by Kathryn C. Seigfried-Spellar (a), Marcus K. Rogers (b)
(a) The University of Alabama, Tuscaloosa, AL 35487, USA
(b) Purdue University, West Lafayette, IN 47907, USA
This study investigated whether deviant pornography use followed a Guttman-like progression in that a person transitions from being a nondeviant to deviant pornography user. In order to observe this progression, 630 respondents from Survey Sampling International’s (SSI) panel Internet sample completed an online survey assessing adult-only, bestiality, and child pornography consumption. Respondents’ “age of onset” for adult pornography use was measured to determine if desensitization occurred in that individuals who engaged in adult pornography at a younger age were more likely to transition into deviant pornography use. Two hundred and fifty-four respondents reported the use of nondeviant adult pornography, 54 reported using animal pornography, and 33 reported using child pornography. The child pornography users were more likely to consume both adult and animal pornography, rather than just solely consuming child pornography. Results suggested deviant pornography use followed a Guttman-like progression in that individuals with a younger “age of onset” for adult pornography use were more likely to engage in deviant pornography (bestiality or child) compared to those with a later “age of onset.” Limitations and future research suggestions are discussed.
Keywords: pornography, Guttman-like progression, age of onset, child pornography, bestiality, Internet crime
Does Deviant Pornography Use Follow A Guttman-Like Progression?
Research suggests child pornography collections not only contain sexualized images of children, but other genres of pornography both deviant and socially acceptable in nature (c.f., Quayle & Taylor, 2002; Quayle & Taylor, 2003). In fact, interviews with child pornography consumers have suggested some offenders move “thorough a variety of pornographies, each time accessing more extreme material” (Quayle & Taylor, 2002, p. 343) as a result of desensitization or appetite satiation, which led to collecting and discovering other forms of deviant pornography (Quayle & Taylor, 2003). Also, some consumers stated they downloaded the images simply because they were available and accessible, making the behaviors primarily a result of compulsivity rather than a specific sexual interest in children (Basbaum, 2010). However, prior analyses rely on case studies of convicted child sex offenders and child pornography users. If a more broadly based representative sample (as utilized here) were employed, then researchers may have a more congruent and complete understanding of the collections of child pornography users.
Some child pornography consumers exhibit a complex array of sexual interests, which may be representative of a more general level of paraphilic tendencies rather than a specific sexual interest in children. In a study conducted by Endrass et al. (2009), the collection of images from 231 men charged with child pornography use also revealed other types of deviant pornography. Specifically, nearly 60% of the sample collected child pornography and at least one other type of deviant pornography, such as bestiality, excrement, or sadism, with at least one out of three offenders collecting three or more types of deviant pornography (Endrass et al. 2009). This research suggests the majority of Internet child pornography consumers are collecting a wider range of deviant pornography, which may reflect a general level of sexual deviance rather than a specific paraphilia, such as pedophilia. In other words, some child pornography consumers may be dissidents within the normal population who exhibit a wider range of sexual interests or curiosity.
Although case studies exist, few quantitative research studies have assessed the question of whether individuals who use nondeviant forms of pornography (e.g., adult pornography) are at a greater risk for consuming deviant forms of pornography (e.g., animal and child pornography). In other words, does deviant pornography use follow a Guttman-like progression (c.f., Holland, 1988) with age of onset being a key factor in whether a person transitions from being a nondeviant to deviant pornography user? Regarding age of onset, the majority of research focuses on the emotional consequences of unwanted exposure to pornography at a young age (c.f., Flood, 2009). For example, Mitchel, Wolak, and Finkelhor (2007) found 10% of 10 to 17 year olds described themselves as being “very or extremely upset” by unwanted exposure to pornography. On the other hand, McKee (2007) interviewed 46 Australians, regarding their exposure to pornography at a young age, who described their pre-pubescent exposure to pornography as “funny” and with “little interest” whereas their post-pubescent exposure was a “right of passage” (p. 10). In addition, research has suggested a relationship between pornography use at a young age and various sexual behaviors. Specifically, Johansson and Hammarén (2007) found young pornography users were more likely to have had sexual intercourse and a one-night stand, and young consumers of violent pornography were more likely to exhibit sexually aggressive attitudes and behaviors (c.f., Flood, 2009).
