Identifying pedophiles “eligible” for community notification under Megan’s Law: A multivariate model for actuarially anchored decisions

This paper illustrates the application of the powerful statistical techniques of multivariate behavioral research to the identification of those offenders about whose presence the community should be notified under terms of Megan’s Law and its variants. A stepwise multiple regression was calculated among a group of 52 pedophiles serving sentences in a specialized prison for the treatment of sex offenders with number of prior arrests for any/all felony offenses as the criterion variable; scores on the MMPI and the Limbic System Checklist, age of the victim in the instant offense, and age of the offender functioned as “post-dictor” variables. Eight variables were extracted (Pd, Mf, victim’s age, Pa, F, SiLSCL score, K), yielding a multiple R of .683. When the criterion is truncated into meta-categories (one or more arrests vs. zero or none), a precision-weighted equation produced 90.4% “true positives” but only 9.6% “false negatives.” It is proposed that the methodology presented herein (rather than the specific results obtained) can be extrapolated to other jurisdictions as a scientifically sound methodology that both reduces the rate of false negatives from that expected by chance and is likely to prove defensible in litigation.