International Journal of Cyber Criminology

 Vol 2 Issue 2

Copyright © 2008 International Journal of Cyber Criminology (IJCC) ISSN: 0974 – 2891 July-December 2008, Vol 2 (2): 346–367

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Identifying Potential Factors of Adolescent Online Victimization for High School Seniors

 

Catherine D. Marcum[1]

Georgia Southern University, USA

 

Abstract

The purpose of this study was to investigate previous Internet usage in a sample of college freshmen, and to reflect on their experiences with online victimization, through variables representing the three constructs of Routine Activities Theory. A survey was administered to 100-level courses at a mid-sized university in the Northeast, which questioned respondents on their Internet behaviors and experiences with victimization during the high school senior period.  The findings of the study indicated that participating in behaviors that increased exposure to motivated offenders and target suitability in turn increased the likelihood of the three types of victimization measured.  Conversely, taking protective measures against victimization to improve capable guardianship generally did not decrease the likelihood of victimization. This research provides a significant contribution to the literature as there are few explanatory studies that attempt to identify reasoning for the victimization of adolescents online.

 

Keywords: Internet; Adolescents; Victimization; Routine Activities Theory; Computer;

 

Introduction

The idea of an electronic global communication system originated from J.C.R. Licklider of the Massachusetts Institute of Technology, in the early 1960s (Licklider & Clark 1962, as cited in Leiner et al. 2003). His “Galactic Network” idea entailed an internationally connected set of computers that allowed for easy accessibility to information.  Now known as the Internet, this inter-continental information highway has enabled people of all ages, especially youth, to drastically expand their social circles and improve their ability to communicate with friends and family (Roberts, Foehr, Rideout & Brodie, 1999; Rosenbaum, Altman, Brodie, Flournoy, Blendon, & Benson, 2000).  Unfortunately, young Internet users are often unable to participate in online activities without the annoyance of uninvited communication from other online users. 

Several studies on the Internet use by youth have found that increasing numbers of young people are experiencing the following types of victimization while using computer-mediated communication (CMC) methods: (a) unwanted exposure to sexual material, (b) sexual solicitation, and (c) unwanted non-sexual harassment (Mitchell et al., 2003; Mitchell, Finkelhor, & Wolak, 2007; O’Connell et al., 2002; Quayle & Taylor, 2003; Sanger et al., 2004; Wolak et al., 2002; Wolak et al., 2003; Wolak et al., 2004; Wolak et al., 2006; Wolak, Mitchell & Finkelhor, 2007; Ybarra, Mitchell, Finkelhor, & Wolak, 2007).  However, majority of these studies are descriptive in nature, thus there is a lack of rigorous research that indicates what online behaviors may increase the likelihood of victimization. 

Roncek and Maier (1991) suggested that Routine Activities Theory is excellent for the examination of predatory or exploitative crimes, which is precisely the type of deviant behavior examined in this study.  According to the Routine Activities Theory, three elements must be present in order for a crime to occur:

  • Exposure to motivated offenders,

  • A suitable target, and

  • Lack of capable guardianship (Cohen & Felson, 1979). 

The purpose of this study was to investigate Internet usage in a sample of college freshmen, and to consider their experiences with online victimization, through variables representing the three constructs of Routine Activities Theory.   The study is expected to provide significant contribution to the literature on adolescent online victimization, considering the overall lack of explanatory research on this topic. 

 

Adolescent Internet Use and Victimization

Past empirical research on adolescent Internet use has demonstrated that Internet use by youth has increased drastically in the past 10 years (Addison, 2001; Izenberg & Lieberman, 1998; Lenhart, Rainie, & Lewis, 2001; Nie & Ebring, 2000; Rainie, 2006; United States Department of Commerce, 2002).  Numerous studies have been conducted to examine the frequency and purposes of adolescent Internet use (Beebe, Asche, Harrison, & Quinlan, 2004; Lenhart, Rainie & Lewis, 2001; Mitchell, Finkelhor, & Wolak, 2003; United States Department of Commerce, 2002). Research suggests that the rate of Internet use in America is increasing, with adolescents becoming heavier users than adults (Subrahmanyam, Kraut, Greenfield, & Gross, 2001). 

The various mediums of communication available on the Internet have been a contributing factor to increased Internet use (Clemmitt, 2006; Kirkpatrick, 2006; Lamb & Johnson, 2006; Rosen, 2006; Simon, 2006; Stuzman, 2006). The mediums of communication available on the Internet, often referred to collectively as social technology (Lamb and Johnson 2006), have enabled people of all ages (especially youth) to expand their social circles and improve their ability to communicate with friends and family in an inexpensive manner (Roberts, Foeher, Rideout, & Brodie, 1999).  Social technology generally refers to computer-mediated communication (CMC) devices that connect people for personal and professional information sharing.  The use of CMC methods allows for ease in the workplace, educational setting, or home to communicate effortlessly with others (Simon 2006). Although there are numerous ways to communicate and socialize with CMCs, this study will focus on the following mediums: chat rooms, instant messaging, e-mail, and social networking websites.  Unfortunately, along with the beneficial use of these CMC methods comes the increased possibility of online victimization. 

