Analysis SPSS files used in the paper to analyze the experiment results. The tests we executed in the paper are as follows, in the SPSS syntax:** PreQuestionnaire.sav, leading to Table 2T-TEST GROUPS=form(1 2) /MISSING=ANALYSIS /VARIABLES=grade USLEC UCLEC /CRITERIA=CI(.95).NPAR TESTS /M-W= CDFAM UCFAM USFAM UCHW USHW CDHW BY form(1 2) /MISSING ANALYSIS.** Anova.sav, leading to the decision of analyzing the two case studies independentlyGLM EntRec EntPre RelRec RelPre TotRec TotPre AdjRelRec AdjRelPre AdjTotRec AdjTotPre BY Domain Form /METHOD=SSTYPE(3) /INTERCEPT=INCLUDE /POSTHOC=Domain Form(TUKEY) /PLOT=PROFILE(Domain*Form) TYPE=LINE ERRORBAR=NO MEANREFERENCE=NO YAXIS=AUTO /PRINT=DESCRIPTIVE ETASQ /CRITERIA=ALPHA(.05) /DESIGN= Domain Form Domain*Form.** DH.sav, leading to Table 3T-TEST GROUPS=Form(1 2) /MISSING=ANALYSIS /VARIABLES=EntRec EntPre RelRec RelPre TotRec TotPre AdjRelRec AdjRelPre AdjTotRec AdjTotPre /CRITERIA=CI(.95).** PH.sav, leading to Table 4T-TEST GROUPS=Form(1 2) /MISSING=ANALYSIS /VARIABLES=EntRec EntPre RelRec RelPre TotRec TotPre AdjRelRec AdjRelPre AdjTotRec AdjTotPre /CRITERIA=CI(.95).** Preferences.sav, leading to Table 5 and Table 6NPAR TESTS /M-W= UCCM USCM UCCDID USCDID UCRID USRID USSTRUCT UCSTRUCT UCOVER USOVER UCREQ USREQ BY Form(1 2) /MISSING ANALYSIS.EXAMINE VARIABLES=UCCM USCM UCCDID USCDID UCRID USRID USSTRUCT UCSTRUCT UCOVER USOVER UCREQ USREQ BY Form /PLOT HISTOGRAM NPPLOT /STATISTICS DESCRIPTIVES /CINTERVAL 95 /MISSING LISTWISE /NOTOTAL.NPAR TESTS /M-W= UCCM USCM UCCDID USCDID UCRID USRID USSTRUCT UCSTRUCT UCOVER USOVER UCREQ USREQ BY Form(1 2) /STATISTICS=DESCRIPTIVES /MISSING ANALYSIS.GLM EntRec EntPre RelRec RelPre TotRec TotPre AdjRelRec AdjRelPre AdjTotRec AdjTotPre BY Domain Form /METHOD=SSTYPE(3) /INTERCEPT=INCLUDE /POSTHOC=Domain Form(TUKEY) /PLOT=PROFILE(Domain*Form) TYPE=LINE ERRORBAR=NO MEANREFERENCE=NO YAXIS=AUTO /PRINT=DESCRIPTIVE ETASQ /CRITERIA=ALPHA(.05) /DESIGN= Domain Form Domain*Form.

Part 1 of the course will offer an introduction to SPSS and teach how to work with data saved in SPSS format. Part 2 will demonstrate how to work with SPSS syntax, how to create your own SPSS data files, and how to convert data in other formats to SPSS. Part 3 will teach how to append and merge SPSS files, demonstrate basic analytical procedures, and show how to work with SPSS graphics.

The RAAAP project surveyed Research Managers and Administrators from across the world, asking questions about why people became RMAs, why they stayed as RMAs, what skills they need for their jobs (soft and hard), what level of seniority they are, demographic information, and so on - overall up to 222 data points were collected from each respondent. This is the output from the SPSS syntax file (DOI:10.6084/m9.figshare.6269090) used to split the processed data into 15 linked datasets in help keep the data anonymous. The process is described in detail in the "RAAAP Data Cleansing Process" DOI:10.6084/m9.figshare.5948461

This group of documents provides the syntax and supporting files in SPSS format that were used to calculate data quality and observed performance. Note: Syntax to calculate risk-standardized performance for each state uses a national, risk-adjusted model that requires child-level data from all states (i.e., national datasets) and thus cannot be replicated by states or other interested parties. Metadata-only record linking to the original dataset. Open original dataset below.

