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\nPlease answer below questions using article .<\/p>\n
“Homework help – Discuss the evidence pertaining to the biological\/genetic contributors to self-control. Based on this evidence, do you think that parental socialization is the only cause of variation in self-control ”<\/p>\n
CRIMINOLOGY VOLUME 43 NUMBER 4 2005 1169
\nDO PARENTS MATTER IN CREATING SELFCONTROL
\nIN THEIR CHILDREN A
\nGENETICALLY INFORMED TEST OF
\nGOTTFREDSON AND HIRSCHI\u2019S THEORY OF
\nLOW SELF-CONTROL*
\nJOHN PAUL WRIGHT
\nUniversity of Cincinnati
\nKEVIN M. BEAVER
\nUniversity of Cincinnati
\nKEYWORDS: ADHD, behavior patterns, genetics, parental influence,
\nself-control
\nGottfredson and Hirschi\u2019s general theory of crime (1990) has
\ngenerated an abundance of research testing the proposition that low
\nself-control is the main cause of crime and analogous behaviors. Less
\nempirical work, however, has examined the factors that give rise to low
\nself-control. Gottfredson and Hirschi suggest that parents are the sole
\ncontributors for either fostering or thwarting low self-control in their
\nchildren, explicitly discounting the possibility that genetics may play a
\nkey role. Yet genetic research has shown that ADHD and other deficits
\nin the frontostriatal system are highly heritable. Our research thus tests
\nwhether \u201cparents matter\u201d in creating low self-control once genetic
\ninfluences are taken into account. Using a sample of twin children we
\nfind that parenting measures have a weak and inconsistent effect. We
\naddress the conceptual and methodological issues associated with the
\nfailure to address genetic influences in parenting studies.
\nMore than a decade ago, Gottfredson and Hirschi (1990) set forth a
\ngeneral theory of crime that assigned low self-control as the causal factor
\nin the etiology of crime and numerous analogous behaviors. Since that
\n* We would like to thank the editor for his commitment to scholarly discourse.
\nDirect all correspondence to John Paul Wright, Division of Criminal Justice,. 600
\nDyer Hall, Ml 0389,University of Cincinnati, Cincinnati, OH 45221 or email at
\njohn.wright@uc.edu.
\n1170 WRIGHT AND BEAVER
\ntime, the theory has occupied a fundamental position in criminology and
\nhas generated an abundance of empirical tests. Its robustness is evident in
\nthe recent meta-analytic review by Pratt and Cullen (2000), who found
\nthat, across various samples and measurement techniques, low self-control
\nis a salient predictor of criminal behavior. With such support, it should be
\nno surprise that low self-control theory continues to be at the heart of
\nmuch criminological debate and investigation (Geis, 2000; Marcus, 2004;
\nSampson and Laub, 1995).
\nGottfredson and Hirschi\u2019s theory focused on the association between
\nlow self-control and crime. Indeed, it is this association that has generated
\nthe greatest amount of empirical interest. Their hypotheses that link the
\ndevelopment of self-control in children to parental behaviors, however,
\nhave been less frequently considered. Parents, they maintain, play the
\ndecisive role in either fostering or thwarting the development of low selfcontrol.
\nBorrowing heavily from the work of Patterson (1982),
\nGottfredson and Hirschi assert that parents who effectively monitor and
\nsupervise their children, and who recognize and respond to their child\u2019s
\nantisocial behavior will effectively instill self-control in that child. Parents
\nwho fail to engage in such parental management techniques will
\nsubsequently fail to help their children develop the ability to resist
\nsituational temptations.
\nGiven the overwhelming support linking low self-control to crime and
\nanalogous behaviors (Pratt and Cullen, 2000), the paucity of research
\nexamining the factors that give rise to low self-control is somewhat
\nsurprising. We have, for instance, been able to locate only a handful of
\nempirical studies that test them. The findings, in general, have been
\nfavorable to Gottfredson and Hirschi\u2019s position that effective child-rearing
\npractices are predictive of self-control in children (but see Cochran, Wood,
\nSellers, Wilkerson, and Chamlin, 1998). Even so, the quality of that
\nevidence is circumspect, for reasons we will detail later.
\nGottfredson and Hirschi attribute the development of low self-control
\nin children solely to parenting practices\u2014rejecting outright potential
\ngenetic effects. At the same time, a large and growing body of clinical and
\nbehavioral genetic research has found that impulsivity, attention deficit
\nhyperactivity disorder (ADHD), and hyperactivity\u2014concepts related
\nclosely to Gottfredson and Hirschi\u2019s construct\u2014are highly heritable
\n(Price, Simonoff, Waldman, Asherson and Plomin, 2001; Rietveld,
\nHudziak, Bartels, van Beijsterveldt and Boomsma, 2003). In a review of
\nthe genetic research on ADHD, for instance, Spencer and his colleagues
\nconcluded that \u201cthe mean heritability of ADHD… is approximately 0.75,
\nwhich means that about 75% of the etiological contribution to this
\ndisorder is genetic\u201d (2002:6). Indeed, after a review of the evidence, the
\nNational Institute of Mental Health published a widely cited brochure
\nDO PARENTS MATTER IN SELF-CONTROL 1171
\nstating that scientists \u201care finding more and more evidence that ADHD
\ndoes not stem from home environment, but from biological causes\u201d
\n(2003:13; for a meta-analytic review of the association between ADHD
\nand criminal behavior see Pratt, Cullen, Blevins, Daigle and Unnever,
\n2002). Other authors have reached similar conclusions. Barkley (2005), for
\nexample, notes that when DSM criteria are used to measure ADHD that
\nstudies indicate it to be 97 percent heritable. \u201cThis trait,\u201d he argues, \u201cis
\nmore inherited than any dimension of human personality\u201d (14).
\nThe potential for genetic heritability to influence levels of low selfcontrol
\nin children poses a serious counter argument to Gottfredson and
\nHirschi\u2019s parenting thesis. For instance, various scholars now openly
\nquestion whether \u201cparents matter\u201d in the development of their children\u2019s
\npersonalities. Harris (1998), for example, has argued forcibly that the
\neffects of parenting on child outcomes have been overstated, and that in
\nmost instances, parents \u201cdon\u2019t matter\u201d when it comes to the child\u2019s
\npersonality (see also Cohen, 1999; Wright and Cullen, 2001). Citing
\nevidence from behavioral genetic studies (see for example, O\u2019Connor,
\nNeiderhiser, Reiss, Hetherington and Plomin, 1998; Plomin, 1995; Scarr
\nand McCartney, 1983), Harris argues that parental socialization practices
\nare likely to be inconsequential once individual differences in parent and
\nchild temperament and genetic heritability are accounted for (Cohen,
\n1999; Pinker, 2002).
\nHarris\u2019s position is more complex than the simple statement that
\n\u201cparents don\u2019t matter\u201d implies. Her critique of the parenting research in
\ngeneral raises serious questions about the validity of many social science
\nfindings relating parenting practices to offspring conduct. The vast
\nmajority of research on parenting, she notes, typically employs samples
\nthat measure one child and one parent, usually the mother, under the
\nassumption that inferences can be made to other children in the household
\n(Rowe, 1994; Walsh, 2002). More detailed research has revealed, however,
\nthat parents enjoy differential relationships with their children. They may
\ntreat one with hostility, yet pamper another (Harris, 1998). Children too,
\nwhen asked, often report substantial differences in their perceptions of
\ntheir parents (Reiss, Neiderhiser, Hetherington and Plomin, 2000).
\nAccording to Harris, numerous \u201cmicro-environments\u201d exist within any
\nhome. It is these micro-environments, or child-specific parenting behaviors
\nas opposed to measures of global parenting, that likely differentiate
\nchildren. Such micro-environments are typically not examined with
\nstandard social methodologies (SSM) (see also Walsh, 2002).
\nNor do SSMs account for commonalities due to genetic similarities.
\nFindings from a broad array of studies converge to show that many
\ntemperamental factors are highly heritable (Caspi, Roberts and Shiner,
\n2005). In turn, associations between parents\u2019 behaviors and the behaviors
\n1172 WRIGHT AND BEAVER
\nof their children may be confused for \u201cstatistically significant parenting
\neffects\u201d in study designs that are insensitive to biological similarities
\nbetween subjects within the home. For example, mothers who are hostile
\nand cold are more likely to be emotionally removed from their daughters\u2019
\nlives. Their daughters, in turn, are more likely to be hostile and cold. SSMs
\nwould correlate the daughters\u2019 hostility with the mother\u2019s removed
\nparenting style and likely infer that maternal hostility caused daughter
\nhostility\u2014all without any recognition that the two phenotypes share
\ncommon genetic backgrounds. These limitations, Harris argues, likely
\nmisspecify or overstate the effects parents may have on their children\u2019s
\ntraits and behaviors.1 We should also add that numerous behavioral
\ngenetic studies have failed to detect significant shared environmental
\neffects (Dunn and Plomin, 1990; Neiderhiser, Reiss, Hetherington and
\nPlomin, 1999; Plomin, Owen and McGuffin, 1994).
\nOur purpose here is twofold. First, we examine the effects of parenting
\non levels of self-control in kindergarten and first-grade children. We do so
\nwith a national dataset that contains mother and teacher reports of child
\nself-control. As do other SSM studies, we use OLS models with appropriate
\ncontrols for demographic and neighborhood influences. Second, and
\nmore important, we also use a sample of twins, taken from the same
\ndataset, to assess the influence of parenting factors. The use of a twin
\ndataset allows us to account for the shared genetic variance between twins.
\nMoreover, we use hierarchical linear regression (HLM) analyses to control
\nfor the clustering of observations caused by genetic similarities. We
\ncontrast results garnered through traditional social science methodologies
\nwith those generated from the sample of twins. It appears, to foreshadow
\nour results somewhat, that Harris\u2019s critiques should no longer be ignored.
\nEFFECTS OF PARENTING ON LOW SELF-CONTROL
\nA long line of literature has placed parents at the forefront of
\ncriminological theories and research (Loeber and Stouthamer-Loeber,
\n1986; Patterson, 1982). Such research has tended to focus on the ways
\nvarious parenting styles, usually measured as global parenting constructs,
\nshape children\u2019s behavioral patterns. Given the dominant role of parents
\nin criminology, it is interesting that very little research has been conducted
\non how parents influence their children\u2019s self-control\u2014especially given the
\nempirical attention dedicated to the theory.
\n1. The interpretation of parenting correlations with child behavioral outcomes is
\nmade difficult by at least three factors: First, parents and their children share genes.
