Intelligence Score Profiles of Female Juvenile Offenders

Abstract

Previous studies have found that male juvenile offenders typically obtain low scores on measures of intelligence, often with a pattern of higher scores on measures of nonverbal relative to verbal tasks. The research on the intelligence performance of female juvenile offenders is limited. This study explored the Wechsler Intelligence Scale for Children–Fourth Edition (WISC-IV) scores of 430 female juvenile offenders. Their mean IQ scores were significantly lower than the standardization sample means. Cluster analysis, which allows for identification of subgroups based on IQ score patterns, revealed five distinct subgroups of girls within the sample based on their WISC-IV Index scores. Although there was a cluster that was similar to the Verbal IQ (VIQ) < Performance IQ (PIQ) pattern described in previous studies, the other clusters serve as a reminder of the diversity of cognitive functioning among juvenile offenders.

Keywords

female juvenile delinquency intelligence risk factors cluster analysis

Intelligence and academic performance have emerged as key risk factors in the development of delinquent behavior, either independently or in concert with other contributing factors (Hoyt & Scherer, 1998; McGloin, Pratt, & Maahs, 2004). Most of these studies have examined the performance of boys, and have found mean full scale IQ scores to be well below the standardization sample mean (Raine et al., 2005). Of the few studies with girls, all have found that female juvenile offenders have lower IQ scores than nonoffenders (e.g., Aiello, 2008; Leve & Chamberlain, 2004).

Analysis of the Verbal IQ (VIQ) and Performance IQ (PIQ) scores of earlier versions of the Wechsler Intelligence Scale for Children (WISC and WISC-R) with juvenile offender samples identified a pattern of significantly higher nonverbal relative to verbal scores (e.g., Cornell &Wilson, 1992; Isen, 2010). An extension of this pattern has been that lower FSIQ scores found in offender populations were artifacts of lower VIQ scores (e.g., Vermeiren, De Clippele, Schwab-Stone, Ruchkin, & Deboutte, 2002). Moffit (1990) used these and related findings to relate poorly developed verbal skills with less well-developed social problem-solving and conflict resolution skills; these deficits, in turn, increase risk to engage in delinquent behavior due to a failure to anticipate the consequences of behavior or mediate impulses.

The aim of the present study was to describe the Wechsler Intelligence Scale for Children–4th edition (WISC-IV) score patterns of a sample of female juvenile offenders by comparing the sample’s scores to the standardization mean (M = 100, SD = 15). Because the majority of this sample (64.7%) was African American, their scores were also compared with those of the African American children who comprised the WISC-IV standardization sample to determine whether any differences were due to score differences that have been observed by race. In addition, cluster analysis was used to determine whether there are patterns of WISC-IV scores that might provide insight into the cognitive functioning or learning needs of female juvenile offenders that might inform rehabilitation efforts.

Method

Participants and Procedure

We examined the test scores of 430 female juvenile offenders ages 10 to 16 (M = 14.61, SD = 1.20) who completed a 14-day residential evaluation program operated by a juvenile court in an urban county in the Midwestern United States that was designed to assist the Court in making disposition decisions. Trained graduate students administered the WISC-IV as part of a standard battery of measures. The majority of participants (n = 278, 64.7%) identified as African American. The most common charge for this group was a Violation of Court Order (VCO); 76% of the sample had only misdemeanor, status or VCO offenses. Therefore, this sample reflects primarily girls who have engaged in few and/or low level offenses. The facility granted permission to access the database and an Institutional Review Board (IRB) approved the study.

Measures

WISC-IV

The WISC-IV (Wechsler, 2003a) produces a Full Scale IQ score, and four Index scores (Verbal Comprehension, Perceptual Reasoning, Working Memory, Processing Speed), which have a mean score of 100 and a standard deviation of 15.

Results and Discussion

The means and standard deviations of the sample’s WISC-IV Full Scale and Index scores are provided in Table 1. Descriptively, the mean scores are below average, and one-sample t tests found all of these scores to be significantly lower than the standardization sample means (M = 100, SD = 15) at the .001 level, which exceeds the level required using a Bonferroni correction to account for multiple comparisons.

Table 1.

Means and Standard Deviations for WISC-IV FSIQ and Index Scores, and t Test Results When Compared With the Standardization Sample (M = 100, SD = 15).

	M	SD	t ^a
WISC-IV
FSIQ	84.04	13.51	−24.49
VCI	84.44	13.77	−23.43
PRI	88.76	13.39	−17.40
WMI	89.23	14.61	−15.28
PSI	86.98	13.21	−20.43

Note. WISC-IV and WJ-III standardization samples have a mean score of 100, SD of 15. WISC-IV = Wechsler Intelligence Scale for Children–Fourth Edition; FSIQ = Full Scale IQ; VCI = Verbal Comprehension Index; PRI = Perceptual Reasoning Index; WMI = Working Memory Index; PSI = Processing Speed Index.

All t values are significant at the .001 level.

A large portion (n = 278; 67.5%) of our sample was African American, and the lower mean scores for the full sample could be related to the known lower performance of African American children on the WISC-IV. Therefore, the mean scores for the African American subgroup of our sample were compared with the African American subgroup of the standardization sample (Wechsler, 2003b). All of the mean FSIQ and Index scores of the African American girls in the present sample (which were lower than those of the entire sample and ranged from 81.13 to 87.35) were significantly lower than the standardizations means reported in the WISC-IV manual (which ranged from 91.72 to 96.12), at p values < .001; these p values meet a Bonferroni correction standard.

