The National Longitudinal Surveys (NLS) are a set of surveys designed to gather information at multiple points in time on the labor market activities and other significant life events of several groups of men and women. For more than 4 decades, NLS data have served as an important tool for economists, sociologists, and other researchers. For more information and data visit: https://www.bls.gov/nls/

Understanding Society, the UK Household Longitudinal Study, is a longitudinal survey of the members of approximately 40,000 households (at Wave 1) in the United Kingdom. The overall purpose of Understanding Society is to provide high quality longitudinal data about subjects such as health, work, education, income, family, and social life to help understand the long term effects of social and economic change, as well as policy interventions designed to impact upon the general well-being of the UK population. The Understanding Society main survey sample consists of a large General Population Sample plus three other components: the Ethnic Minority Boost Sample, the former British Household Panel Survey sample and the Immigrant and Ethnic Minority Boost Sample.

The IMF's World Revenue Longitudinal Data set (WoRLD) is a compilation of government tax and non-tax revenues from the IMF's Government Finance Statistics and World Economic Outlook, and drawing on the OECD Revenue Statistics and Revenue Statistics in Latin American and the Caribbean.

The dataset comprises of spliced revenue data taken from the following sources: WEO, GFS, OECD and various IMF Staff Reports.

The 1975-1981 TIME USE LONGITUDINAL PANEL STUDY dataset combines a round of data collected in 1981 with the principal investigators' earlier TIME USE IN ECONOMIC AND SOCIAL ACCOUNTS, 1975-1976 (ICPSR 7580), collected by F. Thomas Juster, Paul Courant, et al. This combined data collection consists of data from 620 respondents, their spouses if they were married at the time of first contact, and up to three children between the ages of three and seventeen living in the household. The key features which characterized the 1975 time use study were repeated in 1981. In both of the data collection years, adult individuals provided four time diaries as well as extensive information related to their time use in the four waves of data collection. Information pertaining to the household was collected, as well as identical measures from respondents and spouses for all person-specific information. Selected children provided two time diary reports (one for a school day and one non-school day), an academic achievement measure, and survey measures pertaining to school and family life. In addition, teacher ratings were obtained. For each adult individual who remained in the sample through the 1981 study, a time budget was constructed from his or her time diaries containing the number of minutes per week spent in each of some 223 mutually exclusive and exhaustive activities. These measures provide a description of how the sample individuals were currently allocating their time and are comparable to the 87 activity measures created from their 1975 diaries. In addition, respondent and spouse time aggregates were converted to parent time aggregates for mothers and fathers of children in the sample. To facilitate analyses on spouses, a merged data file was created for 868 couples in which both husband and wife had complete Wave I data in either 1975-1976 or 1981.

The Analysis of Longitudinal Claims Databases (R1 Part B): Effect of Variation in Health Coverage, Employment, and Community Resources on Adverse Events and Healthcare Costs and Utilization, United States is the second of a three-part project that examined claims data from Medicare, Medicaid, and/or Optum databases to explore aging trajectories, use of preventative services, and healthcare outcomes for individuals with several types of physical disabilities. This study made use of existing national databases to examine various health outcomes among individuals with disability. Using 2007-2016 Medicaid and Medicare Data, the researchers conducted three separate types of analyses: At the state level, examine the effect of variation in health coverage and related health policies on adverse health events and health outcomes among youth and adults with disability. At the county level, examine the variation in employment and community participatory living on adverse health and health outcomes among youth and adult with disability. At the state level, examine the effect of variation in Medicaid long-term care and community centers on health outcomes among youth and adult with disability.

THIS RESOURCE IS NO LONGER IN SERVICE, documented on August 11, 2015. A searchable database for epidemiologic research on aging changes across the lifespan. In 2003, the National Institute on Aging (NIA) established the Longitudinal Data on Aging (LDA) working group to assist with the development of research initiatives for identifying the physiologic and other types of factors across the lifespan, affecting onset and progression of disease with advancing age, as well as elucidation of protective factors contributing to exceptionally healthy aging. This database was developed based on input from the LDA working group which indicated that establishing a database of existing sources of longitudinal data on aging (e.g., ongoing longitudinal cohorts, longitudinal data sets, biospecimen repositories) would be a valuable resource for facilitating future research on aging changes across the lifespan. The longitudinal studies, data sets and repositories included in this database encompass a wide range of age groups (childhood to old age), studies in minority populations, as well as sources of longitudinal data existing in the United States and abroad. Our primary purpose for establishing this database is to provide a resource for potential applicants for grants to the NIA. No part of this database can be used for commercial purposes.