\nSecond, external biological factors, such as neurotoxins, may influence both parent
\nand child behavior. Third, temporal ordering is very difficult to establish. Child
\ntraits and behaviors likely influence parenting behaviors and vice versa.
\nDO PARENTS MATTER IN SELF-CONTROL 1173
\nUsing data from the Cambridge Study in Delinquent Development,
\nPolakowski (1994) examined the role of parental supervision on child selfcontrol.
\nHis analysis employed two measures of child-rearing practices\u2014
\nparent\u2019s watchfulness and parental supervision\u2014both garnered from
\ninterviews with social workers. These variables were used as indicators for
\na latent measure of supervision. Although not a complete test of
\nGottfredson and Hirschi\u2019s proposition, Polakowski\u2019s findings, generated
\nfrom structural equation models (SEM), were generally consistent with
\nGottfredson and Hirschi\u2019s hypothesis. Children whose parents were
\nvigilant, had, on average, more self-control (see also Lynskey, Winfree,
\nEsbensen and Clason, 2000; Pratt, Turner and Piquero, 2004).
\nAnalyzing data from sixth-grade male students, Feldman and
\nWeinberger (1994), explored the relationships among parenting practices,
\ndelinquent behavior and childhood self-control, or what they termed \u201cselfrestraint.\u201d
\nTheir results indicated that parental management was positively
\nassociated with higher levels of child self-restraint, yet did not have a
\ndirect effect on child misbehavior.
\nSimilarly, two additional studies, conducted by Gibbs and his associates,
\nexamined retrospective accounts of parental management practices on
\nlevels of self-control in a sample of college students. The first\u2014Gibbs,
\nGiever and Martin (1998)\u2014found tentative support for the role parents
\nplay in fostering low self-control. Gibbs et al. measured forty
\ncharacteristics of parental management styles and another forty of low
\nself-control. Through a series of path diagrams they found that parental
\nmanagement had a significant and direct effect on low self-control
\n(Beta=.28). Likewise, Gibbs, Giever and Higgins (2003) performed
\nanother analysis on a sample of college students, again using SEMs. Their
\nfindings paralleled those reported in their 1998 study. Parental
\nmanagement practices maintained a positive relationship with low selfcontrol,
\nwith the coefficient being moderate in magnitude (Beta=.26).
\nSimilar results were garnered in a study replicated by Higgins (2002).
\nHay (2001) also examined the effects of parenting on low self-control in
\na sample of 197 urban high school students. Their analysis also included
\nthe two parenting measures\u2014monitoring and discipline\u2014along with a selfreport
\nmeasure of low self-control in his analyses. The results provided
\npartial support in favor of Gottfredson and Hirschi\u2019s theory. Hay\u2019s
\nanalyses revealed that parental monitoring, but not discipline, was
\nsignificantly associated with child low self-control, even after controls were
\nintroduced for early childhood antisocial behavior. Hay also analyzed an
\nalternative model that combined the two parenting scales into one
\nmonitoring-discipline measure. This was significantly and inversely related
\nto low self-control (Beta=-.24).
\n1174 WRIGHT AND BEAVER
\nMore recently, Unnever, Cullen, and Pratt (2003) found evidence
\nlinking parenting practices to offspring low self-control. Data for their
\nstudy came from 2,437 middle school students in Virginia. Similar to Hay
\n(2001), Unnever and his associates employed measures of parental
\nmonitoring and of consistent punishment. Their analysis also included
\nGrasmick, Tittle, Bursik and Arneklev\u2019s (1993) low self-control scale.
\nTheir findings indicated that monitoring and consistent punishment were
\nsignificantly related to low self-control, even when controlling for the
\nchild\u2019s level of ADHD.2
\nThese studies suggest that Gottfredson and Hirschi\u2019s theory on the
\ndevelopment of low self-control (1990) is at least partly correct. Under the
\nassumptions of SSMs, parenting practices appear to have some influence
\non offspring low self-control. The strength of the relationship between
\nparenting practices and child self-control, however, appears to be
\nmoderate at best. More important, as we will show, there is reason to cast
\ndoubt over the validity of this body of research.
\nGENETIC CONTRIBUTIONS TO LOW SELF-CONTROL
\nGottfredson and Hirschi explicitly discount the possibility that low selfcontrol
\nmay have a genetic component: \u201cthe magnitude of the \u2018genetic
\neffect\u2019\u201d they say, \u201cis near zero\u201d (60). A large body of literature, however,
\nhas arrived at a very different conclusion. In a recent study that examined
\nheritability of attention problems in twins drawn from the Netherlands
\nTwin Registry, Rietveld and colleagues (2002) found that heritability
\nestimates varied between .68 and .76, depending on the age of the twins.
\nTheir longitudinal study of 3,853 twin pairs also found that shared
\nenvironment, which is typically conceived of as family environment, had
\nlittle effect on the child\u2019s level of overactivity. Similar to Barkley\u2019s (1997)
\nconclusions, Rietveld and colleagues suggest strongly that attention
\nproblems are due more to genetic factors than to environmental
\ninfluences.
\nIn a similar vein, Mick, Biederman, Prince, Fischer and Faraone (2002)
\nfound the strongest risk factor for childhood ADHD was having a parent
\nwith ADHD. Children with an ADHD parent were eight times more
\nlikely to be diagnosed with ADHD. The effects of parental ADHD were
\nstronger than fetal exposure to drugs, alcohol, and cigarette smoke, than
\nhaving a low birth weight, and than being born into an economically
\ndisadvantaged family. These findings suggest that even when common
\n2. Some studies do attempt to control partially for child effects, such as Hay (2001)
\nand Unnever et al. (2003), by using autoregressive statistical models. Others do not
\nrely on parental reports and instead utilize individual recollections of parenting
\nbehaviors (Gibbs et al., 1998).
\nDO PARENTS MATTER IN SELF-CONTROL 1175
\nenvironmental risk factors related to offending are taken into account,
\ngenetic factors continue to exert a substantial effect on the child (Reiss,
\nNeiderhiser, Hetherington and Plomin, 2000).
\nAttention problems, problems with hyperactivity, and problems with
\nimpulsivity have repeatedly been shown to have a substantial genetic
\ncomponent. Given the close correspondence between Gottfredson and
\nHirschi\u2019s conception of low self-control and the diagnostic criteria for
\nADHD, it is likely that low self-control is also highly heritable. Indeed, in
\nthe vernacular of developmental neuropsychologists, \u201cexecutive controls\u201d
\nare composed of the ability to regulate emotions, to control impulses, to
\nfocus appropriately on the task at hand, and to delay gratification. These
\ncapacities are housed in the frontal, orbital-frontal and prefrontal cortex
\nof the brain, which are part of the larger frontostriatal system (Aron,
\nRobbins and Poldrack, 2004; Bradshaw, 2001; Miller and Cohen, 2001).
\nVarious neuroscientists have recognized the overlap between executive
\nfunctions and concepts drawn from other fields. Convit and his coauthors
\nnote, for example, that
\nThe characteristics of an individual acting with no forethought
\nand without regard to consequences links the criminologists\u2019
\nexplanation of criminal propensity by inadequate \u201cself-control\u201d
\n(Gottfredson and Hirschi, 1990) or impaired \u201cimpulse control\u201d
\n(Wilson and Herrnstein, 1985) and to results suggested by a role
\nof serotonin in impulsive violence (Virkkunen and Linnoila,
\n1993). The mechanism(s) for impulsive behavior remain unclear.
\nHowever, most brain researchers would agree that the frontal
\nlobes are crucial in complex tasks when planning is required and
\nthat their main function is inhibitory or regulatory (1996:173).
\nMoreover, data from numerous neuroimaging studies vividly show
\nthese areas of the brain to be under substantial genetic control.3 Without
\nexception, however, none of the existing criminological studies into the
\ninfluence of parenting practices on child self-control recognize the
\npossibility that self-control and other executive functions are influenced by
\ngenes and by other biological factors, such as environmental tobacco
\nsmoke (Yolton, Dietrich, Auinger, Lanphear and Hornung, 2005), blood
\nlead levels (Dietrich, Douglas, Succop, Berger and Bornschein, 2001), or
\nbirth complications (Beaver and Wright, 2005).4 Although the evidence
\n3. We note that the brain is also highly intertwined with its immediate environment.
\nEnvironmental stimulation aides in synaptogenesis; likewise, a lack of
\nenvironmental stimulation accelerates neuronal apoptosis.
\n4. We are not the first to recognize the possibility that low self-control may be under
\nsubstantial genetic control. Unnever and associates (2003:495) are the exception
\nand note that \u201c\u2026the origins of self-control are not limited to parental
\n1176 WRIGHT AND BEAVER
\nlinking self-regulation to variation in brain structure and functioning is
\nnow undeniable, it is still far from clear whether features of the social
\nenvironment, namely parental socialization practices, influence the
\ndevelopment of traits such as low self-control. Far from having a \u201cnet
\neffect of zero,\u201d genetic influences may be the dominate influence on
\nexecutive functions.
\n\u201cThe responsible way to tackle the genetic challenge to socialization
\nresearch,\u201d Caspi and his colleagues argue, \u201cis head on, by using genetically
\nsensitive designs that can provide leverage in identifying environmental
\nrisks\u201d (2005:464). Following this advice, we examine the influence of
\nparenting factors on a measure of child self-control in a sample of
\nkindergarten and first-grade students. We also employ, from the same
\nsample, a subsample of twins, and contrast our genetically informed
\nfindings against those detected through common SSM assumptions.
\nMETHODS
\nSAMPLE
\nData for this paper come from the Early Childhood Longitudinal
\nStudy, Kindergarten Class of 1998\u20131999 (ECLS-K). Sponsored by the U.S.
\nDepartment of Education, National Center for Education Statistics
\n(NCES), the ECLS-K is an ongoing study of a nationally representative
\nsample of children designed to assess the impact that the primary
\nschooling years have on learning. The ECLS employed multiple reporting
\nsources to gain detailed information about the children\u2019s behavior, their
\ntemperament, their intellectual skills, their social relationships and their
\nenvironment. Information about such topics was obtained through
\ninterviews with the children, their parents and teachers, and school
\nadministrators. Four waves of data have been collected thus far: two waves
\neach during kindergarten and first grade. Data collection for wave one
\nbegan in the fall of 1998, when the children first entered into kindergarten.
\nThe second wave of questionnaires were administered the following spring
\n(1999). The last two waves of data were obtained during the fall and spring
\nof the first grade (1999\u20132000). Only a small subgroup of students,
\nhowever, were interviewed in the fall of their first grade.
