Hi. I need to write reaction or reflection paper. I don't need to write a summary about it.
You can briefly describe the topic in general but you are expected to critically assess a problem or solution within the reading and describe as reactions to or reflections of the reading. (good, bad, eye opening, questions you may have, etc.).
The majority of these reaction or reflection papers should focus on this.
This paper should be 12-point Times New Roman font, 1 inch margins, Single spaced and it must be 1 page in length. It must be one complete page in length.
Biodemography and Social Biology, 56:137?149, 2010
Copyright © Society for the Study of Social Biology
ISSN: 1948-5565 print / 1948-5573 online
DOI: 10.1080/19485565.2010.524095 Risk Heterogeneity and Recurrent Violent
Victimization: The Role of DRD4 1948-5573
and Social Biology
Biology, Vol. 56, No. 2, Oct 2010: pp. 0?0 Recurring
Victimization and DRD4 LEAH E. DAIGLE
Department of Criminal Justice, Georgia State University, Atlanta, Georgia
For some people, victimization comes with significant costs. One of these costs is the
likelihood of being victimized a subsequent time. Unfortunately, research shows that a
portion of victims do in fact experience more than one victimization. Although this likelihood has been established, the reasons why some people are victimized more than
once are not fully understood. One explanation centers on individual risk factors that,
if left unchanged, will increase risk of further victimization. Previously unstudied,
however, are genetic factors that may place and keep a victim at risk, even after an initial victimization. Using data from the National Longitudinal Study of Adolescent
Health, the current study addresses this gap. The findings reveal that there is in fact a
genetic factor, the 7R allele of the DRD4 gene, that distinguishes individuals who have
been victimized once from those who have been victimized multiple times. Introduction
Recent research shows that, after experiencing an initial victimization, some individuals
are particularly at risk of experiencing a subsequent victimization (see Daigle, Fisher, and
Guthrie 2007). This finding has been found for victims of household burglary (Nicholas,
Povey, Walker, and Kershaw 2005; Townsley, Homel, and Chaseling 2000); sexual
victimization (Daigle, Fisher, and Cullen 2008); theft and property victimization (Barberet,
Fisher, and Taylor 2004; Lauritsen and Davis Quinet 1995; Pease 1998); and violence
(Cattaneo and Goodman 2005; Daigle et al. 2009; Lauritsen and Davis Quinet). For example, results from the British Crime Survey from 2003?-2004 and 2004?2005 indicate that
16% and 14%, respectively, of those experiencing burglary experienced more than one
such incident within the same year (Nicholas et al. 2005). Lauritsen and Davis Quinet
found that almost 60% of assaulted youth in their study were recurrent victims.
Despite this finding that recurrent victimization is a reality that many victims face,
less attention has been given to differentiate why some victims are victimized a single
time and others are victimized multiple times. The research that has been conducted has
largely focused on two perspectives?state dependence and risk heterogeneity. According
to state dependence, something occurs during an initial victimization that impacts risk of
This research uses data from Add Health, a program project designed by J. Richard Udry, Peter
S. Bearman, and Kathleen Mullan Harris 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. Persons interested in obtaining
data files from Add Health should contact Add Health, Carolina Population Center, 123 W. Franklin
Street, Chapel Hill, NC 27516-2524 (email@example.com). No direct support was received from
grant P01-HD31921 for this analysis.
Address correspondence to Leah E. Daigle, Department of Criminal Justice, Georgia State
University, PO Box 4018, Atlanta, GA 30302-4018, USA. E-mail: firstname.lastname@example.org 137 138 L. E. Daigle subsequent victimization. This perspective has received limited support (Fisher, Daigle,
and Cullen 2010; see Farrell, Phillips, and Pease 1995; Pease 1998). Risk heterogeneity,
conversely, argues that characteristics of an individual influence risk of victimization, and
if these characteristics remain unchanged after an initial victimization, that person will
continue to be at risk for subsequent victimization (Pease).
