ICPSR User Support

Important note about using the RECODE or COMPUTE online analysis features for TEDS-A, Concatenated 1992-2009

2012-12-09T17:00:00.002-05:00

This FAQ applies to data users who are creating new variables using the RECODE or COMPUTE functions in the SAMHDA online analysis system (SDA). Due to the large number of cases involved in the TEDS-A Concatenated dataset, it takes the system up to 30 minutes for SDA to complete a RECODE or COMPUTE command. Therefore, it is recommended that users wait for approximately 30 minutes between the creation of a new recoded variable and the analysis of the data on the SAMHDA online analysis system. The system (SDA) will use a new variable (created using RECODE or COMPUTE functions) in an analysis even before the variable is completely created. Thus, users are requested to pay close attention to analytic results that use a created variable to make sure that enough time has elapsed and the new variables have been completely created. When running either RECODE or COMPUTE, you may encounter one of the following: (1) the screen turns blank and the status indicator in your browser says "Done" or (2) the server "times out." If these problems occur, SDA will continue to process your request, although it may take 30 minutes or more to complete.

How do I contact someone at SAMHDA for assistance?

2012-01-18T14:55:00.000-05:00

Questions may be emailed to samhda-support@icpsr.umich.edu. SAMHDA also operates a toll-free helpline (888-741-7242) Monday through Friday, 8:30 a.m. to 4:30 p.m. (EST). The local helpline number is (734) 615-9524. Staff try to respond to email and helpline questions within one business day. Answers to many questions can be found in the SAMHDA help documentation, which is routinely updated.

How often does SAMHDA release new data files for download? Can I be notified?

2012-01-18T14:54:00.000-05:00

New data files are released periodically throughout the year. Staff post announcements about new releases on the SAMHDA home page. You can also receive SAMHDA announcements via email, by subscribing to the SAMHDA email list. Another option is subscribing to RSS feed, which will inform you of any new resources added to the SAMHDA Web site.

How does SAMHDA prepare data files for release?

2012-01-18T14:53:00.000-05:00

SAMHDA follows a series of steps for archiving each new data set. Processing a study for archival use requires that all variables, missing data codes, and coding schemes be standardized across elements of a study. This stage of processing is intensive and may be lengthy depending on the data and completeness of materials received. All variables must be examined to ensure that each is thoroughly identified and labeled. When variables are not thoroughly described, SAMHDA staff consult the documentation and/or questionnaires. In some cases, it may be necessary to contact the principal investigator(s) to remedy any problems uncovered during the review of the data.

Furthermore, each study is assessed to determine if any issues of respondent confidentiality exist, and checks are made for problems arising from either direct or indirect identification. Direct identifiers may be blanked or deleted to safeguard privacy before releasing the data to the public. Eliminating indirect identifiers is a more time-consuming task and may involve recoding the data. For example, dates may be converted to time intervals, allowing for time lapse analyses without providing exact dates that might permit identification of individuals. Variables such as age and income may be converted to categories.

The technical characteristics of the documentation are verified against the data to ensure that the data and documentation match. Items relating to the data collection as a whole are examined (e.g., number of cases, number of variables, number of data files, record length, data structure, and how multiple files are linked).

User-defined missing data codes and weights are documented and inter-field consistency checks are performed. In all cases, corrections to the data through discussions and information exchange with the data collectors are attempted. Data definition statements for SPSS, SAS and Stata are prepared for all data sets and value labels are added when they are not part of the files that were received. When possible, data sets and codebooks are prepared for compatibility with the online analysis system.

After the initial processing is complete, further quality checks are made. For example, the observed frequencies are verified against the reported frequencies and checks are made for consistency of survey responses and skip patterns. Data files are also reformatted to the smallest possible size for optimum speed in file transfer over the Internet.

How much do SAMHDA data sets cost?

2012-01-18T14:12:00.000-05:00

All data and associated files in the archive may be downloaded from SAMHDA's Web site at no cost to the user. SAMHDA is a public resource funded by the Center for Behavioral Health Statistics and Quality (CBHSQ), Substance Abuse & Mental Health Services Administration (SAMHSA), Department of Health and Human Services (DHHS).

What resources are available through SAMHDA?

2012-01-18T14:09:00.000-05:00

SAMHDA studies consist of one or more data files and codebooks, as well as setup files for SPSS, SAS, and Stata. SAMHDA also provides a detailed description file for each study.

Many studies can be analyzed using the online Survey Documentation and Analysis (SDA) system. The SDA system allows users to conduct statistical analyses online without having to download data files or setup files. For more information on the SDA system, visit the SAMHDA SDA Tutorial. Some studies also have Quick Tables and Interactive Maps.

SAMHDA provides links to publications, including a searchable database of bibliographic citations for publications that are based on SAMHDA data.

SAMHDA also provides links to other federal resources, including websites related to substance abuse and mental health research and treatment. Each study page also includes links to relevant reports and related sites for that study.

What is SAMHDA?

2012-01-18T14:08:00.001-05:00

The Substance Abuse & Mental Health Services Administration (SAMHSA), Center for Behavioral Health Statistics and Quality (CBHSQ), formerly the Office of Applied Studies, has primary responsibility for the collection, analysis, and dissemination of behavioral health data.

CBHSQ promotes the access and use of the nation's substance abuse and mental health research data through the Substance Abuse and Mental Health Data Archive (SAMHDA). SAMHDA provides public use data files and documentation to support a better understanding of this critical area of public health. The University of Michigan, Inter-University Consortium for Political and Social Research (ICPSR), is under contract to CBHSQ to disseminate data, and maintain the SAMHDA Web site and bibliography of publications.

For questions about SAMHDA, contact SAMHSA Contracting Officer Technical Representative (COTR) J. Neil Russell, Ph.D. at Neil.Russell@samhsa.gov.

How can I obtain restricted HMCA Data?

2012-01-12T15:26:00.001-05:00

Every data collection is examined by HMCA staff for confidentiality issues. Variables that pose a disclosure risk are restricted from general dissemination. Restricted variables are removed from the public-use datasets available on our Web site or are modified in a way that limits disclosure. For example, a variable that gives respondents' ages in single years may be recoded to five-year age groups and topcoded at 75 so that age 16 becomes age 15-19 and age 92 becomes 75+.

To obtain restricted variables in their original form, analysts must agree to the terms and conditions of a restricted data use agreement. These legally binding agreements place strict controls on who may access restricted data and how the data are used, stored, and conveyed.

If HMCA has restricted one or more variables in a data collection, that will be noted in the "Restrictions" section of its study description (e.g., http://www.icpsr.umich.edu/HMCA/studies/31001/detail) and the "ICPSR Processing Note" in its codebook. The latter may be omitted if the study description or other section of the codebook provides sufficient information on how the restricted variables were handled.

To apply for restricted data from the Community Tracking Study and related surveys, use the special forms that have been prepared for that purpose. For restricted data from other HMCA studies, go to the study downloads page (e.g., www.icpsr.umich.edu/icpsrweb/HMCA/studies/28641) and follow the link under Access Notes.

HMCA is changing the submission process for obtaining restricted data, moving from one that is paper based to one that is Web-based. During the transition, both methods will be in use. Some studies will use the old process while others will use the new.

Is state-level data available for NSDUH?

2012-01-11T13:05:00.000-05:00

The Center for Behavioral Health Statistics and Quality (CBHSQ, formerly the Office of Applied Studies), Substance Abuse Mental Health Services Administration (SAMHSA), does not publicly release certain collected data, including the state-level identifiers. Federal law requires CBHSQ to protect the confidentiality of individual respondent data. For this reason, CBHSQ is unable to release state codes on the NSDUH public-use files. CBHSQ does release tables at the state and sub-state levels. A limited number of estimates are also available for the 20 largest metropolitan areas.

In 2012, CBHSQ is planning to offer researchers two new options for accessing confidential data: a Restricted Data Analysis System (R-DAS) and a Data Portal. A brief description of each system is provided below.

Restricted-use Data Analysis System (R-DAS) - This system will allow for state level analyses and is currently under development. The restricted-use data will be accessible on a secure server but will not be available for download. Users will be able to create tables (not regressions) from these data. Disclosure limitation measures will be embedded in the R-DAS which will ensure that any output will have no disclosures of confidential data. Currently, we estimate a mid-2012 launch of this system.

Data Portal - This pilot system will provide researchers off-site access to individual record level data, including state and substate variables. If this pilot system is successful, we estimate a launch date in late 2012. For access to confidential CBHSQ data through the Data Portal, researchers will be required to submit a research proposal for approval and sign a contract (data use agreement). For approved projects, a researcher will download software onto their computer that will allow them to access servers where the confidential data reside. The researcher can then log into the server remotely and open a virtual desktop to access the confidential data using SAS, SPSS, or Stata. However, at this point in time, it is not clear what the rules of access will be, who will have access, how many researchers will be accepted per year, and if there will be any researcher costs for access. Some of these determinations will be made based on resources and experience gained through testing the pilot system. Current plans call for 2004 to the present NSDUH data being available through the Data Portal.

Please contact SAMHDA in the coming months to receive updates on the expected launch date of the R-DAS and Data Portal. You may also receive automatic email updates by signing up for our listserv.

If you have further questions, please contact J. Neil Russell, Ph.D., the SAMHSA Contracting Officer Technical Representative for SAMHDA at Neil.Russell@samhsa.gov.

Why and how should I cite SAMHDA data?

2011-11-16T14:20:00.003-05:00

Why should I cite data?

Citing data files in publications based on those data is important for several reasons:

Other researchers may want to replicate research findings and need the bibliographic information provided in citations to identify and locate the referenced data.
Citations appearing in publication references are harvested by key electronic social sciences indexes, such as Web of Science, providing credit to the researchers.
Data producers, funding agencies, and others can track citations to specific collections to determine types and levels of usage, thus measuring impact.

Where do I find the citation?

Citations for SAMHDA data can be found in the following locations:

Study descriptions that appear on the Web site
File manifest
PDF study description file

Both the file manifest and the PDF study description file are automatically included with every download. Thus, every download is accompanied by a copy of the standard citation that can be copied and pasted with ease.

What do the citations look like?

Here are some examples:

ABC News, and The Washington Post. ABC News/Washington Post Poll, May 2007 [Computer file]. ICPSR24588-v1. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2009-04-17. doi:10.3886/ICPSR24588

United States Department of Commerce. Bureau of the Census, and United States Department of Labor. Bureau of Labor Statistics. Current Population Survey: Annual Demographic File, 1987 [Computer file]. ICPSR08863-v2. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2009-02-03. doi:10.3886/ICPSR08863

Johnston, Lloyd D., Jerald G. Bachman, Patrick M. O'Malley, and John E. Schulenberg. Monitoring the Future: A Continuing Study of American Youth (12th-Grade Survey), 2007 [Computer File]. ICPSR22480-v1. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2008-10-29. doi:10.3886/ICPSR22480

Hall, David, Clement Leduka, Michael Bratton, E. Gyimah-Boadi, and Robert Mattes. Afrobarometer Round 3: The Quality of Democracy and Governance in Lesotho, 2005 [Computer file]. ICPSR22203-v1. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2009-05-19. doi:10.3886/ICPSR22203

Note that we also include a DOI (Digital Object Identifier) at the end of each citation. A DOI is a unique persistent identifier for a published digital object, such as an article of a study, providing a link to the article or study. This means that if you publish an article using ICPSR data and you include the DOI in the data citation, you make it easy for other researchers to get back to the original data.

How can I let SAMHDA know about my publication?

Users of SAMHDA data are required to send us bibliographic citations for each completed manuscript or thesis abstract. This allows us to provide funding agencies with essential information about use of archival resources and facilitates the exchange of information about the research activities of principal investigators.

Email bibliography@icpsr.umich.edu to submit citations for inclusion in our Bibliography.

Why is the video so wide?

2011-10-27T13:27:00.001-04:00

The original videos are just over 1300 pixels wide. Although we can shrink them within the Web browser, that makes it difficult to perceive the details necessary for observational coding. (Facial expression and such.) If you're attempting to code the videos, you probably want to adjust your screen resolution and maximize your browser window.

How do I code my own dataset?

2011-10-27T13:22:00.000-04:00

This site doesn't feature specialized software for creating your own dataset. In all likelihood, your best bet is to adjust your browser window to fill the top half of your screen and use Microsoft Excel in the lower half of your window to record your codes.

...more to come...

How were the microphones positioned?

2011-10-27T13:21:00.001-04:00

In the video, you can see a black/silver pole interrupting the view. Atop that pole was a microphone that captured the students input. In addition, the teachers wore a microphone. You can switch between these audio tracks using the video player.

Why does the video appear to be distorted?

2011-10-27T13:16:00.004-04:00

The distortion is the result of rendering all sides of the room into one long image, a segmented panorama.

Although the distortion is unusual at first glance, this view enables you to see the entire classroom without having to adjust the camera angle, which is very useful for observational coding.

Can I get access to higher-quality versions of the videos?

2011-10-27T10:48:00.001-04:00

Unfortunately, the video you see on the site is the highest quality version of the video. After video processing (and compression), the original videos were destroyed.

Violence Against Women Resource Guide

2011-10-13T15:30:00.000-04:00

Purpose of the Resource Guide
Sources of the VAW Data at NACJD
VAW Data Collection Highlights
Quick Links to VAW Data
Online Survey Documentation and Analysis
Variable-Level Searching
Help for Complex Data Collections
Human Subjects Issues
Funding Opportunities
Publications
Related Links

Purpose of the Resource Guide

This Violence Against Women (VAW) Resource Guide was designed by the National Archive of Criminal Justice Data (NACJD) staff to provide easy access to data collections on the topic of violence against women, such as Quick Links to certain types of VAW studies and links to studies available for online data analysis. This resource guide also provides useful information for the secondary analysis of these data collections, such as customized help pages for complex data collections, information about how to obtain restricted access data, and links to funding opportunities and publications.

Sources of the VAW Data at NACJD

Many of these data collections are sponsored by the Violence Against Women and Family Violence (VAW&FV) Research and Evaluation program at the National Institute of Justice. This program aims to promote the safety of women and family members, and to increase the efficiency and effectiveness of the criminal justice system's response to these crimes. More information about this program can be found at the VAW&FV Web site.

Their preservation and dissemination through NACJD is also sponsored by the National Institute of Justice (NIJ) Data Resources Program (DRP). In 1984 NIJ established the DRP to ensure the preservation and availability of research and evaluation data collected through NIJ-funded research. NACJD archives these data collections to support new research, replicate original findings, or test new hypotheses based on existing NIJ-funded data collections.

VAW Data Collection Highlights

International and Domestic Trends in Sex Trafficking of Women in the United States, 1999-2000 (ICPSR 3438)
Janice G. Raymond and Donna M. Hughes
Coalition Against Trafficking Women

This study was the first to research both contemporary international and domestic trafficking of women for sexual exploitation in the United States and to include primary research information from interviews with trafficked and prostituted women in the sex industry. Telephone and personal interviews were conducted with people who had experience with or knowledge of sex trafficking in the United States. This data collection consists of the verbatim questions and responses in 107 data files from the following groups of individuals who were interviewed: (1) international and United States women who had been or were in the sex industry in the United States, (2) law enforcement officials who had experience and expertise in sex-industry related cases or immigration, (3) social service workers who provided services to women in prostitution or might have come into contact with women from the sex industry and those providing services to immigrant populations, and (4) health care workers who provided services to women in prostitution or who may have come into contact with women in the sex industry.

Quick Links to VAW Data

The following Quick Links were created to facilitate access to Violence Against Women data collections at NACJD. These collections can also be found through our Download Data web page.

View all VAW studies
View newly archived or updated VAW collections
Other NACJD data collections that can be used for VAW research:

Online Survey Documentation and Analysis

A selection of NACJD data collections is available for use with our online Survey Documentation and Analysis (SDA). This means that users can perform the following tasks without having to download the entire collection and importing it into a statistical package:

Search for variables of interest in a dataset
Review frequencies or summary statistics of key variables to determine what further analyses are appropriate
Review frequencies or summary statistics for missing data
Produce simple summary statistics for reports
Create statistical tables from raw data
Create a subset of cases or variables from a particularly large collection to save downloading time and space on a personal computer

VAW data collections available for online analysis

Learn more about the online analysis system

Help for Complex Data Collections

NACJD is developing specialized web pages to help data users analyze complex data collections. These pages are currently available for the following studies:

Chicago Women's Health Risk Study (ICPSR 3002)

Violence and Threats of Violence Against Women and Men in the United States, 1994-1996 (ICPSR 2566)

Human Subjects Issues

NACJD employs a variety of measures to ensure that subject confidentiality is preserved in all of our data collections. Due to the sensitive nature of data collected about violence against women, many of these data collections are restricted from general access. This means that these data are still freely available to the public, but they cannot simply be downloaded from the NACJD Web site. Access to restricted data collections is possible through a written request to NACJD. Using a Data Transfer Agreement, available online as a PDF file, prospective data users must certify in writing that the data will be used for research or statistical purposes only, and that the confidentiality of respondents or subjects will be protected. More information about the procedures for accessing these data is available on the Restricted Access Data web page.

Funding Opportunities

NIJ provides two types of funding for Violence Against Women Research:

NIJ's Violence Against Women and Family Violence program provides funds for research and evaluation, including research fellowships. The program's objectives are to estimate the scope of violence against women and family violence, identify their causes and consequences, evaluate promising prevention and intervention programs, disseminate research results to the field, and build partnerships among a wide variety of disciplines to accomplish these objectives.
NIJ's Data Resources Program provides funding for the secondary analysis of existing data collections. The program seeks applicants to conduct original research using data from NACJD, especially data from previously funded NIJ projects.

Details regarding the application procedures for both types of funding can be found at NIJ's Funding Opportunities.

Publications

NACJD makes criminal justice data available to the public for secondary analysis. We do not generally archive, produce, or distribute published reports, statistics, charts, or other analyses based upon data holdings.

Users interested in such publications can search our online database of citations for publications related to NACJD data collections. Many of the publications listed in the online database are available in either paper hardcopy or electronic form from the National Criminal Justice Reference Service.

Other publications on the subject of violence against women can be found at NIJ Publications Topical Collections.

What are SAMHDA's Terms of Use?

2011-10-13T13:55:00.004-04:00

All users of data archived at SAMHDA must agree to the Terms of Use prior to downloading or analyzing data online. A copy of the Terms of Use is also included with downloaded data files.

Terms of Use

Please read the terms of use below. If you agree to them, click on the "I Agree" button to proceed. If you do not agree, you can click on the "I Do Not Agree" button and return to the home page.

These data are distributed under the following terms of use. By continuing past this point to the data retrieval process, you signify your agreement to comply with the requirements as stated below:

Privacy of RESEARCH SUBJECTS

Any intentional identification of a RESEARCH SUBJECT (whether an individual or an organization) or unauthorized disclosure of his or her confidential information violates the PROMISE OF CONFIDENTIALITY given to the providers of the information. Disclosure of confidential information may also be punishable under federal law. Therefore, users of data agree:

To use these datasets solely for research or statistical purposes and not for re-identification of specific RESEARCH SUBJECTS.
To make no use of the identity of any RESEARCH SUBJECT discovered inadvertently and to report any such discovery to CBHSQ and SAMHDA (samhda-support@icpsr.umich.edu)

Citing Data

You agree to reference the recommended bibliographic citation in any of your publications that use SAMHDA data. Authors of publications that use SAMHDA data are required to send citations of their published works to ICPSR for inclusion in a database of related publications (bibliography@icpsr.umich.edu).

Disclaimer

You acknowledge that SAMHSA and ICPSR will bear no responsibility for your use of the data or for your interpretations or inferences based upon such uses.

Violations

If CBHSQ or ICPSR determines that this terms of use agreement has been violated, then possible sanctions could include:

Report of the violation to the Research Integrity Officer, Institutional Review Board, or Human Subjects Review Committee of the user's institution. A range of sanctions are available to institutions including revocation of tenure and termination.
If the confidentiality of human subjects has been violated, then report of the violation may be made to the Federal Office for Human Research Protections. This may result in an investigation of the user's institution, which can result in institution-wide sanctions including the suspension of all research grants.
Report of the violation of federal law to the United States Attorney General for possible prosecution.
Court awarded payments of damages to any individual(s)/organization(s) harmed by the breach of confidential data.

Definitions

CBHSQ
Center for Behavioral Health Statistics and Quality

ICPSR
Inter-university Consortium for Political and Social Research

Promise of confidentiality
A promise to a respondent or research participant that the information the respondent provides will not be disseminated in identifiable form without the permission of the respondent; that the fact that the respondent participated in the study will not be disclosed; and that disseminated information will include no linkages to the identity of the respondent. Such a promise encompasses traditional notions of both confidentiality and anonymity. In most cases, federal law protects the confidentiality of the respondent's identity as referenced in the Promise of Confidentiality. Under this condition, names and other identifying information regarding respondents would be confidential.

Research subject
A person or organization that participates in a research study. A research subject may also be called a respondent. A respondent is generally a survey respondent or informant, experimental or observational subject, focus group participant, or any other person providing information to a study.

SAMHDA
Substance Abuse and Mental Health Data Archive

SAMHSA
Substance Abuse and Mental Health Services Administration

Whom do I contact if I have questions about the NSPY restricted files?

2011-10-13T10:38:00.000-04:00

If you had additional questions about obtaining the restricted data, contact ICPSR. If you have questions about analyzing National Survey of Parents and Youth (NSPY), 1998-2004 -- Restricted Use Files, please feel free to contact the investigators directly.

E-mail: NSPYHelp@westat.com
Internet: www.NSPYCENTER.com

Telephone: Toll Free: 888-262-5575
Local: 301-738-3539

Mail: NSPY Center
1700 Research Blvd., RB3252
Rockville, MD 20850
Attn: Victoria Castleman

Inquiry-based Learning Webinar

2011-08-19T09:45:00.002-04:00

Inquiry-based Learning Webinar

How do I find data that I can use for my research project?

2011-07-05T13:53:00.000-04:00

The Data Access/Download Data page allows you to search our data holdings by keyword. The page also gives you several options to browse our data holdings. On the search results page, you may filter your results by subject, geography, time period, funding agency, author, and recency.

Please keep in mind that our data collections consist of ASCII data files and documentation. These data files must be read into a statistical package such as SAS or SPSS in order to generate statistics. Our Web site does not have statistics that have already been produced or published reports, charts, or other analyses based upon data holdings. For more information about these products, see the answer to I don't want to run my own statistics. Where can I get reports or statistics that have already been generated?

What is MyData?

2011-06-17T15:57:00.001-04:00

MyData is a user registration and authentication system for the ICPSR Web site. The system creates a more secure environment for users and enables them to sign up for services that ICPSR is developing. MyData has the following user-friendly features:

Uses an email address as a login ID
Requires a password to authenticate to the Web site
Allows users to create an account and set preferences
Allows registered users to reset a forgotten password
Enables users to view prior download history
Enables users to subscribe to an update notification service that emails users when a study they've previously downloaded is substantially updated

Do I have to establish a MyData account?

Users who desire to download members-only data and some publicly available data may either establish a MyData account or log in using their Facebook or Google account. When a user creates a new account, s/he will be asked to complete a brief questionnaire about user preferences. Users that do not want to share their name and contact information with the Official Representative at their member institution can request to withhold that information.

Resetting File Extensions in Windows 7

2011-05-11T13:25:00.003-04:00

In Windows 7 file extensions are hidden by default. But SAS, SPSS and Stata need to see the file extension to run setup files. This short tutorial shows you how to reset the file extension option in Windows, so your setup files will run properly.