\nSampling waves assessed during the kindergarten year were measured
\nless than 6 months apart. Some of the parenting measures, moreover, were
\nasked only during the spring term. Given the relatively small time
\ndifference between sampling waves, we treat all data collected during the
\npractices\u2026low self-control is not a purely social outcome but is also affected by
\ngenetic predispositions.\u201d
\nDO PARENTS MATTER IN SELF-CONTROL 1177
\nkindergarten year as from one measurement period. However, we use the
\nmeasures of child low self-control measured during the spring to maintain
\ntemporal ordering. Data from the fall wave of the first grade were
\nexcluded from the analysis, making the interval between consecutive
\nmeasurement periods (kindergarten year to spring term of their first
\ngrade), about one year.
\nA unique aspect of the ECLS-K was that when a respondent indicated
\nthe presence of a twin, the proband was subsequently included in the
\nsample, netting n=310 twins. Both twins were subject to identical data
\ncollection processes and instruments. In terms of cluster size, each twin
\nwas reported on by the parent (usually the mother), their teacher and in
\nsome instances the mother and the teacher.
\nThe total sample size for the ECLS-K includes over 21,000 children.
\nRecall that one purpose of our study was to assess results based on SSM\u2019s
\nto those obtained from a sample of twins. With such a large overall
\nsample, very small differences could easily reach levels of statistical
\nsignificance. To make our results as valid as possible, we took a random
\nsample of n=1,000 children from the larger sample of 21,000. We chose a
\nsample size of 1,000 for two reasons. First, most national studies have
\nsample sizes that range from 1,000 to 2,000 subjects. Second, no
\nmeaningful differences were found in our results when random samples
\nranging between n=310 and n=2,000 were analyzed. Thus, the results
\nobtained from our random sample of n=1,000 would be the results
\nreported and published without consideration for the twins in the sample.
\nOverall, the ECLS-K is compatible with our research agenda for four
\nmain reasons. First, the inclusion of twins permits us to control for the
\ngenetic similarity. Second, consistent with Gottfredson and Hirshi\u2019s
\nproposition on the origins of low self-control, a number of parenting
\nquestions were asked that tapped into efficacious parenting practices
\n(Wright and Cullen, 2001). Third, the ECLS-K contains childhood
\nmeasures of low self-control, allowing us to examine the early correlates of
\nlow self-control. And, finally, because multiple reporting sources were
\nused, we were able to construct theoretically consistent measures of low
\nself-control based on parent and teacher reports. Taken together, the
\nECLS-K provides us with a rich data source with which to systematically
\nassess the biological and social origins of low self-control.
\nMEASURES
\nLOW SELF-CONTROL
\nThe ECLS-K employed a version of Gresham and Elliott\u2019s (1990) wellknown
\nSocial Skills Rating Scale (SSRS), a proprietary assessment
\n1178 WRIGHT AND BEAVER
\nbattery, to measure child self-control. The SSRS is a multi-rater,
\nstandardized, normed-referenced assessment battery based on information
\ncollected from mothers and teachers. The SSRS contains subscales that
\ntap into child self-control, including overactivity and hyperactivity. The
\nresponse set for these items were scored 1=never, 2=sometimes, 3=often
\nand 4=very often. Research into the psychometric properties of the SSRS
\nhas found the scales and subscales to be high in reliability, moderate in
\ntest-retest reliability, and valid (Benes, 1995; Gresham, 2001).5
\nBecause the SSRS is a multi-rater assessment instrument, we created a
\nteacher low self-control scale (wave 2 twin sample alpha=.80; wave 2
\nrandom sample alpha=.82; wave 4 twin sample alpha=.82; wave 4 random
\nsample alpha=.85), a parent low self-control scale (wave 2 twin sample
\nalpha=.45; wave 2 random sample alpha=.58; wave 4 twin sample
\nalpha=.57; wave 4 random sample alpha=.59), and a combined low selfcontrol
\nscale (wave 2 twin sample alpha=.61; wave 2 random sample
\nalpha=.63; wave 4 twin sample alpha=.60; wave 4 random sample
\nalpha=.68) for both waves of data. Parental reports, although used widely,
\nare slightly less reliable than information gathered from other sources.
\nData from teachers, however, have proven to be highly efficient and
\nreliable and help measure conduct that occurs away from parents (Cairns
\nand Cairns, 1994; Harris, 1998). Teachers and parents reported on the
\nchild\u2019s ability to manage temper and emotions, on ability to control
\nconduct, and on impulsiveness and activity levels.
\nTo test the robustness of our findings, we also employed an expanded
\nmeasure of low self-control. This scale taps not only into the attention
\nproblems outlined, but also into various social problems experienced by
\nchildren lacking self-control and deficient decision-making processes that
\noften accompany low self-control. This expanded scale contains the
\nfollowing eight items: parental and teacher reports of self-control, parental
\nand teacher reports of approaches to learning, parental reports of the
\nchild\u2019s activity level, parental reports of the child\u2019s social interactions with
\nothers, teacher reports of the student\u2019s interpersonal skills, and teacher
\nreports of the student\u2019s externalizing problem behaviors (twin sample
\nalpha=.75; random sample alpha=.77). The same measures were used to
\ncreate the expanded measure of low self-control during first grade (twin
\n5. The measurement of self-control, or executive control functions in general, is still a
\nmatter of substantial debate. This debate has not escaped criminology. In essence,
\nmuch of the debate centers on whether self-control should be measured
\nattitudinally or through items that capture variation in analogous behaviors. We,
\nhowever, share the view of Rudolph, Lambert, Clark, and Kurlakowsky. (2001:931)
\nthat self-regulation \u201ccan be conceptualized as a combination of cognitive,
\nevaluative, and behavioral processes that guide goal-directed action and emotional
\nresponsiveness.\u201d
\nDO PARENTS MATTER IN SELF-CONTROL 1179
\nsample alpha=.74; random sample alpha=.78). Higher scores on this scale
\nindicate lower levels of self-control. See Appendix A for a description of
\nthe variables and scales. Consult Appendix B for descriptive information
\nabout the samples.
\nSOCIALIZATION MEASURES
\nGottfredson and Hirschi maintain that the ways in which parents
\nsocialize their children will ultimately decide whether their child develops
\nself-control. In particular, they assert that parents who supervise,
\nrecognize and consistently punish childhood transgressions will succeed at
\ninstilling self-control in the child. To partially test this perspective, we
\nincluded five unique measures of parenting behaviors: parental
\ninvolvement, parental withdrawal, parental affection, physical punishment
\nand family rules. Due to data limitations, we were not able to measure all
\nthe parenting variables Gottfredson and Hirschi identify as significant
\npredictors of low self-control. Yet this situation is not unique to the ECLSK
\ndata or our analyses in general; rather, prior research has also been
\nhampered by an inability to measure all dimensions of parental
\nsocialization (Hay, 2001; Unnever et al., 2003). Even so, many of the
\nmeasures used in our analysis are consistent with those Gottfredson and
\nHirschi identify.
\nParental Involvement. This scale taps into the amount of time the
\nparent spends with the child on various activities. Presumably, while
\nengaging in such activities, parents will also be supervising their child. This
\nnine-item measure was constructed using parental responses to the
\nfollowing questions: how often the parent reads, tells stories, sings songs,
\nhelps child with chores, helps with art activities, plays games, teaches the
\nchild about nature, builds things and plays sports (twin sample alpha=.74;
\nrandom sample alpha=.75). Higher scores indicate a greater degree of
\nparental involvement in the child\u2019s life.
\nParental Withdrawal. The scale serves to capture the degree to which
\nparents retreat from, or hold unfavorable attitudes toward, their child.
\nNine items comprised this scale: I am too busy to play with child; I have
\ndifficulty being warm with the child; being a parent is harder than I
\nanticipated; my child does things that bother me; I have to sacrifice to
\nmeet the child\u2019s needs; I feel trapped as a parent; I often feel angry with
\nthe child; my child is hard to care for; and being a parent is more work
\nthan pleasure (twin sample alpha=.67; random sample alpha=.68).
\nParental Affection. This four-item scale measures the degree of
\naffection between the child and the parent, and includes the following
\nitems: we spend warm, close time together; the child likes me; I always
\nshow love for the child; and I express affection to the child. The four items
\n1180 WRIGHT AND BEAVER
\nwere summed, with higher scores representing more parental affection
\n(twin sample alpha=.63; random sample alpha=.59).
\nPhysical Punishment. Gottfredson and Hirschi maintain that parents
\nwho punish consistently will instill self-control in their child. Although the
\nECLS-K data do not include measures tapping into the consistency with
\nwhich parents\u2019 correct misbehavior, two measures do index whether
\nparents would physically punish the child. Parents were presented with a
\nhypothetical scenario, asking them what they would do if the child were to
\nhit them. A list of possible retaliations was then presented, and parents
\nwere subsequently asked which, if any, of the punishment strategies they
\nwould use. We identified two such actions: if the parent stated she would
\n\u201chit the child back\u201d or if she would \u201cspank the child.\u201d These two items
\nwere then summed, forming the physical punishment index, with higher
\nscores indicating the parent is more likely to resort to physical punishment
\nwhen faced with disciplining the child.
\nFamily Rules. This final socialization measure measures a limited
\ndomain of rules within the home. Three questions were asked about rules
\nregarding television viewing. Specifically, parents were asked if there are
\nfamily rules for which television programs the child can watch, the amount
\nof hours the child is permitted to watch television, and if rules exist on
\nhow late or early the child is allowed to watch television. Again, higher
\nscores reflect more family rules (twin sample alpha=.63; random sample
\nalpha=.58).
\nSTATISTICAL CONTROLS
\nGender. Gottfredson and Hirschi maintain that, in general, boys tend to
\nhave lower levels of self-control than girls. We therefore include a
\ndichotomous measure of gender in the analyses (1=male; 2=female).
\nAcademic Preparedness. Cognitive capacity has been found to be a
\nstrong predictor of crime and other behavioral problems (Wilson and
\nHerrnstein, 1985). As such, we included an academic preparedness scale,
\nmeasured during wave one, to assess the degree to which academic ability
\nis related to low self-control. Children in the ECLS-K were subject to a
\ncognitive assessment battery with three distinct components: language and
\nliteracy, mathematical skills, and general knowledge. To ascertain the
\nscores on each of these tests, each child was tested one on one (ECLS-K
\nUser\u2019s Manual). To compute the academic preparedness scale, we
\ncombined these three scores (twin sample alpha=.87; random sample
\nalpha=.84). The scores tap into the acquisition of knowledge and the
\nchild\u2019s preparedness for kindergarten.6
\n6. Reviewers noted that the measure of academic preparedness overlaps with one of
\nDO PARENTS MATTER IN SELF-CONTROL 1181
\nRace. To capture potential differences in self-control between whites
\nand nonwhites, we included race as a control variable. Race was coded
\n(0=white; 1=nonwhite).