Multiple studies have recognized that increasing numbers of young people are experiencing the victimization while using CMC methods, very few studies have attempted to explain why this is happening.  Of the few explanatory studies performed, those using data from the Youth Internet Safety Survey (respondents were between the ages of 10-17) found that use of chat rooms, discussion of sexual topics with online contacts, and a tumultuous relationship with family or friends increased the odds of online victimization (Mitchell et al., 2007; Wolak et al., 2007; Ybarra et al., 2007).  Furthermore, using data from the high school senior and college freshmen time period, Marcum (forthcoming) found that increased exposure to motivated offenders and providing personal information to online contacts also increased the likelihood of online victimization.

  More recent empirical studies examined the effect of different forms of protective measures on adolescent online victimization.  Fleming, Greentree, Cocotti-Muller, Elias and Morrison (2006) and Marcum (forthcoming) found that the installation of filtering and blocking software had no affect on their exposure to inappropriate materials and behaviors and online victimization. Lwin, Stanaland and Miyazaki (2008) further explored protective measures through a quasi-experimental study of 10 to 17 year olds in regard to their experiences with Internet monitoring and mediation by parents. They found that active Internet behavior monitoring by parents decreased the likelihood of participation in risky behaviors online, as well as exposure to inappropriate materials.  However, Lwin et al. (2008) noted that the effectiveness of active monitoring decreased the older the adolescent became, which may be a foreshadowing of the results found in the current study considering the age of the sample.

As stated before, there are few explanatory studies in the literature that attempt to assess the factors of online victimization.  The literature is anemic in regard to studies that use a strong theoretical basis to examine these online outcomes.  In the next section, a brief summary will be provided of the theoretical framework used in the present research to better investigate contributory factors that increase or decrease the likelihood of online victimization.

 

Routine Activities Theory

       Society and its activity patterns are in a constant state of transformation (Madriz, 1996), especially with the development of new technology. For example, daily activities of children have evolved from bicycles and dolls to video games and the Internet.  Rainie (2006) reported that 87% of youth are currently using the Internet, and that number is likely to grow. Yet, as innovative technologies emerge, new methods of victimization also accompany these developments (Mitchell et al., 2003; O’Connell et al., 2002; Sanger et al., 2004; Wolak et al., 2004; Wolak et al., 2006). 

Routine Activities Theory has proved itself to be useful in explaining different types of criminal victimization.  This theory states that there are three components necessary in a situation in order for a crime to occur: a suitable target, a lack of a capable guardian, and a motivated offender (Cohen & Felson, 1979).  Moreover, crime is not a random occurrence; it follows regular patterns that require these three components. 

Based on an examination of the relevant literature, Routine Activities Theory has been supported on both the macro- and micro-level (Arnold et al., 2005; Gaetz, 2004; Schreck & Fisher, 2004; Spano & Nagy, 2005; Tewksbury & Mustaine, 2000).  Although not as plentiful as micro-level research, macro-level investigations of Routine Activities Theory have revealed empirical support for the components of the theory.  In particular, lack of guardianship in areas with large amounts of traffic from non-residents having no ties to the area has shown to produce a significant effect on crime rates in neighborhoods (LaGrange, 1999; Roncek & Bell, 1981; Roncek & Maier, 1991).  Moreover, the lack of guardianship and risky lifestyles of city residents have a significant relationship with victimization (Cao & Maume, 1993; Cook, 1987; Forde & Kennedy, 1997; Sampson, 1987). An examination of countries in different continents revealed support for the theory, by demonstrating how not only a lack of guardianship, but crossing paths with a motivated offender as a suitable target, increases the likelihood of victimization (Tseloni et al., 2004).

Micro-level studies utilize individual-level data, which allows for analysis of factors that specifically apply to individuals, rather than large groups. Literature on offending behavior indicated unstructured peer interaction and lack of parental supervision, reflected a lack of guardianship that was a significant predictor of criminal offending (Bernburg & Thorlindsson, 2001; Schreck & Fisher, 2004; Sasse, 2005).  Personal and property crime victimization studies suggested a person’s routine activities, such as participating in leisure activities away from the home and other lifestyle choices, which significantly increases the likelihood of victimization (Arnold et al., 2005; Cohen & Cantor, 1980; Gaetz, 2004; Moriarty & Williams, 1996; Mustaine & Tewksbury, 1999; Spano & Nagy, 2005; Tewksbury & Mustaine, 2000; Woolredge et al., 1992).  Domain-specific models were noted to better explain routine activities in a specific environment (Mustaine & Tewksbury, 1997; Wang, 2002; Wooldredge et al., 1992).  Finally, current studies revealed that drug and alcohol consumption is a significant predictor of sexual victimization of females (Mustaine & Tewksbury, 2002; Schwartz et al., 2001).

Early tests of Routine Activities Theory, which often is used to examine different types of victimization, focused on the importance of the environment as a vital component of interaction between criminal offenders and victims (Cohen & Felson, 1979).  This is particularly relevant to the current research, as the environment, cyberspace, is a necessary factor that must be present in order to both participate in online activities and become a victim of harassment of other online crime. Cyberspace, which thrives on the possibilities of the unknown, also provides the opportunity for engaging in activities without the presence of a capable guardian. This is true for both the offender and victim, as both parties potentially can participate in deviant behaviors without guardianship present (Beebe et al., 1998; Danet, 1998; Jones, 1999). 