This tutorial shows how to make those file extensions visible to users and to the software packages.

.pdf 691KB
.wmv 17.5MB
.mp4 12.1MB
.mp3 1.3MB
.m4v 5.2MB

When I attempt to play your video files, I get an error message about a missing codec. What's going on?

2011-05-10T08:47:00.002-04:00

In Windows 7, some users will see the following error message when they try to view our Webinars:

Windows Media Player cannot play the file because the required video codec is not installed on your computer.

You will need to install the GoTo Meeting Codec so that Windows Media Player can view .wmv files produced by GoTo Meeting for our Webinars.

How does ICPSR handle authentication for access to its data and members-only content?

2011-04-21T15:10:00.002-04:00

Users may log in to ICPSR using their Facebook or Google account. They may also use an ICPSR MyData account if they prefer not to use third party authentication.

Benefits of Third Party Authentication

Users will not have to remember a separate login/password to access ICPSR data.
If users are logged into their Facebook or Google account and then go to ICPSR, they will already be logged into ICPSR.

Privacy Issues

Using a single identity to log into multiple websites allows users to establish a reputation on several sites with the same ID. However, that may make it easier for others (marketers, law-enforcers) to track them.
Users can choose to share personal information they've stored at the third party provider with ICPSR, in order to save the trouble of re-entering some basic profile information.
If a third party account gets hacked, the hackers would also be able to access that user's ICPSR account.
Third party authentication providers are able to track every site you use your third party account to log into.

Multiple Logins

A user may log into ICPSR using (for example) Facebook one session, Google another session, and MyData a third session. Doing so would create separate profiles in the ICPSR system if the third party accounts are associated with different email addresses.

How do I obtain a data collection from NACJD?

2011-03-19T13:57:00.001-04:00

Most NACJD data collections can be freely downloaded from our web site. Information about how to download data and documentation can be found in the Help section of this web site or users can contact NACJD staff for assistance at nacjd@icpsr.umich.edu.

Selected NACJD data collections cannot be downloaded from our web site due to the sensitive nature of the data. If you are only given the option to download the documentation but no data for a particular collection, then you should read the Restrictions field in the study description for more information about how to obtain the data. In order to ensure respondent confidentiality, certain sensitive data collections are available only through a restricted access archive. While these data are still available free of charge, users must specifically request these collections from the archive. More information about this process and the necessary Data Transfer Agreement Form can be found in our Restricted Access Data Archive as well as in the Restrictions field in the study description.

Why is the text so small on your Web site?

2011-03-18T15:51:00.003-04:00

The default font size varies from browser to browser. We design our sites so that the user can control the font size. By doing this, we guarantee that users with low vision can access our content.

See "How to Increase the Font Size on My Browser" for assistance resizing the text in your preferred browser.

How can I archive crime or criminal justice data that I have collected?

2011-03-18T14:02:00.002-04:00

If you are an NIJ grantee, please see Depositing Data Sponsored by the National Institute of Justice. All other researchers who would like to contribute data to the archive can submit their materials through ICPSR's data deposit procedures and the data will then be forwarded to the criminal justice archive. For more information, see Deposit Data & Findings.

How can I use NACJD data with statistical packages?

2011-03-18T13:48:00.004-04:00

Many NACJD data collections that contain ASCII data files are accompanied by data definition statement files that allow users to read the text files into statistical software packages. NACJD currently provides data definition statements for SAS and SPSS statistical software packages, two of the more commonly used analytical software packages for the social sciences, for many of its data collections.

NACJD can provide help with using data definition statements to read ASCII data files. Users can call our toll-free help line at 1-800-999-0960 or email us at nacjd@icpsr.umich.edu for assistance. For on-line help, see the following help pages:

However, NACJD does not provide technical support for any software package. For information on how to analyze the data once you have read the file into a statistical package, the user should refer to the manual for that particular software. NACJD has compiled a list of suggested Web sites for additional help with software packages on the Statistical Resources page.

Does ICPSR accept qualitative data?

2011-03-18T12:43:00.001-04:00

Yes. Please contact ICPSR prior to submitting qualitative data at deposit@icpsr.umich.edu.

Subsetting using SDA

2011-01-20T09:53:00.007-05:00

This tutorial will walk you through the process of creating a subset of a study in SDA.

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.wmv 44.3MB
.m4v 27MB
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Who is the sponsor for TEDS?

2010-12-15T08:47:00.002-05:00

The Center for Behavioral Health Statistics and Quality (CBHSQ, formerly the Office of Applied Studies), Substance Abuse and Mental Health Services Administration (SAMHSA), Department of Health and Human Services (HHS).

Who is the sponsor for NSDUH?

2010-12-15T08:45:00.002-05:00

When will the next year of data be available for MTF?

2010-12-15T08:30:00.009-05:00

The MTF data files are released after the annual report is published, which is at the end of October in the year following the data collection. For example, the 2009 data files were released in October 2010.

When will the next year of data be available for NSDUH?

2010-12-15T08:30:00.008-05:00

The NSDUH data file is typically released for the preceding year by the end of November or beginning of December.

Are earlier years of data available for the TEDS-Discharges series?

2010-12-15T08:30:00.007-05:00

The earliest year available for TEDS-D is the 2006 file, and there are no plans for making earlier years of TEDS-D available at this time.

What are Interactive Maps and how do I use them?

2010-12-06T12:33:00.016-05:00

Interactive Maps is an online analysis tool that allows for visual exploration of geographic data using a U.S. map and drop-down menus.

Currently, maps are available for the Treatment Episode Data Set - Admissions (TEDS-A) series beginning with the year 2005. The maps allow geographic analysis at the state level. Maps for additional years of TEDS-A, other studies, and more detailed geographic levels are planned for future release.

Access the maps on the Interactive Maps home page (see screen shot below).

To begin using the maps, follow these simple steps

Select the year of data you wish to analyze.
Select the substance of interest. Substances are those reported at admission and may have been reported as the primary, secondary, or tertiary substance of abuse.
Click the "go" button.
Move the cursor over the state of interest to view the count and percentage reporting the substance.

Several resources are available for assistance while using the maps:

A color-coded key to the right of the map defines the varying degrees of response across the geographic level of analysis.
Zoom buttons located at the bottom-left of the map allow a person to hone in on geographic areas. This is useful when trying to select one of the smaller states or the District of Columbia.
A hyperlink beneath the map provides access to a tabular display of the data.
The Related Resources section gives quick access to the selected study or series home page, and links to download the data or analyze them online using SDA.

There are more than 500 citations that I want to export, but the system only allows for up to 500. How can I export the remaining citations?

2010-12-02T14:25:00.001-05:00

After you have completed your search for citations you have the option of downloading your results into RIS or EndNote. Currently the system allows you to export the first 500 results. Most searches will not yield more than 500 citations; however, when this happens you will need to export the citations in stages. The export files can then be merged into one file. To export results beyond the first 500 citations, you will need to alter the URL in the address bar. Below is an illustration of this process using the NSDUH series, which contains over 1,000 citations. To export all of the NSDUH citations you will need to alter the URL two times, after obtaining the initial 500 results. The URL for the first 500 results is:

EndNote: http://www.icpsr.umich.edu/icpsrweb/SAMHDA/biblio/rsxml/resources?sortBy=1&seriesId=64&studyId=0&paging.rows=500

RIS: http://www.icpsr.umich.edu/icpsrweb/SAMHDA/biblio/ris/resources?sortBy=1&seriesId=64&studyId=0&paging.rows=500

To export the citations beginning with 501 up to 1000, replace “rows=500” in the URL with “startRow=501”. To obtain the remaining citations beginning with 1001 you would then use “startRow=1001” .

Notice that the only difference in the above URLs is the notation for the program you want to export the results into. Use “/rsxml/” immediately after “biblio” to export the results into EndNote, or “/ris/” to export the results into RIS. Also notice that the URL contains “seriesId=64”. The number 64 is the series number for the NSDUH. To use a different series, replace the series number (e.g., TEDS-A is 56, MTF is 35).

Can I get assistance from ICPSR in loading the MARC into my card catalog?

2010-11-17T09:25:00.002-05:00

Unfortunately, ICPSR does not have a great deal of MARC expertise in house. If there is a flaw in the XML itself, we can correct that quickly, but we cannot provide guidance on loading the MARC into your catalog. Your best source of support would be to discuss the matter with other ORs.

Can I submit ICPSR MARC records to OCLC?

2010-11-17T09:22:00.002-05:00

Not without express permission from ICPSR. ICPSR is currently in discussion with interested parties. If you would like to assist in submitting our records to OCLC, please contact web-support@icpsr.umich.edu.

Can I just obtain the MARC from OCLC?

2010-11-17T09:18:00.003-05:00

You can, but you'll be charged for the records. In addition, there's no guarantee that the records will be up-to-date.

How do I convert MARC21 XML into MARC?

2010-11-17T09:12:00.003-05:00

According to one of our ORs, you can use an application called MARCEdit to transform the files. Unfortunately, ICPSR can provide guidance beyond that, as we have no in-house expertise on true MARC.

How do I match your MARC records to existing records in my catalog?

2010-11-17T09:07:00.003-05:00

To update your catalog records, please use the 035 field as the matchpoint. 035 contains the ICPSR study number, which is always unique.

How can I get around the 500 results maximum on the metadata export/search results?

2010-11-16T13:17:00.007-05:00

Your export link will look something like this:

http://www.icpsr.umich.edu/icpsrweb/ICPSR/marc/studies?archive=ICPSR&q=&recency=QUARTER&paging.startRow=1&paging.rows=500

That URL will return ICPSR studies in MARC format that were added/updated in the last quarter. It will return the first 500 results. If you change paging.startRow=1 to paging.startRow=501, it will return results 501 through 1000. By doing that, you can snag more than 500 results, but you have to break them into 500-record increments.

You can change paging.rows to a higher value, but it won't let you get around the 500-record limit.

Why doesn't ICPSR just make links for each 500? Or the whole set?

We want to prevent the export functions from taxing the server too much. If we put those links on the site anywhere, then every search engine crawler would hit those links repeatedly, which could cause our server to grind to a halt.

Can I generate MARC for studies updated in a particular date range?

2010-11-16T13:10:00.003-05:00

Unfortunately, no. The Filter by Recency function enables you to select studies added/updated in the last week, month, quarter, or year. We have no plans of implementing a specific date range search for adds/updates. That said, ICPSR will be generating annual update files that feature all adds/updates for the prior year.

Can I obtain MARC from ICPSR? What about Dublin Core?

2010-11-16T13:05:00.003-05:00

ICPSR has a number of export functions for study-level metadata. On the search results page, you can now export results as either MARC21 XML or comma-delimited files (with a maximum of 500 results). This export feature appears at the base of the right-hand column on the search results page. From the study home page, you can now export a study-level metadata record in a variety of formats, including DDI, Dublin Core, and MARC21 XML.

These export options replace ICPSR's previous method of disseminating MARC records. The changes are:

We now provide MARC21 XML, not true MARC.
The new MARC21 XML doesn't include all fields previously indexed; it's a concise metadata record.
We no longer submit our MARC to OCLC.
We no longer have our MARC reviewed/corrected by library staff at the University of Michigan.
Users can easily build custom sets of MARC21 XML, rather than having to re-import all ICPSR metadata.
Users can obtain updated XML at will, rather than waiting for an annual batch from ICPSR.

At the beginning of each year, our staff will generate two large XML files:

a file containing all ICPSR metadata as MARC21 XML
a file containing all studies added or updated in the last year as MARC21 XML

We'll link those files from the OR site, so that the 500 result limit doesn't prevent users from obtaining the full metadata.

If you see any flaws with the XML, please don't hesitate to let us know. The new setup makes it very easy for ICPSR to globally update our XML, and we'd like to ensure that the XML is suitable for our institutions' library catalogs. If you have any questions/concerns, please email web-support@icpsr.umich.edu.

May I use this question in building my own survey?

2010-11-01T13:34:00.005-04:00

Short Answer

Not without further research on your part. The question may be part of a copyrighted instrument. Using it in that case would be copyright infringement and/or plagiarism.

Long Answer

Some instruments utilized as part of the data collection process for a project deposited with ICPSR may contain in whole or in part contents from copyrighted instruments. Reproductions of such instruments are provided as documentation for the analysis of the data of the associated collection. Restrictions on "fair use" apply to all copyrighted content.

Circular 21 from the U.S. Copyright Office provides basic information on fair use and several important legislative provisions and other documents addressing reproduction of copyrighted materials by librarians and educators.

How can I determine if the question is copyrighted?

Read the documentation carefully; contact the investigators directly.

What kind of data formats does the archive distribute? Do you have SPSS Portable files? SAS transport? Stata?

2010-10-29T08:44:00.005-04:00

We primarily distribute data files in eight data formats: three plain text formats (column-delimited ASCII, comma-delimited ASCII, and tab-delimited ASCII), two SAS formats (SAS XPORT and CPORT files), two SPSS formats (SPSS SAV and portable files), and the single Stata data format. Virtually every data file is available in a plain text format. We also supply many data files in one or more of the other formats.

Plain Text

Column-, comma-, and tab-delimited ASCII data files store data, including numeric values, as lines of plain text, with one or more lines per observation (or subject or case). In the plain text format, every character of text--each digit, letter, or other symbol--is encoded in a separate byte in the data file. Thus, the number 133.5 occupies five bytes, the number 8 just one byte, and the string "computer programmer" requires nineteen bytes. Many of ICPSR's plain text data files are encoded with the ASCII character encoding system. However, some use other encodings, such as IBM PC code page 437, which is based on ASCII but supports more characters than ASCII does. Most use the ASCII-based ISO 8859-1 or Windows-1252 encodings.

In all three types of plain text data files, the line(s) allocated to a given observation contains the observation's values for the file's variables. What sets the three types apart is way the values are demarcated on the lines.

In a column-delimited ASCII data file, each variable occupies the same byte(s) on every observation. The bytes are usually called "columns," hence the name of this data format. For example, if a file with one line per observation has just three variables which occupy three bytes each, then the first variable would be located in columns 1-3, the second in columns 4-6, and the third in columns 7-9 on each line in the data file.

To facilitate the use of the column-delimited ASCII data files, which require programming expertise to import them into statistical packages for analysis, ICPSR usually provides programs, called "setups," to read them into SAS, SPSS, or Stata. The setups also assign variable labels and usually assign value labels and define missing values too.

In a comma-delimited ASCII data file, the data values are separated with commas instead of being located in fixed column locations. Thus, in this format, the length of each line varies according to the magnitude of the line's data values. For example, the first two lines of a four-variable data file could look like this:

1,133.5,plumber,250778
2,44,librarian,20000

As with the column-delimited ASCII files, ICPSR usually provides setups to read the comma-delimited ASCII files into SAS, SPSS, or Stata.

Tab-delimited ASCII data files are the same as comma-delimited ASCII files except that values are delimited with a special tab control character instead of a comma. Most of these files were created by ICPSR for use with spreadsheets, such as Excel, into which they can be easily imported. These files can also be read into statistical packages like SAS, SPSS, and Stata. However, ICPSR rarely provides setups for that purpose.

SAS

We distribute two SAS data formats: SAS transport files generated by the SAS CPORT procedure and SAS transport files written by the SAS XPORT engine. Both types of files contain specially formatted SAS data sets, which contain variable labels as well as data. Many of ICPSR's SAS CPORT files also include SAS format catalogs with value labels.

SAS CPORT files should be imported into SAS with the SAS CIMPORT procedure.

Since SAS has an engine that reads SAS XPORT files, they can be read by any SAS command that can read an ordinary SAS data set, such as the SAS set statement or the SAS FREQ procedure. SAS XPORT files can also be converted to standard SAS data sets with the SAS COPY procedure.

SPSS

We distribute two types of SPSS data files: SPSS SAV files written by the SPSS save command and SPSS portable files written by the SPSS export command. Both types of data files include variable labels and usually include value labels and missing value definitions.

To load SPSS SAV files into SPSS use the SPSS get command.

To read SPSS portable files into SPSS use the SPSS import command.

Stata

Like the SAS and SPSS formats, Stata's proprietary data file format, which is written by the Stata save command, is platform independent. Our Stata data files include variable labels and usually include value labels too.

Stata data files should be loaded into Stata with the Stata use command.

Using ASCII data and setup files

Uniform Crime Reporting Program Resource Guide

2010-10-27T13:06:00.017-04:00

About the FBI's Uniform Crime Reporting Program
Using the Resource Guide
ORI-Agencies Lookup Table
Types of UCR Data Available at NACJD: Agency-Level, Incident-Level, and County-Level Data
Online Analysis of UCR Data
Related Resources

About the FBI's Uniform Crime Reporting Program

Uniform Crime Reports (UCR) serve as periodic nationwide assessments of reported crimes not available elsewhere in the criminal justice system. By 1985, there were approximately 17,000 law enforcement agencies contributing reports either directly or through their state reporting programs. By 1998, the number of agencies was over 18,500. More information about the development of the UCR Program can be found on the FBI Web site.

Using the Resource Guide

NACJD, a part of the Inter-University Consortium for Political and Social Research (ICPSR) at the University of Michigan, designed this Resource Guide for World Wide Web users to learn about the FBI's Uniform Crime Reporting Program and to connect to other related information sources.

With this guide, first time users or experienced analysts can:

Find general information about the Uniform Crime Reporting Program.
Connect to other Uniform Crime Reporting Program - related sites.
Access all Uniform Crime Reporting Program data files, codebook, and SPSS and SAS data definition statement files for County-Level, Agency-Level and National Incident-Based Reporting System (NIBRS) data.

Agency-Level UCR Data

The agency-level data, where the unit of analysis is the police agency (or incident for Supplementary Homicide Reports and Hate Crime,) are comprised of five data files per year and contain information on:

Offenses Known and Clearances by Arrest data files include monthly data on the number of Crime Index offenses reported and the number of offenses cleared by arrest or other means. The counts include all reports of Index Crimes (excluding arson) received from victims, officers who discovered infractions, or other sources.
Property Stolen and Recovered data are collected on a monthly basis by all UCR contributing agencies. These data, aggregated at the agency-level, report on the nature of the crime, the monetary value of the property stolen, and the type of property stolen. Similar information regarding recovered property is also included in the data.
Supplementary Homicide Reports (SHR) provide incident-based information on criminal homicides. The data, provided monthly by UCR agencies, contain information describing the victim(s) of the homicide, the offender(s), the relationship between victim and offender, the weapon used, and the circumstance of the incident.
Police Employee (LEOKA) Data provide information about Law Enforcement Officers Killed or Assaulted (hence the acronym, LEOKA) in the line of duty. The variables created from the LEOKA forms provide in-depth information on the circumstances surrounding killings or assaults, including type of call answered, type of weapon used, and type of patrol the officers were on.
Hate Crime Data contain information about crimes that manifest evidence of prejudice based on race, religion, sexual orientation, ethnicity, and mental or physical disabilities. The FBI began to collect these data under the Hate Crime Statistics Act of 1990. Information contained in the data include number of victims and offenders involved in each hate crime incident, type of victims, bias motivation, offense type, and location type.

Select a Year to Browse UCR Agency-Level Data for 1975-Current

Arrests by Age, Sex, and Race, monthly reports
1980 1981 1982 1983 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008

Arrests by Age, Sex, and Race, Summarized Yearly
1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008

Offenses Known and Clearances by Arrest
1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008

Property Stolen and Recovered
1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2008

Supplementary Homicide Reports
1975 1976 1977 1978 1979 1980 1981 1982 1983 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008

Police Employee (LEOKA) Data
1975 1976 1977 1978 1979 1980 1981 1982 1983 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

Hate Crime Data
1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008

Notes: Prior to 1998, all data save for Hate Crime data were merged into one study (ICPSR 9028). Hate Crime data are not available prior to 1992.

1966-1976 Data Collections

Earlier years of agency-level data, where the unit of analysis is the police agency, include one data file per year for the years 1966-1976 and contain the following information:

Return A Monthly Return of Offenses Known to the Police
Supplement I to Return A - Additional Analysis of Motor Vehicle Theft, Burglary, Robbery and Larceny
Supplement II to Return A - Values of Stolen and Recovered Property

Select a Year to Browse or Download UCR Agency-Level Data for 1966-1976

Notes: UCR Agency-Level Data for 1966-1976 was merged into one study, ICPSR 7676.

Incident-Level UCR Data

Incident-level data are collected through the National Incident-Based Reporting System (NIBRS) component of the UCR. NIBRS collects data on each single incident and arrest within 22 offense categories made up of 46 specific crimes called Group A offenses. For each of the offenses coming to the attention of law enforcement, specified types of facts about each crime are collected. In addition to the Group A offenses, there are 11 Group B offense categories for which only arrest data are reported. More information about this study can be found in the NIBRS Resource Guide.

County-Level UCR Data

The county-level Uniform Crime Reporting files contain only arrests and crimes reported data and are distributed annually as four separate data files:

Arrests, All Ages
Arrests, Adults
Arrests, Juveniles
Crimes Reported

Beginning in 1993, an Allocated Statewide Data file is also distributed for each part above. The Statewide data files provide the amount of data from statewide agencies allocated to each county based on the county's share of the state population. These Statewide data files can also be used to "back out" the statewide counts if only the county total is desired.

Allocated Statewide Data for Arrests, All Ages
Allocated Statewide Data for Arrests, Adults
Allocated Statewide Data for Arrests, Juveniles
Allocated Statewide Data for Crimes Reported

Study Design

County-level UCR files are created by NACJD based on agency records in a file obtained from the FBI that also provides aggregated county totals. NACJD imputes missing data and then aggregates the data to the county-level. Specific information about the imputation algorithm can be found in the codebooks for each year of the county-level files. A new imputation algorithm was implemented in 1994. Therefore, data files prior to 1994 are not necessarily comparable to the files from 1994 and onward. The new imputation algorithm more closely approximates the estimation procedures used by the FBI to adjust for incomplete or unreported data from individual law enforcement jurisdictions for their Crime in the United States publications. Users are strongly encouraged to carefully read the study documentation for each year before analyzing these data.

Two major changes to the UCR county-level files were implemented with the 1994 release data and are continued with the 1998 data. A new imputation algorithm to adjust for incomplete reporting by individual law enforcement jurisdictions has been adopted. Also, a new Coverage Indicator has been created to provide users with a diagnostic measure of aggregated data quality in a particular county. These developments are described in greater detail below. The changes were instituted in response to comments from a number of users and after almost a year of discussions by UCR file users, the Uniform Crime Reports Unit of the Federal Bureau of Investigation, the Bureau of Justice Statistics, and the Inter-university Consortium for Political and Social Research.

These changes will result in a break in series from previous UCR county-level files. Data from earlier year files should not be compared to data from 1994 and subsequent years. Changes in procedures used to adjust for incomplete reporting at the ORI or jurisdiction level may be expected to have an impact on aggregates for counties in which some ORIs have not reported for all 12 months. However, the new adjustment procedures should result in county-level data that are less sensitive to changes between years in the extent of reporting by ORIs within a county. Consequently, data from 1994 forward should be more accurate estimates for longitudinal analysis.

