Two statistical tests were developed to test such a "signaling" hypothesis by using data from 10 sites involved in the RAND Corporation's evaluation of intensive supervision programs. Logistic regression models were first estimated to assess whether technical violation charges predicted arrest during community supervision, adjusting for demographic, criminal history, supervision intensity, and community factors related to recidivism, as well as the possibility of confinement during the 1-year follow-up period. Technical violation charges were associated with a decrease in the probability of arrest rather than an increase in the probability of arrest as the "signaling" hypothesis would suggest. A series of multinomial logistic regression models were estimated next to explore whether technical violation charges and arrests appeared to be manifestations of the same underlying propensity to offend consistent with a generality-of-deviance understanding of the "signaling" hypothesis. Results of the analyses showed that technical-violation charges and arrests were not apparently generated by the same underlying process. Probation policies grounded in the "signaling" hypothesis (e.g., policies that seek to protect public safety by revoking the community supervision status of probationers who violate release conditions) should therefore be carefully evaluated. In view of the substantial cost of revocation to corrections systems and the questionable impact of such a policy on public safety, the results of this study support the increased use of intermediate sanctions (short of revocation) to respond to probationer noncompliance. 28 tables, 20 figures, and 113 references