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Comparing effect sizes in follow-up studies: ROC Area, Cohen's d, and r.

Rice, Marnie E., Harris, Grant T.·2005·Law and Human Behavior

DOI 10.1007/s10979-005-6832-7

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Abstract

In order to facilitate comparisons across follow-up studies that have used different measures of effect size, we provide a table of effect size equivalencies for the three most common measures: ROC area (AUC), Cohen's d, and r. We outline why AUC is the preferred measure of predictive or diagnostic accuracy in forensic psychology or psychiatry, and we urge researchers and practitioners to use numbers rather than verbal labels to characterize effect sizes.

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“AUC values below .54 are considered poor, values .55 to .63 fair, values .64 to .70 good, and values above .70 excellent (Desmarais & Singh, 2013; Rice & Harris, 2005).”
Background
Islamist terrorists in Germany and their warning behaviors: A comparative assessment of attackers and other convicts using the TRAP-18.
Böckler, N., Allwinn, M., Metwaly, C., Wypych, B., Hoffmann, J., & Zick, A. (2020) · Journal of threat assessment and management · doi:10.1037/tam0000150 · Cited by 18
Risk scores and reliability of the SARA, SARA-V3, B-SAFER, and ODARA among Intimate Partner Violence (IPV) cases referred for threat assessment
Hilton, N. Z., Pham, A. T., Jung, S. Y., Nunes, K. L., & Ennis, L. P. (2020) · Police Practice and Research · doi:10.1080/15614263.2020.1798235 · Cited by 32
“…range from 0 to 1, where .5 indicates chance association between the predictor and the outcome, AUCs over .56, .64, and .71 are considered small, medium, and large effects predicting the outcome, respectively (Rice & Harris, 2005), and AUCs below .5 indicate prediction in the opposite direction.”
Background
Short-Term and Long-Term Resilience Among At-Risk Adolescents: The Role of Family and Community Settings
Thakur, H., & Cohen, J. R. (2020) · Journal of Clinical Child & Adolescent Psychology · doi:10.1080/15374416.2020.1756296 · Cited by 18
“Correlations demonstrated small to large effect sizes (Rice & Harris, 2005), with the direction and magnitude of effect sizes in the expected direction.”
BackgroundMethod
Predicting Women’s Recidivism Using the Dynamic Risk Assessment for Offender Re-Entry: Preliminary Evidence of Predictive Validity With Community-Sentenced Women Using a “Gender-Neutral” Risk Measure
Scanlan, J., Yesberg, J. A., Fortune, C.-A., & Polaschek, D. (2020) · Criminal Justice and Behavior · doi:10.1177/0093854819896387 · Cited by 18
“Using the widely accepted interpretations of AUCs promulgated by Rice and Harris (2005), these magnitudes would be described as medium.”
Method
Clinical and Genetic Risk Prediction of Cognitive Impairment After Blood or Marrow Transplantation for Hematologic Malignancy
Sharafeldin, N., Richman, J., Bosworth, A., Chen, Y., Singh, P., Patel, S. K., Wang, X., Francisco, L., Forman, S. J., Wong, F. L., et al. (2020) · Journal of Clinical Oncology · doi:10.1200/jco.19.01085 · Cited by 20
A Normative Reference vs. Baseline Testing Compromise for ImPACT: The CARE Consortium Multiple Variable Prediction (CARE-MVP) Norms
Asken, B. M., Houck, Z. M., Schmidt, J. D., Bauer, R. M., Broglio, S. P., McCrea, M., McAllister, T. W., & Clugston, J. R. (2020) · Sports Medicine · doi:10.1007/s40279-020-01263-2 · Cited by 18
“We primarily interpret results using effect size estimates [37].”
Method
The Predictive Properties of Psychiatric Diagnoses, Dynamic Risk and Dynamic Risk Change Assessed by the VRS-SO in Forensically Admitted and Released Sexual Offenders
Eher, R., Hofer, S., Buchgeher, A., Domany, S., Turner, D., & Olver, M. (2020) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2019.00922 · Cited by 18
“For change outcome analyses, the AUCs were computed with the direction of the binary recidivism criterion variable reversed, such that positive AUCs for change scores would represent inverse associations with outcome, and their magnitudes could be interpreted per the Rice and Harris (27) guidelines.”
BackgroundMethod
Cardiorespiratory Fitness Cut-Points are Related to Body Adiposity Parameters in Latin American Adolescents
Prieto-Benavides, D., García-Hermoso, A., Izquierdo, M., Alonso-Martínez, A., Agostinis-Sobrinho, C., Correa-Bautista, J., & Ramírez-Vélez, R. (2019) · Medicina · doi:10.3390/medicina55090508 · Cited by 18
Predictive Validity of the Static-99 and Static-99R in Switzerland
Gonçalves, L. C., Gerth, J., Rossegger, A., Noll, T., & Endrass, J. (2019) · Sexual abuse. A Journal of Research and Treatment · doi:10.1177/1079063218821117 · Cited by 18
“Values above .71 can be considered large in violence risk assessment (Rice & Harris, 2005).”
BackgroundMethodInfluential
Muscle mass to visceral fat ratio is an important predictor of the metabolic syndrome in college students
