The Interconnection between Intellectual Achievement and Self-Control

Abstract

Low self-control has emerged as a strong predictor of criminal conduct and analogous behaviors. Questions remain, however, as to the origins of self-control. Whereas some argue it is a trait instilled solely through a process of parental socialization, more recent research has suggested the possibility that self-control is interconnected with many executive functions deriving from the prefrontal cortex of the brain. Using data from the Child Development Supplement of the Panel Study of Income Dynamics (N = 2,104), this study assesses the degree to which self-control is linked with intellectual achievement in childhood and adolescence. Results from Poisson regression analyses indicate that intellectual achievement is significantly related to variations in self-control, controlling for a variety of parenting measures; age, race, and gender; and previous levels of self-control. A discussion of the relationship between intellectual achievement and self-control is provided.

Keywords

general theory of crime intellectual achievement self-control PSID

Criminologists have long been exploring and debating the link between individual traits and antisocial behaviors. In one of the most prominent and widely cited examples, Gottfredson and Hirschi (1990) developed a general theory of crime in an attempt to explain a wide range of criminal and deviant behaviors. Central to their theory is the concept of self-control, which they argue is developed in early childhood as the result of effective parenting. Parents who fail to implement effective child-rearing techniques will produce children who are “impulsive, insensitive, physical (as opposed to mental), risk-taking, short-sighted, and nonverbal” (Gottfredson & Hirschi, 1990, p. 90). These elements of low self-control described by Gottfredson and Hirschi are aligned with those from other scientific disciplines that also focus on an individual’s ability to control impulses, manage risk tasking, achieve predictability in one’s own life, perform complex thought processes, and control emotions. Thus, there are various ways to describe an individual’s ability to regulate his or her own behaviors. Whereas Gottfredson and Hirschi have coined the term “low self-control” in criminology, the elements underlying this construct are referred to as self-regulation, impulsivity, effortful control, or attention-deficit/hyperactivity disorder in other scientific disciplines (Barkley, 1997, 2006; DeLisi & Vaughn, 2011).

Several empirical studies in the field of criminology have shown that parenting practices, such as monitoring and discipline, are significantly related to variations in self-control in children (Unnever, Cullen, & Pratt, 2003), adolescents (Burton et al., 1995; Hay, 2001; Lynskey, Winfree, Esbensen, & Clason, 2000; Perrone, Sullivan, Pratt, & Margaryan, 2004), and adults (Gibbs, Giever, & Higgins, 2003; Gibbs, Giever, & Martin, 1998; Higgins, 2002). Studies have also shown that other environmental factors, such as neighborhoods and schools, also influence variation in levels of self-control (Pratt, Turner, & Piquero, 2004; Turner, Piquero, & Pratt, 2005).

Brain-Based Functioning and Self-Control

Although there is a large amount of research on the influence of environmental factors on levels of self-control, criminologists have only recently begun to explore the interconnection between self-control and executive functions of the brain (Beaver, Wright, & DeLisi, 2007; Ratchford & Beaver, 2009). Schmeichel and Baumeister (2004) define executive functions as “the active, conscious, and intentional core of the self, responsible for planning, initiating, and revising cognition and behavior” (p. 86). As such, it is argued that self-control is simply one element of a larger group of executive functions partially processed in the frontal lobe of the brain (Beaver et al., 2007). Using data from the National Survey of Children, Ratchford and Beaver (2009) assessed whether neuropsychological deficits covaried with levels of self-control in a sample of children and adolescents. They found that, indeed, individuals with greater neuropsychological deficits, measured by the Peabody Picture Vocabulary Test–Revised, were significantly more likely to exhibit lower levels of self-control. Similar results were reported by Beaver et al. (2007) using a sample of approximately 3,000 young children from the Early Childhood Longitudinal Study, Kindergarten Class of 1998-1999. Specifically, they found that neuropsychological deficits (i.e., fine and gross motor skills) were significantly predictive of lower levels of self-control, even after controlling for various parenting measures, neighborhood disadvantage, gender, race, and prior levels of self-control. Cauffman, Steinberg, and Piquero (2005) found spatial working memory to be a significant predictor of future orientation, an element commonly included in the definition of self-control. Taken together, these results suggest that cognitive deficits may negatively affect one’s level of self-control.

