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p-value Problems? An Examination of Evidential Value in Criminology

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Abstract

Objectives

This study aims to assess the evidential value of the knowledgebase in criminology after accounting for the presence of potential Type I errors.

Methods

The present study examines the distribution of 1248 p-values (that inform 84 statistically significant outcomes across 26 systematic reviews) in meta-analyses on the topic of crime and justice published by the Campbell Collaboration (CC) using p-curve analysis.

Results

The distribution of all CC p-values have a significant cluster of p-values immediately below 0.05, which is indicative of p-hacking. Evidential value (right skewed p-curves) is detected in most meta-analytic topic areas but not motivational interviewing (substance use outcome), sex offender treatment (sexual/general recidivism), police legitimacy (procedural justice), street-level drug law enforcement (total crime), and treatment effectiveness in secure corrections (juvenile recidivism).

Conclusions

More studies, especially carefully designed and implemented randomized experiments with sufficiently large sample sizes, are needed before we are able to affirm the presence of evidential value and replicability of studies in all CC topic areas with confidence.

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Notes

  1. p-curve analysis has been applied to meta-analyses relating to education and intelligence test scores (Ritchie and Tucker-Drob 2018), sugar consumption and self-regulation (Vadillo et al. 2016), cognitive and affective responses to stimuli (Franklin et al. 2014), psychosocial functioning and the cortisol awakening response (Boggero et al. 2017), prosocial behavior (Shariff et al. 2016), expressed emotion, relapse and regional brain structure of schizophrenia patients (Brugger and Howes 2017; Weintraub et al. 2017), child development and working memory training (Sala and Gobet 2017), cognitive/emotional impairments and brain stimulation with Parkinson’s disease (Combs et al. 2015), breast feeding and intelligence scores (Ritchie 2017), and psychological adjustment (Cheng et al. 2014), medical and pharmaceutical clinical trials (Belas et al. 2017), to name a few.

  2. In this section, all actions by researchers that would threaten the evidential value of a given p-curves (regardless of intent) will be referred to as p-hacking for simplicity.

  3. The p-curve app initially had a test to detect significant left skew, but this test has since been removed.

  4. Studies with sufficient power and large enough effect sizes.

  5. See pages 10 and 11 in their supplemental materials.

  6. See also Nelson et al. (2015).

  7. Inclusive of related outcomes that may be non-criminal (e.g., anti-social behavior, delinquency, disorder). Statistically insignificant outcomes from eligible systematic reviews are presented in Appendix Table 1A. Additional coding decisions are noted in Appendix B.

  8. When this information was not provided in the review and the data were not warehoused with the Inter-university Consortium for Political and Social Research (ICPSR), the authors were contacted to request the information.

  9. No statistically significant, beneficial overall outcome: Scared straight and other juvenile awareness programs for preventing juvenile delinquency; Formal system processing of juveniles: Effects on delinquency; Effects of correctional boot camps on offending; Juvenile curfew effects on criminal behavior and victimization; Non-custodial employment programs: Impact on recidivism rates of ex-offenders; Police-initiated Diversion Programs on Delinquent Behaviors; The Effectiveness of Counter-Terrorism Strategies; The effects on re-offending of custodial versus non-custodial sanctions; Parental imprisonment: Effects on child anti-social behavior, crime and mental health.

    No quantitative estimates/Studies not meta-analyzed: Preventative intervention to reduce youth gang violence in low- middle-income countries; Benefit–cost analyses of sentencing; Cross-border Trafficking in Human Beings: Prevention and intervention strategies for Reducing Sexual Exploitation; Interventions for Children, Youth, and Parents to Prevent and Reduce Cyber Abuse; Police strategies to reduce illegal possession and carrying of firearms: Effects on gun crime; Incarceration-based drug treatment review (estimates not provided).

    Not aimed to assess crime reduction among people or places: The effects of stress management interventions among police officers and recruits; Use of DNA testing in police investigative work for increasing offender identification, arrest, conviction, and case clearance; Forensic nurse examiners versus doctors for the forensic examination of rape and sexual assault; Factors associated with youth gang membership in low and middle-income countries; Interview and interrogation methods and their effects on true and false confessions; Corporate Crime Deterrence.

  10. This was able to be done with 3 out of the 9 systematic reviews listed in Table 1, with the smallest p-value dropped in situations where estimates were dependent: treatment effectiveness in secure corrections (serious and general juvenile recidivism was combined), motivational interviewing for substance use (varying follow-up time points measuring substance use were combined), and street-level drug law enforcement (total calls-for-service and total offenses combined).

  11. This entails conducting p-curve analysis again for each outcome but with the smallest p-value removed. The results of the sensitivity analysis are discussed in the next section only when the findings of the p-curve analysis were sensitive to the smallest p-value being removed.

  12. Efforts were made to identify duplicates within and between meta-analytic reviews to ensure that the p-values from only the unique effect sizes were include in Fig. 1. This was done by matching the author/year from which the estimate derived and z-score. For duplicates that were identified, the authors/year were coded using the same label (e.g., Sherman, Gartin, and Buerger 1989; Sherman et al. 1989 would be changed so they are identical) and z-scores were adjusted to match if they differed due to rounding (e.g., z = 4.982 and z = 4.983 would both be changed to 4.983; no adjustments were made that would affect the p-value bin the estimate falls into). Independent samples from a study were distinguished using the author/year label (e.g., Tuffin 2006-Leicestershire and Tuffin 2006-Manchester).

  13. Since p-curve analysis requires that all p-values pertain to the same hypothesis of interest, p-curve analysis on these outcomes were run separately according to the outcomes they informed. The frequency table for the distribution is presented in Appendix Table 2A.

  14. The chances of 36 or more p-values falling in the 0.041 to 0.050 bin as compared to the 22 p-values in the 0.051 to 0.060 bin is 0.044 (one-tailed test; overall N is 58). Test performed using the Binomial Test Calculator provided by https://www.SocSciStatistics.com/.

  15. Disclosure table presented in Appendix Table 3A.

  16. Disclosure tables presented in Appendix Tables 4A and 5A.

  17. Disclosure tables presented in Appendix Tables 6A, 7A, and 8A.

  18. In the sex offender treatment review, there was no longer significant heterogeneity for the general recidivism outcome when an outlier was removed and the overall effect remained significant; however, evidential value was still not found after removing the outlier from the p-curve analysis. Also, estimates were not found to be homogenous after outliers were removed from the sexual recidivism outcome.

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  1. Department of Criminal Justice, Temple University, Philadelphia, PA, USA

    Alese Wooditch, Ryan Fisher & Nicole J. Johnson

  2. Department of Criminology, Law and Society, Center for Evidence-Based Criminology, George Mason University, Fairfax, VA, USA

    Xiaoyun Wu

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  1. Alese Wooditch
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Wooditch, A., Fisher, R., Wu, X. et al. p-value Problems? An Examination of Evidential Value in Criminology. J Quant Criminol 36, 305–328 (2020). https://doi.org/10.1007/s10940-020-09459-5

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