Dataset

This dataset was created by iFinance Tutor

Contents

analyze the current population survey (cps) annual social and economic supplement (asec) with r the annual march cps-asec has been supplying the statistics for the census bureau's report on income, poverty, and health insurance coverage since 1948. wow. the us census bureau and the bureau of labor statistics ( bls) tag-team on this one. until the american community survey (acs) hit the scene in the early aughts (2000s), the current population survey had the largest sample size of all the annual general demographic data sets outside of the decennial census - about two hundred thousand respondents. this provides enough sample to conduct state- and a few large metro area-level analyses. your sample size will vanish if you start investigating subgroups b y state - consider pooling multiple years. county-level is a no-no. despite the american community survey's larger size, the cps-asec contains many more variables related to employment, sources of income, and insurance - and can be trended back to harry truman's presidency. aside from questions specifically asked about an annual experience (like income), many of the questions in this march data set should be t reated as point-in-time statistics. cps-asec generalizes to the united states non-institutional, non-active duty military population. the national bureau of economic research (nber) provides sas, spss, and stata importation scripts to create a rectangular file (rectangular data means only person-level records; household- and family-level information gets attached to each person). to import these files into r, the parse.SAScii function uses nber's sas code to determine how to import the fixed-width file, then RSQLite to put everything into a schnazzy database. you can try reading through the nber march 2012 sas importation code yourself, but it's a bit of a proc freak show. this new github repository contains three scripts: 2005-2012 asec - download all microdata.R down load the fixed-width file containing household, family, and person records import by separating this file into three tables, then merge 'em together at the person-level download the fixed-width file containing the person-level replicate weights merge the rectangular person-level file with the replicate weights, then store it in a sql database create a new variable - one - in the data table 2012 asec - analysis examples.R connect to the sql database created by the 'download all microdata' progr am create the complex sample survey object, using the replicate weights perform a boatload of analysis examples replicate census estimates - 2011.R connect to the sql database created by the 'download all microdata' program create the complex sample survey object, using the replicate weights match the sas output shown in the png file below 2011 asec replicate weight sas output.png statistic and standard error generated from the replicate-weighted example sas script contained in this census-provided person replicate weights usage instructions document. click here to view these three scripts for more detail about the current population survey - annual social and economic supplement (cps-asec), visit: the census bureau's current population survey page the bureau of labor statistics' current population survey page the current population survey's wikipedia article notes: interviews are conducted in march about experiences during the previous year. the file labeled 2012 includes information (income, work experience, health insurance) pertaining to 2011. when you use the current populat ion survey to talk about america, subract a year from the data file name. as of the 2010 file (the interview focusing on america during 2009), the cps-asec contains exciting new medical out-of-pocket spending variables most useful for supplemental (medical spending-adjusted) poverty research. confidential to sas, spss, stata, sudaan users: why are you still rubbing two sticks together after we've invented the butane lighter? time to transition to r. :D

This code merges multiple years of Crime Survey of England and Wales (CSEW) and/or the British Crime Survey (BCS). The current version merges the BCS and CSEW up to the CSEW 2023/2024. The purpose of these code is to help researchers to quickly and easily combine multiple survey sweeps of the CSEW and BCS.

By combining multiple survey sweeps, people are able to look at, for instance, trends in violence. Furthermore, using such a combined file enables you to look at specific offences, population groups, or consequences, that do not have a high enough frequency if you would use only a single year.

This is a Stata do file, access to Stata is therefore required, as is access to all the BCS and CSEW that you want to merge. In specifying the code, you can decide which files you want to merge. Namely, which years of the Crime Surveys you want to merge and if you want the bolt-on datasets that provide uncapped codes, the adolescent and young adult panels, and/or if you want to use the ‘non-white’ panel. This code does not harmonize variables that are different between years.

All original data resources are available via Related Resources.

This code merges multiple years of Crime Survey of England and Wales (CSEW) and/or the British Crime Survey (BCS). The purpose of these code is to help researchers to quickly and easily combine multiple survey sweeps of the CSEW and BCS.