The study of cognition and how it affects behavior has evolved drastically in the past century. For example, in the early 1900s, intelligence was defined and measured as a single general ability (Woodcock, 2002). Today, many scholars view intelligence as a set of interrelated cognitive abilities that contribute to measures of scholastic achievement. Intelligence tests provide an overall assessment of intellectual aptitude by measuring multiple cognitive and brain-based capacities. As such, intelligence is a complex and multidimensional construct, largely because it reflects a host of cognitive processes performed by the human brain.

Although Gottfredson and Hirschi (1990) do not take a firm position on the relationship between intelligence and low self-control in their general theory of crime, it has been suggested by some that low intelligence is in fact embedded in low self-control (Arneklev et al., 1993). Furthermore, studies have shown that an individual’s level of intelligence may affect his or her ability to control immediate impulses. For example, a meta-analysis by Shamosh and Gray (2008) revealed that higher intelligence is associated with greater ability to delay gratification (r = .23), accounting for approximately 5.3% of the variance. Moreover, a functional magnetic resonance imaging (fMRI) study of 108 healthy adults revealed that the relationship between intelligence and the ability to delay gratification is attributable in part to processes occurring in the anterior prefrontal cortex part of the brain, which is one of the last brain structures to mature functionally (Shamosh et al., 2008).

Other studies have examined the relationship between intelligence and specific elements derived from Gottfredson and Hirschi’s (1990) concept of low self-control, such as impulsivity. These studies also appear to demonstrate a modest, inverse relationship between intelligence and impulsivity, whereby reductions in cognitive performance on intelligence testing scales correspond with greater levels of impulsivity (Nussbaum, Choudhry, & Martin-Doto, 1996; Nussbaum & Courbasson, 1997; Russo, De Pascalis, Varriale, & Barratt, 2008; Vigil-Colet & Morales-Vives, 2005). For instance, Nussbaum and Courbasson’s (1997) analysis of forensic psychiatric patients and college students reported a negative relationship between intelligence and impulsivity. Russo et al. (2008) also assessed the relationship between intelligence and impulsivity. Results showed that those who scored higher on the Barratt Impulsivity Scale had reduced scores on the Wechsler Abbreviated Scale of Intelligence. Taken together, extant research suggests that deficits in intellectual functioning may put individuals at increased risk for a propensity toward impulsivity, or low self-control (but cf. Larsen, 1982).

Gottfredson and Hirschi (1990) acknowledge that levels of intelligence are weakly to moderately associated with criminal behavior. Their views on intelligence and self-control, however, are not as definitive. On one hand, they state that dimensions of self-control are “factors affecting calculation of the consequences of one’s acts” (Gottfredson & Hirschi, 1990, p. 95). They further argue that individuals with lower intelligence have fewer negative consequences to consider. As such, an individual’s level of intellectual functioning and level of self-control appear to be highly intertwined. However, Gottfredson and Hirschi go on to argue that although individual differences in intelligence may exist, “effective socialization is always possible whatever the configuration of individual traits” (Gottfredson & Hirschi, 1990, p. 96). In other words, parents are still able to influence their child’s impulses if they practice effective parenting techniques, regardless of their child’s level of intelligence.

The Current Study

Previous research has demonstrated associations between neuropsychological deficits and self-control (Beaver et al., 2007; Ratchford & Beaver, 2009). However, the degree of connection between intellectual achievement and self-control remains unknown. Given insights from the body of literature from neuropsychology, it may be argued that poor intellectual functioning may affect one’s desire or ability to exercise self-control; thus individuals who score lower on cognitive inventories (such as intelligence or aptitude tests) would be more likely to score lower on measures of self-control. Although a number of studies have examined the effects of neuropsychological functioning on self-control or the effects of intelligence on dimensions of self-control (Nussbaum, et al., 1996; Nussbaum & Courbasson, 1997; Russo et al., 2008; Vigil-Colet & Morales-Vives, 2005), there remains a lack of empirical research in the criminological literature on the association between intellectual achievement, a measure closely associated with intellectual functioning, and self-control. The current study seeks to fill this void by examining whether such a relationship exists. In this regard, both intellectual achievement and self-control are measured in early childhood and adolescence in a sample of youths, controlling for the effects of parental socialization, age, race, gender, and previous assessments of self-control. Consistent with the literature on neuropsychological functioning and self-control, it is anticipated that individuals scoring lower on intellectual achievement assessments will also be more likely to score lower on measures of self-control.