Overall, previous research has mainly focused on the emotional impact of unwanted exposure to pornography for young people. The current study focused on the “age of onset” for intentional use, rather than unwanted exposure, of nondeviant and deviant pornography. Since the current study sampled respondents from the United States, definitions of nondeviant and deviant pornography were based on the current obscenity laws in the United States. In the United States, adult pornography is protected by the First Amendment (although there are exceptions); however, child pornography and animal pornography (bestiality) are obscene, therefore, illegal forms of expression. Thus, adult pornography was operationalized as nondeviant, whereas, animal and child pornography were labeled as deviant forms of pornography.
Despite the formal social controls (laws) regulating pornography use, all three genres of pornography remain readily available on the Internet. Therefore, this study explored at what age individuals first knowingly searched for, downloaded, and exchanged/shared the following pornography genres: adult-only, animal (bestiality), and child pornography. By examining the interrelations among the self-reported age and pornography use variables, the authors hoped to understand how nondeviant pornography use either facilitated or predicted the probability of engaging in more deviant forms of pornography.
Three primary objectives were the focus of the current study. The first aim of this study was to determine whether or not the age of onset was a risk factor for engaging in deviant pornography. In other words, are individuals who engage in nondeviant pornography use at an earlier age more likely to engage in deviant forms of pornography use compared to late onset users? The second aim of this study determined whether female respondents were consuming Internet child pornography. Previous research suggests the majority of child pornography users are male; however, the majority of these samples are from the forensic or clinical populations (c.f., Babchishin, Hanson, & Hermann, 2011). In addition, Internet-based research studies suggest women may be engaging in child pornography more than previously expected (c.f., Seigfried, Lovely, & Rogers, 2008; Seigfried-Spellar & Rogers, 2010). Thus, the current study specifically assessed the prevalence of female child pornography use in a sample of Internet users rather than a forensic or clinical sample, in order to provide a broader conceptualization of female users of child pornography (non-convicted and self-reported).
Finally, the third aim of this study explored the frequency of pornography use by collapsing the respondents into pornography categories: none, adult-only, animal-only, child-only, adult-animal, adult-child, animal-child, and adult-child-animal. This methodological analysis allowed assessment of whether self-reported child pornography users were more likely to self-report adult and animal pornography behaviors compared to the other categories of users. Few research studies have specifically assessed the variety of genres collected by Internet child pornography users (c.f., Seigfried-Spellar, in press). Specifically, if child pornography use followed a Guttman-like progression then there should be no “exclusive consumers” of only child pornography; instead, child pornography users should report engaging in other forms of deviant and nondeviant pornography.
This study was exploratory in nature since no previous research has assessed whether individuals who reported a younger “age of onset” for adult pornography use were more likely to engage in deviant pornography use compared to individuals who reported a later “age of onset”. The expectation is to find no relationship between “age of onset” for adult pornography and later deviant pornography use. However, the modest amount of research on child pornography use indicates child pornography collections include both deviant and nondeviant pornographic images. Therefore, it is hypothesized child pornography consumers will be more likely to consume adult-only and bestiality pornography and less likely to be sole consumers of child pornography. Finally, the authors expect to find a sex difference; specifically, men will be more likely to self-report the use of child pornography (e.g., Babchishin et al., 2011). Uniquely, there will be a higher prevalence of female child pornography use in this Internet-based research study due to the difference in sampling methodology.
