This is the final draft, not the in-press version In-press at Early Education & Development
Parenting Practices in Preschool Leading to Later Cognitive Competence: A Family Stress Model
M. Angela Nievar and Amanda Kay Moske University of North Texas
Deborah Jean Johnson Michigan State University
Qi Chen
Abstract
Research Findings: This study investigates the effect of the early home environment on self-regulation in preschoolers, and how self-regulation relates to later school achievement, while taking into account family resources. Participants were part of the National Institute of Child
Health and Human Development’s Study of Early Child Care and Youth Development (NICHD
SECCYD). Our model tested paths from family income and maternal depression through parenting to dyadic and child outcomes, including attachment, self-regulation, and child cognitive outcomes in the first grade. Findings indicate that family income and maternal depression had a substantial effect on parenting practices. Children whose parents did not display negative affect towards them during episodes of depression were more likely to maintain healthy attachment styles. Parenting, which was directly affected by family income, was the most important predictor of children’s cognitive development.
Practice or Policy: Regarding implications for practice, this research indicates that parents who provide a safe and stimulating environment for their children, despite limited
Parenting Practices in Preschool Leading to Later Cognitive Competence: A Family Stress Model
Many parents have struggled to understand why their apparently intelligent and typically developing children have difficulty succeeding in the early elementary grades and beyond. Parents are often unaware that intelligence alone is not sufficient to ensure success in the school setting (NAEYC, 2000). Researchers in the fields of psychology, human development, and education have shown that success in school is influenced by motivation (Linnenbrink & Pintrich, 2002; Wentzel & Wigfield, 1998), social skills (Capara, Barbaranelli, Pastorelli, Bandura, & Zimbardo, 2000), self-concept (Ray & Elliott, 2006; Valentine, DuBois, & Cooper, 2004), self-efficacy (Metallidou & Vlachou, 2007), self-regulation (Martinez-Pons, 1996), and other factors. In recent years, there has been growing interest among researchers and educators in the related concepts of emotional regulation, self-control, and self-regulation. Research has shown that children who can regulate their attention and emotions, control their behavior, and delay gratification perform better in school and in other important arenas of life (Shonkoff & Phillips, 2000; Mischel, Shoda, & Rodriguez, 1989).
In the classic marshmallow studies, Mischel and his colleagues demonstrated that four-year-old children who could control their impulse to eat a marshmallow performed better than their peers who could not delay gratification, when they were tested a decade later (Mischel et al., 1989). Children who could delay gratification at age four were more attentive, planful, and able to cope with stress as adolescents. More current research shows that self-control buffers the impact of risk factors on later adjustment and behavior (Lengua, 2003).
Why are some children able to maintain self-control while others struggle?
behavior (Sanson, Hemphill, & Smart, 2004). In addition, children’s experiences help them learn to control attention, emotion, and behavior; several studies have documented the important role that parents play in helping their children to self-regulate (Brown & Dunn, 1996; Denham, Zoller, & Couchoud, 1994; Denham, von Salisch, Olthof, Kochanoff, & Caverly, 2003;
Schwartz, Thigpen, & Montgomery, 2006). Theoretically, children who have developed a secure relationship with their parents may depend on their internal working model of the attachment relationship to reframe a stressful situation and regulate their emotions (Cassidy, 1994). Researchers have focused less attention on the role of the early environment in promoting regulation of behavior, but some studies suggest that this contributes to the development of self-regulation as well (Bronson, 2000; Kopp, 2000; Supplee, Unikel & Shaw, 2007; Wilson & Gottman, 1996).
The purpose of this study is to investigate how early experiences in the home relate to self-regulation in three-year-old children, and how self-regulation relates to later school achievement, while taking into account the early environment. The conceptual model for our study is presented in Figure 1. We tested this model with data from the NICHD Early Child Care Research Network (NICHD ECCRN, 2005). As shown in the model, the child’s ability to regulate their behavior may develop through interactions with their caregivers, particularly in the relationship with the primary caregiver (Braungart-Reiker, Garwood, Powers, & Wang, 2001; Davies, Harold, Goeke-Morey, & Cummings, 2002; Kochanska, 2001). In this study, we test the effect of family resources on parenting and the early home environment, which in turn are
--- Figure 1 about here --- Applications of Family Stress Theory
Family stress theory suggests that contextual stress overloads family members’ abilities
to cope, thus resulting in poorer relationship quality and functional capacities over time in comparison with families under less stress (McCubbin et al., 1980). Several studies have validated the effect of family resources on maternal stress, parenting skills, and parent availability, and in turn, how these factors affect child development (Ardelt & Eccles, 2001; Brody, McBride, & Kim, 2002; Nievar & Luster, 2006). Economic hardship influences individual well-being and family functioning through the strains and pressures they create in daily living (i.e., being unable to purchase necessary goods and services, making significant cutbacks in daily expenditures because of limited resources, and being unable to pay monthly bills). A study of inner-city adolescents and their mothers indicated that stress and pressures associated with dangerous low-income neighborhoods may explain successful mothers’ encouragement and diligence in preventing problem behaviors (Ardelt & Eccles).
