Unit 1 discussion

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Where can you find the most important information in the article provided? Reading through how was the study conducted? What are the strengths and weaknesses associated with this study? What did the study find?  CJ 4600_Article Assignment A.pdf  

CJ4600 Unit 1

Topic Selection and Digesting Scholarly Material

Introduction

In Unit 1 and through out this senior seminar course, we will discuss becoming a good consumer of research and literature. The ability to digest scholarly research is a helpful tool, especially for students interested in continuing their education beyond an undergraduate degree. In order to do so, we will review techniques that are useful in breaking down the anatomy of an article and understand the focal point of the article. We will put these skills to the test when we use scholarly research to build a literature review, which in turn, will be used to complete the final project. The final project will require students to brainstorm and come up with an idea to solve a current criminal justice issue. The topic selected for Unit 1’s assignment will be the topic of the final paper. In selecting a topic, it is recommended that we select something of interest because once the topic has been approved it cannot be changed since it will be used over the course to build different steps into the final project. It is highly recommended when selecting a topic that time is spent reviewing the options available in crimesolutions.gov. Picking a topic of interest will aid in the writing process sine it is typically easier to write about something that sparks interest. The topic selection is a choice and will not be assigned.

Anatomy of an article

When reading an article, although the entire article is of importance, there are certain parts that need to become the focal point. It is recommended to first skim through the abstract of the article. The abstract is the first thing available on any scholarly type article. The abstract is there to provide a snapshot of what the article addresses, and the outcome of the study. From the abstract, we can figure out what type of article it is: a review of the literature, a meta- analysis, or an empirical article. An empirical article is not only scholarly, in that it has been peer-reviewed, it also involves an actual study. For purposes of this course, in building of the literature review only articles that conduct an actual study can be used. An article must be scholarly first, and if we remember back to research methods, an article is considered scholarly if it went through a peer review process, the type of publication, the authors of the paper, and the journal in which the article appears. After the scholarly onus is met, then the article must meet the requirement of being empirical. Please note, that although literature review, meta-analyses, and ethnographies are scholarly they do not contain actual studies that either test a theory or involve a quasi-experimental study.

The introduction covers an overview of supporting evidence for the study, along with the hook of what makes the study needed or different from existing research. After the introduction, a literature review will follow. Literature reviews are usually broken down chronologically, or organically by topic. For instance, if we were writing a literature review about Routine Activities Theory by Cohen and Felson, our literature review would need to show support for the components of the theory. The literature review could be writing and contain headings such as, Hawley’s theory of humans following a pattern, target suitability, motivated offenders, and lack of guardianship. Alternatively, the literature review could be put together chronologically starting with the inception of the theory in 1979, then the work of the Brantinghams and target hardening, followed by Felson’s additional research on the theory, and other researchers who have expanded upon the theory. As long as the literature review flows and is easy to follow, it can be put together either way as long as it retains clarity. The introduction and literature review portion is very important, however, for the purposes of this class and as a preemptive measure to prevent plagiarism, please only skim through these sections.

Articles where an actual study was conducted will also contain a section dedicated to methodology and sampling. These sections are very important, and we should pay very close attention to them. In these sections, the researcher(s) explain what research questions and/or hypotheses are being explored. Additionally, they will identify how the data was collected and the specific methodology employed. Furthermore, if a secondary data source was utilized it will be indicated in this section. This is also the section where the variables are identified. This section of the article will identify the concepts and explain how they were operationalized. Thinking back to methods, operationalization answers how it was measured, and is the step that converts a concept into a variable. Again, if we remember back to research methods there are two variables that we are extremely interested in: the independent and dependent variables. Our independent variables, or the predictor variables, which goes by the call sign X, and our dependent variable, or the outcome variable, which goes by the call sign y, are being tested to see how they may be linked in a particular study. The researcher (s) is interested to see if a correlation exists between variables.

Also remember back to methods and the charge that correlation does not mean causation.

Following the methodology section, usually, is the data analysis section. This section contains information pertaining to the types of variables and the associated tests run with the data. In our combination of methods and statistics, we know that the type of data drives the statistical test that can be run. For instance, most regression models cannot be run on dichotomous data. Accompanying the statistical analysis are the results from the actual study and charts illustrating the findings. The charts are very helpful in showing us a picture of what was found, and the statistical significance if any. Beyond the analysis section, are the discussion, and conclusion. In the discussion portion of the article the researcher (s) will discuss any limitations encountered during the study and the strengths and weaknesses. In the conclusion, the results are applied and it is used to discuss policy and future research initiatives. Over the next few weeks, we will be discussing evaluations of current programming and the strengths and weaknesses of those programs. Additionally, we will discuss the different limitations that impact studies. For instance, a limitation of a study could be that the program was only tested on male volunteers and the researcher is unsure if the same program would have a similar outcome with female volunteers.

For purposes of this course, the most important sections are the abstract, the methodology section, the results, the discussion and the conclusion. All of the information needed to populate the charts for the literature build will be found in these specific sections.

Writing a Literature Review

The anatomy of an article provided the foundation and acts as a precursor to writing a literature review. In order to prepare a successful literature review we should be mindful of a few things. First, it is best to gather all of the information and scholarly resources prior to writing the literature review. For purposes of this course, we should not entertain writing the literature review prior to receiving feedback on the literature review build. When reading the fifteen scholarly resources that will be used for the literature review, be mindful of themes that emerge, these themes will later serve as sub-headings for the literature review. Next, it is important to check that the resources being used are available through the Kean University databases (ex. EBSCOhost and ProQuest) and they are saved or downloaded for future reference. It is very difficult to write about an article without being able to access the full text of the article. Thirdly, keep track of important information and finally be certain to keep the reference for appropriate APA citation.

After the topic has been selected, and the literature build has been completed it will be time to start on writing the actual literature review. There are many techniques that can be used in providing a summary of the information. Certain students have found it best to create a PowerPoint and dedicate a slide an article then the summaries can be rearranged if needed. Another suggestion is to dedicate an index card per article and manually sort the index cards until the sub-headings develop organically or chronologically. Beyond providing a summary of the article, they will be used to provide support in the final paper to illustrate prior framework. The best way to support a statement is to first acknowledge both sides of an argument, and find strong supporting evidence. In review, the way to determine if the supporting evidence is strong is to see what type of journal was published, who the authors are, and how many times the article has been cited by other researchers.

