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Gender Disparity in Criminal Offenses Among Persons of High IQ Elizabeth Monk-Turner, James Oleson, Paul Cortez, Daniel Dean, Cole Kracke, Jennifer Harmon, Peter Restituto and Greg Trach Int J Offender Ther Comp Criminol 2006; 50; 506 DOI: 10.1177/0306624X06286871 The online version of this article can be found at: http://ijo.sagepub.com/cgi/content/abstract/50/5/506

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Gender Disparity in Criminal Offenses among Persons of High IQ

International Journal of Offender Therapy and Comparative Criminology Volume 50 Number 5 October 2006 506-519 © 2006 Sage Publications 10.1177/0306624X06286871 http://ijo.sagepub.com hosted at http://online.sagepub.com

Elizabeth Monk-Turner James Oleson Paul Cortez Daniel Dean Cole Kracke Jennifer Harmon Peter Restituto Greg Trach Old Dominion University, Norfolk, VA

Criminologists have largely neglected deviance among those with high IQs. This work uses Towers’s (1988) concept of conventional genius to analyze how deviant behavior varies by gender among genius offenders. Like Bisi (2002), the authors expect female patterns of deviance to be lower than that for males even within this genius sample. Their work finds that male geniuses are significantly more likely to self-report ever having committed violent felonies. Among the authors’ conventional genius sample of university students, gender differences in nonviolent felonies, misdemeanor offenses, and unethical behaviors are not significantly different between the female and male respondents. Keywords:

gender; crime; genius

C

riminologists have largely neglected the role of intelligence as a predictor variable in the understanding of crime. When considered, it is generally assumed that less intellectually gifted individuals commit more crime than others (Cullen, Gendreau, Jarjoura, & Wright, 1997; Hirschi, 1969; Rothenberg & Heinz, 1998). More recently, Oleson (1998) attempted to identify the types of crime that high-IQ people commit. However, Oleson’s early research did not examine the role that gender plays with regard to the so-called genius offender. Our work uses Towers’s (1990) concept of conventional genius to see if there are gender differences in self-reported criminal and unethical behavior among this genius type. According to Towers, conventional geniuses find their niche in academia or the Authors’ Note: Please direct all correspondence to Elizabeth Monk-Turner at [email protected]. 506 Downloaded from http://ijo.sagepub.com at OLD DOMINION UNIV LIBRARY on September 10, 2009

Monk-Turner et al. / Criminal Offenses and Gender 507

professions (especially law or medicine) and surround themselves with like others. Unlike other geniuses who drop out of society (and may find themselves incarcerated) or those who pursue unconventional ways to integrate into society (such geniuses may join elite IQ–based societies), conventional geniuses aim to be a part of mainstream society. To capture this group, university students were surveyed. Our respondents attended Oxford University, California Institute of Technology, Cambridge University, Harvard University, Stanford University, University of Washington,Yale University, and University of California, Berkeley. Herrnstein and Murray (1994) described the competitive atmosphere in elite British and American colleges. They reported that graduates of the top universities have IQ scores averaging 2.7 standard deviations above the mean (equivalent to a 143 IQ). We presume that individuals at these institutions are the sort of people Towers (1988, 1990) described as the very gifted who want to be a part of the society as a whole.

Bias Concerns in Measuring Intelligence Before exploring gender differences between genius offenders, it is important to examine whether or not the very mechanisms we rely on to measure intelligence, such as the Weschler Adult Intelligence Scale or Woodcock-Johnson, are biased. IQ tests have long been criticized for bias with regard to race and gender (see Gould, 1983; Lamb, 1997). Although IQ scores are good predictors of academic achievement in elementary and secondary school, IQ is a poor predictor of academic performance at postsecondary levels of education (Horton, 2001; Jensen, 1980). Many have questioned the ability of IQ tests to predict outcomes later in life (Grover, 1981; Senna, 1972). IQ-type tests do not generally measure qualities such as persistence, self-confidence, motivation, or interpersonal skills. The validity of IQ tests has also been questioned in regard to assessing differences in intelligence by ethnicity and race. Herrnstein and Murray (1994) argue that intelligence is measurable by IQ tests, that it is genetically based, and that a person’s IQ essentially remains unchanged with time (also see Miller, 1995; Rushton, 1998). Furthermore, Herrnstein and Murray maintain that IQ is a predictor of many social ills including crime (also see Miller, 1995; Wilson & Herrnstein, 1985). Namely, low-IQ individuals are more apt than others to offend, and the best societal response is punishment. If one is genetically driven to offend, there is clearly no hope of rehabilitation (for a rebuttal of this position, see Lamb, 1997). Cullen et al. (1997), along with many other scholars (see Gardner, 1995; Guay, 2003; Lamb, 1997), dispute Herrnstein and Murray’s work. Reanalyzing the Herrnstein and Murray data, Cullen et al. posit that the IQcrime link Herrnstein and Murray found was not intelligent criminology. Rather, Herrnstein and Murray’s work, they maintain, is misleading and ideologically slanted to promote their crime control agenda (see Cullen et al., 1997; Gardner, 1995).