\nNeighborhood Disadvantage. Research has found self-control to be
\ninfluenced by community structural characteristics (Pratt, Turner and
\nPiquero, 2004). To control for the possibility that neighborhood social
\nfactors may affect the development of self-control, we included a
\nneighborhood disadvantage scale. Six items, reported by the parents, were
\nsummed to form the neighborhood disadvantage scale. Parents were asked
\nhow safe it was for their child to play outside, whether garbage and litter
\nwere visible on the street, whether there were problems with people
\nselling and using drugs or alcohol in the neighborhood, whether there
\nwere problems with burglaries and robberies in the neighborhood, if there
\nwere problems with violent crime, and if there were vacant houses nearby.
\nHigher scores indicate more problems in the neighborhood (twin sample
\nalpha=.60; random sample alpha=.73).
\nANALYTICAL PLAN
\nHarris (1998) offered a stinging critique of standard social science
\nmethodologies, noting that most SSMs include data on one child and one
\nparent, inferring from that relationship to other relationships within the
\nhome (see also Caspi et al., 2005). She also argues that SSMs cannot
\naccount for similarities in genetic commonalities between individuals
\nwithin the same home. To Harris, most of the current empirical literature
\non the role and effects of parenting is seriously biased (referenced in
\nPinker, 2002). We take into account Harris\u2019s criticism by analyzing a
\nrandom subsample of youth as well as a sample of twins. We also use
\nstatistical models unique to each sample. In the random subsample we use
\nOLS regression. This technique does not account for the clustering of
\nGottfredson and Hirschi\u2019s dimensions of low self-control: preference for physical
\nactivities. According to reviewers, we may be committing a tautology primarily
\nbecause we are predicting low self-control with a component of self-control. The
\nacademic preparedness scale does not ask whether students prefer mental activities
\nover physical activities. Rather, the academic preparedness scale tests both what
\nthe student has learned, and the student’s ability to read and to calculate basic
\nmathematical equations. The measure was originally designed to assess individual
\ndifferences in school preparedness for children entering Kindergarten. And as
\nGrasmick, Tittle, Bursik and Arneklev (1993) found, the physical preference
\ndimension of low self-control was the weakest correlate with their well-known and
\nwidely used scale of self-control. Still, we recalculated all of the models without
\nincluding the measure of academic preparedness. The results were virtually
\nidentical to those reported with the inclusion of the academic preparedness
\nmeasure. Future research would benefit by examining the nexus between IQ and
\nother measures of intellect, and low self-control.
\n1182 WRIGHT AND BEAVER
\nindividuals within the household; a serious violation of the technique but
\none that is commonly committed.
\nGenetic similarity within families translates into a loss of statistical
\nindependence between observations. OLS regression assumes that
\nobservations are independent of each other and thus that the correlation
\nbetween error terms across observations is zero (0). Of course, children
\nare not randomly assigned within families, and monozygotic twins share all
\nof their genes. Intra-class correlations, (=rho), a measure of the degree of
\nhomogeneity of phenotypes within families, generally range between .20
\nto .60, indicating that 20 to 60 percent of the variance in the outcome of
\ninterest is accounted for by the clustering of observations (Wright and
\nCullen, 2001). Methodological research, however, has found that ICCs as
\nsmall as .1 can downwardly bias estimates of the standard errors and in
\nsome cases slope estimates (Zyzanski, Flocke and Dickinson, 2004). The
\nresult of ignoring genetic similarities within families likely results in the
\noverestimation of significant effects (Type 1 errors), and the inaccurate
\nattribution of \u201csubstantive meaning\u201d to parenting variables.
\nIn the twin sample we employ a random-effects regression analysis,
\nwhich is virtually synonymous with hierarchical linear modeling (HLM).
\nThis technique allows for the estimation of the proportion of the variance
\nin the dependent variable accounted for by the nonrandom clustering of
\nsubjects within twin dyads. In this case, the clustering of subjects occurs
\ndue to genetic similarity\u2014that is, monozygotic or dyzygotic twin status.
\nHLM and random-effects models account for the loss of statistical
\nindependence and produce robust standard errors through an iterative
\nprocess. The resulting maximum-likelihood slope estimates are assessed
\nagainst standard errors that are comparatively more conservative.
\nUnlike studies in which the central concern is in estimating the
\nheritability of a certain trait, our concern is in estimating the potential
\ninfluences of a range of predictors, controlling in part for genetic
\nsimilarities. As such, we avoid model fitting techniques that are generally
\nreserved for estimating heritability coefficients.7 Our analysis is similar to
\nTully, Arseneault, Caspi, Moffitt and Morgan\u2019s analysis of data from the
\nEnvironmental Risk Longitudinal Twin Study (2004).
\n7. Model fitting procedures revealed that low self-control is 66 percent heritable. The
\nbaseline HLM model produced an ICC of .55, indicating that 55 percent of the
\nvariation in self-control could be attributed to the clustering of twins in the same
\nhousehold. The difference between the results from the SEM model and the HLM
\nmodel indicates that HLM may underestimate the actual amount of clustering in
\nthe twin dyads.
\nDO PARENTS MATTER IN SELF-CONTROL 1183
\nRESULTS
\nTable 1 shows the results of our OLS and HLM analyses. Looking first
\nat the OLS regression of parental reports of their child\u2019s self-control we
\nfound that three of the parenting measures significantly accounted for
\nvariation in low self-control: parental withdrawal, parental affection and
\nfamily rules. As predicted by low self-control theory, parental withdrawal
\nwas positively associated with low control, and family rules and parental
\naffection were inversely related to self-control. Gender, academic
\npreparedness and neighborhood disadvantage were also related to low
\nself-control in the theoretically expected direction. These results are
\nprecisely what would be predicted from low self-control theory under
\nstandard socialization assumptions.
\nTable 1. Effects of Parenting on Child’s Low Self-Control in Kindergarten
\nVariables Parental
\nReports
\nTeacher
\nReports
\nTotal Composite
\nScore
\nOLS HLM OLS HLM OLS HLM
\nSocialization Measures
\nParental
\nInvolvement
\n-.02
\n(-.73)
\n-.01
\n(-.86)
\n-.01
\n(-.31)
\n-.02
\n(-1.28)
\n-.02
\n(-.68)
\n-.04
\n(-1.51)
\nParental
\nWithdrawal
\n.32*
\n(11.49)
\n.05*
\n(2.60)
\n.08*
\n(2.74)
\n.03
\n(1.15)
\n.24*
\n(8.48)
\n.08*
\n(2.26)
\nParental Affection -.09*
\n(-3.32)
\n-.10*
\n(-2.55)
\n-.02
\n(-.66)
\n.01
\n(.24)
\n-.07*
\n(-2.50)
\n-.10
\n(-1.31)
\nPhysical
\nPunishment
\n-.01
\n(-.30)
\n.38*
\n(2.63)
\n.04
\n(1.24)
\n-.04
\n(-.21)
\n.02
\n(.64)
\n.35
\n(1.33)
\nFamily Rules -.08*
\n(-2.97)
\n.09
\n(1.34)
\n-.05
\n(-1.68)
\n-.08
\n(-.89)
\n-.08*
\n(-3.03)
\n.02
\n(.13)
\nControl Variables
\nGender -.10*
\n(-3.73)
\n-.16
\n(-1.55)
\n-.21*
\n(-7.37)
\n-.27*
\n(-2.17)
\n-.20*
\n(-7.48)
\n-.43*
\n(-2.45)
\nAcademic
\nPreparedness
\n-.21*
\n(-7.66)
\n-.00
\n(-1.35)
\n-.16*
\n(-5.46)
\n-.01*
\n(2.29)
\n-.23*
\n(-8.13)
\n-.01*
\n(-2.33)
\nRace .00
\n(.10)
\n-.02
\n(-.11)
\n.03
\n(.89)
\n.19
\n(1.07)
\n.02
\n(.79)
\n.16
\n(.63)
\nNeighborhood
\nDisadvantage
\n.07*
\n(2.46)
\n.07
\n(1.25)
\n-.03
\n(-.94)
\n-.00
\n(-.06)
\n.02
\n(.66)
\n.06
\n(.59)
\nNumber of
\nSignificant Parenting
\nParameters
\n3 3 1 0 3 1
\nIntra-cluster
\nCorrelation
\n.284 .457 .367
\n* Parameter estimate at least twice its standard error
\nThe HLM\/twin model, however, reveals a slightly different pattern.
\nParental withdrawal and parental affection retained statistical significance.
\n1184 WRIGHT AND BEAVER
\nHowever, whereas the measure of family rules dropped out of statistical
\nsignificance, the measure of physical punishment reached it. Moreover,
\nnone of the control measures were significant predictors of self-control,
\nnor was neighborhood disadvantage.
\nEvidence from the first set of equations indicates that the OLS\/SSM
\nmodel likely overestimated the number of significant independent effects
\non parental measures of child self-control. In the HLM model, with the
\nclustering due to genetic similarity accounted for, only three predictors
\nreached significance compared to six in the OLS\/SSM model.
\nTeacher reports of child low self-control reveal a very different pattern
\nof results. In the OLS model, the only significant predictors were parental
\nwithdrawal, gender and academic preparedness. However, in the HLM
\nmodel, none of the parenting measures were significantly associated with
\nlow self-control. Consistent with the OLS model, gender and academic
\npreparedness were also significant predictors.
\nFinally, examining the composite measure of low self-control we find
\nthat, once again, the OLS model overestimated the number of significant
\nparenting effects. In the OLS model, parental withdrawal, parental
\naffection and family rules were significant predictors. In the HLM\/twin
\nmodel, however, only parental withdrawal was. In both models, gender
\nand academic preparedness were significant.
\nThe results from Table 1 indicate that traditional social research
\nmethodologies tend to inflate the effects parents have on their offspring\u2019s
\nself-control, especially if the reporting source is a parent. We note that
\nbecause many of the parenting measures were taken contemporaneously
\nwith the measures of low self-control, that these models offer the highest
\nlikelihood of detecting parenting effects. Still, once clustering due to
\ngenetic similarity was controlled, most of the parenting effects dissipated
\nto statistical insignificance.
\nTable 2 presents the results for child self-control, measured one year
\nlater when the children were in the first grade, regressed on the various
\nparenting measures. Looking first at the OLS regression of parental
\nreports we find that parental withdrawal, parental affection and family
\nrules were significant predictors of child self-control. So too were gender,
\nacademic preparedness and neighborhood disadvantage. Even so, in the
\nHLM\/twin model only parental withdrawal and academic preparedness
\nwere significantly associated with child low self-control. Once again, the
\nOLS\/SSM model overestimated the number of significant parameters. Six
\nparameters reached significance in the OLS model, compared to just two
\nin the HLM\/twin model.
