Abstract
Although narrative reviews have suggested that “youth psychopathy” is a strong predictor of future crime and violence, to date no quantitative summaries of this literature have been conducted. We meta-analyzed recidivism data for the Psychopathy Checklist measures across 21 non-overlapping samples of male and female juvenile offenders. After removing outliers, psychopathy was significantly associated with general and violent recidivism (r w's of .24 and .25, respectively), but negligibly related to sexual recidivism in the few studies examining this low base rate outcome. Even after eliminating outliers, however, considerable heterogeneity was noted among the effects, with some of this variability being explained by the gender and ethnic composition of the samples. Effect sizes for the small number of female samples available for analysis were mostly small and nonsignificant, and psychopathy was a weaker predictor of violent recidivism among more ethnically heterogeneous samples. In relation to predicting both general and violent recidivism, psychopathy performed comparably to an instrument designed specifically to assess risk, the Youth Level of Service/Case Management Inventory (Hoge & Andrews, 2002).
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Notes
The need to weight effect sizes is widely accepted in the meta-analytic literature, with the inverse variance weight being considered a superior method than simply weighting by sample size (see, e.g., Lipsey & Wilson, 2001, pp. 36–37; see also Hedges & Olkin, 1985). In the case of correlations, the formula to compute the inverse variance reduces to n−3 (Lipsey & Wilson, 2001, p. 64). We should note, however, that the mean effect sizes reported later in this article were highly consistent regardless of whether they were weighted or unweighted.
In three cases, effect sizes were available for violent and nonviolent recidivism, but not for general recidivism specifically. In these instances, we computed the mean effect size for these two categories to serve as a proxy for general recidivism. Dropping these three studies from the general recidivism analyses (k=18) had virtually no impact on the mean effect size or the heterogeneity statistics that are reported in Table 2.
We should note that there is debate in the field regarding how best to address outlier effect sizes in meta-analyses, with some commentators expressing concern over the exclusion of such studies (see Lipsey & Wilson, 2001, pp. 107–108 for a discussion of this issue). Given some of the methodological concerns noted above regarding both the Ridenour et al. (2001) and Campbell (2003) studies, however, we believed it was appropriate to remove these effect sizes from the subsequent analyses.
For example, scoring the PCL measures without interviewing the juveniles might result in less valid scores that attenuate predictive validity (see Hare, 2003; for a discussion of concerns regarding file-only PCL scoring). Similarly, coefficient alpha might be lower among studies that neglected to report this information. Because the magnitude of validity coefficients is constrained by the reliability of a scale, this also might result in weaker effect sizes in these studies. Finally, severe range restriction on the PCL scores in a sample also has the potential to attenuate the magnitude of the obtained effect size, as can extreme deviations from an optimal recidivism base rate of 50%.
When the samples were split by gender, it was clear that the outlying low effect (Campbell, 2003) resulted from the male participants in this sample, where the effect size was three standard deviations below the weighted mean and the base rate of violent reoffending was only .08. The effect size for the female sample was quite similar to the other female samples, however, so we retained this small sample (n=34) in the moderator analyses for gender.
Even though methodological factors did not explain a significant amount of heterogeneity in the effect sizes, we were curious as to whether several of the nonsignificant trends noted for some of these factors might account for much of the same heterogeneity in violent recidivism that was explained by the ethnicity variable. We therefore conducted exploratory regression analyses to examine whether ethnicity would continue to explain a significant amount of heterogeneity after controlling for these factors. Only one analysis suggested that the moderating role of ethnicity was substantially reduced after controlling for a methodological variable. Noted earlier, there was a non-significant trend for greater variability on the PCL total score (i.e., larger SDs) to moderate effect sizes for violent recidivism (Q R=2.66, p=.10). When the regression analysis was re-run including the ethnicity variable along with the SD. variable (k=11), the overall model was significant (Q R=6.50, p < .05) and R2 increased substantially to .46. Neither the beta for the SD variable nor the ethnicity variable was significant individually, however. Further detail regarding these analyses is available from the first author.
Although we were unable to code an exact effect size, Marczyk (2002) noted that neither psychopathy nor the YLS/CMI was significantly correlated with violent offenses.
This is the same data set that is reported in Schmidt et al. (2005), except that the sample is slightly smaller (n=102). Schmidt et al. did not include analyses related to the YLS/CMI but these results were included in the original McKinnon thesis.
The conventional values suggested for point biserial correlations such as those that predominate this study are .10 (small), .24 (medium), and .37 (large).
This was a subset of sex offenders who were included in the subsequent Langstrom and Grann (2002) paper, the results of which we were able to code for our general and violent recidivism analyses.
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Edens, J.F., Campbell, J.S. & Weir, J.M. Youth Psychopathy and Criminal Recidivism: A Meta-Analysis of the Psychopathy Checklist Measures. Law Hum Behav 31, 53–75 (2007). https://doi.org/10.1007/s10979-006-9019-y
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DOI: https://doi.org/10.1007/s10979-006-9019-y