IMPUTATION PROCEDURES USED FOR 1994 UCR COUNTY-LEVEL FILES AND ONWARD: The data for any ORI reporting 12 months were used for county aggregation as submitted. Data for an ORI reporting 3 to 11 months were increased by a weight of [12/months reported]. For ORIs reporting 0 to 2 months, data for these ORIs were set to zero and then data were estimated using rates calculated from ORIs reporting 12 months of data located in the ORIs geographic stratum based on UCR Population Groups within their state. UCR Population Groups are defined as follows:

Population Group

Cities 250,000 and over
Cities 100,000 - 249,999
Cities 50,000 - 99,999
Cities 25,000 - 49,999
Cities 10,000 - 24,999
Cities 2,500 - 9,999
Cities under 2,500
Non-MSA counties and non-MSA State Police
MSA counties and MSA State Police

COVERAGE INDICATOR: For releases of UCR county-level files before 1994, data from jurisdictions reporting less than 6 months of data were not included in county totals in an effort to ensure cross-sectional data comparability and quality. With the new procedures to adjust for incomplete reporting, data will be provided for each active ORI that reports less than 12 months of data, whether through weighting of partial year data or substitution of a value based on population group and state. Instead of exercising an a priori judgment that 6 months of data is the minimum threshold for acceptable data quality, a new Coverage Indicator variable has been created that will allow users to set their own threshold for acceptable data quality and to include or exclude data based on the standards they set themselves. The Coverage Indicator variable represents the proportion of county data that is not imputed for a given year. The indicator ranges from 100, indicating that all ORIs in the county reported for 12 months in the year, to 0, indicating that all data in the county are based on estimates (as described above), not reported data. The Coverage Indicator is calculated as follows:

CI_x = (1-(sum((ORI_i pop/county pop)((12-months reported/12))))*100

where

CI = Coverage Indicator
x = county
i = ORI within county

Some ORIs do not have a population associated with their jurisdiction. These ORIs report for jurisdictions such as national parks, colleges and universities, toll bridges and tunnels, and most state police departments. As the coverage indicator is based on months of reporting and the population of each agency, this variable will not show estimation that did occur for statewide ORIs and for ORIs as listed above that do not have a population but reported 3 to 11 months of data. Conversely, the coverage indicator will indicate that estimation has occurred for ORIs with a population that reported 3 to 11 months of data even if the ORIs actually reported no crimes or arrests. Similarly, the coverage indicator will indicate that estimation had occurred for ORIs with a population that reported 0 to 2 months of data, even though no rate was calculated to estimate data because of the lack of ORIs in the agency's geographic stratum reporting 12 months of data. Finally, since data for ORIs that reported 0 to 2 months of information are set to zero, users should be aware that no estimation of data was possible for ORIs without a population that reported 0 to 2 months of data.

It should be emphasized that, while UCR staff were consulted in developing the new adjustment procedures, these UCR county-level files are not official FBI UCR releases and are being provided for research purposes. Users with questions regarding these UCR county-level data files can contact the National Archive of Criminal Justice Data at the ICPSR.

ADDITIONAL NOTES: In the UCR county-level arrest files, the population and data for jurisdictions located in multiple counties are provided only in the county containing the largest population component of the jurisdiction. Counties containing smaller population components of multiple-county jurisdictions will contain no population or arrest data for these jurisdictions. Data in counties affected by one or more multiple-county jurisdictions are indicated by a multi-county jurisdiction flag variable. In the county-level crimes reported files, the population and crime data for jurisdictions located in multiple counties are provided by the UCR porportioned to each county (maximum of three) in which the jurisdiction is located.

Cities designated by the Census Bureau as independent cities are reported separately and have their own "county" codes. Some jurisdictions, such as state parks and some state police, provide data only on a statewide basis. In these cases, data are allocated to counties proportionate to their share of the total state population. State Police data for Vermont that were not reported within a county and the State Police data for Alaska are not allocated to the counties. These two State Police records are identified by the county code 999.

The original data from the FBI contains one record for New York City. Data from New York City are allocated into New York City's five counties on the basis of the proportion of the population in each county. For example, the population for Queens county is divided by the total population of New York City and the resulting proportion is multiplied with data from each of New York City's arrest and offense categories to apportion data to Queens county.

Variables

The three arrest parts contain identical variables: FIPS state and county codes, total county population of agencies reporting arrest data, number of agencies in the county, multi-county jurisdiction flag, coverage indicator, total number of arrests, total number arrests for Part I (index) crimes, total number of arrests for violent crimes, total number of arrests for property crimes, and arrest counts for each Part I and Part II crime.

The crimes reported part contains FIPS state and county codes, state population, total county population of agencies reporting arrests, county population of agencies reporting crimes, number of agencies in county reporting arrests, total number of agencies in county reporting crimes, coverage indicator, total crimes reported for Part I crimes not including arson (index), total crimes reported for Part I crimes including arson (modified index), and counts of crimes reported for each Part I crime.

Data Collection Notes

Users who are interested in the alogorithm used to prepare the county-level UCR data for the years 1977 to 1993 are encouraged to refer to the codebooks for the annual county-level data for a full description.

COMPARING "CRIME IN THE UNITED STATES" TO ICPSR County-Level UCR Data
"Crime in the United States" is a publication prepared by the Federal Bureau of Investigation and provides estimations of national reported crime activity and arrest statistics from law enforcement agencies in the UCR Program. Users of this data collection prepared by ICPSR may not be able to match the statistics presented in "Crime in the United States" due to several factors:

The UCR staff continue to update agency records when additions or corrections are received by the UCR Program. The FBI statistics presented in "Crime in the United States" are based on the data that the FBI has received prior to their established publication deadlines. The data used by ICPSR to prepare the UCR County-Level data may contain additions or corrections to the data submitted to the FBI after their publication deadline for producing "Crime in the United States."
The new imputation algorithm that ICPSR implemented in 1994 approximates the estimation procedures that the FBI uses to adjust for incomplete or unreported data from individual law enforcement jurisdictions. Either limited or no crime or arrest data were available for some states in some years because these states are in the process of converting to the National Incident-Based Reporting System (NIBRS). For some tables in "Crime in the United States," the FBI provided estimated crime counts and arrest totals for each of these states. The ICPSR algorithm estimates data for agencies reporting less than three months of data by calculating rates from agencies reporting 12 months of data located in the agency's geographic stratum within that state. Since data for these states were very limited or not present in the original data, estimates for the entire state could not be produced by ICPSR.
Some tables in "Crime in the United States" were prepared using data only from agencies submitting complete reports for the 12 months for the year prior to the publication deadline. For ICPSR data collections, all law enforcement agency records present in the original FBI data were used in the aggregation to the county level. County records with a coverage indicator value of 100 contain only 12-month reporting agencies (see PDF codebooks for possible exceptions.) Agencies reporting 12 months of data within a county with a coverage indicator value less than 100 cannot be separately identified in this dataset aggregated at the county level.

See a list of County-Level UCR data available from NACJD.

Online Analysis of UCR Data

Uniform Crime Reports County-Level Arrest and Offense Data from 1994 through 2001 (ICPSR 6669, 6850, 2389, 2764, 2910, 3167, 3451, 3721) are available for online analysis using NACJD's Online Survey Documentation and Analysis. Users are strongly encouraged to read the description of the county-level UCR Data before performing online analyses.

With the Online Survey Documentation and Analysis, users can perform online analyses on All Ages Arrest Data, Adult Arrest Variables, Juvenile Arrest Variables, and Crimes Reported Variables. Online data analysis is recommended for users who would like to search for variables of interest in a dataset, review frequencies or summary statistics of key variables to determine what further analyses are appropriate, review frequencies or summary statistics for missing data, produce simple summary statistics for reports, create statistical tables from raw data, and those who would like to create a subset of cases or variables from a particularly large colection to save time in downloading and space on a personal computer. Please see the Online Survey Documentation and Analysis for more information.

Related Resources

Related Official Statistics Data

Sites

The NIBRS Resource Guide
http://www.icpsr.umich.edu/NACJD/NIBRS/

FBI
http://www.fbi.gov/

Homicide Working Group
http://homicideworkinggroup.cos.ucf.edu/

Publications

The link below will search the ICPSR citations database for citations of publications with "Uniform Crime Report" in the title. Users can create their own searches or browse the citations database through our Publications Bibliography Web page.

Search for UCR Publications

Survey of Inmates in State and Federal Correctional Facilities Resource Guide

2010-10-27T12:58:00.008-04:00

About Survey of Inmates in State and Federal Correctional Facilities
Using the Survey of Inmates in Correctional Facilities Resource Guide
About the Data
File Structure
Estimation Procedures
Accuracy of Estimates
Online Analysis of Corrections Data
Other Survey of Inmates in State and Federal Correctional Facilities Resources
Download Survey of Inmates in State and Federal Correctional Facilities Data

Survey of Inmates in State and Federal Correctional Facilities

Designed by the Bureau of Justice Statistics and conducted by the Bureau of the Census, these surveys are part of a series of data gathering efforts undertaken to assist policymakers in assessing and remedying deficiencies in the nation's correctional institutions. The surveys gathered extensive information on demographic, socioeconomic, and criminal history characteristics. Also obtained were details of inmates' military service records such as time of service and branch of service, eligibility for benefits, type of discharge, and contact with veterans' groups. Other variables include age, ethnicity, education, gun possession and use, lifetime drug use and alcohol use and treatment, prior incarceration record, and prearrest annual income. Data on characteristics of victims and on prison activities, programs and services are provided as well.

Using the Resource Guide

NACJD, a part of the Inter-University Consortium for Political and Social Research (ICPSR) at the University of Michigan, designed this Resource Guide for World Wide Web users to learn about the Survey of Inmates in State and Federal Correctional Facilities dataset and to connect to other related information sources.

With this guide, first time users or experienced analysts can:

Find general information about Survey of Inmates in State and Federal Correctional Facilities.
Connect to other Survey of Inmates in State and Federal Correctional Facilities - related sites.
View studies in the series.

About the Data

The Survey of Inmates in State and Federal Correctional Facilities is comprised of two distinct surveys. Both surveys used the same data collection instument, and data files resulting from the combination of the two have the same variables and record layout. The Survey of Inmates in State Correctional Facilities (SISCF) was conducted for the Bureau of Justice Statistics (BJS) by the Bureau of the Census. The Survey of Inmates in Federal Correctional Facilities (SIFCF) was also conducted for the BJS and the Federal Bureau of Prisons (BOP) by the Bureau of the Census. These surveys provide nationally representative data on State prison inmates and sentenced Federal inmates held in Federally owned and operated facilities. Through personal interviews from June through October of the survey year, inmates in both State and Federal prisons provided information about their current offense and sentence, criminal history, family background and personal characteristics, prior drug and alcohol use and treatment programs, gun possession and use, and prison activities, programs and services. Surveys of State prison inmates have been conducted in 1974, 1979, 1986, 1991 and 1997. Sentenced Federal prison inmates were first interviewed in the 1991 survey. Beginning in the year 1997, data collected for the State and Federal surveys were combined into one file.

File Structure

The dataset is comprised of two raw data files, a machine-readable codebook, data collection instruments, and SPSS and SAS data definition statements. The first data file, Numeric data, includes the majority of the responses to the questionnaire items. The second data file, Alphanumeric data, includes all of the literal responses including the "Other-specify" question responses.

Estimation Procedures

The estimation procedures for the SISCF and SIFCF involved weighting the responses from the sampled, interviewed inmates to produce estimates with some calculable degree of sampling error. A series of adjustment factors were applied to the basic weight of each interviewed inmate. Weights for Federal and State inmates were calculated separately.

Basic weight (BW).

The initial weight, or basic weight, for each sampled inmate is the inverse of the probability of selection. This weight changes every year; for the basic weight for a specific year, see the codebook corresponding to that year
Drug Subsampling Factor (DSSF)

The Drug Subsampling Factor was calculated for the SIFCF only. To compensate for subsampling drug offenders by taking only a third of those originally selected, in this adjustment drug offenders were multiplied by 3 and nondrug offenders by 1.
Weighting Control Factor (WCF)

In some prisons, the sampling rate for a facility was adjusted because the actual number of persons in a prison on the sampling date was different from the expected number from earlier Census of State and Federal Correctional Facilities reports or lists from the BOP. When the actual number was less than 80% or more than 120% of the expected number, the weighting control factor was applied to account for adjusting the inmate sampling rate. The weighting control factor is equal to the number of inmates in a facility on the interview date divided by the number expected for that facility. If the expected number was within 20% of the expected number, the weighting control factor was 1.
Duplication Control Factor (DCF)

Several of the very smallest prisons have a total inmate population that is smaller than the number to be sampled in each facility in a particular stratum. For example, if a sample prison contained 15 inmates in a stratum in which 55 were expected to be interviewed, there would be a shortage of inmates. The DCF is used to adjust for the workload shortfall in such prisons. It is equal to the expected number of sample inmates in each facility in a stratum divided by the number of inmates in the prison on the date of the sample. In most prisons, the calculated DCF is less than one because the prison had more total inmates than the expected number in the sample for that stratum; in this case the DCF is set to 1.
Noninterview Factor (NIF)

This factor was applied to adjust the weights to account for noninterviewed inmates. The NIF was calculated as follows:
- Basic demographic data on noninterviewed inmates were obtained by interviewers from prison records after they completed interviewing in a facility for the SISCF or from BOP for SIFCF.
- Inmate records, including noninterviewed inmates, were separated by gender, stratum, race (Black, nonBlack), and age.
- If there were fewer than 30 unweighted cases in a cell, it was collapsed with those in the nearest age category.
- For each cell, the adjusted weights (BW x WCF x DCF) were summed separately for interviewed inmates (I) and for noninterviewed inmates (N).
- A noninterview adjustment factor was calculated for each cell as the sum of the adjusted weights (BW x WCF x DCF) for both interviewed and noninterviewed inmates divided by the adjusted weights for the interviewed, or NIF=(I + N)/I.
Offense Category Ratio Adjustment Factor (OCRAF)

The OCRAF was used to adjust the weighted sample to reflect varying interview rates among inmates in different offense categories. The OCRAF was computed separately for males and females for a number of different offense categories for State inmates and offense categories for Federal inmates. It was calculated as the weighted count of interview and noninterview thru the DCF divided by the weighted count for each stratum through application of the NIF.
Control Count Ratio Adjustment Factor (CCRAF)

CCRAF adjusts the weighted interviews by stratum level counts as of some specific date; this date varies by year. For the date specific to some collection year, see the codebook corresponding to that collection year. For the SISCF these counts were from the National Prisoners Statistics series (NPS-1A). For the SIFCF, the BOP provided counts of sentenced Federal prisoners as of some date (see codebook.)

Thus the final weight (FW) is the product of the basic weight and all the adjustment factors.

For the SISCF:

FW = BW x WCF x DCF x NIF x OCRAF x CCRAF

For the SIFCF:

FW = BW x DSSF x WCF x DCF x NIF x OCRAF x CCRAF

Accuracy of Estimates

Since the SISCF and SIFCF estimates come from a sample, they may differ from figures from a complete census using the same questionnaire, instructions, and enumerators. A sample survey has two possible types of errors: sampling and nonsampling. The accuracy of an estimate depends on both types of errors, but the full extent of the nonsampling error is unknown. Consequently, one should be particularly careful when interpreting results based on a relatively small number of cases or small differences between estimates. The standard errors for SISCF and SIFCF estimates primarily indicate the magnitude of sampling error. They also partially measure the effect of some nonsampling errors in responses and enumeration, but do not measure systematic biases in the data. (Bias is the average over all possible samples of the differences between the sample estimates and the desired value.)

Nonsampling variability

There are several sources of nonsampling errors, including the following:
- Inability to obtain information about all cases in the sample
- Definition difficulties
- Differences in the interpretation of questions
- Respondents' inability or unwillingness to provide correct information
- Respondents' inability to recall information
- Errors made in data collection such as in recording or coding the data
- Errors made in processing the data
- Errors made in estimating values for missing data
- Failure to represent all units within the sample
Nonresponse

Nonresponse in the SISCF and SIFCF resulted from failing to obtain cooperation with sample prisons (first stage nonresponse) or failing to obtain completed interviews with sampled inmates (second stage). In the weighting of the sample, the NIF adjusted the weights for second stage nonresponse. The NIF was calculated based on gender, race, age and stratum. However, biases exist in the estimates to the extent that noninterviewed inmates have different characteristics from those of interviewed inmates in the same age-gender-ethnicity-stratum group. Total nonresponse for each survey includes both first and second stage nonresponse.
Comparability of data

Data obtained from the SISCF and SIFCF are not entirely comparable with data from other sources. This is due to differences in interviewer training and experience and in differing survey processes. This is an example of nonsampling variability not reflected in the standard errors. Caution should be used when comparing results from different sources.

Note on results based upon a small number of cases or small differences in estimates: When summary measures (such as medians and percent distributions) are computed on a base smaller than 5,000 for the SISCF and 1,000 for SIFCF, they probably do not reveal useful information because of the large standard errors involved. In addition, nonsampling errors may result in small differences which may appear to be borderline significant, but are not really different.
Sampling variability

Sampling variability is variation that occurred by chance because a sample was surveyed rather than the entire population. Standard errors are primarily measures of sampling variability, although they may include some nonsampling error. They are measures of the variations that occur by chance because a sample rather than the entire population was surveyed. The sample estimate and its standard error enable one to construct a confidence interval, a range that would include the average result for all possible samples with a known probability. A particular confidence interval may or may not contain the average estimate derived from all possible samples. However, one can say with specified confidence that the interval includes the average estimate calculated from all possible samples. Standard errors may also be used to perform hypothesis testing.
Generalized variance estimates

A number of approximations are required to derive, at a moderate cost, standard errors applicable to estimates from these two surveys. Instead of providing an individual standard error for each estimate, two parameters, a and b, are provided to calculate standard errors for each type of characteristic. For more information, please see the codebook specific to your data.

Variances were calculated using Vplex, a Bureau of the Census software package designed to calculate variances for data derived from multistage complex sample designs. Variances were calculated for the total sample and for gender, marital status and race/ethnicity subgroups (male or female, and black, nonblack, or Hispanic and married or single). Variables for which variances were estimated included criminal justice status, prior sentence to incarceration, prior sentence to probation, current offense (murder or manslaughter, sexual offense, assault, robbery, other violent, drug offense), marital status, ever used marijuana, ever used cocaine or crack, alcohol use, armed during crime, HIV status, military service, one or more victims, education, age (not used for SIFCF), monthly income prior to arrest, whether physically or sexually abused, family member ever in prison, employment status at arrest, sentenced status, whether under the influence of drugs at time of arrest, whether under the influence of alcohol at time of arrest, whether maximum sentence was less or more than 5 years, whether a disability, whether had children, whether received help for a mental or emotional problem, who lived with growing up. These variances were calculated for the general form
σ = ax^2+bx

The variances were then transformed logrythmically and plotted in a regression in several iterations, excluding outliers until a best fit was obtained. Hence, the a values are the intercept and the b values the slope of the line.

Tests may be performed at various levels of significance. A significance level is the probability of concluding that the characteristics are different when, in fact, they are the same. To conclude that two parameters are different at the .05 level of significance, for example, the absolute value of the estimated difference between characteristics must be greater than or equal to 1.96 times the standard error of the difference.

More detailed information on standard errors can be found in the codebook.
Specific variance estimates

Standard error estimates for specific variables can be derived using software packages developed to generate standard errors for data obtained from a complex sample survey design. Variables have been added to the data files to be used in running such software packages. Variables v2068 through v2079 give sample and universe information: v2068 and v2069 give total numbers of males and females in the universe; v2070 and v2071, total numbers of males and females within stratum; v2074 and v2075, total numbers of males and females within each sampled prison; V2076 and v2077, the number of males and females interviewed within each sampled prison; and v2078 and v2079, the total number of prisons in the universe within each stratum. In addition, v2048 gives the stratum, v2050 and v2051, the male and female population according to the universe file, and v2047 the number of inmates sampled.

Using the Online Survey Documentation and Analysis

The online Survey Documentation and Analysis is a set of programs for the documentation and web-based analysis of survey data. It is recommended for users who would like to do the following:

search for variables of interest in a dataset
review frequencies or sumary statistics or key variables to determine what further analyses are appropriate
review frequencies or summary statistics for missing data
produce simple summary statistics for reports
create statistical tables from raw data
create custom subsets of cases or variables from a particularly large collection to save time in downloading or space on a personal computer

Other Survey of Inmates in State and Federal Correctional Facilities Resources

Web Sites

Bureau of Justice Statistics - Corrections Statistics
http://bjs.ojp.usdoj.gov/index.cfm?ty=tp&tid=11

The Survey of Inmates in State Correctional Facilities and the Survey of Inmates in Federal Correctional Facilities
http://bjs.ojp.usdoj.gov/index.cfm?ty=tp&tid=13

Publications

The link below will search the ICPSR citations database for citations of publications with "inmate" in the title. Users can create their own searches or browse the citations database through our Publications Bibliography web page.

Search for Survey of Inmates Publications

National Juvenile Corrections Data Resource Guide

2010-10-27T12:55:00.010-04:00

Summary

The Office of Juvenile Justice and Delinquency Prevention sponsored three series of national juvenile corrections data collections: Census of Juveniles in Residential Placement (CJRP), Juvenile Residential Facility Census (JRFC), and the predecessor to the CJRP series, Children in Custody (CIC).

Census of Juveniles in Residential Placement. The CJRP was administered for the first time in 1997 by the U.S. Bureau of the Census. CJRP replaced the Children in Custody series. CJRP provides the nation with the most detailed picture of juveniles in custody ever produced. The CJRP differs fundamentally from CIC: CIC collected aggregate data on juveniles held in each facility (e.g., number of juveniles in the facility); CJRP collects an individual record on each juvenile held in the residential facility. CJRP data for 1997, 1999, 2001, 2003, and 2006 will be available from NACJD. Access to the form for each year is provided below.

Juvenile Residential Facility Census. The JRFC collects basic information on facility characteristics, including size, structure, security arrangements, and ownership. JRFC also collects information on the health care, education, substance abuse treatment, and mental health treatment provided to youth in these facilities. Congress requires OJJDP to report annually on the number of deaths of juveniles in custody – JRFC collects information on such deaths for the one-year period just prior to the census reference date. JRFC data for 2000, 2002, and 2004 will be available from NACJD. Access to the questionnaire form for each year is provided below.

Children in Custody. The CIC data collection, titled "Census of Public and Private Juvenile Detention, Correctional, and Shelter Facilities" at NACJD, had been conducted since the early 1970s. CIC collected aggregate data on juveniles held in each facility (e.g., number of juveniles in the facility). NACJD holds data from public facilities for 1971 to 1983 and public and private facilities for 1985 to 1995. Access to the form for each year is provided below.

Census of Juveniles in Residential Placement

Purpose

The Census of Juveniles in Residential Placement (CJRP) was administered for the first time in 1997 by the U.S. Bureau of the Census for the Office of Juvenile Justice and Delinquency Prevention (OJJDP). CJRP replaced the Census of Public and Private Juvenile Detention, Correctional, and Shelter Facilities, also known as the Children in Custody (CIC) census, which had been conducted since the early 1970s.

The CJRP, which is conducted biennially, provides the nation with the most detailed picture of juveniles in custody ever produced. The CJRP asks juvenile residential custody facilities in the U.S. to describe each youth assigned a bed in the facility on the last Wednesday in October. Characteristics of the facility, treatment services, and facility population are also collected.

