Click on the links below to hear brief podcasts summarizing research conducted by our graduate students and alumni!


Caitlin Taylor, PhD (Temple) Assistant Professor, LaSalle University
Click here to listen to the podcast titled: “Family Support and the Successful Reentry of Formerly Incarcerated Individuals”

Abstract: This PhD dissertation fills an important gap in the recidivism prediction literature by investigating the role of family support for individuals who have been deemed serious and violent offenders and recently released from state prisons.  As part of the evaluation of the Serious and Violent Offender Reentry Initiative (SVORI), 1,697 adult males and 357 adult females were interviewed 30 days prior to their release and then three, nine and 15 months following release.  Using the data collected from these interviews, this dissertation explores the relationship between emotional family support and instrumental family support and four measures of re offending: any self-reported criminal offending, any self reported violent offending, any self-reported drug offending and whether any arrest occurred (using official records from the National Crime Information Center) during each of the post-release follow up periods.
Controlling for other known predictors of re offending, logistic regression models are used to predict the likelihood of re- offending. Considering respondent attrition over successive interview waves, all analyses are conducted using list wise deletion as well as multiple imputation to handle missing data.  Results generally reveal that emotional support is associated with a significant decrease in re-offending, while instrumental support is not significantly associated with re-offending. These findings have a variety of implications for correctional policies and programming, sentencing policies, post-release supervision policies and programming, criminological theory and future research.

Evan Sorg, PhD student (Temple)
Click here to listen to the podcast titled: “Community Level Impacts of Temperature on Urban StreetRobbery” 

Abstract: This study explores the community-level connections between street robbery and temperature. It examines whether community socioeconomic status (SES) and crime-relevant land uses strengthen or weaken the temperature impact. A theoretical framework relying on routine activity theory, crime pattern theory, and resident-based control dynamics organized predictions. Monthly street robbery counts and temperature data for 36 consecutive months were combined with census and land use data, and analyzed with multilevel models. Results suggest that community robbery counts were higher when temperatures were higher, and in lower SES communities. In support of previous work with property crime, but in contrast to previous work with assault, the effects of temperature were stronger in higher SES communities. In support of the integrated model, commercial land use prevalence and subway stations were associated with heightened temperature impacts on robbery.Community-level fixed and random effects of temperature persist when controlling for land use and community structure; further, the random effects depend in part on both. There are implications for understanding indigenous guardianship or informal resident-based place management dynamics, and for planning seasonal police deployments.

Cory Haberman, Phd student (Temple)
Click here to listen to the podcast titled: “The variable impacts of public housing community proximity on nearby street robberies”

Abstract: Objectives: Use crime pattern theory to investigate the proximity effects of public housing communities on robbery crime while taking into account the presence of nearby nonresidential facilities.
Method: The study uses data describing 41 Philadelphia public housing communities and their surrounds. Surrounds are defined using two increments of street block-sized buffers. Multilevel models(buffer areas nested around public housing communities) allowing the proximity effect to vary across communities and predicting its shape with public housing level predictors are estimated.
Results: The multilevel models show that the shape of proximity effects varies across public housing communities and depends on community size, even after factoring in presence of nonresidential facilities. Spatially,multiple public housing communities close to one another have more intense robbery patterns.
Conclusions: Labeling all public housing communities as equally criminogenic robbery exporters is unwarranted. In fact, some communities have lower robbery counts than the areas surrounding them.Consequently, effectively addressing robbery in and around public housing communities will require careful consideration of where the problem is located. Locating public housing communities more than two blocks apart may reduce robbery.