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Data_Table_Sample_Sex_Offender_Legislation.docx-Copy2.pdf
Data Table: Sex Offender Legislation
Reference Purpose Method Sample Findings Themes Similarities Uniqueness Ackerman, R. A., Levenson,
S. J., Harris, J. A. (2012). How many sex offenders really live among us? Adjusted counts and population rates in five US states. Journal of Crime and Justice, 35(3), 464-474. doi: 10.1080/0735648X.2012. 666407
To provide a more accurate estimate about the number of registered sex offenders in the United States.
Experimental Design: Secondary data analysis Data: Public sex offender registry databases.
Florida, Georgia, Illinois, New York, and Texas.
On average, 43% percent offenders listed in registries were not actually residing in the community; rates ranged from 25% (Florida) to 60% (Texas). Patterns found across the five states: (1) offenders (18% of the total sample) are registered in more than one jurisdiction and (2) sex offenders who are "incarcerated, deported, or diseased distorts the reality of how many RSO's are truly living in our communities" (p. 471).
Sex Offender Registry Accuracy Registered Sex Offender Prevalence
No other studies in this table have assessed the accuracy of sex registry data.
Authors established raw counts of sex offender prevalence in five states.
Bouffard, A. J., & Askew, N.L. (2017). Time-series analyses of the impact of sex offender registration and notification law implementation and subsequent modifications on rates of sexual offenses. Crime and Delinquency, 0(0), 1-30. doi: .org/10.1177/0011128717 722010
Assess sex offense patterns before the SORN policy implementation; impact of SORN policy on monthly counts of filed sexual offenses; impact of SORN implementation on sex crimes against children versus sexual assaults; deterrent effects of SORN on first-time versus repeat offenders; and SORN impact on non-sexual crimes.
Experimental Design: Secondary Data Analysis Data: NeeLaw Criminal Records Database Analysis: Interrupted Time Series Modeling
N=69, 510 sexual offense cases
No significant relationship found between implementation of SORN policies and the number (reduction of) sex offenses filed in court. No relationship found between SORN implementation and reduced sex offense court filings for sex offenses against children or sexual assaults. No deterrent effect found for repeat offenders or first-time offenders; "at least 70% of the sexual offenses were committed by individuals who had not been previously been arrest for an RSO...nor do they appear to have been deterred by the overall threat
SORN Impact on Sex Offense Trends SORN Impact on Sex Offenses Against Children SORN Impact on Sexual Assault SORN Deterrent Effects SORN Impact on
SORN policies have no discernable deterrent effects (Dierfeldt & Carson, 2017; Vasquez, Madden, & Walker, 2008). SORN policies have no significant impact on sexual or non-sexual recidivism (Cadwell, Ziemke, & Vitacco, 2008; Diernfeldt & Carson, 2017; Letourneau et al., 2010; Tewksbury
Study addresses previous, and existing, gaps in the literature regarding SORN/A policy effectivene ss.
Data Table: Sex Offender Legislation
of registration/notification" (p. 21).
Non-Sexual Recidivism
& Jennings, 2010; Vasquez, Maddan, & Walker, 2008; Zevets, 2006; Zgoba et al., 2016)
Cadwell, F. M., Ziemke, H. M., & Vitacco, J.M. (2008). An examination of the sex offender registration and notification act as applied to juveniles. Psychology, Public Policy, and Law, 14(2), 89-114. doi: 10.1037/a0013241
1) Examine difference in the re-offense patterns, if any, for adjudicated juveniles sex offenders versus non-sex offender juveniles. 2) Assess the "predictive utility of methods commonly used to assess the risk of sexual recidivisms in juvenile sex offenders, including risk measures currently in use and the statutory inclusion criteria embedded in SORNA" (p. 96). 3) Assess the "predictive utility of the PCL:YV in predicting sexual recidivism" (p. 96).
