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

analyze the health and retirement study (hrs) with r the hrs is the one and only longitudinal survey of american seniors. with a panel starting its third decade, the current pool of respondents includes older folks who have been interviewed every two years as far back as 1992. unlike cross-sectional or shorter panel surveys, respondents keep responding until, well, death d o us part. paid for by the national institute on aging and administered by the university of michigan's institute for social research, if you apply for an interviewer job with them, i hope you like werther's original. figuring out how to analyze this data set might trigger your fight-or-flight synapses if you just start clicking arou nd on michigan's website. instead, read pages numbered 10-17 (pdf pages 12-19) of this introduction pdf and don't touch the data until you understand figure a-3 on that last page. if you start enjoying yourself, here's the whole book. after that, it's time to register for access to the (free) data. keep your username and password handy, you'll need it for the top of the download automation r script. next, look at this data flowchart to get an idea of why the data download page is such a righteous jungle. but wait, good news: umich recently farmed out its data management to the rand corporation, who promptly constructed a giant consolidated file with one record per respondent across the whole panel. oh so beautiful. the rand hrs files make much of the older data and syntax examples obsolete, so when you come across stuff like instructions on how to merge years, you can happily ignore them - rand has done it for you. the health and retirement study only includes noninstitutionalized adults when new respondents get added to the panel (as they were in 1992, 1993, 1998, 2004, and 2010) but once they're in, they're in - respondents have a weight of zero for interview waves when they were nursing home residents; but they're still responding and will continue to contribute to your statistics so long as you're generalizing about a population from a previous wave (for example: it's possible to compute "among all americans who were 50+ years old in 1998, x% lived in nursing homes by 2010"). my source for that 411? page 13 of the design doc. wicked. this new github repository contains five scripts: 1992 - 2010 download HRS microdata.R loop through every year and every file, download, then unzip everything in one big party impor t longitudinal RAND contributed files.R create a SQLite database (.db) on the local disk load the rand, rand-cams, and both rand-family files into the database (.db) in chunks (to prevent overloading ram) longitudinal RAND - analysis examples.R connect to the sql database created by the 'import longitudinal RAND contributed files' program create tw o database-backed complex sample survey object, using a taylor-series linearization design perform a mountain of analysis examples with wave weights from two different points in the panel import example HRS file.R load a fixed-width file using only the sas importation script directly into ram with < a href="http://blog.revolutionanalytics.com/2012/07/importing-public-data-with-sas-instructions-into-r.html">SAScii parse through the IF block at the bottom of the sas importation script, blank out a number of variables save the file as an R data file (.rda) for fast loading later replicate 2002 regression.R connect to the sql database created by the 'import longitudinal RAND contributed files' program create a database-backed complex sample survey object, using a taylor-series linearization design exactly match the final regression shown in this document provided by analysts at RAND as an update of the regression on pdf page B76 of this document . click here to view these five scripts for more detail about the health and retirement study (hrs), visit: michigan's hrs homepage rand's hrs homepage the hrs wikipedia page a running list of publications using hrs notes: exemplary work making it this far. as a reward, here's the detailed codebook for the main rand hrs file. note that rand also creates 'flat files' for every survey wave, but really, most every analysis you c an think of is possible using just the four files imported with the rand importation script above. if you must work with the non-rand files, there's an example of how to import a single hrs (umich-created) file, but if you wish to import more than one, you'll have to write some for loops yourself. confidential to sas, spss, stata, and sudaan users: a tidal wave is coming. you can get water up your nose and be dragged out to sea, or you can grab a surf board. time to transition to r. :D