Method

Sample

Data for this study are taken from the Panel Study of Income Dynamics (PSID)–Child Development Supplement (CDS) study. The PSID is a nationally representative longitudinal study of individuals and their families developed to study socioeconomics and health throughout the life course and across generations. In 1997, the PSID created a supplemental questionnaire referred to as the CDS-I, which collected information on 2,394 families (88%), who provided information on 3,563 children ages 0 to 12 years. Five years later, the PSID created the CDS-II by reinterviewing 2,021 families (91%) from the CDS-I, providing information on 2,907 children. All data in the PSID and the CDS are collected by the Institute for Social Research at the University of Michigan (Hofferth, Davis-Kean, Davis, & Finkelstein, 1998). In both the CDS-I and the CDS-II, primary caregivers provided information, through telephone and face-to-face interviews, on various parenting measures as well as information on their child’s development. Intellectual achievement tests were also administered to children in 1997 and 2002, including the Woodcock-Johnson Revised Tests of Achievement for Reading and Math (WJ-R).

The data used in this study are a subset of the CDS, which included information on 2,104 children who were at least 5 years old in the CDS-I. The children in the sample ranged from 5 to 13 years of age in 1997, with a mean age of 8.63 years (SD = 2.28). In 2002, the children ranged from 10.4 to 19.3 years of age, with a mean age of 14.76 (SD = 2.31). There was an approximately equal distribution of boys (49.5%) and girls (50.5%). Caucasians represented approximately 45% of the sample, and non-Whites accounted for 55% of the sample. A complete list of items used for creating all measures is provided in the appendix.

Measures

Self-control

Since the PSID-CDS does not contain a direct measure of self-control at either time period (i.e., 1997 or 2002), we replicated Chapple’s (2005) measures of self-control. Specifically, primary caregivers were asked to report their level of agreement with nine statements regarding their child’s self-control (e.g., child is impulsive or acts without thinking; child is restless or overly active, cannot sit still). Individual items were rated from 1 (often true) to 3 (not true). The nine items were then summed to create a measure of self-control, with higher scores reflecting greater self-control (1997, α = .82; 2002, α = .83). This measure of self-control has adequate psychometric properties and moderate statistical validity, and factor analysis reveals that the items load on a single factor (Chapple, 2005).

Parental involvement

Primary caregivers were questioned with regard to their level of involvement in their child’s life in the CDS-I and the CDS-II. Specifically, they were asked how often in the past 6 months they had participated in various activities with their child (e.g., gone to the store with child, done arts and crafts together). These 13 items were derived from the National Survey of Families and Households along with the National Longitudinal Survey of Youth and tap into the level of cognitive stimulation children receive from their primary caregivers. Response categories for each item ranged from 1 (not in the past month) to 5 (every day). The items were then summed together, with higher scores indicating greater parental involvement (1997, α = .76; 2002, α = .82).

Parental affection

Primary caregivers reported their level of affection toward their child in the CDS-I and the CDS-II by indicating how frequently they experienced positive interactions with their child in the past month (e.g., said “I love you,” talked with child about his or her relationships). These items were derived from the Job Opportunities and Basic Skills (JOBS) Child Outcomes Study. The response categories for the six items ranged from 1 (not in the past month) to 5 (every day). The items were then summed, with higher scores indicating greater parental affection (1997, α = .82; 2002, α = .79).

Parental withdrawal

In the CDS-I and the CDS-II, parental withdrawal was measured through four items obtained from the JOBS Child Outcomes Study. This measure taps into the primary caregiver’s level of stress and aggravation associated with parenting (e.g., I feel trapped by my responsibilities as a parent, I find that taking care of my child[ren] is much more work than pleasure). Using a 5-point scale from 1 (not at all true) to 5 (completely true), primary caregivers reported their level of agreement with four statements associated with parental stress. These items were then summed, with higher scores reflecting greater levels of parental withdrawal (1997, α = .66; 2002, α = .68).

Parental rules

Responding to seven items taken from the Detroit Area Study 1997, primary caregivers reported the number of rules or limits placed on their child at both time periods. Specifically, they were asked to indicate whether they implemented a variety of rules (e.g., set limits on how much time their child can watch TV, set a time when their child does homework) with response categories ranging from 1 (never) to 5 (very often) in 1997 and 0 (no) or 1 (yes) in 2002. The seven items were then combined, with higher scores indicating more parental rules (1997, α = .80; 2002, α = .78).