The current study utilized Survey Sampling International (SSI), which provided a panel Internet sample of both male and female respondents, who were at least 18 years of age or older, from the United States. Rather than snowballing the Internet in order to identify respondents, these clients or respondents have already gone through the SSI’s quality control and verification system in order to identify individuals who are at risk of lying on a survey just to qualify or claim any rewards or incentives (SSI, 2009). In addition, SSI prevents the same person from being able to take the survey multiple times (SSI, 2009). Most importantly, these clients or respondents were more likely to be confident in the reliability and confidentiality of this study, as well as comfortable and trusting in the research process itself, which is essential when examining attitudes and behaviors toward socially sensitive topics.
Based on the desire to sample respondents from the “general population of Internet pornography users,” rather than a sample from the clinical or forensic population, and the need to increase the respondent’s confidence in self-disclosure, this sampling methodology best met the needs of the current study. As shown in Table 1, 630 respondents completed the online survey; 502 (80%) were women and 128 (20%) were men (Note: This gender disparity will be discussed later in the paper). Overall, the majority of the sample was white (n = 519, 82.4%), between the ages of 36-55 years (n = 435, 69%), married (n = 422, 67%), and 68% (n = 427) of the respondents had completed some college or post-graduate work.
The respondent’s Internet pornography behavior and age of onset were measured using a shorted version of the Online Pornography Survey (OPS; Seigfried, 2007; Seigfried-Spellar, 2011). The original OPS included 54 questions, which assessed the respondents’ pornography behaviors including intentional searching, accessing, downloading, and exchanging of sexually explicit Internet images. Adult pornography was defined as pornographic images “featuring individuals over the age of 18 years,” whereas child pornography was defined as pornographic materials “featuring individuals under the age of 18 years.” Animal pornography or bestiality was defined as pornographic images “featuring individuals over the age of 18 years with an animal.”
Only 15 items from the Online Pornography Survey, which focused on the respondent’s age of onset for online pornography use, were included in this study. All 15 questions employed the same answer format. The following is an illustrative sample question related to age of onset from the OPS: “How old were you the first time you knowingly accessed a website in order to view pornographic materials featuring individuals under the age of 18 years?” The respondents’ choices for age of onset items were: does not apply to me, under 12 years of age, 12 to under 16 years of age, 16 to under 19 years of age, 19 to under 24 years of age, 24 years of age or older, and decline to respond. Based on item endorsement, the respondents were classified as either users or non-users of adult, animal (bestiality), and child pornography.
Finally, the respondents’ basic demographic information was self-reported via an online questionnaire, which included items such as sex, age, and marital status. The demographics survey appeared at the beginning of the study for all of the respondents. The current study advertised as assessing “attitudes toward adult websites,” and by placing the demographics questionnaire prior to the more socially sensitive questions regarding pornography use, this method increased the accuracy of self-reported sex for this study (c.f., Birnbaum, 2000). Also, all survey items were forced-choice, but the respondents were able to select “decline to respond” to any item, as required by the Institutional Review Board (IRB). Further, all respondents were treated in accordance with the ethical standards set forth by the American Psychological Association (APA).
This study was conducted electronically using an Internet-based survey. This method of conducting research via the Internet has seen increasing used by researchers due to the accessibility of respondents and the perceived anonymity and increased willingness to self-disclose socially unacceptable or controversial behaviors or attitudes (Mueller, Jacobsen, & Schwarzer, 2000). Once the respondents accessed the website, the home page explained the study while acting as a consent form to which the respondents had to agree or decline to participate. If the prospective respondents agreed, they had to click on the “I Agree” button in order to participate. After clicking on the “I Agree” button, the respondents were asked to complete the questionnaires, which took approximately 15 minutes to complete.
At no time were the respondents asked for any identifying information (e.g., name). In order to protect the respondent’s anonymity and confidentiality, the respondents were provided with an ID number so responses to the questionnaires could not be linked or matched to any particular individual.