These data are part of NACJD's Fast Track Release and are distributed as they were received from the data depositor. The files have been zipped by NACJD for release, but not checked or processed except for the removal of direct identifiers. Users should refer to the accompanying readme file for a brief description of the files available with this collection and consult the investigator(s) if further information is needed. This study sought to apply current and advanced Y-STR DNA technology in forensic laboratories to a large in vivo population of proxy-couples, to provide groundwork for future inquiry about the conditions affecting DNA recovery in the living patient, to determine timing for evidence collection, and to attempt to identify variables influencing DNA recovery. The objective of this research was to create the evidence base supporting or limiting the expansion of the 72-hour period for evidence collection. Another objective was to identify conditions that might influence the recovery of DNA, and therefore influence policies related to sample collection from the complex post-coital environment. The collection includes 6 SPSS data files: AlleleRecovery Jun 2014 Allrec.sav (n=70; 34 variables) AlleleRecovery Jun 2014 Used for descriptve analysis.sav (n=66; 58 variables) Condom_collections-baseline-d9-Jun2014 Allrec without open-ended-ICPSR.sav (n=70; 66 variables) DNADemogFemalesJun2014- without open-ended AllRec-ICPSR.sav (n=73; 67 variables) DNADemogFemalesJun2014- without open-ended -For analysis with group variables-ICPSR.sav (n=66; 73 variables) DNADemogMalesJun2014- without open-ended AllRec-ICPSR.sav (n=73; 46 variables) and 1 SAS data file (dnalong.sas7bdat (n=264; 7 variables)). Data from a focus group of subject matter experts which convened to identify themes from their practice are not included with this collection.

This directory contains analytic code used to build cohorts, dependent variables, and covariates, and run all statistical analyses for the study, "Changes in care associated with integrating Medicare and Medicaid for dual eligible individuals: Examination of a Fully Integrated Special Needs Plan."The code files enclosed in this directory are:SAS_Cohorts_Outcomes 23-9-30.sas. This SAS code file builds study cohorts, dependent variables, and covariates. This code produced a person-by-month level database of outcomes and covariates for individuals in the integration and comparison cohorts.STATA_Models_23-6-5_weight_jama.do. This Stata program reads in the person-by-month level database (output from SAS) and conducts all statistical analyses used to produce the main and supplementary analyses reported in the manuscript.We have provided this code and documentation to disclose our study methods. Our Data Use Agreements prohibit publishing of row-level data for this study. Therefore, researchers would need to obtain Data Use Agreements with data providers to implement these analyses. We also note that some measures reference macros with proprietary code (e.g., Medispan® files) which require a separate user license to run. Interested readers should contact the study PI, Eric T. Roberts (eric.roberts@pennmedicine.upenn.edu) for further information.

Behavioral Risk Factor Surveillance System (BRFSS) 2024

Overview

The Behavioral Risk Factor Surveillance System (BRFSS) is the nation's premier system of health-related telephone surveys that collect uniform, state-specific data about U.S. residents regarding their health-related risk behaviors, chronic health conditions, and use of preventive services.

The objective of the BRFSS is to gather consistent, state-level data on preventive health practices and risk behaviors associated with chronic diseases, injuries, and preventable infectious diseases among adults (aged 18 and older).

Established in 1984 with 15 states, the BRFSS now collects data in all 50 states, the District of Columbia, and three U.S. territories. The system completes more than 400,000 adult interviews each year, making it the largest continuously conducted health survey system in the world.

2024 Data Notes

The 2024 BRFSS dataset continues to use the raking weighting methodology (introduced in 2011) and includes both landline and cellphone-only respondents, ensuring more accurate representation of the U.S. adult population.

The aggregate dataset combines landline and cell phone data collected in 2024 from 49 states, The District of Columbia, Guam, Puerto Rico, and The U.S. Virgin Islands.

This original dataset contains responses from 457,670 individuals and has 301 features. These features are either questions directly asked of participants, or calculated variables based on individual participant responses.

⚠️ Note: Tennessee was unable to collect enough responses to meet inclusion requirements for 2024 and is not included in this public dataset.

Certain survey questions and responses have been modified or omitted to comply with federal data policies in effect during the 2024 collection period. As a result, some variables may contain missing values or appear inconsistent due to questions that were removed or restructured.

Data Collection

Data are collected from a random sample of adults (one per household) via telephone interviews.

Factors assessed include: - Tobacco use - Health care access and coverage - Alcohol consumption - Physical activity and diet - HIV/AIDS knowledge and prevention - Chronic health conditions
- Preventive health services and screenings

Content

The annual dataset contains 301 variables, covering both core questions and optional modules. Please refer to the official BRFSS 2024 Codebook for detailed variable definitions and coding.

