Document Type



Doctor of Philosophy


Criminology and Criminal Justice

Date of Defense


Graduate Advisor

Beth Huebner


Eric Baumer, PhD


Stephanie DiPietro, PhD

Brian Johnson, PhD


This study explored the structural sources behind variability in the sentences applied to felons convicted in state courts located across the U.S. Multilevel regression models were used to explore whether various state and county-level attributes help to account for why defendants experience a significantly higher probability of incarceration versus probation in certain jurisdictions. Drawing upon a broad theoretical landscape, the analyses test several hypotheses derived from macro level theories of social control which predict that that the legal and organizational culture of courts, and the socioeconomic and political attributes of the communities they serve, influence sentencing outcomes. This study sought to fill two important gaps in the existing research. First, it broadened the theoretical framework used to interpret community variation in punishment to include the impacts of state sentencing policies that have been linked to the increase in mass incarceration among U.S. states. The second major goal of this study was to bring new data to bear on the issue of whether social and cultural attributes of communities are associated with the severity of the sentences their courts impose. The analysis examines this issue by linking individual sentencing outcomes to aggregate-level General Social Survey (GSS) responses that capture community variation in public sentiment. The sentencing data used to test these hypotheses are derived from the State Court Processing Statistics (SCPS) for the years 1998, 2000, 2002, and 2004. Information on a sample of 26,000 felony cases in the SCPS were appended to a unique county and state level database containing measures that capture variation in sentencing policy, criminal statutes, correctional resources, crime rates, court case load pressures, GSS survey responses, and census-derived demographic attributes.

OCLC Number