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This article critically reviews prior labeling theory research concerning juvenile delinquency and crime, and proposes a new study using a recent data set. The labeling perspective is outlined as it was originally presented, and the theoretical elaborations that have taken place since are highlighted. Distinctions are made between formally applied criminal justice labels and the informal labels that are applied by educational institutions, significant others, and parental figures. An interactionist labeling model is presented to explain levels of juvenile delinquency among a nationally representative sample of American adolescents: the first three waves of the National Longitudinal Study of Adolescent Health (Add Health). Finally, negative binomial regression models are estimated to better explain the dynamic relationship between labels and delinquency. Consistent with labeling theory, formal labeling significantly increased future delinquency.
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Crime & Delinquency
2016, Vol. 62(10) 1313 –1336
© The Author(s) 2014
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DOI: 10.1177/0011128714542504
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Article
Interactionist Labeling:
Formal and Informal
Labeling’s Effects on
Juvenile Delinquency
Daniel Ryan Kavish1, Christopher W. Mullins1,
and Danielle A. Soto1
Abstract
This article critically reviews prior labeling theory research concerning
juvenile delinquency and crime, and proposes a new study using a recent
data set. The labeling perspective is outlined as it was originally presented,
and the theoretical elaborations that have taken place since are highlighted.
Distinctions are made between formally applied criminal justice labels and
the informal labels that are applied by educational institutions, significant
others, and parental figures. An interactionist labeling model is presented
to explain levels of juvenile delinquency among a nationally representative
sample of American adolescents: the first three waves of the National
Longitudinal Study of Adolescent Health (Add Health). Finally, negative
binomial regression models are estimated to better explain the dynamic
relationship between labels and delinquency. Consistent with labeling theory,
formal labeling significantly increased future delinquency.
Keywords
labeling, delinquency, symbolic interactionism
1Southern Illinois University Carbondale, USA
Corresponding Author:
Daniel Ryan Kavish, Doctoral Student, Criminology and Criminal Justice, Southern Illinois
University Carbondale, 4330 Faner, Mailcode 4504, Carbondale, IL 62901, USA.
Email: dkavi2@siu.edu
542504CADXXX10.1177/0011128714542504Crime & DelinquencyKavish et al.
research-article2014
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1314 Crime & Delinquency 62(10)
Introduction
Matsueda (1992) provided empirical evidence that suggested informal labels
could possibly explain both primary and secondary deviance. However, he
conceded that incorporating formal labels, such as those derived from the
juvenile justice system, would allow for stronger tests of a deviance amplifi-
cation proposition. Attentive to the critiques of prior scholars (Barrick, 2014;
Bernburg, 2002; Paternoster & Iovanni, 1989; Tittle, 1980), the current study
provides a test of an interactionist labeling model using multiple types of
formal and informal labels. Furthermore, our measures of youth perceptions
of care allow for an investigation of the relationship between stakes in con-
formity and labeling outcomes.
This article critically reviews prior labeling theory research concerning
juvenile delinquency and crime, and proposes a new study using a recent data
set. This article outlines the labeling perspective as it was originally pre-
sented, and highlights the theoretical elaborations that have taken place since.
Distinctions are made between formally applied criminal justice labels and
the informal labels that are applied by educational institutions, significant
others, and parental figures. Contemporary research is examined to provide a
deeper understanding of the current state of labeling theory literature. Finally,
an interactionist labeling model is presented to explain levels of juvenile
delinquency among a nationally representative sample of American
adolescents.
Review of Literature
Labeling theory’s roots can be traced back to Mead’s (1934) work on self-
concept and the development of symbolic interactionism (see Matsueda,
2014). The contemporary equivalent of this line of labeling research is
Matsueda’s (1992) study of juvenile reflected appraisals. According to Mead
(1934), the actual construction and formation of the self begins during child-
hood. Unlike other theories that examine the self as static across an individu-
al’s life-course development, Mead (1934) asserts that the development of
one’s self continues long after childhood.
Mead was not the only pioneering contributor to the development of label-
ing theory. Cooley (1902) and Tannenbaum (1938) could also claim credit for
assisting in the development of the approach. Tannenbaum’s (1938) “drama-
tization of evil” describes the process by which offenders acquire deviant
labels from members of society. If an act has been characterized as evil by
society, then the offender associated with the act will be simultaneously asso-
ciated with the act and labeled as deviant. Cooley (1902) presented his idea
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Kavish et al. 1315
of the “looking-glass self” before Mead (1934) had fully conceptualized the
idea of an individual’s self-concept. Essentially, Mead (1934) made Cooley’s
(1902) model of self richer and more specific. Cooley (1902) believed that an
individual’s view of self was formed depending on how that individual
thought others in society viewed him or her, and how that individual reacted
to his or her perceptions of their views.
Kinch (1963) formally defined the self-concept as an “organization of
qualities that the individual attributes to himself” (p. 481). If self-conceptions
are associated with occupied social positions, then those self-concepts are
referred to as “role-identities” (Stryker, 1980). The “organization of quali-
ties” results, in part, from the perceptions of other individuals belonging to
important reference groups (Kinch, 1963; see also Markowitz, 2014). Kinch
(1963) referred to this as the reflected appraisals process. This same concep-
tually dynamic complexity can be seen throughout Matsueda’s (1992) con-
temporary discussion of juvenile reflected appraisals. Matsueda (1992)
specifically defined reflected appraisals, quite simply, as “how one perceives
the way others see one” (p.1584).
Throughout the 1950s and 1960s it was the labeling works of Becker
(1963), Lemert (1951), and Schur (1965) that dominated criminological lit-
erature. The works of these three authors were widely popular because they
offered an alternative to deterrence theory. Becker (1963) and Lemert (1951)
used labeling theory to explain an individual’s development of a criminal
identity and the continuation of criminal careers. Each asserted that for vari-
ous reasons, individuals begin to engage in deviant or delinquent behavior.
Some, but not all, individuals will be labeled as deviants or delinquents by
authority figures. Once formally or informally labeled, the individual’s self-
concept changes to include the label, which then drives future deviant acts.
Formal Labels
Formal labels are applied to individuals who have come into contact with
educational or correctional systems with the authority to officially label the
individual (or juvenile) as deviant (Chiricos, Barrick, Bales, & Bontrager,
2007; Ray & Downs, 1986). Stimulated by high recidivism rates, there has
been a recent revival in the research into the criminogenic effects of formal
labels (Chiricos et al., 2007). High recidivism rates suggest that secondary
deviance is likely behavior for convicted felons. Johnson, Simons, and
Conger (2004) make it very clear that there is new support of labeling theory
when they wrote, “Although labeling theory has a history of being very prob-
lematic, current theory and research has reconsidered its merit as an explana-
tion of deviance” (Johnson et al., 2004, p. 5).
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Following labeling theory, Chiricos and his colleagues (2007) claimed
that the transformation of an individual’s identity could lead to increased
criminal behavior or secondary deviance, yet, they add the concept of “struc-
tural impediments” that occur in an individual’s life after going through a
labeling experience. They reiterated the commonly known effects of being
formally labeled by the criminal justice system: “The label of convicted felon
strips an individual of the right to vote, serve on juries, own firearms, or hold
public office” (Chiricos et al., 2007, p. 548). Although these impediments
may not significantly impact recidivism directly, it is quite possible that they
are indirectly affecting secondary deviance by blocking access to legitimate
opportunities (see Barrick, 2014; also Bernburg & Krohn, 2003; Chiricos et
al., 2007). It is also possible that other formal labels, such as an official arrest
or prosecution, could have dramatic implications similar to the “structural
impediments” outlined by Chiricos and his colleagues (2007). After all, even
though some individuals did not receive a formally applied label, the process
of being arrested and prosecuted is likely to lead to the development of infor-
mal labels or negative self-labeling (Chiricos et al., 2007). Addressing this
point, Brownfield and Thompson (2008) supported a labeling hypothesis
when they found that self-reported police contact, or official delinquency, has
a significant positive relationship with delinquent self-concepts.