According to Felson (1987), lack of behavioral controls encourages willingness to participate in criminal activity, and motivated offenders will place themselves in areas that have an abundance of suitable targets.  The current study will examine how the routine activities of adolescents affect their likelihood of online victimization.

 

Methods

 

Research Design

The purpose of this study was to investigate Internet usage in a sample of freshmen enrolled in 100-level courses, as well as to consider their experiences with online victimization.  In order to fully examine the topic, the chosen methodology was developed under the concepts and propositions of Routine Activities Theory, which has been utilized many times in the past to explain various types of victimization. This study employed a survey and was anticipated to produce a more complete understanding of adolescent Internet use and victimization. 

Surveys were administered to enrolled freshmen in the spring of 2008, with a focus on their frequency and types of Internet use, and experiences with different types of Internet victimization.  It is important to note that since college freshmen were polled, they were asked to recall information from their senior year of high school.  Recalling accurate information from the past may be difficult for respondents, which in turn would affect the validity of the findings. However, since this study asked questions that limit the scope of recall (less than one year earlier), the reliability and validity of the findings generally will be greater compared to a study asking for information further in the past.

Through the administration of the survey, the three central elements of Routine Activities Theory were measured. The first element of Routine Activities Theory evaluated was exposure to motivated offenders, which occurred through the examination of independent variables representing general usage of the Internet and specific modes of computer-mediated communication (CMC).  This study asserts that general use of the Internet, including the use of various CMCs, exposes users to potential motivated offenders online as the chances of interaction between the user and the offender are reasonably high.

Students were first asked questions regarding their general usage of the Internet as high school seniors. Next, questions based on the types of activities performed online were asked, accompanied by a set of pre-selected responses. Students were asked to mark, if any, of the Internet activities they performed as a high school senior. These activities included, research, gaming, planning travel, website design, shopping, socializing with others, and/or other.  Respondents also were questioned on the type of social networking website used, if any, as a high school senior.  In general, if a particular site is inhabited by more motivated offenders compared to another site, the respondent may increase his or her chance of victimization by use of that site.

The second element of Routine Activities Theory evaluated was target suitability, which occurred through the examination of independent variables representing behaviors that indicate attractiveness as a suitable target for victimization.  Survey questions addressed this concept by asking respondents to reveal their behaviors regarding privatization of a social networking website, and personal information given to people online and posted on their social networking website. 

The final element of Routine Activities Theory assessed the lack of capable guardianship.  Independent variables represent the amount of monitoring experienced by respondents as high school seniors, and their experiences with protective measures while using the Internet.   

Frequencies for categorical independent variables and descriptive statistics for continuous independent variables in the model are presented in Tables 1 and 2.

 

Table 1: Frequencies for Categorical Variables Representing Independent Variables (N = 483)

________________________________________________________________________

Variable                                                                                N                                                             %

________________________________________________________________________

Activities performed on the Internet

        Research (n = 482)

                        No                                                             23                                                            4.8

                        Yes                                                         459                                                          95.2

        Gaming (n = 482)

                        No                                                           223                                                          46.3

                        Yes                                                         259                                                          53.7

        Planning travel (n = 482)

                        No                                                           326                                                          67.6

                        Yes                                                         156                                                          32.4

        Website design (n = 482)

                        No                                                           406                                                          84.2

                        Yes                                                           76                                                          15.8

        Shopping (n = 482)

                        No                                                           193                                                          40.0

                        Yes                                                         289                                                          60.0

        Socializing with others (n = 482)

                        No                                                             47                                                            9.8

                        Yes                                                         435                                                          90.2

        Other (n = 481)

                        No                                                           429                                                          89.2

                        Yes                                                           52                                                          10.8

Use of email (n = 482)

        No                                                                             91                                                          18.9

        Yes                                                                         391                                                          81.1

Use of instant messaging (n = 482)

        No                                                                             93                                                          19.3

        Yes                                                                         389                                                          80.7

Use of chat rooms (n = 482)

        No                                                                           442                                                          91.7

        Yes                                                                           40                                                            8.3

Use of social networking websites (n = 482)

        No                                                                             89                                                          18.5

        Yes                                                                         393                                                          81.5

Social networking website used

        MySpace (n = 480)

                        No                                                           178                                                          37.1

                        Yes                                                         302                                                          62.9        

Facebook (n = 480)

                        No                                                           180                                                          37.5

                        Yes                                                         300                                                          62.5

        Other (n = 480)

                        No                                                           464                                                          96.7

                        Yes                                                           16                                                            3.3

 

Used a non-privatized

social networking website (n = 481)

                No                                                                           244                                                          50.7                        

                Yes                                                                         237                                                          49.3

 

Information posted on

social networking website[2]

                Age (n = 481)

                                No                                                           120                                                          24.9

                                Yes                                                         361                                                          75.1

                Gender (n = 481)

                                No                                                             91                                                          18.9

                                Yes                                                         390                                                          81.1

                Descriptive characteristics  (n = 481)

                                No                                                           355                                                          73.8

                                Yes                                                         126                                                          26.2

Picture(s) of yourself (n = 481)

                                No                                                             98                                                          20.4

                                Yes                                                         383                                                          79.6

                Telephone number  (n = 481)

                                No                                                           452                                                          94.0