Ramírez‐Vélez, R., García‐Hermoso, A., Prieto‐Benavides, D. H., Correa‐Bautista, J. E., Quino-Ávila, A. C., Barreto, C. M. R., González‐Ruíz, K., Carrillo, H. A., Correa‐Rodríguez, M., González‐Jiménez, E., et al. (2018) · British Journal Of Nutrition · doi:10.1017/s0007114518003392 · Cited by 26
The Reliability and Validity of the Measure of Criminal Attitudes and Associates and the Pride in Delinquency Scale in a Mixed Sex Sample of Justice-Involved Youth
O'Hagan, H., Brown, S. L., Jones, N. J., & Skilling, T. A. (2018) · Criminal Justice and Behavior · doi:10.1177/0093854818810459 · Cited by 23
Assessing Dynamic Violence Risk in a High-Risk Treated Sample of Violent Offenders
Coupland, R., & Olver, M. (2018) · Assessment (Odessa, Fla.) · doi:10.1177/1073191118797440 · Cited by 18
“Values of .50 represent chance level predictive accuracy, values of .556 to .639 correspond to small effects, .639 to .714 moderate, and .714 and higher as large in magnitude predictive effect sizes (Rice & Harris, 2005).”
Background
Uncommonly Observed
Hamilton, Z. K., & Campbell, C. M. (2014) · Criminal Justice and Behavior · doi:10.1177/0093854814546132 · Cited by 18
Situational Familiarity, Efficacy Expectations, and the Process of Credibility Attribution
Reinhard, M., Scharmach, M., & Sporer, S. L. (2012) · Basic and Applied Social Psychology · doi:10.1080/01973533.2012.655992 · Cited by 21
“Binary Lie=Truth Judgments Analogous to the analysis of the credibility judgments, we used an ANOVA to analyze the binary judgment (see Rice & Harris, 2005, for justification).”
Method
An Exemplar-Based Approach to Risk Assessment: Validating the Risk Management Systems Instrument
Shaffer, D. K., Kelly, B., & Lieberman, J. D. (2011) · Criminal Justice Policy Review · doi:10.1177/0887403410372989 · Cited by 18
“The Area Under the Curve (AUC) can be interpreted as the likelihood that a score randomly drawn from one group (e.g., recidivists) will be higher than the one randomly drawn from a second group (e.g., nonrecidivists; Rice & Harris, 2005).”
Background
Toward a Classification of Juvenile Offenders: Subgroups of Serious Juvenile Offenders and Severity of Recidivism
Mulder, E., Brand, E., Bullens, R., & van Marle, H. (2010) · International journal of offender therapy and comparative criminology · doi:10.1177/0306624x10387518 · Cited by 18
Analyzing the Performance of Risk Assessment Instruments: A Response to Vrieze and Grove (2007)
Mossman, D. (2008) · Law and Human Behavior · doi:10.1007/s10979-007-9123-7 · Cited by 18
“(These figures do not include the results from Epperson and colleagues’ MnSOST‐R construction sample, and thus can be regarded as results of ‘‘validation’’ studies.) An effect size of 0.72 is equivalent to an AUC of 0.695 (Swets 1986; Rice and Harris 2005 ) and is comparable to the mean effect size (0.70) reported by…”
BackgroundResultInfluential
Comparing the predictive utility of two screening tools for mental disorder among probationers.
Louden, J. E., Skeem, J., & Blevins, A. (2013) · Psychological Assessment · doi:10.1037/a0031213 · Cited by 18
“This AUC value equates to an effect size (d) of 1.22, a large effect (see Cohen, 1992; Rice & Harris, 2005).”
BackgroundInfluential
Strategies for monitoring mentoring relationship quality to predict early program dropout
Lyons, M. D., & Edwards, K. D. (2022) · American Journal of Community Psychology · doi:10.1002/ajcp.12585 · Cited by 17
Sensing Eating Events in Context: A Smartphone-Only Approach
Bangamuarachchi, W., Chamantha, A., Meegahapola, L., Ruiz-Correa, S., Perera, I., & Gatica-Perez, D. (2022) · IEEE Access · doi:10.1109/access.2022.3179702 · Cited by 17
The degree of cortical plasticity correlates with cognitive performance in patients with Multiple Sclerosis
Balloff, C., Penner, I., Ma, M., Georgiades, I., Scala, L., Troullinakis, N., Graf, J., Kremer, D., Aktaş, O., Hartung, H., et al. (2022) · Brain stimulation · doi:10.1016/j.brs.2022.02.007 · Cited by 17
Evaluating Bias, Shrinkage and the Home-Field Advantage: Results from a Revalidation of the MnSTARR 2.0
Duwe, G. (2021) · Corrections · doi:10.1080/23774657.2021.2011802 · Cited by 17
“To provide additional clarity in the interpretation of this statistic, Rice and Harris (2005) reported the following effect size benchmarks for the AUC: negligible (<0.55), small (0.55– 0.63), moderate (0.64–0.71), and strong (>0.71).”
BackgroundInfluential
Cognitive decline as an outcome and marker of progression toward dementia, in early preventive trials
Mura, T., Coley, N., Amieva, H., Berr, C., Gabelle, A., Ousset, P., Vellas, B., & Andrieu, S. (2021) · Alzheimer's & Dementia · doi:10.1002/alz.12431 · Cited by 17
Predicting environmental risk factors in relation to health outcomes among school children from Romania using random forest model - An analysis of data from the SINPHONIE project.
Lin, Z.-Q., Lin, S., Neamtiu, I., Ye, B., Csobod, É., Fazakas, E., & Gurzău, E. (2021) · Science of the Total Environment · doi:10.1016/j.scitotenv.2021.147145 · Cited by 17
