Relying on the results of validated risk assessments is vital to evidence-based decision making in corrections. Advancements in the approach to risk assessment has seen an emphasis on measuring dynamic risk factors alongside protective factors, as both are expected to be useful for identifying treatment targets and measuring changes in risk over time. Despite these advancements, empirical evidence of change on dynamic risk and protective factors remains limited. Utilizing a sample of 3,976 White and Black men offenders on community supervision with a minimum of three waves of assessment, this thesis tested a three-step analytic approach to establish whether: (a) the Dynamic Risk Assessment for Offender Re-entry (DRAOR) measured the constructs of dynamic risk and protective factors consistently over time, (b) there was evidence of trajectories of within-offender change throughout community supervision, and (c) individual trajectories of change predicted recidivism above and beyond baseline DRAOR score and static risk. Results indicated that the DRAOR was measuring the same constructs in the same way over repeated assessments. Change was observed across each of the DRAOR domains, indicating that on average, offenders were expected to decrease in stable and acute risk over time, and increase in their protect score. Variations in the rates of change were unrelated to static risk, age, and race, but change on each DRAOR domain was partially explained by scores on the remaining domains. Change trajectories were significant predictors of revocations of community supervision after considering the effects of initial score, static risk, and age, but change trajectories were not related to new convictions. The findings indicate that the DRAOR is sensitive to change and information on an offender's level of change should inform case management decisions, such as referrals for intervention. Although findings provide preliminary theoretical support for dynamic risk and protective factors, future research should consider an item-level analysis to identify whether there are specific factors that can be targeted to best support reintegration efforts. Further, increased attention on implementation efforts is needed to gain a thorough understanding of how the DRAOR is used in practice and how it can be optimized moving forward.