Huizinga and Henry (2008) presented a thorough literature review of the
effects of arrest and sanctions. They concluded that their findings suggest that
arrest either increases future delinquency or has very little consequence on
future involvement in delinquency. Even more recently, Lopes and her col-
leagues (Lopes et al., 2012) noted that there is a revived interest in examining
the effects of labeling on non-criminal outcomes that may intensify delin-
quency. They found that formal labeling, such as police intervention during
adolescence, has a significant indirect effect on criminal and non-criminal out-
comes later in life. Formal labeling, or police intervention, significantly affected
non-criminal outcomes such as education, employment, and financial stability
(Lopes et al., 2012). These findings are consistent with labeling theory.
Informal Labels
Informal labels are labels applied to individuals by someone without the offi-
cial or professional authority to distinguish between deviant and non-deviant
behavior (Liu, 2000; Ray & Downs, 1986). This, when viewed as a process,
is known as informal labeling. Ray and Downs (1986) argued that parents are
the primary source of informal labels, and that informal labels can have a
direct affect on an individual’s self-concept or self-esteem. The study of self-
concepts is an intricate part of labeling theory research.
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Kavish et al. 1317
Chassin, Presson, Young, and Light (1981) examined the effects of label-
ing on institutionalized adolescents, focusing on the development of self-
concepts as they pertain to labeling theory. The authors stated that even
though deviant individuals had more deviant self-concepts, the individuals
did not conform to their socially applied labels (Chassin et al., 1981). They
asserted that it is important to examine why deviant labels might not lead to
secondary deviance. They argued that an individual could possibly adopt a
deviant identity in response to society’s labels, but that the deviant identity
may be unimportant in relation to that individual’s self-concept (Chassin et
al., 1981). Another possible alternative is that other interacting positive labels
are playing a role in why a deviant label might not lead to secondary
delinquency.
Sampson and Laub (1997) presented a life-course theory of crime that
focused on cumulative disadvantage and borrowed heavily from labeling
research that examined the relationship between labels and mental illness.
Link (1982, 1987; Link, Cullen, Struening, Shrout, & Dohrenwend, 1989;
Link, Cullen, & Wozniak, 1987) presented a modified labeling theory which
argued that official labeling and subsequent stigmatization produce negative
consequences regarding social networks, jobs, and self-esteem in mental
patients. His research found that labels have negative impacts on psychiatric
patients’ work status, friendships, income levels, and even family relation-
ships. Sampson and Laub (1997) suggested that modified labeling theory
could be revised to examine delinquency and crime, and not just used as a
theory of mental illness. They pointed specifically at the role the family plays
in future delinquency and highlighted that reciprocal social interaction begins
in the family. They specifically asserted that “parenting, at least in part, is a
reaction to children’s temperament, especially difficult ones” (p. 14). To our
knowledge, labeling theory research has yet to fully investigate how formal
labels, delinquency, informal labels of child temperament, and youth percep-
tions of care and acceptance are all interrelated.
Smith and Paternoster (1990) reported no empirical support for the devi-
ance amplification hypotheses commonly theorized by labeling scholars. If
early critics of the labeling perspective figuratively put a stake in labeling
theory’s heart, then Smith and Paternoster (1990) supplied the nails for its
coffin. The popularity of labeling theory began to fade among scholars over
the next decade, but that did not mean that labeling research ceased to con-
tinue. Smith and Paternoster (1990) had hoped that their results would inspire
future empirical studies to address the problem of a selection artifact, but
very few scholars decided to confront the problem over the next decade.
Matsueda (1992) is responsible for not only keeping the labeling perspec-
tive on life support but also as the first major researcher to explain how
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informal labels could possibly explain both primary and secondary deviance.
In his examination of reflected appraisals, parental labeling, and juvenile delin-
quency, he did not elaborate on labeling theory as it was known up to that point,
but rather, he specified a symbolic interactionist theory that primarily examined
the effects of parental labels and reflected appraisals. Both of these types of
labels are considered informal labels by criminologists (Bartusch & Matsueda,
1996; Liu, 2000; Matsueda, 1992). Matsueda (1992) found that disadvantaged
background characteristics increased negative parental labeling and possibly
decrease the probability of positive labeling. Furthermore, consistent with a
deviance amplification hypothesis, his work showed that parental labels had a
substantial affect on delinquency. Reflected appraisals influenced future delin-
quency as well, but even when youth-reflected appraisals were controlled for,
parental labels still had a considerable affect on delinquency.
Also consistent with labeling theory, Matsueda (1992) found that prior delin-
quent behavior influenced youth’s reflected appraisals of self. He found that this
affect worked indirectly through parental appraisals, but that prior delinquency
also affected youth’s reflected appraisals of self directly. This implied that
reflected appraisals, a type of informal label, are the result of earlier behavior,
the individual’s perceptions or understandings of that behavior, and the “selec-
tive perception of actual appraisals” (Matsueda, 1992, p. 1586). In general, he
provided fertile soil for contemporary labeling theorists to place their roots, and
introduced an innovative new method of understanding “the self” as it was origi-
nally presented by Cooley (1902) and others (Chassin et al., 1981; Mead, 1934).
Building on the 1992 article, Bartusch and Matsueda (1996) developed a
micro-level model of gender and delinquency to explain the gender gap.
Using much of the same methods utilized by Matsueda (1992), the authors
tested 15 hypotheses. Bartusch and Matsueda (1996) found that parental
labels had strong affects on youth’s reflected appraisals as a “rule violator.”
Furthermore, reflected appraisals were found to significantly impact delin-
quency levels. The overall message was clearly that reflected appraisals,
especially as a “rule violator,” can increase the likelihood of future delin-
quency (Bartusch & Matsueda, 1996).
Koita and Triplett (1998) examined Matsueda’s (1992) assertion that race
and gender may affect the processes of reflected appraisals and actual apprais-
als. They found that parental appraisals (or labels) significantly affected
reflected appraisals and finally, increased delinquency. Their overall findings
supported the interactionist model of self with one notable exception: Their
models did not result in a proper fit for juvenile Black females.
Brownfield and Thompson (2005) were primarily concerned with the
effects of parental and peer reflected appraisals on delinquency. Their initial
bivariate analyses indicated support for a relationship between parental
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Kavish et al. 1319
reflected appraisals and delinquency. However, this relationship was elimi-
nated upon controlling for peer reflected appraisals and self-concept
(Brownfield & Thompson, 2005). The authors attested that their findings
showed that the way parents, teachers, peers, and siblings react to an indi-
vidual’s behavior could potentially have implications for the probability of
delinquency or a delinquent self-concept.
In the most recent test of reflected appraisals reviewed, Asencio and Burke
(2011) found that criminal and drug-user identities were both a function of
the reflected appraisals of “significant others.” These findings are supportive
of Matsueda (1992) and his colleague’s (Bartusch & Matsueda, 1996; Heimer
& Matsueda, 1994) earlier studies of reflected appraisals. Furthermore, and
most importantly, Asencio and Burke (2011) indicated that the different
sources of reflected appraisals had different affects on the identities of the
respondents. They found that the reflected appraisals of “peers” and “signifi-
cant others” were the most relevant to criminal and drug-user identities
(Asencio & Burke, 2011).
