Arch Sex Behav DOI 10.1007/s10508-013-0244-4

ORIGINAL PAPER

Viewing Child Pornography: Prevalence and Correlates in a Representative Community Sample of Young Swedish Men Michael C. Seto • Chantal A. Hermann • Cecilia Kjellgren Gisela Priebe • Carl Go¨ran Svedin • Niklas La˚ngstro¨m



Received: 9 January 2013 / Revised: 21 June 2013 / Accepted: 28 November 2013 Ó Springer Science+Business Media New York 2014

Abstract Most research on child pornography use has been based on selected clinical or criminal justice samples; risk factors for child pornography use in the general population remain largely unexplored. In this study, we examined prevalence, risk factors, and correlates of viewing depictions of adult–child sex in a population-representative sample of 1,978 young Swedish men (17–20 years, Mdn = 18 years, overall response rate, 77 %). In an anonymous, school-based survey, participants self-reported sexual coercion experiences, attitudes and beliefs about sex, perceived peer attitudes, and sexual interests and behaviors; including pornography use, sexual interest in children, and sexually coercive behavior. A total of 84 (4.2 %) young men reported they had ever viewed child pornography. Most theorybased variables were moderately and significantly associated with child pornography viewing and were consistent with models of sexual offending implicating both antisociality and sexual M. C. Seto (&) Integrated Forensic Program, Royal Ottawa Health Care Group, 1804 Highway 2 East, Brockville, ON K7V 5W7, Canada e-mail: [email protected] C. A. Hermann Department of Psychology, Carleton University, Ottawa, ON, Canada C. Kjellgren  G. Priebe Department of Social Work, Linnaeus University, Kalmar, Sweden G. Priebe Department of Clinical Sciences, Lund University, Lund, Sweden C. G. Svedin Department of Clinical and Experimental Medicine, Linko¨ping University, Linko¨ping, Sweden N. La˚ngstro¨m Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden

deviance. In multivariate logistic regression analysis, 7 of 15 tested factors independently predicted child pornography viewing and explained 42 % of the variance: ever had sex with a male, likely to have sex with a child aged 12–14, likely to have sex with a child 12 or less, perception of children as seductive, having friends who have watched child pornography, frequent pornography use, and ever viewed violent pornography. From these, a 6-item Child Pornography Correlates Scale was constructed and then cross-validated in a similar but independent Norwegian sample. Keywords Child pornography use  Antisociality  Sexual deviance  Sex offenders against children

Introduction Child pornography use is attracting increasing attention as a result of the availability and distribution of this content via the Internet. More individuals are being arrested for child pornography offenses and appear in clinical or criminal justice settings (Bates & Metcalf, 2007; Motivans & Kyckelhahn, 2007; Wolak, 2011). It is very likely, however, that detected offenders represent only the‘‘tip of the iceberg’’and that most child pornography users remain undetected. Data on child pornography traffic in peer-to-peer computer networks indicate that user numbers greatly exceed the number of individuals identified by arrest (Canwest News Service, 2009; Steel, 2009). This gap between activity and detection is partly because of limited resources: Internet crime investigations require specialized officers who are familiar with computer and online technologies and access to forensic computer analysis; smallerpolice departments often lack such specialized resources (see Baines, 2008). Even with more investigators, however, police inquiry is more likely to detect and arrest naı¨ve or careless online child

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pornography users (Jenkins, 2001). Security techniques such as file encryption, anonymous email and proxy services, and datawiping reduce the likelihood of discovery. Yet, a national American study suggested that only 20 % of those arrested for child pornography offenses used one or more of these methods (Wolak, Finkelhor, & Mitchell, 2005). Therefore, most of what we currently know about child pornography use is based on selected clinical or criminal justice samples (Seto, 2013). Riegel (2004) surveyed 290 self-identified pedophiles and found that 95 % had viewed child pornography at least occasionally. Similarly, Neutze, Seto, Schaefer, Mundt, and Beier (2011) recruited 155 self-identified pedophiles and hebephiles in a novel German prevention campaign and found that two-thirds had viewed child pornography at some point in life. In a survey of 307 online pornography users, 30 (approximately 10 %) had ever viewed child pornography (Siegfried, Lovely, & Rogers, 2008). Finally, among 223 individuals in an anonymous, non-representative online survey of pornography use, 41 (18 %) reported viewing child pornography but none had ever been arrested for child pornography offenses (Ray, Kimonis, & Seto, 2013). Large, representative community-based studies conducted in a sensitive manner are needed to elucidate population prevalence and provide less biased correlates of child pornography use. Data based on clinical or criminal justice samples might be affected by considerable selection biases; for example, clinical samples might be more distressed by their child pornography use or more likely to have comorbid psychopathology than communitydwelling child pornography users. Similarly, criminal justice samples may be more likely to engage in antisocial and criminal behavior than community samples of child pornography users.

reflect curiosity, sensation-seeking or other factors) may be associated with viewing child pornography. Excessive Sexual Interest and Behavior Research on community and clinical or criminal justice samples suggests that hypersexuality or excessive sexual preoccupation and behaviors are overrepresented in men who have committed sexual offenses. Community-based data also suggest that number of sexual partners and interest in casual, multiple sexual contacts distinguishes sexually coercive men from non-coercive men and predicts sexual coercion in longitudinal research (Kjellgren, Priebe, Svedin, & La˚ngstro¨m, 2010; Lalumie`re, Chalmers, Quinsey, & Seto, 1996; Malamuth, Linz, Heavey, Barnes, & Acker, 1995). Frequent use of pornography among male adolescents is also associated with increased viewing of child por˚ kerman, & Priebe, 2011). nography (Svedin, A Personality Illegal sexual behavior is associated with certain personality characteristics. Individuals higher on aggressiveness, impulsivity, risk-taking or sensation-seeking are more likely to engage in both nonsexual and sexual criminal behavior and have earlier onset of sexual activity, more sexual partners, and more involvement in casual sex (Kjellgren et al., 2010; Lalumie`re, Harris, Quinsey & Rice, 2005; La˚ngstro¨m & Hanson, 2006; Seto, 2008). As predicted, sensation seeking was associated with child pornography viewing in the survey by Ray, Kimonis, & Seto (2013). Peer Influences

Potential Risk Factors for Child Pornography Viewing In the following sections, we briefly review what is known about potential risk factors for child pornography viewing, rationally organized into domains identified in sexual offending research. Because there is relatively little research on the psychological characteristics of child pornography offenders, we drew primarily on evidence accumulated from men who committed offenses involving sexual contact with children to derive our research hypotheses (Seto, 2008). Sexual Interest in Children There is an intuitive and empirically supported link between pedophilia and child pornography use; many self-identified pedophiles report child pornography use (Riegel, 2004) and a majority of child pornography offenders show greater sexual arousal to children than to adults in laboratory assessments (Blanchard et al., 2007; Seto, Cantor, & Blanchard, 2006). Similarly, self-reported interest in sex with children (not necessarily pedophilia, as the interest in sex with children could also

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The Theory of Planned Behavior (Ajzen, 1991, 2001) has shown great utility in explaining many behaviors, including sexual behavior. Very briefly, behaviors are predicted by intentions to engage in the behavior, which in turn are influenced by attitudes about the behavior and perceived (or subjective) norms about the behavior. For example, favorable attitudes towards depictions of adult–child sex and believing (correctly or not) that peers also hold favorable attitudes should increase intentions and likelihood of viewing adult–child sex. More generally, peer influences have shown substantial explanatory power in models of juvenile delinquency and young adult criminality (Lipsey & Derzon, 1998). Sexual Victimization Sexual victimization in childhood has consistently been found to be associated with sexual offending among adolescents and adults in criminal samples (Forsman, Santtila, Johansson, Sandnabba, & La˚ngstro¨m, 2013; Jespersen et al. 2009; Seto & Lalumie`re, 2010) and was moderately correlated with engaging in