Experimental Design: Secondary Data Analysis Analysis: Cox Proportional Hazard Analysis
N=91 adjudicate d juveniles for sexual felony offense N=174 adjudicate d juveniles for non-sexua l offenses
SORNA's offense-based measures was not a good identifier of juveniles who have higher risk of recidivism (sexual and non-sexual offenses). *SORNA Tier 3 was only predictive of "lower rates of violent offending among participants designated at higher risk" (p. 105). Of the juveniles who met the SORNA Tier 3 criteria, 46.9% versus 70.4% of non-SORNA juveniles had a new violent offense. The risk assessment tests developed by states (New Jersey, Texas, and Wisconsin) were not reliable measures of recidivism risk. Risk assessments that used static variables (typically used to assess adult offender risk), such as "victim selection, previous sex offenses, or level of force...were unrelated to sexual recidivism" (p. 105). "The prevalence rate of new felony sexual offense charges among the juvenile sex offenders (12.1%) was not significantly different from
SORNA Recidivism Impact for Juvenile Sex Offenders Juvenile Risk Assessment SORNA Tier Accuracy
SORN policies have no significant impact on sexual or non-sexual recidivism (Bouffard & Askew, 2017; Diernfeldt & Carson, 2017; Letourneau et al., 2010; Tewksbury & Jennings, 2010; Vasquez, Maddan, & Walker, 2008; Zevets, 2006; Zgoba et al., 2016)
Study focuses on SORN impact for juveniles.
Data Table: Sex Offender Legislation
that non-sex offending delinquents" (p. 101); in other words, the former is not at higher risk of committing sexual offenses.
Diernfeldt, R., & Carson, V. J.
(2017). Examining the influence of Jessica's Law on reported forcible rape: A time-series analysis. Criminal Justice Policy Review, 28(1), 87-101. doi: 10.1177/08874034145631 39
To test the deterrent effect of Jessica's Law on forcible rape.
Experimental Design: Quasi-experiment Data: Monthly report of forcible rape data from six states.
California, Georgia, Louisiana, Missouri, North Caroline, Oregon, and Wisconsin .
Electronic monitoring of sex offenders yielded no significant reduction in the number reported forcible rapes; any noticeable changes in rape rates (California, -12.904; North Carolina, -2.126; Louisiana, -3.77204) became null once "quarterly aggregates were analyzed" (p. 94) Minor reductions of reported rape seen in CA (-12.904) & North Carolina (-2.126) are likely attributed to extraneous variables, which authors did not control for given the methodology.
Sex Offender Electronic Monitoring Sex Offender Legislation and Deterrence
Sex offender legislation, including electric monitoring or registration and notification have no significant impact on reducing the prevalence of rape (Vazquez, Madden, & Walker, 2008). SORN policies have no discernable deterrent effects (Bourfard & Askew, 2017; Vasquez, Madden, & Walker, 2008). SORN policies have no significant impact on sexual or non-sexual recidivism (Bouffard & Askew, 2017; Cadwell, Ziemke, & Vitacco, 2008; Letourneau et al., 2010; Tewksbury & Jennings, 2010;
Jessica's Law (Florida) requires sentencing and lifelong electronic monitoring for some sex offenders. Other states have adopted similar policies under the title Jessica's Law.
Data Table: Sex Offender Legislation
Vasquez, Maddan, & Walker, 2008; Zevets, 2006; Zgoba et al., 2016)
Freeman, J. N., & Sandler, C. J. (2010). The Adam Walsh act a false sense of security or an effective public policy initiative? Criminal Justice Policy Review, 21(1), 31-49. doi: 10.1177/08874034093385 65
To assess whether SORNA's offense-based classification system is a good predictor of sexual recidivism and if other risk factors are better predictors of recidivism (p. 34).
Sex offender registration data (demographics, crime of conviction, criminal history) collected from New York State. Sample was followed from date of release into community to their first re-arrest, after which follow-up was terminated.
N= 17, 165 New York State registered male sex offenders Sample Demograp hics: 63.6% White (n=10, 911), 30.6% Black (n=5, 246), 1.4% Indian and/or Asian (n= 241); Mean age 32.88 (p. 35).