Clearly, a line of research that began in 1992 has established its empirical
merit. The debate, then, is no longer whether reflected appraisals impact
delinquency, but how other markers of identity may interact with labeling
and delinquency. Brownfield and Thompson (2005) noted that future studies
should seek to include measurements of prior delinquency, and appraisals
from parents. The current study does just that, as well as including measures
of school stigmatization, youth perceptions of care, and other key variables
commonly examined by criminologists. Our primary concerns here are
whether formal labeling experiences influence future self-reported involve-
ment in delinquency, and whether that relationship is influenced by other
informal labels or potential contingencies such as race, social class, and bio-
logical sex. Our conceptualization of the impact of labeling on youth percep-
tions and delinquency yields the following hypotheses:
Hypothesis 1: Controlling for prior delinquency, informal labels, and
other important controls, formal labeling will result in an increase in future
delinquency.
Hypothesis 2: Controlling for formal labeling, prior delinquency, addi-
tional forms of labeling, and other important controls, youth perceptions
of care will result in an increase in future delinquency.
Hypothesis 3: Controlling for prior delinquency, youth perceptions of
care will mediate the effect of formal labeling on later delinquency.
Hypothesis 4: Controlling for prior delinquency, additional forms of
labeling, and other important controls, parental labeling will result in an
increase in future delinquency.
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Method
Prior labeling theory analyses have tested only a limited number of labeling
types, but this “interactionist labeling” model incorporates formal labels and
multiple informal labels. These different types of labels, based on prior label-
ing literature, should then either directly or indirectly influence individual
levels of delinquency. The interactionist labeling model dictates that delin-
quent behavior is influenced, in part, due to the application of negative labels.
The sample used is derived from the National Longitudinal Study of
Adolescent Health (Add Health).1 Add Health is a nationally representative
sample of adolescents in Grades 7 to 12 from the United States during the
1994-1995 school year. These adolescents were followed into young adult-
hood with continued in-home interviews. The most recent wave of data used
in this analysis were collected in 2002 (Wave 3), when respondents had
reached young adulthood. Several minority groups were oversampled to
ensure that the respondents included in the survey were racially and ethni-
cally diverse. For a more detailed description, see Harris et al. (2009).
The primary advantages of this data set are that it is a large nationally
representative sample, and it includes a wide variety of possible variables to
be used in a criminological analysis. The longitudinal design of the study
further allows researchers to examine changes in variables over time, allow-
ing the examination of causal relationships between variables or correlations.
One disadvantage of the data is that they are not particularly concerned with
labeling events, dynamics, or theory. This shortcoming prevents us from
properly testing reflected appraisals as originally outlined by Matsueda
(1992). However, the survey does provide enough valid measures for a test of
labeling theory.
The current study utilizes Waves 1, 2, and 3 of the Add Health data. This
means that respondents will have reached adulthood at the third data collection
point, but will have not exceeded the age of 27. This method of analysis allows
research to trace each individual respondent’s behavior, attitudes, and criminal-
ity starting when they were children and ending when they reached adulthood.
The final sample used is limited to survey respondents who had valid weights
and valid data in the independent variables and delinquency measures.
Variables
Dependent Variable
Delinquency. A 13-item delinquency index, incorporating both violent and
non-violent delinquent acts, was constructed to be used as the dependent
variable. The items related to violence are equivalent in both Waves 1 and 3,
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Kavish et al. 1321
and include violent behaviors such as robbery, using weapons in a fight, par-
ticipating in a fight “where a group of your friends was against another
group,” carrying a weapon to school (and/or work in Wave 3), pulling a
weapon on someone, and shooting or stabbing someone. For the first wave of
the study, the index includes non-violent delinquent behaviors such as prop-
erty damage, joyriding, shoplifting, stealing something worth more than $50,
stealing something worth less than $50, burglary, and selling marijuana or
other drugs. The non-violent items included in the delinquency index slightly
change in Wave 3 reflecting more age-normative behaviors. For instance,
shoplifting is removed from the index, and replaced with buying, selling, or
holding stolen property. Likewise, joyriding is replaced with using someone
else’s ATM, debit, or credit card without their permission. In both Wave 1 (α
= .7869) and Wave 3 (α = .7229), respondents are asked about their frequen-
cies of engaging in the aforementioned behaviors. Responses ranged from
“never” (0) to “5 or more times” (3). The items were recoded into dichoto-
mous measures (0 = no, 1 = yes) and summed to create one continuous vari-
able (range = 0-13).
Independent Variables and Controls
Age. The age of the respondent was expressed as the respondent’s age in
years at the time of the survey’s first wave.
Race/ethnicity. Race and ethnicity was measured by constructing dichoto-
mous variables. The four categories constructed were White, Black, His-
panic, or “Other.” White serves as the contrasting category. The variables
indicate whether the respondent identifies primarily as White, Black, His-
panic, or some Other race/ethnicity.
Sex. Sex was measured with a dummy variable (male = 1; female = 0).
Socioeconomic status (SES). The variables concerned with the education level
of the respondent’s residential parents served as a proxy for SES in the cur-
rent study.2 The survey items were concerned with the highest degree com-
pleted by each of the respondents’ residential parents. If only one residential
parent was listed, then that parent’s education level was used as the respon-
dent’s SES. If two parents were available, then their education levels were
averaged. The final variable used was a continuous variable.
Public assistance. Public assistance was measured using a single survey item
from the parent questionnaire. This measurement of public assistance served
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as a second proxy measure of SES for the current analyses. The respondent’s
parents were asked if they were recipients of public assistance. The variable
used was a dichotomous variable with “yes” responses (yes = 1) denoting that
the respondent’s parents answered that they were receiving public assistance
or welfare. “No” (no = 0) responses indicate that an individual’s parents
answered that they were not receiving public assistance or welfare.
Family type. Respondents’ family type was measured with a series of dummy
variables indicating the family type structure in which the respondent lives.
Respondents were categorized based on whether they indicated that they
lived with both biological parents, one biological parent and a step-parent,
one single biological parent, or some other family type. Respondents who
indicated they lived with adoptive parents were coded as living in some
“other” household type.
Formal labeling. Official formal labeling was measured by retroactively track-
ing self-reported arrests listed by respondents in Wave 3. The variable used
was a dichotomous variable with “yes” responses (yes = 1) denoting that the
respondent was officially processed by the criminal justice system. “No”
(no = 0) responses indicate that an individual was not formally processed.
School stigmatization. Respondents’ school stigmatization experiences was
measured by using a summed index of four items indicating stigmatizing
school experiences. Respondents were asked whether they had ever been in
trouble at school due to drinking, been suspended, been expelled, or ever
repeated a grade. Higher scores indicated more experiences of school stigma-
tization. Missing cases were modally imputed (0 = no) prior to being added
to the index. Finally, this index was reduced into a single dichotomous vari-
able indicating any incidence of school stigmatization experiences.
Parental labeling. Parental labeling was measured by constructing a dichoto-
mous variable using a single survey item from the Wave 1 parent question-
naire. The parent questionnaire survey items address a multitude of questions
directly pertaining to the study participants. One survey item asked the
respondents’ parents if they believed their child had a bad temper. “Yes”
responses (yes = 1) denote that the respondent’s parent believes that they
have a bad temper. “No” (no = 0) responses indicate that the parent does not
believe that their child has a bad temper.
Perceptions of care. Youth perceptions of care were measured by constructing
two variables derived from Wave 2 survey items. These survey items asked
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Kavish et al. 1323
respondents how much they felt teachers and family cared about them.
