Purpose: Develop an easy-to-use data product to facilitate comparative effectiveness research involving complex patients. Scope: Claims data can be difficult to use, requiring experience to most appropriately aggregate to the patient level and to create meaningful variables such as treatments, covariates, and endpoints. Easy to use data products will accelerate meaningful comparative effectiveness research (CER). Methods: This project used data from the Medicare Chronic Condition Data Warehouse for patients hospitalized with acute myocardial infarction (AMI) or stroke in 2007 with two-year follow-up and one-year pre-admission baseline. The project joined over 100 raw data files per condition to create research-ready person- and service-level analytic files, code templates, and macros while at the same time adding uniformity in measures of comorbid conditions and other covariates. The data product was tested in a project on statin effectiveness in older patients with multiple comorbidities. Results: A programmer/analyst with no administrative claims data experience was able to use the data product to create an analytic dataset with minimal support aside from the documentation provided. Analytic dataset creation used the conditions, procedures, and timeline macros provided. The data structure created for AMI adapted successfully for stroke. Complexity increased and statin treatment decreased with age. The two-year survival benefit of statins post-AMI increased with age. Conclusion: Claims data can be made more user-friendly for CER research on complex conditions. The data product should be expanded by refreshing the cohort and increasing follow-up. Action is warranted to increase the rate of statin use among the oldest patients. Data Access: These data are not available from ICPSR. The data cannot be made publicly available. Data are stored on University of Iowa College of Public Health secure servers, and may be used only for projects covered within the aims of the original research protocol and Centers for Medicare and Medicaid Services (CMS)-approved data use agreement. Data sharing is allowed only for research protocols approved under data re-use requests by the CMS privacy board. The CMS process for data re-use requests is described at Research Data Assistance Center (ResDac) . Please note that as of May 2013, the DUA covering this work is set to expire February 1, 2014. Thereafter, per the terms of the DUA, datasets created for this project may not be available. User guides are available from ICPSR for detailed descriptions of the data products, including a user guide for Acute Myocardial Infarction (AMI) Analytic Files and a user guide for Stroke and Transient Ischemic Attack (TIA) Analytic Files. Data dictionaries are available upon request. Please contact Nick Rudzianski (nicholas-rudzianski@uiowa.edu or 319-335-9783) for more information.

