Gender Differences in Resistance to Schooling: The Role of Dynamic Peer-Influence and Selection Processes
Gender Differences in Resistance to Schooling: The Role of Dynamic Peer-Influence and Selection Processes
Sara Geven 0 1 2
● Jan O. Jonsson 0 1 2
● Frank van Tubergen 0 1 2
0 Department of Sociology & Social Work, King Abdul Aziz University , Abdullah Suleiman Street, Al Jamiaa District 80200 , Saudi Arabia
1 Institute for Futures Studies, Stockholm University , Box 591, 101 31 Stockholm , Sweden
2 Department of Sociology, University of Amsterdam , Nieuwe Achtergracht 1661018 WV Amsterdam , The Netherlands
Boys engage in notably higher levels of resistance to schooling than girls. While scholars argue that peer processes contribute to this gender gap, this claim has not been tested with longitudinal quantitative data. This study fills this lacuna by examining the role of dynamic peerselection and influence processes in the gender gap in resistance to schooling (i.e., arguing with teachers, skipping class, not putting effort into school, receiving punishments at school, and coming late to class) with two-wave panel data. We expect that, compared to girls, boys are more exposed and more responsive to peers who exhibit resistant behavior. We estimate hybrid models on 5448 students from 251 school classes in Sweden (14-15 years, 49% boys), and stochastic actor-based models (SIENA) on a subsample of these data (2480 students in 98 classes; 49% boys). We find that boys are more exposed to resistant friends than girls, and that adolescents are influenced by the resistant behavior of friends. These peer processes do not contribute to a widening of the gender gap in resistance to schooling, yet they contribute somewhat to the persistence of the initial gender gap. Boys are not more responsive to the resistant behavior of friends than girls. Instead, girls are influenced more by the resistant behavior of lower status friends than boys. This explains to some extent why boys increase their resistance to schooling more over time. All in all, peer-influence and selection processes seem to play a minor role in gender differences in resistance to schooling. These findings nuance under investigated claims that have been made in the literature.
Gender gap ● Student resistance ● Peer influence ● Dynamic social network analysis
In highly developed countries, girls have been academically
outperforming boys at least since the 1990s. Girls obtain
higher reading and language test scores, get higher grade
point averages, are less likely to drop out of school, and
more often enter into higher education than boys
(Buchmann et al. 2008). One possible factor underlying these
differences is the tendency for boys to exhibit more
resistance to schooling (Legewie and DiPrete 2012; Hadjar and
Buchmann 2016). Student resistance to schooling refers to a
lack of adherence to school rules and norms, such as
defying teacher authority and refusing to put effort into
school work (cf. McFarland 2001). This is sometimes also
labelled behavioral disengagement and is generally related
to lower school results and drop-out (Fredricks et al. 2004).
This study addresses the question of why boys are more
resistant to schooling than girls. Previous research has
highlighted the importance of peer processes for student
resistance (McFarland 2001), as well as for the gender gap
in it (Driessen and van Langen 2013). Qualitative studies
suggest that, compared to girls, boys are more exposed to
anti-school attitudes and behaviors in their peer groups and
experience more pressure from their peers to exhibit
anti-school attitudes and behaviors (e.g., Francis 1999;
Warrington et al. 2000). Only a few quantitative studies
have built on these research findings, and all of them are
cross-sectional. These studies use compositional
characteristics of classes or schools as a proxy for the extent to which
boys and girls are exposed to peers with anti-school
attitudes and behaviors. For example, boys’ and girls’ school
attitudes and behavior have been compared across schools
differing in their gender composition (Demanet et al. 2013;
Van Houtte 2004) or average socio-economic status
composition (Legewie and DiPrete 2012).
We improve on previous quantitative studies on the role
of peers in gender differences in student resistance in two
important ways. First, we study gender differences in peer
selection and influence processes directly, instead of
proxying them by compositional effects. Second, we use a
longitudinal approach to examine the role of classroom
peers on gender differences in school resistance. Peer
processes are dynamic, warranting a longitudinal approach,
which allows us to make better inferences about the causal
direction of the relationships between the behavior of peers
and that of adolescents (Hallinan 1981).
Adolescents may behave in similar ways as their
classroom peers for three reasons. First, adolescents tend to
adjust their behavior to the behavior of their classroom
peers (i.e., peer influence effects). Second, adolescents
behave in similar ways as their friends in class, since
adolescents are inclined to befriend peers who are similar to
them, and to unfriend peers who are dissimilar to them (i.e.,
peer selection and deselection effects). Third, adolescents
may be similar to their peers for other reasons than peer
processes. Students who attend the same school or class
tend to be exposed to similar contexts (e.g., neighborhoods)
or come from similar backgrounds (i.e., contextual or
background effects), which causes them to exhibit similar
behaviors. It is impossible to disentangle these different
effects with cross-sectional data. We will apply advanced
longitudinal statistical techniques, including longitudinal
social network analysis (i.e., Simulation Investigation for
Empirical Network Analysis, SIENA), to analyze large
scale panel data on adolescents’ friendships in class and
their school behavior in Sweden, drawn from the CILS4EU
project (Kalter et al. 2013).
Two Peer Effect Explanations
The gender gap in school resistance may be explained by a
differential peer exposure or a differential peer reaction
mechanism (Haynie et al. 2014). The exposure explanation
implies that girls are surrounded, and thus influenced, by
less deviant peers than boys. According to the peer reaction
explanation, girls are less susceptible to the influence of
deviant peer norms than boys, which causes boys to be
more deviant than girls.
In this study, “peers” refer to befriended and
nonbefriended classmates. We focus on classmates, since
student resistance is often enacted in class and classmates play
a pivotal role in a student’s decision to engage in resistant
behavior (McFarland 2001). We examine the role of
befriended and non-befriended classmates separately, since
we assume that both peer groups could be influential, yet for
different reasons. According to normative social influence
theory, people are influenced by the behavior of peers to
avoid social sanctions and to gain social approval by them
(Cialdini and Goldstein 2004). Friends in class could be
influential because they are valued peers, and adolescents
strive to maintain their friendships (Hallinan 1981).
Nonbefriended classmates could be influential because these
classmates can still become friends and adolescents may try
to impress these potential friends (Frank et al. 2008); or
because adolescents try to avoid social sanctions in class,
such as mockery. Compared to friends, non-friends may be
less accepting of “inappropriate” behavior (Müller et al.
2016). Finally, the behavior of non-befriended classmates
may set a norm to which students want to conform.
In previous studies, it is unclear whether befriended or
non-befriended peers are more influential with respect to
boys’ and girls’ school outcomes. For example, Molloy et al.
(2011) suggest that adolescents’ effort in school is
influenced more by friends in class than by classmates that
adolescents are less strongly connected to. However, a
study by Frank et al. (2008) indicates that girls’ decision to
advance in math is not influenced by friends in class, but
only by more distant peer groups (i.e., female schoolmates
and female students who follow the same courses). Finally,
Müller et al. (2016) find that the perceived disruptive
behavior of all classmates, high-status classmates, and
friends in class equally influence a student’s own disruptive
behavior. By studying befriended and non-befriended
classmates separately—rather than assuming that they are
equally influential—we aim to gain a deeper understanding
of the role of peers in the gender gap in student resistance to
Differential Peer Exposure Explanation
While boys and girls who attend the same class are exposed
to the same classmates, they tend to befriend and interact
with different classmates. Boys may not deliberately
befriend peers who exhibit higher levels of resistant
behavior, yet other friendship selection processes are expected to
expose boys to higher levels of student resistance in their
friendship group than girls. First, gender homophily—the
tendency for boys to befriend boys, and for girls to befriend
girls (McPherson et al. 2001)—could lead to gender
differences in the exposure to resistant friends, simply because
boys generally exhibit more resistant behavior than girls
(Buchmann et al. 2008). Second, we expect that adolescents
befriend others with similar values and tastes (McPherson
et al. 2001), including resistant behavior. Previous studies
have found such tendencies with respect to homework
behavior and attentive behavior in school (Geven et al.
2013), externalizing problem behavior in school (Fortuin
et al. 2015), truancy (Rambaran et al. 2016), and academic
achievement (Flashman 2012; Gremmen et al. 2017).
Classmates’ resistance to schooling is highly visible in class,
and adolescents may use this behavior as a signal of
similarity. Moreover, adolescents might engage in resistant
behavior, such as skipping class, together with other
classmates. Such shared “activities” can lead to friendships.