508 International Journal of Offender Therapy and Comparative Criminology

Intelligence and Crime Besides concern regarding conceptualizing and measuring IQ, many question whether intelligence is a significant variable in shaping differences in criminal behavior. Famularo et al. (1992) examined whether neurocognitive factors could discriminate delinquent- from nondelinquent-status offenders brought before the same court. The authors examined 110 delinquents (65 male and 45 female) and 106 highrisk nondelinquent-status offenders on Wechsler Intelligence Scale for Children subtests, the Wide Range Achievement Test and the Memory for Design Test.1 They found that male delinquents were not significantly different from the comparison group of male nondelinquent-status offenders on these measures. Among females, scores on reading, math, and picture completion differed significantly between groups; however, the findings were mixed. Thus, Famularo et al. concluded that intelligence is not an independent risk factor for delinquent behavior (also see Cullen et al., 1997; Kunjukrishnan & Bradford, 1990; Lamb, 1995; Zebrowitz & Lee, 1999). On the other hand, other researchers posit that IQ does shape delinquent behavior and aim to specify the conditions under which this holds (see Hindelang, Hirschi, & Weis, 1981; Murphy & D’Angelo, 1963; Van Brunschot & Brannigan, 1995). For example, Guay (2003) suggests that measured intelligence is lower among certain subgroups of delinquents. Specifically, violent and chronic offenders, they find, have lower IQ scores compared to others (also see Goldner, 1972). Likewise, Lykken (1995) found that IQ plays a part, with other background variables, in understanding violent crime (also see Austin, 1978). Stattin and Magnusson (1991) found that persistent offenders, those who had been arrested in all three time periods they examined, had lower IQ scores than others. Alternatively, Walsh (1987) found a positive relationship between IQ and property crime. Walsh suggests that high-IQ delinquents engage in crimes that require planning and offer deferred gratification. Our work adds to this literature as we examine how self-reported offending behaviors vary within a high-IQ sample of women and men.

Women and Crime A significant body of research examines gender differences in criminal activity aiming to identify differences in the type and amount of crime committed between the sexes (see Broidy & Agnew, 1997; Hannon, 1998). When dealing with any type of criminal or violent act, individuals typically associate it with male offenders (Chesney-Lind & Shelden, 1992).2 Walkate (2001) faults researchers for neglecting how the role of masculinity has gendered the fear of crime. Adler (1975) hypothesized a link between increasing female criminality and the women’s movement. Adler’s masculinazation thesis hypothesized that violent crimes would increase with time among women. Simon (1997) maintained that



arrest and incarceration statistics do not support Adler’s thesis. Rather, he found that incidences of violent crimes among women have remained stable with time. Noting the economic motive behind much female crime, Simon (1975, 1997) predicted increases in white-collar offenses and property offenses, not violent crimes, among women. Steffensmeier and Schwartz (1995) note that although there has been rapid growth in crimes committed by women, the growth largely has been in stereotypical female types of crimes such as credit and welfare fraud, shoplifting, participation in the drug trade, and prostitution (also see Chesney-Lind, 1995). Steffensmeier (1980), using self-reported data, argues that gender differences are narrow with respect to the commission of minor crimes (larceny-theft, fraud, forgery, and embezzlement). Furthermore, gender differences are smaller still for self-report prevalence data on minor offenses such as shoplifting and minor drug use (Steffensmeier & Allan, 1996). Thus, Steffensmeier and Schwartz (1995) sees little benefit in developing gender-specific theories to understand crime. Bisi (2002), however, argues that patterns of female criminality may not follow the same pattern as for men. She posits that female criminality may actually decrease with time (also see Sejcova, 2002; Wan, 2001). Likewise, Levy (2000) posits that crime among women is often a response to a domestic situation or driven by economic motives. Female criminals, Levy argues, share a history of maltreatment (also see Maher & Curtis, 1995; Morash, 1999). Arrigo and Young (1998) posit that our language has prestructured criminology to exclude a theoretical understanding of crime and gender. Like Bisi (2002) and others, we expect to observe gender differences in genius offenders. Using self-reported data, we expect that women will be underrepresented in participation in criminal activity, especially violent felonies, and unethical behavior among conventional genius offenders.