SORNA's three-tier system (based on crime of conviction alone) is not efficient in predicting risk of reoffending; no correlation found between tier level and rearrests for sexual and nonsexual offenses (p. 40). Factors not related to current crime of conviction were better predictors of recidivism (i.e., offender age, prior criminal history, and type of victim); these factors are commonly found in actuarial risk assessments (e.g., Static-99 and MsSOST-R; p. 40). Significant differences found in re-arrest for sexual offenses among tier levels. Tier 1 offenders more likely than Tier 2 and Tier 3 offenders to be rearrested for sexual and non-sexual offenses. Results found nine predictors correlated with sexual offense re-arrest: "(a) number of prior incarceration terms, (b) number of prior supervision violations, (c) number of prior violent felony offense arrests, (d) number of prior registerable sexual offense arrests, (e) variety of
SORNA Tier Classificatio n Accuracy Risk Factors Associated with Recidivism
The tier-system established by the passing of the Adam Walsh Act Title 1 (SORNA) is not a good indicator of an offender’s risk of reoffending (Cadwell, Ziemke, & Vitacco, 2008; Freeman & Sandler, 2010; Letourneau et al. 2010; Sperber et al. 2010; Tewksbury, Jennings, Zgoba, 2011; Zgoba et al. 2016). There exist other factors (other than crime of conviction), such as demographic variables and criminal history, that may be better predictors of recidivism (Letourneau et al. 2010; Tewksbury, Jennings, Zgoba, 2011; Veysey & Zgoba, 2010).
Assessing SORNA Tier Risk Accuracy
Data Table: Sex Offender Legislation
offending history, (f) number of victims in the instant offense, (g) offender age, (h) county of residence, and (i) supervision type" (p. 41). Significant differences between tier levels also found for non-sexual re-arrest; Tier one offenders more likely and more quickly to be re-arrested than Tier 2 (by 34%) and Tier 3 offenders (by 33%). Predictors of non-sexual re-arrest included prior incarceration term served (7%), prior supervision violation (3.9%), and criminal history (24%).
Harris, J. A., Lobanov-Rostovsky, C., Levenson, S. J. (2010). Widening the net the effects of transitioning to the adam walsh act's federally mandated sex offender classification system. Criminal Justice and Behavior, 37(5), 503-519. doi: 10.1177/00938548103638 89
To assess the effects of SORNA implementation in Ohio and Oklahoma (p. 504). Three objectives: (1) impact of SORNA offender classification on the re-distribution of current registrants, (2) individual-level differences among offenders from different tiers, and (3) impact of SORNA on juvenile offenders (p. 509).
Experimental Design: Secondary Data Analysis
Oklahoma Sample (n= 10,187) Ohio Sample (n= 24, 994)
Eighty-two percent of Oklahoma's offenders were reclassified into a higher tier. Fifty-six percent of Ohio's offenders were reclassified into a higher tier. Reclassification and application of retroactive SORNA requirements shifted the characteristics of offenders (e.g., older offenders moved up to higher tiers), which is inconsistent with empirically tested age predictor (higher when young, lower when older). Ohio data indicates that SORNA implementation increases juveniles risk of being re-classified into tier 3.
SORNA Implementati on and Offender Realignment Offender Characteristi c Differences by Tier Tier Classificatio n Impact on Juveniles
Implementation of SORNA guidelines can impact the tier redistribution of offenders, moving them to higher or lower tiers (Zgoeba et al., 2016). SORNA may overestimate an offenders risk since it is based on crime of conviction alone (Sperber et al. 2010; Zgoba et al. 2016).
Study also assessed the impact of SORNA on juveniles.
Justice Policy Institute. (n.d.). To provide an overview of state costs
Estimates calculated
Ohio and Virginia -
Ohio costs: $475,000 initial SORNA implementation
SORNA Costs
No other study or reference in this
Overview of costs for
Data Table: Sex Offender Legislation What will it cost states to
comply with the sex offender registration and notification act? Retrieved from
http://www.justicepolicy.org/ images/upload/08-08_fac _sornacosts_jj.pdf
for implementing SORNA.
Virginia's Department of Planning and Budget data and estimated 2009 federal Byrne grant allocations.
costs for other states estimated based on Ohio and Virginia summary costs.
followed by $85,000 annual maintenance. Cost compared to Byrne Grant deduction ($622,000) would still be less costly in the long-run. Virginia costs: $12, 497, 000 initial SORNA implementation followed by an $8, 887, 000 annual maintenance. Cost compared to Byrne Grant deduction ($400,000) is far larger. Summary of remaining 48 states' costs available in data table (p. 2).
table provides a summary of estimated SORNA costs.
implementi ng SORNA guidelines. Cost may not outweigh the Byrne Grant deduction.