Beyond identifying demographic characteristics that place individuals at risk for
recurrent victimization, research on these individual characteristics that explains why
some individuals are victimized more than once is scant. The research that has attempted
to examine these factors has not revealed a clear picture of relevant individual characteristics. For example, research examining lifestyle and routine activities theory has produced mixed results regarding the factors that distinguished single and recurring sexual
victims, with some research finding that individuals who spend many nights away from
home (Lasley and Rosenbaum 1988), frequently use public transportation after 6 p.m.
(Mukherjee and Carcach 1998), frequently go out at night for entertainment, and engage
in dangerous activities (Outlaw, Ruback, and Britt 2002) are at risk for repeat victimization and others not finding time away from home to be related to an increased risk of
repeat victimization (Outlaw et al.). Lauritsen and Davis Quinet (1995) found that time
spent with delinquent peers was related to the number of assaults experienced. Other
research examining lifestyle and routine activities and recurrent sexual victimization
found no differences between single and recurrent sexual victimizations in individual
characteristics (Fisher et al. 2010).
It is possible, however, that other individual-level characteristics previously unstudied promote recurring victimization. Recent findings indicate that genetic factors are
related to a host of characteristics and behaviors, including crime participation and victimization (Beaver et al.2007; Caspi et al. 2002). Specifically, genes involved in dopaminergic and serotenergic systems have been implicated. Among the genes studied are the
genetic polymorphisms in the dopamine DRD4 receptor gene. In particular, the 7Repeat
(henceforth 7R) allele of the DRD4 gene has been linked to novelty seeking (Benjamin
et al.1996; Ebstein et al. 1996; see Schinka, Letsch, and Crawford 2002). Others have
found support for a link between the DRD4 7R allele and attention-related problems
(Faraone, Doyle, Mick, and Biederman 2001).1 Given that the DRD4 receptor aids in
modulating excitatory signaling in the prefrontal cortex, these findings are not surprising.
Dopamine generally has positive effects on cognitive functioning, but its relationship
is U-shaped, with low and high levels of dopamine related to cognitive impairment
(DeYoung et al. 2006). The 7R allele produces less-efficient receptors; thus, those individuals with this genetic polymorphism may demonstrate more problems. As it may
increase novelty seeking and be related to attention-related problems, it is hypothesized
that those individuals who have more 7R alleles of the DRD4 gene may be at greater risk
for recurring violent victimization. Such individuals may be more likely to engage in
risky activities, find themselves in risky environments, be less attuned to environmental
cues, and otherwise facilitate revictimization?especially if they are unable to change
these characteristics after an initial victimization. To date, however, genetic polymorphisms of the DRD4 gene have not been examined in the victimization literature as a
potential risk factor for recurring victimization after an initial victimization event. The
current study addresses this gap. 1 Other research has found that DRD4 is related to cognitive and behavioral problems in
children, but the finding was in the opposite direction (Birkas et al. 2005). Recurring Victimization and DRD4 139 Method
Data and Sample
Data for this project are derived from the National Longitudinal Study of Adolescent
Health (Add Health), a prospective, longitudinal, and national study of youths in grades 7
through 12. Sample participants were selected using a multi-stage stratified sampling procedure in which 80 high schools and 52 middle schools were identified for inclusion. In
the second stage, more than 90,000 students enrolled in these schools completed the Wave
I In-School Questionnaire (collected in 1994). A subsample of these students (N = 20,745)
were selected to complete the In-Home Questionnaire.
Follow-up data have been collected in three additional waves since the collection of
the Wave I questionnaires. Wave II data were collected between 1 and 2 years after the
first wave of interviews were conducted, and Wave III data were collected in 2001?2002.
Wave II data consist of in-home interviews with 14,738 respondents, and 15,197 respondents participated in the Wave III interviews (Harris et al. 2003).
The Add Health data are especially rich, with measures designed to capture a range of
influences on health and health-related behaviors. It includes measures on parenting and
family dynamics, mental and physical health, engagement in risky behaviors, decision
making, sexual behaviors, education and employment, relationships, and household structure. Add Health data are also unique in that at Wave III, a subsample of the respondents
submitted their DNA for genetic typing and analysis. In total, 2,574 individuals agreed to
participate in this part of the study and were subsequently genotyped (Harris et al. 2003).
As such, the Add Health data can also be used to investigate the relationship between
genes and a range of outcomes.