This dataset contains 3 files: 1. brfss_survey_data_2024.csv # Dataset in .csv format (converted from SAS) 2. codebook_2024.HTML # CDC codebook for variable definitions
3. main_data_brfss_2024.XPT # Main dataset

⚙️ Note: The CSV file were converted from the original SAS format using pandas. Minor conversion artifacts may exist.

Complete description about each column of the CSV file can be found in the codebook.

Source & Acknowledgements

Data provided by the U.S. Centers for Disease Control and Prevention (CDC).

Original source and additional years of BRFSS data: CDC BRFSS Annual Data

Citation:

Centers for Disease Control and Prevention (CDC). Behavioral Risk Factor Surveillance System Survey Data. Atlanta, Georgia: U.S. Department of Health and Human Services, Centers for Disease Control and Prevention, 2024.

License: Public Domain (U.S. Government Work)

Suggested Citation (for Kaggle users)

If you use this dataset in your analysis or publication, please cite as:

Behavioral Risk Factor Surveillance System (BRFSS) 2024. U.S. Centers for Disease Control and Prevention (CDC). Public Domain.

Prepared for Kaggle public dataset publication. All data are in the public domain as U.S. Government works.

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. A Black supplement of 263 respondents, who were asked the same questions that were administered to the national cross-section sample, is included with the national cross-section of 1,571 respondents. In addition to the usual content, the study contains data on opinions about the Supreme Court, political knowledge, and further information concerning racial issues. Voter validation data have been included as an integral part of the election study, providing objective information from registration and voting records or from respondents' past voting behavior. 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.; Performed recodes and/or calculated derived variables.; Checked for undocumented or out-of-range codes.. United States citizens of voting age living in private households in the continental United States. A representative cross-section sample, consisting of 1,571 respondents, plus a Black supplement sample of 263 respondents. 2015-11-10 The study metadata was updated.1999-12-14 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. In addition, SAS and SPSS data definition statements have been created for this collection, and the data collection instruments are now available as a PDF file. face-to-face interview, telephone interviewThe SAS transport file was created using the SAS CPORT procedure.

The Public Use Microdata Sample (PUMS) contains a sample of actual responses to the American Community Survey (ACS). The PUMS dataset includes variables for nearly every question on the survey, as well as many new variables that were derived after the fact from multiple survey responses (such as poverty status). Each record in the file represents a single person, or--in the household-level dataset--a single housing unit. In the person-level file, individuals are organized into households, making possible the study of people within the contexts of their families and other household members. PUMS files for an individual year, such as 2015, contain records of data from approximately one percent of the United States population. As such, PUMS files covering a five-year period, such as 2011-2015, contain records of data from approximately five percent of the United States population.The PUMS files are much more flexible than the aggregate data available on American FactFinder, though the PUMS also tend to be more complicated to use. Working with PUMS data generally involves downloading large datasets onto a local computer and analyzing the data using statistical software such as R, SPSS, Stata, or SAS.Since all ACS responses are strictly confidential, many variables in the PUMS file have been modified in order to protect the confidentiality of survey respondents. For instance, particularly high incomes are "top-coded," uncommon birthplace or ancestry responses are grouped into broader categories, and the PUMS file provides a very limited set of geographic variables

There are several Microsoft Word documents here detailing data creation methods and with various dictionaries describing the included and derived variables.The Database Creation Description is meant to walk a user through some of the steps detailed in the SAS code with this project.The alphabetical list of variables is intended for users as sometimes this makes some coding steps easier to copy and paste from this list instead of retyping.The NIS Data Dictionary contains some general dataset description as well as each variable's responses.

