Help

I need help with . .

Access

Questions about login/password, restricted data, and member-only data

I've forgotten my password. How do I get a new one?

Just go to the MyData login page. At the bottom of the page is a link titled Forgot your password? You'll be asked to enter your email address, and then a new password will be sent to you. After you get the email, you'll probably want to change your password to something easily remembered.

I've changed my email address. How do I change my login? What if I change institutions?

In the navigation bar, you'll see a link titled Login/Account Info. If you've already logged in, this will take you to a list of MyData links. One of those is Edit Account Settings. Here you can change your password, email address, or any other information you have previously provided.

When I attempt to access data from home, I get an "access denied" message, even though my institution is an ICPSR member. Why?

Assuming your OR has provided us with the IP ranges for your institution, you can access data from home, but you first have to login to the ICPSR website from on campus. Once you do so, your MyData account is validated for off campus downloads for the next six months. Every time you log in to the ICPSR website from on campus, that is extended another six months.

In the event that you simply cannot get on campus (for example, you're on sabbatical), simply contact help@icpsr.umich.edu. We'll check with your institution's Official Representative and then manually validate your account.

Whenever I try to login, I get a message stating that I don't have cookies enabled. What's going on?

Under normal operation the website uses the HTTP protocol to deliver content. In a small number of cases we use HTTP with SSL encryption (commonly referred to as HTTPS) to protect the security of the data moving across the network. Our login procedure is one such case.

When one tries to access a resource on the website, the system checks for the presence of a login ticket (or, more generically, a cookie). If there is no ticket available, the browser is redirected to a URL (using HTTP) where the person enters a login and password. When the person clicks the Log In button, the login and password are delivered to the website via HTTPS, and the website returns a login ticket via HTTPS. Finally the website returns the person to the original Web page or resource via HTTP. If anything goes wrong during this process, we deliver an error message about cookies not being enabled, which is the most common cause of failure. The next most common failure is when a site uses a proxy server or firewall, but only proxies HTTP, not HTTPS, and so the transaction fails.

The work around is to force the entire transaction through HTTPS. Here's an easy way to do that:

  1. Go to this page: http://www.icpsr.umich.edu/mydata?path=ICPSR
  2. This should redirect you to the error page about cookies not being enabled. The URL in the "Address bar" should look like this: http://www.icpsr.umich.edu/rpxlogin.
  3. Modify the URL above, adding in an "s" between the "p" in "http" and the semicolon. It should now look like this: https://www.icpsr.umich.edu/rpxlogin
  4. Enter your MyData login and password, and click the Log In button. Or click the Log In Anonymously button.
  5. You should now have this URL in your "Address bar"(http://www.icpsr.umich.edu/mydata?path=ICPSR) and have a list of account-related actions you can take. The important thing, though, is that you now have a ticket (cookie) and should be able to download resources. If you click the logo at the top of the page, that will return you to the home page, and you can then use the website as usual.

Please note that you cannot combine steps (1) and (3) by starting at a HTTPS-delivered version of the home page, because you will still be redirected to an HTTP-type link for the login page after performing step (2). Thus step (3) will still be necessary, and it will also force many web fetches to incur the SSL encryption overhead on both our server and your desktop machine.

There are a number of problems that can pop up when you attempt to download data:

  1. You download a zipped file, but you don't get any data files...just the documentation.

    This means that we can't tell that you're part of a member institution, and the data you're attempting to download is only available to member institutions.

    This happens in four situations: 1) when you're trying to download data from home, but haven't downloaded from on campus in the last six months; 2) when you're trying to use a proxy server, but your settings are incorrect; 3) ICPSR simply doesn't have the complete IP range for your campus; and 4) the data that you are attempting to download are restricted from general dissemination (i.e., the data contain sensitive information and you have to fill out a lot of paperwork to obtain it).

    If you're downloading from home, read the information below on off-campus access. If you're using a proxy server, please stop; just go directly to our website. You'll know you're at a proxy address if the URL looks like http://www.myschool.edu.0-icpsr.umich.edu/ or the like.

