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The purpose of the Census of Juveniles on Probation (CJP) was to collect individual-level data about youth on probation, including their numbers and characteristics. The CJP survey asked respondents to report the total number of juveniles on formal probation within their reporting jurisdiction on the reference date of October 24, 2012.

For each youth on probation, responding agencies were asked to provide the following information: sex, date of birth, race, most serious offense, state and county where most serious offense was committed, and the state and county where the juvenile resided on the census reference date.

This data collection contains the national data.

Using data from all three waves of the Project on Human Development in Chicago Neighborhoods (PHDCN), this secondary data analysis examined the long-term effects of intimate partner violence (IPV) exposure during childhood and adolescence on subsequent externalizing behaviors (i.e., delinquency, violence, and substance use related offending).

The research questions for this study were as follows:

1. Are there significant differences in the mean scores of different externalizing behaviors (measured as "offending" in the present study) in any of the three PHDCN waves between youth exposed to IPV and youth not exposed to IPV?
2. Are there distinct developmental trajectories of externalizing behaviors among youth exposed to IPV when compared to those not exposed to IPV?
3. How do different individual- and neighborhood-level variables act in predicting the developmental paths of externalizing behaviors among youth exposed to IPV?

Propensity score matching (PSM) was employed to match individuals reporting IPV exposure with those not exposed to IPV on key variables. Longitudinal latent class analyses (LLCA) were utilized to estimate the longitudinal developmental trajectories of externalizing behaviors independently for IPV and non-IPV exposed males and females and compared to each other. Multinomial logistic regression models were estimated separately for males and females exposed to IPV during their childhoods to examine the effect of different hypothesized class membership predictors.

This collection contains a master dataset primarily sourced from Emery's (2006) data augmentation along with key variables from all three waves from the PHDCN Longitudinal Cohort Study, cohorts 12 and 15 (DS1); datasets constructed solely for multinomial logistic regressions for youth exposed to IPV, separated by sex (DS2 and DS3); data for the final LLCA models separated by sex and exposure to IPV (DS4 to DS7); and probabilities and latent classes created using Mplus (DS8 to DS9) that can be merged to the multinomial regression data using the SUBID variable. Additionally, syntax for variable and model constructions, as well as Mplus output, have been included as a zip package. Please refer to the P.I. documentation for more information.

This project consists of secondary analysis material (syntax only, no data). The original study that the material pertains to examines two distinct but related types of corporate crime prevention and control mechanisms--one that rests on firm governance (specifically, the Board of Directors) and the other on formal legal interventions. Specifically, the study examines whether (ceteris paribus) firms with more gender diversity on their boards are less involved in offending than firms whose boards are less diverse and whether changes in board diversity over time affect firm offending patterns. Of additional interest is how firms respond to legal discovery and punishment.

Do they change their governance structures (i.e., become more diverse) due to formal legal discovery?

Are firms generally deterred from reoffending (recidivism) when discovered or does deterrence depend on the government's response to offenders?

In particular, are certain regimes (criminal, civil, or regulatory) more successful at crime control than others?

Relevant data are collected from a variety of secondary sources, including corporate financial, statistical, and governance information. These data are then linked to cases of corporate offending (accounting fraud, bribery, environmental and anti-competitive) for 3,327 US based companies between 1996 and 2013.

Analyses-NIJ-5.21.2019--2-.do: Syntax (Stata) used to create type of offense count; domain of processing (civil, criminal, regulatory); offense distribution (by corporate year), female board membership (count and percent); Reoffending (by enforcement type and governance characteristics).

These data are part of NACJD's Fast Track Release and are distributed as they were received from the data depositor. The files have been zipped by NACJD for release, but not checked or processed except for the removal of direct identifiers. Users should refer to the accompanying readme file for a brief description of the files available with this collection and consult the investigator(s) if further information is needed.

This study examined life histories and experiences of individuals involved in the sex trade in New York City.

Also interviewed were twenty-eight criminal justice policymakers, practitioners, and community representatives affiliated with New York City's Human Trafficking Intervention Courts (HTICs).

The collection contains 1 SPSS data file (Final-Quantitative-Data-resubmission.sav (n=304; 218 variables)).

Demographic variables include gender, age, race, ethnicity, education level, citizenship status, current housing, family size, sexual orientation, and respondent's place of birth.

Across the country, child welfare and juvenile justice systems now recognize that youth involved in both systems (i.e., dual system youth) are a vulnerable population who go unrecognized because of challenges in information-sharing and cross-system collaboration. These challenges currently prevent the development of accurate estimations of the number of dual system youth nationally and limit understanding of best practices used by jurisdictions implementing integrated systems models. OJJDP funded this secondary analysis study to address this gap in knowledge.

All data used in the Dual System Youth Design Study were owned or accessed by various partners. No primary data collection occurred in the study. In addition, most of the data accessed by the study partners was confidential, de-identified data that required memorandums of agreement and/or court orders to access and use. Some sites have ongoing or standing agreements with the public agencies who own the data which allow access and use for specific projects. Because, most frequently, the data are owned by the public service agencies and include sensitive information the data are not available to be publicly archived. Instead, here a descriptive overview is provided of the data used and accessed by each study partner as well as contact information of a person at each site that will be able to share syntax and/or coding parameters for those who are considering to replicate the findings or methods.

Researchers interested in inquiring the data and syntax used in this project should refer to the study partners section of the downloadable study documentation. Data provider agency names along with the specific study data that were requested are listed in the documentation.

In the United States, a number of challenges prevent an accurate assessment of the prevalence of hate crimes in different areas of the country. These challenges create huge gaps in knowledge about hate crime--who is targeted, how, and in what areas--which in turn hinder appropriate policy efforts and allocation of resources to the prevention of hate crime. In the absence of high-quality hate crime data, online platforms may provide information that can contribute to a more accurate estimate of the risk of hate crimes in certain places and against certain groups of people. Data on social media posts that use hate speech or internet search terms related to hate against specific groups has the potential to enhance and facilitate timely understanding of what is happening offline, outside of traditional monitoring (e.g., police crime reports). This study assessed the utility of Twitter data to illuminate the prevalence of hate crimes in the United States with the goals of (i) addressing the lack of reliable knowledge about hate crime prevalence in the U.S. by (ii) identifying and analyzing online hate speech and (iii) examining the links between the online hate speech and offline hate crimes.

The project drew on four types of data: recorded hate crime data, social media data, census data, and data on hate crime risk factors. An ecological framework and Poisson regression models were adopted to study the explicit link between hate speech online and hate crimes offline. Risk terrain modeling (RTM) was used to further assess the ability to identify places at higher risk of hate crimes offline.