Arch Sex Behav

sexually coercive behavior in a prior study based on the present sample (Seto et al. 2010a). However, it remains unknown if it is also correlated with child pornography viewing. Recently, Babchishin, Hanson, and Hermann (2011) found that child pornography offenders, like contact sexual offenders, were more likely to have been sexually abused than were comparison subjects. However, child pornography offenders had lower rates of sexual victimization than contact sex offenders. Sexual Behavior Involving Children Finally, we expected child pornography viewing to be correlated with sexual behavior involving children (Seto, Hanson, & Babchishin, 2011). It might also be correlated with sexually coercive behavior more generally, but the latter association should be weaker than for behavior focusing on children. Consistent with this notion, Ray, Kimonis, & Seto (2013) found that mainstream pornography users who admitted viewing child pornography were more likely to report interest in sex with children and actual sexual contact with children than non-child pornography users.

had viewed child pornography and friends’ attitudes about adult–child sex. Finally, we examined the association between viewing child pornography with the self-reported likelihood of any sexual contact with a child and actually engaging in sexually coercive behavior with children age 12 or younger. Following contemporary models of sexual offending against children that emphasize antisociality and sexual deviance as explanatory factors (Lalumie`re et al., 2005; Seto, 2008, 2013; Ward, Polaschek, & Beech, 2006), we postulated that histories of antisocial and atypical sexual behavior would predict child pornography viewing. We also predicted that friends’ involvement with child pornography or perceptions of adult–child sex would be related to child pornography viewing. Last, we hypothesized that child pornography viewing would co-occur with viewing of other atypical pornography, particularly violent pornography and pornography depicting animals and actual sexual offending, generally and against children.

Method Participants

The Present Study We used data from a 2003 survey of 17–20 year old high school students in Sweden (e.g., Kjellgren et al., 2010; Seto et al. 2010a) to examine prevalence and risk factors for viewing pornography depicting adult–child sex. This is a more restrictive definition than viewing any child pornography, which might show children only according to modal child pornography laws. We think the population-representative large sample and psychologically meaningful predictors we were able to examine in this study offset the criticism that child pornography viewing may have changed significantly during the past decade. Though the prevalence of child pornography viewing may have changed as a result of greater availability through more access to high-speed Internet connections, the psychological correlates are likely to remain stable overthisperiod oftime.Forexample, ifindividuals who viewed other pornography were more likely to view child pornography in 2003, we would expect the same finding in a hypothetical survey conducted in 2013. We searched for possible correlates and risk factors for viewing child pornography among variables found to be linked to sexual (re)offending against children in previousresearch with identified sexual offenders, including general antisocial behavior, aggressiveness and risk-taking personality traits, substance misuse, sexual behavior, attitudes about adult–child sex, and sexual victimization (Eke, Seto, & Williams, 2011; Hanson & Morton-Bourgon, 2005; Whitaker et al., 2008). We drew on the sexual offending literature because there is relatively little work on child pornography offenders and undetected child pornography viewers. We also examined information about perceived social norms regarding child-related sexual behavior: if friends

We used data from the Swedish data set of a larger survey of both male and female participants across the Baltic Sea region. This Swedish student sample has been reported on previously (e.g., Kjellgren et al., 2010;Setoetal.2010a).Theoverallresponserate for the Swedish survey was 77 %; remaining students either declined to participate or were absent from school the day the survey was conducted. The participants for this study were 1,978 male third-year high school students from Sweden. The majority of older adolescents attend school (91 %) and the absence rate is relatively low (average absence rate 13 % in 2005), as such participants were considered representative of the older adolescent/ young adult population (Statistics Sweden, 2007; Uppsala kommun, 2005). On average, participants were 18 years old (SD = 0.6, range, 17–20) and most were born in Sweden (85 %). They were recruited from major urban areas, Stockholm (63 %) and Malmo¨ (26 %), and more rural areas represented by Falko¨ping, Haparanda, and Lulea˚ (10 %). Both parents were employed for 65 % of the participants, one parent was employed and one parent was not working for 21 %, neither parent was working for 7 %, one parent employed and the status of the other parent unknown for 4 %, and 3 % ofparticipantshad parentsclassifiedas other. Of the 1,978 participants, 1,780 provided at least one parent’s occupation, coded using the International Socioeconomic Index (ISEI) (Ganzeboom, De Graaf, & Treiman, 1992) that could be used to compute socioeconomic position (SEP). On average, participants had moderate to high SEP based on parental occupation (ISEI index, Mdn = 54, range = 16–88). A total of 7.1 % (n = 127) had low SEP, 46.7 % (n = 831) moderate, and 46.2 % (n = 822) had high SEP.

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Measures1

Offense-Supportive Attitudes and Beliefs

Nonsexual Antisocial Behavior and Substance Abuse

The first set of self-report questions consisted of 15 items regarding attitudes and beliefs about adult–child sex, rated on a 5-point Likert scale from 1 (I disagree completely) to 5 (I agree completely). Sample items were:‘‘An adult and a child should be allowed to have sex together if they both want to’’and‘‘There is nothingwrongwithteachingchildrenabout sexby touching their sexual parts.’’ A five-item rape myth scale assessed attitudes and beliefs about rape and sexual assault. Several of these items were adapted from Burt’s (1980) rape myths scale. Participants reported how much they agreed with each item on a 7-point Likert scale from 1 (strongly disagree) to 7 (strongly agree). Sample items were: ‘‘In the majority of rapes, the victim is promiscuous or has a bad reputation’’ and ‘‘Many guys have an unconscious wish to rape girls.’’

Participants reported if they had ever engaged in any of the following acts: stolen something worth more than the equivalent ofapproximately150USD; committedburglarybybreakingand entering; stolen a car or motorcycle; engaged in violent conflict with a teacher; stayed away from home overnight without parental consent; or been truant from school. Alcohol consumption (defined as at least half a beer, a glass of wine, or 4 cc of spirits) was reported on an 8-point Likert scale from 1 (not applicable) to 8 (almost daily); two or more times a week was chosen to indicate ‘‘frequent alcohol use.’’ Participants also indicated if they had ever used cannabis or‘‘hard’’drugs, defined as cocaine, heroin, amphetamine, or‘‘party’’drugs such as ecstasy. Sexual History

Peer Influences Participants indicated if they ever had had sex with a female, a male (i.e., same-sex sexual contact), and their age at first sexual intercourse. Number of sexual partners was reported on a 3-point Likert scale from 0 (not had intercourse), 1 (1 to 5 partners), or 2 (6 or more partners); 6 or more partners was chosen to indicate ‘‘many sexual partners.’’Participants also responded how often they felt sexual lust on a 5-point Likert scale from 1 (never) to 5 (almost all the time); almost all the time was chosen to indicate ‘‘frequent sexual lust.’’

Participants responded to two questions: ‘‘Some of my friends and acquaintances think sex with children is ok’’ and ‘‘Some of my friends have watched child pornography on the Internet,’’ each rated on a 5-point Likert scale from 1 (not at all true) to 5 (completely true). Each variable was dichotomized into not true at all and any other responses but not true at all; the latter was chosen to indicate any perceived peer interest in sex with children.

