526499
research-article2014
CADXXX10.1177/0011128714526499Crime & DelinquencyMiller and Morris
Article
Virtual Peer Effects in
Social Learning Theory
Crime & Delinquency
1­–27
© The Author(s) 2014
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DOI: 10.1177/0011128714526499
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Brooke Miller1 and Robert G. Morris1
Abstract
The current study examines the differential influence of face-to-face and
virtual peers in predicting digital and traditional offending among college
students through the lens of social learning theory (SLT). SLT components
are explored to discern whether the theory holds for virtual peers, as it has
for face-to-face peers using a structural equation modeling framework, thus
making a substantial contribution to the social learning literature. Findings
provide some support for SLT for both virtual only peers and the face-toface peers model in relation to digital as well as traditional offending. In
addition, findings suggest that virtual peer associations may be as important
as traditional peer associations in explaining certain types of deviant behavior.
Keywords
social learning theory, virtual peers, traditional peers
In recent years, computers and the Internet have become an integral part of
daily life for many, if not most, individuals in terms of personal communication and information sharing. As a result, it is quite easy to keep in direct
contact with friends and relatives across city, state, and international borders,
whereas in the past, communication was limited to either telephone or land
correspondence. As we have progressed into the information age and have
become more dependent on computer technology as part of our everyday
1University
of Texas at Dallas, Richardson, TX, USA
Corresponding Author:
Brooke Miller, Program in Criminology, University of Texas at Dallas, 800 West Campbell
Rd., GR 31, Richardson, TX 75080, USA.
Email: [email protected]
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Crime & Delinquency 
routines, many individuals now communicate with friends more so in the
online world than in the terrestrial world. In fact, there are more than 2 billion
Internet users worldwide (Internet Usage Statistics, n.d.).1 These virtual relationships (i.e., virtual peers) may involve online only relationships or some
combination of online and face-to-face interaction, which are arguably qualitatively different than traditional in-person only relationships. While the
osmosis of digital communication has had a profound pro-social impact,
there are also many negative consequences to consider. These include varying forms of cyber crime and online deviance, the etiology of which we are
only beginning to understand.
Social learning theories (SLTs) of behavior have had much success not
only in explaining the process of learning but also in explaining how much of
an impact this process has on one’s behavior—see Warr (2002). Learning
theories specific to crime and deviance are no exception, however to date,
very few studies have explored how virtual peer relationships operate within
the learning process. More specifically, it remains unclear whether virtual
peers have an equivalent effect on both cyber and traditional forms of deviance through the learning process. Understanding this dynamic relationship
is the primary goal of this study.
True virtual peers can be considered those with whom there is an established relationship but communication transpires solely in an online environment, perhaps never having met the other person face-to-face. Warr (2002)
describes adolescent virtual peer groups as those in which individuals “ . . .
can identify socially and psychologically, from which they can assimilate
tastes and norms of dress, speech, and sexuality, and within which they can
develop a self” (p. 87). People who participate in virtual communication may
be influenced by social interactions with their face-to-face peers as well as
with their virtual peers. The primary difference between the two forms being
ease of communication and geographical proximity. Indeed, virtual peers
may be continuously present in a person’s life (e.g., via chat or video conferencing) and may have a sizable impact on the development of our attitudes
compared with those who are relatively close by. It is possible that the virtual
interactions experienced in this capacity influence behavior, including criminal behavior, exhibited in both on- and offline environments.
Previous research examining the social learning process has focused on
the influence of traditional peer groups while research surrounding the influence of virtual peers is limited at best and warrants further consideration in
developing a deeper understanding of information sharing and differential
peer influences as well as its role in the learning process. There is evidence to
suggest that traditional peer effects on delinquency may operate differently
for youth who spend large amounts of time participating in sedentary
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Miller and Morris
3
behaviors (e.g., playing video games), which calls attention to how peer
effects may be evolving as technology use increases (e.g., see Morris &
Johnson, 2011). That being said, the vast majority of extant literature has
explored the influence of deviant face-to-face peers on traditional deviance
as well as on computer-related deviance (e.g., digital piracy, password guessing, accessing someone else’s computer files, or creating and distributing
malicious computer programming—for example, hacking—etc.). Indeed,
traditional peers warrant continued focus as an influencing factor on traditional and digital offending yet the influence of online only (i.e., virtual)
peers should also be considered (Morris & Higgins, 2010; Morris & Johnson,
2011) as their influence on online and traditionally deviant behavior has not
been determined. From a social learning perspective, virtual peers should
influence individual behavior for those who communicate in an online only
capacity in a manner that is generally similar to traditional peers effects.
Evidence from qualitative research also supports the idea that virtual peer
interactions have a substantive impact on an individual’s behavior. A variety
of studies of cyber crime and technology illustrate the importance of virtual
peers on offending behavior related to computer hacking (Bachmann, 2010;
Holt, 2007, 2009; Jordan & Taylor, 1998; Meyer, 1989; Taylor, 1999), digital piracy (Cooper & Harrison, 2001; Holt & Copes, 2010) and real world
offenses like sexual assault/abuse (Holt, Blevins, & Burkert, 2010; Mitchell,
Finkelhor, & Wolak, 2007), and prostitution (Holt, Blevins, & Kuhns, 2008,
2009; Sharpe & Earle, 2003). For example, Holt et al. (2009) use posts from
web forums frequented by customers of prostitutes, or johns, finding they
openly discuss and share various methods to decrease the risk of arrest as
well as informal threats. Holt, Blevins, and Burruss (2012) use web posts in
hacker discussion forums to assess the importance of virtual peer groups in
developing hacking behaviors. Their findings support the assertion that
hacker groups are generally well connected and that individuals may participate in multiple online communities to both gather information and develop
the density of their social networks (Holt, 2009; Holt, Soles, & Leslie, 2008;
Meyer, 1989). In addition, Holt and Copes’ (2010) interviews with persistent
digital pirates reveal that knowledge can be transmitted through online interactions even when discussants do not perceive themselves as part of a pirating subculture. These findings have repeatedly demonstrated the learning of
virtual deviant behaviors in an online capacity, and lay the foundation for
further exploration of the learning process that occurs online as well as how
it relates to both virtual and traditional offending.