\nAn even greater contrast can be seen when teacher reports are
\nexamined. Only one of the independent variables was associated with low
\nself-control in the HLM\/twin analyses\u2014parental withdrawal. Yet in the
\nDO PARENTS MATTER IN SELF-CONTROL 1185
\nOLS model, parental withdrawal, family rules, gender and academic
\npreparedness each made independent contributions.
\nIn the final set of equations in Table 2 we find that in the OLS model,
\nparental withdrawal, family rules, gender and academic preparedness
\naccounted for variation in low self-control. In the HLM\/twin model,
\nparental withdrawal, academic preparedness and race were significantly
\nassociated with low self-control.
\nTable 2. Effects of Parenting on Child’s Low Self-Control in First Grade
\nVariable Parental
\nReports
\nTeacher
\nReports
\nTotal
\nComposite Score
\nOLS HLM OLS HLM OLS HLM
\nSocialization Measures
\nParental Involvement -.02
\n(-.75)
\n-.01
\n(-.58)
\n-.04
\n(-1.17)
\n-.01
\n(-.63)
\n-.04
\n(-1.13)
\n-.02
\n(-.69)
\nParental Withdrawal .25*
\n(8.49)
\n.07*
\n(3.41)
\n.09*
\n(2.81)
\n.02
\n(.65)
\n.20*
\n(6.33)
\n.06
\n(1.80)
\nParental Affection -.07*
\n(-2.34)
\n-.08
\n(-1.92)
\n.04
\n(1.24)
\n.05
\n(.88)
\n-.02
\n(-.73)
\n-.05
\n(-.76)
\nPhysical Punishment .01
\n(.07)
\n.07
\n(.44)
\n.01
\n(.33)
\n.33
\n(1.80)
\n.02
\n(.72)
\n.38
\n(1.52)
\nFamily Rules -.09*
\n(-3.31)
\n.00
\n(.01)
\n-.07*
\n(-2.30)
\n-.22*
\n(-2.27)
\n-.10*
\n(-3.30)
\n-.28*
\n(-2.07)
\nControl Variables
\nGender -.07*
\n(-2.37)
\n-.15
\n(-1.53)
\n-.21*
\n(-6.94)
\n-.11
\n(-.87)
\n-.18*
\n(-6.07)
\n-.29
\n(-1.65)
\nAcademic
\nPreparedness
\n-.20*
\n(-6.80)
\n-.01*
\n(-3.34)
\n-.20*
\n(-6.48)
\n-.01
\n(-1.88)
\n-.26*
\n(-8.45)
\n-.01*
\n(-3.55)
\nRace .04
\n(1.29)
\n.24
\n(1.69)
\n.02
\n(.47)
\n.29
\n(1.68)
\n.03
\n(1.10)
\n.46*
\n(1.93)
\nNeighborhood
\nDisadvantage
\n.06*
\n(2.19)
\n-.02
\n(-.29)
\n-.01
\n(-.29)
\n.00
\n(.00)
\n.02
\n(.75)
\n-.02
\n(-.16)
\nNumber of Significant
\nParenting Parameters
\n3 1 2 1 2 1
\nIntra-cluster
\nCorrelation
\n.402 .385 .315
\n* Parameter estimate at least twice its standard error
\nConsistent with the findings presented in Table 1, the results shown in
\nTable 2 indicate that standard OLS\/SSM models overestimate the
\ninfluence of various variables on self-control. Of particular interest
\nhowever, is the overestimation of parenting influences. Our parenting
\nmeasures were consistently related to child self-control in the OLS\/SSM
\nmodels in general, but were more consistently related when parents were
\nused as the reporting source. This bias, we note, is the type predicted by
\nHarris (1998) and Pinker (2002). Once similarities due to genetic
\n1186 WRIGHT AND BEAVER
\ninfluences were removed, the effects of other variables, particularly
\nparenting variables, were reduced or eliminated.
\nIn our last set of analyses, depicted in Table 3, we use an expanded
\nmeasure of low self-control. Consistent with our prior analyses, we
\nexamine a contemporaneous measure of low self-control and a prospective
\nmeasure of low self-control.
\nTable 3. Effects of Parenting on Full Measures of Child’s Low Self-Control
\nVariable (Kindergarten) (First Grade)
\nOLS HLM OLS HLM
\nSocialization Measures
\nParental Involvement -.06*
\n(-2.29)
\n-.14*
\n(-3.23)
\n-.10*
\n(-3.18)
\n-.05
\n(-1.05)
\nParental Withdrawal .21*
\n(7.59)
\n.06
\n(.91)
\n.16*
\n(5.20)
\n.10
\n(1.42)
\nParental Affection -.10*
\n(-3.42)
\n-.20
\n(-1.44)
\n-.03
\n(-1.06)
\n-.07
\n(-.50)
\nPhysical Punishment .03
\n(1.01)
\n.25
\n(.51)
\n-.01
\n(-.04)
\n.22
\n(.45)
\nFamily Rules -.05
\n(-1.77)
\n-.10
\n(-.40)
\n-.10*
\n(-3.25)
\n-.46
\n(-1.77)
\nControl Variables
\nGender -.23*
\n(-8.84)
\n-.93*
\n(-2.77)
\n-.22*
\n(-7.59)
\n-.86*
\n(-2.54)
\nAcademic Preparedness -.34*
\n(-12.42)
\n-.04*
\n(-5.51)
\n-.37*
\n(-12.43)
\n-.04*
\n(-5.20)
\nRace .01
\n(.26)
\n.17
\n(.38)
\n.01
\n(.14)
\n.70
\n(1.52)
\nNeighborhood Disadvantage -.01
\n(-.30)
\n.10
\n(.54)
\n.01
\n(.03)
\n.05
\n(.23)
\nNumber of Significant
\nParenting Parameters
\n3 1 3 0
\nIntra-cluster Correlation .328 .374
\n* Parameter estimate at least twice its standard error
\nParental involvement, parental withdrawal, parental affection, gender
\nand academic preparedness made independent contributions to the
\nexplained variance in child low self-control in the OLS model
\n(kindergarten). However, consistent with prior analyses, only parental
\nwithdrawal, gender and academic preparedness were significantly related
\nto low self-control in the HLM\/twin model.
\nA similar pattern was again detected when we regressed the prospective
\nmeasure of child self-control on the independent predictors. In the OLS
\nmodel, parental involvement, parental withdrawal, family rules, gender
\nand academic preparedness were significant predictors. In the HLM\/twin
\nmodel, though, none of the parenting measures were significantly related
\nDO PARENTS MATTER IN SELF-CONTROL 1187
\nto child self-control. Only gender and academic preparedness retained
\nstatistical significance.
\nDISCUSSION
\nOur study sought to answer a single question: do parents matter in the
\netiology of self-control At first glance, this would appear to be a rather
\nsimple empirical question. Empirical research, however, has a bad habit of
\nexposing the underlying complexities of even the simplest research
\nquestion. So it was in this instance. On close inspection, we realized that
\nan answer to this question would require data that allowed for the
\nestimation of genetic similarity between twins and their mother, data that
\ncontained measures of various parenting constructs and data that
\ncontained multiple reporting sources. The ECLS-K fit these requirements.
\nWe address the findings from this study along two intertwined
\ndimensions: substantive findings and methodological implications. In
\ngeneral, our analyses revealed that parenting variables were inconsistently
\nand weakly related to contemporaneous measures of child self-control in
\nkindergarten, and were inconsistently related to prospective measures of
\nself-control in the first grade. Parenting influences typically reached
\nstatistical significance in the OLS models, models that did not account for
\nthe clustering of responses due to genetic similarity. We also note that
\nparenting influences reached significance only when parental reports were
\nused. When teacher reports of child self-control were analyzed, parenting
\nfeatures were consistently unrelated to child self-control. Teachers, we
\nnote, observe children under very difference circumstances than parents;
\ncircumstances that usually require young children to exhibit self-control
\n(Cairns and Cairns, 1994).
\nWhether parenting matters in the etiology of child self-control thus
\nappears to be deeply intertwined with the type of methodology and
\nanalyses employed. Standard social methodologies (SSMs), which usually
\nmeasure only one child and a parent within a household and which
\ntypically ignore genetic similarities between subjects, appear to
\noverestimate the influence of parenting on child self-control. The results
\nof our OLS models, derived from standard SSM assumptions and
\nmethodologies, would generally be accepted as evidence linking parenting
\nto self-control in children.
\nEmploying a genetically informed methodology and analysis, however,
\nalters, or at least conditions, such a conclusion. Overall, employing a more
\nrigorous methodology and analysis reduced or eliminated the influence of
\nseveral variables, most noticeably the parenting variables. Even here,
\nhowever, it appears that methodology makes a difference. Parenting
\neffects were detected only when parent reports of child self-control were
\n1188 WRIGHT AND BEAVER
\nused. The use of teacher reports in our twin\/HLM models detected no
\nsignificant association between parenting variables and child self-control.
\nThis result held contemporaneously and longitudinally.
\nThe use of parent reports, even in our twin sample, confounds the
\nassociation between parenting behaviors and the child\u2019s self-control. That
\nis, parents who are far removed from the daily happenings of their
\nchildren may be likely to report self-control problems in their children.
\nThis effect may be \u201creal,\u201d but it may also reflect individual differences
\nshared between parents and their offspring, or at least differences rooted
\nin behavioral and attitudinal factors that vary between parents. Either
\nway, the use of parent reports may overestimate the influence that
\nparenting behaviors have on a child\u2019s traits. Evidence from the teacher
\nreport models strongly suggests this to be the case.
\nHOW SHOULD OUR FINDINGS BE INTERPRETED
\nThe contrast in findings begs the question of what the appropriate
\nbenchmark to evaluate the validity of our findings is. Some readers will
\npoint to the OLS models as offering evidence sufficient to demonstrate
\nparenting effects. They may also point out that the valid conclusions about
\nparenting behaviors can be derived only from cross-sectional analyses.