Responses ranged from “not at all” to “very much.” Missing cases were
replaced for each item by imputing the mean. The variables were reverse
coded (5 = not at all; 1 = very much). Thus, a higher score represents a more
negative perception of how much respondents felt teachers and family cared
about them.
Plan of Analysis
Contemporary labeling theorists have examined how official labeling impacts
future criminal and non-criminal outcomes. In other words, labeling theorists
have become concerned with the possible intervening variables between
labeling and future criminogenic behaviors and criminal outcomes. For
example, Lopes et al. (2012) recently found that labeling indirectly affected
criminal and non-criminal outcomes. However, their study did not include
measures of reflected appraisals or any other measure of “label internaliza-
tion.” Matsueda (1992) found that reflected appraisals significantly mediated
the affects of informal labels on subsequent delinquency involvement. Yet,
only informal labels and reflected appraisals were included in his symbolic
interactionist model of delinquency. This study addresses this gap in research
by examining the affects of formal labels, informal labels, and youth percep-
tions of care on delinquency.
Negative binomial regression is the analytical strategy employed for the
purpose of this study. This strategy is optimal because the dependent variable
used is continuous and highly skewed (i.e., there are many zeroes in the data).
Poisson regressions are often utilized by researchers dealing with dependent
variables that are not normally distributed. Furthermore, Poisson regression
strategies that better handle problems of overdispersion have been developed
by scholars. However, past research has suggested that negative binomial
regression should be the preferred analytical method employed by research-
ers when it is imperative to estimate the probability distribution of an indi-
vidual count (see Gardner, Mulvey, & Shaw, 1995). An earlier criminological
study that used the same outcome variable that is used in the current analyses
also noted the appropriateness of using negative binomial regression, rather
than a Poisson regression model (see Demuth & Brown, 2004).
Bernburg (2002) declared that the best tests of labeling would be longitu-
dinal, control for prior behavior, and would have samples derived from a
population containing labeled and non-labeled individuals. He also stressed
the importance of controlling for other important variables such as race and
SES. Barrick (2014) noted that proper renderings of labeling theory analyses
will utilize multivariate techniques, control for prior delinquency or criminal
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1324 Crime & Delinquency 62(10)
history, and will examine mediating, conditioning, or intervening variables.
Answering the call of prior scholars (Barrick, 2014; Bernburg, 2002;
Paternoster & Iovanni, 1989; Tittle, 1980), the current study uses a large
nationally representative sample, and it includes a wide variety of variables
thought to be important by labeling theorists. The longitudinal design of the
study further allows researchers to examine changes in variables over time,
allowing the examination of causal relationships. As aforementioned, the
data prevent us from properly testing reflected appraisals as originally out-
lined by Matsueda (1992). However, our measure of youth perceptions of
care, because it points to the respondent’s perceptions of informal social
bonds with family and teachers, allows for an investigation of the relation-
ship between stakes in conformity and labeling outcomes (Sherman, Smith,
Schmidt, & Rogan, 1992). Enough valid measures were available in the data
for a test of labeling theory that is attentive to the main deficiencies of previ-
ous labeling theory research (see Barrick, 2014).
Findings
The first set of findings involve the sample’s basic characteristics. Table 1
shows the ranges, means or weighted proportions, and standard errors for the
variables. Please note that the percentages displayed are weighted propor-
tions. A small weighted proportion (9.76%) of the sample was formally
labeled (n = 877). This finding was expected, as was the finding that a higher
weighted proportion of respondents were informally labeled (27.51% and
38.04%) than formally labeled. The mean age of the sample at Wave 1 was
approximately 15 years old (15.052). More interesting, is that there is an
aging out from delinquency involvement from Wave 1 to Wave 3 in the sam-
ple. The mean delinquency score at Wave 1 was 1.281. Yet, the mean delin-
quency score at Wave 3 was a smaller 0.530. This indicates a natural
desistance from delinquency involvement at Wave 1 to delinquency involve-
ment at Wave 3 throughout the entire sample. Table 2 shows the results of the
bivariate proportions and tests of means. Delinquency scores at both waves
were significantly associated with school stigmatization, parental labeling,
and formal labeling. Furthermore, the bivariate relationships between school
stigmatization, parental labeling, and formal labeling were also significant.
Table 3 shows the results of the five regression models, with the exponen-
tiated coefficients provided to ease interpretation of the data. Model 1 shows
the results of regressing the study’s focal independent variable, formal label-
ing, on delinquency scores measured at Wave 3. Results at this stage indi-
cated that formal labels, without any controls, significantly contribute to later
self-reported incidences of delinquency involvement.
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Kavish et al. 1325
Model 2 included the same variables that were included in Model 1, but
also controlled for respondents’ delinquency scores measured at Wave 1.
Formal labeling was found to be strongly predictive of Wave 3 delinquency
involvement even when controlling for respondents’ prior delinquency
involvement. However, as expected, Wave 1 delinquency scores significantly
contributed to Wave 3 delinquency scores. This finding suggests that formal
labels significantly contribute to future levels of delinquency, net of prior
delinquency involvement.
Model 3 was utilized to determine the affects of formal labels on delin-
quency while controlling for prior delinquency and the two measures of youth
perceptions of care. In Model 3, youth perceptions of teacher care were sig-
nificantly predictive of Wave 3 delinquency scores. Formal labeling was the
strongest significant predictor of Wave 3 delinquency in Model 3, followed by
Table 1. Descriptive Statistics.
Range M or % SE
1. Male 0-1 48.88% 0.007
2. Age 9-20 15.052 0.113
3. Race
White 0-1 67.87% 0.029
Black 0-1 15.09% 0.020
Hispanic 0-1 11.95% 0.017
Other 0-1 5.09% 0.008
4. Family type
Both biological 0-1 57.42% 0.013
One biological/one step-parent 0-1 15.56% 0.005
Single biological 0-1 21.55% 0.010
Other 0-1 5.47% 0.004
5. SES 1-5 2.713 0.047
6. Public assistance 0-1 9.80% 0.008
7. Perceptions of care
Teacher 1-5 2.448 0.024
Family 1-5 2.049 0.015
8. Parental label 0-1 27.51% 0.008
9. School stigmatization 0-1 38.04% 0.014
10. Formal label 0-1 9.76% 0.005
11. Delinquency (Wave 1) 0-13 1.281 0.035
12. Delinquency (Wave 3) 0-13 0.530 0.023
Valid N (listwise) 10,346
Note. SES = socioeconomic status.
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1326 Crime & Delinquency 62(10)
Wave 1 delinquency scores. In Model 2, formal labels contributed to a 264%
increase in Wave 3 delinquency scores. However, after the introduction of
youth perceptions in Model 3, formal labels still resulted in a 257% increase
in Wave 3 delinquency scores. Thus, the introduction of youth perceptions of
care to the model accounted for only a 7% decline in the affect formal labels
had on delinquency.
In Model 4, formal labeling, once again, was the strongest significant pre-
dictor of future delinquency. Like the previous models, the second strongest
significant predictor of subsequent delinquency was prior delinquency. Youth
perceptions of family care, like in Model 3, had no significant impact on delin-
quency. Youth perceptions of teacher care had the same significant influence
on delinquency that was seen in Model 3. Of the two new variables introduced
in Model 4, only school stigmatization had a significant impact on Wave 3
Table 2. Bivariate Proportions and Tests of Means.