The primary objective of the current study is to explore whether virtual
peer effects operate within the confines of SLT equivalently to that of traditional or face-to-face peers. Also explored is whether traditional correlates of
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Crime & Delinquency 
offending are mediated by SLT in a similar manner for virtual versus traditional peers, as posited by the theory. For example, can SLT explain the direct
and indirect influence of virtual peers as it has for traditional peers on participation in traditional deviant activities (theft and assault) as well as for digital
crimes? Relying on data culled from a sample of university students, we test
whether operational variation exists between specific constructs of Akers’
(1985, 1998) SLT that are based on traditional versus online social learning
components. In other words, does SLT explain traditional and online crime
equivalently when traditional (i.e., face-to-face) peer effects are compared
with virtual (i.e., online only) peer effects?
SLT
SLT as a general theory of crime causation has been very successful in
explaining participation in a variety of deviant activities (Akers, 1998; Akers,
Krohn, Lanza-Kaduce, & Radosevich, 1979; Akers & Lee, 1996; Elliott,
Huizinga, & Menard, 1989; Hwang & Akers, 2003; Krohn, Skinner, Massey,
& Akers, 1985; Verrill, 2008; Warr, 2002). Modern day learning theories are
derived from Sutherland’s (1947) differential association theory, which
describes the learning of deviant behavior and definitions favorable to crime
through differential association with deviant others. Burgess and Akers
(1966) further developed the theory to both explain the learning process and
incorporate the concept of differential reinforcement in their SLT. Over the
years, SLT has come to describe the learning process that occurs through the
interaction with deviant others in which individuals learn to define their attitudes and behaviors as deviant, imitate the behavior of others, and have these
ideas reinforced through a balance between experienced or observed rewards
and punishments (Akers, 1985, 1998).
Differential association refers to individual involvement in deviant behavior as the result of social interactions with deviant others. Social interactions
involve the time spent with others including friends, family, and acquaintances as well as the intensity of those interactions. The concept of differential association holds that the learning of deviant behavior occurs when the
number and frequency of exposure to definitions favorable to crime exceeds
definitions unfavorable to crime. The more often and more in-depth interaction one has with individuals espousing favorable definitions toward criminality, the greater the exposure to and learning of criminal definitions. Akers’
(1985) differential association extends Sutherland’s original construct in that
it focuses not only on the definitions of close associates but also on their
behavior. Rather, the behavior of one’s peers, family, and acquaintances are
as important to the learning process as the definitions described by Sutherland.
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Miller and Morris
5
These interactions provide and/or strengthen definitions of acceptable and
unacceptable conduct. Individuals both learn and affirm their definition of
appropriate conduct through their interactions with those around them.
As introduced above, Akers (1998) describes definitions as the beliefs,
attitudes, justifications, or orientations used by an individual to designate certain behaviors as either good or bad. Individual’s will either engage or not
engage in delinquent behavior based on their designation of definitions as
either deviant or non-deviant as well as other stimuli present in a given situation. Individual definitions are both learned and reinforced through the process of differential association and can be positive, neutralizing, or negative.
Positive definitions are those that generally favor a behavior making it morally desirable or permissible; in the case of criminal behavior, positive definitions are those that favor the commission of a criminal act by defining the
behavior as desirable. Neutralizing definitions tend to occur more often and
encourage criminal behavior not by directly supporting the behavior but
through creating excuses or justifications for deviance, which tends to occur
after the neutralizing attitudes are in place—see Morris and Copes (2012).
“The neutralizing definitions view the act as something that is probably undesirable but, given the situation, is nonetheless justified, excusable, necessary,
all right or not really bad after all” (Akers, 2009, p. 79). Negative definitions
are drawn from conventional beliefs regarding criminal behavior. The greater
number of specific negative definitions an individual holds toward different
types of deviance, the less likely they are to engage in criminal behavior. For
purposes of this study, we were interested only in the negative definitions
reinforced by differential association.
Another component of SLT includes the process of learning behavior
through modeling or imitation of others actions through socialization (Akers,
1985, 1998). Peers engaging in deviant or criminal behavior may provide
examples and share knowledge, attitudes, beliefs, and techniques with the
potential to significantly influence an individual’s involvement and participation in deviant conduct. Sources of imitation can include family, friends,
teachers, media, or any source from which an individual may observe and try
to model similar behavior. The learning process does not require that the
model intends to shape the behavior of the observer, and imitation may occur
when an admired model reinforces the repetition of a behavior to the observer
either intentionally or unintentionally. Also unique to the information age is
modeling via secondary peer groups such as via the media or perhaps via
information garnered online (e.g., watching YouTube videos), which has not
been tested previously as applied to SLT.
“Whether individuals refrain from or initiate, continue committing, or
desist from criminal and deviant acts depends on the relative frequency,
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Crime & Delinquency 
amount, and probability of past, present, and anticipated rewards and punishments perceived to be attached to the behavior” (Akers, 2009, p. 66).
Differential reinforcement/punishment involves the balance between both
the anticipated and actual consequences associated with engaging in certain types of behavior. Behavior, whether an individual engages in an act
or refrains from offending, is determined by the balance of rewards and
punishments (Akers, 1985, 1998). Akers (1998) asserts that both positive
and negative reinforcers (i.e., experiences) increase the chances of event
occurrence, whereas punishers decrease the likelihood that an act or behavior will occur. Positive reinforcement may display itself in the form of
approval of certain behaviors from friends, families, and other persons
within an individual’s social environment. Negative reinforcement is generally thought of as the avoidance of unpleasant experiences. Punishments
can be either positive or negative including reprimands or removal/retraction of rewards, praise, or affection, respectively (Akers & Sellers, 2004).