The aim of this specific literature review is to identify common themes and/or variables in prior research on your specific question. However, be mindful of use of direct quotes. Quotes should not be used and if they are used, extremely sparingly. Please remember you are not the first person to engage in research on this question. You should pay particular attention to the findings of the articles. Additionally, think of evaluating and comparing prior research on your topic, and identifying implications and limitations of prior work. Finally, consider what aspects of your research question have not been fully explored yet. When you write your literature review you will be addressing these questions. Furthermore, we will explore comparing and contrasting studies. How do the different studies relate to one another? Which studies are stronger, and why (e.g. better methodology)? Which studies are weaker? What is new, different, or controversial about the various studies? How do the methodologies differ (e.g. empirical articles vs. review articles; quantitative vs. qualitative; longitudinal vs. cross-sectional designs)? While reviewing the articles keep these questions in mind, and use helpful tools to highlight or call attention to this information making it easier to revisit in the future.

(CSLO 3, CSLO4)

REFERENCES:

Cohen, L., and Felson, M. (1979) Routine Activities Theory.

Writing Literature Reviews:

An Overview

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1

Lesson Topics–Chapter 1

What Are Primary Sources?

How Much Literature Must I Cover?

Organization of the Text

Understanding the Writing Process

Start with a Self-Assessment

What Are “Primary Sources”?

First-published, original

Could be:

Empirical research report

Theoretical article

Literature review article

Anecdotal report

Report on professional practice and standards

Avoid:

Non-peer reviewed sources (popular magazines, newspapers)

Wikipedia and other un-vetted websites

Copyright©2017 Taylor & Francis Group, an informa business

How Much Literature Must I Cover?

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Where do you fit in?

Depth of coverage depends on the “specific purpose” of your review

TERM PAPER

Class level (UG vs Grad)?

Nature of class (Survey vs Focused Topic)?

Time constraints (Final paper vs mid-term)?

Instructor’s preference?

THESIS/DISSERTATION

Must be thorough, exhaustive

Consultation with thesis advisor is essential

RESEARCH ARTICLE

Usually shorter, but more focused

Must reflect current state of research

Article based on dissertation often needs to be updated

Organization of the Text

Chapters 1 – 4

Managing the Literature Search

Chapters 5 – 8

Analyzing the Relevant Literature

Chapters 9 – 11

Writing a First Draft

Chapters 12 – 13

Editing and Preparing the Finished Product

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Understanding the Writing Process

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Recognize the reason you are writing this review

Your intended audience will determine the appropriate “writer’s voice” you will adopt

Now you can prepare yourself to “manage” the search for relevant literature

Synthesize your material into a coherent and original statement, which is your first draft

You must then analyze the content of your primary sources; pull out specific details that support your topic

Start with a Self-Assessment

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Step 1: Are you familiar with the University’s electronic resources?

If “yes”, skip to Step 2

If “no”, carefully review Chapter 2 and attend Library workshops as appropriate

Step 2: Have you settled on a topic?

If “yes”, skip to Step 3

If “no”, follow the steps in Chapter 3, and consult with your instructor

Step 3: Identify the relevant literature for your review. (will be covered in Chapter 4)

Summary and Discussion

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Selecting a Topic
and Beginning
the Preliminary Search

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1

Lesson Topics—Chapters 3

Select a Topic

Hone in on the Latest Research

Organize the Search Results to Identify Gaps

Create Spreadsheet to Compile Your Notes, but Remain Flexible

Assemble the Articles Relevant to Your Topic and Compose a Topic Statement

Summary and Discussion

Select Your Topic

Choose a general topic and search the database

Using keywords, modify the search results, as appropriate

Use narrower keywords to limit results, if necessary

Use broader keywords to expand results, if you have too few results

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Hone in on the Latest Research

Try to identify the very latest available research

Look for unpublished conference papers written by established published authors

Find review articles and theoretical articles from the leading journals in your field

Identify the landmark or classic studies on your subject area

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Assemble the Articles Relevant
to Your Topic and Compose
a Topic Statement

Assemble and preview the collection of relevant sources you have identified

Compose a topic statement

Use as much prose as you need to describe your intended topic, as much as several sentences

Refine your topic based on the sources you have collected and previewed

Ask your instructor, or a fellow student, for feedback

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Organize the Search Results to Identify Gaps

Scan each article to get an overview of its contents

Group the articles by categories

When you see gaps, conduct a more focused search of the database

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Create a Spreadsheet to Compile Your Notes, but Remain Flexible

Create a spreadsheet to compile your notes

Make sure to collect complete citations for each source (for ease in developing your reference list)

If copying exact words, take extra care to ensure accuracy

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Summary and Discussion

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Low Self-Control, Deviant Peer Associations,
and Juvenile Cyberdeviance

Thomas J. Holt & Adam M. Bossler & David C. May

Received: 29 July 2010 /Accepted: 1 December 2010
# Southern Criminal Justice Association 2011

Abstract Gottfredson and Hirschi’s (1990) general theory of crime and Akers’
(1998) social learning theory have received strong empirical support for explaining
crime in both the physical and cyberworlds. Most of the studies examining
cybercrime, however, have only used college samples. In addition, the evidence on
the interaction between low self-control and deviant peer associations is mixed.
Therefore, this study examined whether low self-control and deviant peer
associations explained various forms of cyberdeviance in a youth sample. We also
tested whether associating with deviant peers mediated the effect of low self-control
on cyberdeviance as well as whether it conditioned the effect. Low self-control and
deviant peer associations were found to be related to cyberdeviance in general, as
well as piracy, harassment, online pornography, and hacking specifically. Deviant
peer associations both mediated and exacerbated the effect of low self-control on
general cyberdeviance, though these interactions were not found for the five
cyberdeviant types examined.

Keywords Cybercrime . Low self-control . Social learning . Peer offending .