Method To capture Towers’s (1988) hypothesized conventional genius, surveys were distributed to elite university students to tap geniuses who were presumed to conform to traditional patterns of success.3 Surveys were distributed, which included 72 different offense items derived from a variety of sources, representing a thorough spectrum of offending behaviors.4 We classified offenses using the Virginia State and Federal Codes. Of the 72 offenses originally recorded, 4 offenses were eliminated because no specific law or penalty applied.5 Using these codes, we developed four categories: misdemeanor, violent felony, nonviolent felony, and unethical behavior. We coded each of the 72 offenses into one of these four categories again in line with the Virginia State and Federal Code (see Table 1 for coding of each offense). Table 1 contains data on the number of men and women self-reporting each offense (incidence) and the number of offenses committed (prevalence). We examine, using



chi-square analysis, whether men are significantly more likely than women to self-report ever committing these offenses. We do significance tests for incidence data only. We know how many women and men self-reported each of these offenses and can calculate how many did not engage in such behavior (Male [or Female] N − Incidence Reporting Each Offense). For prevalence data, we can only compare basic statistics because these data are in aggregate form. In other words, we know that X number of women (or men) self-reported committing a particular felony offense and of those self-reporting such offending behavior, how many total offenses were reported (prevalence). We cannot determine how many specific offenses each individual committed. Our prevalence data are a mean figure, which were calculated by dividing the number of men (or women) reporting each behavior by the number of times offenders said they committed each offense. Surveys were distributed to research assistants (to then be distributed on campus) by mail between December 1996 and June 1997. For each of the 72 listed offenses, respondents were asked the number of times they had ever committed the offense. Given that genius respondents were being targeted, the university sample was a snowball sample of students from elite schools in the United States and the United Kingdom. Herrnstein and Murray (1994) described the competitive atmosphere in elite American colleges. Students at other elite institutions, such as Cambridge, are presumably of similar ability to students at elite universities in the United States. These individuals, we maintain, are just the people that Towers described as the very gifted conventional genius. Of 580 surveys distributed, 133 students returned the questionnaire. Questionnaires were distributed at Cal Tech, Cambridge, Harvard, Stanford, Yale, and University of California, Berkeley. Again, a snowball sampling procedure was used whereby research assistants (trained by Oleson) at these various elite institutions distributed questionnaires to those on campus. Most of these respondents (42) had already received their bachelor’s degree (37 had a master’s degree, 16 had a PhD or equivalent, 13 had a professional degree, and 26 had some college experience). Among our university respondents, men and women were evenly split (67 males and 66 females). Most described themselves as Caucasian (see Table 2). The most likely religious background reported by respondents was atheist and agnostic (44%) followed by Catholics and Protestants (42%). Most of our respondents were single. Many respondents were familiar with psychometric testing, knew their IQ score(s), and provided detailed information on which IQ test had been used and what raw score was obtained. The university sample had a mean IQ score of 135 on a standard IQ test (see Table 2 for detailed IQ information). This IQ score would qualify an individual for Mensa membership and place him or her at about the 98.5 percentile. Although IQ scores were obtained by self-report, the fact that our university sample was enrolled in elite universities helps corroborate the veracity of the scores.6 We maintain that these university students capture Towers’s (1988) ideal of conventional genius. Again, we are most interested in whether or not these individuals exhibit gender differences in reported criminal and unethical behaviors.



Table 1 Coding of Offenses Incidence

Misdemeanor Purposely damaged or destroyed property that did not belong to you Stolen (or tried to steal) things worth $5 or less (including petty shoplifting) Stolen (or tried to steal) things worth between $5 and $50 Stolen (or tried to steal) something worth more than $50 Picked someone’s pocket or stolen (or tried to steal) from someone’s purse Avoided paying for things such as movies, bus or subway rides, or food Been paid for having sexual relations with someone Paid someone for sexual relations Had sexual relations in a public place Carried a hidden weapon other than a plain pocket knife Beaten someone up seriously enough that they required medical attention of any kind Used marijuana, cannabis, or hashish Bought marijuana, cannabis, or hashish Sold marijuana, cannabis, or hashish Taken pharmaceuticals prescribed for someone else Smuggled illegal drugs or drug paraphernalia Been drunk in a public place Consumed enough alcohol to put you over the legal limit and then driven a car Driven a car without a license Made copies of copyrighted records, tapes, or videocassettes Instigated acts of rebellion against the government or agencies of the government Spread false and injurious statements about someone, either orally or in print Fished or hunted without a license where one is required Resisted arrest Violated the conditions of your parole Knowingly lied while under oath Intentionally trespassed on private or government property Been loud, rowdy, or unruly in a public place (disorderly conduct) Made obscene telephone calls, such as calling someone and saying dirty things Gambled where it is illegal to do so