Since both reinforcers and punishments tend to exist simultaneously for
any act, the balance of these concepts tends to predict behavior (Akers,
1998).
Empirical Support for SLT
As a general theory of crime, SLT has received continual attention over the
past several decades with studies consistently finding differential association
to be an important predictor for involvement in a variety of deviant behaviors
(Akers, 1998; Akers & Lee, 1996; Elliott et al., 1989; Krohn et al., 1985;
Verrill, 2008; Warr, 2002) including property crime (Akers, 1998) and substance abuse (Akers et al., 1979; Hwang & Akers, 2003). Previous studies
also indicate that SLT variables will have a confounding effect on individuallevel predictors of delinquency such as gender, race, and age. For example, in
their study of adolescent smoking behaviors, Spear and Akers (1987) suggest
that social learning variables account for deviance both in males and females
with greater influence on learning in boys than girls. In addition, Akers and
Lee (1996) suggest that the relative influence of SLT variables, peer attitude
influence, will vary by age.
General computer crime (Higgins, Fell, & Wilson, 2006; Higgins &
Makin, 2004a, 2004b; Ingram and Hinduja, 2008; Morris & Blackburn, 2009;
Morris & Higgins, 2009; Rogers, 2001; Skinner & Fream, 1997) and piracy
(Higgins et al., 2006; Higgins & Makin, 2004a; Higgins & Wilson, 2006;
Hinduja & Ingram, 2008, 2009; Morris & Higgins, 2010; Skinner & Fream,
1997) have also been explored using SLT suggesting varying levels of support for theoretical predictors and outcome variables associated with SLT.
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Miller and Morris
7
More specifically, a variety of studies have examined the role of traditional
peer relationships on digital piracy (Higgins et al., 2006; Higgins & Makin,
2004; Higgins & Wilson, 2006; Hinduja & Ingram, 2008, 2009; Morris &
Higgins, 2010; Skinner & Fream, 1997). Hollinger (1993) was among the
first to find a strong correlation between traditional peer influence and computer-related deviance; however, no specific measures of social learning were
used. His findings suggested that individuals are more likely to engage in
computer crimes as the number of friends they have who also participate in
this type of activity increases. Other studies have specifically examined the
influence of traditional peers on digital piracy from a social learning perspective. For example, Skinner and Fream (1997) explored the influence of SLT
on cyber-related deviance. Their findings suggested that association with
deviant peers significantly predicted cyber-related deviance across all categories of cyber crime types. Holt and Morris (2009) found that peer involvement in piracy behaviors are significant predictors for piracy regardless of
MP3 ownership. In addition, Hinduja and Ingram (2009) were among the
first to address the differential impact of online (virtual) and offline peers on
music piracy from a social learning perspective. Their findings indicate that
both online and offline peers significantly predict participation in music
piracy; however, the study was limited to a single indicator of virtual and
offline peer influences.
The Current Study
To date, what literature does mention virtual peer effects provides little to no
discussion on how virtual peer groups may operate through the SLT framework. Here, we expand on previous studies using data collected specifically
to measure the direct and indirect effects of virtual and traditional peers on
digital piracy as well as traditional deviance. Research has generally supported the influence of peers on individual participation in traditional deviance and has found varying levels of support for peer influence on individual
engagement in digital piracy. No study to date has been designed to explicitly
measure the differential impact of traditional and virtual peers on deviance,
cyber or traditional. In response, the research questions used in the current
analysis include the following:
Research Question 1: Do virtual peers matter to the social learning process of engaging in online and offline deviance, specifically, digital piracy
and traditional deviance?
Research Question 2: Does SLT operate according to the theory when
virtual, rather than traditional, peers are modeled?
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Crime & Delinquency 
Method
Data
Data for the current study were obtained through self-administered surveys
completed by undergraduate students during the spring 2010 semester at a
midsize southern university.2 Email correspondence was sent to undergraduate instructors of both required and elective courses for all university students.
Efforts were made to reach students in a variety of majors; however, respondents were only obtained from classes with instructor agreement. Eleven
undergraduate classes were surveyed, of which 51.5% of students reported
non-technical majors (i.e., liberal arts, social science, fine arts, etc.) with the
rest being technical majors (e.g., engineering, math, hard sciences, etc.). Prior
to completing the survey, students were advised that their participation was
voluntary and their responses would be confidential.3 The result was a purposive sample of 454 undergraduate students. Descriptive statistics and bivariate
correlations are displayed in Table 1 and indicate moderate correlations
between reinforcement from online peers and differential association from
online peers as well as with reinforcement from traditional peers. Differential
association with traditional peers also appears to be moderately correlated
with learned definitions. Individual-level predictors suggest relatively low
correlations with SLT correlates and each of the outcome variables.
Measures
Digital piracy. Digital piracy was measured by asking respondents to report
the number of times they had participated in digital piracy during the past 12
months (i.e., how many times did you illegally download full-version commercial software, videos, or music instead of buying it). The respondents
were then asked to report the likelihood they would illegally download fullversion commercial software, videos, or music instead of buying it over the
next 12 months. The outcome measure for the model, participation in digital
piracy, is a weighted measure of self-reported counts of involvement in
piracy by anticipated future involvement in digital piracy. This approach
tapped into not only contemporaneous participation in the outcome behavior
but also into anticipated future behavior.
