meps) instead. confidential to sas, spss, stata, sudaan users: why are you still riding around on a donkey after we've invented the internal combustion engine? time to transition to r. :D

Koch_PACD_SPI_v7Collected in the field; Software: STATA (v.13) (.dta-File)

Code and data to produce final results and summary statistics using Stata 15.1. There are four code files (and a master do file to call them in sequence) and three data sets to produce the results in the paper. All data are included in a single .ZIP file (uncompressed files exceeded upload limits).

Abstract (en): This special topic poll, conducted January 14-19, 1996, is part of a continuing series of monthly surveys that solicit public opinion on the presidency and on a range of political and social issues. The focus of this data collection was campaign finance laws and the pending reform of those laws. Respondents were asked for their opinion and level of knowledge on campaign finance laws, use of campaign funds, the federal government's campaign finance activities, and the public funding of federal elections. Additional topics covered various proposals for campaign finance reform, special interest group contributions, restrictions on political advertising, and the reasons why people contribute to campaigns. Respondents were also asked for their political contribution history, and for their opinions on the influence and access that political contributors gain from their contributions. Demographic variables include sex, age, race, education level, employment status, household income, political party affiliation, political orientation, and voter registration status and participation history. The data contain a weight variable (WEIGHT) that should be used in analyzing the data. This poll consists of "standard" national representative samples of the adult population with sample balancing of sex, race, age, and education. 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: Created online analysis version with question text.. Persons aged 18 and over living in households with telephones in the contiguous 48 United States. Households were selected by random-digit dialing. Within households, the respondent selected was the adult living in the household who last had a birthday and who was home at the time of the interview. 2008-03-13 SAS, SPSS, and Stata setup files, and SAS and Stata supplemental files have been added to this data collection. Respondent names were removed from the data file and a value label for an unknown code was added in the AGE variable. The CASEID variable was created for use with online analysis. Question text has been added to the codebook, and the data collection instrument has been taken out of the codebook and made into its own file. telephone interview (1) The data available for download are not weighted and users will need to weight the data prior to analysis. (2) Additional information about sampling, interviewing, and sampling error may be found in the codebook. (3) Original reports using these data may be found via the Washington Post Opinion Surveys and Polls Web site. (4) The meaning of the variable SELECTB is unknown and may be associated with the sampling method of selecting a respondent based on the adult living in the household who last had a birthday. (5) According to the data collection instrument, code 3 in the variable EDUC includes respondents who answered that they had attended a technical school. (6) A value label for an unknown code was added in the AGE variable. (7) The CASEID variable was created for use with online analysis.

This dataset contains pre- and post-service responses from 175 participants in the Encore Intergenerational Vaccine Corps, a health outreach program within Federally Qualified Health Centers (FQHCs) that was run in the San Francisco Bay Area. The program, sponsored by AmeriCorps Seniors—a federal agency that promotes national and community service for Americans aged 55 and older—ran from May 2021 to April 2022. Participants ranged in age from 18 to 81 years. The volunteers, who aimed to increase awareness and administration of COVID-19 vaccines, were evenly divided between those with medical skills (50%, such as current or retired nurses or doctors) and those without medical skills (50%). The survey questions focused on participants’ perceptions of individuals from different generations (i.e., respondents aged 50+ were asked about younger people, and vice versa) and public issues (e.g., the adequacy of resource allocation to FQHCs), along with their experiences in the program. All responses were collected online, with survey outreach conducted by CoGenerate (formerly Encore.org), the lead operator of the Vaccine Corps. The complete dataset, which includes 145 variables (three of which are string/text variables from open-ended responses), is available in both Stata .do and CSV file formats. A codebook is also provided.

Abstract

The General Household Survey-Panel (GHS-Panel) is implemented in collaboration with the World Bank Living Standards Measurement Study (LSMS) team as part of the Integrated Surveys on Agriculture (ISA) program. The objectives of the GHS-Panel include the development of an innovative model for collecting agricultural data, interinstitutional collaboration, and comprehensive analysis of welfare indicators and socio-economic characteristics. The GHS-Panel is a nationally representative survey of approximately 5,000 households, which are also representative of the six geopolitical zones. The 2023/24 GHS-Panel is the fifth round of the survey with prior rounds conducted in 2010/11, 2012/13, 2015/16 and 2018/19. The GHS-Panel households were visited twice: during post-planting period (July - September 2023) and during post-harvest period (January - March 2024).