Sampling

The CJRP facility inclusion criteria are as follows:

residential facilities in operation on the census reference date
public or private (or tribal since 1999) operation
intended for juvenile offenders (although some hold adults as well)

Specifically excluded are: nonresidential facilities; detention centers operated as part of adult jails; facilities exclusively for drug or mental health treatment or for abused or neglected children; foster homes; and federal correctional facilities (e.g., Immigration and Naturalization Service, Bureau of Indian Affairs, U.S. Marshalls, or Bureau of Prisons).

Inclusion criteria for individual-level data are as follows:

youth under age 21
assigned a bed in a residential facility at the end of the day on the census reference day
charged with an offense or court-adjudicated for an offense
and in residential placement because of that offense

Data Collection Procedures

In late September, the Census Bureau mails out a notification letter to all identified facilities indicating that the CJRP data request forms will soon arrive in the mail. The letter also indicates the reference date for the census and the type of information that will be requested. Respondents are given a contact number for the Census Bureau in case they have questions or problems.

In mid-October, the Census Bureau mails data requests to respondents representing nearly 4,000 public, private, and tribal residential juvenile facilities. Some state and regional agencies provide CJRP data for more than one facility under their jurisdiction. Data are to be returned by the end of November. In early January, facilities that have not yet responded are sent a reminder notice and asked to send a completed response by the end of January. Census staff begin telephone calls to the facilities and central reports that had not responded by that time. The Census Bureau closes out data collection in mid-July. Processing of the data, including error checks, imputation, and editing, continues until the following September.

Data are received and prepared for analysis at the Census Bureau facility in Jeffersonville, Indiana. Respondent questions are fielded by the Government Division of the Census Bureau.

Periodicity

The CJRP is administered every other year, in odd-numbered years. The census reference date is the last Wednesday in October, except for the 2006 CJRP, for which the reference date was the last Wednesday in February.

Access to Data and Forms

1997: ICPSR 4673
1997 CJ-14 form (PDF 223K)

1999: ICPSR 4674
1999 CJ-14 form (PDF 316K)

2001: ICPSR 4670
2001 CJ-14 form (PDF 121K)

2003: ICPSR 23480
2003 CJ-14 form (PDF 598K)

Other CJRP Resources

Easy Access to the Census of Juveniles in Residential Placement provides a web-based data analysis tool to facilitate creating tables at the national level based on several characteristics of youth held in residential placement facilities in the CJRP data.

The National Center for Juvenile Justice (NCJJ) also provides links to several juvenile justice resources, including Quick Links to State Juvenile Justice Profiles and the Statistical Briefing Book.

Juvenile Residential Facility Census (JRFC)

Purpose

JRFC collects basic information on facility characteristics, including size, structure, security arrangements, and ownership. It also provides information on the use of bedspace in the facility to indicate whether the facility is experiencing crowding. The JRFC includes questions about the type of facility, such as detention center, training school, ranch, or group home. This information is complemented by a series of questions about other residential services provided by the facility, such as independent living, foster care, or other arrangements.

JRFC also collects information on the health care, education, substance abuse treatment, and mental health treatment provided to youth in these facilities. While not evaluating the effectiveness or quality of these services, the JRFC gathers important information about the youth the services are directed toward and how the services are provided. The census indicates the use of screenings or tests conducted to determine counseling, education, health, or substance abuse needs, and also examines prominent issues about conditions of confinement, including the restraint of youth and improper absences from the facility. Congress requires OJJDP to report annually on the number of deaths of juveniles in custody; JRFC collects information on such deaths for the one-year period just prior to the census reference date.

Sampling

The inclusion criteria for facilities in the census were:

the facility must house persons under the age of 21
who were charged with or adjudicated for an offense, and
were present in the facility on the reference date because of that offense.

JRFC does not capture data on adult prisons or jails, nor does it include facilities that are used exclusively for mental health or substance abuse treatment or for dependent children. The facility contact list is not publicly available.

Data Collection Procedures

The JRFC data are collected by mail canvass operations. Census Bureau project staff conduct all phases of data processing, corrections and tabulations. Data are provided by a representative of the juvenile residential facility who completes the JFRC questionnaire.

Periodicity

The JRFC is administered every other year, in even-numbered years. The census reference date is the last Wednesday in October.

Access to Data and Forms

2000: ICPSR 4672
2000 CJ-15 form (PDF 189K)

2002: ICPSR 23520
2002 CJ-15 form (PDF 135K)

2004: ICPSR 25282
2004 CJ-15 form (PDF 166K)

2006: ICPSR 25981
2006 CJ-15 form (PDF 116K)

Census of Children in Custody

Purpose

The Children in Custody (CIC) census, administered in odd years from 1971 to 1995, provided information on the number and type of juveniles placed in private, state, and local residential facilities for juvenile offenders across the 50 states and the District of Columbia, and related statistics, such as average length of stay, degree of security, capacity, detention statistics, age ranges, admissions and releases, type of offense, and other facility characteristics such as annual expenditures. The CIC was a complete census of all known juvenile correctional facilities.

The original census occurred in 1971 and supplanted the Survey of Public Institutions for Delinquent Children conducted by the Department of Health, Education, and Welfare. The CIC facility classification system was based on responses to the questionnaire, which asked the respondent to mark the type of facility most applicable. Multifunction facilities, such as training schools with detention or reception centers were classified according to the function having the largest capacity or resident population. This system was used throughout the CIC series.

The first two censuses were restricted to public facilities, but subsequent enumerations were expanded to include privately administered facilities partially in response to the passage of the Juvenile Justice and Delinquency Prevention Act of 1974.

Sampling

All state, county, and city operated public juvenile facilities were included in the census. A facility was classified as private if it was staffed by non-public employees.

Residential programs and group homes in operation on the census reference date were included in the census if:

they housed three or more residents;
at least 50% of the residents were juveniles; and
accused or adjudicated delinquents and status offenders were at least 1% of their average daily population.

In California, however, all California Youth Authority facilities were included in the census.

The following types of facilities were included in the census:

Detention Center
Shelter
Reception or Diagnostic Center
Training School
Ranch, Camp, and Farm
Halfway House and Group Home

Facilities that were specifically excluded from the sample:

Juvenile facilities operated as a part of a local jail, but lacking a separate staff or budget
Non-residential facilities
Facilities operated by federal authorities
Foster homes with fewer than 3 juveniles
Facilities operated exclusively for one of the following:
Drug abuse
Alcoholism
Abused children
Emotionally disturbed/mentally retarded individuals
Unwed mothers
Other nonoffenders
Adults

Data Collection Procedures

The Census Bureau developed the mailing list for public facilities by using various sources throughout the years. Next, the Census Bureau sent to each State's department having responsibility for juvenile corrections a listing of their facilities along with a letter requesting that they verify and update the listing. The Census Bureau also consulted other sources such as annual reports, state criminal justice directories, and personal contacts.

For private facilities, the Census Bureau sent the listing to various local juvenile courts and social service placement agencies asking them to verify and update the list that they use to place juvenile offenders. State juvenile correctional departments were also asked to do the same.

Two questionnaires were used to collect the census data. The CJ-17 was mailed to public facilities and the CJ-29 to private facilities. The CJ-17 form included several items not asked on the CJ-29 form. These additional items asked public facilities about renovation plans, age of the facility, movement by type of population, and any court order for the facility. For a portion of the private facility nonrespondents, a number of data elements were imputed.

Periodicity

The CIC was administered every other year, in odd-numbered years.

CIC data were not collected in 1981. In 1981, OJJDP sponsored the National Survey of Residential Group Care Facilities for Children and Youth, 1981 (ICPSR 6229).

Access to Data and Forms

1971: ICPSR 7637
description and data | 1971 CJ-17 form (PDF 2.7M)

1973: ICPSR 7639
description and data | 1973 CJ-17 form (PDF 3.8M)

1974: ICPSR 7706
description and data | 1974 CJ-17 form (PDF 3.7M)

1975: ICPSR 7707
description and data | 1975 CJ-17 form (PDF 3.8M)

1977: ICPSR 7758
description and data | 1977 CJ-17 form (PDF 3.9M)

1979: ICPSR 7846
description and data | 1979 CJ-17 form (PDF 3.0M)

1983: ICPSR 8205
description and data | 1983 CJ-17 form (PDF 599K)

1985: ICPSR 8495
description and data | 1985 CJ-17 form (PDF 510K) | 1985 CJ-29 form (PDF 500K)

1991: ICPSR 9824
description and data | 1991 CJ-17 form (PDF 854K) | 1991 CJ-29 form (PDF 869K)

1993: ICPSR 6491
description and data | 1993 CJ-17 form (PDF 24.5M)

1995: ICPSR 24260
description and data | 1995 CJ-17 form (PDF 24.7M) | 1995 CJ-29 form (PDF 33.8M)

National Corrections Reporting Program Resource Guide

2010-10-27T12:52:00.010-04:00

About the National Corrections Reporting Program (NCRP)
Using the NCRP Resource Guide
File Structure
NCRP Concepts and Definitions
Online Analysis of NCRP Data
Other National Corrections Reporting Program Resources
Download NCRP Data

About the National Corrections Reporting Program (NCRP)

The National Corrections Reporting Program (NCRP) was conducted by the Bureau of Justice Statistics to provide a consistent and comprehensive description of convicted persons' entrance into and departure from correctional custody and correctional supervision. To accomplish this goal, data were gathered from official state prison records on topics such as race, sex, and age of inmates, length of time in jail, length of time in prison, and type of offense committed. The data were collected from the state prison systems of most of the United States, as well as the Federal Prison System, the California Youth Authority, and the District of Columbia.

The National Prisoner Statistics (NPS) program was established in 1926 by the Bureau of the Census in response to a congressional mandate to compile national information on the populations confined in correctional institutions. This program described the characteristics and counts of prison inmates during each calendar year. Since its initiation, responsibility for this program has shifted among several agencies--in 1950 it was transferred to the Federal Bureau of Prisons and to the Law Enforcement Assistance Administration in 1971. Since 1972, the Bureau of Census, under agreement with the Department of Justice, has had responsibility for compiling the statistical data. Census staff negotiates directly with each state, assembles and edits the data, and prepares the data for analysis and publication.

In 1966 the National Council on Crime and Delinquency (NCCD), with funds from the National Institute of Mental Health, implemented the Uniform Parole Reports (UPR) as an experimental project. At that time, UPR was limited to gathering parole outcome data from selected state parole boards but was eventually expanded into an ongoing program collecting statistics which describe parole populations nationwide. Funding by the National Criminal Justice Information and Statistics Service (renamed the BJS in 1980) has been in effect since 1973. Since 1983, the Bureau of the Census has had responsibility for collecting parole data.

In 1983 the National Prisoner Statistics program on admissions and releases (NPS) and the Uniform Parole Reports (UPR) were combined into one reporting system, the National Corrections Reporting Program (NCRP). The NCRP evolved from the need to improve and consolidate data on corrections at the national-level. Its objective is to provide a consistent and comprehensive description of prisoners entering and leaving the custody or supervision of state and federal authorities.

The NCRP is sponsored by the Bureau of Justice Statistics (BJS), U.S. Department of Justice. The United States Bureau of the Census acts as the data collection agent. The National Corrections Reporting Program is a calendar year report covering the dates January 1 through December 31. Not all states report every year. A list of the participating states can be found in variable V94, "State identifier." Data refer only to those prisoners admitted to prison, released from prison, or released from parole.

Using the Resource Guide

The National Archive of Criminal Justice Data (NACJD), a part of the Inter-University Consortium for Political and Social Research (ICPSR) at the University of Michigan, designed this Resource Guide for World Wide Web users to learn about the National Corrections Reporting Program dataset and to connect to other corrections information sources.

With this guide, first time users or experienced analysts can:

Find general information about the National Corrections Reporting Program.
Connect to other National Corrections Reporting Program - related sites.
Download all of the National Corrections Reporting Program data files, codebook, and SPSS and SAS data definition statement files.

File Structure

National Corrections Reporting Program data are available from the ICPSR in a rectangular format.

There are three parts in the National Corrections Reporting Program. Part 1 contains Prison Admissions data; Part 2 contains Prison Release data, and Part 3 contains Parole Release data. ICPSR has also prepared SPSS and SAS data definition statements for the data file. The SPSS data definition statements file contains the following sections: data list, variable labels value labels, missing value and missing value recode statements. The SAS data definition file contains these sections: proc format, input, label, format, and missing value recode statements.

NCRP Concepts and Definitions

The NCRP applied uniform measurement rules to the data from the states, using the following concepts and definitions.

Prison

A prison was defined as a state or federal correctional facility having custodial authority over persons sentenced to confinement.

Calendar Year Reporting

NCRP collected data for the total number of admissions to prisons, releases from prison and releases from parole for the dates January 1 through December 31. Data were not collected on a fiscal year basis. Records were rejected on individuals if the year of prison admission, prison release or parole release was not reported.

Custody Criteria for Prison Admissions and Releases

The NCRP collected data on all prisoners admitted or released while under the physical custody of state correctional authorities. The NCRP data included prisoners under the immediate control of state authorities, regardless of the jurisdiction in which the prisoners were originally sentenced.

Starting in 1983, all sentenced inmates were counted including those with sentences of a year or less. Prisoners sentenced to a state prison, but admitted to or released from the custody of a local jail, were also included in the NCRP.

Jurisdiction Criteria for Parole Releases

Parole release information was reported for those offenders under the jurisdiction of the paroling authority. Jurisdiction was determined by the legal authority controlling the offender's release from parole supervision, rather than by the authority assuming physical custody of the offender. Parole jurisdiction was defined, for NCRP purposes, as that agency having primary responsibility for supervising of an offender who was conditionally released from prison after having served a portion of the original sentence. The paroling agency has jurisdiction over an offender if it has the legal power to revoke the parole or to decide when parole supervision is to be terminated.

Prisoner Movements

The NCRP included prisoner movements that increased or decreased the custody counts of each reporting state. Additions to the custody count, such as the arrival of new inmates, the return to prison of parole violators, and transfers from other jurisdictions, were classified as admission movements. Removals from custody, such as the release of those completing their sentences, the release to parole and death, were considered release movements. Multiple admissions or releases per person during the year were recorded as separate movements.

Parole and Mandatory Parole Release

Parole signifies the status of an offender who is conditionally released from prison to community supervision. An offender is required to observe the conditions of parole and is under the supervision of a parole agency. Parole differs from probation: unlike parole, probation is determined by judicial authority and is usually an alternative to confinement. Offenders conditionally released from prison to parole are classified in the NCRP as parole admissions movements.

The NCRP also includes mandatory parole release, i.e., those persons released from prison to parole supervision by virtue of statutes that determine the length of time prisoners are incarcerated. Unlike other prisoners released to parole these prisoners were not released as a result of a parole board decision. Offenders released from the jurisdiction of a parole authority were classified as parole release movements. Types of release movements included completion of parole, revocation, absconding, transfer and death.

Online Analysis of NCRP Data

National Corrections Reporting Program Data from 1993 are available for online analysis using NACJD's Online Survey Documentation and Analysis.

With the Online Survey Documentation and Analysis, users can perform online analyses of Prison Admissions Data, of Prison Releases Data, and of Parole Releases Data. Online data analysis is recommended for users who would like to search for variables of interest in a dataset, review frequencies or summary statistics of key variables to determine what further analyses are appropriate, review frequencies or summary statistics for missing data, produce simple summary statistics for reports, create statistical tables from raw data, and those who would like to create a subset of cases or variables from a particularly large colection to save time in downloading and space on a personal computer. Please see the Online Survey Documentation and Analysis for more information.

Other National Corrections Reporting Program Resources

Sites

Bureau of Justice Statistics: Corrections Statistics
http://bjs.ojp.usdoj.gov/index.cfm?ty=tp&tid=11

Bureau of Justice Statistics: Courts and Sentencing Statistics
http://bjs.ojp.usdoj.gov/index.cfm?ty=tp&tid=23

Office of Justice Programs: Corrections
http://www.ojp.usdoj.gov/programs/corrections.htm

Sourcebook of Criminal Justice Statistics
http://www.albany.edu/sourcebook/

National Criminal Justice Reference Service: Corrections
http://www.ncjrs.gov/

American Correctional Association
http://www.corrections.com/aca/

Publications

The link below will search the ICPSR citations database for citations of publications with "National Corrections Reporting Program" in the title. Users can create their own searches or browse the citations database through our Publications Bibliography.

NCRP data from 1983 through 1995 are available on CD-ROM from the National Criminal Justice Reference Service.

Search for NCRP Publications

Federal Justice Statistics Program Resource Guide

2010-10-27T12:49:00.013-04:00

About the Federal Justice Statistics Program
Federal Justice Statistics Program Agencies and Data
User Support

About the Federal Justice Statistics Program

The Bureau of Justice Statistics (BJS) provides comprehensive information on suspects and defendants processed in the federal criminal justice system. Federal agencies provide extracts from their case management systems, which cover various stages of criminal case processing:

Arrest - Covers the arrest of a suspect by a federal law enforcement agency.
Prosecution - Covers the investigation of criminal matters.
Adjudication - Covers the criminal court proceedings through case disposition.
Sentencing - Covers sentences imposed on convicted defendants.
Appeals - Covers cases filed and terminated on appeals.
Corrections - Covers offenders in prison.

The Urban Institute, with sponsorship from BJS, uses these extracts to create Standard Analysis Files (SAFs) for each agency. These SAF data files represent annual cohorts of defendant-cases processed by a single agency within the federal criminal justice system.

BJS sponsors both the Urban Institute and the National Archive of Criminal Justice Data (NACJD). The Urban Institute processes the source data and creates recoded variables suitable for analysis, establishes offense categories that are common across SAFs, and removes information that could identify any individual defendant. The defendant-case, which is the standard unit of observation in all SAFs, is a combination of a defendant, which may be either a person or a corporation, and a matter or case. For example, if a single defendant is involved in three different criminal cases during a specified fiscal year, that individual appears three times (i.e., as three records) in a SAF. Similarly, if a single case involves five defendants, that case appears five times (i.e., as five records) in a SAF. Each SAF is one of three types of cohorts - incoming defendant-cases, outgoing (or "terminating") defendant-cases, and defendant-cases pending at the end of the fiscal year. The universe of each SAF spans one federal fiscal year, October 1 through September 30. NACJD archives and distributes the SAFs.

Federal Justice Statistics Program Agencies and Data

Five federal criminal justice agencies contribute data to the Federal Justice Statistics Program. These agencies and the SAFs associated with them are listed below.

United States Marshals Service (USMS)

The Prisoner Tracking System contains data on all suspects who were arrested for federal offenses and booked by the USMS.

Federal Justice Statistics Program: Arrests and Bookings for Federal Offenses, ... [United States]
Years available: 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

Executive Office for United States Attorneys (EOUSA)

The Central System File contains information on the investigation and prosecution of suspects in criminal matters received and concluded by US Attorneys, while the Central Charge File contains information on criminal cases filed and terminated by US Attorneys.

Federal Justice Statistics Program: Defendants Charged in Criminal Cases Filed in District Court, ... [United States]
Years available: 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

Federal Justice Statistics Program: Defendants in Federal Criminal Cases -- Terminated, ... [United States]
Years available: 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

Federal Justice Statistics Program: Charges Filed Against Defendants in Criminal Cases in District Court, ... [United States]
Years available: 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

Federal Justice Statistics Program: Charges Filed Against Defendants in Criminal Cases in District Court -- Terminated, ... [United States]
Years available: 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

Federal Justice Statistics Program: Suspects in Federal Criminal Matters, ... [United States]
Years available: 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

Federal Justice Statistics Program: Suspects in Federal Criminal Matters Concluded, ... [United States]
Years available: 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

Administrative Office of the United States Courts (AOUSC)

The Criminal Master File contains information about the criminal proceedings against defendants whose cases were filed in US district courts. This file includes information on cases involving felonies, Class A misdemeanors, and lesser misdemeanors that were handled by US district judges. The information in the data file covers criminal proceedings from case filing through disposition and sentencing. The AOUSC Courts of Appeals file contains docket information on criminal appeals filed and terminated in the US courts of appeals. Records of appeals filed, pending, or terminated include information on the nature of the criminal appeal, the underlying offense, and the disposition of the appeal.

Federal Justice Statistics Program: Defendants in Federal Criminal Cases Filed in District Court, ... [United States]
Years available: 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

Federal Justice Statistics Program: Defendants in Federal Criminal Cases Filed in District Court -- Terminated, ... [United States]
Years available: 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

Federal Justice Statistics Program: Defendants in Federal Criminal Cases Filed in District Court -- Pending, ... [United States]
Years available: 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

[Note -- Federal Justice Statistics Program: Criminal Appeals Cases Filed in Courts of Appeals, no updates]

Federal Justice Statistics Program: Criminal Appeals Cases Filed in Courts of Appeals, ... [United States]
Years available: 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006

Federal Justice Statistics Program: Criminal Appeals Cases Filed in Courts of Appeals -- Terminated, ... [United States]
Years available: 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2009

United States Sentencing Commission (USSC)

The Monitoring Data Base contains information on criminal defenders sentenced pursuant to the provisions of the Sentencing Reform Act (SRA) of 1984 whose records have been obtained by the USSC.

Federal Justice Statistics Program: Guideline Computations for Defendants Sentenced Under the Sentencing Reform Act, ... [United States]
Years available: 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

Federal Justice Statistics Program: Defendants Sentenced Under the Sentencing Reform Act, ... [United States]
Years available: 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

Federal Justice Statistics Program: Statutes for Counts of Conviction for Defendants Sentenced Under the Sentencing Reform Act, ... [United States]
Years available: 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

Bureau of Prisons (BOP)

This data file contains information on all sentenced offenders admitted into, or released from, federal prison during a fiscal year, as well as offenders in federal prison at the end of each fiscal year.

Federal Justice Statistics Program: Offenders Admitted to Prison, ... [United States]
Years available: 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

Federal Justice Statistics Program: Offenders Released From Prison, ... [United States]
Years available: 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2009

Federal Justice Statistics Program: Offenders in Prison at Year-End, ... [United States]
Years available: 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

Paired-Agency Linked Files

The paired-agency linked files contain matched-agency pairs (or "dyads"), which permit the linking of records from two different source agencies for adjacent stages of federal case processing.

Federal Justice Statistics Program: Paired-Agency Linked Files
Years available: Cumulative 1994-2009

User Support

Questions regarding the Federal Justice Statistics Program should be directed to the Bureau of Justice Statistics: askbjs@ojp.usdoj.gov

Expenditure and Employment Data for the Criminal Justice System Resource Guide

2010-10-27T12:40:00.009-04:00

About Expenditure and Employment Data for the Criminal Justice System
Using the Expenditure and Employment Resource Guide
File Structure
Data Limitations
Methodological Information (Survey Period, Data Collection, Public Employment, Additional Data Review, Data Not Collected Separately, Imputation Procedures for Missing Data)
Constructing Expenditure Totals (Local Government Expenditure Totals, State-Local Government Expenditure Totals)
Constructing Common Criminal Justice Variables
GIS Analysis of Expenditure and Employment Data
Other Expenditure and Employment Resources
Download Expenditure and Employment Data

Expenditure and Employment Data for the Criminal Justice System

These data collections present public expenditure and employment data pertaining to criminal justice activities in the United States. Information on employment, payroll, and expenditures is provided for police, courts, prosecutors' offices, and corrections agencies. Specific variables include identification of each government, number of full- and part-time employees, level of full- and part-time payroll, current expenditures, capital outlay, and intergovernmental expenditures. Two different data collections are included: the Criminal Justice Expenditure and Employment Extracts and the Criminal Justice Expenditure and Employment Survey. The CJEE Extracts have been extracted from the Census Bureau's Annual Government Finance Survey and Annual Survey of Public Employment since 1982. The CJEE Survey collected detailed annual data for 1971 to 1979, and for 1985, 1988, and 1990, but has been discontinued. The CJEE Extracts data are similar to but not statistically comparable to the CJEE Survey data.