By combining multiple survey sweeps, people are able to look at, for instance, trends in violence. Furthermore, using such a combined file enables you to look at specific offences, population groups, or consequences, that do not have a high enough frequency if you would use only a single year.

This is a Stata do-file, access to Stata is therefore required, as is access to all the BCS and CSEW that you want to merge. In specifying the code, you can decide which files you want to merge. Namely, which years of the Crime Surveys you want to merge and if you want the bolt-on datasets that provide uncapped codes, the adolescent and young adult panels, and/or if you want to use the ‘non-white’ panel. This code does not harmonize variables that are different between years.

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...

Abstract (en): The Longitudinal Study of Generations (LSOG), initiated in 1971, began as a survey of intergenerational relations among 300 three-generation California families with grandparents (then in their sixties), middle-aged parents (then in their early forties), and grandchildren (then aged 15 to 26). The study broadened in 1991 and now includes a fourth generation, the great-grandchildren of these same families. The LSOG, with a fully elaborated generation-sequential design, allows comparisons of sets of aging parents and children at the same stage of life but during different historical periods. These comparisons make possible the investigation of the effects of social change on inter-generational solidarity or conflict across 35 years and four generations, as well as the effects of social change on the ability of families to buffer stressful life transitions (e.g., aging, divorce and remarriage, higher female labor force participation, changes in work and the economy, and possible weakening of family norms of obligation), and the effects of social change on the transmission of values, resources, and behaviors across generations. The LSOG contains information on family structure, household composition, affectual solidarity and conflict, values, attitudes, behaviors, role importance, marital relationships, health and fitness, mental health and well-being, caregiving, leisure activities, and life events and concerns. Demographic variables include age, sex, income, employment status, marital status, socioeconomic history, education, religion, ethnicity, and military service. Presence of Common Scales: Affectual Solidarity Reliability, Consensual Solidarity (Socialization), Associational Solidarity, Functional Solidarity, Intergenerational Social Support, Normative Solidarity, Familism, Structural Solidarity, Intergenerational Feelings of Conflict, Management of Conflict Tactics, Rosenberg Self-Esteem, Depression (CES-D), Locus of Control, Bradburn Affect Balance, Eysenck Extraversion/Neuroticism, Anxiety (Hopkins Symptom Checklist), Activities of Daily Living (IADL/ADL), Religious Ideology, Political Conservatism, Gender Role Ideology, Individualism/Collectivism, Materialism/Humanism, Work Satisfaction, Gilford-Bengtson Marital Satisfaction Datasets:DS0: Study-Level FilesDS1: Waves 1-7DS2: Wave 8 Multi-generation families in California. Smallest Geographic Unit: None Families were drawn randomly from a subscriber list of 840,000 members of a California Health Maintenance Organization in Los Angeles. Families were recruited by enlisting a grandfather over the age of 60 who was part of a three-generation family that was willing to participate. 2019-08-21 The data were updated and resupplied by the data producer; ICPSR has updated the data and documentation to reflect these changes. Additionally, the data producer provided a Stata do file with syntax to merge the two datasets, which is available for download in the study zip folder. The study title was also updated.2016-07-06 Merril Silverstein was added to the collection as a P.I.2015-07-16 Wave 8 was added; including SPSS, SAS, and STATA datasets as well as an ICPSR Variable Description and Frequencies codebook. The codebook for part one was recompiled into a collection level codebook, including both parts one and two. A user guide for the collection has also been added.2009-05-12 Setup files have been updated. Funding institution(s): United States Department of Health and Human Services. National Institutes of Health. National Institute on Aging (2R01AG00799-21A2). computer-assisted self interview (CASI) face-to-face interview mail questionnaire self-enumerated questionnaire telephone interview