Since boys generally show more resistance to schooling
than girls, homophily with respect to school resistance
implies that boys—more often than girls—will befriend
peers who exhibit higher levels of school resistance.
There are two ways in which gender differences in the
exposure to resistant friends could contribute to the gender
gap in student resistance. First, they could lead to an
increase in the gender gap in resistance to schooling over
time. This may occur if adolescents who are exposed to
more resistant friends are inclined to increase their
resistance to schooling more. This type of influence processes is
also referred to as contagion (see Fig. 1). Engagement in
minor forms of deviant behavior is related to social rewards
and status in adolescent peer groups, as it is a way to show
autonomy from adults (Moffitt 1993). Resistance to
schooling can be seen as a form of deviance that is related to
status in some adolescent friendship groups (Demanet and
Van Houtte 2012). In friendship groups in which the level
of resistant behavior is higher, adolescents may experience
more stimulation or pressure to increase their own
resistance to schooling, learning from their friends that this is a
way to gain status. Since boys are generally embedded in
friendship groups in which the level of resistant behavior is
higher, boys will increase their resistance to schooling more
than girls. Consequently, the gender gap in resistance to
schooling will increase.
Second, gender differences in the exposure to friends
could lead to the persistence of initial gender differences in
resistance to schooling. This may occur if adolescents tend
to grow similar to the average behavior of their friends.
More specifically, adolescents will decrease their resistant
behavior when they are exposed to friends who exhibit less
resistant behavior than they do, while they will increase
their resistant behavior when they are exposed to friends
who exhibit more resistant behavior than they do (see
Fig. 1). This type of influence process is also referred to as
convergence. Research has found support for convergence
processes among friends with respect to attention in class,
doing homework (Geven et al. 2013), truancy (Rambaran
et al. 2016), and externalizing problem behavior (Fortuin
et al. 2015). When girls grow similar to the average
behavior of their primarily female friends, and boys grow similar
to the average behavior of their primarily male friends, the
gender gap in resistance to schooling will remain stable
Fig. 1 Convergence and
contagion processes in two peer
groups. Note: Each circle
represents a person. The color of
the circle indicates a person’s
level of student resistance (SR).
Darker colors imply higher
levels of student resistance.
Group A represents a male
friendship group, and group B a
female friendship group. The
right pictures show the level of
SR after convergence (top
picture) and after contagion
(bottom picture) at time point 1.
In male friendship group the
initial level of SR is higher than
in the female friendship group.
After convergence, the average
SR remains the same in both
groups, and the gender
difference in SR is not altered.
After contagion, people in group
A increase their SR more than
people in group B. In other
words: the gender gap increases.
over time (see Fig. 1). In line with this, simulation studies
indicate that, when people interact with others with similar
opinions (homophily) and their own opinions converge to
those of their interaction partners, segregated opinion
clusters emerge (Mäs and Flache 2013). However, the
difference in the average opinions of these clusters do not
become larger over time.
Differential Peer Reaction Explanation
Normative social influence theory predicts that the influence
of befriended and non-befriended classmates is stronger
with respect to types of behavior that lead to social rewards.
In general, engaging in gender-typical behavior is more
socially accepted than engaging in gender atypical behavior
(Rose et al. 2011). According to Adler et al. (1992), boys’
gender roles have traditionally been marked by a more
“active” nature than that of girls. Ethnographic work
indicates that pro-school behavior is in conflict with the male
image (e.g., Francis 1999), and that boys receive their status
from, among other things, breaking the rules and disobeying
adult authority such as teachers (e.g. Jackson 2003).
Although performing well in school is neither for adolescent
boys, nor for adolescent girls, an important source of social
status, pro-school behaviors may be detrimental to boys’
status (Willis 1977). Compared to girls, boys risk social
punishments by peers when they engage in pro-school
behaviors and they experience more peer pressure to exhibit
anti-school behaviors (Warrington et al. 2000).
Quantitative research has shown little support for gender
differences in the relationship between academic
performance or intelligence and social status (Rose et al. 2011).
However, boys may not derive their social status from their
low school performance, but rather from their active
disengagement and rejection of school. In line with this,
research shows that German adolescents allocate higher
social status ratings to descriptions of male student showing
low school effort than to descriptions of female students
showing low school effort (Rentzsch et al. 2011). A recent
German study finds no support for this relation, but shows
that adolescents associate descriptions of students showing
low school effort with masculinity and boy-typicality
(Heyder and Kessels 2016). This may imply that showing
low school effort is more important for boys than for girls.
All in all, we assume that exhibiting resistant behavior
carries more social value for boys than for girls, and it is
also likely that boys will be influenced more by the resistant
behavior of their befriended and non-befriended classmates.
However, social influence theory suggests that boys may
not be more responsive to the resistant behavior of all their
peers, but primarily to high status peers because the
imitation of their behavior is related to greater social rewards
(Shi and Xie 2012). Students may believe that the emulation
of high status peers enhances their own social status (Cohen
and Prinstein 2006) or that it increases their likelihood for
inclusion in (a) high-status peer group(s) (Dijkstra et al.
2008). Moreover, imitating high status peers can lead to
feelings of reflected glory (i.e., feelings of success related to
the association with successful others) (Cialdini et al. 1976;
Dijkstra et al. 2008). Because the engagement in resistant
behavior seems more related to the social status of
boys than to that of girls, we expect that boys will be
influenced more by the resistant behavior of higher status
peers than girls.
It is also possible that boys and girls generally respond to
the resistant behavior of different classmates. According to
social influence theory, people are influenced more by those
who are similar to themselves with respect to central aspects
of their identity (e.g., sex). The behavior of in-group
members forms a reference on how one should behave as a
member of the group (Mason et al. 2007). For example,
boys look at other boys to learn how to behave as males.
Ingroup members are particularly influential on attitudes and
behaviors that are important to the identity of the group
(Wood 2000). Since adolescents may express their
femininity or masculinity through their school behavior (Francis
1999), we expect that the school behavior of same-sex
classmates has a stronger positive influence on adolescents’
school behavior than that of opposite-sex classmates.
The tendency to be primarily influenced by same-sex
classmates could lead to an increasing or persistent gender
gap in student resistance. If students are influenced by the
resistant behavior of same-sex classmates via contagion, a
feedback process may emerge that causes boys—who have
a higher initial propensity to exhibit resistant behavior—to
increase such behavior even more, leading to growing
gender differences in student resistance. If adolescents are
influenced to converge their resistant behavior to those of
same-sex classmates, boys will adjust their behavior to the
average norm for boys, while girls will adjust their behavior
to the average norm for girls. This may lead to a stable
gender gap in student resistance.
This study aims to examine the role of peer-influence and
selection processes in school classes for gender differences
in student resistance. We contribute to previous research by
explicitly and directly assessing the role of befriended and
non-befriended classmates, and by using longitudinal data
and novel statistical methods that enable us to better identify
We argue that gender differences in the exposure and the
response to resistant classmates could lead to time-stable or
even increasing gender differences in student resistance to
schooling. More specifically, we hypothesize that the friends
of boys exhibit higher levels of resistant behavior than the
friends of girls (hypothesis 1a). This in turn will cause boys
to exhibit higher levels of resistance to schooling than girls,
since the resistant behavior of friends positively influences
the resistant behavior of the adolescent (hypothesis 1b).
In addition, we expect that, compared to girls, boys are
more positively influenced by the resistant behavior of their
befriended (hypothesis 2a) and non-befriended classmates
(hypothesis 2b), and that this is accentuated when these have
a higher social status (hypothesis 3a and 3b). Finally, we
hypothesize that adolescents are influenced more by the
resistant behavior of same-sex classmates than that of
opposite-sex classmates (hypothesis 4). We can only test this
hypothesis for non-befriended classmates, as most
adolescents do not have opposite-sex friends (more than 85% of
the friendships in our data are same-sex friendships).
We test the hypotheses on two-wave panel data of
Swedish adolescents (14–15 years old in wave 1). We
believe that it may be possible to generalize the case of
Sweden to other countries, as we expect that social
influence and homophily are predominantly generic processes.
Although gender equality is generally high in Sweden, a
substantial gender segregation is apparent in educational
choices, and girls have dominated higher education since
the early 1980s (Jonsson 1999).