Letourneau, J. E., Levenson, S. J., Bandyopadhyay, D., Sinha, D., Armstrong, S. K. (2010). Effects of south carolina's sex offender registration and notification policy on adult recidivism. Criminal Justice Policy Review, 21(4), 435-458. doi: 10.1177/08874034093531 48
To assess South Carolina's SORN policy influence of sexual recidivism (p. 452).
Experimental Design: Secondary Data Analysis Data: Sex offender criminal history records and registration records from the South Carolina sex offender registry.
N= 6,064 males convicted of one or more sex crimes during 1990-2004 .
South Carolina’s SORN policy not correlated with reduced recidivism rates. Registration status was not a predictor of reduced sexual and non-sexual recidivism, nor did it reduce "time of detection of sex crime recidivism" (i.e., did not decrease the time it took for an offender to reoffend; p. 452). South Carolina’s SORN policy did not decrease rates of recidivism among different types of sexual offenders (p. 453). Results found that offender age, race, and criminal history (e.g., prior convictions) were associated with increased recidivism (p. 454).
SORN Registration Status and Recidivism Risk Factors Associated with Recidivism
Registration status is not an indicator of recidivism, nor does it reduce rates of recidivism (Sperber et al. 2010; Tewksbury & Jennings, 2010). There exist other factors (other than crime of conviction), such as demographic variables and criminal history, that may be better predictors of recidivism (Freeman & Sandler, 2010; Tewksbury, Jennings, Zgoba, 2011).
South Carolina's SORN policy is broad - it requires registration from all offenders regardless of risk and crime of conviction.
Data Table: Sex Offender Legislation McPherson, L. (2007).
Update practitioner's guide to the adam walsh act. Retrieved from
https://smart.gov/pdfs/practiti oner_guide_awa.pdf
To serve as a practioner guide for implementation of SORNA guidelines (under Title 1 of the Adam Walsh Act).
Establish federal standard guidelines for states to update and/or crease sex offender registries. Penalize states with a 10% Byrne Grant deduction for failure to comply with SORNA guidelines, or at least meet the minimum requirements. Expand sex offender registration requirements to juveniles adjudicated of a sex-related crime. Criminalize sex offenders' failure to register as a felony offense.
United States
SORNA establishes a three-tier classification system based on conviction of crime; tier classification determines length of registration and notification. Tier 1: Offenders must register for 15 years, annually. Tier 2: Offenders must register for 25 years, every six months. Tier 3: Offenders must register for life, every three months.
SORN Guidelines - Tier System Details
All other references provide a summary of the contents (in the introduction) of SORNA guidelines.
Adam Walsh Act (2006) Title 1 requires all 50 states and other U.S. territories to bring their existing registries up to compliance (or partial compliance ) with SORNA standardize d guidelines.
National Center for Missing and Exploited Children (2017). Map of Registered Sex Offenders in the United States. Retrieved from http://www.missingkids.c om/content/dam/ncmec/e n_us/documents/sexoffen dersmap.pdf
To provide an overview of current numbers of sex offenders in the United States.
Comparison of sex offenders to census data.
United States.
The total number of registered sex offenders as of May 2017 was 861, 837.
N/A N/A N/A
Przybylski, R. (2015). Recidivism of adult sex offenders. Retrieved from
https://www.smart.gov/pdfs/ RecidivismofAdultSexual Offenders.pdf
To provide an overview of adult sex offender recidivism.
U.S. Department of Justice Summary Report.
N/A Recidivism is difficult to assess given that definitions vary across studies. Recidivism varies by type of sexual offender (e.g., rapists, child molesters, exhibitionists), contrary to
Adult Sex Offender Recidivism
N/A Review of research on sex offender recidivism.
Data Table: Sex Offender Legislation
homogenous assumptions about sex offenders in general.