2.4 Statistical Analyses
After data collection, statistical analyses were conducted using the Statistical Package for the Social Sciences (SPSS) version 19. Statistical significance was set at the alpha level of .05 prior to any analyses. The Fisher-Freeman-Halton Exact Test tested for significant relationships between age of onset, sex, and pornography type. This decision was made for the following reasons: expected cell frequency counts were small due to the study assessing rare occurrences (i.e., child pornography use), it approximates the chi-square test as sample size (N) increases, and the Fisher-Freeman-Halton Exact Test extends the Fisher’s Exact Test to the R x C case (c.f., Freeman & Halton, 1951). Finally, a backward stepwise (Wald) logistic regression was conducted in order to determine if sex and “age of onset” for adult pornography use predicted group membership for nondeviant versus deviant Internet pornography use. Logistic regressions are appropriate for exploratory analyses, for they are more robust with fewer violations of assumptions, such as small and unequal sample sizes (Tabachnick & Fidell, 2007).
As shown in Table 2, 5.2% (n = 33) of the respondents self-reported the use of Internet child pornography. 16 (12.5%) of the male respondents were child pornography users, and 17 (3.4%) of the female respondents were child pornography users. Of the 630 respondents, only 8.6% (n = 54) of the respondents self-reported the use of bestiality pornography, yet nearly half (n = 254, 40.3%) of the respondents reported the use of adult-only pornography. As shown in Table 3, the respondents were further categorized based on their use of adult-only, bestiality, and child pornography.
In support of the study’s premise, no respondents reported the sole use of child pornography. Only 1 female respondent reported only consuming bestiality pornography. In addition, 9.8% (n = 60) of the respondents consumed some mixture of nondeviant and deviant pornography compared to only .5% who reported consuming only deviant pornography (bestiality and child).
Since the descriptive data suggested there was a relationship between adult, animal, and child pornography use (see Table 3), a zero-order correlation was conducted to determine the direction of the relationship. Based on item responses, a dichotomous variable was created for each pornography category: adult, animal, and child. The respondents were coded as either non-users (0) or users (1) for each category of pornography. As shown in Table 4, there was a statistically significant relationship between adult pornography and bestiality use, rϕ (635) = .36 with p < .01, and adult pornography and child pornography use, rϕ (635) = .27 with p < .01. There was a significant positive relationship for individuals who self-reported engaging in adult pornography, animal/bestiality, and child pornography. In addition, men were significantly more likely to self-report the use of adult, rϕ (630) = -.28 with p < .01, animal/bestiality, rϕ (630) = -.18 with p < .01, and child pornography, rϕ (630) = -.17 with p < .01 (See Table 4).
Percentage of Non-Deviant and Deviant Pornography Use by Sex
Classification of Respondents by Self-Reported Use of Adult, Animal, and Child Pornography
Next, respondents were categorized as either: adult-only (adult-only) or adult and child/animal (adult + deviant) pornography users. The “age of onset” was then compared between the two groups to determine if “age of onset” for adult pornography use was related to later use of deviant pornography. Based on the Fisher-Freeman-Halton Exact Test (p < .01), adult + deviant pornography users reported a significantly younger “age of onset” compared to adult-only pornography users. As shown in Table 5, 29% of the adult + deviant pornography users reported an “age of onset” between 12 and 18 years of age compared to only 10% of the adult-only respondents. Instead, the majority (89%) of the adult-only pornography users reported an age of onset of 19 years of age or older compared to 69% for the adult + deviant pornography users (See Table 5).
Based on the significant findings from the zero-order correlations and Fisher-Freeman-Halton Exact Test, the authors conducted a backward stepwise (Wald) logistic regression to determine if “age of onset” and sex were significant predictors of adult-only versus adult + deviant pornography use. As shown in Table 6, the best predictive model for adult-only versus adult + deviant pornography use included both variables, Sex (W = 7.69, p < .01) and Age of Onset (W = 5.16, p < .02). Individuals with a younger “age of onset” for adult pornography use were .8 times more likely to engage in deviant pornography. In addition, men were .4 times more likely to be a deviant pornography user. The Hosmer and Lemeshow test was non-significant, χ2(4) = 6.42 with p = .17, indicating the final model fit the data. In addition, variance inflation factors (VIF) and condition index values were calculated in order to test for multicollinearity, all of which indicated no cause for concern (Sex, VIF = 1.00; Age of Onset, VIF = 1.00; Condition Index < 30).