The PATH Study was launched in 2011 to inform the Food and Drug Administration's regulatory activities under the Family Smoking Prevention and Tobacco Control Act (TCA). The PATH Study is a collaboration between the National Institute on Drug Abuse (NIDA), National Institutes of Health (NIH), and the Center for Tobacco Products (CTP), Food and Drug Administration (FDA). The study sampled over 150,000 mailing addresses across the United States to create a national sample of people who use or do not use tobacco. 45,971 adults and youth constitute the first (baseline) wave, Wave 1, of data collected by this longitudinal cohort study. These 45,971 adults and 9 to 11 sampled at Wave 1) make up the 53,178 participants that constitute the Wave 1 Cohort. Respondents are asked to complete an interview at each follow-up wave. Youth who turn 18 by the current wave of data collection are considered "aged-up adults" and are invited to complete the Adult Interview. Additionally, "shadow youth" are considered "aged-up youth" upon turning 12 years old, when they are asked to complete an interview after parental consent. At Wave 4, a probability sample of 14,098 adults, youth, and shadow youth ages 10 to 11 was selected from the civilian, noninstitutionalized population at the time of Wave 4. This sample was recruited from residential addresses not selected for Wave 1 in the same sampled primary sampling units (PSU)s and segments using similar within-household sampling procedures. This "replenishment sample" was combined for estimation and analysis purposes with Wave 4 adult and youth respondents from the Wave 1 Cohort who were in the civilian, noninstitutionalized population at the time of Wave 4. This combined set of Wave 4 participants, 52,731 participants in total, forms the Wave 4 Cohort. At Wave 7, a probability sample of 14,863 adults, youth, and shadow youth ages 9 to 11 was selected from the civilian, noninstitutionalized population at the time of Wave 7. This sample was recruited from residential addresses not selected for Wave 1 or Wave 4 in the same sampled PSUs and segments using similar within-household sampling procedures. This "second replenishment sample" was combined for estimation and analysis purposes with the Wave 7 adult and youth respondents from the Wave 4 Cohorts who were at least age 15 and in the civilian, noninstitutionalized population at the time of Wave 7 participants, 46,169 participants in total, forms the Wave 7 Cohort. Please refer to the Restricted-Use Files User Guide that provides further details about children designated as "shadow youth" and the formation of the Wave 1, Wave 4, and Wave 7 Cohorts. Wave 4.5 was a special data collection for youth only who were aged 12 to 17 at the time of the Wave 4.5 interview. Wave 4.5 was the fourth annual follow-up wave for those who were members of the Wave 1 Cohort. For those who were sampled at Wave 4, Wave 4.5 was the first annual follow-up wave. Wave 5.5, conducted in 2020, was a special data collection for Wave 4 Cohort youth and young adults ages 13 to 19 at the time of the Wave 5.5 interview. Also in 2020, a subsample of Wave 4 Cohort adults ages 20 and older were interviewed via the PATH Study Adult Telephone Survey (PATH-ATS). Wave 7.5 was a special collection for Wave 4 and Wave 7 Cohort youth and young adults ages 12 to 22 at the time of the Wave 7.5 interview. For those who were sampled at Wave 7, Wave 7.5 was the first annual follow-up wave. Dataset 1002 (DS1002) contains the data from the Wave 4.5 Youth and Parent Questionnaire. This file contains 1,617 variables and 13,131 cases. Of these cases, 11,378 are continuing youth having completed a prior Youth Interview. The other 1,753 cases are "aged-up youth" having previously been sampled as "shadow youth" Datasets 1112, 1212, and 1222, (DS1112, DS1212, and DS1222) are data files comprising the weight variables for Wave 4.5. The "all-waves" weight file contains weights for participants in the Wave 1 Cohort who completed a Wave 4.5 Youth Interview and completed interviews (if old enough to do so) or verified their information with the study (if not old enough to be interviewed) in Waves 1, 2, 3, and 4. There are two separate files with "single wave" weights: one for the Wave 1 Cohort and one for the Wave 4 Cohort. The "single-wave" weight file for the Wave 1 Cohort contains weights for youth who c

This collection provides data on labor force activity for the week prior to the survey. Comprehensive data are available on the employment status, occupation, and industry of persons aged 14 and over. Also shown are personal characteristics such as age, sex, race, marital status, veteran status, household relationship, educational background, and Spanish origin. The collection contains a supplement that includes data on skills and training that workers needed to obtain their current or last job, on-the-job training, skills used on the last job, and workers' perceptions of the adequacy of their skills. This supplement makes it possible to analyze changes in occupation and to assess the relative stability of employment in various industries and occupations. Questions were asked of all persons 15 years of age or older who were living in households and who were members of the experienced labor force, whether they were currently employed or not. (Source: downloaded from ICPSR 7/13/10)

Please Note: This dataset is part of the historical CISER Data Archive Collection and is also available at ICPSR at https://doi.org/10.3886/ICPSR09716.v1. We highly recommend using the ICPSR version as they may make this dataset available in multiple data formats in the future.