We conduct two different types of analyses to model
student resistance to schooling… First, we perform hybrid
models. Second, we use SIENA (Simulation Investigation
for Empirical Network Analysis), a longitudinal social
network approach, to retest all the hypotheses with respect
to befriended classmates (hypothesis 1, 2a, and 3a). In the
SIENA models we also model friendship selection
processes in class as a dependent variable.
Data on Swedish adolescents are drawn from the CILS4EU
data (Kalter et al. 2013). All participants attended 8th and
9th grade (wave 2) of comprehensive school, and a large
majority of them went to a public school (Jonsson and
Mood 2008). The data contain student reports on their
friendships in class (i.e., complete network data) and their
resistance to schooling.
CILS4EU used a multi-stage stratified sampling design.
First, schools were divided into strata according to the
proportion of minority students, oversampling
immigrantdense schools. Within strata, schools were randomly
selected with the sampling probability being proportional to
the number of students. Subsequently, all students in two
randomly selected classes were invited to participate.
Questionnaires were filled out in class and supervised by a
In Sweden, 5025 students in 251 classes in 129 schools
participated in the first wave in the school year of
2010–2011. The response rate was 76.8% at the
schoollevel and 86.1% at the student-level. All but one school
(98.5%) participated again 1 year later. 4110 students
participated in both waves, and 5448 in one of the waves.
The Swedish data are highly suitable for a longitudinal
investigation of peer processes in class. First, mobility
across school classes is relatively low. On average 77% of a
Swedish student’s classmates in the first wave were also his/
her classmates in the subsequent wave. Second, the school
class is a natural unit to which most educational activities
are confined—students usually only mix with those from
other classes in a few subjects. Relatedly, the class is an
important context for friendship formation in school in
Sweden. In the first wave of the data, 75% of all
schoolbased friendships are friendships with classmates. Third,
student participation rates are relatively high in both waves.
Unfortunately, information on students in the Netherlands,
Germany, and England in the CILS4EU data are much less
appropriate for analyzing peer processes in class
longitudinally (see Appendix A1), hence the focus on Sweden.
Five items measure student resistance to schooling: the
extent to which adolescents argue with teachers, get a
punishment in school, skip class, come late to school, and
put a lot of effort into school. Response categories are on a
5-point scale, and range from “never” to “every day” for the
first four items, and from “strongly agree” to “strongly
disagree” for the last item. Higher values thus imply higher
student resistance. The five items load on one factor
(Cronbach’s alpha 0.74 in wave 1 and 0.70 in wave 2), and
item loadings are all above 0.6. For the hybrid model,
student resistant behavior is measured by a student’s
average score on the items. In SIENA only ordinal behavioral
outcome variables with a maximum of ten categories can be
analyzed. Hence, we round the average resistant behavior
score to the nearest half, and recode this value to an ordinal
scale that ranges from 1 to 9 (e.g., a score of 0 becomes 1,
0.5 becomes 2 etc.). The resulting ordinal variable correlates
highly with the non-rounded variable (0.98 in both waves).
Predictors of student resistance
Boy In SIENA, a positive effect indicates that, compared
to girls, boys have a higher tendency to increase their
resistance to schooling. In the hybrid model we estimate a
time-constant and time-varying effect of gender, the latter
by including an interaction between gender and time.
Resistance friends Adolescents were asked to list their best
friends in class (maximum of 5). The resistant behavior of
friends is measured by friends’ average score on the
student resistance variable. In SIENA we specify the
influence of the behavior of friends with the “Average Alter”
effect: If it is positive, an adolescent tends to increase his/her
resistant behavior more when the average resistant behavior
of his/her friends is higher. The average alter effect
represents a “contagion” type of influence (Veenstra et al. 2013)
(see Fig. 1).1
Resistance non-friends In the hybrid models we estimate
the effect of the resistant behavior of classmates who are not
nominated as friends, using their average score on the
student resistance scale.
Resistance male non-friends and resistance female
nonfriends In the final hybrid model we distinguish between
the average student resistance of non-befriended males and
that of non-befriended females.
Status friends and status non-friends The higher number of
incoming friendship ties (i.e., indegree), the higher social
status. In SIENA we include the average indegree of the
adolescent’s friends (i.e., the Popularity Alter effect). In the
hybrid model we include the average indegree of friends
(social status friends) and non-friends in class (social status
Student resistance control variables
To correctly estimate social influence processes in SIENA,
we control for the linear shape and the quadratic shape
effects (Ripley et al. 2017). A positive linear shape effect
implies that people are inclined to have high values on the
dependent variable. A positive quadratic shape effect
implies that students reinforce their own resistant behavior,
whereas a negative quadratic shape effect implies that
students self-correct their resistant behavior. In the hybrid
models we include a time dummy to model whether
students increase or decrease their resistance to schooling
across the two waves.
1 Since we merely examine the increase in the gender gap in resistance
to schooling in SIENA, we use a peer influence effect that is likely to
contribute to the increase in the gender gap over time (see Fig. 1). In
Appendix A5 we present a model in which we use a peer influence
measure in SIENA that captures a convergence type of influence
We control for parental education. When parents value
education and stimulate school work, adolescents might be
influenced less by their friends’ resistance to schooling.
Parental education is used as a proxy for parental values and
support and is measured by the educational level of the
parent with the highest acquired qualification. For most
parents this information is obtained from register data from
Finally, in the hybrid models, we include the proportion
of non-befriended classmates that are boys (Proportion
nonbefriended boys) and the social status of the adolescent as
measured by his/her indegree. In the SIENA models we try
to not include too many (unnecessary) control variables, as
the statistical power of the analyses is somewhat limited.
However, to make sure that we do not fail to include
important controls, we score-type tested two possible
additional control variables in SIENA (Ripley et al. 2017),
namely the adolescent’s indegree and his/her number of
outgoing friendship nominations (i.e., outdegree). None of
these were significant predictors of student resistance to
schooling (results available upon request).
Student friendship nominations in class (up to 5) are
modelled as a dependent variable in the SIENA model.
Predictors of friendship networks
Sex homophily This effect indicates whether adolescents
are more likely to befriend same-sex classmates than
Resistance homophily This effect indicates the tendency to
befriend classmates who exhibit similar levels of resistance
Friendship network control variables
Homophily effects are dependent on characteristics of the
adolescent (i.e., ego) and his/her friend (i.e., alter). Hence,
we include the effect of adolescents’ resistant behavior on
their tendency to nominate friends (Resistance ego) and to
be nominated as a friend (Resistance alter). Similarly, we
control for the effect of a student’s sex on the tendency to
nominate friends (Boy ego) and to be nominated as a friend
Student national-origin background might affect
friendship selection processes. We estimate whether respondents
who have two Swedish-born parents (rather than one or two
foreign-born parents) are more likely to nominate
classmates as a friend (Native Ego) and to be nominated as a
friend (Native Alter). The National-Origin homophily effect
indicates whether adolescents tend to befriend classmates of
the same national-origin background, as defined by the
country of birth of the respondent’s parents. When parents
were not born in the same country, the background of the
student is based on the foreign-born parent. When parents
were born in different foreign countries, the background of
the student is based on the mother’s country of birth. The
national-origin background of the respondent is based on
the respondent’s country of birth when the parental country
of birth is missing (<2 %).
We include several structural network effects. Not
accounting for these effects may lead to biased estimates
(Ripley et al. 2017). The outdegree effect expresses a
student’s tendency to nominate classmates as a friend (Steglich
et al. 2010). Reciprocity refers to the inclination to
reciprocate friendship ties; transitive triplets accounts for the
fact that people tend to befriend friends of friends, and the
3-cycle effect controls for egalitarian triadic closure (which
means that all members in a triad—three actors—are
connected, and receive an equal number of nominations).
Finally, we include two additional outdegree effects, as
initial Goodness of Fit tests indicated that the SIENA
models underestimated the number of students with low
outdegrees. We include the outdegree activity and the
outdegree activity sqrt effects. These are, respectively, the
squared outdegree of the actor, and the outdegree^1.5,
representing non-linear preferences in the number of
outgoing friendship ties.
Plan of Analyses
We estimate hybrid models in Stata 14. Hybrid models
combine the advantages of fixed effect and random effect
models (Allison 2009). Similar to fixed effect models, they
allow for the estimation of changes within individuals,
while accounting for time-invariant effects of time-invariant
characteristics of people (i.e., contextual or background
effects, such as family or neighborhood characteristics).