    If the above suggestions don't apply, please follow the instructions in Helping ICPSR Troubleshoot below, and we'll attempt to fix the problem.

  2. You type in your login and password and get a message that you have successfully logged in, but are then sent back to the login screen.

    This generally means that you're not connecting to your proxy server properly. In this situation, you will need to talk to your Official Representative or your local IT/networking staff. Please note that it is not necessary to use a proxy server to access ICPSR data, and in many cases using a proxy server will interfere with downloads.

    If you're certain that you're not using a proxy server, please follow the instructions below in "Helping ICPSR Troubleshoot".

  3. You get a message that states, "You have successfully authenticated but you are not authorized to access the requested document."

    Some sections of our website are only available to specific users. For example, some functions on the Official Representative (OR) website are only available to users whose MyData account has been flagged as an OR account. Or you have attempted to access an online analysis utility that is only available to member institutions.

    If you believe you have reached this message in error, please e-mail web-support@icpsr.umich.edu, and be sure to send them the URL of the page in question.

  4. You get an error message stating "...[your file] could not be saved, because the source file could not be read. Try again later, or contact the server administrator."

    This is an error that relates to Firefox/Mozilla. Please consult their documentation on the problem.

Helping ICPSR Troubleshoot

In order for ICPSR to investigate most access problems, we need to know your computer's network address and what network address you're forwarding to us. To gather that info, do the following:

  1. Log in to MyData.
  2. Go to our ticket verification tool.
  3. Copy the text there and paste it into an e-mail to web-support@icpsr.umich.edu.

Downloading Data from Off-Campus

If your institution's Official Representative has provided us with the IP ranges for your institution, you can access data from home, but you first have to login to the ICPSR website from on campus. Once you do so, your MyData account is validated for off campus downloads for the next six months. Every time you log in to the ICPSR website from on campus, that is extended another six months.

In the event that you cannot get on campus (for example, you're on sabbatical), contact help@icpsr.umich.edu. We'll check with your and then manually validate your account.

In many cases there is no public version of the data. If our website does not display a public version, then only a restricted version has been authorized for release by the Principal Investigator.

In general, information on how to obtain the data can be found on the study home page. At the top of the page under "Access Notes" there will be a note that will direct you to our online data access request system, or will point you to a PDF file that explains the steps you need to take to obtain the data.

Before you begin filling out the paperwork, you should know the following:

  • Restricted data is generally only made available to PhD-level researchers and their staff; it is not appropriate for undergrad-level projects or class projects.

  • The review process can take as little as a few days or as much as 4-6 weeks.

  • You'll need to fill out paperwork describing your project and how you'll protect the data. This may include providing technical specifications on the security of your work environment.

  • You will need to get approval from the Institutional Review Board on your campus.

ICPSR does not discourage the use of restricted data. In fact, we've put a lot of effort into building systems that make this data available to our users. We are, however, very serious about protecting respondent confidentiality and ensuring that sensitive data is used appropriately.

You can find a video tutorial addressing this topic on the ICPSR YouTube channel.

Analysis

Issues specific to statistical software (SAS, SPSS, Stata, R)

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.

Many of our data collections that contain ASCII data files are accompanied by setup files that allow users to read the text files into statistical software packages. Since a visual interpretation of alphanumeric data files is inefficient, statistical software is needed to define, manipulate, extract, and analyze variables and cases within data files. We currently provide for many of our data collections setup files for SAS, SPSS, and Stata statistical software packages, three of the more commonly used analytical software packages for the social sciences.

Utilizing Setup Files

ICPSR has prepared tutorials on how to analyze data using setup files:

  • ASCII Data File + SAS Setup Files: PDF PPT
  • ASCII Data File + SPSS Setup Files: PDF PPT
  • ASCII Data File + Stata Setup Files: PDF PPT

You can find video tutorials addressing this topic on the ICPSR YouTube channel.

Troubleshooting Setup Files

Many statistical packages will not run a set-up file unless you reset the Windows default setting that hides file extensions. If you continue to have difficulty running a setup file, you can contact ICPSR User Support at help@icpsr.umich.edu.

