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“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? ”

University of Cincinnati
University of Cincinnati
KEYWORDS: ADHD, behavior patterns, genetics, parental influence,
Gottfredson and Hirschi’s general theory of crime (1990) has
generated an abundance of research testing the proposition that low
self-control is the main cause of crime and analogous behaviors. Less
empirical work, however, has examined the factors that give rise to low
self-control. Gottfredson and Hirschi suggest that parents are the sole
contributors for either fostering or thwarting low self-control in their
children, explicitly discounting the possibility that genetics may play a
key role. Yet genetic research has shown that ADHD and other deficits
in the frontostriatal system are highly heritable. Our research thus tests
whether “parents matter” in creating low self-control once genetic
influences are taken into account. Using a sample of twin children we
find that parenting measures have a weak and inconsistent effect. We
address the conceptual and methodological issues associated with the
failure to address genetic influences in parenting studies.
More than a decade ago, Gottfredson and Hirschi (1990) set forth a
general theory of crime that assigned low self-control as the causal factor
in the etiology of crime and numerous analogous behaviors. Since that
* We would like to thank the editor for his commitment to scholarly discourse.
Direct all correspondence to John Paul Wright, Division of Criminal Justice,. 600
Dyer Hall, Ml 0389,University of Cincinnati, Cincinnati, OH 45221 or email at
[email protected]
time, the theory has occupied a fundamental position in criminology and
has generated an abundance of empirical tests. Its robustness is evident in
the recent meta-analytic review by Pratt and Cullen (2000), who found
that, across various samples and measurement techniques, low self-control
is a salient predictor of criminal behavior. With such support, it should be
no surprise that low self-control theory continues to be at the heart of
much criminological debate and investigation (Geis, 2000; Marcus, 2004;
Sampson and Laub, 1995).
Gottfredson and Hirschi’s theory focused on the association between
low self-control and crime. Indeed, it is this association that has generated
the greatest amount of empirical interest. Their hypotheses that link the
development of self-control in children to parental behaviors, however,
have been less frequently considered. Parents, they maintain, play the
decisive role in either fostering or thwarting the development of low selfcontrol.
Borrowing heavily from the work of Patterson (1982),
Gottfredson and Hirschi assert that parents who effectively monitor and
supervise their children, and who recognize and respond to their child’s
antisocial behavior will effectively instill self-control in that child. Parents
who fail to engage in such parental management techniques will
subsequently fail to help their children develop the ability to resist
situational temptations.
Given the overwhelming support linking low self-control to crime and
analogous behaviors (Pratt and Cullen, 2000), the paucity of research
examining the factors that give rise to low self-control is somewhat
surprising. We have, for instance, been able to locate only a handful of
empirical studies that test them. The findings, in general, have been
favorable to Gottfredson and Hirschi’s position that effective child-rearing
practices are predictive of self-control in children (but see Cochran, Wood,
Sellers, Wilkerson, and Chamlin, 1998). Even so, the quality of that
evidence is circumspect, for reasons we will detail later.
Gottfredson and Hirschi attribute the development of low self-control
in children solely to parenting practices—rejecting outright potential
genetic effects. At the same time, a large and growing body of clinical and
behavioral genetic research has found that impulsivity, attention deficit
hyperactivity disorder (ADHD), and hyperactivity—concepts related
closely to Gottfredson and Hirschi’s construct—are highly heritable
(Price, Simonoff, Waldman, Asherson and Plomin, 2001; Rietveld,
Hudziak, Bartels, van Beijsterveldt and Boomsma, 2003). In a review of
the genetic research on ADHD, for instance, Spencer and his colleagues
concluded that “the mean heritability of ADHD… is approximately 0.75,
which means that about 75% of the etiological contribution to this
disorder is genetic” (2002:6). Indeed, after a review of the evidence, the
National Institute of Mental Health published a widely cited brochure
stating that scientists “are finding more and more evidence that ADHD
does not stem from home environment, but from biological causes”
(2003:13; for a meta-analytic review of the association between ADHD
and criminal behavior see Pratt, Cullen, Blevins, Daigle and Unnever,
2002). Other authors have reached similar conclusions. Barkley (2005), for
example, notes that when DSM criteria are used to measure ADHD that
studies indicate it to be 97 percent heritable. “This trait,” he argues, “is
more inherited than any dimension of human personality” (14).
The potential for genetic heritability to influence levels of low selfcontrol
in children poses a serious counter argument to Gottfredson and
Hirschi’s parenting thesis. For instance, various scholars now openly
question whether “parents matter” in the development of their children’s
personalities. Harris (1998), for example, has argued forcibly that the
effects of parenting on child outcomes have been overstated, and that in
most instances, parents “don’t matter” when it comes to the child’s
personality (see also Cohen, 1999; Wright and Cullen, 2001). Citing
evidence from behavioral genetic studies (see for example, O’Connor,
Neiderhiser, Reiss, Hetherington and Plomin, 1998; Plomin, 1995; Scarr
and McCartney, 1983), Harris argues that parental socialization practices
are likely to be inconsequential once individual differences in parent and
child temperament and genetic heritability are accounted for (Cohen,
1999; Pinker, 2002).
Harris’s position is more complex than the simple statement that
“parents don’t matter” implies. Her critique of the parenting research in
general raises serious questions about the validity of many social science
findings relating parenting practices to offspring conduct. The vast
majority of research on parenting, she notes, typically employs samples
that measure one child and one parent, usually the mother, under the
assumption that inferences can be made to other children in the household
(Rowe, 1994; Walsh, 2002). More detailed research has revealed, however,
that parents enjoy differential relationships with their children. They may
treat one with hostility, yet pamper another (Harris, 1998). Children too,
when asked, often report substantial differences in their perceptions of
their parents (Reiss, Neiderhiser, Hetherington and Plomin, 2000).
According to Harris, numerous “micro-environments” exist within any
home. It is these micro-environments, or child-specific parenting behaviors
as opposed to measures of global parenting, that likely differentiate
children. Such micro-environments are typically not examined with
standard social methodologies (SSM) (see also Walsh, 2002).
Nor do SSMs account for commonalities due to genetic similarities.