Based on these analyses, the authors were able to achieve their aims of determining if “age of onset” and sex significantly predicted adult-only versus adult + deviant pornography users. Overall, the hypothesized expectation that child pornography users would be more likely to consume both adult and animal pornography, rather than just solely consuming child pornography, was supported. In addition, the postulation that men were more likely to engage in child pornography use was supported as well as the expectation of a higher prevalence of female child pornography use in this Internet-based sample.
Zero-order Correlation for Sex, Adult, Animal, and Child Pornography Use
Adult-Only Versus Adult and Deviant Pornography Use by Age of Onset
Exploratory Backward (Wald) Logistic Regression for Pornography Use
However, the authors’ expectation of no difference between the “age of onset” for adult pornography use between adult-only and adult + deviant pornography users was not supported. Based on the Fisher-Freeman-Halton Exact Test and logistic regression, adult + deviant pornography users reported a significantly younger “age of onset” for adult pornography use compared to the adult-only pornography users. In other words, deviant pornography users engaged in adult pornography at a significantly younger age compared to those who engaged in only nondeviant pornography.
The current study was the first to assess whether “age of onset” for nondeviant pornography use (i.e., adult-only) was related to later use of deviant pornography (i.e., bestiality, child) using a large Internet-based sample. This study represents an improvement over prior case studies, which rely on samples of convicted offenders. As such, the current study moved away from the clinical or forensic population of child pornography users to child pornography user from the “general population of Internet users.” In addition, this study assessed whether child pornography users collected both deviant and nondeviant pornography or whether they self-reported only consuming child pornography. Overall, significant differences emerged between nondeviant and deviant pornography users for “age of onset” and sex.
A small body of research suggests the majority of Internet child pornography users are collecting a wider range of deviant pornography (c.f., Endrass et al., 2009). In the current study, none of the respondents self-reported the sole consumption of Internet child pornography. Instead, the majority of child pornography users were also collecting other forms of pornography including nondeviant adult pornography and bestiality pornography. Of the 32 child pornography consumers, 60% (n = 19) also collected both nondeviant adult and animal pornography, 34% (n = 11) consumed just nondeviant adult pornography, and only 6% (n = 2) had just animal pornography (See Table 3). These findings support the Seigfried (2007) study, which observed no sole consumers of Internet child pornography. Overall, child pornography users are engaging in a wide range of sexual content and future research should assess whether these collections provide information as to their offline intentions (e.g., hands-on contact offending) as well as personality characteristics (e.g., violent individuals collect violent pornography; Rogers & Seigfried-Spellar, 2012; Seigfried-Spellar, in press).
Consistent with previous research, men continue to be more likely to engage in Internet child pornography use. However, the current study suggests women may be consuming child pornography more than previously suggested by research samples from the clinical for forensic population. For example, Babchishin et al. (2011) conducted a meta-analysis of 27 articles, which included samples of online offenders. The results of the meta-analysis suggest the majority of the child pornography offenders are male, and of the 27 articles, only five studies include female offenders. Thus, less than 3% of the entire sample of online offenders was women (Babchishin et al., 2011). However, previous research including samples from the general population of Internet users, rather than the clinical or forensic population, has reported higher percentages of female consumers of child pornography. For example, the Seigfried et al. (2008) study found 10 of the 30 self-reported child pornography users from an Internet-based research study to be women. In addition, the Seigfried-Spellar (2011) study reported 20% of the self-reported child pornography users were women. Finally, 17 of the 33 (52%) child pornography consumers were women in the current study. Future research should assess why there is a difference in the prevalence of child pornography use for women from different sampling populations.