The Home Mortgage Disclosure Act (HMDA) database (Consumer Financial Protection Bureau, 2022) has compiled mortgage lending data since 1981, but the collection and dissemination methods have changed over time (Federal Financial Institutions Examination Council, 2018), creating barriers to conducting longitudinal analyses. This HMDA Longitudinal Dataset (HLD) organizes and standardizes information across different eras of HMDA data collection between 1981 and 2021, enabling such analysis. This collection contains two types of datasets: 1) HMDA aggregated data by census tract for each decade and 2) HMDA aggregated data by census tract for individual years. Items for analysis include borrower income values, mortgages by loan type (e.g., conventional, Federal Housing Administration (FHA), Veterans Affairs (VA), refinances), and mortgages by borrower race and gender.

The Canadian Longitudinal Tract Database enables the spatial apportionment of Canadian census tract-level data from different census years to common geographic boundaries. This dataset contains the apportionment tables and associated documentation.

ABSTRACT

The Albero study analyzes the personal transitions of a cohort of high school students at the end of their studies. The data consist of (a) the longitudinal social network of the students, before (n = 69) and after (n = 57) finishing their studies; and (b) the longitudinal study of the personal networks of each of the participants in the research. The two observations of the complete social network are presented in two matrices in Excel format. For each respondent, two square matrices of 45 alters of their personal networks are provided, also in Excel format. For each respondent, both psychological sense of community and frequency of commuting is provided in a SAV file (SPSS). The database allows the combined analysis of social networks and personal networks of the same set of individuals.

INTRODUCTION

Ecological transitions are key moments in the life of an individual that occur as a result of a change of role or context. This is the case, for example, of the completion of high school studies, when young people start their university studies or try to enter the labor market. These transitions are turning points that carry a risk or an opportunity (Seidman & French, 2004). That is why they have received special attention in research and psychological practice, both from a developmental point of view and in the situational analysis of stress or in the implementation of preventive strategies.

The data we present in this article describe the ecological transition of a group of young people from Alcala de Guadaira, a town located about 16 kilometers from Seville. Specifically, in the “Albero” study we monitored the transition of a cohort of secondary school students at the end of the last pre-university academic year. It is a turning point in which most of them began a metropolitan lifestyle, with more displacements to the capital and a slight decrease in identification with the place of residence (Maya-Jariego, Holgado & Lubbers, 2018).

Normative transitions, such as the completion of studies, affect a group of individuals simultaneously, so they can be analyzed both individually and collectively. From an individual point of view, each student stops attending the institute, which is replaced by new interaction contexts. Consequently, the structure and composition of their personal networks are transformed. From a collective point of view, the network of friendships of the cohort of high school students enters into a gradual process of disintegration and fragmentation into subgroups (Maya-Jariego, Lubbers & Molina, 2019).

These two levels, individual and collective, were evaluated in the “Albero” study. One of the peculiarities of this database is that we combine the analysis of a complete social network with a survey of personal networks in the same set of individuals, with a longitudinal design before and after finishing high school. This allows combining the study of the multiple contexts in which each individual participates, assessed through the analysis of a sample of personal networks (Maya-Jariego, 2018), with the in-depth analysis of a specific context (the relationships between a promotion of students in the institute), through the analysis of the complete network of interactions. This potentially allows us to examine the covariation of the social network with the individual differences in the structure of personal networks.

PARTICIPANTS

The social network and personal networks of the students of the last two years of high school of an institute of Alcala de Guadaira (Seville) were analyzed. The longitudinal follow-up covered approximately a year and a half. The first wave was composed of 31 men (44.9%) and 38 women (55.1%) who live in Alcala de Guadaira, and who mostly expect to live in Alcala (36.2%) or in Seville (37.7%) in the future. In the second wave, information was obtained from 27 men (47.4%) and 30 women (52.6%).

DATE STRUCTURE AND ARCHIVES FORMAT

The data is organized in two longitudinal observations, with information on the complete social network of the cohort of students of the last year, the personal networks of each individual and complementary information on the sense of community and frequency of metropolitan movements, among other variables.

Social network

The file “Red_Social_t1.xlsx” is a valued matrix of 69 actors that gathers the relations of knowledge and friendship between the cohort of students of the last year of high school in the first observation. The file “Red_Social_t2.xlsx” is a valued matrix of 57 actors obtained 17 months after the first observation.