This file describes the replication material for: Trajectories of mental health problems in childhood and adult voting behaviour: Evidence from the 1970s British Cohort Study. Authors: Lisa-Christine Girard & Martin Okolikj. Accepted in Political Behavior. This dataverse holds the following 4 replication files: 1. data_cleaning_traj.R - This file is designed to load, merge and clean the datasets for the estimation of trajectories along with the rescaling of the age 10 Rutter scale. This file was prepared using R-4.1.1 version. 2. traj_estimation.do - With the dataset merged from data_cleaning_traj.R, we run this file in STATA to create and estimate trajectories, to be included in the full dataset. This file was prepared using STATA 17.0 version. 3. data_cleaning.R - This is the file designed to load, merge and clean all datasets in one for preparation of the main analysis following the trajectory estimation. This file was prepared using R-4.1.1 version. 4. POBE Analysis.do - The analysis file is designed to generate the results from the tables in the published paper along with all supplementary materials. This file was prepared using STATA 17.0 version. The data can be accessed at the following address. It requires user registration under special licence conditions: http://discover.ukdataservice.ac.uk/series/?sn=200001. If you have any questions or spot any errors please contact g.lisachristine@gmail.com or martin.okolic@gmail.com.

The article uses two datasets, which cannot be deposited online, but are freely available to registered users. The data of the German Socio-Economic Panel can be requested via http://www.diw.de/en/diw_02.c.222836.en/access.html. Here we are using version 24 (DOI: 10.5684/soep.v24). The data of the British Household Panel Study can be requested via http://discover.ukdataservice.ac.uk/series/?sn=20000. Here we provide two STATA do-files, one for each dataset, that will create the working file, which was used in the article. The do-files to merge and recode the original data.

Understanding Society, (UK Household Longitudinal Study), which began in 2009, is conducted by the Institute for Social and Economic Research (ISER) at the University of Essex and the survey research organisations Verian Group (formerly Kantar Public) and NatCen. It builds on and incorporates, the British Household Panel Survey (BHPS), which began in 1991.

The Understanding Society: Calendar Year Dataset, 2023, is designed for analysts to conduct cross-sectional analysis for the 2023 calendar year. The Calendar Year datasets combine data collected in a specific year from across multiple waves and these are released as separate calendar year studies, with appropriate analysis weights, starting with the 2020 Calendar Year dataset. Each subsequent year, an additional yearly study is released.

The Calendar Year data is designed to enable timely cross-sectional analysis of individuals and households in a calendar year. Such analysis can however, only involve variables that are collected in every wave (excluding rotating content which is only collected in some of the waves). Due to overlapping fieldwork the data files combine data collected in the three waves that make up a calendar year. Analysis cannot be restricted to data collected in one wave during a calendar year, as this subset will not be representative of the population. Further details and guidance on this study can be found in the xxxx_main_survey_calendar_year_user_guide_2023.

These calendar year datasets should be used for cross-sectional analysis only. For those interested in longitudinal analyses using Understanding Society please access the main survey datasets: Safeguarded (End User Licence) version or Safeguarded/Special Licence version.

Understanding Society: the UK Household Longitudinal Study, started in 2009 with a general population sample (GPS) of UK residents living in private households of around 26,000 households and an ethnic minority boost sample (EMBS) of 4,000 households. All members of these responding households and their descendants became part of the core sample who were eligible to be interviewed every year. Anyone who joined these households after this initial wave, were also interviewed as long as they lived with these core sample members to provide the household context. At each annual interview, some basic demographic information was collected about every household member, information about the household is collected from one household member, all 16+ year old household members are eligible for adult interviews, 10-15 year old household members are eligible for youth interviews, and some information is collected about 0-9 year olds from their parents or guardians. Since 1991 until 2008/9 a similar survey, the British Household Panel Survey (BHPS), was fielded. The surviving members of this survey sample were incorporated into Understanding Society in 2010. In 2015, an immigrant and ethnic minority boost sample (IEMBS) of around 2,500 households was added. In 2022 a GPS boost sample (GPS2) of around 5,700 households was added. To know more about the sample design, following rules, interview modes, incentives, consent, questionnaire content please see the study overview and user guide.