Findings from a broad array of studies converge to show that many
temperamental factors are highly heritable (Caspi, Roberts and Shiner,
2005). In turn, associations between parents’ behaviors and the behaviors
of their children may be confused for “statistically significant parenting
effects” in study designs that are insensitive to biological similarities
between subjects within the home. For example, mothers who are hostile
and cold are more likely to be emotionally removed from their daughters’
lives. Their daughters, in turn, are more likely to be hostile and cold. SSMs
would correlate the daughters’ hostility with the mother’s removed
parenting style and likely infer that maternal hostility caused daughter
hostility—all without any recognition that the two phenotypes share
common genetic backgrounds. These limitations, Harris argues, likely
misspecify or overstate the effects parents may have on their children’s
traits and behaviors.1 We should also add that numerous behavioral
genetic studies have failed to detect significant shared environmental
effects (Dunn and Plomin, 1990; Neiderhiser, Reiss, Hetherington and
Plomin, 1999; Plomin, Owen and McGuffin, 1994).
Our purpose here is twofold. First, we examine the effects of parenting
on levels of self-control in kindergarten and first-grade children. We do so
with a national dataset that contains mother and teacher reports of child
self-control. As do other SSM studies, we use OLS models with appropriate
controls for demographic and neighborhood influences. Second, and
more important, we also use a sample of twins, taken from the same
dataset, to assess the influence of parenting factors. The use of a twin
dataset allows us to account for the shared genetic variance between twins.
Moreover, we use hierarchical linear regression (HLM) analyses to control
for the clustering of observations caused by genetic similarities. We
contrast results garnered through traditional social science methodologies
with those generated from the sample of twins. It appears, to foreshadow
our results somewhat, that Harris’s critiques should no longer be ignored.
A long line of literature has placed parents at the forefront of
criminological theories and research (Loeber and Stouthamer-Loeber,
1986; Patterson, 1982). Such research has tended to focus on the ways
various parenting styles, usually measured as global parenting constructs,
shape children’s behavioral patterns. Given the dominant role of parents
in criminology, it is interesting that very little research has been conducted
on how parents influence their children’s self-control—especially given the
empirical attention dedicated to the theory.
1. The interpretation of parenting correlations with child behavioral outcomes is
made difficult by at least three factors: First, parents and their children share genes.
Second, external biological factors, such as neurotoxins, may influence both parent
and child behavior. Third, temporal ordering is very difficult to establish. Child
traits and behaviors likely influence parenting behaviors and vice versa.
Using data from the Cambridge Study in Delinquent Development,
Polakowski (1994) examined the role of parental supervision on child selfcontrol.
His analysis employed two measures of child-rearing practices—
parent’s watchfulness and parental supervision—both garnered from
interviews with social workers. These variables were used as indicators for
a latent measure of supervision. Although not a complete test of
Gottfredson and Hirschi’s proposition, Polakowski’s findings, generated
from structural equation models (SEM), were generally consistent with
Gottfredson and Hirschi’s hypothesis. Children whose parents were
vigilant, had, on average, more self-control (see also Lynskey, Winfree,
Esbensen and Clason, 2000; Pratt, Turner and Piquero, 2004).
Analyzing data from sixth-grade male students, Feldman and
Weinberger (1994), explored the relationships among parenting practices,
delinquent behavior and childhood self-control, or what they termed “selfrestraint.”
Their results indicated that parental management was positively
associated with higher levels of child self-restraint, yet did not have a
direct effect on child misbehavior.
Similarly, two additional studies, conducted by Gibbs and his associates,
examined retrospective accounts of parental management practices on
levels of self-control in a sample of college students. The first—Gibbs,
Giever and Martin (1998)—found tentative support for the role parents
play in fostering low self-control. Gibbs et al. measured forty
characteristics of parental management styles and another forty of low
self-control. Through a series of path diagrams they found that parental
management had a significant and direct effect on low self-control
(Beta=.28). Likewise, Gibbs, Giever and Higgins (2003) performed
another analysis on a sample of college students, again using SEMs. Their
findings paralleled those reported in their 1998 study. Parental
management practices maintained a positive relationship with low selfcontrol,
with the coefficient being moderate in magnitude (Beta=.26).
Similar results were garnered in a study replicated by Higgins (2002).
Hay (2001) also examined the effects of parenting on low self-control in
a sample of 197 urban high school students. Their analysis also included
the two parenting measures—monitoring and discipline—along with a selfreport
measure of low self-control in his analyses. The results provided
partial support in favor of Gottfredson and Hirschi’s theory. Hay’s
analyses revealed that parental monitoring, but not discipline, was
significantly associated with child low self-control, even after controls were
introduced for early childhood antisocial behavior. Hay also analyzed an
alternative model that combined the two parenting scales into one
monitoring-discipline measure. This was significantly and inversely related
to low self-control (Beta=-.24).
More recently, Unnever, Cullen, and Pratt (2003) found evidence
linking parenting practices to offspring low self-control. Data for their
study came from 2,437 middle school students in Virginia. Similar to Hay
(2001), Unnever and his associates employed measures of parental
monitoring and of consistent punishment. Their analysis also included
Grasmick, Tittle, Bursik and Arneklev’s (1993) low self-control scale.
Their findings indicated that monitoring and consistent punishment were
significantly related to low self-control, even when controlling for the
child’s level of ADHD.2
These studies suggest that Gottfredson and Hirschi’s theory on the
development of low self-control (1990) is at least partly correct. Under the
assumptions of SSMs, parenting practices appear to have some influence
on offspring low self-control. The strength of the relationship between
parenting practices and child self-control, however, appears to be
moderate at best. More important, as we will show, there is reason to cast
doubt over the validity of this body of research.
Gottfredson and Hirschi explicitly discount the possibility that low selfcontrol
may have a genetic component: “the magnitude of the ‘genetic
effect’” they say, “is near zero” (60). A large body of literature, however,
has arrived at a very different conclusion. In a recent study that examined
heritability of attention problems in twins drawn from the Netherlands
Twin Registry, Rietveld and colleagues (2002) found that heritability
estimates varied between .68 and .76, depending on the age of the twins.
Their longitudinal study of 3,853 twin pairs also found that shared
environment, which is typically conceived of as family environment, had
little effect on the child’s level of overactivity. Similar to Barkley’s (1997)
conclusions, Rietveld and colleagues suggest strongly that attention
problems are due more to genetic factors than to environmental
In a similar vein, Mick, Biederman, Prince, Fischer and Faraone (2002)
found the strongest risk factor for childhood ADHD was having a parent
with ADHD. Children with an ADHD parent were eight times more
likely to be diagnosed with ADHD. The effects of parental ADHD were
stronger than fetal exposure to drugs, alcohol, and cigarette smoke, than
having a low birth weight, and than being born into an economically
disadvantaged family. These findings suggest that even when common
2. Some studies do attempt to control partially for child effects, such as Hay (2001)
and Unnever et al. (2003), by using autoregressive statistical models. Others do not
rely on parental reports and instead utilize individual recollections of parenting
behaviors (Gibbs et al., 1998).
environmental risk factors related to offending are taken into account,
genetic factors continue to exert a substantial effect on the child (Reiss,
Neiderhiser, Hetherington and Plomin, 2000).
