Patterns of Change in Assessed Risk for Recidivism: Jointly Modelling Intra-Individual Change and Associated Recidivism

In this thesis, I examine how individuals change following release from incarceration and how these changes are associated with recidivism. Using dynamic risk assessment data collected as part of routine community supervision practice, I present two analytic approaches not previously used in corrections research: joint latent class modeling and shared random effects modeling. This research advances methodology in the field of corrections psychology and contributes to knowledge of how repeated assessment of changeable risk factors can support community supervision's objectives of supporting community re-entry and preventing recidivism.