Personality

Sexual Victimization

Participants self-reported their aggressiveness and proneness to take risks on a 6-point Likert scale, ranging from 1 (not at all true) to 6 (completely true).

A participant was considered to have experienced sexual coercion if he endorsed ever having been pressured or forced into sexual touching, masturbation, oral, anal, or vaginal intercourse, or someone exposing him/herself against the participant’s will. We were able to distinguish between coercion involving nonpenetrative and penetrative acts, but not between coercion involving verbal pressure versus force, based on questions asking about different types of sexually coercive behavior. Participants who had experienced sexual coercion were also asked if there were multiple perpetrators the first time (mostly the only occasion) they were coerced and if they were 12 years old or younger the first time they were coerced.

Interest in Sex with Children Participants reported the likelihood they would have sex with a child if they were certain that no-one would find out and that they would not be punished. The three questions differed regarding the age ranges of the child: between 12 to 14 years; between 10 and 12 years; and younger than 10 years. These items were not worded to be mutually exclusive; hence, participants interested in a 12-year-old child could endorse two items. Each question was rated on a 5-point Likert scale from 1 (very unlikely) to 5 (very likely).

1

Due to lack of obvious theoretical or empirical association, we did not address participants’ perceived parental overprotectiveness or engagement; self-rated single personality items assessing shyness, independence, leadership, strength, and masculinity; gender role stereotypes; or depression.

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Pornography Viewing Frequency of pornography viewing was rated on a 5-point Likert scale ranging from 1 (once) to 5 (almost daily) and responses dichotomized into once to less than daily versus almost daily. Participants also reported if they had ever viewed pornography with depictions of adult–child sex (child pornography), depictions of sex with violence or force, and depictions of sex between humans and animals, respectively. Since the latter specified

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pornography items did not include information about frequency of viewing, we could not distinguish individuals with a single instance of child pornography viewing from those who sought out such content twice or more. The child pornography item was narrowly defined, referred only to depictions of adult–child sex and thus excluded othercontent that could also meet legal criteria for child pornography (such as child–child sex or children in sexually explicit poses). Sexually Coercive Behavior

The research assistant supervised the survey to ensure that the students did not influence each other and that the completed questionnaires were placed in unmarked envelopes that were individually sealed by each participant. Parents were not informed about the study, but approximately 90 % of participants were legally adults. Participants were not financially compensated for their time. All analyses were conducted using SPSS versions 19 and 20. The Human Research Ethics Committee of the Medical Faculty at Lund University, Sweden, approved the study.

Participants were considered to have been sexually coercive if they endorsed ever persuading someone into, using pressure, or forcing somebody to be sexually touched, masturbate them, have sexual intercourse, oral sex, or anal sex, or having exposed themselves. Because of the way these questions were worded, it was not possible to distinguish clearly coercive acts (forcing someone) from those that were less explicitly coercive (persuading or pressing someone into the act):‘‘Have you, yourself, dragged someone into, persuaded, pressured or forced someone to do sexual activities?’’This question was asked after a series of questions about being sexually coerced, where the phrase ‘‘against your will’’was used to make it clear the questions referred to non-consenting activity (copies of the series of questions are available from the last author). All sexual activities involved physical contact, but some would not meet legal criteria for a sexual crime (Kjellgren et al., 2010). We could further distinguish if the first (usually the only) sexually coercive act involved a person aged 12 years or younger.

Univariate associations (reported as odds ratios) between each of the study variables and self-reported child pornography viewing are shown in Table 1. Effect sizes were also reported using Cohen’s (1988) d (converted from odds ratios, see Table 1 note). The number of participants contributing to the association and the proportion with a positive response for dichotomous variables are also shown in Table 1.

Parental Socioeconomic Position

Nonsexual Antisocial Behavior and Substance Abuse

Parental SEP was measured using the ISEI (Ganzeboom et al., 1992). This measure estimates socioeconomic status from occupation and can range from 0–90; higher scores represent higher SEP. Participants’ mother’s and father’s ISEI was recorded and the highest of the two was used to represent SEP. Lower SES was reflected in scores ranging from 0 to 29, moderate SES by scores 30 to 59, and higher SES was represented by scores ranging from 60 to 90.

Reporting ever having committed theft over 150 USD, burglary, or having had a violent conflict with a teacher all had moderately strong univariate associations with child pornography viewing. Ever having been away from home overnight without parental consent had a weak association with child pornography viewing. Substance abuse (e.g., frequent alcohol use, ever used cannabis, or ever used ‘‘hard’’ drugs), stealing a car or motorcycle, and being truant were not significantly associated with child pornography viewing.

Results Prevalence of Child Pornography Viewing A total of 84 (4.2 %) of the 1,978 young men reported ever having viewed child pornography, defined as pornography with ‘‘sex between adults and children.’’ Correlates of Child Pornography Viewing

Procedure Sexual history We obtained permission to conduct the study by contacting the school director in each selected community. We then informed each high school principal about the project and approached students to participate in classes that represented half of the students attending all different vocational or academic school programs in each participating area. Students consented to take part after reviewing both oral and written information about the study. A research assistant visited each class and administered the survey after reminding the students about their anonymity. The students completed the questionnaires in their classrooms.

Data on age at first sexual intercourse was available for 1,374 participants.The meanagewas 15.59 years(SD = 1.57).Reporting ever having sex with a male and frequent sexual lust had strong associations with self-reported child pornography viewing. Experiencing sexual lust almost all of the time was reported by 14.4 % of those who denied any child pornography viewing, compared to 36.9 % of those who reported any child pornography viewing. In contrast, ever having sex with a female, age at first sexual intercourse, and many sexual partners were all

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Arch Sex Behav Table 1 Correlates of viewing child pornography by domain in a representative sample of 1,978 17–20 year-old men in Sweden Correlate

Cohen’s d

Unadjusted odds ratio

95 % CI

n contributing to effect size

% of positive responses

N

Nonsexual antisocial behavior and substance abuse Ever theft over 150 USD

0.44

2.07**

1.19–3.63

147

7.6

1,937

Ever committed burglary

0.62

2.77**

1.62–4.76

189

9.8

1,938

Ever stolen car or motorcycle

0.24

1.49

0.72–3.10

138

7.1

1,944

Ever violent conflict with teacher

0.49

2.26**

1.21–4.22

145

7.5

1,940

Ever away from home overnight without parental consent

0.26

1.54a

0.99–2.39

749

38.7

1,934

Ever truant

0.28

1.59

0.80–3.17

1,561

83.2

1,876

Frequent alcohol use (2? times weekly)

0.18

1.34

0.65–2.79

265

14.8

1,793

Ever used cannabis

0.21

1.42

0.89–2.27

508

26.1

1,946

0.26

1.53

0.74–3.18

135

6.9

1,943

Ever used‘‘hard’’drugs Sexual history Ever had sex with a female

0.04

1.07

0.65–1.77

1,434

73.2

1,960

Ever had sex with a male

0.74

3.38**

1.44–7.93

50

2.6

1,919



1,374

-0.24c

0.88b

0.75–1.02



Many sexual partners (6 ?)