Traditional deviance. Measures of traditional deviance were derived from the
National Youth Survey4 and included asking respondents to report the number of times they engaged in a variety of activities during the previous 12
months. These activities included knowingly buying, selling, or holding stolen goods, using checks or a credit card to illegally pay for something, and
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9
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Gender (female)
Race (White)
Computer knowledge
Age
Piracy
Traditional deviance
Differential association with online peers
Definitions
Imitation
Reinforcement online peers
Differential association with traditional peers
Reinforcement traditional peers
.42
.49
1
−.11
−.39
−.05
−.13
−.04
−.15
−.20
−.07
−.10
−.11
−.04
1
3
.54
.49
1
−.09
−.01
−.07
−.11
−.16
−.03
−.22
−.11
4
1
.00
.07
.23
.07
.26
.07
.26
5
3.21 21.80 4.33
1.00 4.76 2.22
1
.11
1
−.01 −.03
−.06
.11
.04
.08
−.06
.17
−.01
.23
−.11
.20
−.04
.20
.02
.12
−.05
.16
2
Note. All correlations greater than ±.10 are statistically significant at p < .05.
M
SD
1
2
3
4
5
6
7
8
9
10
11
12
Variable
1
.32
.29
.60
.48
.48
7
.16 .00
.47 1.00
1
−.02
−.03
.05
−.04
−.02
−.02
6
.00
1.00
1
.07
.27
.49
.30
8
.00
1.00
1
.19
.29
.16
9
.00
1.00
1
.31
.86
10
Table 1. Bivariate Correlations for Individual-Level Covariates, Deviant Outcomes, and Social Learning (n = 454).
.00
1.00
1
.36
11
.00
1.00
1
12
10
Crime & Delinquency 
purposely assaulting another individual. Measures of traditional deviance
were limited to theft and assault as virtual offenses and peer relationships
were the focus at the time of data collection. Respondents were then asked to
report the likelihood that they would engage in each of these activities during
the next 12 months. The resulting outcome measure for traditional deviance
model is a weighted measure of self-reported counts of involvement in traditional offenses by anticipated future involvement in traditional deviance.
Similar to the digital piracy measure, this approach considers both previous
and future behavior.
Social learning measures. To measure the influence of differential association
with traditional and virtual peers, students were asked to respond to a set of
questions about their peers behavior during the previous 12 months, contextualized to either face-to-face peers or fully online peers.5 Items were worded
equivalently between each context and were derived from previous studies of
SLT and online deviance (e.g., Morris & Higgins, 2010). Respondents were
informed that traditional peers could be considered those with whom an individual has a face-to-face relationship and virtual peers are those with whom
an individual communicates with solely online never having met them in
person, resulting in a 10-item scale (α traditional peers = .86; α virtual peers
= .89).6 Responses to each indicator were recorded on a 1 to 5 scale with 1 =
none and 5 = all friends.
Definitions favorable to illegal behavior were measured by asking students to report how wrong they felt a variety of behaviors were including
both computer-related and traditional deviance (e.g., theft, assault, etc.).
Possible responses were based on a 1 to 5 scale with 1 = not wrong at all and
5 = very wrong. Measures of definitions were reverse coded to simplify interpretation during analysis. This resulted in the use of a nine-item scale (α =
.85) to measure the influence of definitions as a component of SLT.
Imitation7 was measured by asking students to rate how much they have
learned about any, or all, of the computer-related activities included in the survey from their family, friends, Internet searches, and Internet message boards/
chat rooms. Responses were based on a 1 to 10 scale where 1 = nothing and 10
= everything and were derived from Skinner and Fream (1997).8 The four-item
scale (α = .66) was used to represent imitation in the SLT construct.
Differential reinforcement/punishment was measured by assessing the
respondents’ perception of their traditional versus virtual peer approval of a
variety of computer-related activities and traditional deviant behaviors. This
resulted in a six-item scale measuring differential reinforcement for traditional peers and a five-item scale for virtual peers (α traditional peers = .93;
α virtual peers = .95).
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Miller and Morris
11
Individual-level covariates. The individual measures used in this study were
gender (0 = male, 1 = female), race (0 = non-White, 1 = White), respondent
age, and respondent computer skill level. Respondents were asked to selfreport their skill level with computers given the following options: 1 = I am
comfortable using computers, 2 = I can “surf the net,” use common software,
but cannot fix my computer problems, 3 = I can use a variety of software and
fix some of my computer problems, 4 = I can use a variety of operating systems and fix most computer problems I have, or 5 = I am comfortable manipulating or writing computer programming (α = .89)
Analytical Procedure
Structural equation modeling (SEM) was used to examine the influence of
traditional, compared with virtual, peers on recent and anticipated future digital piracy. This was done by estimating models based on the virtual peer
indicators for the SLT component separately from models relying upon traditional peer indicators. We then compare the results across the two outcome
measures (traditional and online deviance). SEM was preferred as it accounts
for measurement error in each of the observed variables that make up the
latent variables included in the model. In addition, it allows for an exploration of the direct and indirect relationships between variables and latent constructs, and it also provides the ability to test an entire theoretical model. The
current analysis uses a second-order latent SLT construct including definitions, modeling (imitation), peer associations, and reinforcement in separate
SEMs examining virtual versus traditional peers on both outcome measures,
respectively (piracy and traditional offending).
There are several indicators of model fit to consider in SEM with the most
common measure of model fit represented by the chi-square test statistic, a direct
test of whether the hypothesized model differs from that generated by the data.
For SEM, evidence that no difference exists between the hypothesized model and
the model generated by the data is indicated by a non-statistically significant chisquare value. In previous studies using SEM, the chi-square statistic rarely provides the desired outcome due to its sensitivity to sample size (Hu & Bentler,
1999). As a result, Raykov and Marcoulides (2006) argued the importance of
additional fit statistics to exploring and determining model fit. Several other fit
statistics for SEM are provided for the current models. The comparative fit index
(CFI) is used as an indicator of model fit; Hu and Bentler (1999) suggest that
values greater than .90 may indicate reasonably good model fit. The root mean
square error approximation (RMSEA) is another common fit measure for SEM;
RMSEA values of .06 or lower generally indicate good model fit when combined
with standardized root mean of the residual (SRMR) values of .08 and below.
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Crime & Delinquency 
Figure 1. Structural model.