\nHowever, more conservative analysts will point out that the OLS
\nestimates are substantially biased and do not meet the criteria outlined by
\nHarris to reject the null hypothesis that parents \u201cdon\u2019t matter.\u201d Nor do the
\nparenting variables generate consistent effects when temporal order is
\nspecified correctly. What, then, do we make of our findings
\nWe suspect the most valid and conservative findings are those that
\nemerge from the composite measure of self-control in the twin\/HLM
\nsample. These are consistent across time and indicate that the measure of
\nparental withdrawal, which captures variation in the extent to which
\nparents report feelings of stress and emotional distance from their
\nchildren, is associated with increases in child low self-control contemporaneously
\nand over a one-year period. The effects are only marginally
\nsignificant, however, which indicates that the true effect size may be
\ntrivial. To buttress our initial findings, we also merged the random sample
\nand the twin sample. The results are shown in Appendix C and converge
\nwith those generated by analyzing the samples separately. As such, these
\nfindings provide tangible evidence in favor of Harris\u2019s (1998) proposition
\nthat, net of genetic similarities within households, parental socialization
\ntechniques minimally influence the individual traits of their children.8
\n8. We also note that Gottfredson and Hirschi claim that self-control materializes prior
\nto the age of twelve and becomes very unlikely to appear thereafter. Neurological
\nstudies, however, dispute this possibility by showing that cerebral volume increases
\nDO PARENTS MATTER IN SELF-CONTROL 1189
\nDoes this mean that parents do not matter Of course not, nor do we
\nencourage readers to blithely accept that possibility given our analyses.
\nInstead, our view is that the influence of parental socialization factors, as
\nwell as other environmental features, are conditioned by the genotypes of
\nthe parent and child. Recent evidence by Caspi and his colleagues, for
\nexample, has documented how individual responses to life-stresses and to
\nexperiencing parental abuse vary by child genotype (Caspi et al., 2002; Caspi
\net al., 2003). As behavioral geneticists frequently tell us, children within the
\nsame family are often very different. These differences cannot be explained
\nby factors that do not vary between children, differences that aggregated
\nparenting measures are not capable of assessing (Plomin, 1995).
\nParents likely influence their children in ways that are more
\ncomplicated than is typically assumed. Parents may moderate the
\ninfluence of specific child traits (Tully et al., 2004), or the traits of parents
\nmay interact in unique ways with the traits of each of their children.
\nParents may also create environments that are so bleak and abusive that
\nthe environmental effects overshadow any genetic influences (Harris,
\n1998). Moreover, the traits of the child likely influence the reactions of
\nparents.9 Difficult children constantly challenge parental authority and
\nlimit-setting efforts. In either case, whatever influence parents have on the
\ntraits of their children, it likely will involve a more sophisticated
\nunderstanding of the genetically influenced, mutually dynamic
\nrelationships that occur within households.
\nAlthough other studies will have to verify our findings, we note that
\nrecent neuroimaging research and studies of twins provide strong evidence
\nof the genetic influence on brain structure and functioning, especially as it
\nrelates to executive control functions (Barkley, 1997; Thompson et al.,
\n2001). Through the use of complex imaging, Thompson and his colleagues
\n(2001) analyzed brain structural differences in a sample of monozygotic
\nand dizygotic twins. Their results graphically depict the strong genetic
\nfoundation underpinning brain growth and functioning. Particularly
\nstriking was their analysis of monozygotic twins. This analysis revealed
\nalmost identical gray matter volume in the frontal lobes of the twins and in
\nthrough the age of 20, while temporal grey matter increases in volume through the
\nage of 16.5 in males and 16.7 in females (Giedd, Blumenthal, Jeffries, Castellanos,
\nLiu, Zijdenbos, Paus, Evans, and Rapoport, 1999). Future research should examine
\nvariation in self-control across a longer age-range, as well as include a broader
\narray of socialization measures.
\n9. As one reviewer noted, the effects generated from the measure of parental
\nwithdrawal could represent the influence of the child on the parent. In subsequent
\nanalyses, we included a measure of prior low self-control (results not shown).
\nInclusion of that measure reduced the effect of parental withdrawal to statistical
\ninsignificance. In the language of behavioral genetics, this dynamic is known as a
\nprovocative gene X environment interaction.
\n1190 WRIGHT AND BEAVER
\nregions that control language acquisition. Other imaging studies have
\nfound that genetic heritability in brain structure is evident through the
\nseventh and eighth decades of life (Pfefferbaum, Sullivan, Swan, and
\nCarmelli, 2000).
\nSELF-CONTROL AND CRIMINOLOGY
\nNeuroimaging studies highlight the close correspondence between
\nspecific traits, such as low self-control, and the genetic controls that direct
\nneuronal growth in specific brain structures related to specific traits. More
\nimportant, they help to explain why common environmental features, such
\nas parental socialization efforts, have only modest to trivial effects (Wright
\nand Cullen, 2001). At least as it relates to self-control, once similarities in
\ngenetic heritability are accounted for, the range of variance left to explain
\nis quite restricted. As it applies to antisocial behavior in young children,
\nArseneault and her colleagues (2003) found that in a sample of 1116 twin
\npairs, genetic factors accounted for 82 percent of the variance, while
\nexperiential factors accounted for only 18 percent of the variance. Recent
\nmeta-analytic reviews also support these findings (Miles and Carey, 1997).
\nGottfredson and Hirschi openly exclude the possibility that self-control
\nhas a genetic base. Our study, along with others from various fields,
\nsuggests that for self-control theory to be a valid theory of crime it must
\nincorporate a more sophisticated understanding of the origins of selfcontrol
\n(Pratt, Turner and Piquero, 2004; see also Pratt et al., 2002). With
\nthe caveats that we have not measured all parental socialization practices
\nand that our data are restricted in age range, the pattern of findings
\nreported here contradicts Gottfredson and Hirschi\u2019s hypotheses that link
\nparental socialization to the development of self-control. Excluding
\ngenetic influences on self-control and related traits likely misspecifies a
\ntheory that, in general, has gained widespread empirical support (Pratt
\nand Cullen, 2000). It also fails to recognize the large body of research that
\nhas linked executive control functions to a range of biological factors, such
\nas prenatal maternal cigarette smoking and drug use (Gibson and
\nTibbetts, 1998), prenatal lead absorption (Bellinger, Leviton, Allred and
\nRabinowitz, 1994), and in utero anoxia (Beaver and Wright, 2005). We
\nsuggest that self-control theory be revised to incorporate this body of
\nliterature.
\nLastly, we note that methodological assumptions shape research
\nfindings, and in this case, it is clear to see that the findings from the OLS
\nmodels overestimated the influence of a range of factors, including
\nparenting and neighborhood variables. Although we do not believe that
\nour methodology is the only way to analyze socialization hypotheses, we
\ndo suggest that it is time for criminologists to come to grips with the
\nDO PARENTS MATTER IN SELF-CONTROL 1191
\nconfluence of genetic similarities and individually specific experiences
\n(Caspi et al., 2005). There is now a voluminous literature that links
\nbiological features to human growth and social development. To the
\nextent that insights from this body of knowledge remain excluded,
\ncriminology will remain on the periphery of other, more established
\ndisciplines (Daly and Wilson, 1988; Rowe, 1994; Udry, 1995; Walsh and
\nEllis, 2004; Wilson, 1998).
\nREFERENCES
\nAron, Adam R., Trevor W. Robbins and Russell A. Poldrack
\n2004 Inhibition and the right inferior frontal cortex. Trends in
\nCognitive Sciences 8: 170\u2013177.
\nArseneault, Louise, Terrie E. Moffitt, Avshalom Caspi, Alan Taylor,
\nFruhling V. Rijsdijk, Sara R. Jaffee, Jennifer C. Ablow and Jeffrey R.
\nMeaselle
\n2003 Strong genetic effects on cross-situational antisocial behaviour
\namong 5-year-old children according to mothers, teachers,
\nexaminer-observers, and twins\u2019 self-reports. Journal of Child
\nPsychology and Psychiatry 44: 832\u2013848.
\nBarkley, Russell A.
\n1997 ADHD and the Nature of Self-Control. New York: Guilford Press.
\n2005 Dr. Russell Barkley on AD\/HD: Excerpts from his lecture in
\nSan Francisco, CA on June 17, 2000. Available online at:
\nhttps:\/\/essays.homeworkacetutors.com\/write-my-essay\/schwablearning.org\/pdfs\/2200_7-
\nbarktran.pdfdate=4-12-05
\nBeaver, Kevin M. and John Paul Wright
\n2005 Evaluating the effects of birth complications on low self-control
\nin a sample of twins. International Journal of Offender Therapy
\nand Comparative Criminology 49: 450\u2013471.
\nBellinger, David, Alan Leviton, Elizabeth Allred and Michael Rabinowitz
\n1994 Pre- and postnatal lead exposure and behavior problems in
\nschool-aged children. Environmental Research 66: 12\u201330.
\nBenes, Kathryn M.
\n1995 Review of the social skills rating system. In Conoley and Impara
\n(eds.), Twelfth Mental Measurements Yearbook. Lincoln, NE:
\nBuros Institute of Mental Measurement.
\nBradshaw, John L.
\n2001 Developmental Disorders of the Frontostriatal System:
\nNeuropsychological, Neuropsychiatric and Evolutionary Perspectives.
\nPhiladelphia, PA: Taylor and Francis.
\n1192 WRIGHT AND BEAVER
\nCairns, Robert D. and Beverley D. Cairns
\n1994 Lifelines and Risks: Pathways of Youth in Our Time.
\nCambridge: Cambridge University Press.
\nCaspi, Avshalom, Brent W. Roberts and Rebecca L. Shiner
\n2005 Personality development: Stability and change. Annual Review
\nof Psychology 56: 453\u2013484.
\nCaspi, Avshalom, Joseph McClay, Terrie E. Moffitt, Jonathan Mill, Judy
\nMartin, Ian W. Craig, Alan Taylor and Richie Poulton
\n2002 Role of genotype in the cycle of violence in maltreated children.
\nScience 297: 851\u2013854.
\nCaspi, Avshalom, Karen Sugden, Terrie E. Moffitt, Alan Taylor, Ian W.
\nCraig, HonaLee Harrington, Joseph McClay, Jonathan Mill, Judy Martin,
\nAntony Braithwaite and Richie Poulton
\n2003 Influence of life stress on depression: Moderation by a
\npolymorphism in the 5-HTT gene. Science 301: 386\u2013389.
\nCochran, John K., Peter B. Wood, Christine S. Sellers, Wendy Wilkerson
\nand Mitchell B. Chamlin
\n1998 Academic dishonesty and low self-control: An empirical test of
\nA General Theory of Crime. Deviant Behavior 19: 227\u2013255.
\nCohen, David B.
\n1999 Strangers in the Nest: Do Parents Really Shape Their Child\u2019s
\nPersonality, Intelligence, or Character New York: John Wiley &
\nSons.
\nConvit, Antonio, Richard Douyon, Kathy F. Yates, Gwenn Smith, Pal
\nCzobor, Jose de Asis, Joseph Vitrai, Louis Camus and Jan Volavka
\n1996 Frontotemporal abnormalities and violent behavior. In D. M.