Sperber, G. K., Lowenkamp, T. C., Carter, E. D., Allman, R. (2010). A sheep in wolf's clothing or a wolf in sheep's clothing? Ohio sex offender registration and the role of science. Criminal Justice Review Policy Review, 21(4), 500-519. doi: 10.1177/08874034093509 09
To assess if registration and notification status/assignment is correlated to a RSO's likelihood of reoffending (p. 505).
Experimental Design: Secondary Data Analysis Analysis: Chi-Square Analyses Variables of Interest: Registry label and probability of reoffending. Data: Collected from Southwest Ohio Residential Facilities.
N=210 adult male sex offenders (Septembe r 1998 - May 2007) from two Southwest Ohio residential facilities. Sample Demograp hics: 91.4% White, 7.1% African American, 1.4% Other; 50.5% Single, 27.9% Married, 21.6 Divorced; 35.7% Under 25, 64.3% 25 or Older.
Ohio’s classification system over-classifies sex offenders into tiers they would not ordinarily be assigned based on actuarial risk assessments. Some offenders are placed into lower tiers even though actuarial risk assessments suggest they belong on a higher tier.
SORNA Tier Accuracy SORNA Registration Status & Recidivism Actuarial Risk of Reoffending Offense-Base d Classificatio n Overestimate s Risk
SORNA may overestimate an offenders risk since it is based on crime of conviction alone (Harris, Lobanov-Rostovs ky, & Levenson, 2010); Zgoba et al. 2016). Registration status is not an indicator of recidivism, nor does it reduce rates of recidivism (Letourneau et al. 2010; Tewksbury & Jennings, 2010).
Study assessed Ohio's sex offender classificatio n system prior to the implementa tion of AWA (i.e., SORNA guidelines), but Ohio's system was still offense-bas ed.
Tewksbury, R. (2005). Collateral consequences
To assess sex offenders' experience
Experimental Design: Survey;
Total Sample
More than one third of sample reported the following
SORN Collateral
No other study in this table assesses
An exploratory
Data Table: Sex Offender Legislation
of sex offender registration. Journal of Contemporary Criminal Justice, 21(67), 68-71. doi: 10.1177/10439862042717 04
with sex offender registries and the associated collateral consequences (p. 69).
authors mailed questionnaires to sex offenders listed on the Kentucky Sex Offender Registry.
Size: n=121 (a 33% survey response rate) Sample Demograp hics: Male 87.8%, Female 12.2%; White 88.8%, Black 8.6%, Other 2.6%; Average Age 44; Metropolit an: 52% (n=63) and Nonmetro politan 48% (n=58)
negative consequences/experiences: job loss, losing a friend, experiencing harassment, and experiencing rude treatment from the general public (p. 78). Offenders with child victims reported that less of their friends/family were aware of their offense(s). Offenders from nonmetropolitan communities reported higher social consequences (p. 78). Offenders who victimized children had "slightly lower rates of social stigmatization, harassment, and loss due to registration" (p. 78).
Consequence s
SORNA consequences on sex offenders.
study assessing how sex offender registration impacts offenders themselves, in all aspects (e.g., employmen t, housing, social support).
Tewksbury, R., & Jennings, G.W. (2010). Assessing the impact of sex offender registration and community notification on sex-offending trajectories. Criminal Justice and Behavior, 37(5), 570-582. doi: 10.1177/00938548103635 70
To assess the impact of SORN laws on sexual recidivism.
Experimental Design: Secondary Data Analysis Data: Collected from the Iowa Department of Corrections.
Iowa sex offenders released from prison prior to SORN implement ation between 1992 and 1996 and offenders released
Overall rate of recidivism (reconviction) for both samples were 12% (p. 580). SORN had no discernable impact of reducing sex offender recidivism (reconviction); it also had no effect on reducing the quantity of subsequent re-offenses by offenders who recidivated. No significant differences on
SORN Registration Status and Recidivism
Registration status is not an indicator of recidivism, nor does it reduce rates of recidivism (Letourneau et al. 2010; Sperber et al. 2010; Tewskbury, Jennings, & Zgoba, 2011). SORN policies have no
SORN is often interchange ably used with SORNA (federal guidelines); SORN refers to general sex offender registration and
Data Table: Sex Offender Legislation
post SORN implement ation between 1997-2001 . Pre-SORN sample (n=759; 98.2% male; 88.7% White; and 96.2% non-Hispa nic; mean age was 37.99 years. Post-SOR N sample (n=823; 98.7% male; 87.1% White, 12.9% non-White .