0.24

1.51

0.86–2.65

310

22.6

1,369

Frequent sexual lust

0.74

3.42**

2.16–5.41

304

15.6

1,946

Aggressiveness

0.40c

1.30**

1.12–1.50





1,953

Prone to take risks

0.00c

1.01

0.83–1.22





1,958

Age at first sexual intercourse

Personality

Interest in sex with children Likely to have sex with a child aged 12–14

1.23

7.56**

4.47–12.79

661

33.8

1,953

Likely to have sex with a child aged 10–12

1.05

5.62**

3.25–9.71

114

5.8

1,953

Likely to have sex with a child under age 10

0.68

3.08**

1.51–6.28

83

4.2

1,953

0.90c

1.14**b

Offense-supportive attitudes and beliefs Child sex liberalism

1.10–1.18





1,858

Seductive children

0.24

1.06*b

1.00–1.12





1,838

Rape myths

0.74c

1.10**b

1.07–1.14





1,879

c

Peer influences Friends think sex with children is OK

0.99

5.17**

3.06–8.73

818

42.3

1,935

Friends watch child pornography

1.26

8.03**

5.11–12.64

300

15.5

1,940

Any sexual victimization

0.25

1.50a

0.93–2.42

445

22.5

1,978

Any sexual victimization involving contact

0.38

1.87**

1.15–3.03

361

18.2

1,978

Multiple abusers at first victimization

1.10

6.14**

2.29–16.47

30

10.0

302

12 years or younger at first victimization

0.43

2.06

0.70–2.40

43

14.5

296

Sexual victimization

Pornography viewing Frequent pornography use

1.14

6.60**

4.16–10.48

197

10.8

1,827

Ever viewed violent pornography

1.85

21.23**

13.08–34.45

236

11.9

1,978

Ever viewed pornography involving sex between humans and animals

0.16

1.30

0.70–2.40

234

11.8

1,978

Sexually coercive behavior Ever exposed your genitals

0.41

1.96

0.73–5.27

56

2.8

1,978

Any coercive sexual touching

0.76

3.48**

2.04–5.92

168

8.5

1,978

Any coercive masturbation

0.86

4.10**

1.73–9.72

43

2.2

1,978

Any coercive sexual intercourse

0.58

2.60*

1.12–6.03

64

3.2

1,978

Any coercive oral sex

0.65

2.94*

1.18–7.35

48

2.4

1,978

Any coercive anal sex

1.06

5.76**

2.21–15.02

27

1.4

1,978

First coercive act against victim under age 12

0.77

3.57*

1.23–10.40

22

14.0

157

NoteSamplesizesvaried dependingon ratesofmissing dataand ifvariableprevalencewascontingentonanothervariable(e.g.,ifsubjectself-reportedsexually coercivebehavior).Odds ratios for dichotomous predictors were calculated using the formula ([b ? 0.5] * [d ? 0.5])/([a ? 0.5] * [c ? 0.5]). 95 % CI for the Odds ratios were computed using the formula e[ln(OR) ± ((H([1/a ? 0.5] ? [1/b ? 0.5] ? [1/c ? 0.5] ? [1/d ? 0.5])) * 1.96)]. Cohen’s d was calculated using the formula d = ln(OR)/1.65 (Sa´nchez-Meca, Marı´n-Martı´nez, & Chaco´n-Moscoso, 2003) a

p\.10, * p\.05, ** p\.01

b

Odds ratios for continuous predictors were calculated using logistic regression

c

Cohen’s d for continuous predictors was calculated using the formula d = ([N1 ? N2] [r])/(H[N1N2][1 - r2]) (Cohen, 1988)

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weakly and non-significantly associated with viewing child pornography. Personality The mean self-rating for aggressiveness was 2.66 (SD = 1.42, N = 1,953) whereas the mean rating for proneness to take risks was 4.50 (SD = 1.17, N = 1,958). Self-reported aggressiveness had a significant moderate association with reported child pornography viewing. Likelihood of taking risks, however, was not significantly related to child pornography viewing. Interest in Sex with Children These variables were dichotomized (i.e., very unlikely vs. any other response but very unlikely) because the responses were positively skewed; the latter was chosen to indicate any interest in sex with children. For each of the three questions, the large majority of the participants responded very unlikely. For a child aged 12–14 years, 275 participants reported 2, 196 reported 3, 90 reported 4, and 100 participants reported 5 (very likely). For a child aged 10–12 years, 31 participants reported 2, 19 reported 3, 5 reported 4, and 59 participants reported 5 (very likely). Finally, for a child aged less than 10 years, 13 participants reported 2, 8 reported 3, 3 reported 4, and 59 participants reported 5 (very likely). Not surprisingly, all three variables representing selfreported interest in sex with children of various ages were strongly and significantly associated with child pornography viewing (Seto et al. 2010b). We also computed a variable where participants’ highest score of the three likelihood to sexual abuse a child was recorded. For this variable, 1,293 participants responded 1 (very unlikely), 275 responded 2, 193 responded 3, 79 responded 4, and 115 responded 5 (very likely); 23 participants were missing data. Offense-Supportive Attitudes and Beliefs Two subscales were created after exploratory factor analysis (results available from last author). Some items in both subscales were reverse keyed; total scores were calculated such that higher scores indicated more positive attitudes and beliefs about adult– child sex. The child sex liberalism subscale contained nine items pertaining to attitudes and beliefs about adult–child sex and summed total scores could range from 9 to 45 (M = 17.1, SD = 5.6, N = 1,858). The seductive children subscale contained six items pertaining to attitudes and beliefs about children’s ability to initiate or consent to sex and total scores could range from 6 to 30 (M = 16.1, SD = 3.9, N = 1,838). Internal consistency for the full scale was moderate (a = .71), and low to moderate for the subscales (a = .65, child sex liberalism subscale; a = .55 seductive children subscale). The child sex liberalism subscale had a strong positive relationship with viewing child pornography

whereas the seductive children subscale only exhibited a weak, significant association with child pornography viewing. Total rape myth scale scores could range from 5 to 35 with a higher score indicating greater endorsement of rape myths (M = 13.7, SD = 6.2, N = 1,879). The internal consistency for this scale was moderate (a = .73). The rape myth scale had a strong positive significant relationship with viewing child pornography. Peer Influences Having friends who think having sex with children is okay or who watched child pornography both had strong associations with reported child pornography viewing. Sexual Coercion Experience Being sexually coerced was weakly associated with self-reported child pornography viewing (marginally significant for any sexual abuse, statistically significant for contact sexual abuse). The likelihood of viewing child pornography was not further increased by young age at time of the first reported sexual coercion experience. The risk of viewing child pornography did increase, however, if multiple perpetrators were reportedly involved. Pornography Viewing Beyond child pornography, frequent pornography use and ever having viewed violent pornography were both strongly related to child pornography viewing. Sexually Coercive Behavior In general, engaging in sexually coercive behavior was moderately to strongly associated with child pornography viewing; however, ever having exposed oneself did not reach statistical significance. Multivariate Modeling Stepwise backward logistic regression modeling was used to identify independent predictors of viewing child pornography from each of the following seven predictor categories: nonsexual antisocial behavior and substance abuse; sexual history; interest in sex with children; offense-supportive attitudes and beliefs; peer influences; other pornography viewing; and sexually coercive behavior. Variables were included only if they had at least a marginally significant (p\.10) univariate association with viewing child pornography (see Table 1). The logistic regression models for each of the predictor categories are shown in Table 2.