Abstract (en): This survey is the first broad-based, systematic examination of the nature of civil litigation in state general jurisdiction trial courts. Data collection was carried out by the National Center for State Courts with assistance from the National Association of Criminal Justice Planners and the United States Bureau of the Census. The data collection produced two datasets. Part 1, Tort, Contract, and Real Property Rights Data, is a merged sample of approximately 30,000 tort, contract, and real property rights cases disposed during the 12-month period ending June 30, 1992. Part 2, Civil Jury Cases Data, is a sample of about 6,500 jury trial cases disposed over the same time period. Data collected include information about litigants, case type, disposition type, processing time, case outcome, and award amounts for civil jury cases. 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.. Forty-five jurisdictions chosen to represent the 75 most populous counties in the nation. The sample for this study was designed and selected by the United States Bureau of the Census. It was a two-stage stratified sample with 45 of the 75 most populous counties selected at the first stage. The top 75 counties account for about 37 percent of the United States population and about half of all civil filings. The 75 counties were divided into four strata based on aggregate civil disposition data for 1990 obtained through telephone interviews with court staffs in the general jurisdiction trial courts. The sample consisted of tort, contract, and real property rights cases disposed between July 1, 1991, and June 30, 1992. 2011-11-02 All parts are being moved to restricted access and will be available only using the restricted access procedures.2006-03-30 File CB6587.ALL.PDF was removed from any previous datasets and flagged as a study-level file, so that it will accompany all downloads.2006-03-30 File CB6587.ALL.PDF was removed from any previous datasets and flagged as a study-level file, so that it will accompany all downloads.2006-03-30 File CB6587.ALL.PDF was removed from any previous datasets and flagged as a study-level file, so that it will accompany all downloads.2006-03-30 File CB6587.ALL.PDF was removed from any previous datasets and flagged as a study-level file, so that it will accompany all downloads.2006-03-30 File CB6587.ALL.PDF was removed from any previous datasets and flagged as a study-level file, so that it will accompany all downloads.2005-11-04 On 2005-03-14 new files were added to one or more datasets. These files included additional setup files as well as one or more of the following: SAS program, SAS transport, SPSS portable, and Stata system files. The metadata record was revised 2005-11-04 to reflect these additions.2004-06-01 The data have been updated by the principal investigator to include replicate weights and a few other variables. The codebook and SAS and SPSS data definition statements have been revised to reflect these changes.2001-03-26 The data have been updated by the principal investigator to include replicate weights. The codebook and SAS and SPSS data definition statements have been revised to reflect these changes.2001-03-26 The data had been updated by the principal investigator to include replicate weights. The codebook and SAS and SPSS data definition statements had been revised to reflect these changes.1997-07-29 The codebook had been revised to correct errors documenting both data files. Column location (and width) of variable WGHT "TOTAL WEIGHT" was incorrectly shown as 10.4 for Part 1, Tort, Contract, and Real Property Data. It was accurately shown in the data definition statements as 9.4. Variables listed after WGHT were inaccurately reported one column off in the codebook. Similarly, column location (and width) of variable WGHT "TOTAL WEIGHT" was incorrectly shown as 10.2 for Part 2, Civil Jury Data. It was accurately shown in the data definition statements as 9.2. Variables listed after WGHT were inaccurately reported one column off in the codebook. Fundi...

This dataset is a collection of variables, morphometrics, derived from rhino footprints using JMP software from SAS. The morphometrics are different combinations of lengths, angles and areas. Each row describes the variables from one footprint, and for each animal trail several footprints are collected to describe the characteristics that make the footprints from that animal unique

The leadership and personal competencies exhibits limitations in terms of construct definition, behavior specifications and valid theory-based measuring strategies. An explanatory design with latent variables and the statistical software SAS 9.4 were used for the validation and adaptation to Spanish of the Leadership Virtues Questionnaire applied to work and organizational psychologists and people who exercise leadership functions in Chile. The levels of agreement between judges for the adaptation to the Spanish language and the confirmatory factor analysis of first order with four dimensions shows insufficient statistical indices for the absolute, comparative and parsimonious adjustments. However, a second-order confirmatory factor analysis with two dimensions presents a satisfactory fit for the item, model, and parameter matrices. The measurement of Virtuous Leadership would provide relevant inputs for further evaluation and training based on ethical competencies aimed at improving management, which would, in turn, allow for its treatment as an independent variable to generate an ethical organizational culture.

Data structure explanation for

Does cash-based operating profitability explain the accruals anomaly in China?