This is important as these may cause (some of) the
similarity between adolescents and their classmates.
Similar to random effect models, hybrid models allow for
the estimation of time-invariant effects of time-invariant
variables. Hence, we are able to estimate the extent to which
boys generally exhibit higher levels of resistant behavior
than girls (i.e., the initial gender gap) and to explore the
extent to which peer processes contribute to the stability of
the initial gender gap.
We account for the nested structure of the data (i.e., time
points are nested in students, who are nested in classes). A
hybrid model with this nested structure can be expressed by
the following formula:
In this formula ytic refers to the resistant behavior at time
point t of student i in class c. β10 is the estimate of an effect
of a time-varying variable, such as time. β10 is the effect of a
time-constant variable, such as gender. β11 represents the
effect of an interaction between time and gender (i.e., the
extent to which boys increase their resistant behavior more
than girls over time). β02 represents the within-individual
effect of the time varying variable x2, such as the resistant
behavior of befriended classmates, while β20 is the
betweenindividual effect of variable x2. More specifically, β20 is the
effect of the respondent’s mean on the time-varying
characteristic; β02 is the effect of respondent’s deviation from
his/her personal mean at a specific time point.
We estimate hybrid models with robust standard errors
(i.e., Huber White estimator), to correct for non-normally
distributed residual errors. We analyze data of all students
who participated in at least one of the two waves. We
impute missing values by means of multiple imputation
with chained equations. We impute ten datasets in a wide
format, so that a non-missing value on a variable in one
wave can be used to impute a missing value on that same
variable in another wave (Young and Johnson 2015). The
imputation model includes all independent variables, the
dependent variable, and a dummy for the student’s school
In the hybrid models we are not able to appropriately
disentangle the influence of friends from friendship selection
processes. Hence, we retest the hypotheses with respect to
befriended classmates in SIENA, specifically designed to
separate these processes by using longitudinal information
and stochastic actor-oriented modeling (Steglich et al.
2010). SIENA has a “friendship selection part” in which
changes in student friendship networks are modelled as a
dependent variable; and a “behavioral part” in which
changes in student resistance are modeled as a dependent
variable. The evolution of student networks and student
behavior are treated as endogenous and interdependent
processes. SIENA assumes that changes in people’s
behavior and their network may occur in between observation
points (Steglich et al. 2010). More specifically, it simulates
the changes in the network and the behavior of respondents
in between the waves. Estimates in SIENA are based on
these simulations. It is possible to control for other
important endogenous network processes, such as the
inclination to reciprocate friendships and to befriend friends
of friends, that may lead to behavioral similarity among
There are also disadvantages of SIENA in comparison to
the hybrid models. First, it is not possible to account for the
effect of unobserved time-invariant characteristics in
SIENA (Steglich et al. 2010). Second, we cannot model the
influence of the behavior of non-befriended classmates.2
Finally, the SIENA data requirements are rather stringent.
This means that in many studies, including the present one,
researchers can only rely on a subsample of their data.
SIENA uses data on relationships between people within
a certain setting, such as a school class (i.e., complete
network data). It requires that no more than 40% of the
students join or leave the class after the first wave (Lubbers
et al. 2011) and that at least 80% of the students participate
in each wave (Ripley et al. 2017). Moreover, for estimates
to be reliable, friendship networks have to be stable enough
(as indicated by a Jaccard index >0.2) (Snijders et al.
2010).3 Two thousand six hundred and seventy one
adolescents in 108 classes and 78 schools meet these data
requirements (46% of the total sample). In addition, we
drop 10 classes (191 students), because they cause
convergence problems.4 In Appendix A2 we provide
information on the extent to which the students that are included in
the SIENA models differ from the students that are
excluded. Although several students are excluded, the SIENA
sample is unique in its size and representativeness. Most
previous studies that use social network techniques rely on
samples from far smaller and more restricted datasets,
e.g., all students from a couple of schools (e.g., Haynie et al.
2014) or students from classes in a particular city
(e.g., Rambaran et al. 2013).
The CILS4EU data contain multiple networks (i.e., school
classes). Ideally, these should be analyzed separately, and
2 We tried to estimate the influence of non-befriended classmates in
SIENA. A student’s non-friends’ network in class is the mirror-image
of their friendship network in class. Hence, the evolution of these
networks cannot be modeled simultaneously. A SIENA model on the
evolution of the non-friends network in class, assumes that the
adolescent and the structure of his/her non-friends network affect who he/
she does not pick as a friend. Unfortunately SIENA models on the
selection of non-friends and the subsequent influence of non-friends do
not converge. We considered including the resistant behavior of
nonfriends as a covariate in the SIENA model. However, with two waves
of data, only constant covariates can be included in the model. This
would assume that the network and the behavior of non-friends are
static, and changes in either one of them will not be accounted for.
3 Two hundred forty six classes participated in the sociometric survey
in both waves. 16 classes are dropped because the composition of the
classes changed too much across the waves. An additional 121 classes
are dropped, because student participation rates in either one of the two
waves was too low. One class was dropped because its Jaccard index
was too low.
4 Overall maximum convergence ratio <0.25.
subsequently be combined in a meta-analysis (Snijders and
Baerveldt 2003). However, we do not have enough statistical
power to apply this approach, as the average school class
only consists of 25 students, and we only have two waves of
data.5 Hence, we take a two-step approach (see Fortuin et al.
2015). First, we combine classes together in multiple
multigroup analyses in SIENA. Second, we perform a
metaanalyses on these multi-group analyses.
Classes that are grouped together with the multi-group
approach in SIENA are not assumed to be related to each
other; ties across the classes are not permitted. However, all
parameters, except for the rate parameter, are assumed to be
the same for classes that are combined (see Appendix A3).
Hence, we combine classes in a multi-group model on the
basis of their gender composition, as the gender composition
may impact the parameters of the hypothesized effects (i.e.,
gender homophily in friendships, student resistance
homophily in friendships, gender differences in resistant behavior,
and (gendered) influence processes with respect to resistant
behavior among friends) and the general level of resistant
behavior in class (Demanet et al. 2013). We sort the classes
by their share of boys, and split the data in 18 groups of six
classes (i.e., groups of about 150 students). We combine six
classes in one multi-group model to ensure that we have
enough statistical power. Because students were only allowed
to nominate up to five classmates, we set the maximum
outdegree for the simulated networks to five in the analyses.
We combine the 18 multi-group analyses in a
metaanalyses. The meta-analyses provide a joint significance test
and an estimate for each effect based on Snijders and
Baerveldt’s (2003) method. The meta-analyses also indicate
whether effects significantly vary across the 18 groups, i.e.,
across classes that differ in their gender composition.
Finally, they provide Fisher-type tests that indicate whether
a parameter is significantly smaller or larger than zero in
any of the subgroups.
Testing the hypotheses
We are interested in the extent to which peer processes
contribute to (time-stable and/or increasing) gender
differences in resistance to schooling. Ideally, we want to test
whether these gender differences are mediated by peer
processes. Unfortunately, there are no formal mediation
tests available in SIENA and multilevel mediation models
on our data do not converge in Stata (i.e., note that we have
a complex multi-level model with three-levels, various
interactions, and multiple imputed data). Hence, we follow
the approach by Stark (2015) who tests for mediation in
5 In a model without interaction effects, we already estimate 7 effects
in the behavioral part, and 15 effects in the friendship selection part of
SIENA by comparing estimates from a model with and a
model without the hypothesized mediator(s). If the
coefficient is reduced after the possible mediator(s) are included,
there is support for mediation. In a first model, we examine
gender differences in resistance to schooling. In a second, we
test whether resistance to schooling is related to friends’
resistance (hypothesis 1b), and whether gender differences
are reduced when accounting for friends’ resistance
(hypothesis 1). In the hybrid models, we are able to examine
the reduction in the time-stable gender difference, and the
reduction in the increase in the gender difference over time.
In the SIENA models, these two effects cannot be separated,
and we examine whether the effect of gender on students’
likelihood to increase their resistance to schooling turns to
insignificance. SIENA estimates are based on simulations
and hence are slightly different in different models.
Moreover, SIENA estimates are expressed as log-odds, which
cannot be compared across models (Mood 2010). The gender
estimate in the SIENA model may go up after the resistant
behavior of friends is added to the model, because the
unobserved heterogeneity in the model decreases.