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Arch Sex Behav Table 2 Stepwise backward logistic regression models with the strongest remaining independent predictors of child pornography viewing by domain among 17–20 year-old men in Sweden Model by domain

B

SE

Adjusted OR

95 % CI

Nonsexual antisocial behavior and substance abuse Ever theft over 150 USD



Ever committed burglary

1.00

Ever violent conflict with teacher



Ever away from home overnight without parental consent



.28

2.71***

1.16 1.24

.46 .24

3.20* 3.47***

1.30–7.86 2.18–5.53

Likely to have sex with a child aged 12–14

1.88

.28

6.52***

3.75–11.35

Likely to have sex with a child aged 12 or lessa

.78

.30

2.19**

1.23–3.90

.12

.02

1.12***

1.08–1.17

Interest in sex with children

Offense supportive attitudes and beliefs Child sex liberalism Seductive children

-.07

.04

.93*

Rape myths

.07

.02

1.07***

1.04–1.11

Friends think sex with children is OK

1.11

.29

3.04***

1.72–5.36

Friends watch child pornography

1.69

.25

5.42***

3.34–8.80

Frequent pornography use

1.40

.27

4.07***

2.42–6.84

Ever viewed violent pornography

2.78

.26

16.14***

9.79–26.61

Pornography viewing



Any coercive sexual touching

1.10

Any coercive masturbation



Any coercive sexual intercourse



Any coercive oral sex



Any coercive anal sex

1.12

.02

1,908

.05

1,891

.13

1,951

.12

1,721

.16

1,924

.30

1,827

.03

1,978

.87–.99

Peer Influences

Sexually coercive behavior Ever exposed yourself

N

1.56–4.73

Sexual history Ever had sex with a male Frequent sexual lust

R2

.29

3.00***

1.70–5.29

.54

3.06*

1.06–8.83

NoteSamplesizesvariedbecauseofmissingdata.Weusedbackwardsstepwiseeliminationofpredictorsbydomainusingalikelihoodratiocriterionofp\.10; that meansamodelincludingallunivariatelysignificant predictors(atp\.10;Table 1)wascomparedtoa modelwithapredictorremoved.Iftheremovalofthe predictor significantly changed how the model fit the data at p\.10 then the predictor was retained (Field, 2009) a Likely to have sex with a child aged 12 or less is a composite variable representing Likely to have sex with a child aged 10 or less and Likely to have sex with a child aged 10–12, which were combined due to issues with multicollinearity

* p\.05, ** p\.01, *** p\.001

Each stepwise backward logistic regression model had less than 15 % missing data and, as a result, listwise deletion was used to exclude participants with missing data. Although listwise deletion can reduce sample size and hence lead to wider confidence intervals, it tends to yield less biased estimates of regression coefficients and more accurate SE estimates compared to pairwise deletion (Allison, 2009). For each stepwise backward logistic regression model, data were screened for multicollinearity using indicators from linear regression (tolerance and VIF) (Field, 2009) and by examining univariate correlations between predictors (correlations[.60 indicate multicollinearity). Multicollinearity was observed for the interest in sex with children domain: likely to have sex with a child under age 10 years and

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likely to have sex with a child aged 10–12 years were too highly correlated to be included in the same model, r = .85. Hence, we computed a composite variable where participants who endorsed anything but unlikely on either variable received a score of 1 (likely to have sex with a child age 12 or less). The models were also examined for outliers using standardized residuals, Cook’s distance, and DF-Beta scores (Field, 2009; Tabachnick & Fidell, 2007) and no problematic cases were found. Finally, linearity of the logit was satisfied for all continuous variables, suggesting the assumption of linearity was satisfied. We did not conduct a stepwise backward logistic regression for the personality domain because aggressiveness was the only variable from this domain to be included in the final multivariate

Arch Sex Behav

logistic regression model. Similarly, a stepwise backward logistic regression was not conducted for the sexual victimization domain because any sexual victimization was the only variable from this domain selected for inclusion in the final multivariate model. The‘‘any sexual victimization’’variable encompassed any sexual victimization involving contact and, therefore, was selected for the final multivariate logistic regression model. Additionally, several variables were not included in multivariate logistic regression models because of small sample sizes:‘‘having multiple perpetrators at first sexual victimization’’ and being‘‘12 years or younger at first sexual victimization’’had smallresultingsamplesizesbecausethesevariablesrequiredthat participants self-reported sexual victimization (n = 302 and 296, respectively). Similarly, having a child victim under age 12 years required that the participant reported having committed a sexual offense, and the sample size for this variable (n = 157) was also too small to include in the logistic regression modeling. Based on the results of the set of logistic regression models for each predictor domain, the following 16 variables were selected to include in the final logistic regression model: ever committed burglary, ever had sex with a male, frequent sexual lust, aggressiveness, likely to have sex with a child aged 12–14, likely to have sex with a child 12 or less, child sex liberalism, seductive children, rape myths, friends think sex with children is OK, friends watch child pornography, frequent pornography use, ever viewed violent pornography, any sexual victimization, any coercive sexual touching, and any coercive anal sex.

Table 3 Multivariate logistic regression model with the strongest independent predictors by domain (from Table 2) of child pornography viewing among 1,511 17–20 year-old men Correlates by domain

B

SE

Adjusted 95 % CI OR

Nonsexual antisocial behavior and substance abuse Ever committed burglary

0.43 0.41 1.54

0.69–3.44

Ever had sex with a male

1.51 0.73 4.53*

1.08–19.00

Frequent sexual lust

0.28 0.34 1.32

0.68–2.58

0.07 0.10 1.07

0.88–1.31

Likely to have sex with a child aged 12–14

1.29 0.37 3.64***

1.76–7.52

Likely to have sex with a child aged 12 or less

0.88 0.41 2.41*

1.08–5.42

Sexual history

Personality Aggressiveness Interest in sex with children

Offense supportive attitudes and beliefs Child sex liberalism

0.04 0.03 1.04

0.98–1.10

Seductive children

-0.10 0.04 0.90*

0.84–0.98

Rape myths

-0.01 0.02 0.99

0.94–1.04

0.21 0.37 1.23

0.60–2.54

1.40 0.33 4.05***

2.13–7.71

Peer influences Friends think sex with children is OK Friends watch child pornography Sexual victimization Any sexual victimization

-0.43 0.36 0.65

0.32–1.33

Pornography viewing

Multivariate Logistic Regression Model

Frequent pornography use

1.37 0.33 3.92***

2.06–7.45

Ever viewed violent pornography

2.08 0.31 8.20***

4.38–14.69

Sexually coercive behavior

For the final multivariate logistic regression model, 23.6 % of the sample had missing data. Little’s MCAR test was non-significant, v2(25) = 13.32, p = .97, suggesting data were missing completely at random. Listwise deletion was used for missing data and this resulted in a final sample size of 1,511 participants. Again, data were screened for multicollinearity and outliers and no problematic cases were identified. Linearity of the logit was satisfied for all of the continuous variables. The final multivariate logistic regression model significantly predicted child pornography viewing better than the constant only model, v2(16) = 220.53, p\.001, n = 1,511. Hosmer and Lemeshow’s test was non-significant, v2(8) = 3.71, p = .88, indicating good model fit (Tabachnick & Fidell, 2007). The model accounted for just under half (Nagelkerke’s R2 = .42) of the variance in viewing child pornography, producing an overall correct classification rate of 95.4 %. The model correctly classified 98.9 % of nonchild pornography users and 26.0 % of child pornography users, indicating high specificity but low sensitivity. Table 3 shows that seven of the included 16 variables significantly predicted viewing child pornography in the final logistic regression model: (1) ever had sex with a male (n = 38 responded positively), (2) likely to havesexwith achild aged 12–

Any coercive sexual touching Any coercive anal sex

-0.21 0.41 0.81 0.73 0.84 2.07

0.37–1.80 0.40–10.68

Note We used listwise case deletion for missing data * p\.05, ** p\.01, *** p\.001