Prevalence

Men

Women

Men

Women

33

24

10

5

51

45

22

5

23 12 7

18 5 7

18 8 8

4 3 4

47

34

17

8

2 12 31 9 10

1 0 31 7 1

1 3 11 49 6

1 0 4 17 1

37 28 15 19 11 45 35

36 18 7 22 6 46 30

588 525 765 66 5 61 44

104 27 3 17 2 17 5

21 46

25 45

16 42

7 26

2

3

4

7

11

11

30

6

16 3 2 0 39 25

8 0 0 0 30 18

11 3 100 0 17 20

2 0 0 0 11 9

8

6

14

9

18

8

77

6 (continued)



Table 1 (continued) Incidence Men Failed to appear in court when ordered to do so by summons Driven a car at unsafe speeds or in a reckless manner Violated safety or environmental standards Used another person’s telephone or phone card without their permission Used another person’s ATM card without his or her permission Made unauthorized copies of commercial computer software Totals Nonviolent felony Stolen (or tried to steal) a motor vehicle, such as a car or motorcycle Knowingly bought, sold, or held stolen goods (or tried to do any of these things) Damaged property or real estate by lighting a fire (arson) Had sexual relations with someone under the age of consent (while above the age of consent yourself) Used hard drugs such as heroin, cocaine, LSD, or ecstasy Bought hard drugs such as heroin, cocaine, LSD, or ecstasy Sold hard drugs such as heroin, cocaine, LSD, or ecstasy Manufactured or cultivated a controlled substance (drugs) Bought or provided liquor for a minor Taken a vehicle for a ride (drive) without the owner’s permission Made an agreement with other people to commit a criminal act Tricked (or tried to trick) a person, group, or company for financial gain (fraud) Broken into a building or vehicle (or tried to break in) to steal something or just to look around Forged another person’s signature on an official document, prescription, or bank check Used privileged information in making investment decisions Manipulated financial accounts in illegal manner Counterfeited fine art or currency Sold or traded government of industrial secrets Intentionally misreported income information on your tax forms Broken into another computer (hacked) Totals

Prevalence

Women

Men

Women

6

1

4

1

44 17 10

35 12 12

65 14 15

10 14 2

0

2

0

1

42

25

18

5

2,657

335

7

2

4

1

15

9

11

2

9 10

1 5

5 22

1 2

22 19 6 4 34 13

17 10 2 3 28 12

183 142 248 5 11 5

8 7 2 1 8 2

15

10

27

8

3

0

34

0

20

9

8

2

14

19

13

7

4 3 2 0 16

1 2 1 1 13

3 40 1 0 3

2 3 3 20 3

7

2

8 773

3 85



Table 1 (continued) Incidence Men Violent felony Used violence or the threat of violence to rob someone Made a serious threat that you meant to carry out Killed another human being (excluding wartime situations) Constructed a bomb or similar explosive device Held someone against his or her will (kidnapping) Had sexual relations with someone against his or her will Totals Unethical behavior Made sexual comments or advances toward someone that you knew were unwanted Plagiarized another person’s work (used it without giving his or her credit) Invented or altered research data Cheated on an examination or test Abused work privileges (e.g., personal phone calls, personal e-mail, or personal use of the copy machine) Totals