Using the Resource Guide

The National Archive of Criminal Justice Data (NACJD), a part of the Inter-University Consortium for Political and Social Research (ICPSR) at the University of Michigan, designed this Resource Guide for World Wide Web users to learn about the Expenditure and Employment Data for the Criminal Justice System dataset and to connect to other capital punishment information sources.

With this guide, first time users or experienced analysts can:

Find general information about Expenditure and Employment Data for the Criminal Justice System.
Connect to other Expenditure and Employment Data for the Criminal Justice System - related sites.
View studies in the series.

File Structure

Expenditure and Employment Data for the Criminal Justice System is available from 1971 through the present from the ICPSR. Files available include CJEE Annual Files from 1971 through 1979; the CJEE Longitudinal File which includes data from 1971-1979, 1985 and 1988; CJEE Individual Units and Estimates File for 1985 and 1988; and CJEE Extracts Files, 1982 through the present, all of which are in logical record length (lrecl) format. SPSS and SAS data definition statements are provided, giving the format and other information for each variable in the lrecl data file. The lrecl data file is constructed with a single logical record for each case.

Data Limitations

Generally, these reflect the fact that the censuses from which this data set was extracted are not designed specifically to obtain data on specific justice functions. The data are subject to possible inaccuracies in classification, response, and processing. Every effort was made to keep such errors to a minimum through care in examining, editing, and tabulating the data submitted by government officials. Follow-up procedures were used extensively to clarify inadequate and inconsistent survey returns.

Readers should be generally cautious in comparing governments. Differences in functional responsibilities from State to State and government to government can affect the comparability of expenditure and employment data. For example, some State governments directly administer certain activities that elsewhere are undertaken by local governments, with or without fiscal aid, and the same variation in the division of responsibilities exists for counties and cities. Numbers reported on the data file may not add to totals due to rounding.

Methodological Information

The Survey Period

Expenditure and employment data in this file cover reporting periods as follows:

Employment data are for March of the collection year after 1995, and for October in all previous years. The U.S. Bureau of the Census changed the reference date for the employment and payroll data from October to March after the 1995 data collection.
Federal Government expenditure data are for the fiscal year, which begins on October 1 of the calendar year prior to the collection year, and ends September 30 of the collection year.
Some agencies operate on a different fiscal year basis from the rest of the parent government. In such instances, figures included are for the agency's fiscal year that ended within the parent government's regular fiscal year.

Data Collection

These data are based on a special compilation of data and sources available from the Census Bureau's censuses of governmental finances and employment. These censuses provide data on expenditure and employment, by function, of Federal, State, and local governments (counties, cities, townships, school districts, and special districts). The data collection procedures for government finance and employment are described below.

Governmental Finances: Federal Government financial data were obtained from "actual" data presented in The Budget of the United States for the Fiscal Year. Certain adjustments were made in Federal data to arrive at Census Bureau "expenditure" amounts. State finance statistics as well as those for large counties and cities were compiled by Census Bureau representatives from official reports and records, with the advice of State and local officers and employees. The figures were classified according to standard census categories for reporting large government finances, and were reviewed intensively. The remaining data were provided either by local officials in response to a mail survey using detailed questionnaires or from central data collection arrangements with State governments. For nonrespondent governmental units data were imputed and the record coded "iiii" in Field 7.

Public Employment

Federal Government civilian employment data were obtained from records maintained by the U.S. Office of Personnel Management. Statistics for State and local governments were obtained by a mail survey. State government data were based on a complete canvass of all State departments, agencies, and institutions. Questionnaires for local governments were generally sent to a local central reporting office, supplemented by special mailings to dependent agencies. For nonrespondent governmental units data were imputed and the record coded "iiii" in Field 8.

For the mail portion of these censuses, figures reported by government officials were generally accepted as correct. In some cases, varying interpretations of the instructions or deficiencies in governmental employment and fiscal records may make it difficult for officials to render complete and accurate reports for their governments. These difficulties are dealt with by (1) careful definition of terms and detailed instructions in difficult cases, (2) supplemental correspondence and telephone follow-up to officials, and (3) intensive examination of data collected, that is, verification of internal consistency and comparison with previous reports and other sources of data. Errors that may be introduced during processing (input preparation, etc.) are minimized through the use of extensive computer editing of the data at various stages of the processing system.

Additional Data Review

In addition to the routine edit procedures for the Census Bureau's annual surveys of government finances and employment, the data extracted from those programs for this file received additional examination. The local government expenditure and employment estimates were reviewed, potential problems investigated, and data revised, where necessary. Data for each of the States and large counties and cities displayed individually in BJS publications (counties over 1,000,000 population and cities over 500,000 population) were scrutinized and compared to the prior CJEE data. Where possible, data were adjusted to correct errors, reclassify activities, and narrow differences with the former CJEE survey series. Specific procedures included referring to alternate sources, estimating missing data, and refining data through peroration (e.g., sheriff offices).

Data Not Collected Separately

Data were not collected separately for some variables for some types of governments. This is because the information was not available from source documents or the function was unusual for the size and type of government. In those instances, the data fields are blanks. When the government had no expenditures or employment of the specified type, the field is zero. For specific justice functions the following are areas where data were not available or collected separately:

Expenditure data are not collected separately for "equipment only" for cities below 500,000 population and counties below 1,000,000. The equipment only data field for units below the population threshold and for balance of county and municipality are blank.

Expenditure data are not collected separately for correctional institutions run by governments below the State level. The correctional institution data field for units below the State level, balance of county, and municipality are blank. Data for correctional institutions run by counties and municipalities have been included in the total correction data field.

Employment data for Federal Government part-time payroll are not available separately; Federal part-time payroll data are included in Federal full-time payroll data.

Imputation Procedures for Estimating Missing Data

For imputation purposes, responding units are grouped within each State by type of government and population, referred to below as "similar units." Each grouping must have at least 15 respondents and a response rate of at least 50%.

Using these groupings of "similar units" two imputation procedures are used to estimate missing data:

For nonresponding governmental units that had previously responded, average growth rates are applied to the prior year imputed or reported data. For employment characteristics average growth rates for full-time and part-time total employees and total payroll, and for total part-time hours are computed using "similar units" that responded in the prior year and in the current year. Likewise, for finance characteristics, growth rates for detailed characteristics are calculated at an aggregate level and used for all details in that aggregate.
For government units that had not responded in a prior year, a "similar unit" is randomly selected and a per capita adjustment is made to its data to account for population differences between the nonrespondent unit and the randomly selected unit.

Field 7 "Finance Year Collected" and Field 8 "Employment Year Collected" are coded "iiii" for units with imputed data.

Constructing State-Local Government Expenditure Totals
and Local Government Expenditure Totals

To avoid including duplicate intergovernmental expenditures in these totals, it is important to observe the following rules (which apply to expenditure data only):

Local Government Expenditure Total

The local government expenditure total is the sum of these records:
County Total
Municipal/Township Total

MINUS any amounts for intergovernmental expenditure to locals, which are shown in these fields:

Function	Field	Record Location
Police protection	60	591-602
Judicial-Legal	66	663-674
Corrections	72	735-746

Thus, at the local level, total expenditure includes intergovernmental payments to the State only.

State-Local Government Expenditure Totals

The State-local government expenditure totals is the sum of these records:
State Government
County Total
Municipal/Township Total

MINUS all amounts for intergovernmental expenditure, which are shown in these fields:

Function	Field	Record Location
Police protection	59	579-590
	60	591-602
Judicial-Legal	65	651-662
	66	663-674
Corrections	71	723-734
	72	735-746

Thus, at the State-local level, total expenditure is equal to direct expenditure (net of all intergovernmental expenditure).

Constructing Common Criminal Justice Variables

There are few totals on the file. The following guidelines address some of the more common criminal justice concepts that can be constructed using the data file.

Total police protection employees = full-time police employees + part-time police employees

Total police protection direct expenditure = current operation + capital outlay (where capital outlay = construction + equipment, land, and existing structures)

Total police protection expenditure = total police direct expenditure + police intergovernmental expenditure to State + police intergovernmental expenditure to locals.

Judicial-Legal and Corrections totals can be constructed in the same fashion.

Total justice expenditure/employment is the sum of police protection, judicial-legal, and corrections.

GIS Analysis of Expenditure and Employment Data

The Expenditure and Employment Data for the Criminal Justice System dataset contains FIPS County Codes as a geographical identifier. These data were not collected for the purpose of mapping or conducting spatial analysis, so users may have to do additional work to make these data compatible with their particular GIS software.

The GIS Resources page at the National Archive of Criminal Justice Data contains tutorials, analysis programs, and other resources, and the Mapping and Analysis for Public Safety (MAPS) Program of the National Institute of Justice has additional mapping tools. General GIS information, data, maps, software publications and links to other resources and Web sites is also available.

Web Sites

Bureau of Justice Statistics: Expenditure and Employment Statistics
http://bjs.ojp.usdoj.gov/index.cfm?ty=tp&tid=5

Publications

The link below will search the ICPSR citations database for citations of publications containing the phrase "justice expenditure." Users can create their own searches or browse the citations database through our Publications Bibliography.

Search for Justice Expenditure and Employment Publications

Homicides in Chicago, 1965-1995 (Chicago Homicide Dataset), Victim-Level Data and Offender-Level Data

2010-10-27T11:29:00.004-04:00

About the Homicides in Chicago Data Set (ICPSR 6399)
Frequently Asked Questions About This Study
Contact Information for Principal Investigator

About the Homicides in Chicago Data Set (ICPSR 6399)

The Chicago Homicide Data Set (CHD), distributed by ICPSR as Homicides in Chicago, 1965-1995 (ICPSR 6399), contains detailed information on every homicide in Chicago police records from 1965 to 1995. To learn more about this study, read the overview of the CHD project (PDF 492K) and the ICPSR study description.

To help users better understand this data collection, Dr. Carolyn Rebecca Block, Dr. Richard L. Block, and the Illinois Criminal Justice Information Authority (ICJIA), the principal investigators for this study, have agreed to post answers to Frequently Asked Questions on our Web site.

Please contact Dr. Block if you have other questions about the study or if you would like further clarification of the answers provided on this Web page. Before submitting new questions, please read all of the questions and answers on this page.

Frequently Asked Questions

Q: What is the difference between the structure of the victim-level data and the offender-level data?

A: The Chicago Homicide Dataset (CHD) consists of two data files, a victim-level file and an offender-level file.

The victim-level data consists of 23,817 records, with one record per victim. For example, one arson homicide incident with ten victims would be represented ten times in the victim-level file. Although offender demographic and relationship variables are included in the victim-level for up to five offenders per victim, you should use the offender-level file to conduct analysis of offenders. There are two reasons why the victim-level file is not accurate for offender-level analysis. 1) For incidents with more than five offenders, information is not included for the additional offenders. 2) For incidents with multiple victims, each offender is double (or triple) counted, depending on the number of victims.

The offender-level data file contains 26,030 records, with one record per offender. It includes a record for each offender known to the police, with no limit on the number of offenders per victim or incident. (The actual maximum in this dataset is 11 offenders.) In addition, there is no "double counting" for incidents with multiple offenders. Each offender appears once per incident. Although victim demographic and relationship information is included for the first victim, you should use the victim-level file to conduct analysis of victims.

Users should be aware that the offender-level CHD contains only homicide incidents in which at least one offender is known to the police. The CHD is periodically updated, and any new information resulting from continuing police investigation is added to both victim-level and offender-level files. However, unsolved homicide incidents will appear in the victim-level file but not in the offender-level file.

In summary, only the offender-level CHD is appropriate for analysis of offender issues, for example, calculating the population-based risk of becoming an offender, or analyzing the proportion of offenders who are juvenile. Only the victim-level CHD is appropriate for analysis of victim issues, for example, calculating the population-based risk of becoming a victim, or analyzing the proportion of victims who are juvenile. It is also possible to use both datasets together because each victim record and each offender record contains identifying numbers that can be used to link one file to the other.

Q: I have downloaded the victim and offender datasets a few times. I seem to be making a mistake because every time I download the offender data it has about 26,000 cases and the victim data always has about 23,000 cases.

A: You have the correct numbers. The number of records differ for several reasons. First, there can be multiple victims per offender, and multiple offenders per victim. Second, for some homicide victims, no offender is ever identified. Therefore, for these victims, there is no offender record in the offender-level CHD file.

Q: It's so complicated to use the victim-level and offender-level files. Why can't I just analyze those homicides that had a single victim and a single offender?

A: If you analyze only single victim/single offender cases, your results will apply only to other single victim/single offender homicides, not to homicides as a whole. If you write a paper that implies that your results apply to all homicides, you would be seriously misleading your audience. A substantial number of homicide incidents have multiple offenders and/or multiple victims, and these homicides are systematically different from single victim/single offender homicides, in aspects such as the offender's age and gender, and the homicide circumstances. Street gang-related homicides and familicides are just two of the many examples. Therefore, in most cases, it would be erroneous to base an analysis on a selection of single victim/single offender homicides.

Q: What homicides are included in the CHD?

A: The CHD contains all murders known to the Chicago police from 1965, with considerable information about each murder. It is based on the Murder Analysis Report (MAR) maintained by the Crime Analysis Unit of the Chicago Police Department (CPD). It includes all homicides known to the police, not only those where an offender is apprehended and convicted, but also homicides that never end in arrest (for example, homicide-suicides), homicides that are later determined to have been justifiable at the prosecutorial or court level, and homicides where the offender is eventually found not guilty. Because the CHD includes murders known to the police, it does not contain cases of concealed death, unless the death was later discovered. However, when such cases are discovered, even years after the death, they are added to the CHD.

Q: Does the CHD include homicides in which the offender commits suicide?

A: Yes. While it is true that suicide is more common among Caucasian (and Latino) males after killing their current or former intimate partner than among African-American male perpetrators, investigators actually are able to rule the death as a homicide and the deceased male as the perpetrator of the homicide. On the other hand, for obvious reasons, correctional datasets of homicide offenders would not include offenders who committed suicide.

Q: What kind of data does the CHD contain on offenders, victims, means of death, and motives?

A:. The CHD is a police-level dataset and reflects information that is important to police investigation. Working closely with the CPD, the principal investigators have added information and detail to the MAR codes, using the narrative. (Note that the narrative does not appear in the archived data.) The CHD provides considerable detail about each homicide. For example, you can separate out elderly victims (defined however you want), and look at weapon, offender's motive, and many other variables. There is also a code for mercy killings and for murder-suicide pacts, and a separate variable for murder-suicide.

Q: Is the CHD updated to reflect newly-discovered information about the homicides?

A: Yes, when the principal investigators add additional years to the CHD, they also review all earlier homicides and edit the data to reflect currently available information. For example, if a homicide has been "cleared" since it was originally coded, the record will be edited to reflect new data about the offender(s) and any updated information about motive or relationship. In addition, new fields were added as the dataset evolved over the years, but these fields were also coded for all the earlier homicides. Almost all codes are consistent back to 1965. A few, for example prior arrest, are not available in 1965 but are coded for all later years. In addition to adding any new information about each homicide, investigators also add new records to the CHD when homicides are discovered at a later date. This may happen after a lengthy investigation (for example, with an arson homicide), when a victim dies months or years after the lethal incident, or when a body is discovered some time after the date of the homicide.

Q: Does the dataset include a Chicago Police Department (CPD) case identification number that could be linked with other data from the CPD?

A: Identifiers have been removed from the archived data. We maintain the original data, but we do not publicly release the data with identifiers.

Q: Does the dataset include detailed information about firearms used in homicides (e.g., make, model, and serial number)?

A: The CHD contains limited information about firearms, such as the type of firearm and caliber. It does not contain serial numbers. For more information, see the weapons variables in the codebook for victim-level data (page 63).

Q: How are the weapon-related data gathered?

A: Weapon in the CHD refers to the cause of death. It is coded on the MAR, and there is often a note in the narrative about the specific cause of death, according to the Medical Examiner of Cook County. Where there was more than one weapon used, the weapon coded is the cause of death. Where there was no weapon, the specific means of death, such as strangulation, "hands and feet" (beating), or "thrown from a height" is coded. In some cases, the cause of death was strangulation or smothering, and the weapon listed is "nylon stocking" or "pillow." There are also specific types of poisoning. For more information, see the CHD codebook.

Q: In the Chicago Homicide Dataset, does the semi-/fully-automatic category for the variable WEAPON (p. 63 of the codebook) contain mostly handguns or long guns (i.e. rifles, shotguns) and is there any way of teasing out which ones are which?

A: If you look at the detailed weapon variables, WEAPCAL and WAUTOMAT, you will see the information we have about the type of automatic or semi-automatic firearm. If you ask someone in your police department or another firearm expert, he or she may be able to tell you the chance that the weapon was a handgun or a rifle, given the available detail. You should also know that we have cleaned the CHD weapon data, and the next archived version will contain some corrections.

Q: What kind of geographic variables does the study contain?

A: The CHD is completely geocoded to address, but for confidentiality reasons, neither the specific address fields nor the X- and Y-coordinates are present in the archived data. Instead, the principal investigators have linked the X- and Y-coordinates to the corresponding Census Tract and Chicago Community Area. In addition, the CPD codes for Police Area and Police District are included from the MAR.

Note that there are relationships among these geographic codes. Generally, the first two digits of the Census Tract are the numeric code for the Chicago Community Area (there are a few exceptions; users should check). Also, the Chicago Police Beat is a four digit number, with the first two digits the Police District (there are multiple beats in a district). In later years, the third digit of the Chicago Police Beat represents the Police Sector (there are multiple beats within a sector). However, there is no relationship between Police Districts, Areas or Beats and Census Tract geography. Note also that there was no change in the Census Tract boundaries in Chicago for the entire period covered by the Chicago Homicide Dataset. Therefore, the tracts are coded consistently throughout. However, the Chicago Police Department did change the boundaries of Police Districts and Areas over time, and added new ones. The incident District and Area is taken from the MAR, and therefore, is not geographically consistent over time. The updated version of the CHD will contain a variable that geocodes each homicide location to the 1993 map of Districts, Areas and Beats (which will, of course, be geographically consistent over time). The Police District and Police Area codes correctly represent the specific Police Department entity that handled the case.

Q: Is there information about social class of victims and offenders in the CHD? If not, are there addresses of victims and offenders, so that we can get neighborhood social class?

A: There is no occupation, income or education information in the CHD, about the victim or the offender. There are, however, codes for Census Tract and Community Area. For more information about demographics in the Chicago Community Areas, see the Community Area Fact Books for 1960, 1970, 1980, 1990, and 2000. Demographics by Census Tract are available from other ICPSR data sets.

Q: Is it possible to identify Latino homicides in the Chicago Homicide dataset?

A: Yes, the racial/ethnic group of all victims and offenders are coded as non-Hispanic black, non-Hispanic white, Latino, and other. In the archived dataset, these codes are consistent from 1965. The original codes in the MAR for some years included different codes, such as codes for specific ethnic groups. Since these codes changed over the years, we have re-coded the racial/ethnic group variables to be consistent over time.

Q: Does the CHD contain day of death data?

A: Yes, there are separate fields for date of injury (year, month, day, day of week, time of day) and date of death (year, month, day). In addition, there is a code for "Book Year," the year that the homicide was recorded in the MAR. In the case of a long investigation, or a homicide discovered well after the incident, the Book Year and the INJYEAR (injury year) will differ.

A: How are the time of death data gathered?

Q: Death time was entered into the MAR by a succession of Crime Analysis Detectives assigned to the job over the years. In some years, however, the forms did not include death time. Also, for some reason, death time (and death date) were not always copied over from the original data files to the archived data files. That is why, in the archived data, death time is missing for some years. We are trying to correct this for the next version of the CHD.

Q: Can these data be used to study homicides of intimate partners, children, parents, or other family members?

A: The dataset has a wealth of information on this, including many aspects of modus operandi. The data contain variables that indicate the relationship between each victim and each offender.

Some researchers, such as Margo Wilson and Martin Daly, have also used the CHD to study familicide. (See: Daly, Martin; Wilson, Margo, "Killing the Competition." Human Nature. 1990, 1, (1), 83 - 109).

Dr. Block also used the CHD for the intimate partner violence study, Community Crime Prevention and Intimate Violence in Chicago, 1995-1998 (ICPSR 3437).

Q: In CHD relationship codes, is common law father/child separate from father or stepfather? Is there a way to determine whether the offender co-resides with the victim? For example, for cases in which the mother's boyfriend is the offender and a child is the victim, can I determine whether the boyfriend lived in the same household as the child? In those cases, which code would I use for the relationship of victim to offender?

A: The CHD does not have a relationship code for "common law father/child." In cases where the man is living with the child's mother, and the MAR indicates "boyfriend," the relationship is coded as stepfather. In cases where the man is not living with the child's mother, or there is no information about co-residence, the relationship is coded as mother's boyfriend. In some cases, where it is not clear whether the man has a romantic relationship with the child's mother, the MAR has a relationship that is not a family member, but the man lives with the family, the relationship is coded as "lives together as family." In cases where we have no information about living arrangements or about the relationship of the man to the child's mother, but the narrative clearly indicates that the man was caring for the child during the incident, the relationship is coded "caretaker, other or unknown." In cases where we have no information about living arrangements or about whether the man is caring for the child, and the MAR indicates that the mother knew the man, the relationship is coded "acquaintance of parent, other or unknown." In the next version of the CHD, there will be a field for "co-reside."

Q: Does the CHD include any homicides where fathers killed their children and the childrens' mother and then killed themselves?

A: Yes the CHD contains such cases. Use the victim-level file, look for multiple-victim incidents in which the victim relationship was a female intimate partner and young children were also killed, and look for cases in which the offender committed suicide. Be careful about the offender's relationship to the children. Biological versus step fatherhood is coded where known, but it is not always known.

Q: What is the difference between the CHD codes "child abuse (causative factor)" and "child abuse (type of homicide syndrome)." Does one refer to a history of child abuse?

A: Data in the CHD refer to the specific lethal incident. Where Child Abuse is coded as a causative factor, the child was killed while being physically abused. Some young children may be killed in gang crossfire, or in a home invasion robbery, or in many other circumstances that would not be coded child abuse. We do not know whether there was a history of child abuse.

Q: Is there literature on homicide committed by the elderly that is connected to this study?

A: Publications on the CHD include population-based risk rates up to age 85. See, for example, "Lethal Violence in the Chicago Latino Community: 1965-1989." pp. 267-343 in Homicide: The Victim-Offender Connection, edited by Anna Victoria Wilson. Cincinnati: Anderson Publishing Co., 1993.

Q: Does this study contain data on child-on-child homicides and whether these homicides led to official policy responses?

A: The Chicago Homicide Dataset does not contain this kind of "system outcome" detail. It does, however, contain basic offense information about 22 homicides occurring from 1965 to 1995, in which at least one offender was 11 years or younger. Updated figures show one other case in 1994, and two in 1993. The 22 homicides were committed by a single offender in 19 cases, two offenders in one case, five offenders in one case, and six offenders in one case. In total, there were 29 offenders age 11 or younger and six older offenders (age 12 to age 19) in the 22 homicides. Of these 29, the youngest was age 5, two were age 7, eight were age 9, seven were age 10 and seven were age 11.