The 2020 Census Demographic and Housing Characteristics Noisy Measurement File is an intermediate output of the 2020 Census Disclosure Avoidance System (DAS) TopDown Algorithm (TDA) (as described in Abowd, J. et al [2022], and implemented in DAS_2020_DHC_Production_Code/das_decennial/programs/engine/primitives.py at main uscensusbureau/DAS_2020_DHC_Production_Code (github.com) The 2020 Census Demographic and Housing Characteristics Noisy Measurement File includes zero-Concentrated Differentially Private (zCDP) (Bun, M. and Steinke, T [2016]) noisy measurements, implemented via the discrete Gaussian mechanism (Cannone C., et al., [2023] ), which added positive or negative integer-valued noise to each of the resulting counts. These are estimated counts of individuals and housing units included in the 2020 Census Edited File (CEF), which includes confidential data collected in the 2020 Census of Population and Housing. The noisy measurements included in this file were subsequently post-processed by the TopDown Algorithm (TDA) to produce the Census Demographic and Housing Characteristics Summary File. In addition to the noisy measurements, constraints based on invariant calculations --- counts computed without noise --- are also included (with the exception of the state-level total populations, which can be sourced separately from data.census.gov). The Noisy Measurement File was produced using the official "production settings," the final set of algorithmic parameters and privacy-loss budget allocations that were used to produce the 2020 Census Redistricting Data (P.L. 94-171) Summary File and the 2020 Census Demographic and Housing Characteristics File. The noisy measurements are produced in an early stage of the TDA. Afterward, these noisy measurements are post-processed to ensure internal and hierarchical consistency within the resulting tables. The Census Bureau has released these noisy measurements to enable data users to evaluate the impact of disclosure avoidance variability on 2020 Census data. The 2020 Census Demographic and Housing Characteristics (DHC) Noisy Measurement File has been cleared for public dissemination by the Census Bureau Disclosure Review Board (CBDRB-FY22-DSEP-004). These data are available for download (i.e. not restricted access). Due to their size, they must be downloaded through the link on this metadata page and not through the standard ICPSR download. The link will take you to the Globus site where these data are housed. A README file is located in the Globus repository. Please refer to that for pertinent information. The Globus holding site requires users to create an account to access these data. Accounts can be created through existing institutional access and by personal access. Please see the Globus "How to get Started" page for more information.

Detailed county and state-level ecological or descriptive data for the United States for the years 1790 to 1970 are contained in this collection. These data files contain extensive information about the social and political character of the United States, including a breakdown of population by state, race, nationality, number of families, size of the family, births, deaths, marriages, occupation, religion, and general economic conditions. Though not complete over the full time span of this study, statistics are available on such diverse subjects as total numbers of newspapers and periodicals, total capital invested in manufacturing, total numbers of educational institutions, total number of churches, taxation by state, and land surface area in square miles. (Source: downloaded from ICPSR 7/13/10)

Please Note: This dataset is part of the historical CISER Data Archive Collection and is also available at ICPSR at https://doi.org/10.3886/ICPSR00003.v1. We highly recommend using the ICPSR version as they may make this dataset available in multiple data formats in the future.

For each county or county equivalent, this file provides the provisional population estimate for July 1, 1986 and the corrected 1980 census population figure. In addition, data are tabulated for births, deaths, and residual migration for the 1980-1986 period. (Source: downloaded from ICPSR 7/13/10)

Please Note: This dataset is part of the historical CISER Data Archive Collection and is also available at ICPSR at https://doi.org/10.3886/ICPSR08862.v1. We highly recommend using the ICPSR version as they may make this dataset available in multiple data formats in the future.

This data collection describes the distribution of full-time and part-time faculty and staff in postsecondary institutions of the United States by occupational category. Collected biennially beginning in 1987, these data permit analysis of trends and distribution of staff in postsecondary education. They also allow comparisons of staffing patterns by institutional type and control and examine the relationship between financial resources and staff resources. The breakdown of the occupational category ranges from executive to faculty to clerical to service/maintenance. Contracted or donated services were also indicated. The unit of analysis is the institution. (Source: downloaded from ICPSR 7/13/10)

Please Note: This dataset is part of the historical CISER Data Archive Collection and is also available at ICPSR at https://doi.org/10.3886/ICPSR09529.v1. We highly recommend using the ICPSR version as they may make this dataset available in multiple data formats in the future.