Analytic sample
(N = 10,346)
Parental label School stigmatization Formal label
Yes No Yes No Yes No
Dependent variable
Delinquency (Wave 3) 0.60* 0.50 0.62** 0.48 1.74*** 0.40
Focal independent variables
Delinquency (Wave 1) 1.65*** 1.14 1.88*** 0.92 2.35*** 1.17
Parental label 36.85%*** 21.77% 36.22%*** 26.57%
School stigma 50.96%*** 33.14% 53.48%*** 36.37%
Formal label 12.85%*** 8.59% 13.72%*** 7.33%
Perceptions of care
Family 2.15*** 2.01 2.16*** 1.98 2.19*** 2.03
Teachers 2.60*** 2.39 2.62*** 2.34 2.69*** 2.42
Control variables
Male 49.91% 48.50% 58.93%*** 42.72% 80.13%*** 45.50%
Age 15.02 15.06 15.47*** 14.80 14.88** 15.07
SES 2.48*** 2.80 2.37*** 2.92 2.79 2.70
Public assistance 13.32%*** 6.48% 13.47%*** 5.22% 7.84% 8.42%
Race
White 67.57% 67.98% 57.54%*** 74.21% 70.11% 67.62%
Black 15.42% 14.96% 23.39%*** 9.99% 16.11% 14.98%
Hispanic 12.72% 11.66% 15.10%*** 10.02% 9.76% 12.19%
Other 4.29% 5.40% 3.98%* 5.78% 4.02% 5.21%
Family processes
Family type
Both biological 51.54%*** 59.65% 44.21%*** 65.53% 50.90%*** 58.13%
Biological/step-parents 16.70% 15.13% 18.07%*** 14.02% 16.15% 15.50%
Single biological 26.05%*** 19.84% 28.70%*** 17.16% 26.03%** 21.07%
Other 5.71% 5.37% 9.02%*** 3.28% 6.92% 5.37%
Note. SES = socioeconomic status.
*p .05. **p .01. ***p .001.
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Kavish et al. 1327
delinquency scores. Unlike the other significant predictors, the relationship
between school stigmatization and Wave 3 delinquency was negative. In other
words, school stigmatization resulted in decreased Wave 3 delinquency scores.
This finding suggests that there may be a specific deterrent value of school
punishment. That being said, once controls were added in the final model, the
affect of school stigmatization was no longer significant.
The final model included all of the variables that were included in Model 4
and the additional control measures (Age, Race, Sex, Family Type, SES, and
Public Assistance). Formal labeling, as in the four previous models, was the
strongest significant predictor of Wave 3 delinquency. The second strongest
Table 3. Negative Binomial Regressions of Delinquency at Wave 3.
Model 1 Model 2 Model 3 Model 4 Model 5
Full sample (N = 10,346) Exp(b) Exp(b) Exp(b) Exp(b) Exp(b)
Independent variables
Formal label 4.38*** 3.64*** 3.57*** 3.65*** 2.85***
Wave 1 delinquency 1.20*** 1.19*** 1.20*** 1.17***
Perceptions of
care–family
1.00 1.01 1.07*
Perceptions of
care–teachers
1.10** 1.10** 1.09**
Parent label 0.95 0.98
School stigmatization 0.87* 0.91
Control variables
Male — — — — 2.49***
Age — — — — 0.85***
SES — — — — 1.13***
Public assistance 1.06
Family type
One biological/one
step-parent
— — — — 1.10
Single biological — — — — 1.00
Other — — — — 0.97
Race
Black — — — — 1.33***
Hispanic — — — — 1.08
Other — — — — 0.89
F statistic 369.25*** 389.97*** 188.67*** 124.52*** 60.44***
Note. SES = socioeconomic status.
*p .05. **p .01. ***p .001.
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1328 Crime & Delinquency 62(10)
predictor of Wave 3 delinquency was being male, followed by being Black.
Both school stigmatization and parental labeling were found to not be signifi-
cant predictors of Wave 3 delinquency scores. On the other hand, both youth
perceptions of family and teachers were found to be significant predictors of
Wave 3 delinquency scores in this model. The control variables influenced the
affect of formal labels on Wave 3 delinquency more so than was influenced by
both of the youth perceptions of care measures.
The first hypothesis stated that controlling for prior delinquency, informal
labels, and other important controls, formal labeling will result in an increase
in future delinquency. The findings supported the first hypothesis. Formal
labels significantly increased subsequent delinquency, and the influence of
formal labels on future delinquency was greater than any other variables
included in the analyses. This finding indicates that prior delinquency, per-
sonal characteristics, and some types of informal labeling are less important
in explaining future delinquency than the application of a formal label.
The second hypothesis stated that controlling for formal labeling, prior
delinquency, additional forms of labeling, and other important controls, youth
perceptions of care will result in an increase in future delinquency. Findings
supported the second hypothesis. Both youth perceptions of teachers and
youth perceptions of family significantly impacted Wave 3 delinquency
scores upon the addition of the control variables to the model. Negative per-
ceptions of care significantly increased subsequent delinquency. The third
hypothesis stated that Controlling for prior delinquency, youth perceptions of
care will mediate the affect of formal labeling on later delinquency. Findings
supported the third hypothesis. Negative youth perceptions of care were
responsible for a moderate increase in subsequent delinquency scores.
Furthermore, youth perceptions of care accounted for 7% of the influence
that formal labeling had on Wave 3 delinquency scores (see Model 3).
The fourth, and final, hypothesis stated that controlling for prior delin-
quency, additional forms of labeling, and other important controls, parental
labeling will result in an increase in future delinquency. The fourth hypothe-
sis is rejected. Parental appraisals did not have a significant impact on Wave
3 delinquency at any stage of the analyses. In sum, the support and rejection
of these four hypotheses has important implications for the future of labeling
theory and criminological research. The findings, and their implications, are
discussed below.
Discussion
The findings indicate that formal labeling, measured as a self-reported arrest,
has a significant affect on delinquency involvement later in life. Furthermore,
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Kavish et al. 1329
the results indicate that this relationship is partially mediated by youth per-
ceptions of care. Arrest is a conceptually poor measure of formal labeling, yet
results reveal substantial and significant influence on subsequent delin-
quency. It is possible, and may be likely, that more extreme labeling experi-
ences would result in an even stronger affect of formal labeling on later
delinquency involvement. For example, it is likely that a formal conviction or
“Felon” label would have a stronger relationship with subsequent delin-
quency than being arrested.
These findings highlight the adverse effects official formal labels can have
on future behavior. The findings also establish that youth perceptions of care
partially mediate the relationship between formal labeling and delinquency.
These findings are particularly supportive of Paternoster and Iovanni’s (1989)
interpretation of the secondary deviance hypothesis and contribute to theo-
retical discourse concerned with labeling and stakes in conformity. According
to Paternoster and Iovanni, a proper rendering of the secondary deviance
hypothesis should be probabilistic, that is, if an individual has experienced
labeling, then that individual may experience a change in his identity, may
discover conventional opportunities to be restricted or limited in access, and
may possibly be excluded from conventional groups. Their rendering of the
secondary deviance hypothesis proposes that as a result of the aforemen-
tioned processes, an individual may illustrate an increased involvement in
delinquency.
The findings indicate that youth perceptions of care significantly impact
subsequent delinquency, and that youth perceptions of care mediate some of
the connection seen between formal labeling and delinquency. This is a sig-
nificant finding because it suggests that youth perceptions of care are impor-
tant in explaining the relationship between formal labeling and secondary
delinquency. It lends merit to the inclusion of youth perceptions in future
labeling theory research. This finding is of further importance because it sug-
gests that labeling experiences, both formal and informal, are mediated by
youth perceptions of care and other intervening variables.