Attention problems, problems with hyperactivity, and problems with
impulsivity have repeatedly been shown to have a substantial genetic
component. Given the close correspondence between Gottfredson and
Hirschi’s conception of low self-control and the diagnostic criteria for
ADHD, it is likely that low self-control is also highly heritable. Indeed, in
the vernacular of developmental neuropsychologists, “executive controls”
are composed of the ability to regulate emotions, to control impulses, to
focus appropriately on the task at hand, and to delay gratification. These
capacities are housed in the frontal, orbital-frontal and prefrontal cortex
of the brain, which are part of the larger frontostriatal system (Aron,
Robbins and Poldrack, 2004; Bradshaw, 2001; Miller and Cohen, 2001).
Various neuroscientists have recognized the overlap between executive
functions and concepts drawn from other fields. Convit and his coauthors
note, for example, that
The characteristics of an individual acting with no forethought
and without regard to consequences links the criminologists’
explanation of criminal propensity by inadequate “self-control”
(Gottfredson and Hirschi, 1990) or impaired “impulse control”
(Wilson and Herrnstein, 1985) and to results suggested by a role
of serotonin in impulsive violence (Virkkunen and Linnoila,
1993). The mechanism(s) for impulsive behavior remain unclear.
However, most brain researchers would agree that the frontal
lobes are crucial in complex tasks when planning is required and
that their main function is inhibitory or regulatory (1996:173).
Moreover, data from numerous neuroimaging studies vividly show
these areas of the brain to be under substantial genetic control.3 Without
exception, however, none of the existing criminological studies into the
influence of parenting practices on child self-control recognize the
possibility that self-control and other executive functions are influenced by
genes and by other biological factors, such as environmental tobacco
smoke (Yolton, Dietrich, Auinger, Lanphear and Hornung, 2005), blood
lead levels (Dietrich, Douglas, Succop, Berger and Bornschein, 2001), or
birth complications (Beaver and Wright, 2005).4 Although the evidence
3. We note that the brain is also highly intertwined with its immediate environment.
Environmental stimulation aides in synaptogenesis; likewise, a lack of
environmental stimulation accelerates neuronal apoptosis.
4. We are not the first to recognize the possibility that low self-control may be under
substantial genetic control. Unnever and associates (2003:495) are the exception
and note that “…the origins of self-control are not limited to parental
linking self-regulation to variation in brain structure and functioning is
now undeniable, it is still far from clear whether features of the social
environment, namely parental socialization practices, influence the
development of traits such as low self-control. Far from having a “net
effect of zero,” genetic influences may be the dominate influence on
executive functions.
“The responsible way to tackle the genetic challenge to socialization
research,” Caspi and his colleagues argue, “is head on, by using genetically
sensitive designs that can provide leverage in identifying environmental
risks” (2005:464). Following this advice, we examine the influence of
parenting factors on a measure of child self-control in a sample of
kindergarten and first-grade students. We also employ, from the same
sample, a subsample of twins, and contrast our genetically informed
findings against those detected through common SSM assumptions.
Data for this paper come from the Early Childhood Longitudinal
Study, Kindergarten Class of 1998–1999 (ECLS-K). Sponsored by the U.S.
Department of Education, National Center for Education Statistics
(NCES), the ECLS-K is an ongoing study of a nationally representative
sample of children designed to assess the impact that the primary
schooling years have on learning. The ECLS employed multiple reporting
sources to gain detailed information about the children’s behavior, their
temperament, their intellectual skills, their social relationships and their
environment. Information about such topics was obtained through
interviews with the children, their parents and teachers, and school
administrators. Four waves of data have been collected thus far: two waves
each during kindergarten and first grade. Data collection for wave one
began in the fall of 1998, when the children first entered into kindergarten.
The second wave of questionnaires were administered the following spring
(1999). The last two waves of data were obtained during the fall and spring
of the first grade (1999–2000). Only a small subgroup of students,
however, were interviewed in the fall of their first grade.
Sampling waves assessed during the kindergarten year were measured
less than 6 months apart. Some of the parenting measures, moreover, were
asked only during the spring term. Given the relatively small time
difference between sampling waves, we treat all data collected during the
practices…low self-control is not a purely social outcome but is also affected by
genetic predispositions.”
kindergarten year as from one measurement period. However, we use the
measures of child low self-control measured during the spring to maintain
temporal ordering. Data from the fall wave of the first grade were
excluded from the analysis, making the interval between consecutive
measurement periods (kindergarten year to spring term of their first
grade), about one year.
A unique aspect of the ECLS-K was that when a respondent indicated
the presence of a twin, the proband was subsequently included in the
sample, netting n=310 twins. Both twins were subject to identical data
collection processes and instruments. In terms of cluster size, each twin
was reported on by the parent (usually the mother), their teacher and in
some instances the mother and the teacher.
The total sample size for the ECLS-K includes over 21,000 children.
Recall that one purpose of our study was to assess results based on SSM’s
to those obtained from a sample of twins. With such a large overall
sample, very small differences could easily reach levels of statistical
significance. To make our results as valid as possible, we took a random
sample of n=1,000 children from the larger sample of 21,000. We chose a
sample size of 1,000 for two reasons. First, most national studies have
sample sizes that range from 1,000 to 2,000 subjects. Second, no
meaningful differences were found in our results when random samples
ranging between n=310 and n=2,000 were analyzed. Thus, the results
obtained from our random sample of n=1,000 would be the results
reported and published without consideration for the twins in the sample.
Overall, the ECLS-K is compatible with our research agenda for four
main reasons. First, the inclusion of twins permits us to control for the
genetic similarity. Second, consistent with Gottfredson and Hirshi’s
proposition on the origins of low self-control, a number of parenting
questions were asked that tapped into efficacious parenting practices
(Wright and Cullen, 2001). Third, the ECLS-K contains childhood
measures of low self-control, allowing us to examine the early correlates of
low self-control. And, finally, because multiple reporting sources were
used, we were able to construct theoretically consistent measures of low
self-control based on parent and teacher reports. Taken together, the
ECLS-K provides us with a rich data source with which to systematically
assess the biological and social origins of low self-control.