Along with the variable sex, “age of onset” was significantly related to deviant pornography use. Respondents who reported a younger “age of onset” for nondeviant pornography use were more likely to engage in deviant pornography use compared to those individuals who reported a later “age of onset.” As shown in Table 5, the adult + deviant pornography users were twice as likely to self-report an “age of onset” between 12-18 years of age compared to the adult-only pornography users. Finally, the logistic regression suggested the best predictive model for deviant pornography use included variables, sex and “age of onset.” That is to say, men were significantly more likely to engage in deviant pornography compared to women. In addition, individuals who started engaging in adult pornography use at a young age were more likely to use deviant pornography compared to those who engaged in adult pornography at a later age.
The findings of the current study suggest Internet pornography use may follow a Guttman-like progression. In other words, individuals who consume child pornography also consume other forms of pornography, both nondeviant and deviant. For this relationship to be a Guttman-like progression, child pornography use must be more likely to occur after other forms of pornography use. The current study attempted to assess this progression by measuring if the “age of onset” for adult pornography use facilitated the transition from adult-only to deviant pornography use. Based on the results, this progression to deviant pornography use may be affected by the individuals “age of onset” for engaging in adult pornography. As suggested by Quayle and Taylor (2003), child pornography use may be related to desensitization or appetite satiation to which offenders begin collecting more extreme and deviant pornography. The current study suggests individuals who engage in adult pornography use at a younger age may be at greater risk for engaging in other deviant forms of pornography. If child pornography use follows a Guttman-like progression, then future research should assess the relationship between age of onset for nondeviant pornography and future appetite satiation leading to other deviant forms of pornography.
Although this study sampled from the “general population of Internet users,” there is no claim that the findings are representative of the entire population of Internet users. While sampling respondents from the same country (United States) limits external validity, the authors were able to increase control over certain confounds, such as the legality of child pornography and animal pornography use. The current methodology targets Internet users who were living in a country where child pornography and animal pornography are illegal. For example, the self-reported Internet child pornography users in the current study were engaging in illegal child pornography behaviors, and legality of child pornography use could be a confound if individuals are sampled from countries where child pornography use is legal (e.g., Russia, Japan, Thailand; see International Centre for Missing & Exploited Children, 2010).
Also, sex representation was disproportionate in the current study. According to the United States Census Bureau (2009a), 50.7% of the United States population was women. When considering only those individuals who had Internet access either inside or outside their household (N = 197,871), 48.6% were women (United States Census Bureau, 2009b). Based on the current panel demographics for Survey Sampling International (personal communication, 2012), 56% of the United States Internet panel is women. It is possible the sex disparity in this study was related to the respondents’ employment status. In the current study, men were significantly more likely to be employed full-time and part-time whereas the women were more likely to be homemakers, χ2 (9) = 73.82, p < .00. Previous research cites respondents who are employed full-time and are “busy” are less likely to complete online surveys (Cavallaro, 2012). So, the sex disparity may have been due to employment status in that the female respondents who were homemakers had more time to complete the online survey. When controlling for employment status, there was a still a significant relationship between “age of onset” and adult-only versus adult + deviant pornography use, rab + c = -.28, p < .01.
Although, the proportion of women to men in the current study was not representative of the United States Internet population, it did sample individuals outside of the clinical or forensic population. In addition, the current study suggests this methodology may reveal more women who are consumers of Internet child pornography compared to other research designs (i.e., clinical or forensic population; Seigfried-Spellar & Rogers, 2010).