February 25, 2020

We provide the final data used in our test and the SAS code to generate all the tables in our paper. The data for the U.S. study is titled “data_pbfj_us”, the data for the Chinese study is titled “data_pbfj_cn”. The SAS code used to generate the results is titled “pbfj_acc_code”. 

Data structure for the U.S. sample Variable names: 1) permno: stock identifier 2) date: date of observation 3) ret: monthly stock return 4) mep: market cap in previous month 5) lnmep: logarithm of mep 6) micro: microcap stock indicator 7) str: short-term reversal, defined as return in previous month. 8) mom: momentum effect (MOM) 9) at: total assets 10) lnbtm: logarithm of firm’s book-to-market equity 11) op_raw: operating profitability (OP) 12) accat: accruals (ACC) 13) opcat: cash-based operating profitability (Cash-OP)

Data structure for the Chinese sample Variable names: 1) permno: stock identifier, same as “stkcd” in CSMAR database 2) date: date of observation 3) ret: monthly stock return 4) mep: market cap in previous month 5) lnmep: logarithm of mep 6) shell: shell stock indicator 7) str: short-term reversal, defined as return in previous month. 8) mom: momentum effect (MOM) 9) at: total assets, same as “A001000000” in CSMAR database 10) lnbtm: logarithm of firm’s book-to-market equity 11) op_raw: operating profitability (OP) 12) accat: accruals (ACC) 13) opcat: cash-based operating profitability (Cash-OP)

Abstract (en): Detailed demographic characteristics of the population of the United States from 1870 to 1960 are contained in this data collection. Included are state-level estimates of the nation's inhabitants by sex, race, nativity and age, as well as intercensal migration calculated by age, race, and sex. The basic information recorded in this collection was obtained from the decennial censuses of the United States or estimated by the principal investigators from material collected by the decennial censuses. The collection is comprised of thirteen separate data files. Each contains information for every state in the nation. All parts have a rectangular file structure with one record per case, with the number of cases ranging from 50 to 2,891, and the record length from 203 to 2,930 per part. Standard geographic identifying codes used in all of the files permit the combination of two or more of the files as research interests dictate. 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 variable labels and/or value labels.. 2011-08-31 SAS, SPSS, and Stata setups have been added to this data collection.2006-01-12 All files were removed from dataset 14 and flagged as study-level files, so that they will accompany all downloads.2006-01-12 All files were removed from dataset 14 and flagged as study-level files, so that they will accompany all downloads. This collection was made available to ICPSR through the Population Studies Center of the University of Pennsylvania.

Abstract (en): The School District Data Book (SDDB) is an education database and information system. It contains an extensive set of data on children, their households, and the nation's school systems. Under the sponsorship of the National Center for Education Statistics, the Bureau of the Census has produced special tabulation files using the basic record files of the 1990 Census of Population and Housing by school district. These tabulation files contain aggregated data describing attributes of children and households in school districts. Data are organized by seven types of tabulation records: (1) characteristics of all households, (2) characteristics of all persons, (3) characteristics of households with children, (4) characteristics of parents living with children, (5) children's household characteristics, (6) children's parents' characteristics, and (7) children's own characteristics. 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: Checked for undocumented or out-of-range codes.. All public elementary and secondary education agencies in operation during 1990-1991 in the 50 states and the District of Columbia. 2006-10-27 Variable names were corrected in SAS and SPSS setup files. The processing note in the codebook was also updated to reflect the corrections.2006-01-12 All files were removed from dataset 139 and flagged as study-level files, so that they will accompany all downloads.2006-01-12 All files were removed from dataset 138 and flagged as study-level files, so that they will accompany all downloads.2006-01-12 All files were removed from dataset 137 and flagged as study-level files, so that they will accompany all downloads.2002-05-29 Seventeen additional datasets (Parts 140-156) were added to the collection, including data for two states previously not covered -- Vermont and Washington -- and additional data for Arkansas, California, Illinois, Massachusetts, Michigan, Minnesota, New Jersey, Pennsylvania, and Texas. (1) Some states have multiple data files because they have large numbers of cases. (2) Two data files are not included in this release. They are: Washington, Part 3, and Wisconsin, Part 4.

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