Q: I have a question regarding the CHD and coding of the victim's age. For those cases in which the child (victim) is under 23 months, is it possible to find out their specific age (i.e., 3 hrs old, 1 week, or 6 months)?

A: This information is not in the currently archived CHD, but it is being added to the next version.

Q: Is it possible to get age of arrested suspect by year for sexual assault of male and female cases for the archived Chicago data?

A: Yes. Use the offender-level CHD. You will have the age of each offender. Under MOTIVSEX, select either "sexual assault of male" or "sexual assault of female." If you want only "arrested suspects," select "cleared by arrest" under CLEARED. Unfortunately, the archived CHD does not indicate which of multiple offenders was arrested. However, this information will be in the next version.

Q: Can I use the CHD to look at trends over time?

A: In both the victim-level or the offender-level CHD, you can look at data by year, by month within year, or by day of the month within month within year, keeping in mind that homicide (fortunately) is rare, and the number per day or even per month will be small.

Q: Does the CHD contain details of the homicide, such as number of times stabbed or location of the injury?

A: We have recently begun to collect more detailed injury data, but the archived dataset doesn't have it yet.

Q: In the variables on the prior arrest record of the victim and the offender(s), what is the meaning of "connected?"

A: "Connected" means that the person has a prior arrest record that includes at least one arrest for a violent offense, such as assault, robbery, rape or homicide. Sometimes the specific prior arrest charges are noted in the narrative, but they are not reflected in the archived data.

Q: How is the CAUSFACT field in the CHD coded?

A: CAUSFACT is initially coded directly from "Causative Factor" in the MAR. The principal investigators then check the narrative, and add a second causative factor (CAUSFAC2), and more detailed categories where indicated. The rule of thumb for coding Causative Factor is that it must be relevant to the homicide incident. For example, if the narrative mentions that the victim was a prostitute, drug dealer, or gang member, but there is no indication that the victim's status was related to the lethal incident, it is not coded. For example, a domestic fight between a prostitute and her boyfriend would be coded as any other domestic altercation, but if the boyfriend is her pimp and beats her to death for refusing to work, then the Causative Factors should be "Pimp doing business" and "Extortion."

Q: How is the CAUSFACT category "Mental Illness" coded?

A: "Mental Illness" is coded on the MAR when police investigation shows that the mental illness of the offender contributed to the homicide. Narratives in typical cases state that the offender had "a history of mental illness" or that the offender "heard voices" telling him to kill. The code is not based on any medical examination of the offender.

Q: The data identify some offenders as cartage thieves. What is cartage theft?

A: Cartage theft occurs when someone steals goods off a truck.

Q: Does NACJD have other studies that provide homicide-related data about specific cities?

A: Studies in the NACJD holdings that provide homicide-related data for specific cities include:

Changing Patterns of Homicide and Social Policy in Philadelphia, Phoenix, and St. Louis, 1980-1994 (ICPSR 2729)
Delinquency in a Birth Cohort II: Philadelphia, 1958-1986 (ICPSR 9293)
Examination of Crime Guns and Homicide in Pittsburgh, Pennsylvania, 1987-1998 (ICPSR 2895)
Examination of Homicides in Houston, Texas, 1985-1994 (ICPSR 3399)
Gun Density, Gun Type, and the Dallas Homicide Rate, 1980-1992 (ICPSR 3145)
Homicides in New York City, 1797-1999 [And Various Historical Comparison Sites] (ICPSR 3226)
Los Angeles Homicides, 1830-2001 (ICPSR 3680)
Murder Cases in 33 Large Urban Counties in the United States, 1988 (ICPSR 9907)
Nature and Patterns of Homicide in Eight American Cities, 1978 (ICPSR 8936) -- the eight cities are Philadelphia, Newark, Chicago, St. Louis, Memphis, Dallas, Oakland, and Ashton
Social Order in Middletown, 1932-1975 (ICPSR 9058)

The following city-specific studies provide homicide rates in regard to homicide:

Crime Changes in Baltimore for 1970-1994 (ICPSR 2352)
-rates for 1970-1992
Anticipating and Combating Community Decay and Crime in Washington, DC, and Cleveland, Ohio, 1980-1990 (ICPSR 6486)
- rates for Washington, DC for 1980, 1983, 1988, and 1990
- Cleveland for 1980 through 1989
Testing Theories of Criminality and Victimization in Seattle, 1960-1990 (ICPSR 9741)
- rates for 1960-1990

Q: Is there an update to the Chicago Homicide Dataset?

A: We have added the years through 2000 and are currently preparing them for archiving.

Q: Can you provide a list of publications related to the CHD?

A: Please see the citations database for this study.

Contact Information for Principal Investigator

Carolyn Rebecca Block, Ph.D.
Illinois Criminal Justice Information Authority
Web site: http://www.icjia.state.il.us/
120 South Riverside Plaza
Chicago, IL 60606
(312) 793-8645
FAX: (312) 793-8422
Email: bblock@icjia.state.il.us

Chicago Women's Health Risk Study

2010-10-27T11:25:00.005-04:00

About the Chicago Women's Health Risk Study (ICPSR 3002)
Frequently Asked Questions About This Study
Related Publications
Contact Information for Principal Investigator

About the Chicago Women's Health Risk Study (ICPSR 3002)

The Chicago Women's Health Risk Study (CWHRS) distributed by ICPSR is a complex study consisting of 15 data files. To learn more about this study read the ICPSR study description.

To help users better understand this data collection, Dr. Carolyn Rebecca Block, the principal investigator for this study, has agreed to post Frequently Asked Questions on our Web site. Please note that the answers to the questions are provided by Dr. Block, and not by NACJD staff.

Frequently Asked Questions

Q: I am having trouble interpreting some of the results in the National Institute of Justice Final Report. For example, in Exhibit 65 (page 148), you have findings from the HARASS scale. I understand what the "yes" responses mean - for instance, with the African American women, 90.6% of those who responded "yes" to "showed up without warning" identified themselves as abused women (AW.) But what does the "no" group mean? 52% of women who responded "no" (he doesn't show up without warning) are AW? If my interpretation is correct, this doesn't seem as useful as simply comparing responses from AW versus non-abused women (NAW).

A: Your interpretation is correct, and you are right that the "percents" could be run either way. I had to make a choice, or the report would have been twice as long and twice as complex. Also, I decided that it would be confusing to go back and forth from one kind of percent to the other. There are good arguments for both ways, but I ended up deciding to do the percents the way they appear. In other reports, of course, we can calculate percents the other way.

In Exhibit 65, for example, I think that it is interesting that half of the women who said that their intimate partner had never "showed up without warning" in the past year interviewed as AW, though over 90% of the women who said that the intimate partner had "showed up without warning" interviewed as AW. From the point of view of "risk factors for serious violence or death," this tells us that the great majority of women who are experiencing this kind of harassment are also currently experiencing physical violence. But, on the other hand, half of the women who are not experiencing this harassing behavior are also experiencing physical violence. Therefore, if we used this question as our only risk factor, we would be in danger of missing a lot of women who are experiencing violence. That's important to know.

But you are right. It is also useful and interesting to look at it the other way. In this case, 68% of the 339 African/American/Black women who had interviewed as AW said that Name had "showed up without warning" in the previous year, compared to only 19% of the 125 African/American/Black women who had interviewed as NAW (chi square p > .0001; Gamma = .800, p < .0001). That tells us that many women (almost a fifth) who are not currently experiencing physical violence from their partner (or who did not tell the interviewer about it) still are experiencing this harassing behavior from a partner. This relates to our other findings that a considerable proportion of women homicide victims had not experienced a prior violent incident from the partner who killed them. We need to be aware of the risk factors for a first, explosive incident. Power and Control, stalking and harassment behavior can be such a risk factor.

So, we should look at things both ways, depending on the question we're asking.
Q: Was prior choking a risk factor for all intimate partner homicide victimization or for choking/strangulation death specifically?

A: More of the women who were choked to death had experienced a non-lethal choking incident in the past year, compared to other women victims and compared to clinic/hospital abused women.

Also, none of the non-lethal choking incidents involved a weapon (e.g., belt, scarf, cord) in the choking, but several of the lethal incidents did.
Q: Are the data from police homicide records and the data with interviews with people close to those who were murdered available in the archive?

A: Yes, all both the police and the interview data for homicide cases are available from the National Archive of Criminal Justice Data under ICPSR study number 3002. The official records data are Part 13 of the data collection and the proxy interview data are in Parts 14 and 15.
Q: In the police records data for the homicide incidents, were you able to look at open cases or just closed cases?

A: Our sample included all homicide cases meeting the study criteria, whether they were open or closed. We followed open cases through the court, and some of them had a final disposition during the data collection phase of the study.
Q: Have you published anything from just the police records or from the interviews you did with the family/associates of the women who were murdered?

A: Almost everything we have published so far has included information from both the clinic/hospital cases and the homicide cases. About 150 pages of the Final Report to NIJ are devoted to the homicide cases. Some publications, such as the "Proxy Methods" article and the "Types of Homicide" article, focus solely on the homicide cases.

If you are looking at the homicide data, you would be particularly interested in the "Proxy Method" article in Homicide Studies. References for CWHRS publications are in the "Project at a Glance," and on the Authority Web site at http://www.icjia.state.il.us.
Q: For the police records data, did you examine files or use police summary data? If you examined files, were they quite detailed?

A: We did not examine complete police investigation files. We used the summary information in the Murder Analysis Reports. Of course, we had a lot of added information from other official records. The official record data include not only police data (the Chicago Homicide Dataset), but medical examiner's office data, court records, newspapers, and other sources. This is explained in all of the CWHRS publications, briefly in the "Project at a Glance," in great detail in the 350-page Final Report to NIJ, and summarized in the other publications. It is also explained in the archive documentation.
Q: Does this study contain information on child witnesses to homicide?

A: The proxy interview data (Parts 14 and 15 of ICPSR 3002) contain information on all of the 87 intimate partner homicides involving a woman as a victim or offender in Chicago in 1995 or 1996. These data also contain information about the children who witnessed the homicides. For example, many of them received no counseling afterwards.
Q: What was the followup time period in the CWHRS?

A: We tried to contact everyone at six months and again at 12 months. However, the actual followup periods varied from woman to woman. This doesn't matter, because the Calendar Histories for each woman start at the date of the previous interview and go to the date of the followup interview. Therefore, it is misleading to call them "6 month" or "12 month" follow-ups.

Related Publications

The link below will search the ICPSR citations database for citations of publications about the CWHRS data collection. Users can create their own searches or browse the citations database through our Publications Bibliography.

Search for CWHRS Publications

Contact Information for Principal Investigator

Carolyn Rebecca Block, PhD
Illinois Criminal Justice Information Authority
http://www.icjia.state.il.us
120 South Riverside Plaza
Chicago, IL 60606
(312) 793-8645
FAX: (312) 793-8422
bblock@icjia.state.il.us

Capital Punishment in the United States Resource Guide

2010-10-27T11:22:00.011-04:00

About Capital Punishment in the United States
Using the Capital Punishment Resource Guide
Capital Punishment Data
File Structure
Methodological Information
Sample Code
Other Capital Punishment Resources
Download Capital Punishment Data

About Capital Punishment in the United States

Capital Punishment in the United States provides annual data on prisoners under a sentence of death, as well as those who had their sentences commuted or vacated and prisoners who were executed. This study examines basic sociodemographic classifications including age, sex, race and ethnicity, marital status at time of imprisonment, level of education, and State and region of incarceration. Criminal history information includes prior felony convictions and prior convictions for criminal homicide and the legal status at the time of the capital offense. Additional information is provided on those inmates removed from death row by yearend.

The dataset provides information on inmates whose death sentences were removed in addition to information on those inmates who were executed. The file also gives information about inmates who received a second death sentence by yearend as well as inmates who were already on death row.

Using the Resource Guide

The National Archive of Criminal Justice Data (NACJD), a part of the Inter-University Consortium for Political and Social Research (ICPSR) at the University of Michigan, designed this Resource Guide for World Wide Web users to learn about the Capital Punishment dataset and to connect to other capital punishment information sources.

With this guide, first time users or experienced analysts can:

Find general information about Capital Punishment in the United States.
Connect to other Capital Punishment in the United States - related sites.
View studies in the series.

Capital Punishment Data

This series of studies contains a descriptive analysis of confinement facilities and state-operated community-based correctional facilities nationwide. The census included prisons; prison farms; reception, diagnostic, and classification centers; road camps; forestry and conservation camps; youthful offender facilities (except in California); vocational training facilities; and correctional drug and alcohol treatment facilities. Variables include physical security, age of facilities, functions of facilities, programs, inmate work assignments, staff employment, facilities under court order/consent decree for conditions of confinement, capital and operating expenditures, custody level of residents/inmates, one-day and average daily population counts, race/ethnicity of inmates, inmate work assignments, in-mate deaths, special inmate counts, and assaults and incidents by inmates. The institution is the unit of analysis.

File Structure

Capital Punishment in the United States, 1973 through the present is available from the ICPSR in logical record length (lrecl) format. SPSS and SAS data definition statements are provided, giving the format and other information for each variable in the lrecl data file. The lrecl data file is constructed with a single logical record for each case.

Methodological Information

Capital punishment information is collected annually as part of the National Prisoner Statistics program (NPS-8). This data series is collected in two parts: data on persons under sentence of death are obtained from the department of correction in each jurisdiction currently authorizing capital punishment and are updated annually; information on the status of death penalty statutes is obtained from the Office of the Attorney General in each of the 50 States, the District of Columbia, and the Federal Government. Data collection forms and more detailed tables are available in Correctional Populations in the United States, published annually. NPS-8 covers all persons under sentence of death at any time during the year who were held in a State or Federal nonmilitary correctional facility. Included are capital offenders transferred from prison to mental hospitals and those who may have escaped from custody. Excluded are persons whose death sentences have been overturned by the court, regardless of their current incarceration status.

The statistics reported in this Bulletin may differ from data collected by other organizations for a variety of reasons: (1) NPS-8 adds inmates to the number under sentence of death not at sentencing but at the time they are admitted to a State or Federal correctional facility. (2) If in one year inmates entered prison under a death sentence or were reported as being relieved of a death sentence but the court had acted in the previous year, the counts are adjusted to reflect the dates of court decisions. (3) NPS counts are always for the last day of the calendar year and will differ from counts for more recent periods.

Sample Code

CAPITAL PUNISHMENT IN THE UNITED STATES records the movement of prisoners onto and off of death row starting in 1973. In order to examine particular cases (individuals) it is necessary to correctly specify the selection criteria. In addition, for certain analyses it is necessary to sort the data file before making selections.

Some individuals are included in the data file more than once. Typically this occurs when someone has been sentenced to death, subsequently had his sentence overturned, and then again sentenced to death. These people have the same identification number in the data set. In order to avoid double counting, BJS tables include only the latest death sentence. The issue of double counting arises most often when the flow of prisoners onto and off of death row is being analyzed.

Below are three examples, using SPSS syntax, of working with CAPITAL PUNISHMENT IN THE UNITED STATES. The first example provides a brief description of the status of the death penalty at year end. The second and third examples detail the flow of prisoners onto and off of death row by year and by state respectively.

EXAMPLE 1

* STATUS OF THE DEATH PENALTY, DECEMBER 31, 1999.
* The output produced by this example corresponds to page 1
* of Bureau of Justice Statistics Bulletin - Capital Punishment 1999.

* Get the capital punishment system file that you created using
* the ASCII data file and SPSS data definition statements
* available from the NACJD website.

GET FILE = "D:\CapPun\cp1999.sav".

* Select REASON FOR REMOVAL FROM DEATH SENTENCE (V31) equal to
* executed and DATE REMOVED FROM DEATH SENTENCE - YEAR (V33)
* equal to 1999.

SELECT IF ( V31 = 1 AND V33 = 1999 ).

* Count executions by state during 1999.

FREQUENCIES VARIABLES = V7.

GET FILE = "D:\CapPun\cp1999.sav".

* Select if inmate STILL ON DEATH ROW DECEMBER 31, 1999 (V27).

SELECT IF ( V27 = 1 ).

* Count prisoners under sentence of death by state and race.

FREQUENCIES VARIABLES = V7 V9.

EXAMPLE 2

* PRISONERS SENTENCED TO DEATH AND THE OUTCOME SENTENCE,
* BY YEAR OF SENTENCING, 1973-99.
* The output produced by this example corresponds to
* Bureau of Justice Statistics Bulletin - Capital Punishment 1999,
* Appendix Table 1.

* Get the capital punishment system file that you created using
* the ASCII data file and SPSS data definition statements
* available from the NACJD website.

GET FILE = "D:\CapPun\cp1999.sav".

* Sort cases by:
* INMATE ID (V6 ascending),
* YEAR REMOVED FROM DEATH SENTENCE (V33 descending),
* MONTH REMOVED FROM DEATH SENTENCE (V32 descending),
* SENTENCE DATE FOR CAPITAL OFFENSE - YEAR (V26 descending),
* SENTENCE DATE FOR CAPITAL OFFENSE - MONTH (V25 descending).
*.

SORT CASES BY V6 (A) V33 (D) V32 (D) V26 (D) V25 (D).

* Set a flag variable to be "on".

COMPUTE FLAG = 1.

* Turn flag variable "off" if inmate is in the data more than once.
* The most recent inmate record is still "on".

IF ( LAG(V6) = V6 ) FLAG = 0.

* Select sentencing dates of 1973 forward and the most recent
* inmate record.

SELECT IF ( V26 >= 1973 AND FLAG = 1).
* Crosstabulate YEAR OF SENTENCE (V26) by
* REASON FOR REMOVAL FROM DEATH SENTENCE (V31).

CROSSTABS V26 BY V31.

EXAMPLE 3

* NUMBER SENTENCED TO DEATH AND NUMBER OF REMOVALS, BY JURISDICTION
* AND REASON FOR REMOVAL, 1973-99.
* The output produced by this example corresponds to
* Bureau of Justice Statistics Bulletin - Capital Punishment 1999,
* Appendix Table 3.

* Get the capital punishment system file that you created using
* the ASCII data file and SPSS data definition statements
* available from the NACJD website.

GET FILE = "D:\CapPun\cp1999.sav".

SORT CASES BY V6 (A) V33 (D) V32 (D) V26 (D) V25 (D).

* Set a flag variable to be "on".

COMPUTE FLAG = 1.

* Turn flag variable "off" if inmate is in the data more than once.
* The most recent inmate record is still "on".

IF ( LAG(V6) = V6 ) FLAG = 0.

* Select sentencing dates of 1973 forward and the most recent
* inmate record.

SELECT IF ( V26 >= 1973 AND FLAG = 1).

* Combine codes in variable - REASON FOR REMOVAL FROM DEATH SENTENCE (V31)
* Capital sentence unconstitutional (3)
* Conviction affirmed, sentence overturned (5)
* Conviction/sentence overturned (6)
* and change value label to reflect combined value.

RECODE V31 (3,5,6 = 3).
ADD VALUE LABELS
V31 3 "Sent/conv overturned"
9 "Still death sent"
.

* Crosstabulate STATE (V7) by
* REASON FOR REMOVAL FROM DEATH SENTENCE (V31).

CROSSTABS V7 BY V31.

Other Capital Punishment Resources

Web Sites

Capital Punishment section of Key Facts at a Glance - Trends in the number of executions and of persons under sentence of death
http://bjs.ojp.usdoj.gov/index.cfm?ty=tp&tid=18#key_facts

Bureau of Justice Statistics: Capital Punishment Statistics
http://bjs.ojp.usdoj.gov/index.cfm?ty=tp&tid=18

Federal Bureau of Prisons, National Institute of Corrections
http://www.bop.gov/

National Criminal Justice Reference Service
http://www.ncjrs.gov/

Texas Department of Criminal Justice: Death Row Statistics
http://www.tdcj.state.tx.us/statistics/stats-home.htm

Publications

The link below will search the ICPSR citations database for citations of publications containing the phrase "capital punishment." Users can create their own searches or browse the citations database through our Publications Bibliography.

Search for Capital Punishment Publications

Homicide Data Resource Guide

2010-10-26T15:37:00.006-04:00

Purpose of the Resource Guide
Overview of Homicide Data Available from NACJD
Homicide Data Collection Highlights
Two Death Penalty Studies Released as Restricted Data
Quick Links to Homicide Data
Online Survey Documentation and Analysis
Help with Individual Data Collections
Human Subjects Issues
Publications
Overview of Data Resources Program
Related Links

Purpose of the Resource Guide

This Homicide Data Resource Guide was designed by the National Archive of Criminal Justice Data (NACJD) staff to provide easy access to data collections related to homicide. For instance, it provides quick links to certain types of homicide studies and links to studies available for online data analysis. This resource guide also provides useful information for secondary analysis of NACJD data collections, such as customized help for complex data collections, information on how to obtain restricted access data, and links to funding opportunities and publications.

Overview of Homicide Data Available from NACJD

Homicide-related data collections at the NACJD can be grouped into three general categories:

Studies focused specifically on homicide
Studies in which homicide is one of many offense types or categories
Data collections about capital punishment in which the offense for which sentences are given is often homicide

These three groups can be further subdivided into data collections that are gathered annually or periodically (serial collections), and data collections that resulted from one-time studies.

Homicide Data Collection Highlights

In the first group of studies, those focused exclusively on homicide, are two major serial data collections:
- U.S. Department of Justice. Federal Bureau of Investigation.
  Supplementary Homicide Reports
  
  These homicide incident data are compiled by law enforcement agencies and submitted to the FBI. The SHR data contain information about characteristics of the victim, the offender, the offender's relationship to the victim, weapon use, location, circumstances, and jurisdiction of the offense.
- United States Department of Health and Human Services. National Center for Health Statistics
  Multiple Cause of Death
  
  These data are produced from death certificates recorded in the nation's vital statistics system. Data are provided concerning underlying causes of death, multiple conditions that caused the death, place of death, residence of the deceased (e.g., region, division, state, county), whether an autopsy was performed, and the month and day of the week of the death. In addition, data are supplied on the sex, race, age, marital status, education, usual occupation, and origin or descent of the deceased.
Important one-time studies that focused exclusively on homicide include:
- Block, Carolyn Rebecca, Richard L. Block, and the Illinois Criminal Justice Information Authority.
  Homicides in Chicago, 1965-1995 (ICPSR 6399)
- Keppel, Robert D., and Joseph G. Weis
  Improving the Investigation of Homicide and the Apprehension Rate of Murderers in Washington State, 1981-1986 (ICPSR 6134)
- Monkkonen, Eric
  Homicides in New York City, 1797-1999 [And Various Historical Comparison Sites] (ICPSR 3226)
- Monkkonen, Eric
  Los Angeles Homicides, 1830-2001 (ICPSR 3680)
In the second group of studies, in which homicide is one of a number of offense types, the most important serial collections come from the FBI's Uniform Crime Reporting Program data and include agency-level reports of:
Homicide as an offense type also appears in two other serial collections:
- United States Department of Justice. Bureau of Justice Statistics.
  National Judicial Reporting Program
- United Nations Office at Vienna, R.W. Burnham, Helen Burnham, Bruce DiCristina, and Graeme Newman
  ">United Nations World Crime Surveys
In the third group of studies, those focused on capital punishment and sentencing, is an annual data series:
- United States Department of Justice. Bureau of Justice Statistics.
  Capital Punishment in the United States
Important one-time studies in this collection include:
- Baldus, David C., George Woodworth, and Charles A. Pulaski Jr.
  Charging and Sentencing of Murder and Voluntary Manslaughter Cases in Georgia, 1973-1979 (ICPSR 9264)
- Baldus, David C., George Woodworth, and Charles A. Pulaski Jr.
  Procedural Reform of Jury Murder Convictions in Georgia, 1970-1978 (ICPSR 9265)
- Espy, M. Watt, and John Ortiz
  Executions in the United States, 1608-1991 (ICPSR 8451)
- Klein, Stephen P., and Richard A. Berk
  Race and the Decision to Seek the Death Penalty in Federal Cases, 1995-2000 [United States] (ICPSR 4533)
- Newton, Phyllis J.; Candace M. Johnson, and Timothy M. Mulcahy
  Investigation and Prosecution of Homicide Cases in the United States, 1995-2000: The Process for Federal Involvement (ICPSR 4540)
- Fagan, Jeffrey, and James Liebman
  Processing and Outcome of Death Penalty Appeals After Furman v. Georgia, 1973-1995: [United States] (ICPSR 3468)

Two Death Penalty Studies Released as Restricted Data

The National Archive of Criminal Justice Data (NACJD) announces the release of information from two studies of the federal death penalty system sponsored by the National Institute of Justice.