You can find a video tutorial addressing this topic on the ICPSR YouTube channel.

Requesting Setup Files from ICPSR

If you're not comfortable building your own setup file, you can contact ICPSR User Support at help@icpsr.umich.edu to inquire about the possibility of ICPSR creating setup files for the study.

If it is determined that ICPSR cannot create them or cannot create them within your time frame, then you are advised to seek assistance from someone on your campus with experience in the statistical package that you have chosen to use.

Understanding Setup Files

If you wish to create a setup file on your own, you should download the documentation for the study, and then consult our tutorial on interpreting a record from an ASCII data file.

The following instructions explain the different components of SAS, SPSS, and Stata setup files. Setup files for certain collections may not contain all of the commands listed below.

SAS Setup Files

SAS setup files can be used to generate native SAS file formats such as SAS datasets, SAS xport libraries, and transport files. Our SAS setup files generally include the following SAS sections. Click on each section to see an example taken from ICPSR 6512 (Capital Punishment in the United States, 1973-1993).

  1. PROC FORMAT: Creates user-defined formats for the variables. Formats replace original value codes with value code descriptions. Not all variables necessarily have user-defined formats.
  2. DATA: Begins a SAS data step and names an output SAS dataset.
  3. INFILE: Identifies the input data file to be read with the input statement. Users must replace the "physical-filename" with host computer-specific input file specifications. For example, users on Windows platforms should replace "physical-filename" with "C:\06512-0001-Data.txt" for the data file named "06512-0001-Data.txt" located on the root directory "C:\".
  4. INPUT: Assigns the name, type, decimal specification (if any), and specifies the beginning and ending column locations for each variable in the data file.
  5. LABEL: Assigns descriptive labels to all variables. Variable labels and variable names may be identical for some variables.
  6. FORMAT: Associates the formats created by the PROC FORMAT step with the variables named in the INPUT statement.
  7. MISSING VALUE RECODES: Sets user-defined numeric missing values to missing as interpreted by the SAS system. Only variables with user-defined missing values are included in the statements.

SPSS Setup Files

SPSS setup files can be used to generate native SPSS file formats such as SPSS system files and SPSS portable files. SPSS setup files produced by generally include the following SPSS sections. Click on each section to see an example taken from ICPSR 6512 (Capital Punishment in the United States, 1973-1993).

  1. DATA LIST: Assigns the name, type, decimal specification (if any), and specifies the beginning and ending column locations for each variable in the data file. Users must replace the "physical-filename" with host computer-specific input file specifications. For example, users on Windows platforms should replace "physical-filename" with "C:\06512-0001-Data.txt" for the data file named "06512-0001-Data.txt" located on the root directory "C:\".
  2. VARIABLE LABELS: Assigns descriptive labels to all variables. Variable labels and variable names may be identical for some variables.
  3. VALUE LABELS: Assigns descriptive labels to codes in the data file. Not all variables necessarily have assigned value labels.
  4. MISSING VALUES: Declares user-defined missing values. Not all variables in the data file necessarily have user-defined missing values. These values can be treated specially in data transformations, statistical calculations, and case selection.
  5. MISSING VALUE RECODE: Sets user-defined numeric missing values to missing as interpreted by the SPSS system. Only variables with user-defined missing values are included in the statements.

Stata Setup Files

Stata setup files can be used to generate native Stata DTA files. Stata setup files produced by ICPSR generally include the following Stata sections. Click on each section to see an example taken from ICPSR 6512 (Capital Punishment in the United States, 1973-1993).