The ECLS-K employed a version of Gresham and Elliott’s (1990) wellknown
Social Skills Rating Scale (SSRS), a proprietary assessment
battery, to measure child self-control. The SSRS is a multi-rater,
standardized, normed-referenced assessment battery based on information
collected from mothers and teachers. The SSRS contains subscales that
tap into child self-control, including overactivity and hyperactivity. The
response set for these items were scored 1=never, 2=sometimes, 3=often
and 4=very often. Research into the psychometric properties of the SSRS
has found the scales and subscales to be high in reliability, moderate in
test-retest reliability, and valid (Benes, 1995; Gresham, 2001).5
Because the SSRS is a multi-rater assessment instrument, we created a
teacher low self-control scale (wave 2 twin sample alpha=.80; wave 2
random sample alpha=.82; wave 4 twin sample alpha=.82; wave 4 random
sample alpha=.85), a parent low self-control scale (wave 2 twin sample
alpha=.45; wave 2 random sample alpha=.58; wave 4 twin sample
alpha=.57; wave 4 random sample alpha=.59), and a combined low selfcontrol
scale (wave 2 twin sample alpha=.61; wave 2 random sample
alpha=.63; wave 4 twin sample alpha=.60; wave 4 random sample
alpha=.68) for both waves of data. Parental reports, although used widely,
are slightly less reliable than information gathered from other sources.
Data from teachers, however, have proven to be highly efficient and
reliable and help measure conduct that occurs away from parents (Cairns
and Cairns, 1994; Harris, 1998). Teachers and parents reported on the
child’s ability to manage temper and emotions, on ability to control
conduct, and on impulsiveness and activity levels.
To test the robustness of our findings, we also employed an expanded
measure of low self-control. This scale taps not only into the attention
problems outlined, but also into various social problems experienced by
children lacking self-control and deficient decision-making processes that
often accompany low self-control. This expanded scale contains the
following eight items: parental and teacher reports of self-control, parental
and teacher reports of approaches to learning, parental reports of the
child’s activity level, parental reports of the child’s social interactions with
others, teacher reports of the student’s interpersonal skills, and teacher
reports of the student’s externalizing problem behaviors (twin sample
alpha=.75; random sample alpha=.77). The same measures were used to
create the expanded measure of low self-control during first grade (twin
5. The measurement of self-control, or executive control functions in general, is still a
matter of substantial debate. This debate has not escaped criminology. In essence,
much of the debate centers on whether self-control should be measured
attitudinally or through items that capture variation in analogous behaviors. We,
however, share the view of Rudolph, Lambert, Clark, and Kurlakowsky. (2001:931)
that self-regulation “can be conceptualized as a combination of cognitive,
evaluative, and behavioral processes that guide goal-directed action and emotional
sample alpha=.74; random sample alpha=.78). Higher scores on this scale
indicate lower levels of self-control. See Appendix A for a description of
the variables and scales. Consult Appendix B for descriptive information
about the samples.
Gottfredson and Hirschi maintain that the ways in which parents
socialize their children will ultimately decide whether their child develops
self-control. In particular, they assert that parents who supervise,
recognize and consistently punish childhood transgressions will succeed at
instilling self-control in the child. To partially test this perspective, we
included five unique measures of parenting behaviors: parental
involvement, parental withdrawal, parental affection, physical punishment
and family rules. Due to data limitations, we were not able to measure all
the parenting variables Gottfredson and Hirschi identify as significant
predictors of low self-control. Yet this situation is not unique to the ECLSK
data or our analyses in general; rather, prior research has also been
hampered by an inability to measure all dimensions of parental
socialization (Hay, 2001; Unnever et al., 2003). Even so, many of the
measures used in our analysis are consistent with those Gottfredson and
Hirschi identify.
Parental Involvement. This scale taps into the amount of time the
parent spends with the child on various activities. Presumably, while
engaging in such activities, parents will also be supervising their child. This
nine-item measure was constructed using parental responses to the
following questions: how often the parent reads, tells stories, sings songs,
helps child with chores, helps with art activities, plays games, teaches the
child about nature, builds things and plays sports (twin sample alpha=.74;
random sample alpha=.75). Higher scores indicate a greater degree of
parental involvement in the child’s life.
Parental Withdrawal. The scale serves to capture the degree to which
parents retreat from, or hold unfavorable attitudes toward, their child.
Nine items comprised this scale: I am too busy to play with child; I have
difficulty being warm with the child; being a parent is harder than I
anticipated; my child does things that bother me; I have to sacrifice to
meet the child’s needs; I feel trapped as a parent; I often feel angry with
the child; my child is hard to care for; and being a parent is more work
than pleasure (twin sample alpha=.67; random sample alpha=.68).
Parental Affection. This four-item scale measures the degree of
affection between the child and the parent, and includes the following
items: we spend warm, close time together; the child likes me; I always
show love for the child; and I express affection to the child. The four items
were summed, with higher scores representing more parental affection
(twin sample alpha=.63; random sample alpha=.59).
Physical Punishment. Gottfredson and Hirschi maintain that parents
who punish consistently will instill self-control in their child. Although the
ECLS-K data do not include measures tapping into the consistency with
which parents’ correct misbehavior, two measures do index whether
parents would physically punish the child. Parents were presented with a
hypothetical scenario, asking them what they would do if the child were to
hit them. A list of possible retaliations was then presented, and parents
were subsequently asked which, if any, of the punishment strategies they
would use. We identified two such actions: if the parent stated she would
“hit the child back” or if she would “spank the child.” These two items
were then summed, forming the physical punishment index, with higher
scores indicating the parent is more likely to resort to physical punishment
when faced with disciplining the child.
Family Rules. This final socialization measure measures a limited
domain of rules within the home. Three questions were asked about rules
regarding television viewing. Specifically, parents were asked if there are
family rules for which television programs the child can watch, the amount
of hours the child is permitted to watch television, and if rules exist on
how late or early the child is allowed to watch television. Again, higher
scores reflect more family rules (twin sample alpha=.63; random sample
Gender. Gottfredson and Hirschi maintain that, in general, boys tend to
have lower levels of self-control than girls. We therefore include a
dichotomous measure of gender in the analyses (1=male; 2=female).