The data is organized in two longitudinal observations, with information on the complete social network of the cohort of students of the last year, the personal networks of each individual and complementary information on the sense of community and frequency of metropolitan movements, among other variables.

In order to generate each complete social network, the list of 77 students enrolled in the last year of high school was passed to the respondents, asking that in each case they indicate the type of relationship, according to the following values: 1, “his/her name sounds familiar"; 2, "I know him/her"; 3, "we talk from time to time"; 4, "we have good relationship"; and 5, "we are friends." The two resulting complete networks are represented in Figure 2. In the second observation, it is a comparatively less dense network, reflecting the gradual disintegration process that the student group has initiated.

Personal networks

Also in this case the information is organized in two observations. The compressed file “Redes_Personales_t1.csv” includes 69 folders, corresponding to personal networks. Each folder includes a valued matrix of 45 alters in CSV format. Likewise, in each case a graphic representation of the network obtained with Visone (Brandes and Wagner, 2004) is included. Relationship values range from 0 (do not know each other) to 2 (know each other very well).

Second, the compressed file “Redes_Personales_t2.csv” includes 57 folders, with the information equivalent to each respondent referred to the second observation, that is, 17 months after the first interview. The structure of the data is the same as in the first observation.

Sense of community and metropolitan displacements

The SPSS file “Albero.sav” collects the survey data, together with some information-summary of the network data related to each respondent. The 69 rows correspond to the 69 individuals interviewed, and the 118 columns to the variables related to each of them in T1 and T2, according to the following list:

 • Socio-economic data.


 • Data on habitual residence.


 • Information on intercity journeys.


 • Identity and sense of community.


 • Personal network indicators.


 • Social network indicators.

DATA ACCESS

Social networks and personal networks are available in CSV format. This allows its use directly with UCINET, Visone, Pajek or Gephi, among others, and they can be exported as Excel or text format files, to be used with other programs.

The visual representation of the personal networks of the respondents in both waves is available in the following album of the Graphic Gallery of Personal Networks on Flickr: .

In previous work we analyzed the effects of personal networks on the longitudinal evolution of the socio-centric network. It also includes additional details about the instruments applied. In case of using the data, please quote the following reference:

Maya-Jariego, I., Holgado, D. & Lubbers, M. J. (2018). Efectos de la estructura de las redes personales en la red sociocéntrica de una cohorte de estudiantes en transición de la enseñanza secundaria a la universidad. Universitas Psychologica, 17(1), 86-98. https://doi.org/10.11144/Javeriana.upsy17-1.eerp

The English version of this article can be downloaded from: https://tinyurl.com/yy9s2byl

CONCLUSION

The database of the “Albero” study allows us to explore the co-evolution of social networks and personal networks. In this way, we can examine the mutual dependence of individual trajectories and the structure of the relationships of the cohort of students as a whole. The complete social network corresponds to the same context of interaction: the secondary school. However, personal networks collect information from the different contexts in which the individual participates. The structural properties of personal networks may partly explain individual differences in the position of each student in the entire social network. In turn, the properties of the entire social network partly determine the structure of opportunities in which individual trajectories are displayed.

The longitudinal character and the combination of the personal networks of individuals with a common complete social network, make this database have unique characteristics. It may be of interest both for multi-level analysis and for the study of individual differences.

ACKNOWLEDGEMENTS

The fieldwork for this study was supported by the Complementary Actions of the Ministry of Education and Science (SEJ2005-25683), and was part of the project “Dynamics of actors and networks across levels: individuals, groups, organizations and social settings” (2006 -2009) of the European Science Foundation (ESF). The data was presented for the first time on June 30, 2009, at the European Research Collaborative Project Meeting on Dynamic Analysis of Networks and Behaviors, held at the Nuffield College of the University of Oxford.

REFERENCES

Brandes, U., & Wagner, D. (2004). Visone - Analysis and Visualization of Social Networks. In M. Jünger, & P. Mutzel (Eds.), Graph Drawing Software (pp. 321-340). New York: Springer-Verlag.

Maya-Jariego, I. (2018). Why name generators with a fixed number of alters may be a pragmatic option for personal network analysis. American Journal of

The National Education Longitudinal Study of 1988 (NELS:88) is a study that is part of the Longitudinal Studies Branch (LSB) program; program data is available since 1988 at . NELS:88 (https://nces.ed.gov/surveys/nels88/) is a longitudinal study that is designed to provide trend data about critical transitions experienced by students as they leave middle or junior high school, and progress through high school and into postsecondary institutions or the work force. A nationally representative sample of eighth-graders were first surveyed in the spring of 1988. A sample of these respondents were then resurveyed through four follow-ups in 1990, 1992, 1994, and 2000. Overall weighted response rate was unavailable as of December 2014. Key statistics produced from NELS:88 data can be used for policy-relevant research about educational processes and outcomes, for example: student learning; early and late predictors of dropping out; and school effects on students' access to programs and equal opportunity to learn.