                                Yes                                                           29                                                            6.0

                School location  (n = 481)

                                No                                                           221                                                          45.9

                                Yes                                                         260                                                          54.1

                Extracurricular activities  (n = 481)

                                No                                                           191                                                          39.7

                                Yes                                                         290                                                          60.3

                Goals/aspirations  (n = 481)

                                No                                                           337                                                          70.1

                                Yes                                                         144                                                          29.9

                Sexual information  (n = 481)

                                No                                                           471                                                          97.9

                                Yes                                                           10                                                            2.1

                Emotional/mental distresses/problems

                (n = 481)

                                No                                                           451                                                          93.8

                                Yes                                                           30                                                            6.2

                Family conflicts (n = 481)

                                No                                                           474                                                          98.5

                                Yes                                                             7                                                            1.5

                Other  (n = 481)

                                No                                                           455                                                          94.6

                                Yes                                                           26                                                            5.4

 

Communicate with strangers online (n = 479)

                No                                                                           272                                                          56.8

                Yes                                                                         207                                                          43.2

 

Personal information to others (n = 482)

                No                                                                           382                                                          79.3

                Yes                                                                         100                                                          20.7

 

Information given to person(s) online[3]           

Age (n = 482)                                       

                                No                                                           381                                                          79.0

                                Yes                                                         101                                                          21.0

                Gender (n = 482)

                                No                                                           378                                                          78.4

                                Yes                                                         104                                                          21.6

                Descriptive characteristics  (n = 482)

                                No                                                           425                                                          88.2

                                Yes                                                           57                                                          11.8

Picture(s) of yourself (n = 482)

                                No                                                           422                                                          87.6

                                Yes                                                           60                                                          12.4

                Telephone number  (n = 482)

                                No                                                           444                                                          92.1

                                Yes                                                           38                                                            7.9

                School location  (n = 482)

                                No                                                           439                                                          91.1

                                Yes                                                           43                                                            8.9

                Extracurricular activities  (n = 482)

                                No                                                           416                                                          86.3

                                Yes                                                           66                                                          13.7

                Goals/aspirations  (n = 482)

                                No                                                           439                                                          91.1

                                Yes                                                           43                                                            8.9

Sexual information  (n = 483)

                                No                                                           468                                                          97.1

                                Yes                                                           15                                                            3.1

                Emotional/mental distresses/problems

                (n = 482)

                                No                                                           467                                                          96.9

                                Yes                                                           15                                                            3.1

Family conflicts (n = 482)

                                No                                                           467                                                          96.9

                                Yes                                                           15                                                            3.1

                Other  (n = 482)

                                No                                                           480                                                          99.6

                                Yes                                                             2                                                            0.4

 

Location of computer use

                Home (n = 481)

                                No                                                             34                                                             7.1

                                Yes                                                         447                                                          92.9

Living room/family room

(n =  475)

                                                No                                           281                                                          59.2

                                                Yes                                         194                                                          40.8

                                Your bedroom (n = 475)

                                                No                                           320                                                          67.4       

Yes                                         155                                                          32.6

                                Parent/guardian’s bedroom

(n = 475)

                                                No                                           467                                                          98.3

Yes                                             8                                                              1.7

                                Other room (n = 475)

                                                No                                           394                                                            82.9

Yes                                           81                                                            17.1

                School computer lab (n = 480)

                                No                                                           458                                                            95.4

                                Yes                                                           22                                                              4.6

                Friend’s home (n = 480)

                                No                                                           474                                                            98.8

                                Yes                                                             6                                                              1.3

Coffee shop (n = 480)

                                No                                                           480                                                          100.0

                                Yes                                                             0                                                             0.0

                Other (n = 480)

                                No                                                           472                                                            98.3

                                Yes                                                             8                                                              1.7

                               

In same room

                Parent/Guardian (n = 481)

                                No                                                           256                                                            53.2

                                Yes                                                         225                                                            46.8

Friend (n = 481)

                                No                                                           223                                                            46.4

                                Yes                                                         258                                                            53.6

                Teacher/Counselor  (n = 481)

                                No                                                           415                                                            86.3

                                Yes                                                           66                                                            13.7

Sibling (n = 481)

                                No                                                           258                                                            53.6

                                Yes                                                         223                                                            46.4

Someone else (n = 481)

                                No                                                           429                                                            89.2

                                Yes                                                           52                                                            10.8

                No one (n = 481)

                                No                                                           275                                                            57.2

                                Yes                                                         206                                                            42.8

 

Restrictions online

                Time spent online (n = 480)

                                No                                                           404                                                            84.2

                                Yes                                                           76                                                            15.8

                Viewing of adult websites (n = 480)

                                No                                                           309                                                            64.4

                                Yes                                                         171                                                            35.6

Use of CMCs (n = 480)

                                No                                                           453                                                            94.4                      

                                Yes                                                           27                                                              5.6

                Other (n = 480)

                                No                                                           467                                                            97.3

                                Yes                                                           13                                                              2.7

                No restrictions (n = 480)

                                No                                                           216                                                            45.0

                                Yes                                                         264                                                            55.0

 

No active monitoring (n = 478)

                No                                                                           184                                                             38.5     

                Yes                                                                         294                                                             61.5

Active monitoring (n = 478)