The ongoing Consumer Expenditure Survey (CES) provides a continuous flow of information on the buying habits of American consumers and also furnishes data to support periodic revisions of the Consumer Price Index. The survey consists of two separate components: (1) a quarterly Interview Survey in which each consumer unit in the sample is interviewed every three months over a fifteen-month period, and (2) a Diary Survey completed by the sample consumer units for two consecutive one-week periods. The Diary Survey contains consumer information on small, frequently-purchased items such as food, beverages, food consumed away from home, gasoline, housekeeping supplies, nonprescription drugs and medical supplies, and personal care products and services. Participants are asked to maintain expense records, or diaries, of all purchases made each day for two consecutive one-week periods. The Consumer Unit Characteristics and Income (FMLY) files supply information on consumer unit characteristics, consumer unit income, and characteristics and earnings of the reference person and his or her spouse. A consumer unit (CU) consists of all members of a particular housing unit who are related by blood, marriage, adoption, or some other legal arrangement. Consumer unit determination for unrelated persons is based on financial independence. Member Characteristics (MEMB) files contain selected characteristics and earnings for each consumer unit member, including information on relationship to reference person. The Detailed Expenditures (EXPN) files present weekly data on expenditures at the Universal Classification Code (UCC) level, while Income (DTAB) files contain data on CU characteristics and income at the UCC level. (Source: downloaded from ICPSR 7/13/10)

Please Note: This dataset is part of the historical CISER Data Archive Collection and is also available at ICPSR at https://doi.org/10.3886/ICPSR03937.v1. We highly recommend using the ICPSR version as they may make this dataset available in multiple data formats in the future.

The data in this file include for each county the number of Federal income tax returns filed and the number of exemptions claimed. Within each category, data are provided on the number of tax filers that migrated into the county, the number that migrated out of the county, and the number for which migration status was unknown. The total number of returns filed is also provided. (Source: downloaded from ICPSR 7/13/10)

Please Note: This dataset is part of the historical CISER Data Archive Collection and is also available at ICPSR at https://doi.org/10.3886/ICPSR08477.v1. We highly recommend using the ICPSR version as they may make this dataset available in multiple data formats in the future.

Data on labor force activity for the week prior to the survey are supplied in this collection. Information is available on the employment status, occupation, and industry of persons 14 years old and over. Demographic variables such as age, sex, race, marital status, veteran status, household relationship, educational background, and Spanish origin are included. In addition to providing these core data, the October survey also contains a special supplement on school enrollment for persons 3 years old and over. This supplement includes the following items: current grade attending at public or private school, whether attending college full- or part-time at a two- or four-year institution, year last attended a regular school, and year graduated from high school. (Source: downloaded from ICPSR 7/13/10)

Please Note: This dataset is part of the historical CISER Data Archive Collection and is also available at ICPSR at https://doi.org/10.3886/ICPSR09544.v1. We highly recommend using the ICPSR version as they may make this dataset available in multiple data formats in the future.

This project includes resting state data for 30 congenitally blind adults and 50 blindfolded sighted controls. All blind participants had minimal light perception from birth at most. These participants are blind due to conditions affecting the eye or optic nerve, rather than brain damage. Cause of blindness information is provided in the aggregate to protect participant privacy. Both blind and sighted participants had no known cognitive or neurological disabilities, as determined through self-report. A subset of the 30 sighted controls can be selected to create a group similar in age and education to the blind group. Participants underwent 1 to 4 resting state scans, each comprising 240 volumes (average scan time = 710.4 seconds per person). During the scans, participants were instructed to relax while staying awake. Sighted participants wore light-excluding blindfolds to ensure uniform light conditions across groups during the scans. T1-weighted anatomical images were also collected. Only the structural and functional images in standard space were shared in this project, in accordance with IRB requirements. For details on image acquisition parameters and data preprocessing methods, please refer to the data description file.