The Longitudinal Social Protection Survey harmonized database contains individual information from Chile, Colombia, El Salvador, Paraguay and Uruguay. It has 320 variables, and 120 of them can be compared in all countries.

The "https://addhealth.cpc.unc.edu/" Target="_blank">National Longitudinal Study of Adolescent to Adult Health (Add Health) is a longitudinal study of a nationally representative sample of adolescents in grades 7-12 in the United States. The Add Health cohort has been followed into young adulthood with four in-home interviews, the most recent in 2008, when the sample was aged 24-32*. Add Health combines longitudinal survey data on respondents' social, economic, psychological and physical well-being with contextual data on the family, neighborhood, community, school, friendships, peer groups, and romantic relationships, providing unique opportunities to study how social environments and behaviors in adolescence are linked to health and achievement outcomes in young adulthood. The fourth wave of interviews expanded the collection of biological data in Add Health to understand the social, behavioral, and biological linkages in health trajectories as the Add Health cohort ages through adulthood. The fifth wave of data collection is planned to begin in 2016.

Initiated in 1994 and supported by three program project grants from the "https://www.nichd.nih.gov/" Target="_blank">Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) with co-funding from 23 other federal agencies and foundations, Add Health is the largest, most comprehensive longitudinal survey of adolescents ever undertaken. Beginning with an in-school questionnaire administered to a nationally representative sample of students in grades 7-12, the study followed up with a series of in-home interviews conducted in 1995, 1996, 2001-02, and 2008. Other sources of data include questionnaires for parents, siblings, fellow students, and school administrators and interviews with romantic partners. Preexisting databases provide information about neighborhoods and communities.

Add Health was developed in response to a mandate from the U.S. Congress to fund a study of adolescent health, and Waves I and II focus on the forces that may influence adolescents' health and risk behaviors, including personal traits, families, friendships, romantic relationships, peer groups, schools, neighborhoods, and communities. As participants have aged into adulthood, however, the scientific goals of the study have expanded and evolved. Wave III, conducted when respondents were between 18 and 26** years old, focuses on how adolescent experiences and behaviors are related to decisions, behavior, and health outcomes in the transition to adulthood. At Wave IV, respondents were ages 24-32* and assuming adult roles and responsibilities. Follow up at Wave IV has enabled researchers to study developmental and health trajectories across the life course of adolescence into adulthood using an integrative approach that combines the social, behavioral, and biomedical sciences in its research objectives, design, data collection, and analysis.

* 52 respondents were 33-34 years old at the time of the Wave IV interview.
** 24 respondents were 27-28 years old at the time of the Wave III interview.

To provide an array of community characteristics by which researchers may investigate the nature of such contextual influences for a wide range of adolescent health behaviors, selected contextual variables have been calculated and compiled. These are provided in this Contextual Database, already linked to the Add Health respondent IDs.

The Joint Unemployment and Vacancies Operating System Cohort (JUVOS) is a 5% sample of all computerised claims for unemployment-related benefits selected by reference to a claimant's National Insurance (NI) number. Each time a person with a relevant NI number makes a claim for unemployment-related benefits their details are added to the cohort file.

The purpose of the study is to provide a means of examining long-term dynamics of the labour market. It enables analysis of claimant unemployment, the number of unemployment spells experienced by individuals, and the length of time between spells of unemployment. It provides information on the number of previous claims that a claimant has made within a specified period, and also on the gap between the start of their more recent (or in some cases current) claim, and the end of their previous claim (if one existed).

The JUVOS database is used to inform policy decisions on employment and training, welfare and social security. It assists in monitoring the impact of government schemes, and is used by various government departments, including the Department for Education and Skills, the Department for Work and Pensions, local authorities, consultants and researchers.

The date coverage of the 13th edition is October 1982 - January 2006. Please see READ file for full details.