DRD4. Buccal cells were genotyped, in part, for a polymorphism in the DRD4 gene
for those respondents who participated in the DNA subsample during Wave III.2 The gene
maps to the 11p15.5 chromosome and contains a polymorphism 48 bp VNTR in the third
exon. This polymorphism codes for a 16-amino acid segment in the third cytoplasmic loop
of the receptor protein. Two primer sequences were used to amplify the polymorphism:
forward, 5??AGGACCCTCATGGCCTTG-3? and reverse, 5?GCGACTACGTGGTCTACTCG-3? that resulted in polymerase chain reaction products of 379, 427, 475, 523,
571, 619, 667, 715, 763, and 811 bps (Smolen and Hewitt n.d). Ten alleles were found at
the DRD4 locus. The alleles for 379 (2R), 427 (3R), 475 (4R), 523 (5R), and 571 (6R)
were pooled together into a single group. The alleles for 619 (7R), 667 (8R), 715 (9R), and
763 (10R) were also grouped together. The polymorphism was then coded co-dominantly,
with respondents with two alleles less than 7R in a group, those with one allele 7R or
greater and one less than 7R in a second group, and individuals with 2 alleles 7R or greater
in the third group. This method of coding the alleles has been used in previous research
examining DRD4 (Hopfer et al. 2005; Vaughn, Beaver, DeLisi, Howard, and Perron
2009). 2 Only those participants who had full siblings or twins in the sample were asked to participate
in the genetic subsample. 140 L. E. Daigle Attention deficits. Victimization has been linked to attention deficits and low self-control
(Schreck 1999). To measure a person?s attention deficits, a four-item additive scale was created. Respondents were asked in four items how often they had trouble in terms of interacting with peers and teachers, paying attention, and getting homework done. Item responses
ranged from never to every day. As has been done in previous research (Beaver and Wright
2005; Daigle, Cullen, and Wright 2007), answers to these questions were summed to create
the attention deficits scale, with higher scores reflecting more problems (a = .69).
Problem solving. Poor impulse control has also been linked to victimization (Schreck
1999). One aspect of impulse control is the ability to make rational, thoughtful analyses of
problems before making decisions and evaluating decisions after they are made. To measure a person?s ability to do so, respondents were asked to evaluate how they approach
making decisions when faced with a problem by addressing whether one of the first things
they do is to get as many facts about the problem as possible, whether they usually try
to think of as many different ways to approach a problem as possible, whether they generally use a systematic method for judging and comparing alternatives, and whether after
carrying out a solution to a problem they usually try to analyze what went right and
what went wrong. As done in previous research (Beaver and Wright 2005; Daigle et al.
2007), a problem-solving scale was created by summing the responses that ranged from
1 = strongly disagree to 5 = strongly agree (a = .73).
Delinquent peers. Exposure to persons who engage in delinquency and criminal
activity places individuals at risk for victimization (Haynie and Piquero 2006; Schreck,
Fisher, and Miller 2004; Schreck and Fisher 2004). To examine whether having delinquent peers impacts recurring victimization, a measure of the number of delinquent
friends a person has was created by using Wave I data. Following other work using the
Add Health data, three items that asked about the number of their three best friends who
use marijuana, drink alcohol, and smoke cigarettes were used to create the delinquent
peers variable (Bellair, Roscigno, and McNulty 2003; Daigle et al. 2007). The values of
the three questions were added together to create the final delinquent peers variable.
Time spent with non-family members. According to the lifestyle-routine activities
perspective, the more time individuals spend outside of their home with non-family members, the more likely they are to be victimized (Hindelang, Gottfredson, and Garafalo
1978). To capture the extent to which people interact with such individuals outside of the
home, two items are used.
Time spent hanging out with friends. First, a measure of time spent hanging out with
friends was included. Respondents were asked during Wave I how many times during the
past week they just hung out with friends. Higher values indicate more time spent hanging
out with friends.
