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

IntroductionLoneliness and cognitive decline are pressing concerns among older adults, yet little research has explored cognition as a predictor of loneliness. This study investigates the dynamic relationship between loneliness and cognitive function in older adults using the random intercept cross-lagged panel model (RI-CLPM).MethodsData were drawn from Waves 9–14 of the Health and Retirement Study (HRS), encompassing 8,473 individuals aged 65 years and older. Loneliness was assessed using the UCLA Loneliness Scale, and cognitive function was measured using immediate and delayed word recall and serial 7s from the HRS RAND file. Age, gender, education, marital status, self-health report, and depression were included as covariates. Using Mplus, we computed RI-CLPMs. The first three models were conducted on loneliness and cognitive functions. Then unconditional RI-CLPMs with no exogenous predictors were computed.ResultsThree conditional model results showed that age, gender, marital status, self-health report, and depression were significantly associated with loneliness in the first wave, but only age and self-health report were significantly associated with immediate and delayed word recall at the first wave, not with serial 7s. For carry-over effects, loneliness showed significant positive associations across consecutive waves, but cognitive functions showed significant positive associations just in the last two waves. Some spill-over effects were found between loneliness and cognitive functions. For within-person effects, although initially non-significant, a negative association between loneliness and immediate and delayed word recall emerged in later waves (11–12 and 13–14). The conditional models indicated that older age, not being married, male gender, low self-reported health, and high depression levels were positively associated with loneliness. However, only older age and lower self-reported health were positively linked to cognitive functions.DiscussionThis study underscores the link between loneliness and cognitive function decline in older adults, emphasizing the need to address loneliness to potentially reduce cognitive decline. Insights into demographic predictors of loneliness and cognitive function could inform targeted interventions for promoting successful aging.

The English Longitudinal Study of Ageing (see the main study under SN 5050) is a longitudinal household survey for the study of health, economic position, and quality of life among the elderly.

The ELSA COVID-19 Study, Waves 1-2, 2020 (SN 8688) is a follow-up substudy based on the sample of the main ELSA study. Within the context of the Coronavirus Disease 2019 (COVID-19) outbreak, all participants for the COVID-19 substudy were selected from the existing ELSA sample to measure the socio-economic effects/psychological impact of the lockdown on the 50+ population of England. The ELSA COVID-19 substudy allows a cross-sectional analysis of the dynamics of the lockdown, enabling too the possibility to link the data collected with previous and future waves of ELSA for longitudinal analysis.

Subsequent to that, the University of Southern California (USC) Gateway to Global Aging Data team has created the Harmonized ELSA COVID data file, along with a codebook, to facilitate cross-country comparisons across international harmonized COVID studies, including the harmonized Health and Retirement Study (HRS) COVID study and the harmonized Survey of Health, Ageing and Retirement in Europe (SHARE) COVID study. This is a separate data product which is a user-friendly version of a subset of the ELSA COVID-19 substudy. The Harmonized ELSA COVID dataset uses data from the third edition of the ELSA COVID-19 substudy, released in February 2022. It follows the conventions of variable naming and data structure first developed by the RAND Center for the Study of Aging.

The harmonized ELSA COVID data file is built using variables from the harmonized ELSA data file (SN 5050) and the ELSA COVID-19 substudy data files (SN 8688). It does not include any data which is not released under UKDS End User Licence access conditions.