\nStoff and R. B. Cairns (eds.), Aggression and Violence: Genetic,
\nNeurobiological, and Biosocial Perspectives. Mahwah, NJ:
\nLawrence Erlbaum.
\nDaly, Martin and Margo Wilson
\n1988 Homicide. New York: Aldine de Gruyter.
\nDietrich, Kim N., M. Douglas Ris, Paul A. Succop, Omer G. Berger and
\nRobert L. Bornschein
\n2001 Early exposure to lead and juvenile delinquency.
\nNeurotoxicology and Teratology 23: 511\u2013518.
\nDunn, Judy and Robert Plomin
\n1990 Separate Lives: Why Siblings are so Different. New York: Basic
\nBooks.
\nDO PARENTS MATTER IN SELF-CONTROL 1193
\nFeldman, S. Shirley and Daniel A. Weinberger
\n1994 Self-restraint as a mediator of family influences on boys\u2019
\ndelinquent behavior: A longitudinal study. Child Development
\n65: 195\u2013211.
\nGeis, Gilbert
\n2000 On the absence of self-control as the basis for a general theory
\nof crime: A critique. Theoretical Criminology 4: 35\u201353.
\nGibbs, John J., Dennis Giever and George E. Higgins
\n2003 A test of Gottfredson and Hirschi\u2019s generally theory using
\nstructural equation modeling. Criminal Justice and Behavior 30:
\n441\u2013458.
\nGibbs, John J., Dennis Giever and Jamie S. Martin
\n1998 Parental management and self-control: An empirical test of
\nGottfredson and Hirschi\u2019s general theory. Journal of Research in
\nCrime and Delinquency 35: 40\u201370.
\nGibson, Chris L. and Stephen G. Tibbetts
\n1998 Interaction between maternal cigarette smoking and apgar
\nscores in predicting offending behavior. Psychological Reports
\n83: 579\u2013586.
\nGiedd, Jay N., Jonathan Blumenthal, Neal O. Jeffries, F. X. Castellanos,
\nHong Liu, Alex Zijdenbos, Tomas Paus, Alan C. Evans and Judith L.
\nRapoport
\n1999 Brain development during childhood and adolescence: A
\nlongitudinal MRI study. Nature Neuroscience 2: 861\u2013863.
\nGottfredson, Michael and Travis Hirschi
\n1990 A General Theory of Crime. Palo Alto, CA: Stanford University
\nPress.
\nGrasmick, Harold G., Charles R. Tittle, Robert J. Bursik, Jr. and Bruce J.
\nArneklev
\n1993 Testing the core empirical implications of Gottfredson and
\nHirschi\u2019s general theory of crime. Journal of Research in Crime
\nand Delinquency 30: 5\u201329.
\nGresham, Frank M.
\n2001 Assessment of social skills in children and adolescents. In Jac
\nJ.W. Andrews, Donald H. Saklofske, and Henry L. Janzen
\n(Eds.), Handbook of Psychoeducational Assessment:Ability,
\nAchievement, and Behavior in Children. New York: Academic
\nPress.
\n1194 WRIGHT AND BEAVER
\nGresham, Frank M. and Stephen N. Elliott
\n1990 The Social Skills Rating System. Circle Pines, MN: American
\nGuidance Service.
\nHarris, Judith R.
\n1998 The Nurture Assumption: Why Children Turn Out the Way They
\nDo. New York: The Free Press.
\nHay, Carter
\n2001 Parenting, self-control, and delinquency: A test of self-control
\ntheory. Criminology 39: 707\u2013736.
\nHiggins, George E.
\n2002 General theory of crime and deviance: A structural equation
\nmodeling approach. Journal of Crime and Justice 25: 71\u201395.
\nLoeber, Rolf and Magda Stouthamer-Loeber
\n1986 Family factors as correlates and predictors of juvenile conduct
\nproblems and delinquency. In Michael Tonry and Norval Morris
\n(eds.), Crime and Justice: An Annual Review of Research. Vol. 7.
\nChicago: University of Chicago Press.
\nLynskey, Dana Peterson, L. Thomas Winfree, Finn-Aage Esbensen and
\nDennis L. Clason
\n2000 Linking gender, minority group status, and family matter to selfcontrol
\ntheory: A multivariate analysis of key self-control
\nconcepts in a youth-gang context. Juvenile and Family Court
\nJournal 51: 1\u201319.
\nMarcus, Bernd
\n2004 Self-control in the general theory of crime: Theoretical
\nimplications of a measurement problem. Theoretical
\nCriminology 8: 33\u201355.
\nMick, Eric, Joseph Biederman, Jefferson Prince, Marianna J. Fischer and
\nStephen V. Faraone
\n2002 Impact of low birth weight on attention-deficit hyperactivity
\ndisorder. Developmental and Behavioral Pediatrics 23: 16\u201322.
\nMiles, Donna R. and Gregory Carey
\n1997 Genetic and environmental architecture of human aggression.
\nJournal of Personality and Social Psychology 72: 207\u2013217.
\nMiller, Earl K. and Jonathan D. Cohen
\n2001 An integrative theory of prefrontal cortex function. Annual
\nReview of Neuroscience 24: 167\u2013202.
\nDO PARENTS MATTER IN SELF-CONTROL 1195
\nNational Institute of Mental Health
\n2003 Attention Deficit Hyperactivity Disorder. Bethesda, MD:
\nNational Institute of Mental Health, National Institutes of
\nHealth, US Department of Health and Human Services.
\nAvailable from: https:\/\/essays.homeworkacetutors.com\/write-my-essay\/nimh.nih.gov\/publicat\/adhd.cfm
\nNeiderhiser, Jenae, M., David Reiss, E. Mavis Hetherington and Robert
\nPlomin
\n1999 Relationships between parenting and adolescent adjustment
\nover time: Genetic and environmental contributions.
\nDevelopmental Psychology 35: 680\u2013692.
\nO\u2019Connor, Thomas G., Jenae M. Neiderhiser, David Reiss, E. Mavis
\nHetherington and Robert Plomin
\n1998 Genetic contributions to continuity, change, and the cooccurrence
\nof antisocial and depressive symptoms in
\nadolescence. Journal of Child Psychology and Psychiatry and
\nAllied Disciplines 39: 323\u2013336.
\nPatterson, Gerald R.
\n1982 Coercive Family Process. Eugene, OR: Castilia.
\nPinker, Steven
\n2002 The Blank Slate: The Modern Denial of Human Nature. New
\nYork: Viking.
\nPfefferbaum, Adolf, Edith V. Sullivan, Gary E. Swan and Dorit Carmelli
\n2000 Brain structure in men remains highly heritable in the seventh
\nand eighth decades of life. Neurobiology of Aging 21: 63\u201374.
\nPlomin, Robert
\n1995 Genetics and children\u2019s experiences in the family. Journal of
\nChild Psychology and Psychiatry 36: 33\u201368.
\nPlomin, Robert, Michael J. Owen and Peter McGuffin
\n1994 The genetic basis of complex human behaviors. Science
\n264:1733-1739.
\nPolakowski, Michael
\n1994 Linking self- and social control with deviance: Illuminating the
\nstructure underlying a general theory of crime and its relation to
\ndeviant identity. Journal of Quantitative Criminology 10: 41\u201378.
\nPratt, Travis C. and Francis T. Cullen
\n2000 The empirical status of Gottfredson and Hirschi\u2019s general
\ntheory of crime: A meta-analysis. Criminology 38: 931\u2013964.
\n1196 WRIGHT AND BEAVER
\nPratt, Travis C., Michael G. Turner and Alex R. Piquero
\n2004 Parental socialization and community context: A longitudinal
\nanalysis of the structural sources of low self-control. Journal of
\nResearch in Crime and Delinquency 41: 219\u2013243.
\nPratt, Travis C., Francis T. Cullen, Kristie R. Blevins, Leah Daigle and
\nJames D. Unnever
\n2002 The relationship of attention deficit hyperactivity disorder to
\ncrime and delinquency: A meta-analysis. International Journal
\nof Police Science and Management 4: 344\u2013360.
\nPrice, Thomas S., Emily Simonoff, Irwin Waldman, Philip Asherson and
\nRobert Plomin
\n2001 Hyperactivity in preschool children is highly heritable. Journal
\nof the American Academy of Child and Adolescent Psychiatry
\n12: 1362\u20131364.
\nReiss, David, Nenae M. Neiderhiser, E. Mavis Hetherington and Robert
\nPlomin
\n2000 The Relationship Code: Deciphering the Genetic and Social
\nInfluences on Adolescent Development. Cambridge: Cambridge
\nUniversity Press.
\nRietveld, M.J.H., J. J. Hudziak, M. Bartels, C.E.M. van Beijsterveldt and
\nD. I. Boomsma
\n2003 Heritability of attention problems in children: Cross-sectional
\nresults from a study of twins, age 3 to 12 years. Neuropsychiatric
\nGenetics 1176: 102\u2013113.
\nRowe, David C.
\n1994 The Limits of Family Influence: Genes, Experience, and
\nBehavior. New York: Guilford.
\nRudolph, Karen D., Sharon F. Lambert, Alyssa G. Clark and Kathryn D.
\nKurlakowsky
\n2001 Negotiating the transition to middle school: The role of selfregulatory
\nprocesses. Child Development 72: 929\u2013946.
\nSampson, Robert J. and John H. Laub
\n1995 Understanding variability in lives through time: Contributions
\nof life-course criminology. Studies on Crime and Crime
\nPrevention 4: 143\u2013158.
\nScarr, Sandra and Kathleen McCartney
\n1983 How people make their own environments: A theory of
\ngenotypeenvironment effects. Child Development 54: 424\u2013435.
\nDO PARENTS MATTER IN SELF-CONTROL 1197
\nSpencer, Thomas J., Joseph Biederman, Timothy E. Wilens and Stephen
\nV. Faraone
\n2002 Overview and neurobiology of attention-deficit\/hyperactivity
\ndisorder. Journal of Clinical Psychiatry 63:3-9.
\nThompson, Paul, Tyrone D. Cannon, Katherine L. Narr, et al.
\n2001 Genetic influences on brain structure. Neuroscience 4: 1\u20136.
\nTully, Lucy A., Louise Arseneault, Avshalom Caspi, Terrie E. Moffitt and
\nJulia Morgan
\n2004 Does maternal warmth moderate the effects of birth weight on
\ntwins\u2019 attention-deficit\/hyperactivity disorder (ADHD)
\nsymptoms and low IQ Journal ofConsulting and Clinical
\nPsychology 72: 218\u2013226.