reducing maternal stress, which in turn, predicted parenting practices, and finally child outcomes during the school years (Conger et al., 2002; Lee, Lee, & August, 2011; Nievar & Luster, 2006). Thus, resources are important to parenting success. However, little research has examined the family stress model with a normative sample, and even fewer studies have examined this model during the early years of life.
More frequently research has investigated parenting as a predictor of later behavior. Bowlby (1973) and Baumrind (1967) found children exposed to positive family experiences were more likely to grow into trusting and self-reliant adults. Yet, children in poverty are more likely to have negative family experiences. Parents with few economic resources are less likely to express warmth, nurturance, and responsiveness, and to be less consistent in their caregiving than economically advantaged parents (Easterbrooks & Graham, 1999; Hanson, McLanahan, & Thomson, 1997; McLoyd, 1998). Economically advantaged parents tend to spend more time talking to and reading to their infants and preschoolers than low-income parents (Bradley & Corwyn, 2003). Inadequate physical environments, associated with poverty, often contribute to child stress and subsequent behavioral problems, such as hyperactivity, helplessness, low frustration tolerance, and distractibility (Evans, 2006).
average, are more negative, guilt-inducing, critical, unsupportive and intrusive with their children and demonstrate more negative affect than non-depressed parents (Cummings & Davies, 1999; Cummings, Keller, & Davies, 2005; Dix & Meunier, 2008).
Although research generally indicates negative outcomes for infants with depressed mothers, there is some variation in effects on children. Among clinically depressed mothers, mothers with securely attached children often appeared to limit their expression of depressed affect and anxiety when observed with their children, and maternal behavior predicted
children’s security of attachment (Radke-Yarrow, 1991; Teti, Heaton, Benjamin, & Gelfand,
1995). In general, however, the literature suggests that maternal depression predicts insecure attachment and lower levels of self-regulation among children (Atkinson et al., 2000; Campbell et al., 2004; Hoffman, Crnic, & Baker, 2006; Raikes & Thompson, 2006), and rates of maternal depression are highest among families with low income and few resources.
Limited educational opportunities and a lower social status cause many low-income parents to feel a sense of powerlessness and lack of influence in their formal relationships outside of the home. For example, at work they must obey rules of others in positions of power and authority. When they return home, their parent-child interactions seem to duplicate these experiences with the parent in the position of authority (Pinderhughes, Dodge, Bates, Pettit, & Zelli, 2000). A sense of powerlessness outside of the home and higher levels of stress combined with a stronger belief in the value of physical punishment may contribute to a greater use of coercive discipline among families in poverty.
Egeland, Carlson, & Collins, 2005). Furthermore, studies that include low-income families often relegate income to a control variable, concealing developmental processes specific to
low-income families (Evans, 2004; Johnson, Jaeger, Randolph, Cauce & Ward, 2003). Contexts may create different environments that fit with parental expectations for young children (Ogbu, 1985; Chen & French, 2008). Obviously, the contexts of children in poverty widely differ from those of their middle-class peers due to fewer resources and greater stress on the parents and children; processes may differ based on social class (Nievar & Becker, 2008). Previous studies of family stress often have a sample limited to at-risk families, but in this investigation we test family stress theory (McCubbin et al., 1980) in a national sample, viewing limited resources as a stressor and examining factors that mediate that stress to improve child outcomes. It is hypothesized that positive experiences and resources in the early years may help children overcome difficult circumstances, allowing them to succeed despite the odds.