Juvenile delinquency

Am J Crim Just
DOI 10.1007/s12103-011-9117-3

T. J. Holt (*)
School of Criminal Justice, Michigan State University, 434 Baker Hall, East Lansing, MI 48824, USA
e-mail: [email protected]

A. M. Bossler
Justice Studies Program, Department of Political Science, Georgia Southern University, P.O. Box
8101, Statesboro, GA 30460, USA
e-mail: [email protected]

D. C. May
Correctional & Juvenile Justice Services, Eastern Kentucky University, 521 Lancaster Avenue,
Stratton 110, Richmond, KY 40475, USA
e-mail: [email protected]

Over the last two decades, an increasing body of research has examined the problem
of cybercrime. Cybercrime refers to offenses where special knowledge of cyberspace
is used to violate the law (Furnell, 2002; Wall, 2001). Awide range of behaviors can
be facilitated or enhanced by electronic communications and the Internet, such as
harassment (Bocij, 2004; Finn, 2004; Holt & Bossler, 2009), pornography (Buzzell,
Foss, & Middleton, 2006), media piracy (e.g., Higgins, 2005), and theft (Jordan &
Taylor, 1998). Criminologists have used two primary theories to examine
cybercrime: Gottfredson and Hirschi’s (1990) general theory of crime and Akers’
(1998) social learning theory. Gottfredson and Hirschi (1990) argue that those with
low self-control tend to be impulsive, insensitive, short sighted, risk takers who are
unable to resist the opportunity to offend (Gottfredson & Hirschi, 1990). Akers’
(1998) social learning theory, however, suggests that crime is a learned behavior
stemming from peer associations that provide sources of deviant imitation,
definitions and justifications, and reinforcement for offending.

These theories offer strikingly different explanations for why individuals commit
crime, though both have received significant empirical support with both street
crimes (e.g., Akers & Jensen, 2006; Gottfredson, 2006; Pratt & Cullen, 2000; Pratt et
al., 2009; Tittle, Ward, & Grasmick, 2003) and cybercrime (e.g., Higgins, 2005,
2006; Holt, Burrus, & Bossler, 2010; Morris & Higgins, 2010; Skinner & Fream,
1997). Scholars have also examined how low self-control and deviant peer
associations interact with each other to explain both traditional offenses and
cybercrime (e.g., Gibson & Wright, 2001; Higgins, Fell, & Wilson, 2006). Though
these studies provide significant insight into how low self-control and social learning
influence the commission of cybercrime, few have considered how these relation-
ships may operate with non-university populations, particularly juvenile populations
who are thought to engage in a variety of cybercrimes (Taylor, Fritsch, Liederbach,
& Holt, 2010; Yar, 2005). As young people continuously gain access to computer
technology at earlier ages (Wolack, Mitchell, & Finkelhor, 2006), it is critical that
researchers consider the phenomena of cybercrime offending in juvenile populations
to better understand its causes and correlates.

This study addresses these issues by examining the effects of low self-control and
deviant peer associations on multiple forms of cybercrime offending in a population
of middle and high school students. In addition, we examine how associating with
deviant peers interacts with low self-control, specifically exploring mediating and
moderating effects. The findings benefit the literature on low self-control and social
learning and their ability to explain novel forms of offending in juvenile populations
as well as the scant literature on how low self-control and deviant peer associations
interact.

Understanding Cybercrime

The problem of cybercrime is diverse, encompassing a range of behaviors with
economic and emotional consequences (Taylor et al., 2010). One of the most
recognized typologies of cybercrime, developed by Wall (2001), suggests that there
are four forms of offending in virtual environments: deception/theft, pornography,
violence, and cyber-trespass. The most common type of cyber-theft committed by

Am J Crim Just

young adults and youth is digital piracy, where illegal copies of digital media are
created without the explicit permission of the copyright holder (Gopal, Sanders,
Bhattacharjee, Agrawal, & Wagner, 2004). Piracy is particularly challenging for law
enforcement because illegally copied media can be downloaded through websites
and file sharing services distributed across the Internet (Gopal et al., 2004; Hinduja,
2001). The costs of piracy are thought to be quite high, as the U. S. music industry
reports losses of over $12 billion each year due to theft (Taylor et al., 2010).

The second form of cybercrime with particular significance for juvenile
populations is cyber-pornography, as individuals under the age of 18 can easily
view and obtain sexually explicit through the World Wide Web (DiMarco, 2003;
Edleman, 2009; Lane, 2000). In fact, the adoption and popularity of various forms of
media, including DVDs, webcams, digital photography, and streaming web content,
are directly tied to the pornography industry (Lane, 2000). This may account for the
fact that one in three children were exposed to unwanted images of nude individuals
or people having sex while online in 2005 (Wolack et al., 2006).

There is a growing body of research focusing on online harassment, a form of
cyber-violence, which is common among juvenile populations. This offense can lead
victims to feel fear or distress in much the same manner as real-world stalking and
harassment (Bocij, 2004; Finn, 2004; Wall, 2001). Harassment can take a variety of
forms, including threatening or sexual messages delivered via e-mail, instant
messaging services, or posts in chat rooms. In addition, the popularity of social
networking sites like Myspace among youths allows them to post mean or cruel
messages about other people for the public to see (Hinduja & Patchin, 2008, 2009).
Although some victims may view harassing communications to be nothing more
than a nuisance, some victims report physical or emotional stress (Finn, 2004).

The final type of cybercrime noted by Wall (2001) is cyber-trespass, where
individuals utilize computers and technology to access computer systems they do not
own or legally have permission to use (Holt, 2007; Yar, 2005). This most often
involves computer hacking which is often attributed to juveniles who spend their
time exploring computer networks without authorization from the system owners
(Furnell, 2002; Yar, 2005). While media reports of hacking suggest these offenses are
often complex and involve significant financial losses (Furnell, 2002), simple forms
of hacking involve guessing passwords and accessing accounts without permission
from the system owners (Bossler & Burruss, 2010; Holt, 2007).

Low Self-Control, Deviant Peers, and Cybercrime

In light of the diverse range of offending opportunities facilitated by the Internet,
criminologists have begun to explore the ways in which traditional theories of crime
may account for these behaviors. One of the most heavily tested theories used to
examine cybercrime is Gottfredson and Hirschi’s (1990) general theory of crime.
They argue humans are rational beings who weigh the costs and benefits of potential
behavior, including crime, and act accordingly. Individuals with low self-control,
however, are impulsive, insensitive, short sighted, risk-takers who prefer simple
tasks. As a result, they are likely to choose the immediate gains of crime even
through the long-term consequences are greater. The general theory of crime has

Am J Crim Just

been tested extensively and has received strong support as one of the most
significant correlates of street crime (Gottfredson, 2006; Pratt & Cullen, 2000) and
juvenile delinquency (Brownfield & Sorenson, 1993; DeLisi & Vaughn, 2008;
Winfree, Taylor, He, & Esbensen, 2006

Low self-control has also been linked to various forms of cybercrime, including
illegal music downloading (Higgins, Wolfe, & Marcum, 2008; Hinduja & Ingram,
2008), movie piracy (Higgins et al., 2006; Higgins, Fell, & Wilson, 2007), software
piracy (Higgins, 2005, 2006; Higgins & Makin, 2004a, b; Higgins & Wilson, 2006;
Moon, McCluskey, & McCluskey, 2010), and viewing sexual material online
(Buzzell et al., 2006). Tests of self-control and computer hacking, however, have
produced mixed effects that do not fully support the general theory of crime (Bossler
& Burruss, 2010). Thus, self-control appears to have some value in explaining
individual participation for a wide variety of cybercrimes.