recidivism were found between groups released prior to and after SORN (not SORNA).
significant impact on sexual or non-sexual recidivism (Bouffard & Askew, 2017; Cadwell, Ziemke, & Vitacco, 2008; Diernfeldt & Carson, 2017; Letourneau et al., 2010; Vasquez, Maddan, & Walker, 2008; Zevets, 2006; Zgoba et al., 2016)
notification laws (these may vary across states).
Tewksbury, R., Jennings, G. W., Zgoba, M. K. (2011). A longitudinal examination of sex offender recidivism prior to and following the implementation of SORN. Behavioral Sciences and the Law Behav. Sci. Law, 30, 308-328. doi: 10.1002/bsl.100
To assess the difference in recidivism rates among pre-SORN and post-SORN offenders in New Jersey (p. 313). To assess whether heterogeneity is present in offender trajectories for
Data were collected from the New Jersey Department of Corrections' Offender-Based Correctional Information System. Authors used official re-arrest records to measure recidivism. Authors
Pre-SORN sample (n=247) Post-SOR N sample (n=248)
Overall recidivism rate for both groups was low and consistent with prior recidivism research; three-quarters of offenders were identified as low-risk. SORN registration status not an adequate predictor of sexual reoffending. SORN registration status not
SORN registration status effects on recidivism Other, better, predictors of recidivism.
There exist other factors (other than crime of conviction), such as demographics and criminal history, that may be better predictors of recidivism (Freeman & Sandler, 2010;
Assessment of heterogenei ty in re-arrest trajectories following release from prison.
Data Table: Sex Offender Legislation
re-arrest (i.e., "are there distinct risk profiles among sex offenders with regard to their recidivism trajectories; and are these profiles similar or different for sex offenders pre- and post-SORN" [p. 313]). To examine if other variables (e.g., demographics, substance use, mental health, criminal history, etc.) have an influence on recidivism trajectories.
followed offender data for 8 years.
an adequate predictor of non-sexual reoffending. "Sex offenders with diagnosed drug problems, who are rapists rather than child molesters, who have female victims and victims who are either strangers or non-family members and who have been previously arrested for a non-sex offense significantly distinguish the high-risk sex offenders from those sex offenders considered to be low-risk – hence being more frequent and shorter survival recidivists" (p. 324)
Vesey & Zgoba, 2010). Registration status is not an indicator of recidivism, nor does it reduce rates of recidivism (Letourneau et al. 2010; Sperber et al. 2010; Tewksbury & Jennings, 2010). SORN policies have no significant impact on sexual or non-sexual recidivism (Bouffard & Askew, 2017; Cadwell, Ziemke, & Vitacco, 2008; Diernfeldt & Carson, 2017; Letourneau et al., 2010; Tewksbury & Jennings, 2010; Vasquez, Maddan, & Walker, 2008; Zevets, 2006; Zgoba et al., 2016)
Vasquez, E. B., Maddan, S., Walker, T. J. (2008). The influence of sex offender registration and notification laws in the united states a time-series analysis. Crime & Delinquency, 54(2),
To assess the impact of Megan's Law on forcible rape prevalence (p. 175).
Experimental Design: Quasi-experiment Data: UCR monthly report of forcible rape data.
10 U.S. States
Overall, comparison of reported forcible rapes prior to and after implementation of Megan's Law indicate no significant deterrent effects of rape. Arkansas, Connecticut,
Megan's law impact on forcible rapes
Sex offender legislation, including electric monitoring and/or registration and notification have no significant impact on the
Focused on the impact of sex registration and notification policies on the
Data Table: Sex Offender Legislation
175-192. doi: 10.1177/00111287073116 41
Nebraska, Nevada, Oklahoma, West Virginia: no increase or decrease in rape. California rape reporting increased by "41 rapes per month" (p. 186).
prevalence of rape (Dierfeldt & Carson, 2017). Implementation of Megan’s Law (prior to SORNA) has no apparent effects of reducing rates of recidivism, for sexual and non-sexual offenses (Tewksbury, Jennings, & Zgoba, 2011; Zevitz, 2006).
influence of forcible rape.