  1. FILE SPECIFICATIONS: Assigns values to local macros that specify the locations of the files used to build a Stata system file. Users must replace the "physical-filename" with host computer-specific input file specifications. For example; users on Windows platforms should replace "raw-datafile-name" with "C:\06512-0001-Data.txt" for the data file named "06512-0001-Data.txt" located on the root directory of "C:\". Simarlarly, the "dictionary-filename" should be replaced with "C:\06512-0001-Stata_dictionary.dct". The "stata-datafile" specification should be named with the specification for where you wish to store the Stata system file.
  2. INFILE COMMAND: Reads the columnar ASCII data into a Stata system file.
  3. VALUE LABEL DEFINITIONS: Defines descriptive labels for the individual values of each variable.
  4. MISSING VALUES: Replaces numeric missing values (i.e., -9) with generic system missing ".". By default the code in this section is commented out. Users wishing to apply the generic missing values should remove the comment at the beginning and end of this section. Note that Stata allows you to specify up to 27 unique missing value codes.
  5. SAVE OUTFILE: This section saves out a Stata system format file. There is no reason to modify it if the macros in Section 1 were specified correctly.

Many of our datasets offer delimited files as a download option. Delimited files can be imported into Excel. ICPSR has a tutorial on how to read tab-delimited files into Excel.

Please note that this process will not work with the majority of ASCII data disseminated by ICPSR as they are not tab-delimited. For instructions on how to import non-tab-delimited ASCII data into Excel, please seek help from your campus' Official Representative in finding someone on your campus who can help you accomplish this.

R acts as an alternative to traditional statistical packages such as SPSS, SAS, and Stata such that it is an extensible, open-source language and computing environment for Windows, Macintosh, UNIX, and Linux platforms. Such software allows for the user to freely distribute, study, change, and improve the software under the Free Software Foundation's GNU General Public License. It is a free implementation of the S programming language, which was originally created and distributed by Bell Labs. However, most code written in S will run successfully in the R environment. R performs a wide variety of basic to advanced statistical and graphical techniques at little to no cost to the user. These advantages over other statistical software encourage the growing use of R in cutting edge social science research.

Where can I obtain R?

Installation files for Windows, Mac, and Linux can be found at the website for the Comprehensive R Archive Network, http://cran.r-project.org/. The site also contains documentation for downloading and installing the software on different operating systems. There is no cost for downloading and using R.

Where can I find more information on R?

Books

Braun, W. and Murdoch, D. (2007). A First Course in Statistical Programming with R. Cambridge, MA: Cambridge University Press.

Chambers, J. M. (1998). Programming with Data: A Guide to the S Language. Murray Hill, NJ: Bell Laboratories.

Dalgaard, P. (2008). Introductory Statistics with R (2nd edition). New York: Springer.

Everitt, B., and Hothorn, T. (2006). A Handbook of Statistical Analyses Using R. Boca Raton, FL: Chapman & Hall/CRC.

Faraway, J. J. (2005). Linear Models with R. Boca Raton, FL: Chapman & Hall/CRC.

Faraway, J. J. (2006). Extending the Linear Model with R: Generalized Linear, Mixed Effects and Nonparametric Regression Models. Boca Raton, FL: Chapman & Hall/CRC.

Fox, J. (2002). An R and S-Plus Companion to Applied Regression. Thousand Oaks, CA : Sage Publications.

Muenchen, R. A. (2009). R for SAS and SPSS Users. Springer Series in Statistics and Computing. New York: Springer.

Murrell, P. (2005). R Graphics. Boca Raton, FL: Chapman & Hall/CRC.

Pinheiro, J. C. and Bates, D. M. (2004). Mixed Effects Models in S and S-Plus. New York: Springer.

Spector, P. (2000). Data Manipulation with R. New York: Springer.

Venables, W. N., and Ripley, B. D. (2002). Modern Applied Statistics with S. Fourth Edition. New York: Springer.

Zuur, A. F., Ieno, E. N., and Meesters, E. H. W. G. (to be published 2009). A Beginner's Guide to R. Use R. New York: Springer.

Web Resources
Seminal Journal article

Ihaka, R., and Gentleman, R. (1996). R: A language for data analysis and graphics. Journal of Computational and Graphical Statistics, 5(3):299-314.

References:

How do I read ICPSR data into R?

We have a brief tutorial available on how to read data into R.

Can I use R without having to learn the details of the R language?