Method Participants
Data source. The NICHD ECCRN longitudinal study was developed to answer questions about child care experiences for young children (NICHD, 2005). Domains of study included
children’s health, language, cognitive growth and social-emotional development. Although not a
nationally representative sample, demographic status of participants is roughly comparable to that of the nation as a whole (n = 1364). The sample was somewhat restricted, however,
excluding a number of family types and contexts, including teen mothers less than 18 years old, families living in dangerous neighborhoods, and children with disabilities at birth.
differences related to attrition, however, on income, ethnicity, depression, parenting subscales, attachment, self-regulation, or cognitive outcomes. There were some slight changes in racial proportions that were not significant. For example, in the original data collection, 80% of the children were classified as White, which included Hispanics. In our sample, 82% of the children were classified as White. Most of the children had a father figure; 86% of the mothers reported living with a partner or spouse. Fifteen percent of the families were living in poverty. Additional demographics are presented in Table 1.
Measures
Maternal depression. The Center for Epidemiological Studies Depression Scale (CES-D) was used as a measure of maternal depression. The CES-D is a widely-used measure of depression in non-clinical samples. When the participating child was 15 months old, mothers reported the occurrence of 20 symptoms over the past week. Sample items are: “I felt sad” and
“I was bothered by things that usually don’t bother me.” Possible responses to the items were: 0
(rarely or none of the time--less than 1 day), 1 (some or a little of the time—1 - 2 days), 2 (occasionally or a moderate amount of time—3 - 4 days), and 3 (most or all of the time—5 - 7 days). According to Radloff, raw scores ≥16 indicate depressive risk (1977). Within our sample, 16% exhibited depression risk. Previous research assessing community samples of men and women had equal or higher rates of depressive risk, ranging from 16-20% (Ritchey, Gory, Fitzpatrick, & Mullis, 1990). Cronbach’s alpha was .85 in the general population (NICHD, 1999a; Radloff, 1977; Roberts, 1980); however, in this sample it was .90 at 15 months.
self-report survey items, is one of the most widely used measures of the home environment. The Research Triangle Institute created three subscales from the HOME for the 15-month data collection to enhance reliability, including Home Enrichment, Positive Parenting, and Lack of Negativity (Research Triangle Institute, 1993). The three subscales were derived from 45 items using exploratory factor analysis with varimax rotation. Based on analyses results and a close examination of the items, the three-factor solution with some items eliminated was optimal. Items included did not measure the safety of the physical environment or family structure, but were limited to interactions or materials directly provided by the parent. The retained items loaded .40 or higher on each of the factors. Cronbach’s alphas were, respectively, .56, .54, and .69.
Each item is scored in binary fashion (yes/no), resulting in somewhat lower alphas than traditional Likert scales. For example, one of the 8 items on the Home Enrichment Subscale is,
“Parent provides toys for child.” Parents who provided toys for their child were given a “1,” and
parents who did not provide toys for their child were given a “0.” The subscale ranged from 0 to
8. An example item from the Lack of Negativity Subscale (ranged from 0 to 6) is, “Parent neither slaps nor spanks the child during visit.” One of the items on the Positive Parenting Subscale (ranged from 0 to 6) is, “Parent’s voice conveys positive feelings toward child.” A high score reflects a more positive home environment for all three scales.
the amount of income equal to federal poverty guidelines for participants, based on family size and location. For example, housing costs are generally higher in urban areas than in rural areas. These ratios were averaged for the purpose of this study, and trimmed at 7 and above to reduce skewedness.
Attachment security in early childhood. The Attachment Behavior Q-Set (AQS) (Waters & Deane, 1985) measured attachment security at 24 months. During a 2-hour home visit, trained observers made notes on child behavior in naturally occurring or semi-structured situations. Immediately afterwards, observers sorted 90 items pertaining to the mother-child relationship into a rank order on a 9-point Likert scale. To determine whether an infant’s behavior is organized in ways similar to the secure base phenomenon, scores on each of the 90 items are correlated with a standardized sort with 90 items indicating optimal use of the
caregiver as a secure base. This correlation results in a number ranging from –1 to +1, with +1 representing the most secure attachment possible.
