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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

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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...

Endovascular therapy (EVT) has changed the landscape of acute stroke treatment in the context of large vessel occlusion (LVO). Still, procedural success is typically determined by the degree of large vessel recanalization, despite the fact that large vessel recanalization does not always result in microvascular reperfusion. To address this discrepancy, we performed bedside optical CBF monitoring (with diffuse correlation spectroscopy) during endovascular therapy. This allowed comparison of CBF pre vs post-recanalization.Note 3 files uploaded:The .dta file is a stata file which contains all variables labels which includes all necessary variables details.The .xlsx file database is in numerical format (i.e. without applying text labels).The .xlsx file data dictionary contains all variable names, variable labels, and code to translate the numerical values.

The files provided within this .zip file are meant to reproduce the tables and figures included in the article "Tabloid Media Campaigns and Public Opinion: Quasi-Experimental Evidence on Euroscepticism in England" by Florian Foos and Daniel Bischof in the APSR. Notice: - This is a fully reproducible archive written in Stata's project environment: https://www.statalist.org/forums/forum/general-stata-discussion/general/1302147-how-project-from-ssc-is-different-from-stata-built-in-project. - As the code is written in a project environment we advise all users to carefully read the README.TXT in order to understand how reproduction in Stata's project environment works. - The largest part of our analyses are based on yearly attitudinal data from the British Social Attitudes Survey (BSA): https://www.bsa.natcen.ac.uk. The BSA does not allow researchers to upload these data as part of their replication files; we are also not allowed to upload a recoded version of the data file. However, all yearly BSA surveys are available via the UK Data Service. In order to reproduce the results reported in this paper, you will need to a) register with the UK Data Service (https://beta.ukdataservice.ac.uk/myaccount/login) and b) access and download the relevant .dta files and place them into the replication archive (data_original/BSA/*YEAR*).

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

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

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.

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

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

This upload contains a Stata 14.2 do file, the main dataset, and several mini-datasets needed to replicate the analyses in our paper. Thanks for reading!

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.

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

Association between socio-demographic and other characteristics of HCWs and sero-protection status.

Data, Stata code, and output

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.)

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.

Predictors for sero-protection status of healthcare workers.

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).