Abstract (en): Since 1972, the General Social Survey (GSS) has been monitoring societal change and studying the growing complexity of American society. The GSS aims to gather data on contemporary American society in order to monitor and explain trends and constants in attitudes, behaviors, and attributes; to examine the structure and functioning of society in general as well as the role played by relevant subgroups; to compare the United States to other societies in order to place American society in comparative perspective and develop cross-national models of human society; and to make high-quality data easily accessible to scholars, students, policy makers, and others, with minimal cost and waiting. GSS questions include such items as national spending priorities, marijuana use, crime and punishment, race relations, quality of life, and confidence in institutions. Since 1988, the GSS has also collected data on sexual behavior including number of sex partners, frequency of intercourse, extramarital relationships, and sex with prostitutes. In 1985 the GSS co-founded the International Social Survey Program (ISSP). The ISSP has conducted an annual cross-national survey each year since then and has involved 58 countries and interviewed over one million respondents. The ISSP asks an identical battery of questions in all countries; the U.S. version of these questions is incorporated into the GSS. The 2016 GSS added in new variables covering information regarding social media use, suicide, hope and optimism, arts and culture, racial/ethnic identity, flexibility of work, spouses work and occupation, home cohabitation, and health. 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 noninstitutionalized, English and Spanish speaking persons 18 years of age or older, living in the United States. Smallest Geographic Unit: census region For sampling information, please see Appendix A of the ICPSR Codebook. computer-assisted personal interview (CAPI), face-to-face interview, telephone interview Please note that NORC may have updated the General Social Survey data files. Additional information regarding the General Social Surveys can be found at the General Social Survey (GSS) Web site.

This dataset contains 15 semi-structured interview transcripts from a study of research data managers in the United States. Study participants worked at small, medium, and large data archives. These archives were affiliated with universities but served varied constituents and provided a range of services. The study asked interview participants to describe their jobs and describe problems or dilemmas they encountered in the course of everyday work. We explicitly asked participants to describe cases where they had to exercise discretion when creating, implementing, or enforcing policy. We framed policy in an organizational and legal sense. Study participants stated that data-related policy challenges may stem from any stage of the research lifecycle.

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. This study was a secondary analysis of Survey of Youth in Residential Placement (SYRP) 2003 United States. This study examined which observed disparities in placement and incarceration experiences relate to disparities in other aspects of a justice-involved youth's life. Distributed here are the codes used to create the datasets and preform the secondary analysis.

Version 4 release notes:I am retiring this dataset - please do not use it. The reason that I made this dataset is that I had seen a lot of recent articles using the NACJD version of the data and had several requests that I make a concatenated version myself. This data is heavily flawed as noted in the excellent Maltz & Targonski's (2002) paper (see PDF available to download here and important paragraph from that article below) and I was worried that people were using the data without considering these flaws. So the data available here had the warning below this section (originally at the top of these notes so it was the most prominent thing) and had the Maltz & Targonski PDF included in the zip file so people were aware of it. There are two reasons that I am retiring it. First, I see papers and other non-peer reviewed reports still published using this data without addressing the main flaws noted by Maltz and Targonski. I don't want to have my work contribute to research that I think is fundamentally flawed. Second, this data is actually more flawed that I originally understood. The imputation process to replace missing data is based off of a bad design, and Maltz and Targonski talk about this in detail so I won't discuss it too much. The additional problem is that the variable that determines whether an agency has missing data is fatally flawed. That variable is the "number_of_months_reported" variable which is actually just the last month reported. So if you only report in December it'll have 12 months reported instead of 1. So even a good imputation process will be based on such a flawed measure of missingness that it will be wrong. How big of an issue is this? At the moment I haven't looked into it in enough detail to be sure but it's enough of a problem that I no longer want to release this kind of data (within the UCR data there are variables that you can use to try to determine the actual number of months reported but that stopped being useful due to a change in the data in 2018 by the FBI. And even that measure is not always accurate for years before 2018.).!!! Important Note: There are a number of flaws in the imputation process to make these county-level files. Included as one of the files to download (and also in every zip file) is Maltz & Targonski's 2002 paper on these flaws and why they are such an issue. I very strongly recommend that you read this paper in its entirety before working on this data. I am only publishing this data because people do use county-level data anyways and I want them to know of the risks. Important Note !!!The following paragraph is the abstract to Maltz & Targonski's paper: County-level crime data have major gaps, and the imputation schemes for filling in the gaps are inadequate and inconsistent. Such data were used in a recent study of guns and crime without considering the errors resulting from imputation. This note describes the errors and how they may have affected this study. Until improved methods of imputing county-level crime data are developed, tested, and implemented, they should not be used, especially in policy studies.Version 3 release notes: Adds a variable to all data sets indicating the "coverage" which is the proportion of the agencies in that county-year that report complete data (i.e. that aren't imputed, 100 = no imputation, 0 = all agencies imputed for all months in that year.). Thanks to Dr. Monica Deza for the suggestion. The following is directly from NACJD's codebook for county data and is an excellent explainer of this variable.The Coverage Indicator variable represents the proportion of county data that is reported for a given year. The indicator ranges from 0 to 100. A value of 0 indicates that no data for the county were reported and all data have been imputed. A value of 100 indicates that all ORIs in the county reported for all 12 months in the year. Coverage Indicator is calculated as follows: CI_x = 100 * ( 1 - SUM_i { [ORIPOP_i/COUNTYPOP] * [ (12 - MONTHSREPORTED_i)/12 ] } ) where CI = Coverage Indicator x = county i = ORI within countyReorders data so it's sorted by year then county rather than vice versa as before.Version 2 release notes: Fixes bug where Butler University (ORI = IN04940) had wrong FIPS state and FIPS state+county codes from the LEAIC crosswa