This dataset contains a SPSS (v23) syntax file (.sps) and the relating graph template (.sgt) that can be used to create anonymized graphs of spinal motion in the transversal plane relative to phases of gait. All sections are commented within the script. The algebraic sign of the pelvis gets corrected. It required correction since the pelvis is in alignment of surface rotation, and in order to be in parallel with the vertebral body’s rotation it has to be reversed. So, if the surface above the spinous process runs to the right, this implies a rotation to left of the vertebral body beneath.

Dataset survey methods document and report. There is a dataset in R format plus an SPSS .sav file and an accompanying .sps syntax codefile. Running the syntax file on the .sav file should provide labels etc for the .sav file.

NATIONAL FACE - TO - FACE SURVEYS OF REPRESENTATIVE SAMPLES OF PARENTS OF ELEMENTARY SCHOOL CHILDREN IN 10 SOUTH EAST EUROPEAN COUNTRIES Center for Educational Policy Studies (CEPS) in cooperation with Open Society Institute Education Support Program

Raw experimental data (Approach Avoidance Task- Excel file). Survey data and processed experimental data (summary Approach Avoidance Task measures - SPSS Data file). Syntax for analyses reported in the paper (SPSS Syntax file). Submitted paper (methods and results contain details on data collection and data analysis)

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. The purpose of this study was to produce knowledge about how to prevent at-risk youth from joining gangs and reduce delinquency among active gang members. The study evaluated a modification of Functional Family Therapy, a model program from the Blueprints for Healthy Youth Development initiative, to assess its effectiveness for reducing gang membership and delinquency in a gang-involved population. The collection contains 5 SPSS data files and 4 SPSS syntax files: adolpre_archive.sav (129 cases, 190 variables), adolpost_archive.sav (119 cases, 301 variables), Fidelity.archive.sav (66 cases, 25 variables), parentpre_archive.sav (129 cases, 157 variables), and parentpost_archive.sav {116 cases, 220 variables).

SPSS Syntax File for Racial Categorization Study 1.

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 represents the first comprehensive national assessment of law enforcement uses of and perspectives on sex offender registration and notification (SORN) systems. The two-year, mixed-method study featured collection and analysis of interview data from over two-dozen jurisdictions, and administration of a nationwide survey of law enforcement professionals. The study examined ways in which law enforcement leaders, uniformed staff, and civilian staff engaged in SORN-related duties perceive SORN's roles and functions, general effectiveness, and informational utility. Additionally, the study elicited law enforcement perspectives related to promising SORN and related sex offender management practices, perceived barriers and challenges to effectiveness, and policy reform priorities. This collection includes two SPSS data files and one SPSS syntax file: "LE Qualitative Data.sav" with 55 variables and 101 cases, "LE Quantitative Data-ICPSR.sav" with 201 variables and 1402 cases and "LE Quantitative Data Syntax.sps". Qualitative data from interviews conducted with law enorcement professionals are not available at this time.

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 directly examined the nature and characteristics of cases prosecuted in the federal courts by analyzing prosecutorial decisions to proceed with charges (or not) once an arrest is initiated, and to investigate any adjustment from the arresting offense to the charging offense. These decisions were analyzed to document their correlates and identify variation across case type. The collection contains 1 SPSS data file (2002-10-Arrest-cases--FINAL-ANALYSIS.sav (n=794,807; 43 variables)) and 1 SPSS syntax file.

Data in SPSS formatMeasured language variables across the cultural groups, in SPSS data file format.Data.savData in CSV formatEquivalent data to the SPSS upload, in CSV format.Data.csvAnalysis syntax for SPSSSyntax used to generate the reported results using SPSS.Syntax.sps

SMaRteN, in partnership with Vitae, conducated research into the impact of COVID-19 on the working lives of doctoral researchers and research staff. This is the Time 2 data set. Data was collected at the end of September and start of October 2020. Please see link at bottom of page for the first data set.SMaRteN www.smarten.org.ukThe UK Research and Innovation (UKRI) funded Student Mental Health Research Network (SMaRteN) is working to support and encourage better research into student mental health. SMaRteN is based at Institute of Psychiatry, Psychology and Neurosciences at King’s College London.Vitae is a non-profit programme supporting the professional and career development of researchers. www.vitae.ac.uk @vitae_newsCovid-19 and the associated lock down has caused substantive disruption to the study and work of doctoral students and researchers in universities. The response to the pandemic has varied across universities and research funders.SMaRteN and Vitae aim to develop a national picture for how doctoral researchers and research staff have been affected by the pandemic.​The survey includes questions relating to the impact of COVID-19 on research work, mental wellbeing, social connection. We further address the impact of COVID-19 on changes to employment outside of academia, living arrangements and caring arrangements and the consequent effect of these changes on research work. The survey considers the support provided by supervisors / line managers and by universities.Data available here as either an SPSS or Excel download:SPSS file contains labelsExcel file contains labels and brief notes about codingRecoding data for CV19 impact - SPSS Syntax file describes steps taken to code dataCV19_impact_on_researchers - word document, export from Qualtrics of the survey.Please note, data has been removed from this data set to ensure participant anonymity.For further information, please contact Dr Nicola Byrom - nicola.byrom@kcl.ac.uk