Although there was a sex disparity in the current study, the relationship between adult-only versus adult + deviant pornography use and “age of onset” was still significant when controlling for sex, rab + c = -.30 with p < .01. When only assessing the male respondents, men who engaged in adult + deviant pornography reported a significantly younger “age of onset” for adult pornography use compared to the men who engaged in adult-only pornography, Fisher-Freeman-Halton Exact Test = 15.79 with p < .01. When only assessing the female respondents, women who engaged in adult + deviant pornography also reported a significantly younger “age of onset” for adult pornography use compared to the women who engaged in adult-only pornography, Fisher-Freeman-Halton Exact Test = 7.36 with p < .05.
Finally, a recent study using the same Internet-based research design but with a snowball sample of Internet respondents replicated the findings of this study in that individuals who self-reported a younger age of onset for adult pornography use were more likely to engage in deviant pornography (Seigfried-Spellar, 2013).
There is a debate in the literature regarding the effects of unwanted exposure to pornography by young children; however, few studies assess the age of intentional use of nondeviant and deviant pornography. Despite attempts of monitoring, filtering, or deleting images or websites on the Internet, nondeviant and deviant of pornography will continue to be accessible, affordable, and anonymous (c.f., Seigfried-Spellar, Bertoline, & Rogers, 2012). Growth in the number of deviant pornography users (i.e., child pornography) will only increase as the current 2.45 billion of the world’s population (35%) with Internet access continues to increase (ITU, 2011). This growth will only add importance to understanding “why” some people view, download and exchange deviant pornography when others do not. This exploratory study suggests “age of onset” for nondeviant pornography use is related to later deviant pornography use. In addition, women are engaging in child pornography, but men are still more likely to be consumers of child pornography. As suggested by Quayle and Taylor (2003), desensitization may put an individual at risk for progression from nondeviant to deviant pornography behaviors. Future research should assess whether individual differences (e.g., openness to experience, consciousness, extraversion, agreeableness, and neuroticism; see Seigfried-Spellar & Rogers, 2013) are related to this Guttman-like progression for deviant (i.e., child) pornography use.
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(1), 92-123.
Basbaum, J.P. (2010). Sentencing for possession of child pornography: A failure to distinguish voyeurs from pederasts. Hastings Law Journal, 61, 1-24.
Birnbaum, M.H. (Ed.). (2000). Psychological experiments on the internet. San Diego, CA: Academic Press.
Cavallaro, K. (2012). Revealing the Answer to Life’s Most Difficult Market Research Question: Why Don’t People Join Panels? Retrieved from Survey Sampling International’s website www.surveysampling.com
Endrass, J., Urbaniok, F., Hammermeister, L.C., Benz, C., Elbert, T., Laubacher, A., & Rossegger, A. (2009). The consumption of internet child pornography and violent and sex offending. BMC Psychiatry, 9(43), 1-7.
Flood, M. (2009). The harms of pornography exposure among children and young people. Child Abuse Review, 18, 384-400.
Freeman, G.H. & Halton, J.H. (1951). Note on exact treatment of contingency, goodness of fit and other problems of significance. Biometrika, 38, 141-149.
Hollinger, R.C. (1988). Computer hackers follow a guttman-like progression. Sociology and Social Research, 72(3). 199-200.
International Centre for Missing & Exploited Children (2010). Child pornography: Model legislation and global review. (6th ed.). Retrieved from www.icmec.org
International Telecommunication Union. (2011). The world in 2011: Facts and figures. Retrieved from www.itu.int/ict
Johansson, T. & Hammaren, N. (2007). Hegemonic masculinity and pornography: Young people’s attitudes toward and relations to pornography. Journal of Men’s Stutides, 15, 57- 70.
McKee, A. (2007). Saying you’ve been at dad’s porn books I apart of growing up: youth, pornography and education. Metro Magazine, 155, 118-112
Mitchell, K., Wolak, J., & Finkelhor, D. (2007). Trends in youth reports of sexual solicitations, harassment, and unwanted exposure to pornography on the internet. Journal of Adolescent Health, 410, 116-126.