We also test whether boys are more exposed to resistant
friends than girls (hypothesis 1a). We perform a t-test to
examine gender differences in the resistant behavior of
friends. Moreover, in the friendship selection part in SIENA,
we test for friendship selection processes that are expected to
be responsible for boys’ greater exposure to resistant friends.
More specifically, we model the tendency of adolescents to
befriend same-sex classmates and to befriend classmates who
exhibit similar levels of resistance to schooling.
To test hypothesis 2a and 2b, we include an interaction
between gender and the within-individual resistant behavior
of (non-)friends in the hybrid model. Positive interaction
effects indicate that an increase in the resistant behavior of
(non-)friends is more positively related to an increase in boys’
resistance to schooling than that of girls. In SIENA, we test an
interaction between the respondent’s gender and the resistance
of friends by means of score-type tests (i.e., an interaction
between the Average Alter effect and the Boy ego effect in the
behavioral part of the SIENA model). A left-sided test
indicates whether the effect is smaller than zero, and a right-sided
test indicates whether the effect is larger than zero. Because
the effect is tested twice, we use a significance level of α/2
(=0.05) (Ripley et al. 2017). Score-type tests do not provide
an estimate for the interaction effect, but are preferred over
directly estimating the effect, since the latter is likely to lead to
convergence problems (Mercken et al. 2010).
In a subsequent model we test whether boys, as
compared to girls, are more influenced by the resistant behavior
of (non-)friends with a higher social status (hypothesis 3a
and 3b). In the hybrid model, we include a three-way
interaction between gender, the within-individual effect of
the resistant behavior of (non-)friends, and the
betweenindividual effect of the social status of (non-)friends.6 We
control for all the two-way interaction effects that are
underlying this three-way interaction. In SIENA, we use
score-type tests for the three-way interaction between the
respondent’s gender (i.e., Boy ego effect), the social status
of friends (i.e., Popularity Alter effect), and the resistance of
friends (i.e., Average Alter effect) as well as for all
underlying two-way interactions.
In the final hybrid model, we test hypothesis 4. We
include separate variables for the resistant behavior of
nonbefriended girls and boys. Moreover, we include
interactions between the respondent’s gender and the
withinindividual effect of the resistant behavior of non-befriended
boys and girls. We test whether an increase in the resistant
behavior of non-befriended classmates of the same sex is
more positively related to an increase in resistant behavior
than the corresponding increase of those of the opposite sex.
Table 1 presents the descriptives of the sample for the
hybrid models (i.e., the full sample), and Table 2 shows the
descriptives of the SIENA subsample. As expected, boys
seem to exhibit higher levels of resistance to schooling than
girls. In the full sample, boys’ level of resistance is 0.11
higher in wave 1, and 0.14 higher in wave 2. T-tests indicate
that these gender differences are significant (wave 1: t
(5011) = −6.676, p < 0.001; wave 2: t(4502) = −7.481, p
< .001), and that boys increase their resistant behavior
slightly more than girls over time (gender difference in
increase is 0.034, t(4091) = −2.231, p = 0.026). Table 1
also indicates that the friends of boys exhibit higher levels
of resistant behavior than the friends of girls. In line with
hypothesis 1a, the resistant behavior of the friends of boys
is 0.08 higher than the resistant behavior of the friends
of girls in both waves (Table 1). T-tests indicate that these
differences are significant (wave 1:t(4767) = −6.085,
p < 0.001; wave 2:t(4383) = −5.912, p < .001).
The results of the hybrid models are presented in Table 3. In
the first model we include the effect of time, gender, the
interaction between them, and the control variables. The
model shows that boys’ level of resistance is 0.125 (0.21 of
6 We do not include it as a time-variant characteristics, because of
ceiling effects (i.e., (non-)friends with a high status may not be able to
increase their status even more). Moreover, the theory does not posit
that (non-)friends who increase their social status have a larger
a standard deviation) higher than that of girls, and that boys
increase their resistant behavior by 0.029 more than girls
(i.e., boy*time interaction) between the two waves.
In model 2, we add the resistant behavior of friends. We
find that a one-unit increase in the resistance of friends is
related to a 0.115 increase in adolescents’ resistance to
schooling (supporting hypothesis 1b). Compared to model
1, the time-stable gender difference in resistance to
schooling is reduced by 15% (i.e., (1-(0.106/0.125)*100).7
However, the increase in the gender gap over time is not
reduced. Hence, we find limited support for hypothesis 1.
Model 3 adds the behavior of non-friends in class. A
one-unit increase in the resistant behavior of non-befriended
classmates is related to a 0.120 increase in the resistant
behavior of the respondent,8 very similar to the effect of
In model 4, we test whether the resistant behavior of
friends and non-friends, respectively, is more positively
related to the resistant behavior of boys than that of girls
(hypothesis 2a and 2b).9 Our results are in fact contrary to
these assumptions. For girls, a one-unit increase in the
resistant behavior of friends is related to a 0.158 increase in
their resistance to schooling (p < 0.001), while the
corresponding figure for boys is 0.077 (p = 0.012). The
interaction between gender and the resistant behavior of
nonfriends is also negative, and the point estimate of similar
size, but not statistically significant. Compared to the
previous model without the interaction effects, the (increase in
the) gender gap is not altered.
Next, we examine whether boys are more influenced than
girls by the resistant behavior of high-status friends (model
5) and non-friends (model 6). In model 5, the interaction
between the between-individual effect of the social status of
friends and the within-individual effect of the resistant
7 It is not possible to conduct a statistical test to examine whether the
‘boy’ coefficient significantly varies across the multi-level models in
Stata (i.e., the suest command does not work for xtmixed models).
8 At the between-individual level in model 3, the resistant behavior of
befriended and non-befriended classmates are negatively related to that
of the student. These effects do not capture influence effects, but are a
logical consequence of the model. Model 3 expresses the relation
between the resistant behavior of friends and that of the adolescent,
given the resistant behavior of non-friends. We are thus comparing
students whose non-friends exhibit the same level of resistance. This
makes it likely that we are comparing students who are part of the
same/a similar friendship group. A student who is the most resistant
person in his/her friendship group will logically have less resistant
friends than a student who is the least resistant person in his/her
friendship group (i.e., the relation is negative). The relation between
the resistant behavior of non-friends and that of the student could be
negative for similar reasons. Students who exhibit relatively high
levels of resistant behavior in a class will logically have classmates
who exhibit lower levels of resistant behavior than students who
exhibit relatively low levels of resistant behavior in a class.
9 We also tested these interactions in separate models, this did not
alter the conclusions.
behavior of friends is positive and borderline significant.
This indicates that an increase in the resistant behavior of
friends is more positively related to an increase in the
adolescent’s resistant behavior when the average social
status of friends is higher. In line with hypothesis 3a, this is
more so for boys than for girls (i.e., the three-way
interaction between gender, the resistant behavior of friends, and
the social status of friends is positive and significant).
To shed more light on this three-way interaction effect,
we plot the average marginal effects of the resistant
behavior of friends for different values of friends’ social status
for boys and girls. Figure 2 indicates that when the average
social status of friends is low (about two incoming
friendship ties or less), an increase in their resistant behavior is
more positively related to an increase in the resistant
behavior of girls than that of boys. The corresponding
gender difference in the influence of friends with a higher
social status is well covered by the confidence intervals.
The average social status of friends is positively
correlated with their average resistant behavior (0.2 in wave 1
and 0.4 in wave 2). The significant three-way interaction
thus implies that, compared to girls, boys are less influenced
by friends who tend to exhibit low levels of resistance to
schooling. This accounts for some of the increase in the
gender gap over time. Compared to the previous model, the
increase in the gender gap (i.e. the boy*time interaction) is
reduced by 23% (from 0.030 to 0.023).10 We do not find
statistically significant interactions between the social status
of non-friends, the resistant behavior of non-friends and
gender (hypothesis 3b; model 6).
Finally, we test whether the resistant behavior of same-sex
non-friends is more positively related to adolescents’ school
resistance than the resistant behavior of non-friends’
opposite-sex classmates (model 7, hypothesis 4). For girls the
results are in line with the hypothesis. A one-unit increase in
resistant behavior of male non-friends is related to a 0.068
increase in the resistant behavior of girls, while the
corresponding effect of female non-friends is 0.119. However, the
difference in the sizes of these two relationships is
statistically insignificant (F-test (1, 0) = 0.77, p = 0.379). For boys,
neither the same-sex effect of 0.042 (p = 0.207) nor the
opposite-sex effect of 0.055 (p = 0.334) is statistically
significant, which also is true of the difference between these
estimates (F-test (1, 116.9) = 0.03, p = 0.856).