Veysey, M. B., & Zgoba, M. K. (2010). Sex offenses and offenders reconsidered an investigation of characteristics and correlates over time. Criminal Justice and Behavior, 32(2), 583-595. doi: 10.1177/00938548103638 90.
To assess whether any changes to sex offender characteristics resulted from the implementation of Megan’s law, as well to assess if any changes occurred in the predictors of recidivism.
Collected data from the New Jersey Department of Corrections to make comparison of offender characteristics, prior to and after Megan’s Law was implemented in New Jersey.
Sample of sex offenders from the New Jersey Departme nt of Correction s (n=550).
Variables associated with risk of reoffending, both prior to and after Megan’s Law, were having never been married, no child victims, unemployment, prior rape offenses, and use of weapon. Offenders with higher sexual recidivism had more behavioral health problems (p. 593). Generally, characteristics of sex offenders prior to and after Megan’s Law, are similar.
Megan's Law Effect on Sex Offender Characteristi cs and Risk Factors.
There exist other factors (other than crime of conviction), such as demographic variables and criminal history, that may be better predictors of recidivism (Freeman & Sandler, 2010; Letourneau et al. 2010; Tewksbury, Jennings, Zgoba, 2011).
Assessment of sex offender characterist ics before and after sex registration laws.
Zevitz, R. G., (2006). Sex offender community notification: Its role in recidivism and offender reintegration. Criminal Justice Studies, 19(2), 193-208. doi:
To assess the effects of Megan’s Law on sex offender recidivism for offenders “attempting to successfully reintegrate into society” (p. 203).
Authors followed two samples with varying levels of public exposure (registry) over a four and half year period and
Two groups: Extensive Notificatio n sample (n=47) Limited
Public exposure of sex offenders had minimal effect on an offender’s rate of recidivism (p. 204). "After controlling for relevant demographic and criminal history variables, careful
Megan's Law Impact on Recidivism
Implementation of Megan’s Law (prior to SORNA) has no apparent effects of reducing rates of recidivism, for
Sex offender reintegratio n and recidivism.
Data Table: Sex Offender Legislation
10.1080/14786010600764 567
collected data from (official records) from Wisconsin’s Department of Corrections. Authors measured recidivism as resentencing for a new crime.
notificatio n group (n=166).
analysis of the data did not reveal any significant differences between the extensive notification subjects and the comparison subjects in terms of their likelihood of being recommitted during the follow-up period. Nor do the findings suggest that alerting the community to their presence significantly shortened the amount of time before recommitment for those offenders who did recidivate" (p. 204). Forty-eight percent of the extensive notification sample recidivated (went back to prison) for sexual and/or nonsexual offenses, compared to 49.3% of the limited notification sample.
sexual and non-sexual offenses (Vasquez, Maddan, & Walker, 2008; Tewksbury, Jennings, & Zgoba, 2011). SORN policies have no significant impact on sexual or non-sexual recidivism (Bouffard & Askew, 2017; Cadwell, Ziemke, & Vitacco, 2008; Diernfeldt & Carson, 2017; Letourneau et al., 2010; Tewksbury & Jennings, 2010; Vasquez, Maddan, & Walker, 2008; Zgoba et al., 2016)
Zgoba, M. K., & Levenson, J. (2012). Failure to register as a predictor of sex offense recidivism: The big bad wolf or a red herring? Sexual Abuse: A Journal of Research and Treatment, 24(4), 328-349. doi: 10.1177/10790632114210 19
To describe and compare the characteristics of New Jersey sex offenders who failed to register (FTR) versus those who registered (non-FTR [p. 332]). To evaluate "the role of registration noncompliance in contributing to general and sexual recidivism
Study used a quasi-experimental design and employed purposive sampling from a cohort of sex offenders released from New Jersey State Prison between 1980 and 2008. Researchers used descriptive statistics to show
The total sample size was n=1,125 and the demograp hics were as follows: 49.3% Black, 36.6% White,
Study found a 15% recidivism rate for sexual offenses over the follow-up period (FTR and non-FTR). FTR group had an 18% re-arrest for new sexual offenses. Although authors note this rate is "slightly above the average sexual re-offense rate", it is still not significant enough to imply that these individuals are "especially sexually dangerous" (p. 340).