                No                                                                           411                                                            86.0

                Yes                                                                           67                                                            14.0

Unsure of active monitoring (n = 478)

                No                                                                           361                                                            75.5

                Yes                                                                         117                                                            24.5

No filtering/blocking software (n = 478)

                No                                                                           291                                                            60.9                      

                Yes                                                                         187                                                            39.1

 

Filtering/blocking software (n = 478)               

No                                                                           239                                                            50.0

                Yes                                                                         239                                                            50.0

Unsure of filtering/blocking software (n = 478)

                No                                                                           426                                                            89.1

                Yes                                                                           52                                                            10.9

 

________________________________________________________________________

 

Table 2: Descriptive Statistics for Recoded Continuous Variables Representing Exposure to Motivated Offenders (N = 483)

__________________________________________________________________________________

Variable                                Minimum                              Maximum              Mean      Standard Deviation             

__________________________________________________________________________________

Hours per week on the

Internet (n = 479)                                  0                              35                            15.14                         8.97

 

Hours per week of

use of email (n = 479)                           0                              4                                1.29                         1.06      

 

Hours per week of

use of instant messaging 

(n = 480)                                                 0                              15                              4.39                         4.09

 

Hours per week of

use of chat rooms  (n = 480)                0                                1                              0.08                         0.27

 

Hours per week of use of

social networking websites

(n = 477)                                                 0                              15                              4.05                         3.79

______________________________________________________________________________________

 

Three dependent variables were examined in this particular study.  Respondents were asked if, during their high school senior year, they had received the following from a person online: sexually explicit material (e.g., pornography), non-sexual harassment (e.g., unwanted emails, instant messages) and sexual solicitation (e.g., request for either online or offline sexual interaction).  Dependent variables for this study were measured as dichotomous variables.  Frequencies for categorical dependent variables are presented in Table 3.

 

Table 3: Frequencies for Categorical Variables Representing Dependent Variables (N = 483)

______________________________________________________________________________________

Variable                                                                                 N                                                             %

______________________________________________________________________________________

 

Received unwanted sexually explicit material                 

(n = 473)                                                                

No                                                                           365                                                          77.2

Yes                                                                         108                                                          22.8

 

Received harassment in non-sexual manner

(n = 468)

                No                                                                           324                                                          69.2

                Yes                                                                         144                                                          30.8

Received solicitation for sex (n = 470)             

                No                                                                           425                                                          90.4

                Yes                                                                           45                                                            9.6

______________________________________________________________________________________

 

Sample

The population for the present research included all freshmen enrolled in 100-level course at a mid-sized university in the northeast during the spring 2008 academic term.  In order to obtain a representative sample of freshmen, a sampling frame of all 100-level courses potentially available to freshmen at the main campus in spring 2008, along with the respective sections available for each course.  Course sections were randomly selected and permission was requested from the professor of the course to administer the survey to the class of students.  This process continued until a sample of 483 freshmen (out of the 744 surveys collected) was collected for analysis.

In regard to the demographics of the sample, approximately 40% of the respondents were male. This is comparable to the entire freshman population at this university (42.6% male).  Also, much like the freshmen population, the majority of the sample (83.7%) was white non-Hispanic.  Finally, 51.3% of the sample was 18 years old and the remaining members were 19 years old (this information was not available for the population).

  

Analysis

Data obtained through administration of the survey was analyzed in different manners through various techniques. Since the dependent variables initially were measured as a dichotomy, logistic regression models were used to assess relationships between the independent variables and the likelihood of victimization. Due to the large number of independent variables measured in this study, stepwise logistic regression was utilized to determine the appropriate variables to assess in the models. In multivariate analysis, some variables can have a statistically significant effect only when another variable is controlled, which is called a suppressor effect (Agresti & Finlay, 1997). As a result, backward elimination was selected as the method of stepwise regression, whereby all possible variables are initially contained in the model, and there is less risk of ruling out variables involved in suppressor effects (Menard, 2002). 

Another step taken to enhance the discovery of potential relationships was to relax the p < .05 criterion for retention of variables in the models.  Bendel and Afifi (1977) asserted that p < .05 is too low and further recommended that the criterion for retention in the stepwise model be set at .15 or .20, so important variables are not excluded.  The criterion for retention of variables in this study was set at .20, to better reveal any possible statistically significant relationships.  Furthermore, linear probability models first were utilized to identify any possible problems with multi-collinearity, through the use of tolerance statistics and variance inflation factors.  These factors were found to be normal and therefore were not an issue in this study.

 

Results

Table 4 presents the logistic regression estimates for the dependent variable “receipt of sexually explicit material.” The high school senior time period model was shown to explain a range of 12.3% to 18.3% of the variation in the dependent variable. Respondents who shopped online (Shop) and those who used chat rooms one or more hours per week (ChatHour) were over two times more likely to be victimized, and those who provided various types of information to online contacts also were more likely to receive sexual material. In addition, two control variables emerged as significant predictors. First, respondents who were white (White) were less likely than minorities to receive sexually explicit material online (b = -.750, p < .05). Second, respondents whose parents more often took away privileges (Privileges) during the high school senior time period were more likely to be victimized (b = .142 p < .01).  The temporal ordering of the latter relationship may be important to consider, as it is possible that when respondents received sexually explicit material, parents then took away computer privileges.