Prevalence

Women

Men

Women

1 12 3 15 2 1

0 6 0 2 1 3

2 13 6 9 3 2 35

0 2 0 2 2 2 8

15

3

18

8

14

19

11

3

12 42 51

11 42 48

7 8 105

2 4 32

149

49

Analysis We examined differences in the number of female and male respondents ever committing four types of offenses: misdemeaners, nonviolent felonies, violent felonies, and noncriminal but unethical behavior. We also looked at how often such offenses were committed (prevalence). Men committed about three times the number of violent felony offenses compared to the number of violent felony offenses committed by women (34 men versus 12 women). Men committed 74% of all violent felony offenses ever reported for this sample. Self-reported violent felonies include threatening or using violence to rob another, seriously threatening another, killing, constructing a bomb, or kidnapping. Forty-six mean instances (prevalence data) of violent felonies were selfreported in this sample. Men committed a mean of 35 violent felonies compared to a female mean of 8 (see Table 3). We hypothesized that men would self-report ever committing more felony offenses compared to women. Our data support this hypothesis. Men are significantly more likely to self-report such offenses compared to women (χ2 = 16.08; p = .001). Overall, the number of mean nonviolent felony offenses committed by men was also higher than those committed by women (223 men versus 147 women). Nonviolent



Table 2 Characteristics of the University Sample Variable

Total Number

Ethnicity Asian White African American Others Religious background Catholic Jewish Atheist or agnostic Protestant Others Age 17 to 20 21 to 30 31 to 40 Above 40 Educational attainment Less than college Some college Bachelor’s degree Master’s degree PhD or equivalent Professional degree Marital status Married Cohabiting Divorced Single IQ (SD above M based on standard IQ test with a M of 100 and a SD of 16) 0.00 to 0.99 1.00 to 1.99 2.00 to 2.99 3.00 to 3.99 4.00 to 4.99

25 103 1 5 29 6 59 27 13 8 14 101 11 2 24 42 37 16 13 19 12 3 100 18 34 58 21 3

felonies include using, buying, and selling hard drugs and forging another person’s signature on an official document (including a bank check). The majority (60%) of these crimes were committed by our male respondents. Still, only four specific nonviolent felony offenses show a significant gender difference. Men were significantly more likely than women to report ever buying or selling hard drugs (χ2 = 5.39; p = .05). We did not find a significant gender difference in whether men and women used such drugs. Men were significantly more likely than women to self-report taking a vehicle



Table 3 Prevalence (Mean Number of Offenses Ever Committed) Data by Type of Offense and Gender for University Student Sample

Violent felony offenses ever committed Nonviolent felony offenses ever committed Misdemeanor offenses ever committed Unethical behaviors ever committed

Male

Range

Female

Range

35 773 2,657 149

2 to 13 0 to 248 0 to 765 7 to 105

8 85 335 49

0 to 2 0 to 20 0 to 104 2 to 32

for a drive without the owner’s permission (χ2 = 4.11; p = .05). However, women self-reported breaking into a building or vehicle (or trying to break in) to steal something significantly more often than men (χ2 = 6.39; p = .05). Thus, the specific nonviolent felony offenses men self-report significantly more often than women are buying and selling hard drugs. Overall, our hypothesis is not supported that men are more likely than women to self-report committing nonviolent felonies. Looking at prevalence data for nonviolent felonies, men self-reported a mean of 773 such offenses compared to a mean of 85 for women. The gender gap in nonviolent felonies is in the mean number of times women and men use, buy, and sell hard drugs. Men self-report engaging in these three activities 573 (mean) times compared to 17 (mean) times for women. For men, half (53%) of all self-reported nonviolent felonies are drug related. Women reported more instances of selling or trading government secrets (24% of all female nonviolent felonies) than using, buying, or selling hard drugs (20% of all female nonviolent felonies). In our sample, there is no significant gender difference in the number of women (17) and men (22) using hard drugs. However, of those who report such use, men are engaging in the behavior much more frequently than are women (183 mean times for men versus 8 for women). Men are more likely than women to self-report buying and selling hard drugs. Gender differences were not marked for misdemeanor offenses. Nonetheless, men still self-reported more misdemeanors than did women (748 men versus 582 women). Fifty-six percent of all misdemeanor offenses self-reported by this sample were committed by men. Of the misdemeanor offenses we examined, only five specific offenses reveal a significant gender difference. In each case, men self-report committing such misdemeanor offenses more often than women do. Our male respondents self-report committing significantly more of these crimes: avoiding paying for things such as movies, bus or subway rides, or food (χ2 = 10.034; p = .001); paying someone for sexual relations (χ2 = 13.19; p = .05); beating someone up seriously enough that he or she required medical attention of any kind (χ2 = 6.39; p = .05); making unauthorized copies of commercial computer software (χ2 = 7.66; p = .05); and gambling where it is illegal to do so (χ2 = 4.77; p = .05). Thus, in the vast majority of misdemeanor offenses we examine, our data reveal no significant gender difference in the likelihood of ever committing