Failure to register and its connection to recidivism is not significant. Characteristi cs of sex offenders who fail to register.
No other study in this table examined whether failure to register is a predictor of reoffending risk.
Exploratory study focuses on failure to register, not registration status and notification as done in most prior studies.
Data Table: Sex Offender Legislation
risk" (p. 332). To identify "failure to register" risk factors (p. 332).
the sample's characteristics and then used t tests and x2 analysis for comparison of groups.
12.8% Hispanic; average age 35 years old. FTR (n=644) and non-FTR (n=481)
Additionally, FTR group had slightly higher sexual offense re-arrest rates than non-FTR group. Participants, FTR and non-FTR, were more likely to re-arrested for new non-sexual offenses, and to have more "technical violations" (p. 340). FTR offenders were likely to be (a) younger, (b) a minority, and (c) have no prior marriage compared to non-FTR offenders. Factors associated with recidivism in this study include "prior sexual criminal history and prior nonsexual criminal history" (p. 340); failure to register was not a predicting factor for recidivism.
Zgoba, M. K., Miner, M., Levenson, J., Knight, R., Letourneau, E., & Thornton, D. (2016). The adam walsh act: An examination of sex offender risk classification systems. Sexual Abuse: A Journal of Research and Treatment, 28(8), 772-740. doi: 10.1177/10790632155695 43
Purpose #1: Compare SORNA tier-classification system with risk assessment tests to assess which is a better indicator of sex offender reoffending risk. Purpose #2: “Evaluate the predictive accuracy of existing state risk assessment classification schemes” (p. 729). Purpose #3: “Examine the distribution of risk assessment scores within and across tier categories as defined by the AWA" (p. 729).
Data collected from automated databases. Project was divided into two phases. Phase 1: Static-99R scores based coding. Phase 2: Recidivism coding. Recidivism measure: new arrest.
Sample consisted of 1,789 formerly incarcerat ed male sex offenders from New Jersey, Florida, and South Carolina.
SORNA classification system is questionable in terms of accuracy and utility (p. 735). Tier-system not a good identifier of high-risk sex offenders. No significant recidivism differences found across the 5 and 10-year mark between Tier 3 and Tier 2 offenders. Florida’s results indicate that those in Tier 2 had “higher recidivism rates than Tier 3" (p. 735)."Existing state classification showed a more consistent trend in the expected direction, with lower tier offenders recidivating at lower rates than higher tier offenders at both 5 and 10-year follow-up times" (p. 736). Results from actuarial
AWA/SORN A: can the tier system predict/identi fy offenders at higher risk for reoffending (recidivism)?
The tier-system established by the passing of the Adam Walsh Act Title 1 (SORNA) is not a good indicator of an offender’s risk of reoffending (Freeman & Sandler, 2010; Letourneau et al. 2010; Sperber et al. 2010; Tewksbury, Jennings, Zgoba, 2011). Implementation of Megan’s Law (prior to SORNA)
Assessment of SORNA tier-system classificatio n.
Data Table: Sex Offender Legislation
measures were not consistent with SORNA tier levels; “AWA Tier 3 offenders did not have higher Static-99R scores than Tier 2" (p. 736). Offenders who scored low on Static-99R were classified into SORNA Tier 3 for all four states, suggesting SORNA is not a good measurement of risk. SORNA may overestimate and overclassify offender risk levels.
has no apparent effects of reducing rates of recidivism, for sexual and non-sexual offenses (Vasquez, Maddan, & Walker, 2008; Tewksbury, Jennings, & Zgoba, 2011; Zevitz, 2006). The tier-system established by the passing of the Adam Walsh Act Title 1 (SORNA) is not a good indicator of an offender’s risk of reoffending (Freeman & Sandler, 2010; Letourneau et al. 2010; Sperber et al., 2010; Tewksbury, Jennings, Zgoba, 2011).