 

Table 4. Logistic Regression Estimates for the Dependent Variable of Receipt of Sexually Explicit Material (N = 483)

____________________________________

Variable                  B(SE)                      Exp(B)                   

____________________________________

 

Travel                     -.456(.269)              .634                        

Design                   ..477(.305)              1.611      

Shop                       .812(.263)               2.253** 

OtherActivity       ..592(.357)              1.808                      

ChatHour               .774(.393)               2.169*                    

ProvidedInfo         .107(.046)               1.113*    

ParInRm                 .446(.237)               1.562                      

OthInRm                .489(.357)               1.630                      

RestrictTime          .498(.303)               1.645                      

Sex                          -.436(.242)              .646

White                     -.750(.297)              .472*      

GPA                        -.222(.109)              .801

Privileges               .142(.043)               1.153**

Constant                -1.464(.440)            .231**

_____________________________________

 

-2 Log-likelihood                  467.669                                                  

Model Chi-Square                62.651                                    

Cox & Snell R2                      .123                                                        

Nagelkerke R2                       .183                                                        

______________________________________

p < .05

** p < .01

*** p < .001

 

Table 5 presents the logistic regression estimates of the dependent variable “receipt of non-sexual harassment” during the high school time period. The variables retained at the .20 level were shown to explain 15.7% to 21.9% of the variation in the dependent variable during the college freshman time period model. Socializing online (Social) continued to increase the likelihood of non-sexual harassment (b = 1.537, p < .05). Furthermore, hours per week spent using email (EmHours) now emerged as a variable that significantly increased the likelihood of this type of victimization (b = .232, p < .05).  Providing various types of personal information to online contacts (Provided Info) was the most statistically significant predictor of non-sexual harassment (b = .178, p < .001).  Finally, the only significant control variable in the model was placing an importance on succeeding in school.  Respondents who had a stronger desire to succeed in school (Succeed) were less likely to receive non-sexual harassment (b = -.184, p < .05).

 

Table 5. Logistic Regression Estimates for the Dependent Variable of Receipt of Non-Sexual Harassment (N = 483)

______________________________________

Variable                  B(SE)                      Exp(B)                   

________________________________

Shop                       .344(.221)               1.410                      

Social                     1.537(.631)             4.651*                                    

EmHours                .232(.111)               1.261*                    

IMHours                ..048(.028)              1.049                      

ProvidedInfo         .178(.043)               1.195***               

LivRm                     -.391(.221)              .677                        

SchLab                   -1.420(.809)            .242                        

RestrictCMC         .767(.459)               2.152                      

DKActMon           -.331(.256)              .718                        

Sex                          .381(.240)               1.464                                                                         

GPA                        -.181(.112)              .835                        

Grades                    .154(.089)               1.167                      

Succeed                 -.184(.075)              .832*

Constant                -2.707(.872)            .067**

____________________________________

 

-2 Log-likelihood                  525.572                                                  

Model Chi-Square                82.029***                                             

Cox & Snell R2                      .157                                                        

Nagelkerke R2                       .219                                                        

____________________________________

* p < .05

** p < .01

*** p < .001

 

Finally, the full logistic regression model examining the last type of victimization analyzed in this study, receipt of sexual solicitation, is presented in Table 6.  Variables retained at the .20 level were shown to explain 15.4% to 30.0%[4] of the variation in the dependent variable.  Two independent variables were statistically significant predictors, along with three control variables.  Providing personal information to online contacts (ProvidedInfo) had the most highly significant impact on this type of victimization, as it increased the likelihood of receipt of sexual solicitation by approximately 38% [Exp(B) = 1.377] for each type of information provided.  Main use of the Internet in locations noted as “Other Place” (OthPl) (i.e., not in the parent’s or friend’s home, or school computer lab) also significantly increased this likelihood (b = 2.196, p < .05).  With regard to the control variables, respondents who reported they could share thoughts and feelings with friends (ShareFriends) (b = -.228, p < .05) and those who had greater respect for their teachers (RespectTeachers) (b = -.214, p < .05) were significantly less likely to receive sexual solicitation online. In addition, a respondent whose parents more often took away privileges (Privileges) was more likely to receive sexual solicitation online (b = .143, p < .05).  Again, this could indicate that privileges were removed as a result of inappropriate online behaviors, or that conflict with parents actually influenced the likelihood that a respondent would be in a position to receive sexual solicitation online.

 

 Table 6. Logistic Regression Estimates for the Dependent Variable of Receipt of Sexual Solicitation (N = 483)

______________________________________

Variable                  B(SE)                      Exp(B)                                                   

______________________________________

 

Gaming                   -.567(.343)              .567                        

Social                     1.366(1.089)           3.919                      

SNWHours           .081(.044)               1.085                      

ProvidedInfo         .320(.055)               1.377***               

OthPl                      2.196(.949)             8.988*                    

FriInRm                  .605(.349)               1.831                      

SibInRm                  -.630(.347)              .532                        

RestrictTime          -.761(.511)              .467                        

ShareFriends         -.228(.089)              .796*                      

RespectParents    .203(.125)               1.225      

RespectTeachers -.214(.098)              .080*                      

Succeed                 .146(.082)               1.157                      

Nag                         -.122(.072)              .885

Privileges               .143(.071)               1.153*

Constant                 -3.214(1.484)          .040*

____________________________________

 