A competing theory that has been frequently applied to offending on and off-line
is Akers’ (1998) social learning theory. Social learning theory argues that individuals
become deviant and maintain criminal careers through a dynamic social learning
process hinging on differential associations. Individuals become exposed to deviant
definitions, models, and reinforcement based on their differences in association
patterns. Associating with deviant peers is one of the strongest correlates of crime
(Akers, 1998; Akers & Lee, 1996; Krohn, 1999; Lee, Akers, & Borg, 2004; Pratt et
al., 2009; Warr, 2002), and is a stronger predictor than low self-control for most
forms of crime (e.g., Pratt & Cullen, 2000; Pratt et al., 2009; Tittle et al., 2003; Warr,
2002). Deviant definitions, which consist of an individual’s attitudes, perceptions,
and justifications for deviance, influence a wide variety of crime and other deviant
behaviors (e.g., Akers & Jensen, 2006). In addition, associating with deviant
associates provides deviant models to imitate (Boeringer, Shehan, & Akers, 1991).
Finally, differential reinforcement, referring to the balance between past, present, and
future rewards and punishments, increases the probability of future deviant behavior
(Akers & Jensen, 2006; Akers & Lee, 1996; Lee et al., 2004; Pratt et al., 2009).

Social learning theory has significant intrinsic value for understanding the
commission of various forms of cybercrime since offenders must “learn not only
how to operate a highly technical piece of equipment but also specific procedures,
programming, and techniques for using the computer illegally” (Skinner & Fream,
1997, 498). Scholars have found consistent evidence that associating with deviant
peers is one of the strongest predictors of a wide variety of cyberdeviance (Bossler &
Burruss, 2010; Higgins, 2005; Higgins & Makin, 2004a, b; Higgins et al., 2006;
Higgins & Wilson, 2006; Higgins et al., 2007, 2008; Hinduja & Ingram, 2008; Holt
et al., 2010; Ingram & Hinduja, 2008; Morris & Higgins, 2010; Skinner & Fream,
1997). In addition, evidence also exists that the other three components of the social
learning process are related to cybercrime as individuals are more likely to commit
cybercrime if they have definitions favoring the violation of laws controlling the use
of computers and the Internet (e.g., Higgins, 2005; Higgins et al., 2006; Hinduja &
Ingram, 2008; Holt et al., 2010; Ingram & Hinduja, 2008; Morris & Higgins, 2010;
Skinner & Fream, 1997), have deviant computer models to imitate (Holt et al., 2010;
Ingram & Hinduja, 2008; Skinner & Fream, 1997), and experience positive
reinforcement supporting the violation of computer laws (Hinduja & Ingram,
2008; Holt et al., 2010; Ingram & Hinduja, 2008; Skinner & Fream, 1997).

Am J Crim Just

Since research has consistently identified that both low self-control and
deviant peer associations are strong predictors of both traditional and cybercrime
offending, emerging scholarship has explored the interactions between these two
variables. Gottfredson and Hirschi (1987: 597) provide a theoretical basis for a
mediation effect, stating that “people acquire the propensity to delinquency, find
delinquent friends, and then commit delinquent acts, including serious criminal
acts.” Research indicates that individuals with lower levels of self-control self-
select into deviant peer groups in the real world (Chapple, 2005; Evans, Cullen,
Burton, Dunaway, & Benson, 1997; Longshore, Chang, Hsieh, & Messina, 2004;
Mason & Windle, 2002) and online (e.g., Bossler & Holt, 2010; Higgins et al.,
2006; Wolfe & Higgins, 2009). In addition, some studies have found that
associating with deviant peers mediates the effect of low self-control on both
traditional deviant behavior (e.g., Chapple, 2005; Longshore et al., 2004; Mason &
Windle, 2002) and cybercrime (e.g., Bossler & Burruss, 2010; Bossler & Holt,
2010; Higgins et al., 2006).

An alternative explanation for this relationship is that criminal propensities
interact with social settings to exacerbate the impact of individual characteristics on
crime (Evans et al., 1997). It would therefore be predicted that lower levels of self-
control would be a stronger predictor of deviance for those with more deviant peers
than those with fewer deviant friends. The evidence for this conditioning effect,
however, is inconsistent in both the traditional and cybercrime research. Gibson and
Wright (2001) found that high school students with lower levels of self-control and
more exposure to coworker delinquency were at the greatest risk of occupational
offending. Higgins and colleagues extensive research examining the conditioning
effect of deviant peer association on the relationship between low self-control and
various forms of digital piracy (Higgins, 2005; Higgins et al., 2006, 2007, 2008;
Higgins & Makin, 2004a, b), suggest that low self-control might be a stronger
predictor of piracy for those with more deviant peer associations; however, z-tests
(Paternoster, Brame, Mazerolle, & Piquero, 1998) comparing regression coefficients
found no statistically significant differences Other researchers, however, have found
that the impact of low self-control became less influential as peer groups became
more delinquent (Hinduja & Ingram, 2008; Meldrum, Young, & Weerman, 2009).
Thus, it is not clear whether associating with deviant peers strengthen or weakens the
effect of low self-control on deviance.

The Present Study

There is substantial evidence that both low self-control and deviant peer associations
affect crime and delinquency in the real world and virtual environments. The
evidence is mixed, however, regarding the ability of deviant peer associations to
mediate and condition the effect of low self-control. In addition, most studies have
primarily examined the causes of computer crime and deviance within college
samples, reducing our understanding of this phenomenon in juvenile populations.
Thus, this study examined whether Gottfredson and Hirschi’s (1990) general theory
of crime and deviant peer associations explained a substantial proportion of
cyberdeviance in sample of Kentucky middle and high school students. In addition,

Am J Crim Just

we explored how low self-control and deviant peer associations interact to explain
cybercrime.