Geographic coverage

National

Analysis unit

• Households • Individuals • Agricultural plots • Communities

Universe

The survey covered all de jure households excluding prisons, hospitals, military barracks, and school dormitories.

Kind of data

Sample survey data [ssd]

Sampling procedure

The original GHS‑Panel sample was fully integrated with the 2010 GHS sample. The GHS sample consisted of 60 Primary Sampling Units (PSUs) or Enumeration Areas (EAs), chosen from each of the 37 states in Nigeria. This resulted in a total of 2,220 EAs nationally. Each EA contributed 10 households to the GHS sample, resulting in a sample size of 22,200 households. Out of these 22,200 households, 5,000 households from 500 EAs were selected for the panel component, and 4,916 households completed their interviews in the first wave.

After nearly a decade of visiting the same households, a partial refresh of the GHS‑Panel sample was implemented in Wave 4 and maintained for Wave 5. The refresh was conducted to maintain the integrity and representativeness of the sample. The refresh EAs were selected from the same sampling frame as the original GHS‑Panel sample in 2010. A listing of households was conducted in the 360 EAs, and 10 households were randomly selected in each EA, resulting in a total refresh sample of approximately 3,600 households.

In addition to these 3,600 refresh households, a subsample of the original 5,000 GHS‑Panel households from 2010 were selected to be included in the new sample. This “long panel” sample of 1,590 households was designed to be nationally representative to enable continued longitudinal analysis for the sample going back to 2010. The long panel sample consisted of 159 EAs systematically selected across Nigeria’s six geopolitical zones.

The combined sample of refresh and long panel EAs in Wave 5 that were eligible for inclusion consisted of 518 EAs based on the EAs selected in Wave 4. The combined sample generally maintains both the national and zonal representativeness of the original GHS‑Panel sample.

Sampling deviation

Although 518 EAs were identified for the post-planting visit, conflict events prevented interviewers from visiting eight EAs in the North West zone of the country. The EAs were located in the states of Zamfara, Katsina, Kebbi and Sokoto. Therefore, the final number of EAs visited both post-planting and post-harvest comprised 157 long panel EAs and 354 refresh EAs. The combined sample is also roughly equally distributed across the six geopolitical zones.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The GHS-Panel Wave 5 consisted of three questionnaires for each of the two visits. The Household Questionnaire was administered to all households in the sample. The Agriculture Questionnaire was administered to all households engaged in agricultural activities such as crop farming, livestock rearing, and other agricultural and related activities. The Community Questionnaire was administered to the community to collect information on the socio-economic indicators of the enumeration areas where the sample households reside.

GHS-Panel Household Questionnaire: The Household Questionnaire provided information on demographics; education; health; labour; childcare; early child development; food and non-food expenditure; household nonfarm enterprises; food security and shocks; safety nets; housing conditions; assets; information and communication technology; economic shocks; and other sources of household income. Household location was geo-referenced in order to be able to later link the GHS-Panel data to other available geographic data sets (forthcoming).

GHS-Panel Agriculture Questionnaire: The Agriculture Questionnaire solicited information on land ownership and use; farm labour; inputs use; GPS land area measurement and coordinates of household plots; agricultural capital; irrigation; crop harvest and utilization; animal holdings and costs; household fishing activities; and digital farming information. Some information is collected at the crop level to allow for detailed analysis for individual crops.

GHS-Panel Community Questionnaire: The Community Questionnaire solicited information on access to infrastructure and transportation; community organizations; resource management; changes in the community; key events; community needs, actions, and achievements; social norms; and local retail price information.

The Household Questionnaire was slightly different for the two visits. Some information was collected only in the post-planting visit, some only in the post-harvest visit, and some in both visits.

The Agriculture Questionnaire collected different information during each visit, but for the same plots and crops.

The Community Questionnaire collected prices during both visits, and different community level information during the two visits.

Cleaning operations

CAPI: Wave five exercise was conducted using Computer Assisted Person Interview (CAPI) techniques. All the questionnaires (household, agriculture, and community questionnaires) were implemented in both the post-planting and post-harvest visits of Wave 5 using the CAPI software, Survey Solutions. The Survey Solutions software was developed and maintained by the Living Standards Measurement Unit within the Development Economics Data Group (DECDG) at the World Bank. Each enumerator was given a tablet which they used to conduct the interviews. Overall, implementation of survey using Survey Solutions CAPI was highly successful, as it allowed for timely availability of the data from completed interviews.