-2 Log-likelihood                  265.676                                                  

Model Chi-Square                80.393***                                             

Cox & Snell R2                      .154                                                        

Nagelkerke R2                       .300                                                        

____________________________________

* p < .05

** p < .01

*** p < .001

 

Discussion and Conclusion

 

Daily use of the Internet is a customary behavior for so many Americans, whether it is for socialization, research, or various other activities. Considering the idea for the Internet was not conceived until 1962 (Leiner et al., 2003) and just became a familiar facet of businesses and homes in the early 1990s (Sanger et al., 2004), this new commodity of communication has become a prevalent mainstay in American homes. Due to its easy accessibility and availability, the frequency of Internet use has increased in all age groups; however, Internet use by adolescents has had the largest increase of use compared to any other age group (Addison, 2001). 

Today’s adolescents have grown up using the Internet, and in turn they are extremely familiar with the multiple opportunities of use available online.  Youth are especially involved in online socialization with various methods of computer-mediated communication (CMC), such as email, chat rooms, instant messaging, and social networking websites. Moreover, not only are more adolescents using the Internet to socialize, they are also spending more time online (Izenberg & Lieberman, 1998; Nie & Erbring, 2000; United States Department of Commerce, 2002).  Unfortunately, while the use of CMCs can produce positive interaction and develop enjoyable relationships for its users, these young people spending extensive amounts of time online are also placing themselves at risk for an increased likelihood of victimization.

The purpose of this study was to further investigate past Internet usage in a sample of college freshmen, as well as to consider their experiences with online victimization.  In order to more fully examine this topic area, the chosen methodology was developed under the concepts and propositions of Routine Activities Theory, which has been utilized many times in the past to explain various types of victimization.  As few studies have attempted to provide an explanation for adolescent online victimization, this study employed a survey utilizing a theoretical basis and was anticipated to produce a more complete understanding of adolescent Internet use and victimization. 

Examination of the data showed that behaviors that increased exposure to motivated offenders had a sizeable impact on the likelihood of victimization. Consistent with the findings of Wolak et al. (2007), respondents in this study reported that participation in certain activities while online, and amplified used of CMCs, increased the likelihood of victimization through receipt of sexual material as well as non-sexual harassment.  These results, which indicated that exposure to motivated offenders increased a person’s likelihood to experience victimization, are also consistent with previous victimization research using Routine Activities Theory. 

The examination of the data also showed that behaviors that increased target suitability had a large impact on the likelihood of victimization. In fact, participating in behaviors that increased target suitability was shown to have the largest affect on dependent variables. Supporting findings by Mitchell et al. (2007), this study indicated that communicating with people online and providing personal information to online contacts increased the likelihood of all three types of victimization measured in the study for respondents during the high school senior time period.          

These findings were analogous with previous studies examining victimization through Routine Activities Theory. Multiple studies have found that decreasing a person’s target suitability in turn decreases his or her likelihood of becoming a victim of crime (Felson, 1986; Horney, et al., 1995; Schreck & Fisher, 2004).  For example, Arnold et al. (2005) discovered that if the main activities of respondent involve drinking and other leisure activities, their level of target suitability is increased and in turn, they are more likely to be a victim of crime.  Moreover, Wang (2002), during his examination of causal factors associated with bank robberies, determined that banks who presented themselves as suitable targets (i.e., excessive amounts of cash and located close to a major highway) were more likely to be robbed.

Unlike the other two constructs of Routine Activities Theory, protective measures taken during Internet use (measured under the theoretical construct of lack of capable guardianship) had minimal affect on the dependent variables measured in the study. In regard to measures examining lack of capable guardianship, findings from this study indicated that protective software had no significant effect on victimization for survey respondents. Contrary to what was expected in the findings, the use of filtering and blocking software did not appear to decrease victimization for the respondents.  Conversely, some respondents who were unsure if the software was present were more likely to be victimized; in other words, although there was a possibility that software was present to filter unwanted materials, respondents were still more likely to receive some type of sexual material.

Little support was found supporting that online restriction given to respondents would decrease the likelihood of victimization online, as only one type of restriction (viewing of adult websites) had a statistically significant effect on the victimization.  High school seniors who had this type of restriction were less likely to be victimized online, while college freshmen were not affected. Furthermore, little support was found for the expectation that adolescents who were monitored while online were less likely to be victimized. 

The findings of this study were not similar to previous studies examining victimization through Routine Activities Theory, as past research revealed that uses of protective measures, which decreased lack of capable guardianship, decreased the likelihood of victimization (Cao & Maume, 1993; Cook, 1987; Sampson, 1987).  In regard to the Wang (2002) study mentioned previously, he discovered that banks that increased security and had armed security guards were less likely to be robbed.  Tseloni et al. (2004), who used data from the British Crime Survey to examine victimization through burglary, found further support.  He discovered that single parent families were more likely to have their homes burglarized due to a lack of guardianship.