The current study involved the presentation of four SEMs designed to test
study research questions surrounding both traditional and virtual peers. We
first examined the direct effects between each of the predictor variables as
well as the social learning construct in relation to digital piracy. In addition,
direct effects between each of the predictor variables on traditional deviance
as well as SLT are presented. Each SEM included, (a) four exogenous
observed predictor variables (age, gender, race, and computer knowledge),
(b) four exogenous latent components of social learning, (c) an endogenous
latent social learning construct as a potential mediator of the structural factors
(differential association for both traditional and virtual peers, definitions,
modeling, and reinforcement for both traditional and virtual peers), and (d)
an observed outcome (either digital piracy or traditional deviance). This
framework is considered a second-order SEM where measurement error is
captured from each component of SLT, as well as in the SLT factor itself. The
same fit statistics were used for both models to determine how well the traditional and virtual peer models fit the data. The path diagrams in Figure 1
depict the final structural model used for each analysis where items in the
ovals represent latent constructs and boxed items represent observed constructs. The first model (Table 2) examined peer effects on participation in
digital piracy and the second model (Table 3) examined the effects on
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−0.065 (0.056)
0.102 (0.056)
0.054 (0.070)
−0238 (0.096)*
0.907 (0.106)***
−0.039 (0.065)
−0.161 (0.059)**
−0.341 (0.058)***
0.496 (0.067)***
−0.120 (0.201)
−0.489 (0.199)*
−0.109 (0.025)***
0.743 (0.162)***
0.9
0.058
0.066
—
—
—
—
—
—
—
0.92
0.054
0.064
−0.122 (0.053)*
−0.136 (0.051)**
−0.172 (0.056)**
0.316 (0.076)***
0.022 (0.059)
0.092 (0.056)
−0.084 (0.061)
−0.104 (0.080)
0.708 (0.100)***
−0.172 (0.069)*
−0.192 (0.063)**
−0.243 (0.064)***
0.446 (0.067)***
Coefficient (SE)
Coefficient (SE)
−0.28 (0.06)***
−0.07 (0.05)
−0.37 (0.09)***
0.09 (0.06)
—
—
—
—
—
Coefficient (SE)
Online peers SLT model
Traditional peers SLT model
Note. SEM = structural equation modeling; SLT = social learning theory; CFI = comparative fit index; RMSEA = root mean square error
approximation; SRMR = standardized root mean of the residual.
*p < .05. **p < .01. ***p < .001.
Direct effects
Gender → piracy
Race → piracy
Age → piracy
Computer knowledge → piracy
Social learning → piracy
Gender → social learning
Race → social learning
Age → social learning
Computer knowledge → social learning
Indirect effects
Gender → SLT → piracy
Race → SLT → piracy
Age → SLT → piracy
Computer knowledge → SLT → piracy
Fit statistics
CFI
RMSEA
SRMR
Base model
Table 2. SEM Results for Traditional and Online Peer Models on Digital Piracy.
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−0.616 (0.090)***
0.196 (0.090)*
0.007 (0.010)
−0.022 (0.096)
0.293 (0.063)***
−0.421 (0.114)***
−0.166 (0.113)
−0.061 (0.011)***
0.429 (0.114)***
−0.123 (0.044)**
−0.049 (0.035)
−0.018 (0.005)***
0.126 (0.043)**
0.90
0.057
0.067
—
—
—
—
—
—
—
0.926
0.055
0.063
−0.127 (0.044)**
−0.058 (0.037)
−0.008 (0.004)*
0.096 (0.041)*
−0.613 (0.090)***
0.205 (0.090)*
−0.003 (0.009)
0.008 (0.094)
0.288 (0.059)***
−0.439 (0.119)***
−0.201 (0.118)
−0.027 (0.012)*
0.332 (0.121)**
Coefficient (SE)
Coefficient (SE)
−0.752 (0.093)***
0.150 (0.092)
−0.011 (0.009)
0.106 (0.095)
—
—
—
—
—
Coefficient (SE)
Virtual peers SLT model
Traditional peers SLT model
Note. SEM = structural equation modeling; SLT = social learning theory; CFI = comparative fit index; RMSEA = root mean square error
approximation; SRMR = standardized root mean of the residual.
*p < .05. **p < .01. ***p < .001.
Direct effects
Gender → traditional offenses
Race → traditional offenses
Age → traditional offenses
Computer knowledge → traditional offenses
Social learning → traditional offenses
Gender → social learning
Race → social learning
Age → social learning
Computer knowledge → social learning
Indirect effects
Gender → SLT → traditional offenses
Race → SLT → traditional offenses
Age → SLT → traditional offenses
Computer knowledge → SLT → traditional
offenses
Fit statistics
CFI
RMSEA
SRMR
Base model
Table 3. SEM Results for Traditional and Virtual Peer Models on Traditional Offending.
Miller and Morris
15
traditional offending. Note that construct indicators and outcomes were
changed to assess differences in the traditional versus virtual peer effect on
each outcome, respectively.
Results
Table 2 presents the standardized SEM results from the traditional peer and
virtual peer digital piracy models. While chi-square test statistics were significant for each of the models, the alternative fit statistics were within
acceptable ranges indicating the models fit the data. For the traditional peers
model, the other fit statistics (CFI = .90, RMSEA = .058, SRMR = .066)
indicate reasonably good fit for this model given the current data set. The
other fit statistics for the virtual peers model also indicate good model fit
(CFI = .92, RMSEA = .054, SRMR = .64).9
The first column of Table 2 displays the results of the base model using
ordinary least squares (OLS) regression.10 The results of this model suggest
that both gender and age significantly predict involvement in digital piracy
prior to the introduction of the social learning constructs. When the SLT construct is introduced into the model, in columns 2 and 3 (i.e., the SEM models), the strength of the base model is weakened in accordance with Akers’
theory. For example, the impact of age remains significant in both the traditional and virtual peers models as indicated by a reduction in the age coefficient in both models. In addition, race and level of computer knowledge have
a significant indirect effect on digital piracy through the SLT construct indicating that the SLT construct weakens the predictive power of the base model.