• SPSS file containing extracted general, patient and intervention characteristics - SPSS syntax file for descriptive statistics and associations of/within subsets of studies - RevMan 5.3 file containing meta-analyses

The project aimed to understand whether young adults who take care of a loved-one (young adult caregivers; YACs) differ in their perceived life balance and psychosocial functioning as compared to young adults without care responsibilities (non-YACs). In addition, this project aimed to understand how YACs evaluated a tool to support informal careg

ivers. This tool (“Caregiver Balance”; https://balans.mantelzorg.nl) is specifically designed to support informal caregivers taking care of a loved-one in the palliative phase and could potentially be adapted to meet the needs of YACs.

In this project, we collected data of 74 YACs and 246 non-YACs. Both groups completed questionnaires, and the YACs engaged in a usability test. The questionnaire data was used to compare the perceived life balance and psychological functioning between YACs and non-YACs, aged 18-25 years, and studying in the Netherlands (study 1). Furthermore, we examined the relationship between positive aspects of caregiving and relational factors, in particular, relationship quality and collaborative coping among YACs (study 2). Finally, we conducted a usability study where we interviewed YACs to understand the needs and preferences towards a supportive web-based solution (study 3).

Table: Study details and associated files

    Number
    Study Name
    Study Aim
    Study Type
    Type of data
    Associated Files


    1
    Perceived life balance among young adult students: a comparison between caregivers and non-caregivers
    Compare the perceived life balance and psychological functions among student young adult caregivers aged 18-25 years (YACs) with young adult without care responsibilities
    Survey study
    Quantitative

ENTWINE_YACs_nonYACsSurvey_RawData

ENTWINE_PerceivedLifeBalanceSurvey_YACs_nonYACs_CleanedData

ENTWINE_ PerceivedLifeBalanceSurvey _Syntax

ENTWINE_YACs_nonYACsSurvey_codebook

    2
    Examining the relationship of positive aspects of caregiving with relational factors among young adult caregivers
    Examine the relationship of positive aspects of caregiving with relational factors, in particular, relationship quality and collaborative coping among a particular group of ICGs, young adult caregivers (YACs), aged 18-25 years.
    Survey study
    Quantitative

ENTWINE_YACs_nonYACsSurvey_RawData

ENTWINE_PositiveAspectsCaregiving_Survey_YACs_cleanedData

ENTWINE_PositiveAspectsCaregiving_Survey_Syntax

ENTWINE_YACs_nonYACsSurvey_codebook

    3
    Exploring the support needs of young adult caregivers, their issues, and preferences towards a web-based tool
    Explore (i) challenges and support needs of YACs in caregiving, (ii) their needs towards the content of the ‘MantelzorgBalans’ tool, and (iii) issues they encountered in using the tool and their preferences for adaptation of the tool.
    Usability study

Qualitative and Quantitative

ENTWINE_Needs_Web-basedTools_YACs_Interview_Usability_RawData [to be determined whether data can be shared]

ENTWINE_Needs_Web-basedTools_YACs_Questionnaires_RawData

Description of the files to be uploaded

Study 1: Perceived life balance among young adult students: a comparison between caregivers and non-caregivers

ENTWINE_YACs_nonYACsSurvey_RawData: SPSS file with the complete, raw, pseudonomyzed survey data. The following cleaned dataset ‘ENTWINE_PerceivedLifeBalanceSurvey_YACs_nonYACs_CleanedData’ was generated from this raw data.