We also subjected WISC-IV subtest scores to cluster analysis to create subgroups using a Ward’s method hierarchical cluster analysis, which has been widely used and recommended because it minimizes within-group variance and maximizes between group variance (Field, 2000). A five-cluster solution best fit this sample, as it both adequately differentiated the participants and included clusters with sufficient numbers of participants to be considered relevant. We labeled the clusters Average (n = 74; 17.21% of sample), Low Average (n = 130; 30.23% of the sample), Verbal Weakness (n = 130; 30.23% of the sample), Extremely Low (n = 59; 13.72% of the sample), and Low VCI/Low PRI (n = 37; 8.60% of the sample). Table 2 provides descriptive statistics for the WISC-IV FSIQ and Index scores by cluster. A one-way multivariate analysis of variance (MANOVA) across the five clusters was significant, F(60, 1606.57) = 26.73, p = .001; post hoc analyses found that the WISC-IV Index scores of three of the five clusters differed significantly from one another. For the remaining two clusters, the WISC-IV VCI scores did not differ from each other, but all of other Index scores differed significantly from all other clusters.

Table 2.

Means and Standard Deviations for WISC-IV Summary Scores by Cluster.

Cluster	Average (n = 74)	Low average (n = 130)	Verbal weakness (n = 130)	Extremely low (n = 59)	Low VCI/PRI (n = 37)
FSIQ
M	104.41	88.51	77.9^a	63.76	81.51^a
SD	6.29	5.85	5.89	6.78	5.77
VCI
M	100.42	92.65	76.49^a	69.22	75.81^a
SD	9.20	6.84	7.83	10.51	8.45
PRI
M	105.14	92.35	87.08	69.68	79.76
SD	8.28	7.29	9.22	10.17	8.45
WMI
M	106.16	91.53^b	83.07	72.88	95.05^b
SD	8.99	9.00	10.56	13.74	12.95
PSI
M	99.77^c	86.88^d	84.41^c	70.47	97.11^d
SD	11.76	9.69	8.47	9.84	11.75

Note. Superscripts indicate nonsignificant differences between clusters. All other between-Index differences were statistically significant at the .001 level. WISC-IV = Wechsler Intelligence Scale for Children–Fourth Edition; VCI = Verbal Comprehension Index; PRI = Perceptual Reasoning Index; FSIQ = Full Scale IQ; WMI = Working Memory Index; PSI = Processing Speed Index.

Whereas the present study’s findings of significantly low mean IQ and Index scores in a sample of juvenile offenders are consistent with many previous studies (e.g., Raine et al., 2005), the cluster analysis highlights the wide range of abilities among this sample of juvenile offenders. Two of the clusters accounted for just more than 60% of the total sample. One of those clusters (n = 130; 30.23% of total sample), which we named “Verbal Weakness,” reflected verbal abilities that were a mean of 10.59 points lower than the mean PRI score, and that seemed to reflect the VIQ < PIQ difference that has been described in the IQ and delinquency research on boys for decades (e.g., Wong & Cornell, 1999).

By comparison, the other clusters we identified represented fairly small portions of our overall sample. The Average cluster represented 17.21% of the overall sample, and this group would appear to be adequately prepared to manage the demands at school, and to have the cognitive abilities to help them navigate their social world. For this group, factors other than cognitive deficits likely account for their involvement in delinquent behavior. For the Extremely Low (13.72%) group, with cognitive weaknesses in a number of areas, the likelihood that these deficits played at least some role in their involvement in delinquent behavior seems greater.

Finally, the Low VCI/PRI cluster represents the smallest (8.60%) portion of our sample and indicates deficits in both fluid reasoning and crystallized intelligence (Flanagan & Kaufman, 2004). Whereas deficits in verbal skills have been described in previous studies, low PRI scores have not previously been found, or at least not explored, in juvenile offender samples.

Our study is limited by the use of test scores from the WISC-IV, which has now been replaced by an updated version. Furthermore, the test scores were generated by girls who had engaged primarily in misdemeanor, status, or VCO offenses, which does not allow us to generalize our findings to all girls who come before the Juvenile Court. Future studies utilizing broader samples of girls whose juvenile court contact involved a wider range of offenses would provide insight into the degree to which our findings are typical of female juvenile offenders at large.

Nonetheless, these data serve as a reminder that, rather than being a homogeneous group, the female juvenile offenders present varied cognitive profiles that are related to the important risk factor of school adjustment. In addition to girls who demonstrate the verbal deficits that have informed theory (e.g., Moffit, 1990) and intervention (e.g., McGloin et al., 2004) for juvenile offenders, many of the girls in this sample have other patterns of cognitive strength and weakness that might require alterations in widely used interventions for juvenile offenders; the variability of the cognitive profiles in this sample indicates that at least some offenders are more or less prepared to understand and engage in the widely used interventions. In particular, empirically supported cognitive-behavioral programs require verbal skills that many of the girls in our sample do not possess (e.g., Willner, 2005); this challenge has begun to be acknowledged in studies of intervention for male offenders. Increased knowledge of the cognitive profiles of female offenders will allow intervention program developers to create new interventions or modify current interventions that capitalize on offenders’ cognitive strengths and recognize cognitive weaknesses, helping to maximize the effectiveness of these programs (Vieira, Skilling, & Peterson-Badali, 2009).

Footnotes

Acknowledgements

The authors thank the Hamilton County Juvenile Court for their support of this study, and Abby Lonneman for her assistance in preparing this manuscript.

Authors’ Note

Shelby Werner is now at Bethel Olentangy Psychological Services, Columbus, Ohio; Susan Ficke is now at TriHealth, Cincinnati, Ohio. Portions of this study served as the dissertation of the first author.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

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