these crimes. Again, our hypothesis that men would self-report ever committing more misdemeanor offenses than women was not empirically supported. As was true for violent felony and nonviolent felony offenses, the prevalence data for misdemeanors are marked by gender. On average, men report committing 2,657 misdemeanor violations compared to only 335 for women. In our sample, men are not significantly more likely than women to use, buy, or sell marijuana, cannabis, or hashish. However, as was true for hard drug use, of those men who engage in these behaviors, they do so much more frequently than women do. The mean use of marijuana for our male respondents was 588; for women, it was 104. The same pattern holds for buying and selling marijuana. Looking at gender differences among conventional geniuses for self-reported unethical behavior, the gender difference is very small. Female university students report 123 such offenses compared to 134 among male students. Thus, men are committing 52% of all self-reported unethical behaviors in our sample. These behaviors include making unwanted sexual comments or advances, plagiarism, and abusing work privileges. There was no significant difference in the likelihood of women or men ever committing any of these unethical behaviors. Looking at prevalence data for unethical behaviors, men once again self-report more total mean offenses compared to women (149 men versus 49 women). Again, our hypothesis that men would be overrepresented among those who ever reported committing unethical behaviors was not supported by our data.

Discussion Little work to date examines gender differences in genius offenders. This is in part because of the difficulty of sampling this population. Our work uses Towers’s (1990) concept of conventional genius. Sampling elite university students to capture conventional genius, we examine gender differences in self-reported ever-committed offenses. Respondents were sent a questionnaire asking how many times they had ever committed any of the 72 offenses. These offenses were categorized as violent or nonviolent felonies, misdemeanors, and unethical behaviors. We hypothesized that women would be less likely to self-report ever committing any of these offenses. Our work shows that among conventional geniuses, males are more likely to self-report committing felony offenses. However, looking at other offenses, on average, men do not self-report committing significantly more offenses compared to women. Only 4 of our nonviolent felony offenses and 5 misdemeanor offenses show a significant gender difference in self-reported offending behavior. One of the four significant differences found for nonviolent felony offenses, in fact, shows that women are more likely than men to commit the offense (breaking into a building or vehicle to steal something). Although men do self-report more buying and selling of hard drugs, taking a car for a drive without the owner’s permission, avoiding paying for miscellaneous items, paying someone for sex, gambling illegally, and beating another up, most offenses do not reveal significant



gender differences in offending behavior. For example, women are no more likely than men to buy or sell marijuana. Both women and men are equally likely to report unethical behaviors in this sample. Our work does show-marked difference in prevalence data by various offenses. Overall, men consistently report committing more total mean offenses compared to women. Thus, of those who self-reported engaging in various behaviors, men are generally reporting more mean times of committing various offenses. Our work, save for violent felonies, does not support that of Bisi (2002) who argues that male and female patterns of deviance are different and that males are more apt to engage in norm-violating behavior compared to women. Rather, our work suggests a gender difference among violent offenders but not among offenders generally. We suggest that social and cultural factors shape the gender differences we observe in offending behavior. We believe our findings add to what little is known about genius offenders.

Notes 1. The Wechsler Intelligence Scale for Children subtests give IQ scores and several other indices of intellectual ability. The Wide Range Achievement Test and Memory for Design Test measure reading, spelling, arithmetic, and the ability to learn or memorize. 2. Hannon (1998), in a content analysis of 376 articles from four journals (Criminology, the British Journal of Criminology, the Journal of Research in Crime and Delinquency, and the Journal of Criminal Law and Criminology) selected between 1974 and 1978 and 1992 and 1996, quantified androcentricity. Researchers coded the articles by sex composition of the sample, specification of generality with regard to gender in the title, and space devoted to the discussion of gender differences. Hannon shows that even though there has been a remarkable increase in research including both males and females, males continue to be the sample norm in criminological research. 3. The data for this work was provided by Oleson. See Oleson (1998) for a detailed description of questionnaire construction and sampling considerations. Many of these items were drawn from the National Longitudinal Survey of Youth. 4. The offenses were electronic eavesdropping, draft dodging, blackmailing, and attempted suicide. 5. In the few cases in which respondents did not self-report their IQ score, the following estimate was made: On a standard IQ test (with a mean of 100 and a standard deviation of 16), those with a PhD received a 130 IQ (+1.88 SD; see Jensen, 1980). Because scores were reported from more than a dozen different IQ and achievement tests with various means and standard deviations, measures of intellectual ability were standardized into z scores.

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