Klein, Stephen P. and Richard A. Berk
Race and the Decision to Seek the Death Penalty in Federal Cases, 1995-2000 [United States] (ICPSR 4533)

The purpose of this project was to examine possible defendant and victim race effects in capital decisions in the federal system. RAND researchers selected cases received by the Department of Justice Capital Cases Unit (CCU) between January 1, 1995, and July 31, 2000. These cases were handled under the revised Death Penalty Protocol of 1995, and were processed during Attorney General (AG) Janet Reno's term in office. The database contains 312 cases for which defendant- and victim-race data were available from 71 federal judicial districts in 40 states, the District of Columbia, Puerto Rico, and the Virgin Islands. The database was structured to allow researchers to examine two stages in the federal prosecution process, namely the U.S. Attorney's Office recommendation to seek or not to seek the death penalty and the final AG charging decision. Data are available through the NACJD restricted data access procedures

Newton, Phyllis J., Candace M. Johnson, and Timothy M. Mulcahy
Investigation and Prosecution of Homicide Cases in the United States, 1995-2000: The Process for Federal Involvement (ICPSR 4540)

This study addressed questions related to potential geographic and racial disparity in the investigation and prosecution of federal capital cases and examined the process by which criminal cases, especially homicide cases, enter the federal criminal justice system. Between 2000 and 2004, face-to-face interviews were conducted with all criminal justice officials in the state and federal criminal justice systems in nine federal districts who potentially would play a role in determining whether a homicide case was investigated and prosecuted in the state or federal systems. The technical report is available through the NACJD's restricted data access procedures. Data are forthcoming and will be available through use of the ICPSR Data Enclave.
2006-07-26

Quick Links to Homicide Data

View all homicide studies
View newly archived homicide-related data collections
Search for data collections (Note: Most Group 1 data collections focused on homicide have homicide, murder, assassination, death, or deadly in the study title)
Other data collections that can be used for homicide research:

Online Survey Documentation and Analysis

Search for variables of interest in a dataset
Review frequencies or summary statistics of key variables to determine what further analyses are appropriate
Review frequencies or summary statistics for missing data
Produce simple summary statistics for reports
Create statistical tables from raw data
Create a subset of cases or variables from a particularly large collection to save downloading time and space on a personal computer

Homicide data collections available for online analysis

Learn more about the online analysis system

Help with Individual Data Collections

NACJD is developing specialized web pages to help data users analyze some data collections. These pages are currently available for the following studies:

Homicides in Chicago, 1965-1995 (ICPSR 6399)

Chicago Women's Health Risk Study (ICPSR 3002)

Human Subjects Issues

NACJD employs a variety of measures to ensure that subject confidentiality is preserved in all of our data collections. Since dta collected about homicide can be sensitive, some homicide-related data collections are restricted from general access. This means that these data are still freely available to the public, but they cannot simply be downloaded from the NACJD Web site.

Access to restricted data collections is possible through a written request to NACJD. Using a Data Transfer Agreement, available online as a PDF file, prospective data users must certify in writing that the data will be used for research or statistical purposes only, and that the confidentiality of respondents or subjects will be protected. More information about the procedures for accessing these data is available on the Restricted Access Data Archive web page.

Publications

Users interested in such publications can search our online database of citations for publications related to NACJD data collections. Some publications listed in the online database are available in either paper hardcopy or electronic form from the National Criminal Justice Reference Service at http://www.ncjrs.org/.

Overview of Data Resources Program

A number of data collections highlighted in this online resource guide are available through the National Institute of Justice (NIJ) Data Resources Program (DRP). In 1984 NIJ established the DRP to ensure the preservation and availability of research and evaluation data collected through NIJ-funded research. NACJD archives these data collections to support new research, replicate original findings, or test new hypotheses based on existing NIJ-funded data collections.

Law Enforcement Management and Administrative Statistics Resource Guide

2010-10-26T15:04:00.009-04:00

About Law Enforcement Management and Administrative Statistics
Using this Resource Guide
File Structure
Sampling Information
Weighting the Sample
Data Limitations
GIS Analysis of Law Enforcement Management and Administrative Statistics Data
Related Resources
Download Law Enforcement Management and Administrative Statistics Data

Law Enforcement Management and Administrative Statistics

These surveys were first collected in 1987 and are administered approximately every three years. They present information on law enforcement agencies in the United States: state police, county police, special police (state and local), municipal police, and sheriff's departments. Variables include size of the population served by the police or sheriff's department, levels of employment and spending, various functions of the department, average salary levels for uniformed officers, policies and programs, and other matters related to management and personnel.

Using the Resource Guide

The National Archive of Criminal Justice Data (NACJD), a part of the Inter-University Consortium for Political and Social Research (ICPSR) at the University of Michigan, designed this Resource Guide for World Wide Web users to learn about the Law Enforcement Management and Administrative Statistics dataset and to connect to other capital punishment information sources.

With this guide, first time users or experienced analysts can:

Find general information about Law Enforcement Management and Administrative Statistics.
Connect to other Law Enforcement Management and Administrative Statistics - related sites.
Download all of the Law Enforcement Management and Administrative Statistics data files, codebook, and SPSS and SAS data definition statement files.

File Structure

Law Enforcement Management and Administrative Statistics is available from 1987 through the present from the ICPSR. All files are in logical record length (lrecl) format. SPSS and SAS data definition statements are provided, giving the format and other information for each variable in the rectangular data file. The rectangular data file is constructed with a single logical record for each case.

Sampling Information

The law enforcement agencies were separated into two groups for the purpose of sample selection: self-representing (SR) and non-self-representing (NSR) agencies. All state police are SR. The definitions of SR and NSR are shown below.

SELF-REPRESENTING AGENCIES

An agency is SR if it meets one of the following two criteria:
- An agency is a State Police agency
- An agency has 100 or more sworn full-time equivalent (FTE) employees.
Average number of sworn FTE = rounded [(# sworn full time employees) + 0.5 * (# worn part-time employees)]
NON-SELF-REPRESENTING AGENCIES

All remaining agencies in Sheriff's Department, Local Police, and Special Police categories are NSR.

An agency can be out-of-scope for the collection year because it disbanded after the previous collection year but before the current collection year. An agency can also be out-of-scope because it never should have been in the universe in the first place. An attachment in the codebook lists agencies that are out-of-scope for the purposes of that data's collection year.

Weighting the Sample

Information on creating cells for weighting and imputation can be found in the codebook for each collection year.

Each agency that responds to the questionnaire receives:

a base weight,
a final universe post-stratification factor from the previous collection year,
a final universe post-stratification factor for the current collection year,
a non-response adjustment factor, and
a final weight.

These are each detailed in the codebook.

THE NON-RESPONSE ADJUSTMENT FACTOR Some of the agencies selected for the sample did not return a questionnaire. To account for the non-response, we use a non-response adjustment factor (see codebook for variable information). The non-response adjustment factor is applied within a collapsed cell. A list of non-response agency identification numbers is available in the codebook.

The non-response adjustment factor for a given imputation cell i can be calculated using a formula provided in the codebook.

Data Limitations

National estimates from a given data year, such as the total number of agencies or the total number of employees, cannot be directly compared to national estimates from the previous data year due to changes in the universe.

Users should be aware that estimates of law enforcement employees from this survey will differ from estimates from other sources, such as the Justice Expenditure and Employment Extracts, which are based on the Annual Finance Survey and the Annual Employment Survey. In the Justice Expenditure and Employment Extracts, data are presented by governmental function, whereas in this survey, employees are classified by type of agency.

In both the public employment and finance statistics, special police (i.e. park, airport, school, etc) are classified in the function of the parent agency. For example, the Washington DC Metro transit police would not be coded as law enforcement personnel, but rather as public transportation personnel. Also the finance and employment definitions specify that the police must have full (as opposed to limited) arrest powers to be classified under the law enforcement function. In many cases, such as schools, the collection instruments don't allow for the enumeration of law enforcement employees under a separate category.

Although the universe for this survey is state and local law enforcement agencies that are publicly funded and employ at least one full-time or part-time sworn officer with general arrest powers, there can be agencies in the sample who reported 0 sworn officers. This can happen in small agencies, which employed a sworn officer when the universe information was collected. However, if that sworn officer is no longer employed by the agency when the sample information is collected and has yet to be replaced, the agency reports 0 sworn officers.

GIS Analysis of Law Enforcement Management and Administrative Statistics Data

The Law Enforcement Management and Administrative Statistics dataset contains FIPS County Codes as a geographical identifier. These data were not collected for the purpose of mapping or conducting spatial analysis, so users may have to do additional work to make these data compatible with their particular GIS software.

The GIS Resources page at the National Archive of Criminal Justice Data contains tutorials, analysis programs, and other resources, and the Mapping and Analysis for Public Safety (MAPS) Program of the National Institute of Justice has additional mapping tools at http://www.ojp.usdoj.gov/nij/maps/. General GIS information, data, maps, software publications and links to other resources and Web sites can be found on the Related Sites page.

Other Law Enforcement Management and Administrative Statistics Resources

Web Sites

Program Assessments & Policy Support at the U.S. Department of Justice's Community Oriented Policing Services Site http://www.cops.usdoj.gov/

Bureau of Justice Statistics: Law Enforcement Statistics http://bjs.ojp.usdoj.gov/index.cfm?ty=dcdetail&iid=248

Publications

The link below will search the ICPSR citations database for related publications with select words in their title. Users can create their own searches or browse the citations database through our Publications Bibliography Web page.

Search for Law Enforcement Management Publications

Search for Local Police Departments Publications

Search for Sheriffs' Departments Publications

How do I import a study citation into EndNote/RefWorks/etc.?

2010-09-30T10:27:00.013-04:00

EndNote X4+ Users

If you click on Export > Citation > RIS, EndNote should automatically import the citation. If the citation comes through with the reference type "Computer Program," you'll need to update your RIS import filter, which can be obtained from EndNote's Web site. Simply download the file and place it in the EndNote X4/Filters folder; it will replace the pre-existing "RefMan RIS.enf" file.

Older Versions of EndNote

If you click on Export > Citation > RIS, EndNote should automatically import the citation. Unfortunately, the citation will come through with the reference type "Computer Program," due to how the EndNote software interprets the "DATA" publication type in RIS. To correct this issue, you can either upgrade your software, or follow the instructions on the EndNote Web site.

Other Bibliographic Software

Just click on Export > Citation > RIS. Your software will either import it automatically, or it will save a small citation file to your hard drive. Open your bibliographic software and look for an "Import" option in the "File" menu. In the dialogue box, point it at that file. There may be an additional menu that lets you specify the type of import; look for a filter labeled "RIS" or "RefMan RIS" or "Reference Information Systems."

Please note that RIS is a standard, but not all bibliographic software adheres to the standard. I.e., if you import a study citation into Zotero via RIS, it will call the citation a "Web Page," even though the RIS standard labels it a "Data file."

What fields were dropped in the revised MARC?

2010-09-30T10:26:00.001-04:00

We removed the following elements when we switched from distributing MARC to MARC21 XML:

035a: OCLC id
490: series name
505a: dataset names
516a: number of datasets
518a: time period
522a: geographic coverage
536a: funding agency
536c: grant number
567a: universe

I need to deposit data in an archive as a requirement for a journal article and obtain a DOI. How do I do that?

2010-09-13T09:04:00.004-04:00

ICPSR's Publication-Related Archive (PRA) is a self-archiving mechanism that facilitates the deposit of data supporting publications. Go to the ICPSR Deposit Form and select PRA as the archive for deposit. Then follow the instructions provided for entering descriptive metadata and uploading the data. Upon publication of the data you will receive a notification that includes the Digital Object Identifier to the data, which you can include in your article. If you need a DOI sooner, please contact deposit@icpsr.umich.edu to make a special request.

Note that the PRA requires a connection between the data and the published article, so please be sure to include in the metadata you provide the article’s citation and a DOI if available.

How do the batch export utilities work?

2010-09-01T12:43:00.007-04:00

The developer utilities enable you to export your current search results to a standard format so that you can import the results into various software packages. Currently, we've enabled batch export of citations (as RIS or EndNote XML) so that you can import your search results into bibliographic software (perhaps to include in a report).

What other formats are available?

ICPSR is working on a variety of study-level exports, including DDI2, DDI3, Dublin Core, and RIS/EndNote XML (for the citation of the study itself). We plan to have these done by the end of 2010.

How can I get more than 500 results in the batch export?

We had to put a limit on how much one could export to prevent search engine crawlers from slowing down our site by repeatedly hitting export links that taxed the server a lot. Thus we instituted the 500 cap on batch exports. To get around that, just do the following:

Copy the link of the export you want.
Paste the link into a new browser window.
Add the following text to the end of the link: &paging.startRow=501
Press "return."

This will cause the export to have results 501-1000. Adjust the number to get more. We could add a drop-down menu to enable this, but then the search engine crawlers would ping it repeatedly.

Is the longitudinal panel data available for Monitoring the Future?

2010-08-09T15:50:00.003-04:00

The information below comes directly from Monitoring the Future. Please refer to their Web site for more information.

A subset of high school seniors are selected each year for follow-up, which is conducted in an alternating biennial fashion, with the first half of the subset receiving their first follow-up questionnaire one year after high school, and the second half receiving their follow-up two years after high school. They receive a series of six questionnaires within this arrangement, so the second half of the subset is 12 years past high school when they receive their last young adult "FU-12" questionnaire. Then, the follow-up procedure changes to 5-year intervals to cover middle adulthood.
The questionnaires in the young adult follow-ups are directly comparable to the base year questionnaires, both in content and in numbers of questionnaire forms. The core drug use questions are included along with the same types of related attitude and behavioral items, many of which are unique to each form, so respondents receive the same questionnaire form throughout the base year and young adult follow-up series.
All data for a particular individual are linked (or, in the case of form-specific items, capable of being linked) in the panel dataset. The sheer amount of information greatly increases the risk of breaching confidentiality. Thus, based on policies approved by our funding source and IRB, the panel data set cannot be made available to the public in totality and without modification.
Special data requests can be made through the Web site email address. Once we get a request, information about policies and procedures is sent out. Requests are considered on a case-by-case basis, and may be fulfilled - at requestor's cost - typically by providing data analytic access.

Additional information about the design of the panel component of the design and procedures used in the study are included in our annual NIDA report, Volume II, and in more detail in the MTF "Occasional Papers." See, for example, "The Aims and Objectives of the Monitoring the Future Study and Progress Toward Fulfilling Them as of 2006" (pdf).

To make a request for this data and for further information, please contact MTF staff at: MTFinfo@isr.umich.edu

What are Quick Tables?

2010-08-09T15:32:00.002-04:00

Quick Tables are streamlined data analysis tools that allow you to produce analytic tables by choosing from among pre-selected high-interest variables in drop-down menus. Currently, Quick Tables are available for the following series: HBSC, NSDUH, TEDS-A, and TEDS-D.

How do I deposit data in the SAMHDA collection?

2010-08-09T15:03:00.001-04:00

SAMHDA considers archiving data that meets the standards established with our funder, the Office of Applied Studies, Substance Abuse and Mental Health Services Administration. When making a determination to archive data, SAMHDA considers the quality of the research design and methods, the completeness of the data and documentation files, the scope of the study, and the significance of the study to the field.

Prior to a public release, SAMHDA processes the data and documentation files in order to ensure the quality of the public-use file(s), enhances their user friendliness, and resolves any confidentiality issues that may be present.

For New Depositors:

If you would like SAMHDA to consider archiving your data, please send us an inquiry at samhda-support@icpsr.umich.edu. Also, please visit ICPSR's How to Deposit Data page to view guidelines, instructions, and answers to frequently asked questions about the data deposit process.

Previous / Authorized Depositors:

Please access our Electronic Deposit Form.

How do I get the TEDS-A Concatenated file?

2010-08-09T10:21:00.004-04:00

The TEDS-A concatenated data file is now available for download. This public-use file is provided in an ASCII rectangular format with SPSS and SAS data definition statements. A SPSS System and ASCII tab-delimited files are also available. Please note that because of the size of the data file normally available Stata files and the SAS Transport s file were unable to be produced.

If you are unable to download the file in one of the available formats, SAMHDA can provide you with the file. You can obtain the entire data set or select a subset of cases and/or variables. We can make the files available to you through a special Web link where you can access them directly. This is the quickest and easiest way for you to get the data you request. However, we can send you a CD of the files as well.

Please email us the following information at samhda-support@icpsr.umich.edu

Data File Delimiter (choose one of the following):
None -- the preferred delimiter for analyzing the data using SAS, SPSS, or Stata. Blank -- one of two delimiters for using Excel, Access, or other similar software package. Comma -- another delimiter for using Excel, Access, or other similar software package.
Type of setup file(s) (choose one or more of the following):
SAS SPSS Stata DDI (XML) SDA (DDL)
Cases: Please indicate whether you desire all cases or a subset of cases. One common example of a subset would be to request only those cases for a given state. You may also request more than one filter to define a subset (e.g., males in the 12-14 year old age group). Please refer to the TEDS codebook when considering filters. Please note that some variables, such as age, are categorized and therefore only allow for certain specific age ranges.
Variables: Please indicate whether you desire all variables or a subset of variables. If you request a subset of variables, please refer to the TEDS codebook when choosing variables for the subset. We prefer that you make your requests using Variable Groups (e.g., CLIENT CHARACTERISTICS, SUBSTANCES OF ABUSE: ORIGINAL VARIABLES, etc.).

As soon as we get this information from you, we will begin to process your request. We will email you instructions on how to retrieve the file from the Web site.

What enhancements are available when using SDA?

2010-08-05T15:47:00.001-04:00

SAMHDA recently upgraded the Survey Documentation and Analysis (SDA) system to version 3.4. All of the previous statistical procedures are still available. Users who prefer to use the original interface may still use it by selecting the link entitled Use Classic Interface in the upper-left corner of the screen. SDA 3.4 improves the calculation of statistics for complex samples in the TABLES and MEANS programs.

For the TABLES program, enhancements include:

Corrections to the calculation of standard errors and confidence intervals.
Addition of Rao-Scott F-tests.
Ability to display weighted or unweighted N of cases.
Option to set the number of decimals for all statistics.

For the MEANS program, enhancements include:

Corrections to the calculation of the standard errors and confidence intervals.
Option to display the p-value of each difference from the cells in a base row or column.
Default reporting of the weighted N of cases in each cell for weighted analyses.
Option to include charts in output.
Optional diagnostic table for design variables.

Further information is provided in the SDA Manual (SDA 3.4).

SDA 3.3, released in June 2009, contained the following changes to the analysis programs and features:

Disclosure Protection: SAMHDA now has the ability to suppress output that may compromise the confidentiality of survey respondents by applying disclosure protection rules to a data file. Analysis programs, including RECODE and COMPUTE, now check for the presence of disclosure rules and enforce them. Disclosure rules may be specified to: a) prevent an analysis from being run; b) suppress the output after running an analysis; and c) suppress the unweighted number of cases from being reported in the output. The SDA 3.3 Documentation for Disclosure provides greater detail on the disclosure rules that may be specified.
List Created Variables - View Button: The output from the listing of recoded and computed variables now includes a "View" button that provides access to descriptions of the variables. This feature can be accessed under the SDA Create Variables menu.
Title: A title or label can be entered for each analysis request and will appear at the top of the HTML output produced by SDA analysis programs.
Customized Subset: This procedure has also been revised in that recoded and computed variables may now be included in a subset. If pre-set selection filters have been defined by SAMHDA, these filters now apply to the interactive version of the subset procedure as well as to the analysis programs. A Comma Separated Values (CSV) file is available for output.

Content adapted from the SDA Manual (version 3.3).

For further information on SDA, please select the Getting Started button located in the upper-right portion of the screen or visit the SDA Tutorial.

What are the main components of the SDA interface?

2010-08-05T15:46:00.001-04:00

In 2008 SAMHDA upgraded the appearance of the Survey Documentation and Analysis (SDA) system. The new interface allows users greater navigational ability within SDA. All of the statistical procedures are still available as they were previously. Users who prefer to use the original interface may still do so by selecting the link entitled Use Classic Interface in the upper-left corner of the screen. For further information about SDA, please select the Getting Started button located in the upper-right portion of the screen.Users no longer need to open the codebook in a separate browser to view a list of variables. Also, users can now switch between the various statistical procedures without having to return to the main analysis page. These improvements are possible because the screen splits into the following four windows:

Program Selection Window. Select from programs to perform analysis, create or recode variables, download the dataset or a customized data subset, view the codebook, or view the help file Getting Started.
Variable Selection Window. The buttons within this window change depending on the type of analysis selected. Variables are selected and placed into the box. The user can then specify which analysis field the variable should go into (i.e., row or column for a crosstabulation, independent or dependent for a regression, or used as a control or filter variable). Users can also obtain a frequency table and accompanying question text for that variable by selecting the View button.
Variable Tree Window. All variables and variable labels are listed and organized as they appear within the codebook, into groups with headings and subheadings. Click on the +/- boxes next to the heading to view all the variables within a selected group. When a variable of interest is located, select it and the program will place it into the variable selection window.
Analysis Window. This screen will display the required and optional fields for the type of analysis you have selected. This screen looks identical to the classic interface for each of the analytic features available.

Why do I have to scroll to the bottom of the page to view my search results?

2010-08-05T15:45:00.002-04:00

This is a bug that we run into now and again that relates to a combination of the Web browser you use and the screen width of your monitor. If you run into this error, please email web-support@icpsr.umich.edu so that we can fix it. We'll need to know your screen resolution, your operating system, your Web browser, and the browser version. For example, "I'm on Mac OSX using Chrome 5. My screen is set to 1024 x 768." With that information, we can reproduce the problem and fix it.

How do I find a study by Principal Investigator?

2010-08-05T15:11:00.002-04:00

Perform a keyword search in the "Study descriptions" tab. Then you can use the "Filter by Author" facet in the right column.

HELP! I'm trying to locate alcohol and drug treatment resources.

2010-08-05T15:08:00.001-04:00

The Substance Abuse and Mental Health Services Administration (SAMHSA) provides a quick guide to finding effective alcohol and drug addiction treatment. SAMHSA also sponsors a treatment locator that will help you find treatment professionals in your geographic area.