In this study, average interrater reliability was .92, based on correlation of item rankings between observers. Before observers were allowed to rate items for participants, they went through an extensive training process administered by Brian Vaughn, an expert on attachment and q-sort methodology. Observers needed to have .55-.65 correlation between exact scores coded by Vaughn; they also needed to have no more than 10% of items coded more than 2
categories apart from Vaughn’s coding. Periodic tests retested reliability using videotapes after 2
The mean Attachment Q-Set score in this sample was lower than some typically developing samples. According to NICHD documentation, there were some discrepancies between sites due to either measurement methods or population differences. Post-hoc Tukey tests indicated that sites from three states were significantly different than average, with two of the three states being significantly lower than average. Although the total mean was lower than
Waters’ (1995) normal sample, a test for differences between means indicated that there were no
significant differences between Waters’ sample (M = .33) and the NICHD sample (M = .33.
Self-regulation. Researchers videotaped and coded children’s performance on a self -regulation task at 36 months. In this task, a researcher in a laboratory setting asked the
participating child to delay handling an attractive toy while their mother was occupied filling out surveys. Mothers were instructed to tell the child they were busy if the child requested help and to continue doing so with any additional requests from the child. After about an hour of free play, the researcher showed the child a new and exciting toy, a crocodile that moved quickly across the room. The researcher showed the child how to operate the toy but then told them not to touch the toy until given permission. Videotaping occurred during a waiting period of 2.5 minutes.
Coders scored videotapes of children at a central location with computer-based coding at one second intervals. Behaviors such as the length of time the child refrained from touching the toy following initial instructions from the experimenter, the amount of time the child spent actively playing with the toy after being asked not to touch it, the amount of time the child spent
tentatively and/or furtively touching the toy, and the child’s focus of attention (social, elsewhere)
engagement, total away time, focus on toy, social focus, and a reverse-scored measure of time spent actively playing with the toy. These behaviors accounted for attempts to self-regulate and complete the task, in addition to the measure of self-control (time spent playing with the toy).
This five-item measure had an internal consistency reliability of .79. Coding reliability was measured in two ways. Most of the 5 behavior focus codes used percentage agreement. Overall percentage agreement across behaviors was .84. The latency to first active engagement was an exception to percentage agreement due to the repeated measurement over time. This focus code used Pearson correlation for measuring interrater reliability, r = .97.
Cognitive outcomes in first grade. This measure is a computed average of the standard scores of subscales from the Woodcock-Johnson Psycho-Educational Battery-Revised (WJ-R) (Woodcock & Johnson, 1989; Woodcock, 1990). The WJ-R tests cognitive abilities, aptitude, and achievement. Subscales included applied problems, incomplete words, letter-word identification, memory for sentences, and picture vocabulary. These subscales measured
cognitive factors and achievement such as short-term memory, comprehension, basic knowledge, and auditory processing. All subscales were standardized with a mean of 100 and standard deviation of 15.
Results
The purpose of this study was to test a model explaining interrelations between the early home environment and parenting, self-regulation in preschool, and cognition in first grade. Preliminary analyses included correlations of study variables (see Table 2) and tests of mediation (see Table 3). Longitudinal models tested the pathways between parent and family
characteristics, the home environment at 15 months, children’s attachment security at 24 months,
for site location, ethnicity and race, and child gender on endogenous variables. Data Imputation
Structural equation modeling requires a set of complete data. Missing data are likely to result in less accurate computations than when data are replaced by estimation of maximum likelihood (Little & Rubin, 1989). Missing data also make inferences to the general population less meaningful. To address missingness in data, the models were analyzed using the full information maximum likelihood (FIML) method under Mplus, which applies the expectation maximization algorithm described in Little and Rubin (2002). This method estimates the best possible value for missing data through multiple iterations, while comparing cases with missing data to other cases with complete data. Only cases that had a child outcome variable, either self-regulation or attachment security, and at least one of the cognitive ability tests, were included in these analyses. Two additional cases were deleted because they were missing data on over 50% of the study variables. Only 1.9% of the data was actually computed as missing data, resulting in 1023 complete cases.
Preliminary Analyses
All preliminary analyses were conducted using SAS Release 9 (2003). The correlation matrix is presented in Table 2 along with means and standard deviations for study variables. According to the cutoff values of 2 for skewedness and 7 for kurtosis (West, Finch, & Curran, 1995), only the scores for positive parenting were skewed. After identifying potential outliers and examining the frequencies and distribution of the skewed variable (Barnett & Lewis, 1994),
all potential outliers’ responses on positive parenting were found to be within reasonable ranges
Intercorrelations of factors of the Home Observation for Measurement of the
Environment (HOME) ranged from small to medium in effect size (Cohen, 1988). All three HOME factors, Enrichment, Positive Parenting, and Lack of Negativity, showed medium effects in correlations with income. Although the subscales of the HOME were based on a continuous measure, in this sample the data violated assumptions of normality and were treated as ordinal variables. Although all correlations were significant, driven by the large sample size, only the relation between attachment and self-regulation did not reach an effect size considered to be
substantive according to Cohen’s (1988) convention.