The Vietnam Longitudinal Survey, 1995-1998 (VLS) sought to analyze the impact of changing household economies on demographic phenomena such as marriage, pregnancy, and family composition in Vietnam. The VLS was the first longitudinal sociological survey and one of the largest sociological surveys ever conducted in Vietnam. The study was part of a long-term collaborative research program between the Institute of Sociology (IOS), Hanoi - Vietnam, and Professor Charles Hirschman from the University of Washington-Seattle. The VLS emerged as the result of extensive exchange between IOS researchers and Charles Hirschman following their first collaborative project, the Vietnam Life History Survey (VLHS), which was conducted in 1991 (ICPSR 31101). During the 1994-95 academic year, Hirschman and IOS jointly developed a detailed plan for the VLS based on their previous experiences from the VLHS. Ten communes in the provinces of Nam Ha and Ninh Binh were selected for the VLS survey using probability sampling methods. In July 1995, the pretest survey was carried out in the Dai Xuyen commune approximately 40km south of Hanoi. Baseline interviews were conducted from September to November of 1995, with 1,855 households and 4,464 individuals surveyed for the first round. The second round of interviewing was carried out from August to September of 1996, with 1,820 households and 4,340 individuals successfully re-interviewed. The third round was carried out in July and August of 1997, with 1811 households and 4309 individuals re-interviewed. The fourth and final round of the survey was conducted in July and August of 1998, with a final household count of 1,795 and 4,222 individual respondents. Data were collected at the individual and household level for each survey year. Household-level variables measured several household attributes, including size of land and living space, house construction materials, number of rooms and amenities, ownership of appliances, vehicles, and livestock, types and amount of agricultural production. Individual-level variables measured traditional courtship and wedding customs, familial marriage negotiations, marital history, pregnancy and birth history, as well as experiences with abstinence, various contraceptive methods, abortion, pregnancy, and breastfeeding. Household-level demographic variables provide information on household composition, including number of members, age, sex, ethnicity, education level, marital status, and occupation of each household member, as well as total household income. Individual-level demographic variables include age, sex, ethnicity, religion, education level, occupation, job history, income, marital status, and information on children of respondents.

The Long Beach Longitudinal Study (LBLS) was created in 1978 to obtain normative data for the Schaie-Thurston Adult Mental Abilities Test (STAMAT). From 1994 to 2003 it was extended under the guiding principle that cognitive aging is a largely contextual phenomenon. Individual differences in abilities and change in those abilities over adulthood are associated not only with cognitive mechanisms, but with sociodemographic phenomena such as birth cohort, or gender, and within-individual characteristics, including health, affect, self-efficacy, personality, and other variables that impact health. This principle is reflected in the testing measures added to the original panel. Besides the original ability measures used by Schaie, the Life Complexity Inventory, has been included in all testing. Because these measures were included in the later generations of testing, independent and direct comparisons can be made with Seattle Longitudinal Study (ICPSR 00158) to replicate findings and to generalize longitudinal samples. Panel 1 The initial panel was sampled in 1978 and consisted of 65 adults aged 28-33 and 518 adults aged 55-84. This sample was tested using the STAMAT, as well as a 20-item list of common English nouns for testing free recall, and a brief essay to test text recall. In 1981, 264 participants from this sample were retested, 106 were again retested from 1994-1995, and 42 in 1997. Finally, 15 participants of the original sample were tested from 2000-2002 using additional tests adopted for the creation of a second panel, described below, as well as a test for measuring executive function. Panel 2 In 1994, a second panel of 630 participants aged 30-97, a third of which were over 80, was added to the study. The testing for this sample included multiple indices of list recall, text recall, working memory, perceptual speed, and vocabulary for structural equation modeling. Assessment of language, autobiographical memory, personality, depression, health, health behaviors and other measures were also incorporated into the study. In 1997, 352 members of this second panel were retested. From 2000-2002, 179 participants of this second panel completed the 1994-1995 measures, as well as several tests extending the battery to indices of executive function. In 2003, 133 participants were retested. Panel 3 A third sample was recruited during the 2000-2002 time frame consisting of 911 participants aged 30-98, again approximately a third of which were over the age of 80. In 2003, 513 members of this third panel were retested. Datasets The data are provided in 6 datasets. Panel 1 and 2 1978 - 2003 Longitudinal File Dataset 1 is a longitudinal file of data from Panel 1 for tests performed in 1978, 1981, 1994, 1997, and 2000-2002, and data from Panel 2 for tests performed in 1994, 1997, 2000-2002 and 2003. Panels 1 and 2 1994 STAMAT File Dataset 2 contains the STAMAT test variables for Panels 1 and 2. Panel 1 and 2 1994-2000 Master Data Longitudinal File Dataset 3 is a second longitudinal file containing the complete catalog of variables from Panels 1 and 2 for test performed in 1994, 1997 and 2000. Panel 2 Wave 1 1994 Cross File Dataset 4 contains variables for the first wave of Panel 2 which took place in 1994. Panel 2 Wave 2 1997 Cross File Dataset 5 contains variables for the second wave of Panel 2 which took place in 1997. Panel 3 Wave 1 2000 Master File Dataset 6 contains variables from the first wave of Panel 3 which took place in 2000.