Data

The sample for this study was developed at a middle and high school, both
adjacent to a large metropolitan area in central Kentucky. These institutions
consist of mostly suburban populations, selected based on existing relationships
with the research team. One of the authors visited the schools in May 2008 to
supervise and explain the data collection process. An online survey instrument
was used where students used their school log-in accounts to access the
questionnaire that was hosted on the district server. All eighth graders were given
the opportunity to participate in the survey. In the high school, only the freshmen
class, technology classes, and business classes were provided the opportunity to
participate since each of these groups had at least one period in which they had
access to computers during the school day.

Due to missing cases and listwise regression, a total of 435 cases were analyzed from
the total sample of 518 respondents. Further analyses indicated that the missing cases did
not differ significantly on key measures from the cases analyzed. Thus, missing data did
not substantively alter the findings and conclusions. The sample represents approxi-
mately 25% of the high school population and 35% of the middle school. The sample
was 50% female and 79% White, consistent with the school’s demographics.

Measures

Dependent Variable

Cyberdeviance On a nine-point ordinal scale (0 = never; 1 = once or twice a year;
2 = once every two to 3 months; 3 = once a month; 4 = once every two to 3 weeks;
5 = once a week; 6 = two to three times a week; 7 = once a day; 8 = two to three
times per day), students were asked how many times they committed the following
acts within the previous 12 months: 1) knowingly use, make, or give to another
person “pirated” media (music, television show, or movie) (mean = 1.29; std.dev. =
2.05); 2) knowingly use, make, or give to another person a “pirated” copy of
commercially-sold computer software (mean = .51; std.dev. = 1.39); 3) go to
websites to view sexual materials on purpose (mean = .99; std. dev. = 2.08); 4) post
mean or threatening messages about another person for others to see (mean = .40;
std. dev. = .1.09); and 5) access another’s computer account or files without his/her
knowledge or permission to look at information or files (mean = .50; std. dev. =
1.30) (Rogers, 2001; Skinner & Fream, 1997; Wolack et al., 2006). Principle
components analysis with a varimax rotation indicated a one-factor solution based
on the eigenvalues and a scree discontinuity test. The responses to these five items
were standardized and averaged (alpha = .713). In order to decrease skewness and
kurtosis, the outlier with the highest score was changed to the second highest. In
addition, we took the square root of the scores plus one. The final measure ranged
from .75 to 2.00 (mean = .96; std. dev. = .28) (see Table 1).

Am J Crim Just

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Am J Crim Just

Independent Variables

Low Self-Control Low self-control was measured with the 24-item scale (coded 1 =
strongly disagree to 4 = strongly agree) created by Grasmick, Tittle, Bursik, and
Arneklev (1993) in order to better compare the results with prior studies on crime in
general and cybercrime specifically. The scores for the 24 items were summed,
creating a measure ranging from 30 to 96 (mean = 59.37; std. dev. = 12.11). Higher
scores on the scale indicate less self-control; a positive correlation was thus expected
between this measure and the dependent variables. Cronbach’s alpha for the index
was .87, indicating good reliability. Similar to previous research, principle
components analysis indicated six factors with eigenvalues over one. The scree
discontinuity test, however, revealed a one-factor solution with the largest drop
between the first and second factors.

A small number of scholars have found that there are biased items in the Grasmick et
al. scale using Rasch rating scale model analysis (Gibson, Ward, Wright, Beaver, &
Delisi, 2010; Higgins, 2007; Piquero, MacIntosh, & Hickman, 2000). Although
Gibson, Ward, Wright, Beaver, and Delisi (2010) found that their reduced scale had
similar effects on criminal behavior as the full Grasmick et al. scale, we reran all
analyses (Tables 1, 2, 3 and 4) with low self-control scales created from the Higgins
(2007) (min. 21; max. 64; mean = 40.75; std. dev. = 7.96) and Gibson et al. (2010)
(min. 19; max. 64; mean = 41.29; std. dev. = 8.38) models to test the robustness of our
findings.1 In order to compare this study with previous research, we present the
traditional Grasmick et al. scale findings in the tables and note the few significant
differences (i.e., z-tests between regression coefficients) in the text and footnotes.

Deviant Peer Association On a five-point scale (0 = none; 1 = very few; 2 = about
half; 3 = more than half; 4 = all of them), respondents were asked to assess how
many of their friends engaged in the following acts of cyberdeviance in the past
12 months: 1) pirating media; 2) pirating computer software; 3) viewing sexual
offensive materials online; 4) harassing other online; and 5) engaging in computer
hacking (Rogers, 2001; Skinner & Fream, 1997).2 Peer deviance was then computed

1 We focused on the work of Higgins (2007) and Gibson et al. (2010) because they examined the construct
validity of the original Grasmick et al. scale with four options ranging from strongly agree to strong
disagree. Piquero, MacIntosh, and Hickman (2000) tested a revised version of the Grasmick et al. scale
that had five options measuring how frequent someone acted in that fashion.
2 Pirating media, pirating software, and viewing offensive sexual materials online were measured with the
following three items respectively: 1) knowingly used, made, or gave to another person “pirated” media
(music, television show, or movie); 2) knowingly used, made, or gave to another person a “pirated” copy
of commercially-sold computer software; 3) looked at pornographic, obscene, or offensive materials
online. Peer harassment was computed by taking the average of the standardized scores for the following
four measures (alpha = .919): 1) posted or sent a message about someone for other people to see that made
that person feel bad; 2) posted or sent a message about someone for other people to see that made that
person feel threatened or worried; 3) sent a message to someone via e-mail or instant message that made
that person feel threatened or worried; and 4) sent a message to someone via e-mail or instant message that
made that person feel bad. Peer computer hacking was created by averaging the standardized scores for the
following three items (alpha = .832): 1) tried to guess another’s password to get into his/her computer
account or files; 2) accessed another’s computer account or files without his/her knowledge or permission
just to look at the information or files; and 3) added, deleted, changed or printed any information in
another’s computer files without the owner’s knowledge or permission.