\nUdry, Richard J.
\n1995 Sociology and biology: What biology do sociologists need to
\nknow Social Forces 73: 1267\u20131278.
\nUnnever, James D., Francis T. Cullen and Travis C. Pratt
\n2003 Parental management, ADHD, and delinquent involvement:
\nReassessing Gottfredson and Hirschi\u2019s general theory. Justice
\nQuarterly 20: 471\u2013500.
\nVirkkunen Matti and Markku Linnoila
\n1993 Serotonin in personality disorders with habitual violence and
\nimpulsivity. In S. Hodgins (ed.), Mental Disorder and Crime.
\nNewbury Park, CA: Sage Publications.
\nWalsh, Anthony
\n2002 Biosocial Criminology: Introduction and Integration. Cincinnati,
\nOH: Anderson.
\nWalsh, Anthony and Lee Ellis
\n2004 Ideology: Criminology\u2019s Achilles\u2019 heel Quarterly Journal of
\nIdeology 27: 1\u201325.
\nWilson, Edward O.
\n1998 Consilience: The Unity of Knowledge. New York: Alfred A.
\nKnopf.
\nWilson, James Q. and Richard Herrnstein
\n1985 Crime and Human Nature: The Definitive Study of the Causes of
\nCrime. New York: Simon and Schuster.
\nWright, John Paul and Francis T. Cullen
\n2001 Parental efficacy and delinquent behavior: Do control and
\nsupport matter Criminology 39: 677\u2013706.
\n1198 WRIGHT AND BEAVER
\nYolton, Kimberly, Kim Dietrich, Peggy Auinger, Bruce P. Lanphear and
\nRichard Hornung
\n2005 Exposure to environmental tobacco smoke and cognitive
\nabilities among U.S. children and adolescents. Environmental
\nHealth Perspectives 113: 98\u2013103.
\nZyzanski, Stephen G., Susan A. Flocke and L. Miriam Dickinson
\n2004 On the nature and analysis of clustered data. Journal of Family
\nMedicine 2: 199\u2013200.
\nJohn Paul Wright is associate professor of criminal justice at the
\nUniversity of Cincinnati. He has published articles on the effects of
\nadolescent employment on delinquency, the effects of parenting on
\noffspring misbehavior, and on the role of money in youthful misconduct.
\nHis current research focuses on the genetic heritability of traits related to
\ncrime, including self-control, stability in antisocial behavior over time, and
\nthe biosocial development of serious violence.
\nKevin M. Beaver is a doctoral candidate in the Division of Criminal
\nJustice at the University of Cincinnati. He received his BA in sociology
\nfrom Ohio University and his MS in criminal justice from the University of
\nCincinnati. His research and teaching interests include life-course and
\nbiosocial criminology, the heritability of antisocial behavior, and the
\nstability of violent offending. He has published in the Journal of
\nQuantitative Criminology, Youth Violence and Juvenile Justice, and
\nInternational Journal of Offender Therapy and Comparative Criminology.
\nDO PARENTS MATTER IN SELF-CONTROL 1199
\nAPPENDIX A. DESCRIPTION OF VARIABLES AND SCALES
\nLow Self-Control Scales
\nTeacher Ratings of Low Self-Control in Kindergarten (wave 2)
\nScale created by summing the following items: Teacher reports of the student\u2019s …
\n1) externalizing problem behaviors
\n2) self-control
\nTeacher Ratings of Low Self-Control in First Grade (wave 4)
\nScale created by summing the following items: Teacher reports of the student\u2019s …
\n1) externalizing problem behaviors
\n2) self-control
\nParental Ratings of Low Self-Control in Kindergarten (wave 2)
\nScale created by summing the following items: Parental reports of the child\u2019s …
\n1) self-control
\n2) impulsivity
\nParental Ratings of Low Self-Control in First Grade (wave 4)
\nScale created by summing the following items: Parental reports of the child\u2019s …
\n1) self-control
\n2) impulsivity
\nTotal Composite Score of Low Self-Control in Kindergarten (wave 2)
\nScale created by summing the following items:
\nTeacher reports of the student\u2019s …
\n1) externalizing problem behaviors
\n2) self-control
\n3) approaches to learning (e.g., attentiveness and persistence)
\n4) interpersonal skills (e.g., ability to form friendships)
\nParental reports of the child\u2019s …
\n5) self-control
\n6) impulsivity
\n7) approaches to learning (e.g., concentration and persistence)
\n8) social interactions (e.g., positive interactions with peers)
\nTotal Composite Score of Low Self-Control in First Grade (wave 4)
\nScale created by summing the following items:
\nTeacher reports of the student\u2019s …
\n1) externalizing problem behaviors
\n2) self-control
\n3) approaches to learning (e.g., attentiveness and persistence)
\n4) interpersonal skills (e.g., ability to form friendships)
\n1200 WRIGHT AND BEAVER
\nParental reports of the child\u2019s …
\n5) self-control
\n6) impulsivity
\n7) approaches to learning (e.g., concentration and persistence)
\n8) social interactions (e.g., positive interactions with peers)
\nSocialization Measures
\nParental Involvement in Kindergarten (wave 1)
\nScale created by summing the following items (according to parental reports):
\nHow often the parent …
\n1) reads to the child
\n2) tells the child stories
\n3) sings songs with the child
\n4) helps the child with art activities
\n5) helps the child with chores
\n6) plays games with the child
\n7) teaches the child about nature
\n8) helps the child build things
\n9) plays sports with the child
\nParental Withdrawal in Kindergarten (wave 2)
\nScale created by summing the following items (according to parental reports):
\n1) Does the parent have to sacrifice to meet the child\u2019s needs
\n2) Does the respondent feel trapped as a parent
\n3) Is the parent too busy to spend time with the child
\n4) Does the parent often feel angry with the child
\n5) Is it hard for the parent to be warm to the child
\n6) Is the child harder to care for than anticipated
\n7) Being a parent is harder than the parent expected
\n8) Does the child do things that bother the parent
\n9) Is being a parent more work than pleasure
\nParental Affection in Kindergarten (wave 2)
\nScale created by summing the following items (according to parental reports):
\n1) Does the parent and child spend warm, close time together
\n2) Does the child like the parent
\n3) Does the parent always show love for the child
\n4) Does the parent express affection to the child
\nPhysical Punishment in Kindergarten (wave 2)
\nIndex created by summing the following items (according to parental reports): If
\nthe child hit the parent, the parent would …
\n1) hit the child back
\n2) spank the child
\nDO PARENTS MATTER IN SELF-CONTROL 1201
\nFamily Rules (wave 2)
\nScale created by summing the following items (according to parental reports):
\nAre there family rules …
\n1) for which television programs the child can watch
\n2) limiting the number of hours the child can watch television
\n3) pertaining to how early\/late the child can watch television
\nScale Included for Statistical Control
\nNeighborhood Disadvantage Scale (wave 2)
\nScale created by summing the following items (according to parental reports):
\n1) How safe is it for the child to play outside
\n2) Is there garbage and litter on the street
\n3) Are there problems with people using or selling drugs in the neighborhood
\n4) Are there problems with burglaries or robberies in the neighborhood
\n5) Are there problems with violent crime in the neighborhood
\n6) Are there vacant houses in the neighborhood
\nAppendix B. ECLS-K Descriptive Statistics
\nTwin
\nSample
\nRandom Sample Combined Sample
\nMean SD Mean SD Mean SD
\nSocialization Measures
\nParental Involvement 24.72 4.60 25.10 4.51 25.03 4.52
\nParental Withdrawal 11.73 3.04 12.15 3.25 12.08 3.22
\nParental Affection 14.66 1.59 14.76 1.53 14.74 1.54
\nPhysical Punishment .20 .40 .22 .43 .22 .43
\nFamily Rules 2.23 .91 2.23 .90 2.24 .90
\nControl Variables
\nPercentage Male 40% 51% 49%
\nAcademic Preparedness 147.16 26.11 152.44 25.37 151.56 25.56
\nPercentage White 63% 56% 57%
\nNeighborhood
\nDisadvantage
\n17.34 3.04 17.10 1.56 17.15 1.50
\nKindergarten
\nParental Reports 3.84 .88 4.05 1.01 4.02 1.00
\nTeacher Reports 3.25 1.05 3.48 1.14 3.44 1.13
\nComposite Score 7.08 1.51 7.51 1.69 7.43 1.66
\nFull Measure 15.68 2.92 16.18 3.13 16.09 3.10
\nFirst-Grade
\nParental Reports 3.93 .88 3.92 .99 3.92 .97
\nTeacher Reports 3.24 1.01 3.49 1.16 3.45 1.14
\nComposite Score 7.0 1.49 7.39 1.71 7.34 1.68
\nFull Measure 15.75 2.80 16.21 3.17 16.13 3.12
\n1202 WRIGHT AND BEAVER
\nAppendix C. Effects of Parenting on Full Measures in Combined Sample
\nVariable (Kindergarten) (First Grade)
\nOLS HLM OLS HLM
\nSocialization Measures
\nParental Involvement -.06*
\n(-3.51)
\n-.14*
\n(-3.52)
\n-.07*
\n(-3.49)
\n-.04
\n(-1.05)
\nParental Withdrawal .19*
\n(7.42)
\n.05
\n(.91)
\n.16*
\n(5.62)
\n.12*
\n(2.03)
\nParental Affection -.19*
\n(-3.80)
\n-.25*
\n(-2.09)
\n-.07
\n(-1.27)
\n-.13
\n(-1.08)
\nPhysical Punishment .21
\n(1.18)
\n.35
\n(.81)
\n-.00
\n(-.00)
\n.32
\n(.75)
\nFamily Rules -.19*
\n(-2.24)
\n-.25
\n(-1.13)
\n-.40*
\n(-4.15)
\n-.45
\n(-1.88)
\nControl Variables
\nGender -1.36*
\n(-9.42)
\n-.94*
\n(-2.83)
\n-1.36*
\n(-8.44)
\n-1.05*
\n(-3.09)
\nAcademic Preparedness -.04*
\n(-13.67)
\n-.04*
\n(-6.08)
\n-.05*
\n(-13.21)
\n-.04*
\n(-5.28)
\nRace .09
\n(.58)
\n.14
\n(.34)
\n.18
\n(1.02)
\n.66
\n(1.61)
\nNeighborhood Disadvantage -.01
\n(-.12)
\n-.09
\n(-.60)
\n-.02
\n(-.33)
\n-.03
\n(-.16)
\nNumber of Significant
\nParenting Parameters
\n4 2 3 1
\n* Parameter estimate at least twice its standard error