14 (n = 548 responded positively), (3) likely to have sex with a child 12 or less (n = 94 responded positively), (4) seductive children subscale (n = 747 responded positively, that is, greater than the median score of 16), (5) friends watch child pornography (n = 238 responded positively), (6) frequent pornography use (n = 168 responded positively), and (7) ever viewed violent pornography (n = 197 responded positively). Of note, the significant association between the seductive children subscale scores and viewing child pornography was in the opposite direction than expected. Also,‘‘likely to havesex with a childaged 12– 14’’ and ‘‘ever viewed violent pornography’’ were attenuated to about half their predictive strengths from the category-by-category analyses (Table 2). Several putative predictors from each predictor category (Table 2) were substantially attenuated in the final model: frequent sexual lust, rape myths, friends think sex

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with children is okay, aggressiveness, and any sexual victimization (Table 1). Child Pornography Correlates Scale We were interested in creating and cross-validating a Child Pornography Correlates Scale that could be potentially useful for future researchers interested in characterizing the likelihood that an individual has viewed child pornography. Hence, six of the seven significantpredictors were used to create a Child Pornography Correlates Scale. The seductive children subscale was not included because of its unexpected, inverse association with child pornography viewing. Responses on the six significant dichotomous predictors were summed to create a total score ranging from 0 to 6, with higher scores reflecting more positive responses. To simplify scoring and maximize potential generalizability to other samples (since the scale was constructed and validated on the same sample), the items were not weighted (e.g., Grann & La˚ngstro¨m, 2007). Child Pornography Correlates Scale scores were only computed for participants with complete data on all predictors. Participants who had not viewed child pornography had a mean score of 0.73 (SD = 0.90, n = 1,665) and participants who had viewed child pornography had a mean score of 2.74 (SD = 1.18, n = 81) on the Child Pornography Correlates Scale. Not surprisingly, the construction procedure led to a significant and large mean difference between participants who had viewed child pornography and those who had not, d = 2.20, 95 % CI (1.96, 2.43). The Area Under the ROC curve (AUC) was .90, 95 % CI (.87, .93), p\.001, indicating the scale was very good at discriminating those who had viewed child pornography from those who had not.2 Figure 1 shows the proportion of participants who had viewed child pornography across Child Pornography Correlates Scale scores. The association between Child Pornography Correlates Scale score and child pornography viewing was linear, as expected. We also computed the sensitivity, specificity, positive predictive values, and negative predictive values for two potential cut-off scores for the Child Pornography Correlates Scale. For a cut-off score of 3 (i.e., participants who scored 3 or higher were classified as high risk for child pornography viewing), sensitivity was 59 %, specificity 95 %, the positive predictive value was 36 %, and the negative predictive value was 98 %. For a cut-off score of 4, sensitivity was 26 %, specificity 99 %, the positive predictive value was 60 %, and the negative predictive value was 96 %.

2

AUC = .90, 95% CI [.87, .94] if likely to have sex with a child aged 12 to 14 and likely to have sex with a child 12 years or younger were combined into one item on the Child Pornography Correlates Scale in the Swedish sample.

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Percentage of Participants Who Viewed Child Pornography

Arch Sex Behav 100

n=1

90 80

n= 3

70 n= 17

60 50 40 n= 27

30 20 10 0

n= 21 n= 2

n= 10

0

1

2

3

4

5

6

Score on Child Pornography Correlates Scale

Fig. 1 Proportion of 81 participants who viewed child pornography by score on the Child Pornography Correlates Scale based on 6 independent predictors obtained from a multivariate logistic regression model (from Table 3). Three participants who admitted viewing child pornography had missing data on one of the Child Pornography Correlates Scale items

Cross-Validation of the Child Pornography Correlates Scale We cross-validated the Child Pornography Correlates Scale using a separate sample of 1,748 male third year students from Norway. Participants were recruited from 41 different high schools in the 9 largest urban areas of Norway. Data for this sample were taken from the Norwegian component of the same Baltic Sea region survey as the Swedish participants (response rate 82 %; see Seto et al. 2010a). On average, Norwegian participants were 18 years of age (SD = 0.60, range, 17–20 years). Of these 1,748 participants, 98 (5.6 %) had viewed child pornography. The computation of Child Pornography Correlates Scale scores for the Norwegian sample differed slightly from the Swedish sample. Norwegian participants were only asked to respond to one question regarding the likelihood that they would have sex with a child (age unspecified), if they were certain that no one would find out and that there would be no negative consequences. Of the 1,748 participants, 1,387 reported 1 (very unlikely), 126 reported 2, 89 reported 3, 59 reported 4, and 87 reported 5 (very likely). Thus, five rather than six variables were included in the computation of scale scores. As for the Swedish sample, all variables were recoded into dichotomous predictors where a score of one represented a positive response. Responses on the five predictors weresummed to create a total score ranging from 0 to 5, with higher scores reflecting more child pornography viewing risk factors. Again, Child Pornography Correlates Scale scores were only computed for participants with data on all predictors (N = 1,748). Participants who had not viewed child pornography had a mean score of 0.49 (SD = 0.75, n = 1,650) compared to 2.07 (SD = 1.19, n = 98) for those who had. Once more, there was a significant and large mean difference between participants who had viewed child pornography and those who had not, d = 2.02,

Arch Sex Behav

95 % CI (1.08, 2.24). The AUC was .87, 95 % CI (.83, .90), p\.001, indicating the modified scale was very good at discriminating those who had viewed child pornography from those who had not in a construction-independent sample.

Discussion We examined the correlates of child pornography viewing in a population-representative sample of almost 2,000 young men in Sweden. Many of our predictions were supported. Child pornography viewing was significantly, univariately associated with aspects of antisocial behavior, sexual history, viewing of other atypical pornography,and subjectivepeercharacteristicsregarding adult–child sex. The strongest correlate by far was viewing violent pornography, indicating a strong association of different types of atypical pornography use. This might reflect the cooccurrence of atypical sexual interests for children and sexual violence, hypersexuality or other shared causes (third variables or confounders) of atypical pornography use. Self-reported interest in having sex with a child also had a strong association with self-reported child pornography viewing. These results were consistent with prior research by Seto et al. (2006) including surveys of self-identified pedophiles and hebephiles suggesting strong associations between sexual interest in children and child pornography viewing (see Seto, 2010). Regression analyses identified seven variables that independently contributed to the prediction of child pornography viewing. All of these predicted child pornography viewing in the expected direction except for a puzzling finding for the seductive children subscale; individuals with higher scores were less likely to view child pornography than individuals with lower scores. However, the effect size was weak, b = –.10, opposite to the weak positive univariate association, r = .05, at p\.05. Hence, this may represent a chance finding, particularly since theoretical models and past studies would predict a positive association. Putting this puzzling variable aside, we created a simple correlates scale of the six remaining unweighted dichotomized variables. There was a clear and positive relationship between the Child Pornography Correlates Scale and the likelihood of child pornography viewing, ranging from 0 % at the lowest possible score to 100 % at the highest. An adapted version of this scale (minus one item) was cross-validated in a similar sample of young Norwegian men, producing very comparable results. The Child Pornography Correlates Scale could be useful in future research when investigators want to characterize the likelihood that someone has viewed child pornography without directly asking the question, because child pornography viewing is illegal in most jurisdictions whereas none of the scale items pertain to criminal behavior. Individuals with a high score on the scale are more likely to have viewed child pornography whereas those with a low score are unlikely to have done so.