Looking at the direct effects of the individual predictors on digital piracy for
traditional peers, a lower level of computer knowledge is the only significant
predictor of digital piracy. None of the individual-level predictors were significant predictors for the virtual peers model. However, the direct effects of
SLT predicting piracy were significant for both the traditional and virtual
peer models. The direct effects of each of the individual predictors, for the
traditional peers model, on SLT were significant with the exception of gender. Non-Whites, younger individuals, and those with higher levels of computer knowledge appear to have a significant influence on the social learning
process. All individual demographics for virtual peers display a significant
direct relationship with social learning.
For traditional peers, SLT significantly mediates the relationship between
age and digital piracy. The finding that gender is not a significant predictor of
digital piracy through SLT is contrary to previous studies and Akers’ assertion that gender should have an indirect effect on offending through SLT. In
addition, while significant indirect effects appear to mediate the relationship
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Crime & Delinquency 
between race and computer knowledge through SLT, a lack of reduction in
these values when compared with the base model runs contrary to Akers’
theory. Findings suggest SLT mediates the indirect effect of age on digital
piracy for the traditional peer model.
The results from the virtual peer model were similar to those associated
with the traditional peer model. Rather, the direction of the relationship
between each individual predictor and SLT was the same in both the traditional and virtual peer models in regard to the direct and indirect effects on
digital piracy. Results from the virtual peer model suggest that SLT significantly mediates the relationship between gender and age on digital piracy. As
suggested by the theory, being younger and male significantly predicts digital
piracy for the virtual peers model through SLT. The indirect effect of race
through social learning on digital piracy is significant; however, it does not
appear to explain participation in digital piracy any more than the base model.
In addition, the indirect effects of level of computer knowledge appear to
have a significant influence on participation in digital piracy through SLT.
Table 3 presents the standardized SEM results for the traditional offending
outcome. Similar to the piracy model, chi-square test statistics were significant for each of the models, and the other fit statistics were within acceptable
ranges as well. The data indicate reasonably good fit for the traditional peers
model (CFI = .90, RMSEA = .057, SRMR = .067) as well as the virtual peers
model (CFI = .92, RMSEA = .055, SRMR = .63).
The first column of Table 3 displays the results of the base model using
OLS regression with the traditional offending outcome. The results of this
model indicate that gender significantly predicts involvement in traditional
deviant behaviors prior to the introduction of the social learning constructs.
Similar to the piracy models, when the SLT construct is introduced into the
model, in columns 2 and 3 (i.e., the SEM models), the strength of the base
model is weakened in accordance with Akers’ theory. Specifically, gender
remains significant in both the traditional and virtual peers models indicating
that social learning has a stronger influence in the results of the SEM. In addition, age and level of computer knowledge have a significant indirect effect
on traditional deviance through social learning.
Columns 2 and 3 of Table 3 also outline the direct effects of each demographic variable on traditional deviance and social learning. The direct effects
of individual predictors on traditional deviance for traditional as well as virtual peers indicate both gender and race were significant predictors of traditional deviance. Similarly, the direct effect of SLT in predicting traditional
deviance for both the traditional and virtual peer models was significant. The
direct effects of each of the individual predictors on social learning for both
the traditional and virtual peer model were significant with the exception of
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Miller and Morris
17
race. The social learning process appears to play a significant role for males,
younger individuals, and those with higher levels of computer knowledge.
Similar to the online offense models, the direction of the relationship
between each individual predictor and SLT was the same in both the traditional and virtual peer models in regard to the direct and indirect effects on
traditional offending. SLT significantly mediates the relationship between
gender and traditional deviance for both traditional and virtual peers in accordance with Akers’ theory. Race remains non-significant for both models.
However, both the indirect effects of age and level of computer knowledge
through SLT on digital piracy are stronger in both the traditional and virtual
peer models suggesting that the operation of SLT is robust to context specific
influences on the learning process relative to the qualitative nature of the
outcome.
Discussion
The current study sought to examine the direct and indirect differences in the
social learning process through both traditional and virtual peers in relation
to both online and traditional offending. SEM was used to examine each
component of the SLT construct in relation to both traditional and virtual peer
relationships. The primary objective was to explore whether virtual peers
have the same predictive power as traditional peers for involvement in digital
piracy and traditional deviance consistent with SLT. Individual variables
were examined to determine their influence when interacting with traditional
and virtual peers on each of these types of offending.
Regarding the first research question (Do virtual peers matter to the social
learning process of engaging in digital piracy and traditional deviance?), it is
clear that virtual peers influence the SLT process regarding participation in
digital piracy as we might expect for traditional peers. However, the learning
process appears to operate somewhat differently when virtual, rather than
traditional peers are modeled in the social learning process of digital piracy.
For example, gender and age each appear to have a stronger impact on the
virtual version of the SLT construct in explaining participation in digital
piracy as well as traditional deviance, rather the effects on SLT are stronger
in these models. In addition, the direct effects in the virtual peer model of
gender and age on digital piracy indicate that correlates of crime will be
mediated by the learning process as suggested by SLT.
Regarding the second research question (Does SLT operate according to
the theory when virtual, rather than traditional, peers are modeled?), several
differences between the traditional and virtual peer models emerged, in terms
of the indirect effects of individual characteristics through SLT on digital
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Crime & Delinquency 
piracy. The findings suggest that differences in peer effects lie more with how
individual-level variables operate within SLT, rather than with the direct
effects of SLT.11
Specifically, gender operates via SLT in the virtual peer model, but not in
the traditional peer model. The effects of being male on digital piracy were
significantly mediated through SLT for virtual peers but not for traditional
peers. While there were no significant indirect effects of gender for traditional peers, the effect of being male on digital piracy in the virtual peer
model is in line with previous findings; being male is more predictive of
engagement in digital piracy than being female (Hinduja & Ingram, 2009;
Morris & Higgins, 2009, 2010; Skinner & Fream, 1997). This suggests that
males may be more likely to be influenced by their virtual peers than females.