Intellectual achievement

The full version of the WJ-R provides a reasonable estimate of overall intellectual ability by measuring two primary subcomponents: cognitive ability and intellectual achievement (Woodcock & Johnson, 1989). Based on the Cattell-Horn-Carroll theory (Carroll, 1993), the full version of the WJ-R taps into nine cognitive and intellectual abilities (e.g., processing speed, comprehension-knowledge, visual-spatial thinking, etc.). Of the two subcomponents that make up the WJ-R, the PSID-CDS includes only measures of intellectual achievement. Specifically, in the CDS-I, children ages 6 and older were administered four subtests: Letter-Word Identification (i.e., 57 items), Passage Comprehension (i.e., 43 items), Applied Problems (i.e., 60 items), and Calculations (i.e., 58 items). With the exception of Calculations, these subtests were then readministered in the CDS-II. For both tests, the level of difficulty increased throughout the test, with the starting point based on the child’s level of education. All items were measured as a dichotomy with scores of 0 (incorrect) or 1 (correct). The items from each subtest were then summed to provide an overall score for the Woodcock-Johnson test, with higher scores indicating greater intellectual achievement (1997, α = .89; 2002, α = .84). These scores were then standardized prior to being included in the analyses.

Control variables

Three control variables (i.e., age, gender, race) were included in this study. Age was included as a continuous variable measured in years, whereas gender and race were included as dichotomous variables with scores of 0 (male) or 1 (female) and 0 (White) or 1 (non-White).

Analyses

First, average levels of intellectual achievement were calculated for six collapsed standardized Woodcock-Johnson scores on the basis of standard deviations (i.e., standard deviation less than −2.5, between −2.5 and −1, between −0.99 and 0, between 0.1 and 1, between 1.1 and 2.5, and greater than 2.5) in both childhood and adolescence. Second, because of the non-normal distribution of the measure of self-control, Poisson regression was used to estimate the independent effects of intellectual achievement on levels of self-control in childhood and adolescence.¹ Although some may argue that Tobit regression might have been a good fit to use here (Osgood, Finken, & McMorris, 2002), Tobit regression would not allow us to account for the clustering of participants within families (Rogers, 1993). As such, we elected to use Poisson regression with Huber/White variance estimates to account for the overdispersion in the measure of self-control as well as the nesting of siblings within the same family (Rogers, 1993).

Two analytical models were created for adolescents. Model 1 contains the measure of intellectual achievement, as well as all of the parenting measures, and Model 2 includes the measure of prior levels of self-control measured in childhood. This model examines whether intellectual achievement can explain changes in self-control from childhood to adolescence. All analyses included age, gender, and race as control variables. Regression coefficients from these analyses were then used to calculate the percentage increase or decrease in self-control resulting from each standard deviation increase or decrease in the Woodcock-Johnson score ([exp(b) − 1]*100).

Results

Table 1 provides the descriptive statistics for all variables included in this study from the CDS-I and the CDS-II.

Table 1:

Descriptive Statistics for Measures in the CDS-I (1997) and CDS-II (2002)

	1997		2002
Measure	Range	M (SD)	Range	M (SD)
Parental involvement	13-65	34.14 (8.06)	13-60	30.51 (8.09)
Parental withdrawal	4-20	9.63 (3.59)	4-20	9.53 (3.74)
Parental affection	6-30	26.04 (3.82)	6-30	22.99 (4.14)
Parental rules	8-36	16.35 (5.12)	0-7	2.70 (2.21)
Total Woodcock-Johnson score (prior to standardizing)	154-583	415.53 (60.56)	120-525	302.94 (45.90)
Self-control	9-27	22.85 (3.69)	9-27	22.86 (3.67)

Note. CDS = Child Development Supplement.

The pattern of results shown in Table 2 reveals a positive relationship between self-control and intellectual achievement; the average level of self-control increases as intellectual achievement increases. For example, those who scored between −2.5 and −1.0 standard deviations below zero on the Woodcock-Johnson scale in childhood scored, on average, 20.84 (SD = 4.33) on the measure of self-control, compared to 24.07 (SD = 2.87) for those who scored between 1.1 and 2.5 standard deviations above zero. Similar results are reported for adolescents. Those who scored between −2.5 and −1.0 standard deviations below zero on the Woodcock-Johnson scored, on average, 21.51 (SD = 4.11) on the measure of self-control, compared to 24.13 (SD = 2.72) for those who scored between 1.1 and 2.5 standard deviations above zero.