Co-funders

In addition to the Economic and Social Research Council, co-funders for the study included the Department of Work and Pensions, the Department for Education, the Department for Transport, the Department of Culture, Media and Sport, the Department for Community and Local Government, the Department of Health, the Scottish Government, the Welsh Assembly Government, the Northern Ireland Executive, the Department of Environment and Rural Affairs, and the Food Standards Agency.

End User Licence and Special Licence versions:

There are two versions of the Calendar Year 2023 data. One is available under the standard End User Licence (EUL) agreement, and the other is a Special Licence (SL) version. The SL version contains month and year of birth variables instead of just age, more detailed country and occupation coding for a number of variables and various income variables have not been top-coded (see document '9471_eul_vs_sl_variable_differences' for more details). Users are advised to first obtain the standard EUL version of the data to see if they are sufficient for their research requirements. The SL data have more restrictive access conditions; prospective users of the SL version will need to complete an extra application form and demonstrate to the data owners exactly why they need access to the additional variables in order to get permission to use that version. The main longitudinal versions of the Understanding Society study may be found under SNs 6614 (Safeguarded (EUL)) and 6931 (Safeguarded/SL).

Low- and Medium-level geographical identifiers produced for the mainstage longitudinal dataset can be used with this Calendar Year 2023 dataset, subject to SL access conditions. See the User Guide for further details.

Suitable data analysis software

These data are provided by the depositor in Stata format. Users are strongly advised to analyse them in Stata. Transfer to other formats may result in unforeseen issues. Stata SE or MP software is needed to analyse the larger files, which contain about 1,800 variables.

analyze the area resource file (arf) with r the arf is fun to say out loud. it's also a single county-level data table with about 6,000 variables, produced by the united states health services and resources administration (hrsa). the file contains health information and statistics for over 3,000 us counties. like many government agencies, hrsa provides only a sas importation script and an as cii file. this new github repository contains two scripts: 2011-2012 arf - download.R download the zipped area resource file directly onto your local computer load the entire table into a temporary sql database save the condensed file as an R data file (.rda), comma-separated value file (.csv), and/or stata-readable file (.dta). 2011-2012 arf - analysis examples.R limit the arf to the variables necessary for your analysis sum up a few county-level statistics merge the arf onto other data sets, using both fips and ssa county codes create a sweet county-level map click here to view these two scripts for mo re detail about the area resource file (arf), visit: the arf home page the hrsa data warehouse notes: the arf may not be a survey data set itself, but it's particularly useful to merge onto other survey data. confidential to sas, spss, stata, and sudaan users: time to put down the abacus. time to transition to r. :D

This data depository contains all experimental materials, data, and code for Spamann, Lawyers' Role-Induced Bias ... All experimental materials (i.e., exercise and survey instrument) are in the pdf file Spamann_experimentalmaterials_all.pdf. The dataset Newman.dta (Stata 14.2) contains the data collected. The Stata do-file Spamann_role_bias_code.do generates the three figures and other reported statistical information reported in the version of the paper originally posted to SSRN in May 2019. Spamann_role_bias_code_revised.do generates the four figures and other reported statistical information reported in the revision submitted to JLS in March 2020 and ultimately accepted by the journal. Both do-files use Newman.dta. Newman.dta is the result of merging 6 csv files generated by Qualtrics in each of the six semesters from students' survey responses. These 6 csv files, and the do-file rawdata_merge_clean.do to merge them, are also included.

This tool--a simple csv or Stata file for merging--gives you a fast way to assign Census county FIPS codes to variously presented county names. This is useful for dealing with county names collected from official sources, such as election returns, which inconsistently present county names and often have misspellings. It will likely take less than ten minutes the first time, and about one minute thereafter--assuming all versions of your county names are in this file. There are about 3,142 counties in the U.S., and there are 77,613 different permutations of county names in this file (ave=25 per county, max=382). Counties with more likely permutations have more versions. Misspellings were added as I came across them over time. I DON'T expect people to cite the use of this tool. DO feel free to suggest the addition of other county name permutations.