In addition, gender predicted reported involvement in both online and traditional deviant behavior through the SLT construct for both traditional and
virtual peers.
A more noticeable difference is represented by the differential impact of
race between the two models. The effect of race via SLT on digital piracy was
much stronger for the virtual peers model. Race was not a significant predictor of traditional deviance for either model. However, the finding that nonWhites are significantly more likely to engage in digital piracy through the
social learning construct in the virtual peer model is consistent with previous
findings regarding the influence of race (Morris & Higgins, 2010) and may
be explained by the prospect that online social interaction is immune to many
of the cultural and racial distinctions found in traditional social settings.
Since the virtual environment lends itself toward individual anonymity, it
may be that race does not play the same role in the learning process of individuals participating in online activities as users may be less aware or unconcerned with racial differences.
Results also indicated an increased likelihood for younger individuals to
report on- and offline deviant behaviors through the SLT construct. However,
the effect of age on digital piracy, through SLT differs between models with
the effect being stronger in the traditional peers model. Younger students may
be expected to spend more time online than older students, though it may also
be that younger students also spend more time with their traditional peers, as
they are generally earlier in their college years. The findings from the traditional deviance model support this conclusion; the effect of age with SLT was
found stronger in the traditional peer model.
It remains unclear whether the differential use of the Internet among individuals reaching college age is affecting this relationship. While current
undergraduate populations are blended with those who are computer savvy
having spent the majority of their lives online with those who are slightly
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Miller and Morris
19
older but still comfortable using the computer, we might expect that future
undergraduate students will view their interactions online in an entirely different manner. As this population spends more time online developing different types of relationships, future analyses may find that the additional hours
of online activity result in higher levels of digital piracy as a result of their
online relationships or levels may even out as online activity increases.
The results from the current study differ from the only previous study to
have examined the differential impact of traditional and virtual peers (Hinduja
& Ingram, 2009). Such findings suggest that traditional peers are the most
important predictor of involvement in digital piracy, followed by virtual
sources (peers and other media sources). The results from the current study
suggest that the social learning construct operates in a similar manner for
both traditional and virtual peers for both digital piracy and traditionally
deviant behaviors. In tandem, these findings suggest that virtual peers should
be examined further in relation to their influence on the learning process as
their influence operates in a similar manner as traditional peers.
In sum, the findings offer modest support for SLT via virtual peers; however, limitations within this study offer ample opportunity for future
research.12 The current data were based on a sample of undergraduate students from a single university, future studies should consider extending the
sample to a diverse set of universities. Similarly, future studies should examine the differential influence of traditional and virtual peers on a more diverse
age group. College students comprise a unique social stratum that may differ
in peer influences from individuals of the same group in a non-university setting. Rather, peer influences may operate differently between college students and non-college students of similar ages. In addition, exploration of the
differential learning process at different age points, such as high school students, may provide additional clarification of the mediating impact of SLT on
digital piracy.
Results from the current analysis should also be considered in terms of the
implications for SLT. While findings indicate modest support for SLT for the
influence of virtual peers on digital piracy and traditional deviance, the current models do not allow for any conclusions regarding the influence of individual characteristics through SLT on other cyber-related deviance (such as
computer hacking) or other acts of traditional deviance (such as drug use).
Further exploration of the influence of various individual predictors both on
and through social learning constructs including relationships with both traditional and virtual peers will further define the importance of peer relationships in offending. Further exploration of the influence of these two types of
peers could define the importance of each of the constructs of SLT. The current study is an important first step in an attempt to explore whether there is
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Crime & Delinquency 
a differential impact of traditional and virtual peers on digital piracy. In the
end, SLT as a general theory of crime may need to be expanded to account for
differing learning processes resulting from virtual peer interaction. The
potential for this was driven by our finding that virtual peers was found to
have a stronger mediating effect on digital piracy than those with traditional
peers, suggesting that the learning process operates differently for different
types of crimes. However, considering the finding that the virtual peer SLT
construct predicted traditional offending in a statistically indistinguishable
manner to that of traditional peers, it is apparent that virtual peers also play
an important part of the learning of traditional deviance.
In closing, our findings support SLT in explaining digital piracy as well as
traditional deviance. Results suggest that virtual peers matter in a manner
similar to that of traditional peers, and their influence should be further examined in relation to both traditional and digital deviance. Future research
should examine the influence of individual characteristics as well as each of
the components of SLT as relationships with virtual peers may operate differently in certain constructs when compared with traditional peer driven models. In addition, future studies should consider the relative influence of virtual
peers on other types of crime to determine the extent to which they affect
learning of deviant activities, both digital and traditional. Understanding the
role of virtual peers in the learning process and its subsequent effect on
behavior may provide useful information in developing policy aimed at
reducing propensities toward criminal behavior.
Appendix
Social Learning Measures
Differential association with traditional peers (10 items). To the best of your
knowledge, about how many of your FACE-TO-FACE friends have engaged
in the following activities within the last year? (none) 1----2----3----4----5 (all)
1. suggested that you do something that was against the law?
2. suggested you pirate music or software online?
3. done something serious that was against the law (i.e., something that
might be punishable by jail time)?
4. tried to guess someone else’s password?
5. used the Internet to retaliate against someone who they felt did them
some wrong?
6. accessed another’s computer files without authorization?
7. used marijuana?
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8. used illicit drugs other than marijuana? (e.g., cocaine, LSD, Ecstasy,
etc.)
9. used prescription drugs that were not prescribed to them?
10. stolen someone else’s property?
Differential association with virtual peers (10 items). Think of all of your friends
with whom you only have an ONLINE relationship. To the best of your
knowledge, how many of them have done the following in the past year or so:
(none) 1----2----3----4----5 (all)
1. suggested you do something that was against the law?