The Early Childhood Longitudinal Study, Birth Cohort (ECLS-B), is a study that is part of the Early Childhood Longitudinal Study program; program data is available since 1998-99 at . ECLS-B (https://nces.ed.gov/ecls/birth.asp) is a longitudinal study that is designed to provide policy makers, researchers, child care providers, teachers, and parents with detailed information about children's early life experiences. The study was conducted using multiple data collection methods (computer-assisted in-person interviews, computer-assisted telephone interviews, self-administered questionnaires, and direct observation) to collect information about children's characteristics, behaviors, development, and experiences from the adults who were important in the children's lives, including mothers, fathers, early care and education providers, and teachers. Direct child assessments were used to measure children's development, knowledge, and skills from the time the children were about 9 months old. A nationally representative sample of approximately 14,000 children born in the U.S. in 2001 was fielded. Key statistics produced from ECLS-B focus on children's health, development, care, and education during the formative years from birth through kindergarten entry.

The LIFE-M project combines of U.S. vital records (birth, marriage, death certificates) with census information into longitudinal and intergenerational micro-data. Using cutting-edge, machine learning techniques, the resulting dataset consists of four generations and millions of high-quality links for 20th century Americans. For more details about the project, check out the website (https://life-m.org/). Additionally, these data can be linked to the LIFE-M Ohio Causes of Death Project (https://doi.org/10.3886/E149841).

Initial data analysis checklist for data screening in longitudinal studies.

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

The Australian Longitudinal Study of Ageing, which ran from 1992 to 2014, was devised to generate longitudinal data over multiple time points. Thirteen waves were carried out. Waves 1, 3, 6, 7, 9, 11 and 12 comprised of a full face-to-face ‘household’ interview and a clinical assessment. Waves 2, 4, 5, 8, 10, 13 consisted of shorter telephone household interviews.The initial sample of the older old (70 and older) was randomly drawn from the database of the South Australian Electoral Roll. Persons in the older age groups as well as males were deliberately oversampled to compensate for the higher mortality that could be expected over the study period. In addition, spouses of primary respondents (aged 65 and over) and other household members aged 70 and over were asked to participate. 2087 participants were initially interviewed at Wave 1 in 1992. Over the years, attrition due to either death, ill health, moving out of scope, being uncontactable, or refusal has reduced the number of participants to 94 in 2014. Information covering the data, questionnaires and relevant details are openly available.Items in the household interview schedule represent a comprehensive set of measures chosen for their reliability and validity in previous studies, sensitivity to change over time, and suitability for use in a study of elderly persons. The domains assessed included demography, health, depression, morbid conditions, hospitalisation, hearing and vision difficulties, cognition, gross mobility and physical performance, activities of daily living and instrumental activities of daily living, lifestyle activities, exercise education and income.At the completion of the household interview, participants were left with self-administered questionnaires, which were mailed back in pre- paid envelopes or collected at the time of the clinical assessment. The domains covered by the questionnaires were dental health, sexual activity and psychological measures of self-esteem, morale and perceived control.The individual clinical assessment objectively measured both physical and cognitive functioning. The physical examination included measures of blood pressure, anthropometry, visual acuity, audiometry and physical performance. The cognitive assessment included measures of memory, information processing efficiency, verbal ability and executive function. The clinical assessments were conducted by nurses who received special training in the standard administration of all psychological instruments and the anthropometric measures. In addition, fasting blood samples and urine specimens were collected on the morning following the clinical assessment at Wave 1, and blood samples were again taken at Wave 3.Some data have been provided by secondary sources. Participant deaths have been systematically monitored through the government Registry of Births, Deaths and Marriages.From Wave 7 onward, collateral data were gathered from the files of the Health Insurance Commission (HIC). Permission was sought for access to the Health Insurance Commission HIC for purposes of establishing use of medical care and services and expenditure. The information sought from the HIC database included: the number of medical care services, and for each service, the nature of the service, date, charge, and benefit; the number of PBS prescriptions, and for each prescription, the drug prescribed, number of repeats, date, charge, and benefit.