Hours worked. A second item was included to measure the extent to which individuals spend time working. Research suggests that the number of hours worked per week is
positively linked to violent victimization (Spano, Freilich, and Bolland 2008). One reason
why hours worked is linked to victimization may be because working outside of the home
increases the likelihood that individuals will interact with non-family members and thus
be exposed to motivated offenders, an explanation that is consistent with the lifestylesroutine activities perspective (Hindelang et al. 1978). To gauge the extent to which
respondents spend time outside of the home working, respondents were asked in Wave I
about the number of hours they spent working during the school year. Recurring Victimization and DRD4 141 Neighborhood advantage. Because victimization risk has been tied to neighborhood
context, a measure of neighborhood advantage is included (Sampson 1985). Similar to
measures used by other Add Health researchers (Beaver et al. 2007), a three-item neighborhood advantage scale was created from questions in Wave I that asked whether the
respondent knows most people in their neighborhood, whether they stopped on the street
to talk, and whether people in the neighborhood look out for each other. Higher scores
reflect more neighborhood advantage (a = .57).
Drug use. Drug use has been linked to an increased risk of being victimized, by making individuals less aware of their external environment, lowering inhibition, and by
increasing their vulnerability. In this sense, drug use can be conceived as a measure of
involvement in a risky activity that likely promotes victimization.3 It also has been linked
to risk of revictimization (Center for Substance Abuse Treatment 1997). Accordingly, a
measure of whether an individual had used any illicit drugs was included (0 = no, 1 = yes).
Demographics. To control for the fact that younger people are more likely than others to be victims of crime (Rand 2009), a measure of age in years is included. Race has
also been identified as impacting risk of victimization (Haynie and Piquero 2006). A measure of race (0 = white, 1 = non-white) is used.
To identify what distinguishes single from recurring victims, the factors that predict any
victimization must first be considered. Because of the dependence of recurring victimization on an initial victimization, statistical models that take this into account are used. As
such, estimation is done by simultaneously examining one model that examines the factors
that predict being victimized and one model that examines the factors that predict being a
recurring victim, given the fact that to be a recurring victim, an initial victimization must
have occurred. Two dependent variables are thus used in the analysis.
Victim/nonvictim. To assess whether someone was victimized, a measure of personal
victimization across Waves I, II, and III was created. Individuals were asked how many
times in the past 12 months someone shot them, stabbed them, pulled a gun or knife on
them, jumped them, or physically fought them in Waves I and II. In Wave III, individuals
were also asked whether they had been beaten up. For each of the questions in all of the
waves, respondents could choose from the following responses: never, once, twice, or
more than two times. Individuals who had not experienced any of these victimizations
across all waves (those who responded never to all questions at all waves) were classified
as nonvictims (coded 0). If individuals reported that they had experienced at least one victimization event in any of the three waves, they were classified as victims (coded 1).
Single/recurring victim. To assess whether someone experienced a single victimization or more than one victimization across the three waves of data, the same victimization
items were used as in the victim/nonvictim variable. If individuals reported they had experienced any of the five victimizations and reported only one incident, they were considered single victims (coded 0). If they had experienced more than one incident across the
three waves, they were classified as recurring victims (coded 1). Recurring victimization,
3 Given the link between involvement in delinquency and victimization, a measure of delinquent
involvement was created but not included in the final model as it was significantly and strongly correlated with each exogenous variable, except for race. Inclusion in the model resulted in coefficients
changing signs from the original model, which is an indication of multicollinearity (Greene 1997). 142 L. E. Daigle then, is a measure that assesses whether someone experienced more than one victimization
or a single victimization only in the three Waves. Using Waves I, II, and III to create the
recurring victimization measure was necessary to be able to have a large enough sample
size to conduct multivariate analysis, given the restriction of the sample size owing to the
inclusion of the DRD4 gene variable. As noted previously, only a subsample of individuals were genotyped; thus, to have a large enough sample size to have sufficient variation
on the dependent variables, revicitmization was measured as experiencing more than one
victimization in any of the three waves (Wave I, II, and III).