2. suggested you pirate music or software online?
3. done something serious that was against the law (i.e., something that
might be punishable by jail time)?
4. tried to guess someone else’s password?
5. used the Internet to retaliate against someone who they felt did them
some wrong?
6. accessed another’s computer files without authorization?
7. used marijuana?
8. used illicit drugs other than marijuana? (e.g., cocaine, LSD, Ecstasy,
etc.)
9. used prescription drugs that were not prescribed to them?
10. admitted to stealing someone else’s property?
Imitation (four items). Please rate how much you have learned about any or all
of the above listed computer activities from the following people: (nothing)
1----2----3----4----5----6----7----8----9----10 (everything)
1.Family
2.Friends
3. Internet searches
4. Internet message boards/chat rooms
Negative definitions (nine items)
1. How wrong is it for you, or anyone else, to try to guess someone
else’s password on:
a. A social networking website (such as Myspace or Facebook)
b. A school website
c. A banking website
d. An Internet email account
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Crime & Delinquency 
2. How wrong is it for you, or anyone else, to use a social networking
site (such as Myspace, Facebook, or Twitter) to retaliate against
someone you felt did you some wrong?
3. How wrong is it for you, or anyone else, to access another’s computer
files without authorization?
4. How wrong is it for you, or anyone else, to add, delete, change, or
print another person’s computer file information without them knowing it?
5. How wrong is it for you, or anyone else, to download commercial
software, videos, or music instead of buying it (i.e., using a peer-topeer file sharing network, or torrent site)?
6. How wrong is it for you, or anyone else, to knowingly buy, sell, or
hold stolen goods or try to do any of these things?
7. How wrong is it for you, or anyone else, to use checks or credit cards
illegally to pay for something (including intentional overdrafts)?
8. How wrong is it for you, or anyone else, to use marijuana?
9. How wrong is it for you, or anyone else, to use illicit drugs other than
marijuana (e.g., cocaine, LSD, ecstasy, etc.)?
(not wrong at all) 1----2----3----4----5 (very wrong)
Differential reinforcement/punishment traditional peers (five items). To what
extent would you expect that your face-to-face friends would approve or disapprove of you participating in each of the below computer-related activities? (strongly disapprove) 1----2----3----4----5 (strongly approve)
1. Guessing someone else’s password
2. Using a social networking site (Such as Myspace, Facebook, or
Twitter) to retaliate against someone you felt did you some wrong?
3. Accessing computer files without authorization?
4. Adding, deleting, changing, or printing another person’s computer
file information without them knowing it?
5. Downloading music or software for free, instead of buying it?
Differential reinforcement/punishment virtual peers (six items). To what extent
would you expect that your online friends would approve or disapprove of
YOU participating in each of the below computer-related activities? (strongly
disapprove) 1----2----3----4----5 (strongly approve)
1. Guessing another’s password.
2. Using a social networking site (such as Myspace, Facebook, or
Twitter) to retaliate against someone you felt did you some wrong?
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Miller and Morris
23
3. Altering someone else’s personal information on a public webpage
without that person’s permission?
4. Accessing computer files without authorization?
5. Adding, deleting, changing, or printing another person’s computer
file information without them knowing it.
6. Downloading music, video, or software files without paying a fee.
Declaration of Conflicting Interests
The author(s) declared no potential conflicts of interest with respect to the research,
authorship, and/or publication of this article.
Funding
The author(s) received no financial support for the research, authorship, and/or publication of this article.
Notes
1. http://www.internetworldstats.com/stats.htm
2. See Higgins, Fell, and Wilson (2006) for complete discussion of the appropriateness of a sample of college students for studies of computer-related deviance and
Wiecko (2010) for discussion of the appropriateness of using college students for
studies in criminology and criminal justice.
3. Students were asked to refrain from completing the survey if they had already
taken it in another class. Also, six cases included missing data and were excluded
from analysis. The final sample was 454.
4. Traditional offending measures were adapted from National Youth Survey measures of deviance and anti-social behavior (see Elliott, Huizinga, & Menard,
1989). The current study did not specifically measure alternative types of traditional deviance; however, future studies should consider alternative measures
that may be more appropriate for college samples.
5. Survey questions and response categories used to measure each construct can be
found in the appendix.
6. A limitation to the measurement of peer influence is that the current data does not
allow for comparison of the proportion of an individual’s traditional to virtual
peers.
7. Survey questions addressed imitation of online deviance and did not include
measures of traditional deviance.
8. A limitation to the imitation measurement is that it deviates from Akers, Krohn,
Lanza-Kaduce, and Radosevich (1979) intended measures. However, the scale
derived from Skinner and Fream’s (1997) study allow for the inclusion of additional online modeling sources in addition to traditional models.
9. Modification indices were used to correlate specified error terms improving the
fit of each model; however, overall results were not changed.
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Crime & Delinquency 
10. In the base model in Table 2, the digital piracy outcome is log transformed to
adjust for positive skew.
11. Differential association with traditional and virtual peers was tested for both the
digital piracy and traditional offending model using the Z test for equality of
coefficients (Paternoster, Brame, Mazerolle, & Piquero, 1998) and reflected no
significant difference in the social learning process with either peer type.
12. We also ran models for each outcome that incorporated both the traditional and
virtual social learning theory (SLT) indicators simultaneously as part of SLT
measurement. As expected, the explanatory power of the SLT construct increased
using this approach.
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Miller and Morris
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Author Biographies
Brooke Miller is a doctoral student in the Criminology Program at The University of
Texas at Dallas. Her research interests include criminological theory testing, cyberrelated offending, and violent offending.
Robert G. Morris is an associate professor of criminology and director of the Center
for Crime and Justice Studies at the University of Texas at Dallas. His research surrounds contemporary issues in criminology and criminal justice and quantitative
methods and has been published in journals such as Justice Quarterly, Crime &
Delinquency, Journal of Quantitative Criminology, Intelligence, among others.
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