Recrafting Youth Risk Assessment: Developing the Modified Positive Achievement Change Tool for Iowa
Hamilton, Z. K., Kowalski, M. A., Schaefer, R., & Kigerl, A. (2020) · Deviant Behavior · doi:10.1080/01639625.2019.1609302 · Cited by 17
“Based on industry-standard AUC ranges, an average increase of 7% represents roughly a full effect size increase (see Rice and Harris 2005).”
BackgroundInfluential
Using Administrative Data to Assess the Risk of Permanent Work Disability: A Cohort Study
Bethge, M., Spanier, K., & Streibelt, M. (2020) · Journal of occupational rehabilitation · doi:10.1007/s10926-020-09926-7 · Cited by 17
Predicting risk of recidivism in Spanish young offenders: Comparative analysis of the SAVRY and YLS/CMI.
Ortega-Campos, E., García-García, J., Sánchez, L. D. L. F., & Zaldívar-Basurto, F. (2020) · Psicothema · doi:10.7334/psicothema2019.275 · Cited by 17
“For the total and partial scores of the SAVRY, AUC estimates are statistically signifi cant, with values over .70, indicating large magnitude (Rice & Harris, 2005) for all domains.”
The Assessment of Upper Limb Functionality in Friedreich Ataxia via Self-Feeding Activity
Nguyen, K., Corben, L., Pathirana, P., Horne, M. K., Delatycki, M., & Szmulewicz, D. (2020) · IEEE transactions on neural systems and rehabilitation engineering · doi:10.1109/tnsre.2020.2977354 · Cited by 17
“Gini index (GI) = 2*AUC-1 [32], due to distribution independence and the consistent interpretation of the effect [33].”
BackgroundMethod
Telephone-based cognitive behavioural therapy for female patients 1-year post-bariatric surgery: A pilot study
Sockalingam, S., Leung, S., Hawa, R., Wnuk, S., Parikh, S. V., Jackson, T. D., & Cassin, S. E. (2019) · Obesity Research & Clinical Practice · doi:10.1016/j.orcp.2019.07.003 · Cited by 25
Protective Factors in Violence Risk Assessment: Predictive Validity of the SAPROF and HCR-20 V3
Neil, C., O’Rourke, S. J., Ferreira, N. B., & Flynn, L. (2019) · International Journal of Forensic Mental Health · doi:10.1080/14999013.2019.1643811 · Cited by 18
“Rice and Harris (2005) suggest an AUC value of .639 is regarded as a medium effect and .714 is a large effect.”
Background
Mentally Disordered Firesetters: An Examination of Risk Factors
Wyatt, B., Gannon, T. A., McEwan, T. E., Lockerbie, L., & O’Connor, A. (2018) · Psychiatry · doi:10.1080/00332747.2018.1534520 · Cited by 21
Sexual Sadism Among Sex Offenders in Switzerland
Gonçalves, L. C., Rossegger, A., Gerth, J., Singh, J. P., & Endrass, J. (2018) · Sexual Abuse · doi:10.1177/1079063218800473 · Cited by 19
“It must be noted that point-biserial correlation coefficients tend to be smaller than Pearson correlation coefficients and, therefore, the magnitude of the associations may be biased downward (Rice & Harris, 2005).”
BackgroundInfluential
Investigating Characteristics of the Nonrecidivating Psychopathic Offender
Burt, G., Olver, M., & Wong, S. C. P. (2016) · doi:10.1177/0093854816661215 · Cited by 17
“Research demonstrates that the VRS has moderate to high predictive accuracy (cf. Rice & Harris, 2005) for future violence in a large general prison inmate sample (Wong & Gordon, 2006), treated high-risk violent offenders (Lewis, Olver, & Wong, 2013), and psychiatric inpatients (Dolan & Fullam,…”
Background
L’évaluation du risque et des besoins criminogènes à la lumière des données probantes : une étude de validation de la version française de l’inventaire de niveau de service et de gestion des cas – LS/CMI
Guay, J.-P. (2016) · European Review of Applied Psychology · doi:10.1016/j.erap.2016.04.003 · Cited by 17
Developing Nonarbitrary Metrics for Risk Communication
Lehmann, R. J. B., Thornton, D., Helmus, L., & Hanson, R. K. (2016) · Criminal Justice and Behavior · doi:10.1177/0093854816651656 · Cited by 17
“…(not accounting for the nesting in different samples, nor for differences in follow-up) were reported using the area under the curve (AUC) statistic (Swets, Dawes, & Monahan, 2000), where values of .556, .639, and .714 were considered small, moderate, and large, respectively (Rice & Harris, 2005).”
Method
An Examination of a Risk/Needs Assessment Instrument and Its Relation to Religiosity and Recidivism Among Probationers in a Muslim Culture
Bhutta, M. H., & Wormith, J. (2016) · Criminal Justice and Behavior · doi:10.1177/0093854815604011 · Cited by 17
“ROC analyses were also conducted to examine the validity of the LS/CMI sections on recidivism because ROC is relatively unaffected by base rates that deviate substantially from 50% (Rice & Harris, 2005) as they did in the current investigations.”
Method
Predictive validity of the START for unauthorised leave and substance abuse in a secure mental health setting: a pseudo-prospective cohort study.
O'Shea, L., & Dickens, G. (2015) · International Journal of Nursing Studies · doi:10.1016/j.ijnurstu.2015.02.007 · Cited by 17