Am J Crim Just

Ta
b
le

2
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in
ea
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re
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n
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Am J Crim Just

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o
d
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χ
2

1
7
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4
8
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1
5
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7
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2
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R
2

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9
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6
6

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1
6

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5
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0
8

U
n
st
an
d
ar
d
iz
ed

co
ef
fi
ci
en
ts

ar
e
p
re
se
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te
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(s
ta
n
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ar
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rs

in
p
ar
en
th
es
es
);

*
p
<
.0
5
;
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*
p
<
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1
.
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ll

χ
2

w
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if
ic
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ar
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u
se
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in

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e
li
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.
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is
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p
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th
at
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sp
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p
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p
ir
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Am J Crim Just

Ta
b
le

4
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b
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(n
=
4
3
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)

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a


a

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M
o
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χ
2

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1
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3
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U
n
st
an
d
ar
d
iz
ed

co
ef
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ci
en
ts
ar
e
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re
se
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te
d
(s
ta
n
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in
p
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th
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);
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p
<
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;
A
ll
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2
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t
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as
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en
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.
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as

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(−
1
7
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4
5
)
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d
st
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r
(4
8
8
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)
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en

in
cl
u
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in
th
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w

as
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ci
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.
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h
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ly

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o
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e
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b
le
.
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h
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er
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st
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ti
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re
n
t
re
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lt
s
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r
th
e
o
th
er

m
ea
su
re
s

Am J Crim Just

by averaging the standardized scores for these five items, giving each of the five
items equal weight in the measure (a=.776).

Control Variables

Non-school hours was assessed by asking the respondents how many hours per week
they spent on computers for non-school related reasons over the last 6 months (1 =
less than 5 h; 2 = five to 10 h; 3 = 11 to 15 h; 4 = 16 to 20 h; 5 = 21 or more hours).
Spending more time in online environments for personal use unrelated to school or
work may provide greater opportunities to offend, such as sending threatening or
emotionally hurtful messages (Hinduja & Patchin, 2009; Holt & Bossler, 2009).

Computer skill was a four-point ordinal measure based on general categories of
computer proficiency: (0) I am afraid of computers and don’t use them unless I
absolutely have to (afraid); (1) I can surf the ‘net, use common software, but cannot fix
my own computer (beginner); (2) I can use a variety of software and fix some computer
problems I have (intermediate); and (3) I can use Linux, most software, and fix most
computer problems I have (advanced) (Rogers, 2001). This measure was included to
understand the influence that computer skill may have on an individual’s capacity to
offend, since those with greater knowledge of technology may be better able to engage
in more complex offenses (Higgins, 2005; Hinduja, 2001; Holt, 2007).

Computer location was a dichotomous measure (0 = public setting; 1 = bedroom
or laptop). Parents can more easily detect computer deviance when the computer is
in a public area such as a family room or kitchen (Bocij, 2004; Wolack et al., 2006).
Thus, requiring computer use in heavily trafficked areas can reduce opportunities for
juveniles to engage in cybercrime.

Demographics Sex (0 = male; 1 = female) was a dichotomous measure. Age was a
continuous measure ranging from 13 to 19 (mean = 14.99; std. dev. = 1.38). Grades
on report cards was a self-reported ordinal measure consisting of: (0) mostly Fs; (1)
mostly Ds; (2) mostly Cs; (3) mostly Bs; and (4) mostly As.

Results

The correlation matrix, presented in Table 1, indicated that the hypothesized
relationships were supported. Low self-control (r=.34) was positively correlated with
the commission of cyberdeviance. Associating with deviant peers had a strong
correlation with respondent cyberdeviance (r=.74). Low self-control and deviant peer
associations were correlated with each other (r=.24). In addition, the three opportunity
variables were correlated with cyberdeviance. Spending more time online for non-
school related reasons (r=.23), possessing greater computer skill (r=.28), and having a
computer in a personal setting (r=.18) were all positively related with the commission
of cyberdeviance. Age (r=.11) was positively correlated with cyberdeviance while
being female (r=−.27) and having higher grades (r=−.24) were negatively correlated
(Hinduja, 2001; Jordan & Taylor, 1998). Thus, these bivariate analyses provided
strong support to further explore the hypotheses via multivariate analyses.

Am J Crim Just

We estimated a linear regression model with cyberdeviance as the dependent variable
(see Table 2, Model 1). Multicollinearity did not appear to bias the parameter estimates
as the independent variables were not strongly correlated with each other (see Table 1)
and the highest VIF and lowest tolerance were 1.223 and .817 respectively (results not
shown). The linear regression model indicated that individuals with lower levels of
self-control were more likely to commit cyberdeviance in general. Associating with
peers who commit computer deviance, however, remained significant and was the
strongest predictor of cyberdeviance overall (Bossler & Burruss, 2010; Higgins et al.,
2006). Keeping a computer in a personal setting increased participation in
cyberdeviance by decreasing parental monitoring. Females were also less likely to
commit online deviance in keeping with larger research on cybercrime (Hinduja, 2001;
Jordan & Taylor, 1998; Skinner & Fream, 1997). Most of the other measures were not
significant in the full model, although they had significant zero-order correlations.

An additional model was run without the peer cyberdeviance measure in order to
examine whether associating with deviant peers mediated the effect of low self-
control (see Table 2, Model 2). Model 2 explained 25.9% of the variation, while
Model 1 explained 59.5%, illustrating the importance of peer deviance in predicting
cyberdeviance (Bossler & Burruss, 2010; Higgins et al., 2006). Model 2 also
indicated that the effect of low self-control on cyberdeviance significantly increased
when peer deviance was not included in the model (z score = −2.12).3 In addition,
the number of hours spent online for non-school related reasons, computer skill, and
grades were significant when peer deviance was not included in the model. Thus,
peer deviance appears to mediate the relationship between self-control, opportunity,
and cyberdeviance. Additionally, low self-control appears to have both a direct and
indirect effect via peer offending on youth cyberdeviance.4

In order to examine whether different levels of deviant peer associations conditioned
the effect of low self-control on cyberdeviance, the sample was partitioned by the
median of peer deviance (−.181) and models were run separately for those with low and
high deviant peer associations (Higgins et al., 2007; Higgins & Makin, 2004a). Model
3 indicated that low self-control was significant for both groups, but that low self-
control had a significantly larger effect for those with more deviant peer associations,
thus strengthening its effect (z=−2.24). This indicated that associating with deviant
peers exacerbated the effect of low self-control, supporting the finding of Gibson and
Wright (2001) and the suggestive evidence of Higgins and colleagues work on digital
piracy (e.g., Higgins et al., 2006, 2007; Higgins & Makin, 2004a, b).5