Table 2:

Average Level of Self-Control (SC) for Collapsed Standardized Woodcock-Johnson Scores in Childhood (1997) and Adolescence (2002)

Woodcock-Johnson Score	SC in 1997		SC in 2002
Range	n	M (SD)	n	M (SD)
1997
<−2.5	13	20.77 (4.19)	10	19.60 (3.50)
−2.5 to −1.0	193	20.84 (4.33)	162	21.16 (4.26)
−0.99 to 0	477	22.56 (3.75)	399	22.72 (3.74)
0.1 to 1.0	444	23.51 (3.24)	398	23.43 (3.37)
1.1 to 2.5	191	24.07 (2.87)	167	23.81 (3.12)
>2.5	9	24.11 (1.69)	8	23.75 (2.66)
2002
<−2.5			23	19.52 (4.18)
−2.5 to −1.0			244	21.51 (4.11)
−0.99 to 0			667	22.83 (3.58)
0.1 to 1.0			421	23.49 (3.36)
1.1 to 2.5			173	24.13 (2.72)
>2.5			13	24.77 (1.96)

Table 3 displays the results from the Poisson regression models for measures of self-control in childhood and adolescence. Specifically, columns 1 and 2 in Table 3 reveal that greater intellectual achievement is associated with greater self-control during childhood (b = .042, SE = .005), when controlling for race, gender, age, and parenting measures. Specifically, self-control increased 4.3% for each standard deviation increase in the Woodcock-Johnson score ([exp(.042) −1]*100 = 4.3).

Table 3:

Poisson Regression Analyses of the Relationship Between Intellectual Achievement and Self-Control in Childhood (1997) and Adolescence (2002)

	Childhood (SC in 1997)		Adolescence (SC in 2002)
	Model 1		Model 1		Model 2
Variable	B (SE)	z	B (SE)	z	B (SE)	z
1997
Race	.012 (.012)	1.02	−.000 (.013)	−0.01	−.005 (.011)	−0.48
Gender	.042 (.010)	4.30***	.019 (.011)	1.70	−.003 (.010)	−0.31
Age	.001 (.002)	0.42	.009 (.003)	3.12**	.007 (.002)	2.78**
Parental involvement	−.000 (.001)	−0.04	.001 (.001)	0.70	.000 (.001)	0.16
Parental withdrawal	−.011 (.002)	−6.93***	−.007 (.002)	−3.84***	−.001 (.002)	−0.79
Parental affection	.006 (.002)	2.65**	.000 (.002)	0.03	−.002 (.002)	−1.18
Parental rules	.002 (.001)	1.95	.001 (.001)	0.93	−.001 (.001)	−0.54
Intellectual achievement	.042 (.005)	7.67***	.038 (.006)	5.82***	.014 (.006)	2.58*
Self-control					.025 (.002)	12.31***
2002
Race			.036 (.009)	4.10***	.031 (.008)	3.93***
Gender			.015 (.008)	2.00*	−.005 (.007)	−0.65
Age			.007 (.002)	3.56***	.005 (.002)	2.88**
Parental involvement			.002 (.001)	3.06**	.002 (.001)	2.93**
Parental withdrawal			−.011 (.001)	−9.60***	−.008 (.001)	−7.20***
Parental affection			.000 (.001)	0.40	.000 (.001)	0.43
Parental rules			.004 (.002)	1.95	.005 (.002)	2.57*
Intellectual achievement			.038 (.004)	9.09***	.026 (.004)	6.59***
Self-control 1997					.020 (.001)	15.63***

p < .05. **p < .01. ***p < .001.

The second section of Table 3 presents the estimated coefficients and standard errors for self-control during adolescence. As shown in Model 1 (i.e., columns 3 and 4), greater intellectual achievement in both childhood (b = .038, SE = .006) and adolescence (b = .038, SE = .004) is significantly associated with greater self-control in adolescence, controlling for race, gender, age, and parenting measures. More specifically, self-control increases 3.9% for each standard deviation increase in the Woodcock-Johnson score measured both in childhood and adolescence.

As evident in columns 5 and 6 (i.e., Model 2), the relationship between greater intellectual achievement in childhood (b = .014, SE = .006) and adolescence (b = .026, SE = .004) remains significantly associated with self-control in adolescence, even after controlling for prior levels of self-control in childhood. As such, after controlling for prior self-control scores, each standard deviation increase in the Woodcock-Johnson score measured in childhood and adolescences is associated with a 1.4% and 2.6% increase in self-control in adolescence, respectively.