Table 4 shows the results of the SIENA analyses on
students’ resistance to schooling and their friendships in class.
10 It is not possible to conduct a statistical test to examine whether the
‘boy*time’ coefficient significantly varies across the multi-level models
in Stata (i.e., the suest command does not work for xtmixed models).
Table 1 Descriptive statistics
for the hybrid analyses. N
individuals = 5448; N school
classes = 251
Mean (s.d.) Range
Mean (s.d.) Range
Individual independent variablesc
5.40 % join
class in w2
class in w2
6 6 1 6 4 7 4 6
.000 .010 .090 .107 .000 .001 .208 .002
.0600 .1700 .8800 .8810 .0400 .1600 .8100 .0900 .1600 .2400
.3000 **.0700 .6900 **.3970 **.1100 .3200 **.5220 .4100 +.2600 *.4900
The estimates are presented in log-odds-ratio’s (Ripley et al.
2017). Model 1 indicates that the odds for boys to
increase their resistance to schooling (rather than not) is
1.267 (OR = e0.237 = 1.267) greater than the corresponding
odds for girls.
We hypothesized that boys are more exposed to resistant
behavior in their friendship group (hypothesis 1a, which
was supported by t-tests), because adolescents tend to
befriend same-sex peers and peers who exhibit similar
levels of resistant behavior. We do find positive and
significant gender homophily effects and resistance homophily
effects in all the SIENA models, supporting the idea that
adolescents tend to befriend classmates of the same sex and
who engage in similar levels of resistance to schooling.
In model 2 we find that the resistant behavior of friends
positively and significantly influences the resistant behavior
of adolescents, supporting hypothesis 1b. When friends
score one point higher on student resistance, adolescents’
odds of increasing their resistant behavior are 1.261 higher
than their odds of maintaining their initial level (e 0.232).
When accounting for the effect of friends’ resistant
behavior, the boy effect reduces slightly, and remains
statistically significant. While we cannot conclude that there is no
mediation (see page 18; and Mood 2010), the results
suggest that the resistant behavior of friends does not fully
account for the gender gap in the increase in resistance to
Is there a gender difference in the responsiveness to the
resistant behavior of friends (hypothesis 2a)? Score type
tests show that the interaction between gender and the
resistant behavior of friends in model 3 is not statistically
significant (χ2(36) = 30.705, p = 0.718; left-sided
scoretype test: χ2(36) = 46.011, p = 0.123).11 This finding differs
from the findings of the hybrid model (Table 3), which is
probably because we use a restricted sample for the SIENA
analyses (the interaction between gender and the resistant
behavior of friends is not statistically significant in a hybrid
model on the SIENA sample (see Appendix A4)).
Finally, we test whether the influence of the resistant
behavior of friends with a higher social status is stronger for
boys than for girls (hypothesis 3a). It appears not to be, as
the interaction between the social status and resistant
behavior of friends is not statistically significant (right-sided
score-type test χ2(32) = 31.128, p = .510; left-sided
score11 It could be that boys increase their problem behavior in school
more when friends’ resistant behavior in school is more common,
while girls decrease their problem behavior more when the resistant
behavior of friends is less common. If this is the case, the interaction
effect will be insignificant. There are effects available in SIENA to
separately model the influence of peers on an increase and a decrease
in behavior. However, these effects are still under investigation for
behavioral variables with more than two values, and their
interpretation for such behavioral variables is still uncertain (see SIENA manual
p. 37, Ripley et al. 2017).
Fig. 2 Average marginal effect of resistance of friends by the social
status of friends and gender
type test: χ2(36) = 33.235, p = .407). Moreover, we find
no statistically significant three-way interaction between
gender, the social status of friends, and the resistant
behavior of friends (right-sided score-type test χ2(32) = 37.002,
p = .249; left-sided score-type test: χ2(32) = 36.280,
p = .276). Again, hybrid models on the SIENA sample
indicate that this may be due to our restricted sample, as we
find no support for these interactions in the hybrid models
on the SIENA sample (see Appendix A4).
Goodness of fit tests and robustness checks of the SIENA
We assess the Goodness of fit (GoF) of the SIENA models
with a method that uses auxiliary statistics (see
Appendix A5). In 92 classes or more, the model fit was adequate
for the indegree, the geodesic distance, and resistance to
schooling. Outdegree and transitive ties seemed to be
modelled inadequately in more classes (see Appendix A5).
We estimate several additional models to improve the
model fit for these statistics (see Appendix A5 and A6).
Some of these model modifications improve the model fit
for some classes, yet they worsen the fit for others.
Reassuringly, however, modifications to the specifications of the
SIENA model do not alter our main findings (see
Previous research has indicated that boys exhibit more
resistance to schooling than girls (for reviews on the gender
gap in school outcomes, see: Buchmann et al. 2008;
Driessen and van Langen 2013). While scholars have
highlighted the role of peer processes in this gender gap,
there are relatively few quantitative studies that actually test
the role of peers, and existing quantitative research has been
limited to cross-sectional data. This study contributed to
past research by explicitly and longitudinally studying the
role of peer processes in gender differences in student
resistance to schooling. We hypothesized that gender
differences in both the exposure and the response to resistant
peers may lead to time-stable or increasing gender
differences in resistant behavior. We estimated hybrid models on
panel data on more than 5000 adolescents (age 14–15 in
wave 1) in over 200 school classes in Sweden. On a
subsample of the data, we employed novel statistical social
network techniques. We found that, overall, boys show
more resistance to schooling than girls, and that the gender
gap slightly widened across a one-year-period.
The findings suggested that, compared to girls, boys are
more exposed to friends who exhibit resistance to
schooling. Moreover, and importantly, adolescents seemed to be
positively influenced by the resistant behavior of their
friends. These peer selection and peer influence processes
did not account for the widening gender gap in resistance to
schooling over time. However, our results indicated that
they contributed somewhat to the persistence of the initial
gender gap. It could be that boys are influenced to behave
similarly to the average behavior of their male friends, and
girls are influenced to behave similarly to the average
behavior of their female friends, which would lead to
timeconstant gender differences in resistant behavior. Gender
differences in resistance to schooling may possibly be less
persistent over time if boys and girls would befriend
different (e.g., opposite-sex) peers.
We did not find that boys responded more to their
friends’ resistant behavior than girls. Instead, our results
suggested that girls were more influenced by the average
behavior of their friends than boys, seemingly due to the
fact that girls were more positively influenced by the
resistant behavior of low-status friends. Boys’ emulation of
the resistant behavior of friends may be motivated by their
desire to gain status, and therefore they tend not to be
influenced by low status friends. Girls may be influenced by
the resistant behavior of friends for other reasons, such as
the maintenance of friendship ties. The fact that, compared
to girls, boys were less influenced by the resistant behavior
of low status friends somewhat explained why boys
increased their resistant behavior more than girls.
We did not find that girls and boys differed in their
responsiveness to the behavior of non-befriended classmates.
Relatedly, we did not find that students’ school resistance was
influenced more by the resistant behavior of same-sex
nonfriends than that of opposite-sex non-friends. In general, peer
processes did not seem to account for much of either the
time-stable or the increasing gender gap in resistance to
schooling over time. Although our findings should be
looh ..SE .5601 .6005 .0204 .0504 .1702 .6504 .0002 .0026 .1040 .0088 .0071 .0094 .0042 .0091 .0047 .1035 .0026 .0065 .0071 .0025 .0003 ..NA ..NA ..NA ..NA tae
repeated on other data in different countries, they are
potentially of great theoretical importance. Policies that aim to
tackle gender differences in educational outcomes by
focusing on gendered peer processes related to school resistance
may, according to our results, only have limited effects.
This study also knows some limitations. First, the
evolution of resistant behavior and the formation of friendships
are interdependent processes. Although our SIENA models
handled the feedback processes between friendship selection
and the influence of friends, we could not fit a corresponding
model for the feedback processes between friendship
selection and the influence of non-befriended classmates. In
the hybrid models, we were unable to rule out that a change
in student resistance was (also) related to a change in the
resistance of (non-)befriended classmates, rather than the
other way around. This is related to the fact that we only had
two waves of data. With more observation points, we could
have shed more light on the temporal ordering of the
relationships in the hybrid models. This would have also
allowed us to examine whether boys consistently increase
their resistance to schooling more than girls.