The results were largely consistent with current models of sexual offending, which could include illegal pornography use (Lalumie`re et al., 2005; Seto, 2008; Ward et al., 2006). In these models, sexual offending is explained by antisociality and sexual deviance, where individuals high in antisocial tendencies and high in atypical sexual interests and/or sexual preoccupation are the most likely to engage in criminal sexual behavior. Our correlational results support that some child pornography viewers may engage in this behavior because they are risk-takers and curious about the taboo and illegal content; some, however, may be motivated by a sexual interest in children or young adolescents (Seto et al., 2006, 2010a, 2010b). The association between experiencing sexual coercion and child pornography viewing was also consistent with the idea that adverse early sexual experiences somehow affect psychosexual development and increases the likelihood of sexual interest or behavior involving children. Two recent meta-analyses suggest that sexual abuse history is associated with sexual offending in both adolescents and adults (Jespersen et al., 2009; Seto & Lalumie`re, 2010). This also held for self-reported sexual coercion history and coercive behavior in the Swedish and Norwegian samples weexamined in this study (Seto et al. 2010a), as well as recent analyses of Finnish twin data that also accounted for familial confounding of the victimization–victimizer association (Forsman et al., 2013). These correlational studies are consistent with causal explanations, but further research using more informative study designs is needed. Sexual coercion experience did not remain a unique predictor of child pornography viewing in the multivariate analyses, suggesting that any potential effects of sexual abuse may be mediated by its effects on sexual or antisocial interests or behaviors.

Limitations First, data on age of onset, frequency, and intensity for child pornography viewing werenot available, because the survey was abroadpopulationsurvey ofsexualityratherthan aspecific study of child pornography viewing. The definition was sufficiently broad that a single exposure to pornography depicting an adult and someone who appeared to be under the legal age of consent (15 years in Sweden) would be viewed as a positive response. Participants did not necessarily engage in recurrent child pornography viewing that is more characteristic of identified child pornography offenders. Others could have endorsed the item because they viewed pornography depicting adults interacting with underage teens, which would meet the legal definition of child pornography. Given the expected correspondence between sexual interests and pornography content (Seto et al., 2006), as well as the fact that child pornography viewing correlated with self-reported sexual interest in children ages 14 and younger, our results indicates at least some participants had viewed illegal child pornography involving younger minors. Because our

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Arch Sex Behav

results were generally consistent with theory and past research on child pornography or sexual offending, we expect the associations to be even stronger if we had had a stronger measure of child pornographyviewingthat referredtointentional,recurrent viewing of pornographic content depicting children ages 14 or younger. Second, cross-sectional surveys preclude strong causal inferences from observed associations, in this case between predictor variables and reported child pornography viewing. However, our results agree substantially with findings from different lines of prior research, usually with smaller and more selected clinical samples, suggesting that antisociality and sexual deviance have substantial explanatory value. Another limitation of the survey data reported here were recall and other self-report biases, given the sensitive nature of many items. We have confidence in our results, however, because the survey was anonymous and primarily covered less sensitive aspects of sexuality, the survey achieved a high response rate, and participants did endorse items that described behaviors that are illegal in Sweden. Future Directions Improved understanding of causal risk factors is important so that prevention and intervention efforts appropriately address truly causal mechanisms, not correlates or epiphenomena. Hence, future studies should use more etiologically informative designs. Experimental work is obviously ethically impossible, but prospective designs using better measures of constructs of interest would allow more sophisticated modeling of putative causal relationships. Further correlational research using contemporary samples and better measures would also be informative, because child pornography viewing and the strength of correlations (though probably not the correlates themselves) may have changed since the survey data analyzed in this study were collected. Acknowledgments The Swedish survey was funded by the Swedish Ministry of Health and Social Affairs and the Norwegian survey was funded by the Norwegian Ministry of Child and Family Affairs. The research network that conducted the Baltic Sea Regional Study on Adolescent Sexuality was funded by the Norwegian Research Council. Niklas La˚ngstro¨m was funded by the Swedish Research Council. We thank Dr. Svein Mossige for access to the Norwegian data used to cross-validate our Child Pornography Correlates Scale. The authors have no financial interests to disclose regarding this study.

References Ajzen, I. (1991). Theory of planned behavior. Organizational Behavior and Human Decision Processes, 50, 179–211. doi:10.1016/0749-5978(91) 90020-T. Ajzen, I. (2001). Nature and operation of attitudes. Annual Review of Psychology, 52, 27–58. doi:10.1146/annurev.psych.52.1.27.

123

Allison, P. D. (2009). Missing data. In R. E. Millsap & A. Maydeu-Olivares (Eds.),TheSAGEhandbookofquantitativemethodsinpsychology (pp. 72–90). London, England: Sage. Babchishin, K. M., Hanson, R. K., & Hermann, C. A. (2011). The characteristics of online sex offenders: A meta-analysis. Sexual Abuse: A Journal of Research and Treatment, 23, 92–123. doi:10.1177/107 9063210370708. Baines, V. (2008, November). Online child sexual abuse: The law enforcement response. Paper presented at the Third World Congress of ECPAT International, Rio de Janeiro, Brazil. Retrieved from http://www.ecpat. net/worldcongressIII/PDF/Publications/ICT_Law/Thematic_Paper_ ICTLAW_ENG.pdf Bates, A., & Metcalf, C. (2007). A psychometric comparison of Internet and non-Internet sex offenders from a community treatment sample. Journal of Sexual Aggression, 13, 11–20. doi:10.1080/135526007013 65654. Blanchard, R., Kolla, N. J., Cantor, J. M., Klassen, P. E., Dickey, R., Kuban, M. E., & Blak, T. (2007). IQ, handedness, and pedophilia in adult male patients stratified by referral source. Sexual Abuse: A Journal of Research and Treatment, 19, 285–309. doi:10.1007/s11194-0079049-0 Burt, M. R. (1980). Cultural myths and supports for rape. Journal of Personality and Social Psychology, 38, 217–230. doi:10.1037/00223514.38.2.217. Canwest News Service. (2009). Retrieved from http://www.canada.com/ ottawacitizen/news/story.html?id=392a7d0f-e5dc-4e5b-b163-ef347 11a9994&k=79268 Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale, NJ: Lawrence Erlbaum. Eke, A. W., Seto, M. C., & Williams, J. (2011). Examining the criminal history and future offending of child pornography offenders: An extended prospective follow-up study. Law and Human Behavior, 35, 466–478. doi:10.1007/s10979-010-9252-2. Field, A. (2009). Discovering statistics using SPSS. London, England: Sage. Forsman, M., Santtila, P., Johansson, A., Sandnabba, K., & La˚ngstro¨m, N. (2013). Childhood maltreatment and risk of sexual and non-sexual violent behavior. Manuscript submitted for publication. Ganzeboom, H. B. G., De Graaf, P. M., & Treiman, D. J. (1992). A standard International Socioeconomic Index of Occupational Status. Social Science Research, 21, 1–56. Grann, M., & La˚ngstro¨m, N. (2007). Actuarial assessment of violence risk: To weigh or not to weigh? Criminal Justice and Behavior, 34, 22–36. doi:10.1177/0093854806290250. Hanson, R. K., & Morton-Bourgon, K. (2005). The characteristics of persistent sexual offenders: A meta-analysis of recidivism studies. Journal of Consulting and Clinical Psychology, 73, 1154–1163. doi:10. 1037/0022-006X.73.6.1154. Jenkins, P. (2001). Beyond tolerance: Child pornography on the Internet. New York: New York University Press. Jespersen, A. F., Lalumie`re, M. L., & Seto, M. C. (2009). Sexual abuse history among adult sex offenders and non-sex offenders: A metaanalysis. Child Abuse and Neglect, 33, 179–192. doi:10.1016/j.chiabu. 2008.07.004. Kjellgren, C., Priebe, G., Svedin, C. G., & La˚ngstro¨m, N. (2010). Sexually coercive behavior in male youth: Population survey of general and specific risk factors. Archives of Sexual Behavior, 39, 1161–1169. doi: 10.1007/s10508-009-9572-9. Lalumie`re,M.L.,Chalmers,L.J.,Quinsey,V.L.,&Seto,M.C.(1996).Atest of the mate deprivation hypothesis of sexual coercion. Ethology and Sociobiology, 17, 299–318. doi:10.1016/S0162-3095(96)00076-3. Lalumie`re, M. L., Harris, G. T., Quinsey, V. L., & Rice, M. E. (2005). The causes of rape: Understanding individual differences in the male propensity for sexual aggression. Washington, DC: American Psychological Association.