DATA COMMUNICATION SYSTEM: The data communication system used in Wave 5 was highly automated. Each field team was given a mobile modem which allowed for internet connectivity and daily synchronization of their tablets. This ensured that head office in Abuja had access to the data in real-time. Once the interview was completed and uploaded to the server, the data was first reviewed by the Data Editors. The data was also downloaded from the server, and Stata dofile was run on the downloaded data to check for additional errors that were not captured by the Survey Solutions application. An excel error file was generated following the running of the Stata dofile on the raw dataset. Information contained in the excel error files were then communicated back to respective field interviewers for their action. This monitoring activity was done on a daily basis throughout the duration of the survey, both in the post-planting and post-harvest.

DATA CLEANING: The data cleaning process was done in three main stages. The first stage was to ensure proper quality control during the fieldwork. This was achieved in part by incorporating validation and consistency checks into the Survey Solutions application used for the data collection and designed to highlight many of the errors that occurred during the fieldwork.

The second stage cleaning involved the use of Data Editors and Data Assistants (Headquarters in Survey Solutions). As indicated above, once the interview is completed and uploaded to the server, the Data Editors review completed interview for inconsistencies and extreme values. Depending on the outcome, they can either approve or reject the case. If rejected, the case goes back to the respective interviewer’s tablet upon synchronization. Special care was taken to see that the households included in the data matched with the selected sample and where there were differences, these were properly assessed and documented. The agriculture data were also checked to ensure that the plots identified in the main sections merged with the plot information identified in the other sections. Additional errors observed were compiled into error reports that were regularly sent to the teams. These errors were then corrected based on re-visits to the household on the instruction of the supervisor. The data that had gone through this first stage of cleaning was then approved by the Data Editor. After the Data Editor’s approval of the interview on Survey Solutions server, the Headquarters also reviews and depending on the outcome, can either reject or approve.

The third stage of cleaning involved a comprehensive review of the final raw data following the first and second stage cleaning. Every variable was examined individually for (1) consistency with other sections and variables, (2) out of range responses, and (3) outliers. However, special care was taken to avoid making strong assumptions when resolving potential errors. Some minor errors remain in the data where the diagnosis and/or solution were unclear to the data cleaning team.

Response

Replication data, Stata code for analysis, and R code for figures

This study delves into the intricate political dynamics that influence legislators’ policy stances concerning the import of US meat into Taiwan over the last decade. It specifically centers on the instances of US beef importation in 2012 and US pork importation in 2021. Within this folder, you will find two datasets along with a Stata .do file, all of which are instrumental for the analysis of quantitative data as presented in the paper. Additionally, the folder encompasses a spreadsheet that facilitates the creation of Figure 5.

Stata “do-file” containing the code used to run the analyses in the manuscript using the data from “S1 Dataset.”

BackgroundWorldwide, there is a growing concern about the rising number of people with declining cognitive functioning. However, findings on this phenomenon are inconclusive. Our study aimed to assess the prevalence of cognitive impairment and the associated factors in women with a history of pregnancy complications in rural southwestern Uganda.MethodsThis was a cross-sectional study carried out among women above 40 years of age in the greater Kabale district of southwestern Uganda between March and April 2022. Study participants were identified using a consecutive sampling method. Predictor variables included pregnancy complications and other social demographic factors that were assessed by semi-structured interviews while cognitive functioning as an outcome variable was assessed by Montreal Cognitive Assessment (MoCA-B) tool. Data were analyzed using STATA at a 95% Confidence level. Logistic regression analyses were selected for statistical modelling while odds ratios were calculated to assess the strength of associations between the predictor and outcome variables.ResultsIn total, 75% (212/280) of participants had some form of cognitive impairment, with 45% (123/280) falling into mild CI, 31% (86/280) moderate CI and 4% (10/280) severe CI. Twenty-three percent (68/280) of participants fell into category of normal cognitive functioning. Participants with >65 years of age had higher odds of developing cognitive impairment (OR = 2.94; 95%CI: 0.96–9.04, p = 0.06) than those with < 65 years of age. Protective factors to cognitive impairment include delivering from a health facility (OR = 0.31,95% CI:0.16–0.60, p = < .001), primary and post primary levels of education (OR = 0.05; 95% CI: 0.02–0.13, p

Sociodemographic and other relevant characteristics of study participants (N = 711).

This is a Stata do file replicating analysis for How divided is Britain? Symbolic boundaries and social cohesion in Post-Brexit Britain. The data are stored at the UK Data Service. The data are safeguarded and require login for access. https://beta.ukdataservice.ac.uk/datacatalogue/studies/study?id=8926