The Response2covid19 dataset tracks governments’ responses to COVID-19 all around the world. The dataset is at the country-level and covers the January-October 2020 period; it is updated on a monthly basis. It tracks 20 measures – 13 public health measures and 7 economic measures – taken by 228 governments. The tracking of the measures allows creating an index of the rigidity of public health measures and an index of economic response to the pandemic. The objective of the dataset is both to inform citizens and to help researchers and governments in fighting the pandemic.The dataset can be downloaded and used freely. Please properly cite the name of the dataset (“Governments’ Responses to COVID-19 (Response2covid19)”) and the reference: Porcher, Simon "A novel dataset of governments' responses to COVID-19 all around the world", Chaire EPPP 2020-03 discussion paper, 2020.

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 study was designed to begin to build a knowledge base to address the challenges of crossover youth in Maryland - those involved at some point in their lives in the dependency and delinquency systems. Employing a mix of qualitative and quantitative methods, the research focused on the five most populous jurisdictions in the state, Baltimore City, and Anne Arundel, Montgomery, Prince George's, and Baltimore Counties. This collection includes 4 SPSS data files: CINA BCity_Archive_final_Corrected-ICPSR.sav (n=400; 64 variables) CY Stakeholder Survey_Archive_final_Corrected_Update2016-ICPSR.sav (n=164; 302 variables) Delinquency_Risk_Archive_final_Corrected_Update2016-ICPSR.sav (n=1,127; 62 variables) Needs_Archive_final-ICPSR.sav (n=700; 67 variables) Data from interviews with 26 officials in state and local agencies to collect information on policies and practices affecting crossover youth in Maryland are not available as part of this collection.

This data collection contains a stratified 1-percent sample of households, with separate records for each household, each "sample line" respondent, and each person in the household. These records were encoded from microfilm copies of original handwritten enumeration schedules from the 1950 Census of Population. Geographic identification of the location of the sampled households includes Census regions and divisions, states (except Alaska and Hawaii), Standard Metropolitan Areas (SMAs), and State Economic Areas (SEAs). The data collection was constructed from and consists of 20 independently-drawn subsamples stored in 20 discrete physical files. The 1950 Census had both a complete-count and a sample component. Individuals selected for the sample component were asked a set of additional questions. Only households with a sample line person were included in the 1950 Public Use Microdata Sample. The collection also contains records of group quarters members who were also on the Census sample line. Each household record contains variables describing the location and composition of the household. The sample line records contain variables describing demographic characteristics such as nativity, marital status, number of children, veteran status, education, income, and occupation. The person records contain demographic variables such as nativity, marital status, family membership, and occupation. (Source: downloaded from ICPSR 7/13/10)

Please Note: This dataset is part of the historical CISER Data Archive Collection and is also available at ICPSR at https://doi.org/10.3886/ICPSR08251.v1. We highly recommend using the ICPSR version as they may make this dataset available in multiple data formats in the future.