ENTWINE_PerceivedLifeBalanceSurvey_YACs_nonYACs_CleanedData: SPSS file with the cleaned dataset having the following metadata -

Population: young adult caregivers and young adult non-caregivers aged 18-25 years studying in the Netherlands;

Number of participants: 320 participants in total (74 young adult caregivers and 246 young adult non-caregivers)

Time point of measurement: Data was collected from December 2020 till March 2022

Type of data: quantitative

Measurements included, topics covered: perceived life balance (based on the Occupational balance questionnaire [1]), burnout (Burnout Assessment Tool [2]), negative and positive affect (Positive and Negative Affect Schedule [3]), and life satisfaction (Satisfaction with Life Scale [4])

Short procedure conducted to receive data: online survey on Qualtrics platform

SPSS syntax file ‘ENTWINE_ PerceivedLifeBalanceSurvey _Syntax’ was used to clean and analyse ENTWINE_PerceivedLifeBalanceSurvey_YACs_nonYACs_CleanedData dataset

ENTWINE_YACs_nonYACsSurvey_codebook: Codebook having the variable names, variable labels, and the associated code values and code labels for ENTWINE_PerceivedLifeBalanceSurvey_YACs_nonYACs_CleanedData dataset

Study 2: Examining the relationship of positive aspects of caregiving with relational factors among young adult caregivers

ENTWINE_YACs_nonYACsSurvey_RawData: SPSS file with the complete, raw survey data. The following cleaned dataset ‘ENTWINE_PositiveAspectsCaregiving_Survey_YACs_cleanedData’ was generated from this raw data.

ENTWINE_PositiveAspectsCaregiving_Survey_YACs_cleanedData: SPSS file with the cleaned dataset having the following metadata -

Population: young adult caregivers aged 18-25 years studying in the Netherlands;

Number of participants: 74 young adult caregivers

Time point of measurement: Data was collected from December 2020 till March 2022

Type of data: quantitative

Measurements included, topics covered: positive aspects of caregiving (positive aspects of caregiving scale [5]), relationship quality (Relationship Assessment Scale [6]), collaborative coping (Perception of Collaboration Questionnaire [7] )

Short procedure conducted to receive data: online survey on Qualtrics platform.

SPSS syntax file ‘ENTWINE_PositiveAspectsCaregiving_Survey_Syntax’ was used to clean and analyse ‘ENTWINE_PositiveAspectsCaregiving_Survey_YACs_cleanedData’ dataset.

ENTWINE_YACs_nonYACsSurvey_codebook: Codebook having the variable names, variable labels, and the associated code values and code labels for ENTWINE_PositiveAspectsCaregiving_Survey_YACs_cleanedData dataset.

Study 3: Exploring the support needs of young adult caregivers, their issues, and preferences towards a web-based tool

ENTWINE_Needs_Web-basedTools_YACs_Interview_Usability_RawData: Pseudonymized word file including 13 transcripts having the qualitative data from interview and usability testing with the following metadata –

Population: young adult caregivers aged 18-25 years studying in the Netherlands; 13 participants in total

Time point of measurement: data was collected from October 2021 till February 2022

Type of data: qualitative and quantitative

Measurements included, topics covered: Caregiving challenges, support needs and barriers, usability needs, preferences and issues towards eHealth tool

Short procedure conducted to receive data: Online interviews

ENTWINE_Needs_Web-basedTools_YACs_Questionnaires_RawData: Excel sheet having the quantitative questionnaire raw data with the following metadata

Population: young adult caregivers aged 18-25 years studying in the Netherlands; 13 participants in total

Time point of measurement: data was collected from October 2021 till February 2022

Type of data: qualitative and quantitative

Measurements included, topics covered: User experience (user experience questionnaire [8]), satisfaction of using the web-based tool (After scenario questionnaire [9]), Intention of use and persuasive potential of the eHealth tool (persuasive potential questionnaire [10])

Short procedure conducted to receive data: Online questionnaire

Data collection details

All data was collected, processed, and archived in accordance with the General Data Protection Regulation (GDPR) and the FAIR (Findable, Accessible, Interoperable, Reusable) principles under the supervision of the Principal Investigator.

The principal researcher and a team of experts (supervisors) in the field of health psychology and eHealth (University of Twente, The Netherlands) reviewed the scientific quality of the research. The studies were piloted and tested before starting the collection of the data. For the survey study, the researchers monitored the data collection weekly to ensure it was running smoothly.

The ethical review board, Centrale Ethische Toetsingscommissie of the University Medical Center Groningen, The Netherlands (CTc), granted approval for this research (Registration number: 202000623).

Participants digitally signed informed consent for participating in the study.

Terms of use

Interested persons can send a data request by contacting the principal investigator (Prof. dr. Mariët Hagedoorn, University Medical Center Groningen, the Netherlands mariet.hageboorn@umcg.nl).

Interested persons must provide the research plan (including the research question, methodology, and analysis plan) when requesting for the data.

The principal investigator reviews the research plan on its quality and fit with the data and informs the interested person(s).

(Pseudo)anonymous data of those participants who agreed on the reuse of their data is available on request for 15 years from the time of completion of the PhD project.