Table 3 also reveals that two out of the four parenting measures in childhood were significantly related to variations in self-control. Specifically, parental affection and parental withdrawal during childhood were significantly related to self-control in childhood. Parental withdrawal during childhood was also significantly related to self-control in adolescence, except when controlling for prior measures of self-control. On the other hand, parental involvement and parental rules during childhood were not significantly related to variations in self-control in childhood or adolescence. With regard to adolescents, parental involvement and parental withdrawal during adolescence were significantly associated with self-control in adolescence, even after controlling for prior measures of self-control in childhood. On the other hand, parental affection and parental rules during adolescence were not significantly related to variations in self-control in adolescence.

Discussion

The current study extended previous research by exploring the relationship between intellectual achievement and self-control. Although some previous research has suggested that parental socialization is the primary influence in the development of self-control (Burton et al., 1995; Hay, 2001; Lynskey et al., 2000; Perrone et al., 2004; Unnever et al., 2003), more recent research points to the importance of neuropsychological factors (Beaver et al., 2009; Beaver, DeLisi, Vaughn, Wright, & Boutwell, 2008; Beaver, Wright, DeLisi, & Vaughn, 2008; Wright, Beaver, DeLisi, & Vaughn, 2008), brain structure (Toga & Thompson, 2005), and executive functioning (Beaver et al., 2007; Ratchford & Beaver, 2009). The purpose of the current study was to expand this research by examining the degree to which self-control is linked with an individual’s level of intellectual achievement.

Overall, the results revealed that intellectual achievement is significantly related to self-control in childhood and adolescence. Specifically, lower scores on the Woodcock-Johnson test were associated with measured decreases in self-control, when controlling for various parenting measures, age, race, and gender. These results remained significant even after controlling for prior measures of self-control obtained 5 years earlier. Thus, results from the current study provide further support for the suggestion that self-control may be interconnected with functions in the prefrontal and temporal cortex of the brain, such as intellectual achievement (Beaver et al., 2007; Ratchford & Beaver, 2009).

It is possible that intellectual achievement and self-control are linked because of the same genetic factors operating on both. Although data limitations of the current study do not allow for the exploration of this possibility, other studies assessing constructs related to self-control (e.g., impulsivity, inattentiveness, and other self-regulatory problems) and intellectual functioning have shown that their covariation may be attributed to common genetic effects (Kuntsi et al., 2004; Paloyels, Rijsdiijk, Wood, Asherson, & Kuntsi, 2010; Polderman et al., 2006, 2009). For example, Kuntsi et al.’s (2004) analysis of data from two birth cohorts from the E-Risk Longitudinal Twin Study (N = 1,116) showed that 91% of the phenotypic correlation between impulsivity and IQ (r = −0.31) could be attributed to a shared genetic factors. Future studies should continue to use twin samples and follow behavioral genetic techniques to decompose the variance and covariance in self-control and intellectual functioning to determine the extent to which their overlap is attributed to shared genetic factors operating on both.

With regard to parenting measures, the results from the current study are similar to previous research in that parental withdrawal appears to be a consistent predictor of self-control in childhood and adolescence (Beaver et al., 2007; Beaver, Wright, & Maume, 2008; Wright & Beaver, 2005). In addition, the results appear to suggest that different aspects of parenting influence self-control at different developmental stages. For example, parental affection appears to be an important factor influencing self-control in childhood yet not in adolescence. On the other hand, parental involvement appears to be an important factor influencing self-control in adolescence but not in childhood. It may be that parent–child relationships are more relevant to children during childhood than in adolescence. Finally, similar to previous studies, the presence of more parental rules does not appear to have a significant effect on self-control (Beaver et al., 2007; Beaver, Wright, & Maume, 2008; Cretacci, 2008).

These findings are congruent with previous research that has shown parenting measures to be weakly or inconsistently related to measures of self-control. For example, Wright et al. (2008) found little evidence to suggest that parenting, in and of itself, is a causal factor in the development of childhood self-control. In contrast, they attribute much of the variation in childhood self-control to genetic linkages shared between parents and their offspring. Known as passive gene-environment correlation, it is possible that the relationship between ineffective parenting and a child’s level of self-control is a result of biological parents’ providing their child with both their genetic material as well as the environment in which they are raised (Scarr & McCartney, 1983). Therefore, the relationship between parenting and childhood behavior may result from common genetic factors operating in both the parent and the child.