Abstract (en): In 1995-1996, the MacArthur Midlife Research Network carried out a national survey of over 7,000 Americans aged 25 to 74 [ICPSR 2760]. The purpose of the study was to investigate the role of behavioral, psychological, and social factors in understanding age-related differences in physical and mental health. The study was innovative for its broad scientific scope, its diverse samples (which included siblings of the main sample respondents and a national sample of twin pairs), and its creative use of in-depth assessments in key areas (e.g. daily diary of stressful experiences [ICPSR 3725] and cognitive functioning [ICPSR 3596]) on a subset of participants. A detailed description of the study and findings generated by it are available at: http://www.midus.wisc.edu With support from the National Institute on Aging, a follow-up of the original Midlife Development in the United States (MIDUS) sample was conducted in 2004 (MIDUS 2 [ICPSR 4652]). The daily stress and cognitive functioning projects were repeated and expanded at MIDUS 2; in addition the protocol was expanded to include biomarkers and neuroscience. In 2013 a third wave (MIDUS 3) of survey data was collected on longitudinal participants. Data collection for this follow-up wave largely repeated baseline assessments (e.g., phone interview and extensive self-administered questionnaire), with additional questions in selected areas such as economic recession experiences. Cognitive functioning data were also collected at the same time, while data collection for the daily diary, biomarker, and neuroscience projects commenced in 2017. MIDUS also maintains a Colectica portal, which allows users to interact with variables across waves and create customized subsets. Registration is required. The Aggregate Data dataset contains 2,613 variables and 3,294 cases. This dataset includes information about the following types of variables: recession experience, health, education, occupation, marital status, household roster, children, caregiving, living arrangements, race and ethnicity, life satisfaction, health insurance, personal beliefs, finances, community involvement, neighborhood, social networks, sexuality, religion and spirituality, discrimination, childhood family background, images of life change, and psychological turning points. The Disposition Codes dataset contains 6 variables and 7,108 cases. This dataset contains final disposition codes for the M3 phone interview. The Coded Text Data dataset contains 183 variables and 3,137 cases. This dataset contains coded text responses to open-ended questions and those which had "Other-specify" responses. 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 online analysis version with question text.; Checked for undocumented or out-of-range codes.. Presence of Common Scales: For information regarding scales, please refer to the Psychosocial Constructs and Composite Variables. Response Rates: 77 percent of living longitudinal participants completed the M3 phone survey. Details of fielding and final disposition codes for Phone, SAQ, as well as Cognitive data collection projects can be found in the Field Report. Datasets:DS0: Study-Level FilesDS1: Aggregate DataDS2: Disposition CodesDS3: Coded Text DataDS4: National Survey of Midlife Development in the United States (MIDUS 3), 2013-2014, Coded Text Data The noninstitutionalized, English-speaking population of the United States. Smallest Geographic Unit: none Only living respondents who completed the M2 phone interview were eligible for participation in the M3 survey. 2019-04-30 This collection has been updated to include new data supplied by the P.I. The resupplied data includes new cases for the Self-Administered Questionnaire (SAQ) as part of the MIDUS M3 re-fielding effort undertaken to increase participant completion during 2015 fielding. The re-fielding cases can be identified and/or filtered with C1STATUS or the new M3RE_FILTER variables. Ten post-stratification weight variables (C1PWGHT1 through C1PWGHT10), and a new occupation variable based on Standard Occupation Classification system, have b...

This collection offers data on basic characteristics of postsecondary institutions in the United States and covers institutional characteristics for 1989-1990. The data were used for sample design and selection for other IPEDS surveys. Key data elements include the name, address, and telephone number of the institution as well as information about levels of course offerings, calendar system, admissions requirements, student services, accreditation, modes of instruction, and institutional eligibility for student financial aid programs. Updated information on tuition and fees and room and board charges for the current academic year also is available. The unit of analysis is the postsecondary institution. (Source: downloaded from ICPSR 7/13/10)

Please Note: This dataset is part of the historical CISER Data Archive Collection and is also available at ICPSR at https://doi.org/10.3886/ICPSR09527.v2. We highly recommend using the ICPSR version as they may make this dataset available in multiple data formats in the future.