3 There are two statistically significant differences (Paternoster et al., 1998) between the three self-control
measures in Model 2. First, the Gibson measure had a statistically significant stronger effect on
cyberdeviance (b = .009; std. error = .001). Second, a statistically significant mediation effect is found in
Model 2 when using the traditional Grasmick et al. scale and the Gibson scale, but not the Higgins scale.
The standard error for low self-control using the Higgins scale is .002 rather than .001 as is found for the
other two measurements.
4 In order to further verify this conclusion, a linear regression was conducted with peer deviance as the
dependent variable (results not shown). This model indicated that low self-control, spending more time
online for non-school related reasons, computer skill, and lower report card grades all increased the
association with delinquent others.
5 Although there were no statistically significant differences between the three different measures of low
self-control within the two subgroups, the Gibson et al. measure indicated a conditioning effect (z=−3.13),
congruent with that of the unstandardized scale. The Higgins measure had no such effect (z=−1.58),
though this was because of the larger standard errors.

Am J Crim Just

The above analyses were also run for each of the five components of the
cyberdeviance measure to examine whether the findings were consistent for all
cybercrime types. The correlation matrix (see Table 1) indicated that low self-control
and deviant peer associations were significantly correlated with each form. We ran a
logistic regression model for each of the dichotomized components (see Table 3,
Model 1 s).6 These models indicated that the effects of both low self-control and
deviant peer associations were robust. Lower levels of self-control and higher levels of
deviant peer associations increased the odds of each of the five forms of
cyberdeviance, representing the entire range of Wall’s (2001) typology. In fact, z-
tests (results not shown) indicated that low self-control had consistent effects for all
five cyberdeviance types, meaning that low self-control did not predict one type of
cyberdeviance better than another. Thus, low self-control was able to predict simple
forms of cyberdeviance, like viewing online sexual material and harassing others
online, as well as cybercrimes that require some knowledge of computer technology,
including piracy (see Higgins, 2005; Higgins et al., 2006) and hacking (Bossler &
Burruss, 2010).7 Z-tests (results not shown) found this to be true for peer deviance as
well, meaning that associating with deviant peers did not increase the odds of
committing one form of cyberdeviance more than another.

Although the logistic regression models indicated that low self-control and peer
deviance predicted each form of cyberdeviance (see Table 3), not all of the other factors
predicted the cyberdeviance types equally. For example, students with higher levels of
computer skill and higher grades were more likely to pirate software (Hinduja, 2001).
Computer skills did not, however, predict other forms of offending when controlling
for peer deviance. Gender was not related to either media piracy or hacking,
incongruent with previous research (Higgins et al., 2006; Holt, 2007; Jordan & Taylor,
1998). Males were more likely to pirate software (Higgins, 2005, 2006; Hinduja,
2001) and view sexual materials online. Females, however, were more likely than their
male counterparts to harass others online (Hinduja & Patchin, 2009).

We also examined whether the mediating and conditioning effects found in the above
analyses for cyberdeviance in general (see Table 2) would hold for each of the five
cyberdeviance types. The model 2s in Table 3 exclude their respective peer deviance
measure to examine for mediating effects. Z-tests (results not shown) indicated that the
low self-control coefficients were not significantly different between the model 1s and
model 2s. Table 4 contains the models for each type partitioned by its respective peer
deviance measure to test for conditioning effects.8 Z-tests (results not shown) did not

6 We dichotomized the components because of heavy skew and little variation. Most students did not
commit these offenses or performed them at lower levels (see descriptives Table 1). Therefore, we
dichotomized software piracy, pornography, harassment, and computer hacking (0 = no; 1 = yes) in order
to examine whether self-control predicted the probability of committing these acts. Media piracy (mean =
1.29; std. dev. = 2.05) was a partial exception to the trend, as 55.9% of the sample did not engage in this
offense within the last year. Another 16.8% stated that they had pirated media once or twice in the last
year. This total (72.7%) is similar to the percentages of students who had not committed any of the other
offenses; thus we dichotomized media piracy based on students who engaged in piracy less than twice in
the last 12 months (0) and those who committed it more often (1).
7 Z-tests indicate that there were no significant differences in the abilities of the Grasmick et al., Higgins,
and Gibson scales in predicting the five types of cyberdeviance.
8 Since the individual peer deviance measures were five-point ordinal measures, we could not partition the
models by medians. Instead, we partitioned on whether or not the person had deviant peer associations for
that specific item (i.e. low = 0; high = all other options).

Am J Crim Just

find any significant differences between the low self-control regression coefficients
between the subgroups. Thus, no significant evidence was found that deviant peer
associations mediated or conditioned the effect of low self-control on any of the five
cyberdeviance types. Deviant peer associations, however, mediated and conditioned
the effect of low self-control on cyberdeviance in general (see Table 2).

Discussion and Conclusions

Gottfredson and Hirschi’s (1990) general theory of crime and Akers’ (1998) social
learning theory are two of the most widely supported theories in criminology (Akers
& Jensen, 2006; Gottfredson, 2006; Pratt & Cullen, 2000; Pratt et al., 2009). Both
low self-control and deviant peer associations have been linked to numerous forms
of real world crime and cybercrime. Most of our knowledge on the link between low
self-control, peer associations, and cyberdeviance, however, is primarily based on
college samples (e.g., Higgins, 2005, Higgins & Makin 2004a, b; Holt et al., 2010).
Few studies have considered the applicability of low self-control and social learning
theory to juvenile participation in cybercrime. This study utilized a sample of middle
and high school students and found that low self-control predicted the commission
of cyberdeviance in general and various forms of cyberdeviance specifically (e.g.,
Higgins, 2005, 2006; Higgins & Makin, 2004a, b; Higgins et al., 2006, 2007). In
addition, self-control did not significantly predict any form of cyberdeviance with
more emphasis than another.