To assess mediation based on the literature and our theoretical model, we used the most common method for testing mediation in psychological research (Baron & Kenny, 1986; Judd & Kenny, 1981; Kenny, Kashy, & Bolger, 1998). First, we tested attachment as a mediator between subscales of the HOME (Enrichment, Positive Parenting and Lack of Negativity) and
Structural Equation Models
Based on our theoretical model, we tested linkages among income and maternal
depression, children’s home environment, and child outcomes (n = 1023). Structural equation
modeling (SEM) allows for simultaneous entry of factors into a regression-like analysis, but it also explains direct and indirect paths unlike linear regression models. SEM analyses were conducted using Mplus (v.6.12, Muthén & Muthén, 1998-2010) to test the hypothesized model. As the scores of positive parenting were skewed, maximum likelihood estimation with robust standard errors (MLR) was used as the estimator in analyses, because it is robust to non-normality (Muthén & Muthén). Furthermore, nonnon-normality conditions had nearly no effect on the standard errors of parameter estimates especially with sample sizes larger than 100 (Lei & Lomax, 2005). Given the MLR estimator used and the sample size of 1,023, the parameter
estimates and test of significance should be relatively unaffected even with the skewed variables. The model investigated in this study consisted of one latent variable measured by three manifest indicator variables and five manifest variables. The latent variable, Parenting, was measured by constructed factors of the HOME focusing on parental provision of social and cognitive experiences: Enrichment, Lack of Negativity, and Positive Parenting. The five manifest variables included income, depression, attachment, self-regulation, and cognitive outcomes. Income and maternal depression were co-varying exogenous variables. As expected, maternal depression and income were significant predictors of the latent parenting variable, which in turn predicted self-regulation, attachment, and cognitive outcomes. Standard deviations and
correlations for the study’s manifest variables were presented in Table 2.
statistic is sensitive to sample size and departures from multivariate normality, and will often result in the rejection of a well-fitting model (Joreskog & Sorbom, 1989). Additional goodness of fit indices are provided in Table 4. The value of the CFI exceeds .9 in absolute magnitude and the RMSEA is below .05, indicating an acceptable fit between the model and data. The distribution of normalized residuals was symmetrical and centered near zero.
Figure 2 displays the model with standardized path coefficients. Both standardized and unstandardized coefficients and significance levels are shown in Table 4. Only one path was not statistically significant: depression to attachment. The R² values indicated income and depression accounted for 51% of the variance in parenting; depression and parenting accounted for 11% of the variance in attachment; parenting and self-regulation accounted for 33% of the variance in cognitive outcomes. In this model, the effect of income on cognitive outcomes was reduced from a correlation of .41, a medium effect, to a direct path of .13, a small effect, due to mediation of parenting and self-regulation.
Scale (CES-D) because this widely-used self-report measure has been validated across racial-ethnic groups (Roberts, 1980).
Although income and race-ethnicity are related, the process model itself did not vary significantly by ethnicity in our multi-group test of African Americans and European Americans. Only African Americans and European Americans had sufficient numbers to test for significant differences in process between groups. Although process models did not vary, individual mean differences in parenting exist by racial-ethnic groups, even with multiple demographic controls. Thus, the exclusion of controls on the exogenous variables of income and maternal depression is appropriate while controls were used on all endogenous variables within the model. As apparent from the model (Figure 2) and mediation analyses (Table 3), parenting variables have the strongest mediation effect on cognitive outcomes.
--- Figure 2 about here ---
Discussion
The purpose of this study was to test a process model with stressors affecting parenting and the home environment, which in turn may affect attachment, self-regulation, and cognition. This study takes into account the association of income with psychosocial and environmental variables as a test of family stress theory (Evans, 2004). By including income and associated variables, we are able to view processes of development within the context of the environment.