Second, and relatedly, our data pertain to a particular age
(mainly 14–15), and a particular observation window (1
year), and we must be careful to generalize over and above
those. Longitudinal school (network) data over longer
periods of time are however scarce, one reason being that
school classes often change their composition, and previous
studies have also encountered this problem. In fact,
although our data had shortcomings, they were still unique.
Network studies are often based on case studies (e.g., in one
school). This study included schools representing the whole
of Sweden, making it much more possible to draw
inferences to the population of adolescents. Moreover, we
studied adolescents at a crucial period in their development in
which deviant behavior is around its peak (Moffitt 1993),
and during which peers act as central socializing agents
(Veenstra et al. 2013).
Scholars have frequently argued that peers play a pivotal
role in the gender gap in resistance to schooling (Driessen
and Van Langen 2013), and the subsequent
underachievement and attainment of boys in school (Hadjar and
Buchmann 2016). Boys would be pressured in their peer
groups to be defiant in class, as this would lead to higher
social status and prevent them from being ostracized. The
idea that peers play a significant part in boys’ resistance to
schooling is mainly based on small-scale qualitative studies,
or cross-sectional quantitative studies. However, peer
processes are dynamic, and can only be studied adequately
with longitudinal data. In this research we were able to
overcome this lacunae by explicitly studying the role of
peers for gender differences in student resistance, using
large-scale panel data for Sweden. Our analyses supported
the hypothesis of peer influences on school resistance. At
the same time, our study somewhat nuanced the role of
peers for boys’ greater resistance to schooling, as we found
that peer processes contributed only slightly to this gender
gap and increases herein.
Acknowledgements We would like to thank the anonymous
reviewers and the editor for their helpful suggestions and feedback.
We would like to thank Tom Snijders, and other members of the
StocNet forum for helping with SIENA related questions.
Funding This study is part of the research program ‘Immigrants,
Natives and the Occupational Career: Do Social Contacts Matter?’,
supported by a Netherlands Organization for Scientific Research
(NWO) grant to Frank van Tubergen as principal investigator. Jan O.
Jonsson acknowledges support from the Swedish Research Council for
Health, Working Life and Welfare (FORTE 2012–1741; 2016-07099).
Financial support from the NORFACE research programme on
Migration in Europe—Social, Economic, Cultural and Policy
Dynamics for the CILS4EU project is acknowledged.
Author Contributions S.G. conceived of the study, drafted the
manuscript and performed the statistical analyses; J.J. and F.v.T. were
involved in the theoretical framework, and interpretation of the data.
All authors were involved in the revisions, and read and approved the
final version of the manuscript.
Compliance with Ethical Standards
Conflict of Interest The authors declare that they have no
Ethical Approval All procedures were in accordance with the
ethical standards of the 1964 Helsinki declaration and its later
amendments or comparable ethical standards. The Swedish part of the
CILS4EU study received approval from the Regional Ethics
Committee, Stockholm. Approval reference number 2010/1557–31/5.
Informed Consent Informed consent was obtained from all students
that participated in the study and their parents.
Open Access This article is distributed under the terms of
the Creative Commons Attribution 4.0 International License
(http://creativecommons.org/licenses/by/4.0/), which permits
unrestricted use, distribution, and reproduction in any medium, provided you
give appropriate credit to the original author(s) and the source, provide
a link to the Creative Commons license, and indicate if changes were
A1 Information on the English, German, and Dutch
data: reasons for exclusion and the gender gap
In the Netherlands, student mobility across school classes
is very high, especially for students in the higher tracks. On
average 52% of a Dutch respondent’s classmates in the first
wave are not part of his/her class in the second wave. The
opportunity structure for friendships in class thus changes
over time, and changes in the behavior of friends and/or
non-friends may purely stem from changes in the class
composition. All in all, none of the Dutch class networks
are suitable for longitudinal social network analyses (Ripley
et al. 2017; CILS4EU 2016).
In England, the school class is not the prime context for
educational activities and friendship formation in school.
Students tend to move to different classes for different
subjects, and only 41% of all friendships in school are
friendships to classmates in the first wave of the data
collection.12 Hence, peer processes outside of class are likely to
play a more prominent role in the gender gap in resistance to
schooling in England, and a study on peer processes in class
would therefore be misleading. Moreover, about 80% of the
English class networks are unsuitable for longitudinal social
network analyses (see CILS4EU 2016).13
In 18% of the German schools, students left their school
after the first wave, as they finished their education. This
implies that we only have longitudinal information on
resistance to schooling and friendship networks in class for a
selective German sample, as students who finished school
after the first wave are students from low ability tracks.
Students from lower ability tracks generally exhibit higher
levels of resistance to schooling (Dumont et al 2017). Besides
this issue, about 75% of the German classes are unsuitable for
longitudinal social network analyses (CILS4EU 2016).
While gender differences in resistance to schooling are
apparent in Sweden, they tend to be larger in the other
countries in the CILS4EU data. Compared to Sweden, the
gender gap in resistance to schooling is significantly larger
in Germany in the first wave (1.9 times larger), and larger in
all the other three countries in the second wave (1.6 times
larger in Germany; 1.9 times larger in England; 1.5 times
larger in the Netherlands).14 Nevertheless, there are no
significant country differences in the gender gap in the
increase in resistance to schooling over time.
A2 Representativeness of the analytical sample
We use two sample t-tests to examine whether students
that were included in the SIENA analyses significantly
12 Students were not asked about their five best friends in general in
the second wave of the data in England, Germany, and Sweden.
13 Note that in the CILS4EU report they use less strict criteria for class
inclusion (i.e., a student participation rate of 75% in each wave,
instead of 80%).
14 We performed multi-level regression analyses in which we account
for the nesting of students in classes. First, we regressed students’
resistance to schooling on gender, the survey country, and an
interaction between the survey country and gender. Second, we regressed
students’ change in resistance to schooling across the waves on gender,
the survey country, and an interaction between the survey country and
differ from students that were excluded with respect to
resistance to schooling. For these test we use the average
score on the resistance to schooling items, and not the
ordinal variable that is used in the SIENA analyses.
Compared to students that are excluded from the SIENA sample,
students that are included in the sample score 0.074 points
lower on resistance to schooling in the first wave, and 0.039
points lower in the second wave. These differences are
statistically significant (wave 1:t(5003) = 4.311, p < 0.001;
wave 2:t(4786) = 2.245, p = 0.025). Little’s MCAR tests
show that students resistance to schooling is not missing
completely at random in the sample we use for the SIENA
analyses (Little χ2(2) = 62.344, p < 0.001). Moreover,
students are also not missing completely at random in the full
sample (Little χ2(2) = 160.166, p < 0.001). We have to be
aware of this when drawing conclusions.
A3 Test of the ‘equality of parameters’ assumption of
the SIENA multi-group models
We combine several class networks in a multi-group
analysis in SIENA (i.e., with the sienaGroupCreate
function). The analyses take into account that adolescents can
only befriend students who attend their own class
By combining multiple classes in one big network the
power and convergence of the models is improved.
However, multi-group models assume that parameters15 are the
same in the different classes that we combine in one
analysis. We test this assumption for the hypothesized effects
with the sienaTimeTest function for model 2–4 in Table 4.
For the behavioral part of the analyses these effects are: the
effect of the resistant behavior of friends, the boy effect, the
interaction between boy and the resistant behavior of
friends, and the interaction between the boy, social status of
friends, and the resistant behavior of friends. For the
friendship formation part of the model, we test this
assumption for the gender homophily and resistant behavior
The ‘equality of parameter’ assumption is met for the
hypothesized effects in 10 of the 18 groups. In the groups
for which the assumption is not met, we delete classes that
violate the assumption and rerun the multiple group models
and the meta-analyses until the assumption is met for all
The results (full tables available upon request) are highly
similar to the results reported in Table 4, and in line with
our main conclusions. In line with the results reported in the
main text, we still find support for gender homophily and
resistant behavior homophily effect in models 2–4.