Quayle, E. & Taylor, M. (2002). Child pornography and the internet: Perpetuating a cycle of abuse. Deviant Behavior: An Interdisciplinary Journal, 23, 331-361.
Quayle, E. & Taylor, M. (2003). Model of problematic internet use in people with a sexual interest in children. Cyberpsychology and Behavior, 6(1), 93-106.
Rogers, M. & Seigfried-Spellar, K. (2011). Internet child pornography: legal issues and investigative tactics. In T. Holt (ed.), Crime On-Line: Correlates, Causes, and Context. Durham, NC: Carolina Academic Press.
Rogers, M.K. & Seigfried-Spellar, K.C. (2012, February). Applied predictive behavioral modeling: The role of behavioral sciences in digital forensics. Presentation at the American Academy of Forensic Sciences 64th Annual Scientific Meeting, Atlanta, GA.
Seigfried, K., Lovely, R., & Rogers, M. (2008). Self-reported consumers of Internet child pornography: A psychological analysis. International Journal of Cyber Criminology, 2(1), 286-297.
Seigfried-Spellar, K.C. (in press). Measuring the Preference of Image Content for Self-Reported Consumers of Child Pornography. In M. Rogers & K. Seigfried-Spellar (Series Eds.), Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering: Digital Forensics and Computer Crime. New York: Springer. Accepted for Publication 2012.
Seigfried-Spellar, K.C. (2013, February). Replicating the Seigfried-Spellar and Rogers (2011) study on deviant pornography use by age of onset and sex. Presentation at the American Academy of Forensic Sciences 65th Annual Scientific Meeting, Washington, D.C.
Seigfried-Spellar, K.C. (2011). The role of individual differences in predicting the type of images collected by internet child pornography consumers. Unpublished Dissertation, Purdue University, West Lafayette, IN.
Seigfried-Spellar, K.C., Bertoline, G.R., & Rogers, M.K. (2012). Internet child pornography, U.S. sentencing guidelines, and the role of internet service providers. In P. Gladyshev & M. Rogers (Series Eds.), Lecture Notes of the Institute for Computer sciences, Social- Informatics and Telecommunications Engineering: Vol. 88. Digital forensics and cyber crime (pp. 17-32).
Seigfried-Spellar, K. & Rogers, M. (2010). Low neuroticism and high hedonistic traits for female Internet child pornography consumers. Cyberpsychology, Behavior, and Social Networking, 13(6), 629-635.
Seigfried-Spellar, K.C. & Rogers, M.K. (2013). Discriminating self-reported internet child pornography users by individual differences. Manuscript submitted for publication.
Sheldon, K. & Howitt, D. (2008). Sexual fantasy in paedophile offenders: Can any model explain satisfactorily new findings from a study of Internet and contact sexual offenders? Legal and Criminological Psychology, 13, 137-158.
Survey Sampling International (2009). Verification in the Online World. Retrieved from Survey Sampling International’s Web site: http://www.surveysampling.com
Tabachnick, B. G. & Fidell, L. S. (2007). Using Multivariate Statistics (5 ed.). Boston, MA: Pearson Education, Inc.
United States Census Bureau (2009a). State and County Quick Facts. Retrieved from United States Census Bureau’s website: www.quickfacts.census.gov
United States Census Bureau (2009b). Computer and Internet Use in the United States: October 2009. Retrieved from United States Census Bureau’s website: www.census.gov
Wolak, J., Finkelhor, D, Mitchell, K., & Ybarra, M. (2008). Online “predators” and their victims: Myths, realities, and implication for prevention and treatment. American Psychologist, 63(2), 111-128.
Reprinted from Computers in Human Behavior 29 (2013) 1997–2003, Kathryn C. Seigfried-Spellar, Marcus K. Rogers, “Does deviant pornography use follow a Guttman-like progression?”, with permission from Elsevier.
1 thought on “Does Deviant Pornography Use Follow A Guttman-Like Progression?”