Data will be available in Excel or SPSS format alongside the variable codebook after the completion of this PhD project and publication of the study results.

References

1. Wagman P, Håkansson C. Introducing the Occupational Balance Questionnaire (OBQ). Scand J Occup Ther 2014;21(3):227–231. PMID:24649971

2. Schaufeli WB, Desart S, De Witte H. Burnout assessment tool (Bat)—development, validity, and reliability. Int J Environ Res Public Health 2020;17(24):1–21. PMID:33352940

3. Watson D, Clark LA, Tellegen A. Development and Validation of Brief Measures of Positive and Negative Affect: The

These data are part of NACJD's Fast Track Release and are distributed as they there received from the data depositor. The files have been zipped by NACJD for release, but not checked or processed except of the removal of direct identifiers. Users should refer to the accompany readme file for a brief description of the files available with this collections and consult the investigator(s) if further information is needed.The study used a multi-method approach including 1. a process evaluation in all eight sites involving yearly site visits from 2012 to 2014 with key stakeholder interviews, observations, and participant focus groups; 2. a prospective impact evaluation (in four sites) including interviews at release from jail or prison and at 12 months after release (as well as oral swab drug tests) with reentry court participants and a matched comparison group; 3. a recidivism impact evaluation (in seven sites) with a matched comparison group tracking recidivism for 2 years post reentry court entry and 4. a cost-benefit evaluation (in seven sites) involving a transactional and institutional cost analysis (TICA) approach. Final administrative data were collected through the end of 2016.This collection includes four SPSS data files: "interview_archive2.sav" with 746 variables and 412 cases, "NESCCARC_Archive_File_3.sav" with 518 variables and 3,710 cases, "Interview Data1.sav" with 1,356 variables and 412 cases, "NESCCARC Admin Data File.sav" with 517 variables and 3,710 cases, and three SPSS syntax files: "Interview Syntax.sps", "archive_2-17.sps", and "NESCCARC Admin Data Syntax.sps".

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 includes data that was used to investigate the effect of legislative and judicial factors on system responses to sex trafficking of minors (STM) in metropolitan and non-metropolitan communities. To accomplish this, researchers evaluated the effectiveness of the immunity, protection, and rehabilitative elements of a state safe harbor law. This project was undertaken as a response to a growing push to pass state safe harbor laws to align governmental and community responses to the reframing of the issue of sex trafficking of minors that was ushered in with the passage of the Trafficking Victims Protection Act (TVPA). This collection includes 4 SPSS files, 3 Excel data files, and 2 SPSS Syntax files: Child-Welfare-Human-Trafficking-Reports-2013-2017-data.xlsx Judicial-Interview-De-identified-Quantitative-Data-for-NACJD_REV_Oct2018.sav (n=82; 36 variables) Judicial-online-survey-data-for-NACJD_REV_Dec2018.sav (n=55; 77 variables) Juvenile-Justice-Screening-for-HT-2015-MU-MU-0009.xlsx Post-implementation-survey-data-for-NACJD_REV_Dec2018.sav (n=365; 1029 variables) Pre-implementation-survey-data-for-NACJD_REV_Dec2018.sav (n=323; 159 variables) Recode-syntax-for-pre-implementation-survey-for-NACJD.sps Statewide-juvenile-court-charges-2015-MU-MU-0009-to-NACJD.xlsx Syntax-for-post-implementation-survey-data-to-NACJD.sps Qualitative data from judicial interviews and agency open-ended responses to Post-Implementation of the Safe Harbor Law Survey are not available as part of this collection.

(1) Part2FSWDataJan312018_Final.SAV This file contains the SPSS data file with deidentified and clean data on female sex worker sample. (2) Part2TruckerDataJan312018_Final.SAV This file contains the SPSS data file with deidentified and clean data on female sex worker sample. (3) SPSS Syntax for part 2 Female Sex Worker paper.docx This is a word document with the SPSS syntax used for the analysis of the FSW sample that is presented in the 3IE report. (4) SPSS syntax for part 2 trucker paper.docx This is a word document with the SPSS syntax used for the analysis of the trucker sample that is presented in the 3IE report.

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 examines the experiences of Arab versus non-Arab households with crime and their relationships with and attitudes towards the police in their communities. Face to face interviews were conducted in 414 households. Data were analyzed to gauge respondents' level of fear regarding crime and other factors that affect their risk of victimization.This collection includes one SPSS data file: "Arab_study_data.sav" with 201 variables and 414 cases and one SPSS syntax file: "Arab_study_syntax.sps".

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