Moreover, we still find support for a positive effect of the
resistant behavior of friends and a positive effect of being a
boy on the evolution of resistant behavior in models 2–4. In
model 3, the left-sided score type test for the interaction
15 All parameters except for the rate parameters.
between gender and the resistant behavior of boys is
borderline insignificant16 (Left one-sided score type test:
χ2 = 45.505, p = 0.090; Right-one-sided test Fisher type
tests χ2 = 31.695, p = 0.583). A significant left-one sided
test would indicate that, compared to girls, boys are less
likely to increase their resistance to schooling when their
friends exhibit higher levels of resistance to schooling. The
three-way interaction that is tested in model 4 is not
significant (Left one-sided score type test: χ2 = 36.280,
p = 0.276; Right-one-sided test Fisher type tests
χ2 = 37.002, p = 0.249). The left-sided score type tests for
the interaction between the resistant behavior of friends and
gender is again borderline insignificant (Left one-sided
score type test: χ2 = 42.823, p = 0.096; Right-one-sided test
Fisher type tests χ2 = 33.337, p = 0.402)
A4 Hybrid models on the SIENA sample
See Table 5.
A5 Goodness of fit tests of the SIENA model
We assess the Goodness of fit (GoF) of the SIENA
model with a method that uses auxiliary statistics. We use
model 2 to assess the GoF. Networks are simulated on the
basis of the parameters in this SIENA model. The simulated
networks are compared to the observed data with respect to
several auxiliary statistics. More specifically, we compare
the simulated network data with respect to four auxiliary
network statistics—outdegree distribution, indegree
distribution, geodesic distance, and triad census—and one
auxiliary behavior statistic—the behavior distribution of
resistance to schooling. A significant statistic indicates that
the effects in the SIENA model do not adequately represent
friendship or behavioral patterns in the observed data.
This may indicate that additional effects should be included
in the model. Statistics are calculated for each of the 98
classes (see Table 6). As we test the same hypothesis
multiple times, we use the Bonferroni correction for
multiple testing (also see Block 2015). More specifically,
we take a significance level of α/n. α is 0.05 and n
is the number of classes in the multiple group model. When
the p-value < α/n, the multiple group model fit is
inadequate, and the p-value is printed bold in Table 6.
For resistance to schooling, indegree, and geodesic
distance, the model fit seems adequate in most classes.
Resistance to schooling has an inadequate fit for 6 classes in
6 groups. The findings of the behavioral part of the model
are not altered when these groups are excluded from the
meta-analysis. However, homophily with respect to
resistance to schooling turns to insignificance in the friendship
part of the model (0.242 (s.e. 0.143), p = 0.118). The
indegree effect has an inadequate fit in one class in group 8.
Excluding this group from the meta-analysis does not alter
16 Because the test is performed twice, we take a significance level of
α/2 = 0.05.
the conclusions. For the geodesic distance, the fit is
inadequate for 6 classes in 4 groups. Again, conclusion remain
unaltered when these groups are excluded from the
The outdegree effect seems to be modeled inadequately
for 20 classes in 13 of the 18 multi-group models. Plots
indicate that in most classes this was due to an
underestimation of students with a low outdegree in the model.
Initially, the fit for outdegree was even worse, and therefore
we included the outdegree activity and the outdegree
activity sqrt effects. The conclusions of the model remained
the same. Moreover, we tried several other model
specifications (see Appendix A5). More specifically, we estimated
a model with a truncated outdegree effect instead of the
outdegree activity sqrt effect. The truncated outdegree effect
models people’s tendency to have no outgoing ties. Adding
this effect improves the fit for several classes, but worsens it
for others. Moreover, conclusions are not altered by
including this effect.
The triad census specifies whether possible relationships
among three actors (i.e., triads) are well represented by the
model. The triad census is modeled inadequately for 9 of
the 98 classes in 8 of the 18 groups. We inspect the plots of
the GOF of the triad census to examine which triadic
relationships are misrepresented by our model. In several
classes, the inadequate fit for the triad census statistic
is due to a misrepresentation of closed triads with one
reciprocated tie. In some classes, triads with one-directional
ties between the actors were misrepresented. Finally, triads
with no or only one tie were sometimes misrepresented.
Hence, the inadequate modeling of the transitive census
may be related to an inadequate modeling of outdegree. To
improve the fit of the model, we first estimated a model with
the truncated outdegree effect instead of the outdegree
activity sqrt effect. Second, we estimated a model with
the transitive ties effect, instead of the transitive triplets
effect. Finally, we estimated models with an interaction
between the transitive triplets effect and the reciprocity
effect (i.e., the transRecTrip effect) (see Appendix A6).
Some of these modifications—especially the last one—
improve the model fit for the triad census statistic for
several classes. However, it worsens the model fit for other
classes. Moreover, the conclusions are not altered by these
A6 Alternative model specifications of the SIENA
We check whether the SIENA results are robust to
several alternative model specifications. We apply several
modification to model 2 in Table 4. Based on the Goodness
of Fit results, we estimate models in which we use different
effects to model outgoing friendship ties and triadic
configurations in the network (see Appendix A4). Moreover,
we estimate a model in which we use a different measure to
8 0 6 5 6 6 3 3
.00 .20 .09 .25 .00 .02 .76 .04
0 0 0 0 0 0 0 0
.000 *.048 .034 *.588 +.012 *.054 .057 .025
0 0 0 0 0 0 0 0
.0008 .0027 .0005 .0208 .0060 .0032 .0186 .0071 .0072 .0084
+ + *
.0001 .0203 .0040 .5009 .0011 .0048 .2006 .0020 .0023 .0052
eb sm iang tits
hT tebw tInh +<0
specify the influence of the resistant behavior of friends on
the adolescent’s tendency to change his/her resistance to
schooling. The models are presented in Table 7. The results
are highly similar to the ones we obtain in model 2 in
In the first model in Table 7, we replace the ‘outdegree
activity sqrt’ effect with the ‘truncated outdegree’ effect. The
truncated outdegree effect models the likelihood to
nominate at least one classmate as a friend. The effect is
negative, indicating that, controlled for the other effects in the
model, people tend to not nominate any classmates as
friends. In the questionnaire we asked people to nominate
their ‘best’ friends in class. It may be that some people do
not have their best friends within the class context.
In the second model we replace the transitive triplets
effect with a transitive ties effect. The transitive ties effect
resembles the transitive triplets effect, as it represents the
tendency to befriend friends of friends (i.e., a triad is
closed). However, the transitive triplets effect considers
how many triads will be closed by forming a specific tie,
while the transitive ties effect considers whether triads are
closed by forming a specific tie.
In model 3 we add an interaction between the transitive
triplets effect and the reciprocity effect (i.e., transRecTrip
effect). This effect appears to be negative, indicating that
the tendency of transitive closure (befriending the friends of
friends) is larger for one-directional friendships.
In model 4 we measure the influence of the resistant
behaviour of friends with a different effect. In the main
SIENA analyses we used the average alter effect to specify
this effect. This peer influence effect represents a contagion
effect (Veenstra et al. 2013).17 In model 4 in Table 7, we
use the ‘average similarity effect’ to specify the influence of
the behaviour of friends. The average similarity effect
indicates whether adolescents tend to minimize the
difference between their own resistant behavior and the average
resistant behavior of their friends (i.e., adolescents try to
behave in similar ways as their friends). This effect
represents a convergence type of influence. We find a positive
and significant average similarity effect, indicating that
adolescents try to engage in similar levels of resistance to
schooling as their friends. The results obtained in model 4
in Table 7 are in line with those in model 2 in Table 4.
Although the resistance homophily estimate is not
significant in model 4 in Table 7, the Fisher type tests indicate
that this homophily effect is positive and significant in some
17 In SIENA we model gender differences in the tendency to increase
resistant behavior in schooling. Hence, we focus on contagion effects,
as we expected that contagion effect are able to contribute to an
increase in the gender gap in resistance to schooling over time (see
Table 6 P-values of the goodness of fit statistics for the SIENA model
Resistance to schooling
Table 6 continued
Resistance to schooling
Sara Geven works as a postdoctoral researcher at the Department of
Sociology at the University of Amsterdam. Her research interests
include educational inequality, social networks, and stratification
Jan O. Jonsson is Professor at Nuffield College at the University of
Oxford. His research interests include social mobility and the class
structure, educational inequality, poverty, family, ethnic inequality and
integration, as well as children’s wellbeing.
Frank van Tubergen is Professor at the Department of Sociology at
Utrecht University. His research interests include social networks,
religion, ethnic intermarriage and interethnic friendships, language
acquisition of immigrants, schooling and employment.
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