Academic Preparedness. Cognitive capacity has been found to be a
strong predictor of crime and other behavioral problems (Wilson and
Herrnstein, 1985). As such, we included an academic preparedness scale,
measured during wave one, to assess the degree to which academic ability
is related to low self-control. Children in the ECLS-K were subject to a
cognitive assessment battery with three distinct components: language and
literacy, mathematical skills, and general knowledge. To ascertain the
scores on each of these tests, each child was tested one on one (ECLS-K
User’s Manual). To compute the academic preparedness scale, we
combined these three scores (twin sample alpha=.87; random sample
alpha=.84). The scores tap into the acquisition of knowledge and the
child’s preparedness for kindergarten.6
6. Reviewers noted that the measure of academic preparedness overlaps with one of
Race. To capture potential differences in self-control between whites
and nonwhites, we included race as a control variable. Race was coded
(0=white; 1=nonwhite).
Neighborhood Disadvantage. Research has found self-control to be
influenced by community structural characteristics (Pratt, Turner and
Piquero, 2004). To control for the possibility that neighborhood social
factors may affect the development of self-control, we included a
neighborhood disadvantage scale. Six items, reported by the parents, were
summed to form the neighborhood disadvantage scale. Parents were asked
how safe it was for their child to play outside, whether garbage and litter
were visible on the street, whether there were problems with people
selling and using drugs or alcohol in the neighborhood, whether there
were problems with burglaries and robberies in the neighborhood, if there
were problems with violent crime, and if there were vacant houses nearby.
Higher scores indicate more problems in the neighborhood (twin sample
alpha=.60; random sample alpha=.73).
Harris (1998) offered a stinging critique of standard social science
methodologies, noting that most SSMs include data on one child and one
parent, inferring from that relationship to other relationships within the
home (see also Caspi et al., 2005). She also argues that SSMs cannot
account for similarities in genetic commonalities between individuals
within the same home. To Harris, most of the current empirical literature
on the role and effects of parenting is seriously biased (referenced in
Pinker, 2002). We take into account Harris’s criticism by analyzing a
random subsample of youth as well as a sample of twins. We also use
statistical models unique to each sample. In the random subsample we use
OLS regression. This technique does not account for the clustering of
Gottfredson and Hirschi’s dimensions of low self-control: preference for physical
activities. According to reviewers, we may be committing a tautology primarily
because we are predicting low self-control with a component of self-control. The
academic preparedness scale does not ask whether students prefer mental activities
over physical activities. Rather, the academic preparedness scale tests both what
the student has learned, and the student’s ability to read and to calculate basic
mathematical equations. The measure was originally designed to assess individual
differences in school preparedness for children entering Kindergarten. And as
Grasmick, Tittle, Bursik and Arneklev (1993) found, the physical preference
dimension of low self-control was the weakest correlate with their well-known and
widely used scale of self-control. Still, we recalculated all of the models without
including the measure of academic preparedness. The results were virtually
identical to those reported with the inclusion of the academic preparedness
measure. Future research would benefit by examining the nexus between IQ and
other measures of intellect, and low self-control.
individuals within the household; a serious violation of the technique but
one that is commonly committed.
Genetic similarity within families translates into a loss of statistical
independence between observations. OLS regression assumes that
observations are independent of each other and thus that the correlation
between error terms across observations is zero (0). Of course, children
are not randomly assigned within families, and monozygotic twins share all
of their genes. Intra-class correlations, (?=rho), a measure of the degree of
homogeneity of phenotypes within families, generally range between .20
to .60, indicating that 20 to 60 percent of the variance in the outcome of
interest is accounted for by the clustering of observations (Wright and
Cullen, 2001). Methodological research, however, has found that ICCs as
small as .1 can downwardly bias estimates of the standard errors and in
some cases slope estimates (Zyzanski, Flocke and Dickinson, 2004). The
result of ignoring genetic similarities within families likely results in the
overestimation of significant effects (Type 1 errors), and the inaccurate
attribution of “substantive meaning” to parenting variables.
In the twin sample we employ a random-effects regression analysis,
which is virtually synonymous with hierarchical linear modeling (HLM).
This technique allows for the estimation of the proportion of the variance
in the dependent variable accounted for by the nonrandom clustering of
subjects within twin dyads. In this case, the clustering of subjects occurs
due to genetic similarity—that is, monozygotic or dyzygotic twin status.
HLM and random-effects models account for the loss of statistical
independence and produce robust standard errors through an iterative
process. The resulting maximum-likelihood slope estimates are assessed
against standard errors that are comparatively more conservative.
Unlike studies in which the central concern is in estimating the
heritability of a certain trait, our concern is in estimating the potential
influences of a range of predictors, controlling in part for genetic
similarities. As such, we avoid model fitting techniques that are generally
reserved for estimating heritability coefficients.7 Our analysis is similar to
Tully, Arseneault, Caspi, Moffitt and Morgan’s analysis of data from the
Environmental Risk Longitudinal Twin Study (2004).
7. Model fitting procedures revealed that low self-control is 66 percent heritable. The
baseline HLM model produced an ICC of .55, indicating that 55 percent of the
variation in self-control could be attributed to the clustering of twins in the same
household. The difference between the results from the SEM model and the HLM
model indicates that HLM may underestimate the actual amount of clustering in
the twin dyads.
Table 1 shows the results of our OLS and HLM analyses. Looking first
at the OLS regression of parental reports of their child’s self-control we
found that three of the parenting measures significantly accounted for
variation in low self-control: parental withdrawal, parental affection and
family rules. As predicted by low self-control theory, parental withdrawal
was positively associated with low control, and family rules and parental
affection were inversely related to self-control. Gender, academic
preparedness and neighborhood disadvantage were also related to low
self-control in the theoretically expected direction. These results are
precisely what would be predicted from low self-control theory under
standard socialization assumptions.
Table 1. Effects of Parenting on Child’s Low Self-Control in Kindergarten
Variables Parental
Total Composite
Socialization Measures
Parental Affection -.09*
Family Rules -.08*
Control Variables
Gender -.10*
Race .00
Number of
Significant Parenting
3 3 1 0 3 1
.284 .457 .367
* Parameter estimate at least twice its standard error
The HLM/twin model, however, reveals a slightly different pattern.
Parental withdrawal and parental affection retained statistical significance.
However, whereas the measure of family rules dropped out of statistical
significance, the measure of physical punishment reached it. Moreover,
none of the control measures were significant predictors of self-control,
nor was neighborhood disadvantage.
Evidence from the first set of equations indicates that the OLS/SSM
model likely overestimated the number of significant independent effects
on parental measures of child self-control. In the HLM model, with the
clustering due to genetic similarity accounted for, only three predictors
reached significance compared to six in the OLS/SSM model.