This study also found that peer offending had a stronger effect on offending than low
self-control (Pratt & Cullen, 2000; Tittle et al., 2003) and consistently predicted each
type of cyberdeviance. In fact, it appears that peer offending partially mediated and
conditioned the relationship between low self-control and cybercrime offending in
general. As a consequence, peers with low self-control appear to coalesce in virtual
environments in much the same way as in the real world (Higgins et al., 2006; Wolfe
& Higgins, 2009). In addition, associating with deviant peers exacerbated the effect of
low self-control on cyberdeviance in general (e.g., Gibson & Wright, 2001). However,
these interactions were not found when examining the five components of the
cyberdeviance measure. Thus, further research is needed to disentangle the influence
and interaction of peers and low self-control in on and off-line contexts.

Additionally, the findings from this research raise questions about the relationship
between juvenile participation in cybercrime and juvenile delinquency off-line.
There is a clear relationship between low self-control, deviant peers, and cybercrime,
though it is less clear if there is an association between real world offending and
participation in cybercrime. The anonymity afforded by the Internet and computer
technology, coupled with the relative ease and innocuous appearance of most online
activities in public settings, may make cyberdeviance more attractive to some youths
than real world offenses. Limited research suggests individuals who engage in
bullying in online environments also bully others in the real world (Hinduja &
Patchin, 2009). Thus, future research is needed to explore the relationship between
low self-control, deviant peers, and participation in real world crime and cybercrime.

The limitations of this study, however, necessitate further research on the problem
of cybercrime. Specifically, a cross-sectional study was conducted at one middle

Am J Crim Just

school and one high school in Kentucky. The findings appear to be generalizable to
other groups, though sampling in other parts of the country would determine whether
a regional effect exists. In addition, the cross-sectional nature of the study does not
allow for a causal examination of deviant peer association and delinquency.
Longitudinal data would better allow the disentanglement of these effects.
Additionally, while basic demographic correlates were examined, there were no
measures for parental income or other factors that may affect access to technology
and the Internet on a regular basis. Future research is needed to consider the ways in
which ecological conditions may affect cybercrime offending at an early age.

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Thomas J. Holt is an Assistant Professor in the School of Criminal Justice at Michigan State University.
He received his Ph.D. in criminology and criminal justice from the University of Missouri—Saint Louis.
His research focuses on cybercrime and the ways that technology and the Internet facilitate deviance.

Adam M. Bossler is an Assistant Professor of Justice Studies at Georgia Southern University. He has a
doctorate in criminology and criminal justice from the University of Missouri—St. Louis. His current
research focuses on the application of traditional criminological theories to cybercrime offending and
victimization.

David C. May is a Professor and Kentucky Center for School Safety Research Fellow in the Department
of Criminal Justice at Eastern Kentucky University. He has published numerous articles in the areas of
responses to school violence, perceptions of the severity of correctional punishments, fear of crime, and
weapon possession, two books examining the antecedents of gun ownership and possession among male
delinquents, and a book on perceptions of the punitiveness of prison.

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  • Low Self-Control, Deviant Peer Associations, and Juvenile Cyberdeviance
    • Abstract
    • Understanding Cybercrime
    • Low Self-Control, Deviant Peers, and Cybercrime
    • The Present Study
      • Data
    • Measures
      • Dependent Variable
      • Independent Variables
        • Control Variables
    • Results
    • Discussion and Conclusions
    • References

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/KOR <FEFFc7740020c124c815c7440020c0acc6a9d558c5ec0020d654ba740020d45cc2dc002c0020c804c7900020ba54c77c002c0020c778d130b137c5d00020ac00c7a50020c801d569d55c002000410064006f0062006500200050004400460020bb38c11cb97c0020c791c131d569b2c8b2e4002e0020c774b807ac8c0020c791c131b41c00200050004400460020bb38c11cb2940020004100630072006f0062006100740020bc0f002000410064006f00620065002000520065006100640065007200200035002e00300020c774c0c1c5d0c11c0020c5f40020c2180020c788c2b5b2c8b2e4002e>
/NLD (Gebruik deze instellingen om Adobe PDF-documenten te maken die zijn geoptimaliseerd voor weergave op een beeldscherm, e-mail en internet. De gemaakte PDF-documenten kunnen worden geopend met Acrobat en Adobe Reader 5.0 en hoger.)
/NOR <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>
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/ENU (Use these settings to create Adobe PDF documents best suited for on-screen display, e-mail, and the Internet. Created PDF documents can be opened with Acrobat and Adobe Reader 5.0 and later.)
/DEU <FEFF004a006f0062006f007000740069006f006e007300200066006f00720020004100630072006f006200610074002000440069007300740069006c006c0065007200200037000d00500072006f006400750063006500730020005000440046002000660069006c0065007300200077006800690063006800200061007200650020007500730065006400200066006f00720020006f006e006c0069006e0065002e000d0028006300290020003200300031003000200053007000720069006e006700650072002d005600650072006c0061006700200047006d006200480020>
>>
/Namespace [
(Adobe)
(Common)
(1.0)
]
/OtherNamespaces [
<<
/AsReaderSpreads false
/CropImagesToFrames true
/ErrorControl /WarnAndContinue
/FlattenerIgnoreSpreadOverrides false
/IncludeGuidesGrids false
/IncludeNonPrinting false
/IncludeSlug false
/Namespace [
(Adobe)
(InDesign)
(4.0)
]
/OmitPlacedBitmaps false
/OmitPlacedEPS false
/OmitPlacedPDF false
/SimulateOverprint /Legacy
>>
<<
/AddBleedMarks false
/AddColorBars false
/AddCropMarks false
/AddPageInfo false
/AddRegMarks false
/ConvertColors /ConvertToRGB
/DestinationProfileName (sRGB IEC61966-2.1)
/DestinationProfileSelector /UseName
/Downsample16BitImages true
/FlattenerPreset <<
/PresetSelector /MediumResolution
>>
/FormElements false
/GenerateStructure false
/IncludeBookmarks false
/IncludeHyperlinks false
/IncludeInteractive false
/IncludeLayers false
/IncludeProfiles true
/MultimediaHandling /UseObjectSettings
/Namespace [
(Adobe)
(CreativeSuite)
(2.0)
]
/PDFXOutputIntentProfileSelector /NA
/PreserveEditing false
/UntaggedCMYKHandling /UseDocumentProfile
/UntaggedRGBHandling /UseDocumentProfile
/UseDocumentBleed false
>>
]
>> setdistillerparams
<<
/HWResolution [2400 2400]
/PageSize [595.276 841.890]
>> setpagedevice

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