The present study provides normative data for the Child Pornography Offender Risk Tool (CPORT) by aggregating individual-level data from six routine samples (N = 956) across Canada, Europe, and the United Kingdom. All cases were men adjudicated for child sexual abuse and exploitation material (CSAEM) offenses. Meta-analyses revealed stable predictive accuracy for the CPORT across studies (AUCs = .728 and .713, respectively, for any sexual recidivism and for CSAEM-specific recidivism). Estimated recidivism rates increased with CPORT scores, ranging from 3% to 41% for any sexual reoffending and from 2% to 32% for CSAEM-specific reoffending. Risk ratios indicated that individuals with a CPORT score of 5 reoffended at approximately five times the rate of those with the median score (CPORT = 2), with even higher rates observed among those scoring 6. Percentile norms were also derived, with a median CPORT score of 2 corresponding to the 64th percentile in terms of risk for sexual recidivism. These CPORT norms can help facilitate improved risk communication and align practice with the Risk-Need-Responsivity (RNR) model.
AebiM.PlattnerB.ErnestM.KaszynskiK.BesslerC. (2014). Criminal History and Future Offending of Juveniles Convicted of the Possession of Child Pornography. Sexual Abuse, 26(4), 375–390. https://doi.org/10.1177/1079063213492344
2.
AzizianA.EkeA. W.FarmusL.ScottS.SetoM. C. (2025). Convergent and divergent validity of the Child Pornography Offender Risk Tool (CPORT) in a clinical sample from California. Sexual Abuse, 37(4), 399–425. https://doi.org/10.1177/10790632241271245
3.
BabchishinK. M.DibayulaS.McCullochC.HansonR. K.HelmusL. M. (2023). ACUTE-2007 and STABLE-2007 predict recidivism for men adjudicated for child sexual exploitation material offending. Law and Human Behavior, 47(5), 606–618. https://doi.org/10.1037/lhb0000540
4.
BabchishinK. M.EkeA. W.LeeS. C.LewisN.SetoM. C. (2022). Applying offending trajectory analyses to men adjudicated for child sexual exploitation material offenses. Criminal Justice and Behavior, 49(8), 1095–1114. https://doi.org/10.1177/00938548211040849
5.
BabchishinK. M.HansonR. K.HelmusL. M. (2012). Absolute recidivism rates predicted by Static-99R and Static-2002R sex offender risk assessment tools vary across samples: A meta-analysis. Criminal Justice and Behavior, 39(9), 1148–1171. https://doi.org/10.1177/0093854812443648
6.
BabchishinK. M.HansonR. K.VanZuylenH. (2015). Online child pornography offenders are different: A meta-analysis of the characteristics of online and offline sex offenders against children. Archives of Sexual Behavior, 44(1), 45–66. https://doi.org/10.1007/s10508-014-0270-x
7.
BabchishinK.MerdianH.L.BartelsR.M.PerkinsD. (2018). Child sexual exploitation materials offenders. European Psychologist, 23(2). https://doi.org/10.1027/1016-9040/a000326
8.
BaskurtS.BabchishinK. M.HilkesG.SetoM. C. (2025). A meta-analysis of recidivism rates among individuals who commit child sexual exploitation material (CSEM) offending. Aggression and Violent Behavior, 85, 1–18. https://doi.org/10.1016/j.avb.2025.102080
BickartW.McLearenA. M.GradyM. D.StolerK. (2019). A descriptive study of psychosocial characteristics and offense patterns in females with online child pornography offenses. Psychiatry, Psychology and Law, 26(2), 295–311. https://doi.org/10.1080/13218719.2018.1506714
11.
BlackC. (2018). Predicting recidivism among an adult male child sexual abuse imagery offender population with the Child Pornography Offender Risk Tool Short Version (CPORT-SV): A NEW ZEALAND validation study. [Master’s Thesis]. University of Canterbury.
12.
BonettD. G. (2012). Confidence interval for a proportion. In CooperH.CamicP. M.LongD. L.PanterA. T.RindskopfD.SherK. J. (Eds.), APA handbook of research methods in psychology: Vol. 3. Data analysis and research publication (pp. 245–246). American Psychological Association.
BorensteinM.HedgesL. V.HigginsJ. P. T.RothsteinH. R. (2009). Introduction to meta-analysis. Wiley.
15.