The effect produced by youth perceptions of care being added to the mod-
els was minimal, especially when viewed in contrast to the effect formal
labels had on subsequent delinquency. This suggests that youth perceptions
of care may significantly influence future delinquency involvement directly,
but also that there may be a change in identity, or at least perception, for some
individuals that have been formally labeled. Matsueda (1992) found that
informal labeling’s affect on delinquency was mediated by reflected apprais-
als as a “rule violator,” but the current findings suggest that a similar process
may also be occurring with formal labels and youth perceptions as “cared
about” by teachers and family.
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1330 Crime & Delinquency 62(10)
It should be cautioned that our measures of youth perceptions were uncon-
cerned with delinquent self-concepts. Rather, our measures of youth percep-
tions of care specifically measured how an adolescent perceived how much
their family and teachers care about them. Therefore, perceptions of care, as
operationalized within this study, measured the perceived strength of infor-
mal social bonds with family and teachers. Sherman and his colleagues
(1992) suggested that informal control, in the form of weak informal social
bonds to family and teachers, may condition the impact of formal labeling on
future delinquency. Dejong (1997), while investigating specific deterrence,
found that individuals with minimal bonds to a job, family, or education were
more likely to recidivate after being formally labeled and incarcerated.
Similarly, our results indicate that negative perceptions of informal social
bonds to family and teachers increase future delinquency and partially medi-
ate the relationship between formal labeling and subsequent delinquency.
The hypothesis concerned with parental labeling was rejected. Parental
labeling did not have a significant impact on future delinquency at any stage
of the analyses. Likewise, school stigmatization, upon the inclusion of the
control variables, did not have a significant impact on future delinquency.
School stigmatization may be insignificant in predicting secondary delin-
quency simply because it is unrelated to future delinquency involvement.
Another possibility is that the methods used in this study to measure school
stigmatization may not have accurately accounted for school stigmatization
and labeling experiences. For example, an additional supplemental survey of
the respondents’ teachers would have allowed for more specific items regard-
ing school labeling and stigmatization experiences. For instance, being
expelled from school is a very different stigmatizing experience than being
labeled as a deviant or “rule breaker” by a teacher.
The control variables added in the final model (Age, Race, Sex, and SES)
were shown to be significant predictors of secondary delinquency. The age
variable performed as expected: having a negative impact on Wave 3 delin-
quency scores. Being male strongly influenced Wave 3 delinquency scores,
second in strength only to being formally labeled. Race was also a significant
predictor of secondary delinquency, supporting labeling theory’s contention
that racial minorities are more prone than non-minorities to being negatively
labeled, and as a result, engage in secondary delinquency.
Individuals with higher SES scores were significantly more likely than
those with lower SES scores to engage in secondary delinquency. These
quantitative findings are similar to Chambliss’ (1973) qualitative observa-
tions. To be more specific, Chambliss (1973) claimed that the “Saints” in his
study were more actively involved in delinquent behavior than the
“Roughnecks.” His qualitative work established that it is possible that social
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Kavish et al. 1331
status and social markers of SES influence the likelihood of encountering
negative labels or experiencing negative labeling events. His work further
established that individuals identified as upper class or middle class may pos-
sibly engage more frequently than lower class individuals in delinquent
activities or behavior as they are less surveilled and thus have greater oppor-
tunity (Chambliss, 1973). Alternatively, others have argued that members of
more privileged groups with a greater stake in conformity are more subject to
the power of a formal label in self-image construction (see Sherman et al.,
1992). For example, Chiricos and colleagues’ (2007) study of a deferred
adjudication program in Florida found that experiencing formal adjudication
over deferred adjudication was more likely to lead to recidivism for Whites
over Blacks or Hispanics. The current study used two proxy measures of SES
due to the problems with income reporting among the respondents in the
sample, and this may affect the validity of these findings.
The current study found both direct and indirect linkages between label-
ing and subsequent delinquency. Formal labels were the strongest predictors
of secondary delinquency throughout the study. It is likely that more indirect
linkages would be found, and the extant of formal labeling’s direct relation-
ship with delinquency diminished, upon the inclusion of variables attempt-
ing to measure social exclusion from conventional groups and opportunities.
“Structural impediments,” as Chiricos and his colleagues (2007) have sug-
gested, explain how formal labeling could have such a significant impact on
future criminal or delinquent behavior. Formal labeling was the strongest
predictor of subsequent delinquency in the current study, but labeling was
measured as an arrest. An arrest, arguably, is a weak measure of formal
labeling because there are relatively few “structural impediments” after
being arrested, especially when compared with the possible “structural
impediments” an individual must overcome after being officially convicted
and sanctioned.
The current study is not without its methodological limitations. The sam-
ple and data used only allow the findings to be generalized to adolescents in
the United States. Furthermore, the data itself were not particularly concerned
with labeling events or processes. It is strongly suggested that future surveys
strive to include the items needed for a proper test of labeling theory. In fact,
for the purposes of improving criminological research, social surveys of ado-
lescents should begin including items considered to be the most pertinent
among criminologists of all types. This would allow social research of all
types to improve, and would simultaneously foster a new wave of theoretical
elaboration and integration. Arguably, the most important limitation of the
current study is that a specific grounded labeling theory has not been estab-
lished by prior research (see Melossi, 1985). A more grounded and precise
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1332 Crime & Delinquency 62(10)
labeling theory would allow for the wide-scale use of replication and com-
parative studies that are essentially the backbone for proper theory testing.
As aforementioned, another limitation of the current study is that only one
formal label was examined. The current study operationalized a self-reported
arrest as an important formal labeling experience. Existing criminological
and criminal justice research shows that there are other noteworthy formal
labels that could influence secondary deviance and future criminal justice
outcomes. For example, Quinn (2010) examined the relationship between a
formal “gang member” label and juvenile justice dispositions. Other studies
have operationalized formal labeling as an official conviction or adjudication
(Chiricos et al., 2007).
To compound this limitation, all labels do not impact or influence an indi-
vidual’s life equally. Becker (1963) made this clear when he described the
idea of a “master status.” Quinn (2010) elaborated by pointing out that not all
labels are negative, and that labels might be more or less important to indi-
viduals based on their individual and family characteristics. To put it another
way, specific labels can hold more or less weight for certain individuals.
Future research should make a greater attempt to elaborate conceptually on
Becker’s notion of a “master status” and to better explain how different types
of labels specifically affect different types of people.
Conclusion
The findings were generally supportive of labeling theory. The strongest sig-
nificant affect on subsequent delinquency was found to be caused by formal
labeling. Therefore we suggest that formal labels should continue to be
emphasized by theorists as extremely important. The current study found that
formal labels were much more important than parental appraisals, school
stigmatization, and youth perceptions of care. The current study did not find
a significant connection between parental appraisals and subsequent delin-
quency, but this is not to say that parental appraisals should be played down
in the future or ignored. Rather, it is likely that this finding is simply a func-
tion of how parental labeling was operationalized in the current study. The
true emphasis of contemporary labeling theorists should be on the develop-
ment of a general theory of crime that incorporates all dimensions of prior
labeling theory research.
This study contributed to existing criminological research by providing a
contemporary test of labeling theory using a nationally representative and
longitudinal data set. The data allowed for a test of an interactionist labeling
model of delinquency using multiple types of formal and informal labels.
Furthermore, a new and innovative conceptual approach toward labels and
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Kavish et al. 1333
delinquency was taken. Substantial support for labeling theory was found in
a nationally representative sample of American adolescents. Still, there are
lingering questions in need of answers. Future research should attempt to
more closely examine the significant relationships found in the current study
to conceptually expand upon the dynamic social processes that may occur
after being formally or informally labeled.
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 publi-
cation of this article.