Teacher reports of child low self-control reveal a very different pattern
of results. In the OLS model, the only significant predictors were parental
withdrawal, gender and academic preparedness. However, in the HLM
model, none of the parenting measures were significantly associated with
low self-control. Consistent with the OLS model, gender and academic
preparedness were also significant predictors.
Finally, examining the composite measure of low self-control we find
that, once again, the OLS model overestimated the number of significant
parenting effects. In the OLS model, parental withdrawal, parental
affection and family rules were significant predictors. In the HLM/twin
model, however, only parental withdrawal was. In both models, gender
and academic preparedness were significant.
The results from Table 1 indicate that traditional social research
methodologies tend to inflate the effects parents have on their offspring’s
self-control, especially if the reporting source is a parent. We note that
because many of the parenting measures were taken contemporaneously
with the measures of low self-control, that these models offer the highest
likelihood of detecting parenting effects. Still, once clustering due to
genetic similarity was controlled, most of the parenting effects dissipated
to statistical insignificance.
Table 2 presents the results for child self-control, measured one year
later when the children were in the first grade, regressed on the various
parenting measures. Looking first at the OLS regression of parental
reports we find that parental withdrawal, parental affection and family
rules were significant predictors of child self-control. So too were gender,
academic preparedness and neighborhood disadvantage. Even so, in the
HLM/twin model only parental withdrawal and academic preparedness
were significantly associated with child low self-control. Once again, the
OLS/SSM model overestimated the number of significant parameters. Six
parameters reached significance in the OLS model, compared to just two
in the HLM/twin model.
An even greater contrast can be seen when teacher reports are
examined. Only one of the independent variables was associated with low
self-control in the HLM/twin analyses—parental withdrawal. Yet in the
OLS model, parental withdrawal, family rules, gender and academic
preparedness each made independent contributions.
In the final set of equations in Table 2 we find that in the OLS model,
parental withdrawal, family rules, gender and academic preparedness
accounted for variation in low self-control. In the HLM/twin model,
parental withdrawal, academic preparedness and race were significantly
associated with low self-control.
Table 2. Effects of Parenting on Child’s Low Self-Control in First Grade
Variable Parental
Composite Score
Socialization Measures
Parental Involvement -.02
Parental Withdrawal .25*
Parental Affection -.07*
Physical Punishment .01
Family Rules -.09*
Control Variables
Gender -.07*
Race .04
Number of Significant
Parenting Parameters
3 1 2 1 2 1
.402 .385 .315
* Parameter estimate at least twice its standard error
Consistent with the findings presented in Table 1, the results shown in
Table 2 indicate that standard OLS/SSM models overestimate the
influence of various variables on self-control. Of particular interest
however, is the overestimation of parenting influences. Our parenting
measures were consistently related to child self-control in the OLS/SSM
models in general, but were more consistently related when parents were
used as the reporting source. This bias, we note, is the type predicted by
Harris (1998) and Pinker (2002). Once similarities due to genetic
influences were removed, the effects of other variables, particularly
parenting variables, were reduced or eliminated.
In our last set of analyses, depicted in Table 3, we use an expanded
measure of low self-control. Consistent with our prior analyses, we
examine a contemporaneous measure of low self-control and a prospective
measure of low self-control.
Table 3. Effects of Parenting on Full Measures of Child’s Low Self-Control
Variable (Kindergarten) (First Grade)
Socialization Measures
Parental Involvement -.06*
Parental Withdrawal .21*
Parental Affection -.10*
Physical Punishment .03
Family Rules -.05
Control Variables
Gender -.23*
Academic Preparedness -.34*
Race .01
Neighborhood Disadvantage -.01
Number of Significant
Parenting Parameters
3 1 3 0
Intra-cluster Correlation .328 .374
* Parameter estimate at least twice its standard error
Parental involvement, parental withdrawal, parental affection, gender
and academic preparedness made independent contributions to the
explained variance in child low self-control in the OLS model
(kindergarten). However, consistent with prior analyses, only parental
withdrawal, gender and academic preparedness were significantly related
to low self-control in the HLM/twin model.
A similar pattern was again detected when we regressed the prospective
measure of child self-control on the independent predictors. In the OLS
model, parental involvement, parental withdrawal, family rules, gender
and academic preparedness were significant predictors. In the HLM/twin
model, though, none of the parenting measures were significantly related
to child self-control. Only gender and academic preparedness retained
statistical significance.
Our study sought to answer a single question: do parents matter in the
etiology of self-control? At first glance, this would appear to be a rather
simple empirical question. Empirical research, however, has a bad habit of
exposing the underlying complexities of even the simplest research
question. So it was in this instance. On close inspection, we realized that
an answer to this question would require data that allowed for the
estimation of genetic similarity between twins and their mother, data that
contained measures of various parenting constructs and data that
contained multiple reporting sources. The ECLS-K fit these requirements.
We address the findings from this study along two intertwined
dimensions: substantive findings and methodological implications. In
general, our analyses revealed that parenting variables were inconsistently
and weakly related to contemporaneous measures of child self-control in
kindergarten, and were inconsistently related to prospective measures of
self-control in the first grade. Parenting influences typically reached
statistical significance in the OLS models, models that did not account for
the clustering of responses due to genetic similarity. We also note that
parenting influences reached significance only when parental reports were
used. When teacher reports of child self-control were analyzed, parenting
features were consistently unrelated to child self-control. Teachers, we
note, observe children under very difference circumstances than parents;
circumstances that usually require young children to exhibit self-control
(Cairns and Cairns, 1994).
Whether parenting matters in the etiology of child self-control thus
appears to be deeply intertwined with the type of methodology and
analyses employed. Standard social methodologies (SSMs), which usually
measure only one child and a parent within a household and which
typically ignore genetic similarities between subjects, appear to
overestimate the influence of parenting on child self-control. The results
of our OLS models, derived from standard SSM assumptions and
methodologies, would generally be accepted as evidence linking parenting
to self-control in children.
Employing a genetically informed methodology and analysis, however,
alters, or at least conditions, such a conclusion. Overall, employing a more
rigorous methodology and analysis reduced or eliminated the influence of
several variables, most noticeably the parenting variables. Even here,
however, it appears that methodology makes a difference. Parenting
effects were detected only when parent reports of child self-control were
used. The use of teacher reports in our twin/HLM models detected no
significant association between parenting variables and child self-control.
This result held contemporaneously and longitudinally.