CohenT. H. (2023). Building a risk tool for persons placed on federal post-conviction supervision for child sexual exploitation material offenses: Documenting the federal system’s past, current, and future efforts. Federal Probation, 87(1), 19–31.
16.
EkeA. W.BabchishinK. M.PaquetteS.ScottS. B.BatinicM.FortinF.SetoM. C. (2024). Comparing the Static-99R and the Child Pornography Offender Risk Tool (CPORT) in two Canadian samples adjudicated of child sexual exploitation material offenses. Journal of Criminal Justice, 95(102303). https://doi.org/10.1016/j.jcrimjus.2024.102303
17.
EkeA. W.HelmusL. M.SetoM. C. (2018). Scoring Guide for the Child Pornography Offender Risk Tool (CPORT): Version 2.
18.
EkeA. W.HelmusL. M.SetoM. C. (2019). A validation study of the Child Pornography Offender Risk Tool (CPORT). Sexual Abuse, 31(4), 456–476. https://doi.org/10.1177/1079063218762434
19.
GottfredsonM. R.HirschiT. (1990). A general theory of crime. Stanford University Press.
20.
GunnarsdóttirH. Ó. (2019). Risk assessment of convicted child pornography offenders in Iceland 2000-2017. Master's Thesis. Reykjavík University.
21.
HansonR. K. (2021). Prediction statistics for psychological assessment. American Psychological Association. https://doi.org/10.1037/0000275-000
22.
HansonR. K.BabchishinK. M.HelmusL. M.ThorntonD.PhenixA. (2017). Communicating the results of criterion referenced prediction measures: Risk categories for the Static-99R and Static-2002R sexual offender risk assessment tools. Psychological Assessment, 29(5), 582–597. https://doi.org/10.1037/pas0000371
23.
HansonR. K.BabchishinK. M.MayK.ReidB.WilsonR. J. (2026). Risk tools predict for me with low intellectual ability and a history of sexual offending. Manuscript accepted for publication. Sexual Abuse.
24.
HansonR. K.BourgonG.HelmusL.HodgsonS. (2009). The principles of effective correctional treatment also apply to sexual offenders: A meta-analysis. Criminal Justice and Behavior, 36(9), 865–891. https://doi.org/10.1177/0093854809338545
HansonR. K.HarrisA. J. R.HelmusL.ThorntonD. (2013). High-risk sex offenders may not be high risk forever. Journal of Interpersonal Violence, 28(12), 2568–2589. https://doi.org/10.1177/0886260513479032
27.
HansonR. K.ThorntonD. (2000). Improving risk assessments for sex offenders: A comparison of three actuarial scales. Law and Human Behavior, 24(1), 119–136. https://doi.org/10.1023/A:1005482921333
28.
HansonR. K.ThorntonD.HelmusL.BabchishinK. M. (2016). What sexual recidivism rates are associated with Static-99R and Static-2002R scores?Sexual Abuse, 28(3), 218–252. https://doi.org/10.1177/1079063215574710
29.
HelmusL.HansonR. K.ThorntonD.BabchishinK. M.HarrisA. J. R. (2012). Absolute recidivism rates for the Static-99R and Static-2002R sex offender risk assessment tools: A meta-analysis. Law and Human Behavior, 36(5), 399–412. https://doi.org/10.1037/h0093938
30.
HelmusL. M.EkeA. W.FarmusL.SetoM. C. (2024). The CPORT and Risk Matrix 2000 for men convicted of child sexual exploitation material (CSEM) offenses: A predictive accuracy comparison and meta-analysis. Criminal Justice and Behavior, 51(1), 3–23. https://doi.org/10.1177/00938548231208194
31.
HelmusL. M.EkeA. W.SetoM. C. (2025). What risk assessment tools can be used with men convicted of child sexual exploitation material offenses? Recommendations from a review of current research. Law and Human Behavior, 49(1), 71–88. https://doi.org/10.1037/lhb0000594
HigginsJ.ThompsonS. G.DeeksJ. J.AltmanD. G. (2003). Measuring inconsistency in meta-analyses. British Medical Journal, 327(7414), 557–560. https://doi.org/10.1136/bmj.327.7414.557
KahnR. E.AmbroziakG.HansonR. K.ThorntonD. (2017). Release from the sex offender label. Archives of Sexual Behavior, 46(4), 861–864. https://doi.org/10.1007/s10508-017-0972-y
36.