SAMHDA does not have the data I need. Where else should I look?

2010-08-05T15:06:00.002-04:00

The SAMHDA holdings are housed at the Inter-university Consortium for Political and Social Research (ICPSR), which was established in 1962 to preserve and distribute social science research data. ICPSR is a membership-based organization, with over 600 member colleges and universities around the world. If you are in a school or organization belonging to ICPSR, you have unlimited access to ICPSR data.

ICPSR sponsors many topical archives, in addition to SAMHDA, that provide data resources to individuals and institutions outside the consortium's membership.

SAMHDA also provides a listing of other data sources and related sites. Or you can search the publications based upon our data.

I don't want to run my own statistics. Where can I find reports or pre-run tables?

2010-08-05T15:05:00.001-04:00

The primary function of the SAMHDA archive is to provide access to raw research data for secondary analysis. However, there are several options if you are looking for tabulated reports or summary information:

A feature called Quick Tables is available for some of the studies in our archive. Quick Tables allows you to produce analytic tables by choosing from among pre-selected high-interest variables available in drop-down menus.
If you are seeking reports for a data set that we archive, please check our Web site for the "reports and related sites" links for that study.
For links to general publication resources, please see our Related Sites. Two Web sites that may be of particular interest are SAMHSA's National Clearinghouse for Alcohol and Drug Information (NCADI) and SAMHSA's Office of Applied Studies, which includes both detailed and short reports for SAMHSA studies.

Can I select multiple datasets for a download? What about multiple stat packages?

2010-08-05T15:04:00.002-04:00

For every study in the archive you have the option of either downloading all of the files or selecting individual files. Use the option of downloading all files if you want more than one part (dataset) of a multiple part study, or if you want to download the study into multiple statistical packages. There is not a way to "cherry pick" datasets or statistical packages. You cannot select datasets 1 and 3 with a single click. Similarly, you cannot select SAS and Stata, but not SPSS. Once you have downloaded the entire study you can then select which individual files to extract from the zip file provided.

What is SDA and why should I use it?

2010-08-05T15:02:00.003-04:00

The Survey Documentation and Analysis (SDA) system allows users to conduct statistical analysis quickly and efficiently on the Internet using their Web browser. It was developed by the Computer-assisted Survey Methods Program (CSM) at the University of California at Berkeley. The SDA system is capable of performing a wide range of statistical analyses from bivariate crosstabulation to multiple regression and analysis of variance. The system allows users to design and implement custom recodes as well as generate subsets of data for download and analysis with traditional statistical applications.

For an overview on how to analyze data online, please consult our SDA Tutorial.

Additional information about SDA and its capabilities can be found in the SDA online documentation from Berkeley.

The variables I want to examine have been removed or modified. How do I gain access to them?

2010-08-05T15:02:00.002-04:00

SAMHDA provides access to public-use files. Given the sensitive nature of these studies, great lengths are taken to ensure that respondent identity is protected. For this reason, variables that pose an identification (or disclosure) risk are modified or removed from the data file. Researchers wishing to access protected variables must contact the study's principal investigator(s). This information can be located in the study abstract or codebook.

What are weights?

2010-08-05T15:01:00.001-04:00

Many of the datasets archived by SAMHDA are designed to represent particular populations. For instance, the National Household Survey of Drug Abuse is designed to represent citizens of the United States, ages 12 and up. Since not every citizen can be surveyed, such studies utilize a stratified random sampling strategy. That means that the population is divided into broader groups (called sampling units) from which individual persons are randomly selected for participation. Depending on the focus of the research, more participants may be selected from particular sampling units than is necessary for proportional representation (a practice known as oversampling). This is done to ensure that the dataset will have an adequate number of cases to facilitate analysis of the subpopulation. Mathematical weights are used to adjust the findings to be proportionally accurate. Thus, weights are adjustments required to ensure that findings accurately represent the population. Users should always consult the codebook to determine which weight, if any, is required.

Why do I get prompted for a username and password when trying to download a file? I thought SAMHDA data were freely available.

2010-08-05T15:00:00.001-04:00

Data archived in SAMHDA are in the public domain, you should have the option to log in anonymously. If you are not given the option to log in anonymously, then you are either attempting to download a study that is not part of the SAMHDA archive or there is a problem with the website and you should notify the SAMHDA help desk immediately.

How do I search SAMHDA's holdings for datasets or particular variables?

2010-08-05T14:57:00.004-04:00

You may search the SAMHDA Web site for a particular study or specific variables in many different ways:

One way to locate a particular study archived by SAMHDA is to Browse All Studies. This list features our holdings with links to a brief synopsis page for each study. From the description page, you may download files associated with the study, initiate an online analysis session with the online Survey Documentation and Analysis (SDA) system (when available), or access the links to related reports or Web sites.
The search options section on SAMHDA's homepage provides three ways to search SAMHDA holdings.
- The variables search option searches question text and value and variable labels for all studies on the online Survey Documentation and Analysis (SDA) system. SAMHDA is currently working on making every study available on the variable search. You can limit your search to only the SAMHDA holdings or you may expand your search to include all of the ICPSR holdings. Note that this searches thousands of variables at once and therefore, may be slow depending on the search terms entered. The Office of Applied Studies (OAS), Substance Abuse and Mental Health Services Administration (SAMHSA), offers a short report on how to use this search option to find specific variables.
- The Study Descriptions search option searches for keywords or phrases within the descriptions of all the studies in SAMHDA's holdings.
- The Web Site search option searches for keywords or phrases on the SAMHDA Web site.
SAMHDA's holdings are indexed by the search utility on ICPSR's Web site. Simply enter a keyword into the search box on this page to generate a list of data files that meet your search criteria. Note that this procedure may return studies that are not held or supported by SAMHDA, and that are restricted to ICPSR member institutions. Please refer to the ICPSR help documentation for more on interpreting results from the ICPSR search page.

Do I have to create a MyData account to download SAMHDA holdings?

2010-08-05T14:57:00.003-04:00

No. Because data archived in SAMHDA are in the public domain, you have the option to log in anonymously.

How do I get permission to use the data archived at SAMHDA?

2010-08-05T14:48:00.005-04:00

Because data archived in SAMHDA are in the public domain, you do not need to obtain permission to access, analyze, or publish findings based on them. We do ask, however, that all users of SAMHDA data sets abide by the terms of use included in your download and properly cite the data files.

What is the distinction between a series and a study?

2010-08-05T10:29:00.001-04:00

By study, we mean a one-time data collection, a single year, or a collection within a series.

For example, the National Survey on Drug Use and Health (NSDUH) has been conducted annually since 1990, and every 2-3 years prior to that (as far back as 1979). When we talk about all the NSDUH surveys, we refer to the series; when we talk about a particular year, we refer to a study, which is also the downloadable object you can obtain from our Web site.

When we divide our collection into "Series" and "Other Studies," we're making a distinction between surveys repeated over time, and surveys that were a one-time event.

What is faceted searching? How does it work?

2010-08-05T10:19:00.001-04:00

With the new SAMHDA Web site, SAMHDA has enhanced searching our data holdings using SOLR. SOLR offers the following advantages:

Faceted searching
No more limit of 500 results
Date searching of multiple fields
Same search rules for data holdings, bibliography, and variables database

Faceted searching alone is a significant improvement.

Easy to shift between refining and expanding search results
Unlikely to hit "no results found" page as facets provide an indicator of the size of your result set
Seamless integration with keyword searching

The SAMHDA site features two types of searches: variables and study descriptions. By default, you search the variables in the studies.

Variables

In the screen above, I've done a search on "methamphetamine." The results page lists all the studies that have variables on methamphetamine, sorted by the number of matching variables. I'm not sure I want to limit myself to one study just yet, so I'm going to click on "Find matching variables in all studies" atop the page to return a list of all variables.

This returns nearly 2300 variables, which is a bit more than I want to page through. Looking to the right, I can see several facets for narrowing my results. Since I'm interested in looking at relatively current data, I select "2000-2009" under "Filter by Time Period." You'll note that it provides an indication of how many results I'll find. I've also sorted by "Time Period (newest)" to pull the most current variables to the top of the list.

Now that I've selected a facet, it no longer appears on the right, and I can see "time period:2000-2009" just above my search results. If I were to click on the "X" next to it, it would re-execute the search, removing that particular term. Thus I can narrow my search quickly by selecting a link to the right, or expand my search by removing a previously selected filter/facet. I can see a really good candidate in the list: "Ever used methamphetamine."

Clicking on the variable label takes me to a screen with additional information, including the full question text, responses, and frequencies.

Scrolling further down the page, I can see additional options. The option "view the study home page" will take me to the main entry point for the study, where I can read more on the sample, download the data files (in SAS, SPSS, or Stata format), and perform online analysis.

Study Descriptions

You can also search the study descriptions if you're looking for a particular study, investigator, or agency. If you click on the "Go" button without entering a search term, it automatically returns all studies.

Thus I can see that SAMHDA has 134 different studies. Looking to the right, I can see more facets, some of which are not available for variables. The subject facet gives a broad description of SAMHDA's holdings in this case. The "more" link expanded the facet from the top 5 terms to the top 15. A "less" link appears if you want to shrink the list again. There's a "view all" link at the bottom of the list, which I'll now follow.

The "view all" link returns a comprehensive list of all the subject terms assigned to SAMHDA studies, along with a count of how many studies were thus tagged. The book icon enables you to go to the thesaurus entry for that term if you want to find broader, narrower, or related terms, and clicking on the word itself performs a search for that term.

I've selected "cocaine," which returned 29 results. The subject facet no longer appears, and so other facets have risen to the top. I can use the other facets to narrow my results further by geography, time period, investigator, series, or recency (added to the site).

I attempted to download a study and saw a message that I "had no saved files." Why can't I download the files?

2010-07-26T08:26:00.005-04:00

This error can occur in the following situations:

You may have double-clicked the download icon on the previous page. In this case, the first click should have offered you the chance to download your files and, consequently, cleared your saved files. The second click generated this error message. In spite of the error message, the files were probably successfully downloaded. If not, please try again without double-clicking.
You are attempting to download member-only data and are not flagged as being part of a member institution, and this study is atypical in terms of how the documentation files are archived. On the study home page, you should have seen a note under "Access Notes" that stated, "These data are available only to users at ICPSR member institutions; you are not at a member institution. Thus you may only download the documentation files." You will need to use the "Download documentation files" option.

If you believe you received this message in error, please contact web-support@icpsr.umich.edu.

SDA Tutorial on Frequencies and Crosstabs

2010-06-22T13:03:00.006-04:00

Part 2 presents the practical application of SDA's features, using a specific ICPSR dataset to demonstrate how to conduct analyses on the data, as well as how to use other features like new variables creation, subsetting, and downloading data.

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.mp3 6.5MB

How do I provide information on my OpenURL/SFX server?

2010-06-11T08:53:00.006-04:00

ICPSR no longer records information on the OpenURL/SFX servers maintained by member institutions. Rather, we direct users to WorldCat, a service provided by OCLC. As long as your information is up-to-date with WorldCat, the full-text links from our site will work correctly.

My data are collected from very vulnerable populations. How can I prevent these data from being used to portray them in an injurious way?

2010-05-26T08:53:00.003-04:00

It is the policy of ICPSR that responsible science, which includes appropriate analytic methods and peer reviewed venues for research results, is adequate to protect vulnerable populations from inappropriate, unfair, and inaccurate portrayals. In order to participate in a valid scientific discussion of the issues that face vulnerable populations, researchers must be willing to share their data and methods in an ethically responsible manner with other researchers who wish to replicate or refute their findings. One must be willing to trust the peer review process to screen out analyses that do not conform to methods appropriate to the question at hand. ICPSR is strongly committed to protecting vulnerable individuals from being identified by data analyses, but the scientific process must be used to protect vulnerable populations from inaccurate representations.

I don't mind depositing the baseline study from my longitudinal data system, but is it possible to delay release of subsequent waves of data?

2010-05-26T08:51:00.003-04:00

The utility of longitudinal studies lies primarily in the follow-up embedded in the research design. While the baseline data will be valuable in the short run, NAHDAP will work with depositors on a time frame for acquisition and release of the subsequent waves of data. Without a reasonable time frame, baseline studies will not be acquired for the NAHDAP. Depositors can work with NAHDAP staff to develop a method for acquiring and releasing the additional waves under a delayed-dissemination agreement. These agreements allow the subsequent waves to be acquired and prepared but not released for secondary analysis until the appropriate time.

Can my data be embargoed until I or my research team finish all our planned analyses?

2010-05-26T08:50:00.003-04:00

ICPSR has a delayed-dissemination policy that allows researchers to deposit data earlier in the research process so that they may benefit from the data and documentation preparation services offered by staff. Delayed-dissemination contracts require depositors to commit to a timeline, which is usually two years from deposit to data release. Depositors have access to ICPSR files as soon as they are prepared and need not wait for the public release. They must, however, be willing to commit to the timeline for release.

Is it possible for me to read and approve research proposals based on my data? I wish to determine the nature of the research done with my data.

2010-05-26T08:49:00.002-04:00

The policy of ICPSR is that responsible use of secondary data should be unfettered by the research agenda of the original data producer. When the data are distributed under restricted-use contracts, a research proposal is required in order to screen users for a credible research agenda and to ascertain whether the data will meet their research needs. The proposal, however, is screened only by the contract administrator at NAHDAP.

If I deposit data with NAHDAP, who owns the data?

2010-05-26T08:48:00.003-04:00

ICPSR only asks for the right to redistribute the data, but does not acquire or retain the original copyright or transfer rights. ICPSR users must sign a terms-of-use agreement in order to download data that includes a clause that prevents the redistribution of the data for commercial purposes. The original owner of the data, which is usually the university or not-for-profit that received the grant or contract, retains copyright and other legal rights associated with the data.

In the informed consent documents, I promised the data would only be used by an approved research team. How can I now share my data with others?

2010-05-26T08:46:00.004-04:00

Unless the informed consent document names the members of the research team specifically, an amended Institutional Review Board application that includes a plan for data protection and dissemination can be filed with the lead institution to define the research team. Restrictive informed consent documents may prevent the release of data in purely public releases, but do not preclude the possibility of a research team that is defined by a group of restricted- or limited-use contract holders. The research team may be defined as those persons known to the original researchers. In the case of restricted-use or limited-use contracts, the researchers using the data are known to ICPSR and to the original research team.

My data are on very sensitive topics; the risk to participants is very high should they be re-identified. How can I protect the respondents?

2010-05-26T08:42:00.004-04:00

ICPSR evaluates all data files for disclosure risk using state-of-the-art techniques developed under a grant from the National Institutes of Health. From this evaluation, staff recommend a method of data release that protects the respondents from re-identification while retaining the analytic utility of the data. Release options include public release; public release with disclosure control practices put in place; restricted release with a user contract; enclave only release; and online analysis only with no micro-data download. A full public release is only warranted when there is little risk of re-identification or the data have been sufficiently transformed to substantially reduce that risk.

My data are very complicated. I am not sure users will be able to use the data. Will NAHDAP staff provide user support?

2010-05-26T08:40:00.002-04:00

ICPSR has three levels of user support. Our central email and telephone service uses help desk software to track and prioritize all user support inquires. Technical questions about data downloading and software issues are answered by tier 1 support staff. Questions about specific data files will be sent to NAHDAP staff who prepared the data for release to provide user support on data content and structure. The NAHDAP director and manager will provide more sophisticated, tier 3 support for complex technical questions. Depositors will not be expected to provide ongoing user support, but rather to provide all the documentation necessary for secondary data users to make sense of the original data collection. The ICPSR archival collection includes many very complex data systems that have been successfully analyzed by responsible researchers.

My data/documentation are not in a format that can be released to secondary users. How do I find the resources to prepare it for broader distribution?

2010-05-26T08:37:00.004-04:00

The National Institute on Drug Abuse has funded the National Addiction & HIV Data Archive Program (NAHDAP) to assist grant recipients in preparing data for release. NAHDAP staff will help clean and prepare data files, metadata and documentation in consultation with the grant staff. NAHDAP is built on the infrastructure of the Interuniversity Consortium for Political and Social Research (ICPSR), which is designed to easily create standardized, digitally stable data files, and to disseminate SAS, Stata, and SPSS files and searchable PDF codebooks and documentation. The staff of NAHDAP will standardize the data and documentation with input from the original data producers.

Why is sharing data useful to me? Why should I share data that I have worked very hard to collect and analyze?

2010-05-26T08:33:00.002-04:00

While data sharing is primarily useful for expanding scientific knowledge, it does provide benefits for individual researchers. Data systems that are in the public domain often generate additional research which is credited to the original source. For instance, the National Longitudinal Study of Adolescent Health, which has been in the public domain since its inception, has generated over 3,000 publications in the last 20 years authored by persons not on the original research team. In addition, data citation practices and the norms of scientific practice have changed substantially in the past 20 years so that the production of data is now considered a scholarly pursuit. A 2009 committee report by the National Academy of Sciences has emphasized the emerging role of data sharing both in science and in the careers of scholars.

What is the importance of the Integrated Fertility Survey Series?

2010-04-20T12:55:00.000-04:00

Though researchers from a broad range of disciplines have produced a large body of research on patterns in families and fertility, the ability to make comparisons over time -- a central task for understanding family change -- has been constrained by difficulties in using multiple datasets to perform time-series analyses. Such difficulties include changes in respondent universe, weighting procedures, imputation protocols, question wording, and variable availability across studies. This is especially true when attempting to include surveys from the earlier years. The IFSS project attempts to address these limitations by developing a data set that allows for comparisons across longer periods than were previously feasible. The primary aim of the IFSS is to establish a harmonized set of data and documentation across ten nationally representative surveys of fertility and family. It is expected that harmonization of common variables across multiple surveys will allow researchers, policymakers, students, and other constituencies to make comparisons across time.

What is harmonization? Why harmonize variables?

2010-04-19T12:59:00.000-04:00

Harmonization is a process by which variables are made comparable across survey years. Harmonization schemes must be developed for each IFSS variable so that comparisons can be made across time. In general, harmonization in the IFSS project involves combining, into a single variable, information covering comparable substantive ground but from different files in the original GAF, NFS, and NSFG data sets.

Differences in question text, sample design, and respondent universes further complicate the harmonization process. Challenges posed by this part of harmonization include varying universes by survey (and survey year) and question text. For example, the IFSS data set will contain a variable for ever having used contraception. In the 1970 National Fertility Survey, the universe of respondents of whom the question is asked contains all respondents in the sample. However, in the 1965 National Fertility Survey, the universe is smaller; it contains only respondents who were not pregnant at the time of the survey interview. For other variables, differences in universe are not observed. For example, respondent's age is asked of all respondents in all ten component surveys; therefore, respondent's age poses no universe problems to those using the IFSS data set. Given the subtle variation in variables across surveys, the IFSS staff must exercise care in evaluating variable comparability.

To assist users of the IFSS data, IFSS staff are developing comprehensive variable documentation that will address important comparability notes across variables and studies. Especially serious, for example, are comparability problems that are not evident from the coding structure, including alterations in the survey question wording and changes in the variable universe. Such documentation will include notes about changes in respondent universe, question design, data collection instrument design, and other information necessary for informed use of IFSS data. The IFSS project staff will seek to make transparent all decisions made in the harmonization process so researchers can choose whether to use the harmonized version or to create one of their own.

How are variables selected to be harmonized? Are all variables harmonized across all data sets?

2010-04-18T13:00:00.000-04:00

With consultation from the IFSS advisory panel -- composed of distinguished experts in fields as diverse as demography, economics, public health, survey methodology, and sociology -- variables selected for inclusion in the IFSS data set will be identified on the basis of their expected interest to social science researchers, graduate students, policymakers, and other constituencies. Not all variables in all surveys will be harmonized. In most cases, variables selected for inclusion in the IFSS data set will have comparable equivalents across three or more surveys.

Broadening the Scope and Range of Minority Data -- Making Data on Underrepresented Populations Accessible

2010-04-07T09:12:00.005-04:00

This webinar outlines ICPSR's continuing efforts to provide and make available more data about underrepresented populations for researchers to explore important questions and relationships . In late 2009, ICPSR redesigned and renamed the Minority Data Resource Center (MDRC) to the Resource Center for Minority Data (RCMD). The focus will be on the website's new layout and resources, the revised structure of the RCMD, and the current goals and programs for this archive's future.

Original webcast date: April 6, 2010

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ICPSR User Support

Important note about using the RECODE or COMPUTE online analysis features for TEDS-A, Concatenated 1992-2009

How do I contact someone at SAMHDA for assistance?

How often does SAMHDA release new data files for download? Can I be notified?

How does SAMHDA prepare data files for release?

How much do SAMHDA data sets cost?

What resources are available through SAMHDA?

What is SAMHDA?

How can I obtain restricted HMCA Data?

Is state-level data available for NSDUH?

Why and how should I cite SAMHDA data?

Why should I cite data?

Where do I find the citation?

What do the citations look like?

How can I let SAMHDA know about my publication?

Why is the video so wide?

How do I code my own dataset?

How were the microphones positioned?

Why does the video appear to be distorted?

Can I get access to higher-quality versions of the videos?

Violence Against Women Resource Guide

Purpose of the Resource Guide

Sources of the VAW Data at NACJD

VAW Data Collection Highlights

Quick Links to VAW Data

Online Survey Documentation and Analysis

Help for Complex Data Collections

Human Subjects Issues

Funding Opportunities

Publications

Related Links

What are SAMHDA's Terms of Use?

Terms of Use

Privacy of RESEARCH SUBJECTS

Citing Data

Disclaimer

Violations

Definitions

Whom do I contact if I have questions about the NSPY restricted files?

Inquiry-based Learning Webinar

How do I find data that I can use for my research project?

What is MyData?

Do I have to establish a MyData account?

Resetting File Extensions in Windows 7

When I attempt to play your video files, I get an error message about a missing codec. What's going on?

How does ICPSR handle authentication for access to its data and members-only content?

Benefits of Third Party Authentication

Privacy Issues

Multiple Logins

How do I obtain a data collection from NACJD?

Why is the text so small on your Web site?

How can I archive crime or criminal justice data that I have collected?

How can I use NACJD data with statistical packages?

Does ICPSR accept qualitative data?

Subsetting using SDA

Who is the sponsor for TEDS?

Who is the sponsor for NSDUH?

When will the next year of data be available for MTF?

When will the next year of data be available for NSDUH?

Are earlier years of data available for the TEDS-Discharges series?

What are Interactive Maps and how do I use them?

There are more than 500 citations that I want to export, but the system only allows for up to 500. How can I export the remaining citations?

Can I get assistance from ICPSR in loading the MARC into my card catalog?

Can I submit ICPSR MARC records to OCLC?

Can I just obtain the MARC from OCLC?

How do I convert MARC21 XML into MARC?

How do I match your MARC records to existing records in my catalog?

How can I get around the 500 results maximum on the metadata export/search results?

Can I generate MARC for studies updated in a particular date range?

Can I obtain MARC from ICPSR? What about Dublin Core?

May I use this question in building my own survey?

What kind of data formats does the archive distribute? Do you have SPSS Portable files? SAS transport? Stata?

Plain Text

SAS

SPSS

Stata

Using ASCII data and setup files

Uniform Crime Reporting Program Resource Guide

Select a Year to Browse UCR Agency-Level Data for 1975-Current

Select a Year to Browse or Download UCR Agency-Level Data for 1966-1976