Authors: Rice, M. E., & Harris, G. T.
Affiliations: Waypoint Centre for Mental Health Care (CA)
Venue: Law and Human Behavior
Publisher: American Psychological Association (APA)
Published: 2005-10
Volume: 29
Issue: 5
Pages: 615-620
ISSN: 0147-7307 (ISSN-L)
1573-661X
DOI: 10.1007/s10979-005-6832-7
OpenAlex: W2060774914
Cited by: 1,608
References: 0
Citations by year: 2026: 60 · 2025: 84 · 2024: 90 · 2023: 121 · 2022: 151 · 2021: 150 · 2020: 174 · 2019: 137 · 2018: 112 · 2017: 80 · 2016: 88 · 2015: 65 · 2014: 64 · 2013: 58 · 2012: 51
FWCI: 11.55
Type: journal article
Language: en
Open access: closed
Locations: Law and Human Behavior (publishedVersion)
PubMed (publishedVersion)
Topics: Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
Social Sciences › Psychology › Experimental and Cognitive Psychology › Psychology Research and Bibliometrics
Keywords: effect size, Cohen's d, diagnostic accuracy, prediction accuracy, forensic psychology, follow-up study
MeSH terms: Forecasting, Humans, ROC Curve, Risk Assessment

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Comparing effect sizes in follow-up studies: ROC Area, Cohen's d, and r.

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