 

Policy Suggestions

The findings of this study indicated that respondents who spent an increased amount of time using the Internet and specific CMCs (in turn exposing their likelihood of encountering a motivated offender) were more likely to be victimized.  Nevertheless, it would be futile to attempt to develop prevention programs that encouraged youth to reduce their use of the Internet.  Use of the Internet is often necessary for educational purposes, and many youth use the Internet to socialize and connect with others. In fact, after the administration of the first Youth Internet Safety Survey, Wolak et al. (2002) determined that over half of the youth (55%) examined reported the use of chat rooms, instant messages, and e-mail to communicate with people they had never met, with the hopes of forming relationships. Rather than encouraging youth to stop socializing on the Internet, it would be more effective to educate youth on the dangers present online so they are aware of the potential for victimization. 

Adolescents using the Internet should be educated to only participate in online communication with people they know and trust.  Many of the respondents in this study reported that they communicated with and provided personal information to people they met online, as well as participated in offline relationships with these online contacts. In other words, these youth were revealing personal information to complete strangers (people who may intend to prey upon a vulnerable population) and were likely to continue the virtual relationship offline through various modes of communication, often in person. Although none of the respondents in this study reported participating in unwilling sexual relationships with people met online, past research has shown that there are adolescents who are physically victimized by contacts met online (Kendall, 1998; Tarbox, 2000).  If adolescents limit their online communication to people they know, the risk of offline victimization should be lower.

With limited past research available, this study sought to generate greater understanding about the relationships between Internet behaviors and activities (representing the three constructs of Routine Activities Theory) and online victimization and relationship formation. Providing personal information to online contacts and communicating with people met online (variables representing the theoretical construct of target suitability) were the strongest and most consistent predictors of online victimization.  Moreover, use of certain CMCs (variables representing the theoretical construct of exposure to motivated offenders) also was shown to predict certain types of victimization. However, variables representing the third construct of Routine Activities Theory, lack of capable guardianship, were not shown to be strong or consistent predictors of online victimization of youth. 

From the knowledge gained through this study, hopefully more effective policies and programs can be developed to educate youth and families about protecting themselves while online. Youth should be aware of who they are conversing with online and refrain from providing any type of personal information to people they do not know and trust. No matter what the preferred solution by parents is, the reality is that as children get older and become more independent, they become more technologically savvy and therefore are able to participate in online communication without the watchful eye of a parent or guardian. We as adults have the responsibility to educate youth that predators come in many forms, not just the stereotypical “creepy old man” preying on little children on the playground. This especially is true on the Internet where multiple identities can be created and used to prey on young online users.  The main goal we should have is not to create paranoia, but rather intelligent awareness.

Finally, there is ample of opportunity for future research in this area. Surveying a wider age range of adolescents, as well as those in different geographical areas, would add to the knowledge base.  Also, further investigation of the use of social networking websites and the offending behaviors of adolescents, as well as their familiarity with deceptive Internet practices, will advance our knowledge of the online behaviors and experiences of adolescents. With this knowledge, better protective measures and policies can be developed to keep adolescents safe online.

 

Limitations of the study

A sample of adolescents was chosen for this study because past research has shown that youth between the ages of 12 to 17 years old are at a high risk for online victimization (Mitchell, Finkelhor, & Wolak, 2003; O’Connell, Barrow, & Sange, 2002; Sanger, Long, Ritzman, Stofer, & Davis, 2004; Wolak, Finkelhor, & Mitchell, 2004; Wolak, Mitchell, & Finkelhor, 2002; Wolak, Mitchell, & Finkelhor, 2006). The ideal sample for this particular study would include respondents who fall into this age group.  However, based on human subject issues that would have been encountered while trying to survey this group, college students who were legally able to participate in research (without parental consent) were chosen. The sample included adolescents ages 18 and 19 years old and was lacking the inclusion of younger adolescents.   

A second limitation regarding the representativeness of this sample is based on the geographical area from which the sample was drawn.  The mid-sized university in the northeast is located in a rural area, and many of its students originate from surrounding rural areas. This limited the number of students from urban and suburban settings in the sample, thereby decreasing the general ability of the findings.  In comparison, the YISS-1 and YISS-2 utilized a nationally representative survey by collecting data from adolescents throughout the United States, making the results more generalized. Nonetheless, since this is one of the few explanatory studies performed in this topic area, issues of recall and geographical location did not prevent a significant contribution from being made to the knowledge and understanding of potential causes of adolescent online victimization.

A final issue involved the wording of survey items, specifically pertaining to the measurement of persons in the room with the respondent during Internet use. The variable representing having a person in the room designated as “Other” during Internet use was shown to be a significant predictor. However, a qualitative response to elaborate on the identity of the person designated as “Other” was not available in the survey. Since this was shown to be a significant independent variable, it should be investigated further in the future.

 

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_____________________________________________________________________________________
 


[1] Assistant Professor, Department of Political Science, Georgia Southern University, PO Box 8101, Statesboro, GA 30460, United States of America Email: cmarcum@georgiasouthern.edu

 [2] Independent variables listed as “Information posted on social networking websites” were combined into one variables termed “SNWInfo” for statistical analysis.

[3] Independent variables listed as “Information given to person(s) online” were combined into one variables termed “ProvidedInfo” for statistical analysis.

[4] There is a notable spread between the Cox & Snell and Nagelkerke R2 in this model.  After careful evaluation of the model, the author believes the reason for this spread is the low amount of respondents who experienced this dependent variable (n = 45) compared to the total sample.