The use of parent reports, even in our twin sample, confounds the
association between parenting behaviors and the child’s self-control. That
is, parents who are far removed from the daily happenings of their
children may be likely to report self-control problems in their children.
This effect may be “real,” but it may also reflect individual differences
shared between parents and their offspring, or at least differences rooted
in behavioral and attitudinal factors that vary between parents. Either
way, the use of parent reports may overestimate the influence that
parenting behaviors have on a child’s traits. Evidence from the teacher
report models strongly suggests this to be the case.
The contrast in findings begs the question of what the appropriate
benchmark to evaluate the validity of our findings is. Some readers will
point to the OLS models as offering evidence sufficient to demonstrate
parenting effects. They may also point out that the valid conclusions about
parenting behaviors can be derived only from cross-sectional analyses.
However, more conservative analysts will point out that the OLS
estimates are substantially biased and do not meet the criteria outlined by
Harris to reject the null hypothesis that parents “don’t matter.” Nor do the
parenting variables generate consistent effects when temporal order is
specified correctly. What, then, do we make of our findings?
We suspect the most valid and conservative findings are those that
emerge from the composite measure of self-control in the twin/HLM
sample. These are consistent across time and indicate that the measure of
parental withdrawal, which captures variation in the extent to which
parents report feelings of stress and emotional distance from their
children, is associated with increases in child low self-control contemporaneously
and over a one-year period. The effects are only marginally
significant, however, which indicates that the true effect size may be
trivial. To buttress our initial findings, we also merged the random sample
and the twin sample. The results are shown in Appendix C and converge
with those generated by analyzing the samples separately. As such, these
findings provide tangible evidence in favor of Harris’s (1998) proposition
that, net of genetic similarities within households, parental socialization
techniques minimally influence the individual traits of their children.8
8. We also note that Gottfredson and Hirschi claim that self-control materializes prior
to the age of twelve and becomes very unlikely to appear thereafter. Neurological
studies, however, dispute this possibility by showing that cerebral volume increases
Does this mean that parents do not matter? Of course not, nor do we
encourage readers to blithely accept that possibility given our analyses.
Instead, our view is that the influence of parental socialization factors, as
well as other environmental features, are conditioned by the genotypes of
the parent and child. Recent evidence by Caspi and his colleagues, for
example, has documented how individual responses to life-stresses and to
experiencing parental abuse vary by child genotype (Caspi et al., 2002; Caspi
et al., 2003). As behavioral geneticists frequently tell us, children within the
same family are often very different. These differences cannot be explained
by factors that do not vary between children, differences that aggregated
parenting measures are not capable of assessing (Plomin, 1995).
Parents likely influence their children in ways that are more
complicated than is typically assumed. Parents may moderate the
influence of specific child traits (Tully et al., 2004), or the traits of parents
may interact in unique ways with the traits of each of their children.
Parents may also create environments that are so bleak and abusive that
the environmental effects overshadow any genetic influences (Harris,
1998). Moreover, the traits of the child likely influence the reactions of
parents.9 Difficult children constantly challenge parental authority and
limit-setting efforts. In either case, whatever influence parents have on the
traits of their children, it likely will involve a more sophisticated
understanding of the genetically influenced, mutually dynamic
relationships that occur within households.
Although other studies will have to verify our findings, we note that
recent neuroimaging research and studies of twins provide strong evidence
of the genetic influence on brain structure and functioning, especially as it
relates to executive control functions (Barkley, 1997; Thompson et al.,
2001). Through the use of complex imaging, Thompson and his colleagues
(2001) analyzed brain structural differences in a sample of monozygotic
and dizygotic twins. Their results graphically depict the strong genetic
foundation underpinning brain growth and functioning. Particularly
striking was their analysis of monozygotic twins. This analysis revealed
almost identical gray matter volume in the frontal lobes of the twins and in
through the age of 20, while temporal grey matter increases in volume through the
age of 16.5 in males and 16.7 in females (Giedd, Blumenthal, Jeffries, Castellanos,
Liu, Zijdenbos, Paus, Evans, and Rapoport, 1999). Future research should examine
variation in self-control across a longer age-range, as well as include a broader
array of socialization measures.
9. As one reviewer noted, the effects generated from the measure of parental
withdrawal could represent the influence of the child on the parent. In subsequent
analyses, we included a measure of prior low self-control (results not shown).
Inclusion of that measure reduced the effect of parental withdrawal to statistical
insignificance. In the language of behavioral genetics, this dynamic is known as a
provocative gene X environment interaction.
regions that control language acquisition. Other imaging studies have
found that genetic heritability in brain structure is evident through the
seventh and eighth decades of life (Pfefferbaum, Sullivan, Swan, and
Carmelli, 2000).
Neuroimaging studies highlight the close correspondence between
specific traits, such as low self-control, and the genetic controls that direct
neuronal growth in specific brain structures related to specific traits. More
important, they help to explain why common environmental features, such
as parental socialization efforts, have only modest to trivial effects (Wright
and Cullen, 2001). At least as it relates to self-control, once similarities in
genetic heritability are accounted for, the range of variance left to explain
is quite restricted. As it applies to antisocial behavior in young children,
Arseneault and her colleagues (2003) found that in a sample of 1116 twin
pairs, genetic factors accounted for 82 percent of the variance, while
experiential factors accounted for only 18 percent of the variance. Recent
meta-analytic reviews also support these findings (Miles and Carey, 1997).
Gottfredson and Hirschi openly exclude the possibility that self-control
has a genetic base. Our study, along with others from various fields,
suggests that for self-control theory to be a valid theory of crime it must
incorporate a more sophisticated understanding of the origins of selfcontrol
(Pratt, Turner and Piquero, 2004; see also Pratt et al., 2002). With
the caveats that we have not measured all parental socialization practices
and that our data are restricted in age range, the pattern of findings
reported here contradicts Gottfredson and Hirschi’s hypotheses that link
parental socialization to the development of self-control. Excluding
genetic influences on self-control and related traits likely misspecifies a
theory that, in general, has gained widespread empirical support (Pratt
and Cullen, 2000). It also fails to recognize the large body of research that
has linked executive control functions to a range of biological factors, such
as prenatal maternal cigarette smoking and drug use (Gibson and
Tibbetts, 1998), prenatal lead absorption (Bellinger, Leviton, Allred and
Rabinowitz, 1994), and in utero anoxia (Beaver and Wright, 2005). We
suggest that self-control theory be revised to incorporate this body of
Lastly, we note that methodological assumptions shape research
findings, and in this case, it is clear to see that the findings from the OLS
models overestimated the influence of a range of factors, including
parenting and neighborhood variables. Although we do not believe that
our methodology is the only way to analyze socialization hypotheses, we
do suggest that it is time for criminologists to come to grips with the
confluence of genetic similarities and individually specific experiences
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John Paul Wright is associate professor of criminal justice at the
University of Cincinnati. He has published articles on the effects of
adolescent employment on delinquency, the effects of parenting on
offspring misbehavior, and on the role of money in youthful misconduct.