Yes (at least for the basics), there are a number of "front ends" that have been constructed in order to make it easier for users to interact with the R statistical computing environment. For example, a graphical user interface (or "GUI") allows the analyst to carry out data analysis tasks by selecting items from menus and lists, rather than entering commands.

One such GUI is the R Commander, written by John Fox. The R Commander is accessed by installing and loading the Rcmdr package within R. The R Commander provides an easy-to-use, menu-based system for loading data into R, manipulating data values, performing statistical analyses, creating graphical displays, and carrying out diagnostic tests on statistical models. Documentation for the R Commander is available on John Fox's website and in the following paper:

Fox, John. 2005. "The R Commander: A Basic-Statistics Graphical User Interface to R." Journal of Statistical Software 14(9).

There are several other GUI systems, in addition to the R Commander, for interacting with R.

The advantage provided by the R Commander or another GUI is that the user does not need to learn a language in order to carry out his or her analysis. Instead, each step is taken by making one or more selections from a menu of available options. The disadvantage of interacting with the R environment through a GUI is that the course of the analysis is limited to those actions that have been programmed into the GUI. Thus, one could argue that using a GUI removes much of the flexibility that is inherent in the R environment.

In order to overcome the preceding limitation, the R Commander and most other GUIs allow the user to employ both methods of interacting with the environment within a single R session. For example, one could invoke the R Commander, and use its GUI to read the contents of an external file and create an R data frame. For many types of analyses, other features of the R Commander could be used to estimate model parameters, construct graphical displays, and so on. But, if the user wanted to carry out a task that is not available in the R Commander (e.g., a multidimensional scaling analysis), then the data frame created in the GUI could still be treated like any other currently defined R object (say as an argument to a function or the target of an assignment) on the R command line. In this manner, a user could exploit the advantages of both the GUI and the command-line interface.

The National Archive of Criminal Justice Data (NACJD) has prepared a detailed tutorial on how to use ICPSR data with GIS software. Please consult this tutorial for more information.

ICPSR uses software called SDA to enable users to do statistical analysis via their Web browser. Online analysis is available for over 750 studies on our website.

The Survey Documentation and Analysis (SDA) system allows users to conduct statistical analysis quickly and efficiently 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.

You can also find video tutorials on using SDA on the ICPSR YouTube channel.

Download

Specific concerns related to the download process

Our files are compressed using WinZip and have the .zip file suffix. Users who download compressed files will have to decompress the files before using them.

Windows

Windows XP has a built-in decompression tool that decompresses .zip files. Users with other Windows versions may need to download the utility from the WinZip website.

WinZip and the Saved Files Utility

WinZip users (and those who use the built-in decompression tool in Windows XP), should be aware that WinZip has two ways to extract files: by using drag-and-drop and by choosing "Extract" from underneath the "Actions" menu. These two methods produce different results. If you use drag-and-drop, then you will only get the files...not the folders that enclose them. Hence you'll lose the hierarchy that we've set up (including folders that are titled with study names and dataset names). If you use the "Extract" command from the "Actions" menu, then the folder hierarchy is preserved if 'Use Folder Names' is specified in the extraction dialog box.

When I attempt to uncompress the files I downloaded from your site, WinZip complains that the file name is insensible. How can I uncompress the file?

The total path length (not file name length) has to be less than 255 characters. Our file names can be lengthy. If the path to which you wish to extract your files is also lengthy, then WinZip will fail.

Extract your files to the root directory of your hard drive. I.e., extract the files to c:/ instead of c:/User/My Documents/Various Social Science Projects On Which I Work/ICPSR Data/.

Macintosh

For Macintosh OSX users, decompression software is built into the operating system; you can open compressed files by double-clicking on the .zip file.

If you're encountering problems with the MacOSX built-in decompression software, you may wish to download StuffIt Expander.

UNIX/Linux

Users in the UNIX/Linux environment can simply use the unzip command to decompress .zip files.

Once you have the appropriate software on your local machine, follow the instructions supplied by your software to decompress the zipped files.