In addition to the data limitation preventing further analyses with respect to the genetic overlap between intellectual achievement and self-control, the current study has four additional limitations. The first limitation is related to the measures of self-control and intellectual achievement. Unfortunately, the PSID-CDS does not contain a direct measure of self-control, such as the Grasmick, Tittle, Bursik, and Arneklev (1993) scale. As such, we recommend replicating these analyses using different data sets with more direct measures of self-control. Also, the PSID-CDS administered only the intellectual achievement subtest of the WJ-R. Had the full WJ-R been administered, a more complete measure of intelligence would have been available to study its relationship with self-control. Furthermore, the WJ-R test provides a quantitative measure of intellectual achievement while ignoring the structural composition of the brain. As a result, including various neuroimaging techniques (see Toga & Thompson, 2005) and administering a more expanded battery of neuropsychological tests would strengthen the investigation of both the structural and functional aspects of the brain that may be involved in the development of self-control.

For example, a study using fMRI data showed that individuals who scored high on intelligence tests displayed a greater level of activity in the anterior prefrontal cortex part of the brain and the most restraint in delayed gratification tests (Shamosh et al., 2008). Future studies should continue to use brain imaging techniques to further explore the relationship between intellectual ability and self-control. In addition, it has been suggested that the effects of neuropsychological functioning on self-control may be underestimated because of different regulatory systems that exist for cognition, emotion, and motivation (Bass & Nussbaum, 2010; Levi, Nussbaum, & Rich, 2010). This line of evidence suggests that distinct neuropsychological measures of cognitive, motivational, and emotional regulation are needed.

Second, this study included only primary caregiver reports regarding specific parenting behaviors and youth levels of self-control. The reliance on a single source of information concerning both parenting techniques and youth self-control introduces the possibility of internal validity bias. Although the PSID-CDS has information from teachers and secondary caregivers, the abundant amount of missing data inhibited the inclusion of those measures in analyses. Including these additional sources of information would have been beneficial as a means of validating the measures of parenting and self-control gathered from primary caregivers. In addition, although our results highlight the connection between intellectual achievement and self-control, we do not consider alternative hypotheses. For example, it is possible that children with lower self-control exhibit more problem behaviors that interfere with their ability to achieve academically.

Finally, it was not possible to include any subsequent measure(s) of criminal or delinquent behavior for the PSID-CDS sample. In a recent meta-analysis, Ogilvie et al. (2011) found that poor neuropsychological executive functioning was strongly associated with criminality and various forms of antisocial behaviors. Furthermore, McGloin, Pratt, and Maahs (2004) have reported that IQ is indirectly related to delinquency through self-control. The current investigation, however, could not assess the complex interrelationship between deficits in intellectual functioning, self-control, and various forms of maladaptive and criminal behaviors. It is suggested that future studies use a behavioral genetic design to further examine this complex relationship.

Despite these limitations, the current study is part of a recent trend in the literature that seeks to further examine the origins of Gottfredson and Hirschi’s (1990) concept of low self-control. Whereas the original formulators of the theory attribute the development of self-control to parenting techniques, results from this study, as well as previous research, suggest that self-control may be part of a broader complex of higher-order brain-based functions housed in the prefrontal cortex of the brain, all of which have genetic foundations.

Footnotes

Appendix

Authors’ Note:

The authors would like to thank the editor and the anonymous reviewers for their constructive comments and insightful suggestions.

Notes

Danielle Boisvert (PhD, University of Cincinnati) is an Assistant Professor in the College of Criminal Justice at Sam Houston State University. Her research interests include life-course/developmental criminology, biosocial criminology, and behavioral genetics.

William Stadler, PhD, is a rehabilitation and behavioral health solution designer for Cerner Corporation. His research interests include assessment and process improvement, bioinformatics, and the intersection between behavioral health and adverse events.

Jamie Vaske, PhD, is an assistant professor at Western Carolina in the Department of Criminology and Criminal Justice. Her research interests include biosocial criminology, gender and crime, and quantitative methods and analyses.

John P. Wright is an associate professor of criminal justice in the School of Criminal Justice at the University of Cincinnati. He has published broadly on biological influences and criminal behavior.

Matthew Nelson is a doctoral student in the Criminology, Law, and Society Program at George Mason University. He received his MA in criminal justice from the Pennsylvania State University in 2011. His research interests include research methods, statistics, and offender reentry.

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