Notes
1. This research uses data from Add Health, a program project directed by Kathleen
Mullan Harris and designed by J. Richard Udry, Peter S. Bearman, and Kathleen
Mullan Harris at the University of North Carolina at Chapel Hill, and funded
by Grant P01-HD31921 from the Eunice Kennedy Shriver National Institute of
Child Health and Human Development, with cooperative funding from 23 other
federal agencies and foundations. Special acknowledgment is due Ronald R.
Rindfuss and Barbara Entwisle for assistance in the original design. Information
on how to obtain the Add Health data files is available on the Add Health website
(http://www.cpc.unc.edu/addhealth). No direct support was received from Grant
P01-HD31921 for this analysis.
2. Using the income of the respondents’ residential parents as a proxy for socioeco-
nomic status was initially considered for the study. However, the income mea-
sures were found by the data collectors and other scholars to be highly unreliable.
To be more specific, there is a substantial amount of missing data pertaining to
parental income. Recent studies have concluded that these missing data may not
be random, but rather, represent a distinct subset of the study’s population (see
Harris et al., 2009).
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Author Biographies
Daniel Ryan Kavish is a doctoral student in the Criminology and Criminal Justice
program at Southern Illinois University Carbondale. He received an MA in
Criminology and Criminal Justice from Southern Illinois University Carbondale and
a BA in Criminal Justice from the University of Illinois Springfield. His current
research interests include labeling theory, convict criminology, and racial disparities
within the criminal justice system.
Christopher W. Mullins is an associate professor of Criminology and Criminal
Justice at Southern Illinois University Carbondale. He has published 4 books and
more than 30 journal articles and book chapters. His work primarily focuses on vio-
lence and its control.
Danielle A. Soto is an assistant professor in the Department of Criminology and
Criminal Justice at Southern Illinois University Carbondale. Her research currently
focuses on racial/ethnic minorities and crime/delinquency, especially Hispanic delin-
quency. She also looks at female delinquency and teen dating violence.
at Southern Illinois University Carbondale on September 21, 2016cad.sagepub.comDownloaded from
... Recent reviews of empirical research generally find that studies, using longitudinal designs and comparing groups that have no or minimal contact with the juvenile/criminal justice system with those who have been formally processed, support labeling theory (Barrick, 2014, Huizinga and Henry, 2008, Liberman, Kirk, and Kim, 2014Kavish, Mullins and Soto, 2016;Ward, Krohn, and Gibson, 2014). Moreover, Petrosino, Turpin-Petrosino and Guckenburg (2014) systematically identified 29 experiments that used random or quasirandom assignment and found that overall juvenile justice system processing was associated with increased future crime. ...
... In line with previous research in criminology (e.g. Demuth and Brown, 2004;Kavish et al., 2016), a count variable 10 from the combined measure was created and analyzed using negative binomial regression to test the relationships (described in more details below). ...
... The log of the outcome is predicted with a linear combination of the predictors. This statistical strategy is in line with previous research in criminology (Demuth and Brown, 2004;Kavish, et al., 2016;Dennison, 2019). The models in from Wave 3 as well as from Wave 4 when the youngest respondents were 24 years old. ...
Thesis
Full-text available
The overrepresentation of minority youth in the juvenile justice system has been well documented. More research has, however, been needed on levels of discrimination, particularly on potential biases in the earliest point of contact, such as police decisions to stop and arrest young people. Further, few studies have examined individual and neighborhood characteristics simultaneously, which has limited the understanding of citizens’ experiences with the police. Focusing on potential biases in the juvenile justice system is essential as recent studies indicate that most types of interventions have negative consequences for the lives of young people, such as increasing the probability of crime in adulthood. The current study addresses some of the limitations of previous research and uses data from the National Longitudinal Study of Adolescent to Adult Health (Add Health) to test several hypotheses related to the probability of having been stopped or arrested by the police in youth, and the long-term impact of punitive interventions by the police and school authorities. Results generated from the multilevel analyses fail to show that racial and ethnic minorities are more likely than White youth to be stopped by the police. Independent of differences in behavior, Black youth are, however, more likely to be arrested than White adolescents. There is no significant difference between the probability of police stops or arrest for Hispanic and White youth. The probability of arrest also increases with increased concentrated disadvantage (concentrated poverty, a high proportion of single-parent households, and a high proportion of residents without a high school diploma). Interventions in adolescence (being arrested or suspended/expelled from high school) do not decrease subsequent crime but instead lead to more crime in adulthood. The findings indicate that this is partly because these interventions decrease adult SES, particularly interventions by school authorities. The current study also indicates that Black youth and young women are more vulnerable to the negative consequences of interventions than other groups.
... Our hypothesis that grandiosity would be associated with low responsiveness to negatively valenced stimuli was not supported. In contrast to previous findings adopting the same multidimensional approach of psychopathy (Fanti et al., 2017;Hansen et al., 2007;Kavish et al., 2016), grandiosity was associated with high startle reactivity to fear stimuli during picture tasks. A positive association between startle reactivity to sad stimuli and grandiosity was also revealed, although it dropped to non-significance after accounting for conduct problems and other dimensions of psychopathy. ...
Article
Full-text available
Previous studies have revealed associations between grandiosity, callous unemotionality, and impulsive dimensions of psychopathy with psychophysiological measures during adolescence and young adulthood. However, it is largely unknown if such associations can be identified earlier in life. The main aim of the current study was to investigate the associations between diverse psychophysiological measurements (heart rate, skin conductance, and startle reflex) assessed at rest and during exposure to emotional stimuli with the three dimensions of psychopathy. This was done in a sample of 147 children (Mage = 7.30, SD = 1.42; 44.2% girls) selected from a large screening sample (N = 1652). Participants watched video scenes and pictures eliciting different emotions, while their physiological reactions were monitored. Regarding baseline measures, results showed a negative relation between the impulsive dimension with baseline skin conductance. Hierarchical regression models controlling for age, gender, conduct problems, and the interrelation between psychopathic traits, revealed several important associations. Lower heart-rate reactivity in response to sad video scenes and fearful pictures was uniquely associated with the callous-unemotional dimension. High startle reactivity in response to fearful emotional stimuli was associated with the grandiose (fearful pictures) and impulsive (fearful videos) dimensions. The present study provides new evidence and adds to existing knowledge regarding the distinct physiological processes associated with each dimension of psychopathy assessed in childhood.
... Students often responded to failure by self-handicapping and become defensive [52,53], but first-time-juvenile delinquents, except for the lowest quartile, responded by maintaining a positive self-image and concept of ability in the face of external stimuli which should seriously refute such appraisals. Kavish, et al. [54] suggested students adapted to negative labels to lessen the problem of being seen as a failure in social, emotional, and academic situations. In other situations, people experiencing failure coped by rationalizing reasons for failure and seek self-enhancements and selfprotection [55]. ...
Article
Full-text available
Most studies on grit examined participants who were more successful than others and found grit was a significant factor. There was a gap in the literature for participants with extreme failure, first-time-detained juvenile delinquents, and the impact of grit. The purpose of the present study was an explanatory and exploratory study of grit and the interaction with other factors for first-time-detained juvenile delinquents. A sample of juvenile delinquents incarcerated for the first time in the United States was used. The results, using analyses of variances and correlational analysis, suggested grit in juvenile delinquents incarcerated correlated negatively with a mental illness screener and positively with higher social self-esteem. Examining grit at different levels revealed juvenile delinquents had other factors which impact grit. A discussion about the meaning of labeling theory followed from the results, and recommendations to improve educational outcomes in juvenile detention centers were given.
... Finally, studies have found that formal labeling significantly increases future delinquency (Kavish et al., 2016). Using longitudinal data, Bernburg, Krohn, and Rivera (2006) examined how contact with the criminal justice system and being labeled a juvenile delinquent predicted future deviance. ...