Arch Sex Behav La˚ngstro¨m, N., & Hanson, R. K. (2006). High rates of sexual behavior in the general population: Correlates and predictors. Archives of Sexual Behavior, 35, 37–52. doi:10.1007/s10508-006-8993-y. Lipsey, M., & Derzon, J. H. (1998). Predictors of violent or serious delinquency in adolescence and early adulthood: A synthesis of longitudinal research. In R. E. Loeber & D. P. Farrington (Eds.), Serious and violent juvenile offenders: Risk factors and successful interventions (pp. 86– 105). Thousand Oaks, CA: Sage. Malamuth, N. M., Linz, D., Heavey, C. L., Barnes, G., & Acker, M. (1995). Using the confluence model of sexual aggression to predict men’s conflict with women: A 10-year follow-up study. Journal of Personality and Social Psychology, 69, 353–369. doi:10.1037/0022-3514.69. 2.353. Motivans, M., & Kyckelhahn, T. (2007). Federal prosecution of child sex exploitation offenders, 2006. Bureau of Justice Statistics Bulletin (ReportNo.NCJ219412).Washington,DC:BureauofJusticeStatistics. Neutze, J., Seto, M. C., Schaefer, G. A., Mundt, I. A., & Beier, K. M. (2011). Predictors of child pornography offenses and child sexual abuse in a community sample of pedophiles and hebephiles. Sexual Abuse: A Journal of Research and Treatment,23,212–242.doi:10.1177/107906 3210382043. Ray, J. V., Kimonis, E. R., & Seto, M. C. (2013). Correlatesand moderators of child pornography consumption in a community sample. Sexual Abuse: A Journal of Research and Treatment. doi:10.1177/10790632 13502678. Riegel, D. L. (2004). Effects on boy-attracted pedosexual males of viewing boy erotica [Letter to the Editor]. Archives of Sexual Behavior, 33, 321–323. doi:10.1023/B:ASEB.0000029071.89455.53. Sa´nchez-Meca, J., Marı´n-Martı´nez, F., & Chaco´n-Moscoso, S. (2003). Effect-size indices for dichotomized outcomes in meta-analysis. Psychological Methods, 8, 448–467. doi:10.1037/1082-989X.8.4.448. Seto, M. C. (2008). Pedophilia and sexual offending against children: Theory,assessment,andintervention.Washington, DC: American Psychological Association. Seto, M. C. (2010). Child pornography use and Internet solicitation in the diagnosis of pedophilia [Letter to the Editor]. Archives of Sexual Behavior, 39, 591–593. doi:10.1007/s10508-010-9603-6. Seto, M. C. (2013). Internet sex offenders. Washington, DC: American Psychological Association. Seto, M. C., Cantor, J. M., & Blanchard, R. (2006). Child pornography offenses are a valid diagnostic indicator of pedophilia. Journal of Abnormal Psychology, 115, 610–615. doi:10.1037/0021-843X.115.3. 610. Seto, M. C., Hanson, R. K., & Babchishin, K. M. (2011). Contact sexual offending by men with online sexual offenses. Sexual Abuse: A Journal of Research and Treatment, 23, 124–145. doi:10.1177/107906 3210369013.

Seto, M. C., Kjellgren, C., Priebe, G., Mossige, S., Svedin, C. G., & La˚ngstro¨m, N. (2010a). Sexual coercion experience and sexually coercive behavior: A population study of Swedish and Norwegian male youth. Child Maltreatment, 15, 219–228. doi:10.1177/1077559510 367937. Seto, M. C., & Lalumie`re, M. L. (2010). What is so special about male adolescent sexual offending? A review and test of explanations using meta-analysis. Psychological Bulletin, 136, 526–575. doi:10.1037/ a0019700. Seto, M. C., Reeves, L., & Jung, S. (2010b). Explanations given by child pornography offenders for their crimes. Journal of Sexual Aggression, 16, 169–180. doi:10.1080/13552600903572396. Siegfried, K. C., Lovely, R. W., & Rogers, M. K. (2008). Self-reported online child pornography behavior: A psychological analysis. International Journal of Cyber Criminology, 2, 286–297. Statistics Sweden. (2007). Population statistics. Retrieved from http://www. scb.se/en_/Finding-statistics/Publishing-calendar/Show-detailedinformation/?publobjid=6636??. Steel, C. M. (2009). Child pornography in peer-to-peer networks. Child Abuse and Neglect, 33, 560–568. doi:10.1016/j.chiabu.2008.12.011. ˚ kerman, I., & Priebe, G. (2011). Frequent users of pornogSvedin, C. G., A raphy. A population based epidemiological study of Swedish male adolescents. Journal of Adolescence, 34, 779–788. doi:10.1016/j. adolescence.2010.04.010. Tabachnick, B. G., & Fidell, L. S. (2007). Using multivariate statistics. Boston, MA: Pearson Education Inc. Uppsala kommun. (2005). Uppsala kommun utva¨rderingsenheten. Utva¨rdering av elevers fra˚nvaro i gymnasieskolan i Uppsala kommun [The evaluation unit of the Uppsala local authority: 2005:6. Evaluation of students’absencefrom high school in Uppsala].Retrievedhttp://www. uppsala.se; Accessed March 6, 2007. Ward, T., Polaschek, D. L. L., & Beech, A. R. (2006). Theories of sexual offending. West Sussex, England: John Wiley & Sons. Whitaker, D. J., Le, B., Hanson, R. K., Baker, C. K., McMahon, P. M., Ryan, G., Klein, A., & Rice, D. D. (2008). Risk factors for the perpetration of child sexual abuse: A review and meta-analysis. Child Abuse & Neglect, 32, 529–548. doi:10.1016/j.chiabu.2007.08.005 Wolak, J. (2011, October). What we know (and don’t know) about Internet sex offenders. Plenary presented at the annual meeting of the Association for the Treatment of Sexual Abusers, Toronto, Ontario, Canada. Handout from http://www.atsa.com/sites/default/files/ConfHO2011 Wolak.pdf Wolak, J., Finkelhor, D., & Mitchell, K. J. (2005). Child pornography possessors arrested in internet-related crimes: Findings from the National Juvenile Online Victimization Study (Report CV81). Alexandria, VA: National Center for Missing and Exploited Children.

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Viewing child pornography: prevalence and correlates in a representative community sample of young Swedish men.

Most research on child pornography use has been based on selected clinical or criminal justice samples; risk factors for child pornography use in the ...
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