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The effectiveness of Sex Offender Registration and Notification: A meta-analysis of 25 years of findings

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Abstract

Objectives

Examine 25 years of Sex Offender Registration and Notification (SORN) evaluations and their effects on recidivism.

Methods

We rely on methodology guidelines established by the Campbell Collaboration for meta-analyses to systematically synthesize results from 18 research articles including 474,640 formerly incarcerated individuals. We estimate the effect of SORN policies on recidivism from 42 effect sizes and determine if the effect of SORN varies by sexual or non-sexual recidivism when examining arrest or conviction as outcomes.

Results

The random-effects meta-analysis model demonstrated that SORN does not have a statistically significant impact on recidivism. This null effect exists when examining a combined model and when disaggregating studies by sexual or non-sexual offenses, or conceptualizing recidivism by arrest or conviction.

Conclusions

SORN policies demonstrate no effect on recidivism. This finding holds important policy implications given the extensive adoption and net-widening of penalties related to SORN.

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Notes

  1. This number was released via personal communication with Dr. David Bierie from the US Marshals Service. NCMEC data were utilized to create this count and the USMS approved the release and publication of the information.

  2. It is important to note that, while important, a small number of the studies outlined in the research review are not included in the meta-analysis due to expiration of data storage rules and data output reporting styles that inhibited calculation of an effect size. Efforts were made with many authors to retrieve all relevant data for inclusion in the current study. Some of the studies not included in the analysis are Adkins et al., (2000); Petrosino and Petrosino (1999); Sandler et al., (2008); Schram and Milloy (1995); Vasquez et al., (2008); and Zgoba et al., (2010).

  3. No exclusions were made solely based on this category. Meaning, no study was excluded because they had studied parole or probation released offenders in exclusion. Parolees were included in this analysis because in many states sex offenders are held on parole supervision for life (PSL) or community supervision for life (CSL) after release from prison. Individuals who received only probation would not have been; however, no studies were excluded for this reason.

  4. FTR is a felony in many states and with the federal government. It was not included in any recidivism analyses, however, because the studies were excluded for various other reasons.

  5. Meta-analysis procedures were originally developed to demonstrate a bivariate and isolated effect of a treatment on an outcome. These effects are ideal in experimental and quasi-experimental designs; however, those study designs are not always feasible in social sciences which has resulted in researchers relying on multivariate designs to control for confounding effects. Within multivariate analyses, controls may be operationalized or measured differently resulting in an increase in bias and inability to compare controls consistently across studies (Borenstein et al., 2009). Due to this difference in statistics and operationalizations, multi-variate effects were not included in this meta-analysis. These studies include Ackerman et al., 2012; Freeman & Sandler, 2010; Maurelli & Ronan, 2013; Park et al., 2014; Vasquez et al., 2008; and Zgoba et al., 2018.

  6. In instances where multiple effects existed, if the authors could combine those effects to create an combined study effect, they did so. In studies where effects could not be combined, the authors chose one effect for the main model and noted that in Table 1.

  7. The authors recognize that when the number of studies is small, it is difficult to properly apply the random-effects model given the large variability in effect size (Borenstein et al., 2009). Thus, we also provide the fixed-effects for each model although we feel confident that the random-effects models are most appropriate for our data given theoretical and statistical rationales.

  8. Heterogeneity demonstrates the variation that exists in the true effect size underlying a certain population. This is essentially the effect that would exist for an infinite number of cases (Borenstein et al., 2009). I2 statistics are interpreted as a ratio and demonstrate inconsistency across studies by determining the extent to which confidence intervals overlap—determining the true effect underlying studies (Higgins et al., 2003; Higgins & Thompson, 2002).

  9. Although this study had an effect size that was much larger than our sample, we retained it for analyses.

  10. The fixed-effects model demonstrated that SORN reduced the mean of recidivism by 13.5%, (OR = 0.865 [0.837–0.894]) which was statistically significant (Z18 = −8.612, p = 0.000).

  11. The fixed-effects model demonstrated that for sexual offenses SORN increased the mean of recidivism by 6.9%, (OR = 1.069 [1.014–1.126]) which was statistically significant (Z17 = 2.475, p = 0.013).

  12. For non-sexual offense types, the fixed effects model demonstrated that SORN reduced the mean of recidivism by 21.8%, (OR = 0.782 [0.746–0.819]) which was statistically significant (Z9 = −10.255, p = 0.000).

  13. The fixed-effects model demonstrated that SORN decreased the mean of recidivism by 22.4%, (OR = 0.776 [0.746–0.807]) which was statistically significant (Z13 = −12.646, p = 0.000).

  14. For studies examining conviction as an outcome, the fixed-effects model demonstrated that SORN increased the mean of recidivism by 11.7%, (OR = 1.117 [1.037–1.203]) which was statistically significant (Z5 = 2.905, p = 0.004).

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Zgoba, K.M., Mitchell, M.M. The effectiveness of Sex Offender Registration and Notification: A meta-analysis of 25 years of findings. J Exp Criminol 19, 71–96 (2023). https://doi.org/10.1007/s11292-021-09480-z

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