In general, the model explained the development of mother-child attachment and
depression predicted parenting practices, which in turn predicted toddler attachment, self-regulation, and cognitive outcomes during the preschool years.
It is interesting, and somewhat unexpected, that attachment was not a substantive
predictor of self-regulation in the model. We hypothesized that a secure attachment relationship would assist the child in controlling their impulses as preschoolers. In fact, one of the strategies that children often use in trying to resist the attractive object during the self-control procedure is that of looking to their mother for support. We theorized, therefore, that having a secure base present (the mother) would help children in this experimental setting. Yet, a contrasting tenet of attachment theory states that avoidantly attached children learn to self-regulate their negative emotions at an early age to avoid anger and negative repercussions from their caregivers (Cassidy, 1994). Thus, this may confound the relation between attachment and self-regulation skills. Although attachment was significantly correlated with self-regulation (r = .16, p < .01), there is no direct path between these two variables in the process model.
Mediational Processes
Observation for the Measurement of the Environment (HOME). The inclusion of these highly influential parenting factors may reveal how some parents buffer their children from their depression.
Certainly, other factors are important in the development of self-regulation that also involve parenting and predict attachment. Our measure of the home environment, which includes positive parenting, the avoidance of negative discipline, and enriching, educational
activities, explained a substantial amount of variance in children’s self-regulation. As parents
work with their children in educational activities, they teach children to delay instant
gratification and attend to the task at hand. Parents who best facilitate children’s developing self-regulation are more likely to be sensitive and supportive in their parenting role. Children who are unafraid of negative responses from their parents are more likely to accept parental instruction at this early age, and more likely to explore their environment within reasonable bounds that the parent has set.
Self-regulation does, however, have components such as the ability to focus related to child temperament, and we note a child effect on cognitive outcomes through self-regulation in
our test of mediation. Simply put, it is not only parenting that predicts children’s cognitive
abilities, but the child’s innate ability to self-regulate also plays a part. This is consistent with literature on early temperament and later self-regulation (Sanson, Hemphill, & Smart, 2004). The effect of self-regulation on cognitive outcomes is, however, minimal, when contrasted with the combined effect of parenting and family resources.
Family Resources and Resilience
cognitive child development. Certain aspects of the environment associated with poverty, such as crowding and safety issues, may influence the process of children’s development (Evans, 2006; Gershoff, Aber, Raver, & Lennon, 2007). Other factors associated with poverty, such as crime, noise, and pollution, may not be an issue in rural areas. However, unsafe housing is associated with poverty in both rural and urban settings (Berger, 2004).
Safety hazards in the home prohibit infants from being free to fully explore their
that they cannot touch everything or go anywhere; self-control becomes an important component for safely navigating the neighborhood or community.
Many parents raise well-adjusted and capable children regardless of income or
neighborhood effects. We found that an enriched environment mediates over one fourth of the
effect of income on children’s cognitive outcomes. More importantly, the final model shows
only a small effect of income on cognitive outcomes when family and child socialization processes are taken into account. The simple association between income and cognitive outcomes was over 3 times as large as the final model’s path between income and cognitive outcomes. This finding indicates that some parents actively foster resilience in low-income families. Parents who sing songs, tell stories, name objects, teach counting, and provide
opportunities for their children to draw pictures foster cognitive development even in developing countries where expensive play materials are often unavailable (Bornstein & Putnick, 2012). Similarly, parents in the United States can foster cognitive development with limited financial resources.
We also found that harsh parenting mediated the relation between depression and
their caregivers, than in later years, when that trust may be well-established and the meaning of disciplinary methods may be more easily understood.
Limitations and Future Directions
Certain limitations are inherent in secondary data analysis. In this particular study, the diversity of measurement methods and the quality of measures selected is above average. Yet, multiple measures for some of the constructs may have yielded different findings. This study focused on cognitive outcomes and self-regulation, yet our measure of self-regulation is based on an experimental procedure conducted at one time-point 3 years previous to children's entry into first grade. Having some additional observational or mother report measures of self-regulation at additional times would have been preferred. Our methods also presume a single direction of effects and base family effects on maternal parenting alone. We acknowledge the importance of siblings and fathers in the family environment. Indeed, the family environment is complex, and children may affect their parents’ behavior as well as parents affecting their children (Cox, 2010).