His current research focuses on the genetic heritability of traits related to
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the biosocial development of serious violence.
Kevin M. Beaver is a doctoral candidate in the Division of Criminal
Justice at the University of Cincinnati. He received his BA in sociology
from Ohio University and his MS in criminal justice from the University of
Cincinnati. His research and teaching interests include life-course and
biosocial criminology, the heritability of antisocial behavior, and the
stability of violent offending. He has published in the Journal of
Quantitative Criminology, Youth Violence and Juvenile Justice, and
International Journal of Offender Therapy and Comparative Criminology.
Low Self-Control Scales
Teacher Ratings of Low Self-Control in Kindergarten (wave 2)
Scale created by summing the following items: Teacher reports of the student’s …
1) externalizing problem behaviors
2) self-control
Teacher Ratings of Low Self-Control in First Grade (wave 4)
Scale created by summing the following items: Teacher reports of the student’s …
1) externalizing problem behaviors
2) self-control
Parental Ratings of Low Self-Control in Kindergarten (wave 2)
Scale created by summing the following items: Parental reports of the child’s …
1) self-control
2) impulsivity
Parental Ratings of Low Self-Control in First Grade (wave 4)
Scale created by summing the following items: Parental reports of the child’s …
1) self-control
2) impulsivity
Total Composite Score of Low Self-Control in Kindergarten (wave 2)
Scale created by summing the following items:
Teacher reports of the student’s …
1) externalizing problem behaviors
2) self-control
3) approaches to learning (e.g., attentiveness and persistence)
4) interpersonal skills (e.g., ability to form friendships)
Parental reports of the child’s …
5) self-control
6) impulsivity
7) approaches to learning (e.g., concentration and persistence)
8) social interactions (e.g., positive interactions with peers)
Total Composite Score of Low Self-Control in First Grade (wave 4)
Scale created by summing the following items:
Teacher reports of the student’s …
1) externalizing problem behaviors
2) self-control
3) approaches to learning (e.g., attentiveness and persistence)
4) interpersonal skills (e.g., ability to form friendships)
Parental reports of the child’s …
5) self-control
6) impulsivity
7) approaches to learning (e.g., concentration and persistence)
8) social interactions (e.g., positive interactions with peers)
Socialization Measures
Parental Involvement in Kindergarten (wave 1)
Scale created by summing the following items (according to parental reports):
How often the parent …
1) reads to the child
2) tells the child stories
3) sings songs with the child
4) helps the child with art activities
5) helps the child with chores
6) plays games with the child
7) teaches the child about nature
8) helps the child build things
9) plays sports with the child
Parental Withdrawal in Kindergarten (wave 2)
Scale created by summing the following items (according to parental reports):
1) Does the parent have to sacrifice to meet the child’s needs
2) Does the respondent feel trapped as a parent
3) Is the parent too busy to spend time with the child
4) Does the parent often feel angry with the child
5) Is it hard for the parent to be warm to the child
6) Is the child harder to care for than anticipated
7) Being a parent is harder than the parent expected
8) Does the child do things that bother the parent
9) Is being a parent more work than pleasure
Parental Affection in Kindergarten (wave 2)
Scale created by summing the following items (according to parental reports):
1) Does the parent and child spend warm, close time together
2) Does the child like the parent
3) Does the parent always show love for the child
4) Does the parent express affection to the child
Physical Punishment in Kindergarten (wave 2)
Index created by summing the following items (according to parental reports): If
the child hit the parent, the parent would …
1) hit the child back
2) spank the child
Family Rules (wave 2)
Scale created by summing the following items (according to parental reports):
Are there family rules …
1) for which television programs the child can watch
2) limiting the number of hours the child can watch television
3) pertaining to how early/late the child can watch television
Scale Included for Statistical Control
Neighborhood Disadvantage Scale (wave 2)
Scale created by summing the following items (according to parental reports):
1) How safe is it for the child to play outside
2) Is there garbage and litter on the street
3) Are there problems with people using or selling drugs in the neighborhood
4) Are there problems with burglaries or robberies in the neighborhood
5) Are there problems with violent crime in the neighborhood
6) Are there vacant houses in the neighborhood
Appendix B. ECLS-K Descriptive Statistics
Random Sample Combined Sample
Mean SD Mean SD Mean SD
Socialization Measures
Parental Involvement 24.72 4.60 25.10 4.51 25.03 4.52
Parental Withdrawal 11.73 3.04 12.15 3.25 12.08 3.22
Parental Affection 14.66 1.59 14.76 1.53 14.74 1.54
Physical Punishment .20 .40 .22 .43 .22 .43
Family Rules 2.23 .91 2.23 .90 2.24 .90
Control Variables
Percentage Male 40% 51% 49%
Academic Preparedness 147.16 26.11 152.44 25.37 151.56 25.56
Percentage White 63% 56% 57%
17.34 3.04 17.10 1.56 17.15 1.50
Parental Reports 3.84 .88 4.05 1.01 4.02 1.00
Teacher Reports 3.25 1.05 3.48 1.14 3.44 1.13
Composite Score 7.08 1.51 7.51 1.69 7.43 1.66
Full Measure 15.68 2.92 16.18 3.13 16.09 3.10
Parental Reports 3.93 .88 3.92 .99 3.92 .97
Teacher Reports 3.24 1.01 3.49 1.16 3.45 1.14
Composite Score 7.0 1.49 7.39 1.71 7.34 1.68
Full Measure 15.75 2.80 16.21 3.17 16.13 3.12
Appendix C. Effects of Parenting on Full Measures in Combined Sample
Variable (Kindergarten) (First Grade)
Socialization Measures
Parental Involvement -.06*
Parental Withdrawal .19*
Parental Affection -.19*
Physical Punishment .21
Family Rules -.19*
Control Variables
Gender -1.36*
Academic Preparedness -.04*
Race .09
Neighborhood Disadvantage -.01
Number of Significant
Parenting Parameters
4 2 3 1
* Parameter estimate at least twice its standard error

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