Plan of Analysis
The analysis for this study entails several steps. First, bivariate analyses of explanatory
variables, including the key variable of interest DRD4, and victim type are conducted. The
sample is limited to those individuals who are males as DRD4 did not significantly vary
across the victim types for females and because DRD4 has been shown to be more relevant for various outcomes for males (DeYoung et al. 2006; Laucht, Becker, Blomeyer,
and Schmidt 2007).4 In addition, one monozygotic twin was randomly excluded from each
twin pair to provide conservative parameter estimates, and individuals with missing data
were excluded. In the victim-recurring victim model, the sample is further reduced by
including only those individuals who were genotyped. The final sample includes 6,213
male respondents. Table 1 shows the descriptive characteristics of the sample. On average,
the youths were 15.9 years of age at the time of Wave I data collection. The majority of
the sample, 61.3%, were white. Most individuals had not used any illicit drug during the Table 1
Characteristics of total sample in analysis
Sample characteristic Mean (SD) Percent DRD4* (%)
Time Spent with Friends
Drug Use (% yes)
Race (% Non-white)
Age (Wave 1) ?
15.88 (1.58) 65.6
38.7 *Only a subsample of respondents were genotyped. The final sample
size for DRD4 is 949.
Note: n = 6,213.
4 Analyses were run for the subsample of female respondents. DRD4 was not significant in
either the model predicting victimization or the model predicting recurring victimization. Recurring Victimization and DRD4 143 previous month (82.9%). Slightly fewer than one-third (34.3%) of individuals possessed at
least one 7R allele, whereas almost 5% (4.8) had two.
The second stage of the analysis includes a bivariate probit model with sample selection. This analysis is appropriate when examining recurring victimization, in that being a
single or recurrent victim is observed only if a person is a single victim (see Fisher et al.
2010; Osborn Ellingworth, Hope, and Trickett 1996). Because the probability of being a
recurrent victim is conditioned on the probability of being a single victim, their error terms
are correlated, thus creating selectivity bias. The bivariate probit model addresses this
relationship statistically (for a detailed discussion about the bivariate probit model see
Greene 1997). The model estimates two equations, one estimating the probability of being
a victim versus a nonvictim and another estimating the probability of being a recurring
victim, relative to being a single victim. Huber/White standard errors are used to correct
for clustering of individuals within schools. Results
Table 2 depicts the results of two sets of bivariate analyses. In the first set of analyses, the
results show the distribution of the explanatory variables included in the final models
across nonvictims and victims (individuals who experienced at least one victimization
across the three waves). Several key findings can be gleaned. First, when considering
victimization across the three waves of data, most males have been victimized at least
once. Slightly more than half (56.1%) had experienced at least one violent victimization.
Second, many of the explanatory variables significantly differed across nonvictims and
victims, with victims possessing higher values on the various risk factors. Victims experienced more attention deficits, had more problems with problem solving, had more delinquent peers, spent more time with their peers, worked more hours, lived in more
advantaged neighborhoods, had greater drug use, and were more likely to be non-white
compared to nonvictims.
In the second set of analyses, to be consistent with the multivariate analyses, the
explanatory variables are compared across single and recurring victims (those individuals
who experienced more than one victimization across the three waves of data) for those
individuals who are in the second model for the bivariate probit analysis. Overall, of those
individuals who were victimized, 65.2% had experienced more than one victimization
across the three waves. Most notably, the key variable of interest, DRD4, significantly
varies across single and recurring victims. Although only 5.5% of the single victims have
two 7R alleles, 7.4% of the recurring victims have two 7R alleles. This difference is statistically significant at a = .05. Other differences for recurring and single victims were
found. Recurring victims were more likely to have attention deficits, to have delinquent
peers, to use drugs, and to be non-white than nonvictims.
The results of the bivariate probit model are presented in Table 3. In the first set of
results in column two are the results of the model distinguishing victims from nonvictims.
Marginal effects are presented, which represent the probability that is associated with
change in an independent variable on the outcome being observed (victimization), when
the other covariates are kept fixed, in this case at their respective means (Greene 1997; Jarl
and Gerdtham n.d.). Several factors increase the risk of being victimized at least once
across the three waves of data. A marginal change in attention deficits increased the likelihood of being victimized by 2%. Having delinquent peers is also significant; a marginal
change in the number of delinquent peers increased the risk of victimization by 2%. Time
spent with friends and number of hours spent working were also significantly...
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