Some older Web browsers set a limit on the maximum file size that can be downloaded. For example, Internet Explorer 6 will not download files larger than 2GB, and Internet Explorer 7 will not download files larger than 4GB.

If you're having trouble downloading a large study, we recommend you try the following:

  1. Verify that you have enough hard disk space for the download.
  2. Update/upgrade your Web browser.
  3. Try using a different Web browser.
  4. Download a single dataset at a time.

Restricted Data

 

In general, information on how to obtain the data can be found on the study home page. At the top of the page under "Access Notes" there will be a note that will direct you to our online data access request system, or will point you to a PDF file that explains the steps you need to take to obtain the data.

Before you begin filling out the paperwork, you should know the following:

  • Restricted data is generally only made available to PhD-level researchers and their staff; it is not appropriate for undergrad-level projects or class projects.

  • The review process can take as little as a few days or as much as 4-6 weeks.

  • You'll need to fill out paperwork describing your project and how you'll protect the data. This may include providing technical specifications on the security of your work environment.

  • You will need to get approval from the Institutional Review Board on your campus.

ICPSR does not discourage the use of restricted data. In fact, we've put a lot of effort into building systems that make this data available to our users. We are, however, very serious about protecting respondent confidentiality and ensuring that sensitive data is used appropriately.

You can find a video tutorial addressing this topic on the ICPSR YouTube channel.

The time it takes for a researcher to receive data varies and depends on the quality and completeness of the application, the number of applications NACJD/ICPSR is currently handling, and the Archive Director's schedule and availability to review. Typically, it takes two to four weeks for an application to be approved and for the data to be sent to the researcher.

One application will suffice for all restricted data archived by NACJD, with the exception of Census of Juveniles in Residential Placement (CJRP) and Juvenile Residential Facility Census (JRFC) data. These series are only available for online analysis through remote access or through the physical enclave and require a separate application. For all other data requests, please list all studies by title and number.

If the requested data do not meet your needs, you may request additional datasets by adding them to the existing Agreement in IDARS and resubmitting it for review. Moreover, if new serial data are released and you were approved for an earlier year(s) of the dataset, you may append the newly released data to your existing agreement.

An Investigator can add as many staff members to an existing agreement as needed. To add staff members, the Investigator and staff member(s) must complete and sign the Confidentiality Pledge.

All agreements are valid for two years, given IRB approval/exemption. To extend your Agreement for an additional two years, you must complete the Request for Extension of Use (PDF 107K) and send updated IRB approval (if the original IRB documentation has expired and was not exempted).

You will need to complete a new application. An agreement is valid only under the terms it was signed with the institution, which will no longer be valid.

No. The Agreement is a standard template that is required to be signed by the authorized institutional representative for each request. The terms and conditions are written to apply uniformly to all data users, without modification.

General

Other questions about data and documentation

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 ICPSR data can be found in the following locations:

  1. Study descriptions that appear on the website
  2. File manifest
  3. 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 ICPSR know about my publication?

Users of ICPSR 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.

A persistent URL is one that never changes. Persistent URLs are designed so that your bookmarks and links don't break when a website gets updated.

On the ICPSR website, our study descriptions now have persistent URLs. If you want to bookmark a study page or link to it from your website, you should bookmark/link the persistent URL. In most browsers, you can do this by right-clicking on the link. In the menu that appears, there will be an option to add it to your bookmarks.

DOIs

DOI stands for Digital Object Identifier (see The DOI System). A DOI is a unique persistent identifier for a published digital object, such as an article or a study. A DOI also links to an article or study. Here is an example of a citation for an ICPSR study with a DOI:

Deschenes, Elizabeth Piper, Susan Turner, and Joan Petersilia. INTENSIVE COMMUNITY SUPERVISION IN MINNESOTA, 1990-1992: A DUAL EXPERIMENT IN PRISON DIVERSION AND ENHANCED SUPERVISED RELEASE [Computer file]. ICPSR06849-v1. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2000. doi:10.3886/ICPSR06849

The DOI in this example is 10.3886/ICPSR06849 and it links to the URL:

http://dx.doi.org/10.3886/ICPSR06849

ICPSR maintains the DOI so that the link will always work. On most websites, when you see a DOI it is a clickable object.