... Indeed, research has confirmed that parental appraisals can strongly influence adolescents' reflected appraisals of themselves as well as their delinquency (Kavish, Mullins, & Soto, 2016;Matsueda, 1992). Matsueda's (1992) research indicated that parents' appraisals that their children would get into trouble and break rules were interpreted as meaningful by adolescents and transmitted into reflected appraisals of themselves as rule violators. ...
Article
This research sought to identify a potential process by which intergenerational crime occurs, focusing on the effect of parental incarceration on adolescents’ subsequent arrests. We drew from Matsueda’s work on reflected appraisals as an explanatory mechanism for this effect. Thus, the present research examined whether caregivers’ and adolescents’ expectations for adolescents’ future incarceration sequentially mediated the effect of parental incarceration on adolescents’ actual arrest outcomes. Propensity score matching was used to examine this effect in a sample of 1,735 15- to 16-year-olds using NLSY97 data. Parental incarceration was positively related to caregivers’ expectations of adolescents’ future arrest. Moreover, caregivers’ expectations were strongly associated with adolescents’ expectations. Finally, the effect of parental incarceration on adolescents’ actual future arrest likelihood was partially mediated by caregivers’ and adolescents’ expectations for this outcome. This study revealed support for the proposition that the experience of parental incarceration may influence adolescents’ negative outcomes through reflected appraisals.
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This study examines deviant identity in relation to youth offending by combining items tapping both self-appraisal and reflected appraisal. In particular, using survey data from 3,446 Korean youth across five waves of the Korea Youth Panel Survey (KYPS), findings from group-based trajectory modeling (GBTM) present four distinct offending groups—a high-rate chronic group, stable non-offending group, adolescence-limited group, and declining group. Then, findings from the multinomial logit model reveal that deviant identity is a robust predictor of offending for subgroups of adolescents involved in offending at any level in comparison to stable non-offenders. Accordingly, this study supports the idea that deviant identity should be considered as a prominent predictor of a variety of types of youth offending.
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Despite extensive research into juvenile justice interventions, there is a limited focus on family engagement, including parent–child experiences in these various programs. Even less research explores how families, specifically youth and parents, are affected by diversion from the traditional juvenile justice system. The current study fills this gap by drawing from in-depth interviews with 19 parents and 19 youths participating in a juvenile pretrial diversion program in Southern California. This research highlights how a diversion program can influence how families understand the justice system and law-related behaviors. The themes discussed include how diversion programs shape parent–child bonds, how parents navigate negative indictments of youth and themselves for participating in diversion, and the influence of external challenges and social forces shaping youth and parent experiences. Findings support the theoretical contributions from social bond and labeling theory. Implications and future research will also be discussed.
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The bulk of the desistance literature has focused on social/contextual factors (marriage, employment, peers) and their criminogenic consequences. Less attention has been devoted to the role of criminal justice system involvement in the desistance process, and most of the existing research indicates that system involvement tends to inhibit or delay desistance from crime. One recent effort to combat that pattern was implemented with the Responsive Interventions for Change (RIC) Docket in Harris County, Texas, in 2016. The RIC Docket was intended to increase defendants’ access to a pretrial release bond and to reduce rates of felony convictions, thus lowering the risk of disrupting important prosocial ties and avoiding potentially stigmatizing labels. In the present study, we use case processing data on rates of pretrial release and felony convictions from one year prior to (N = 6,792) and three years following (N = 12,152) the implementation of the RIC Docket. Results show that those processed through the RIC Docket were 24% more likely to have access to pretrial release and 45% less likely to have their cases result in a conviction. We conclude by discussing the importance of policy changes intended to reduce barriers to the successful desistance process for individuals involved in the justice system.
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Full-text available
The stigma associated with mental illness results in discrimination, loss of socioeconomic status, lowered sense of self-worth, and increased symptoms. Labeling theory is an explanatory framework that accounts for these effects. In light of developments in the understanding of the causes and treatment of mental illness, the theory has undergone modification from its original version to show how internalized stigma affects well-being. Recent research integrates modified labeling theory with the reflected appraisals process of identity formation to understand how attitudes toward mental illness affect recovery through their effects on the self-concept.
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Criminology has long been concerned with many questions that are inherently longitudinal. What is the developmental life-course of criminal behavior? Is there one general offending pattern or multiple offending patterns? Which early risk factors, if any, are strongly predictive of criminal behavior? Do particular interventions prevent or retard future criminal behavior? Longitudinal research following individuals over many years has unique potential to answer such questions, although such studies take many years to conduct. Many longitudinal studies of crime and delinquency initiated since the 1980s have produced hundreds of published papers, providing an unprecedented opportunity to address such questions. What have we learned? The six reviews in The Long View of Crime synthesize findings from about 200 papers from over 60 longitudinal studies. Three considerations guided the choice of topics for review: (a) a critical mass of studies; (b) an emphasis on longitudinal methods; and (c) policy relevance. The volume focuses on adolescence. Several adolescent experiences are considered directly, including employment, gang involvement, and first arrests. Adolescence is also considered in relation to early childhood, from a focus on the end of adolescence, and as situated in the longer context of criminal careers. The volume begins with an introduction and executive summary, and concludes with a chapter considering future directions in using longitudinal research to study causes of delinquency. In addition, an Appendix lists each longitudinal study in the volume along with essential study features, and cross-lists the studies with the reviews. This shows which longitudinal studies informed each topic, and also indicates analytic opportunities not yet explored. © 2008 Springer Science+Business Media, LLC. All rights reserved.
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In this paper, we consider the relationship between self-concept or identity and juvenile delinquency. Major contributions to the study of deviance based on interactionist theory are reviewed. We conduct an empirical analysis, with a focus on the effects of parental and peer reflected appraisals. Measures based on social control theory are included in the analysis as a comparison with interactionist measures. We find strong support for symbolic interactionist theory.
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Critics of labeling theory vigorously dispute Scheff's (1966) provocative etiological hypothesis and downplay the importance of factors such as stigma and stereotyping. We propose a modified labeling perspective which claims that even if labeling does not directly produce mental disorder, it can lead to negative outcomes. Our approach asserts that socialization leads individuals to develop a set of beliefs about how most people treat mental patients. When individuals enter treatment, these beliefs take on new meaning. The more patients believe that they will be devalued and discriminated against, the more they feel threatened by interacting with others. They may keep their treatment a secret, try to educate others about their situation, or withdraw from social contacts that they perceive as potentially rejecting. Such strategies can lead to negative consequences for social support networks, jobs, and self-esteem. We test this modified labeling perspective using samples of patients and untreated community residents, and find that both believe that "most people" will reject mental patients. Additionally, patients endorse strategies of secrecy, withdrawal, and education to cope with the threat they perceive. Finally, patients' social support networks are affected by the extent to which they fear rejection and by the coping responses they adopt to deal with their stigmatized status.
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Does referring a case to juvenile court or diverting it affect a person's future delinquent/criminal behavior? Labeling theory suggests that it does, arguing that formal processing by the juvenile justice system is part of a deviance amplification process that ultimately results in increased criminal/delinquent activity. But critics point out that a higher rate of future offending among those referred to court, often interpreted as evidence supporting the deviance amplification argument, could be nothing more than a selection artifact. Specifically, those referred to juvenile court may have more attributes that are related to future offending than do those who are diverted from the system. Under this scenario, differences between these groups in later offending could simply reflect preexisting differences in criminal propensity. This article discusses approaches for testing the deviance amplification argument against the alternative hypothesis of a selection artifact.