KelleyS. M.AmbroziakG.ThorntonD.BarahalR. M. (2020). How do professionals assess sexual recidivism risk? An updated survey of practices. Sexual Abuse, 32(1), 3–29. https://doi.org/10.1177/1079063218800474
37.
LeeS. C.BrankleyA. E.HansonR. K. (2023). There is no such thing as zero risk of sexual offending. Canadian Journal of Criminology and Criminal Justice, 65(3), 1–31. https://doi.org/10.3138/cjccj-2022-0025
PaquetteS.Brouillette-AlarieS. (2025). Online sexual offending against children: Recidivism rates and predictors. Sexual Abuse, 37(6), 635–666. https://doi.org/10.1177/10790632241309631
40.
PilonA. (2016). The predictive validity of general and offense-specific risk assessment tools for child pornography offenders’ reoffending. Master’s thesis. University of Saskatchewan.
41.
RalstonC. A.EppersonD. L. (2013). Predictive validity of adult risk assessment tools with juveniles who offended sexually. Psychological Assessment, 25(3), 905–916. https://doi.org/10.1037/a0032683
42.
R Core Team. (2025). R: A language and environment for statistical computing(Version 4.5.0) [Computer software]. R Foundation for Statistical Computing. https://www.R-project.org/
43.
RiceM. E.HarrisG. T. (2005). Comparing effect sizes in follow-up studies: ROC area, Cohen’s d, and r. Law and Human Behavior, 29(5), 615–620. https://doi.org/10.1007/s10979-005-6832-7
RuscioJ. (2008). A probability-based measure of effect size: Robustness to base rates and other factors. Psychological Methods, 13(1), 19–30. https://doi.org/10.1037/1082-989X.13.1.19
46.
SavoieV.QuayleE.FlynnE.O'RourkeS. (2022). Predicting risk of reoffending in persons with child sexual exploitation material offense histories: The use of child pornography offender risk tool in a Scottish population. Sexual Abuse, 34(5), 568–596. https://doi.org/10.1177/10790632211047190
47.
SchulzeR. (2007). Current methods for meta-analysis: Approaches, issues, and developments. Zeitschrift Für Psychologie/Journal of Psychology, 215(2), 90–103. https://doi.org/10.1027/0044-3409.215.2.90
48.
SeiserA.EherR.HelmusL. M.TurnerD.RettenbergerM. (2025). The psychometric properties of the Child Pornography Offender Risk Tool (CPORT) in different subgroups of individuals convicted of offenses related to child sexual exploitation material (CSEM). Law and Human Behavior, 49(5), 426–439. https://doi.org/10.1037/lhb0000616
49.
SetoM. C. (2025). Online sexual offending: Theory, practice, and policy (2nd ed.). American Psychological Association.
50.
SetoM. C.EkeA. W. (2015). Predicting recidivism among adult male child pornography offenders: Development of the Child Pornography Offender Risk Tool (CPORT). Law and Human Behavior, 39(4), 416–429. https://doi.org/10.1037/lhb0000128
51.
SetoM. C.EkeA. W. (2017). Correlates of admitted sexual interest in children among individuals convicted of child pornography offenses. Law and Human Behavior, 41(3), 305–313. https://doi.org/10.1037/lhb0000240
52.
SoldinoV.Carbonell-VayáE. J.Seigfried-SpellarK. C. (2021). Spanish validation of the child pornography offender risk tool. Sexual Abuse, 33(5), 503–528. https://doi.org/10.1177/1079063220928958
53.
SwetsJ. A.DawesR. M.MonahanJ. (2000). Psychological science can improve diagnostic decisions. Psychological Science in the Public Interest, 1(1), 1–26. https://doi.org/10.1111/1529-1006.001
54.
van den BergJ. W.SmidW.SchepersK.WeverE.van BeekD.JanssenE.GijsL. (2018). The predictive properties of dynamic sex offender risk assessment instruments: A meta-analysis. Psychological Assessment, 30(2), 179–191. https://doi.org/10.1037/pas0000454
55.
VergouweY.SteyerbergE. W.EijkemansM. J.HabbemaJ. D. (2005). Substantial effective sample sizes were required for external validation studies of predictive logistic regression models. Journal of Clinical Epidemiology, 58(5), 475–483. https://doi.org/10.1016/j.jclinepi.2004.06.017
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