If you're accessing an article, you'll probably be forwarded to a reference service like JStor or ProQuest, and if your campus has a subscription with that service, you should be able to access the full text. In other words, DOIs also have built-in OpenURL functionality.

DOIs are concise and easy to include in a citation. DOIs appear in the citations that we display on the ICPSR website and include with each download. If you publish an article using ICPSR data and you include the DOI in the data citation as one of your references, you make it easy for other researchers to get back to the original data.

DOIs are also part of an integrated network of linkages between articles and datasets that is maintained by publishers and archives through registration agencies like CrossRef. The inclusion of DOIs in citations makes it much easier for us to see how a report or dataset generates other research, which in turn assists researchers in demonstrating the value and scientific impact of their work.

How do I get a DOI for my survey?

DOIs are created/registered by publishing organizations, not by individuals. If you deposit your data with us, we'll assign a DOI for your study and it will appear in the citation on our website and in downloads.

How do I get a DOI for my report or article?

The journal/publisher of the article should assign the DOI. If your publisher isn't registering DOIs, you should encourage them to do so. The CrossRef website has useful information on how to get started.

How do I use DOIs?

Include them in your footnotes and references. Just copy and paste the citation, as you normally would, along with the DOI. By properly citing the data and including the DOI, you're giving proper credit to the investigators who conducted the research and giving the scholarly community a clearer picture of the impact of the research.

How do I demonstrate the impact of a study I've deposited with ICPSR?

The related literature link, available with each study, connects to a list of all the publications based upon your study that ICPSR has been able to find. DOIs enable us to harvest those citations more easily, because publishers submit these linkages to the DOI repositories, and we can capture them from there. If you want to help the process along (to build a complete list of citations), please:

  • Email us any citations to publications we've missed: bibliography@icpsr.umich.edu.
  • Include the DOI for the dataset whenever you publish articles about the data.
  • Educate other researchers about the power and utility of DOIs. The system works best when everyone uses it.
  • When writing reports, always cite the data. Encourage other researchers to cite the data as well.
  • Encourage journal editors to include citations to data and their DOIs, along with citations to publications.

Yes, ICPSR can supply an earlier release version of a study unless that version is unavailable for confidentiality reasons or at the request of the principal investigator. Please send your request to help@icpsr.umich.edu and include the ICPSR study title, study number, principal investigator, and the date and/or version of the data of interest. Data will be supplied on cdrom, generally within a week or two of receipt of request.

How does ICPSR manage versioning?

  • What triggers a new edition or version of a study?
    • A change in any of the data and/or documentation files.
    • The addition of withdrawal of data and/or documentation files.
  • How and where is such a change to a study documented?

    In the metadata record:

    • Version field (in version notation "ICPSRXXXXX-v3")
    • Version history field (collect.changes, which provides a text description of what has changed, and a datestamp)
    • Citation display includes the version statement
  • What happens to unchanged files (if changes don't apply to all files)?
    • ICPSR does not currently version at the individual file level - our version statement references the collection as a whole. If only one file of a multiple file collection changes, the collection version changes.
  • Are previous editions/versions kept?
    • Yes, through a back-up system and a searchable 'browse archive' feature available to authorized staff.
  • Are these made available to users?
    • Upon request only, previous versions can be made available to users.
  • Clarification on terminology, do we use 'edition', 'version', or other terms?
    • ICPSR uses 'version' exclusively. (Historically, ICPSR used three different terms: edition, version, and release, but these have all been rolled into the single term "version" and the notation "ICPSRXXXXX-v3").
    • What do we mean by "version" : A form or variant of the original ICPSR-archived data collection.

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User support staff are available from 9:00 a.m. to 5:00 p.m., EST, Monday to Friday. Contact us if you cannot find the answer to your question on this site. Our goal is to respond to inquiries on the same day they are received. Many of our most frequently asked questions are available as tutorials or webinars on YouTube and are always accessible.