We also wish to note that this study does not examine all of the many factors responsible for cognitive development. Our goal is not to create an overarching model of cognitive
development, and there are certainly alternate explanations for children’s cognitive abilities. Instead, our focus is on the development of self-regulation in the context of the family
importance, the sample excludes families living in unsafe neighborhoods, infants with perinatal problems requiring extensive hospitalization, underage mothers, and mothers with insufficient English skills. In addition, those who initially declined to participate (42%) may have had more difficulties in their families than those who agreed to participate. Thus, the sample is somewhat lower in risk level than the population as a whole.
Another limitation of the data was the high percentage of European American families. We controlled for ethnicity and race; however, we would like to note that previous research has found differences in the meaning of parenting and attachment styles across cultures (e.g., Rothbaum et al., 2000). In general, these findings should not be applied to all groups; earlier research has indicated some cultural differences with respect to parenting between African Americans and Euro-Americans in this sample (NICHD, 1999b; Nievar & Moske, 2009). Although we found no differences in our process model between African Americans and
European Americans, future research could investigate the culture and meaning of parenting. In addition, Hispanic and Asian families are too limited in number to analyze separately with this model. Attrition was relatively low for this study; however, attrition was substantially higher for ethnic minority groups.
Conclusion
development, and that same environment provides a basis for the development of attachment and self-regulation. A stimulating, enriched home environment leads to children doing better in school, and this kind of environment buffers the effects of stressors that low-income families face. A positive, enriched environment aids in the development of self-regulation as well.
Given the importance of the early home environment and parenting skills, it is evident that parent education and programming are valuable tools for positive change. Home visiting programs have demonstrated success in predicting academic achievement (Nievar, Jacobson, Chen, Johnson, & Dier, 2011) and preventing child abuse (Eckenrode, Ganzel, Henderson et al., 2000). Other programs, such as Avance and Triple P, provide parent education to low-income or at-risk families in a group setting (Bayer, Hiscock, Scalzo et al., 2009; Schaller, Rocha, &
Barshinger, 2007). One program, Getting Ready, combines home visits with school-based
programming to improve children’s social competence and parent engagement (Sheridan et al.,
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Table 1.
Participant Demographics (n = 1023)
Age of Mother at Birth M = 28.52, SD = 5.58 Race of Child
White 82%
African American 12% Asian or Pacific Islander 1.5%
American Indian, Eskimo, Aleutian 0.3% Other 4.5% Gender of Child
Male 511
Female 512
Maternal Educational Attainment
Did not complete HS 9%
Completed HS 20%
Completed some college 33%
Completed college 23%
Graduate-level coursework 16% Partner Educational Attainment
Did not complete HS 5%
Completed HS 23%
Completed some college 28%
Completed college 25%
Graduate-level coursework 19%
Table 2
Correlations Between Study Variables (n = 1023)
Variables 1 2 3 4 5 6 7 8
1. Enrichment -
2. Positive Parenting .374* -
3. Lack of Negativity .242* .129* -
4. Income .397* .256* .256* -
5. Attachment .206* .169* .138* .173* - 6. Depression -.202* -.115* -.157* -.264* -.128* - 7. Self-regulation .219* .112* .130* .201* .160* -.090* - 8. Cognitive Outcomes .398* .240* .169* .407* .211* -.181* .258* - M 6.953 5.572 5.080 3.176 .296 8.974 .016 105.028
SD 1.401 .816 1.086 2.032 . 202 8.065 .732 11.181
Note: Point-biserial correlations are provided for the subscales of the HOME (Enrichment, Positive Parenting, and Lack of Negativity).
Table 3
Results of Sobel Tests for Significant Mediation Paths
______________________________________________________________________________
Path t SE % Mediated
Enrichment → Attachment → Self-regulation 3.580*** .004 11.38
Depression → Positive Parenting → Attachment -2.972** <.001 1.04
Depression → Lack of Negativity → Attachment -.3042** <.001 14.38
Depression → Enrichment → Attachment -4.276*** <.001 28.35
Income → Enrichment → Cognitive Outcomes 7.706*** .080 27.40
Enrichment → Self-regulation → Cognitive Outcomes 4.788*** .068 10.17
Note. % Mediated = ab/c where a = unstandardized regression coefficient between predictor and mediator; b = unstandardized regression coefficient between mediator and outcome variable; c = unstandardized regression coefficient between predictor and outcome variable.
Table 4
Path Coefficients and Estimates for Total Sample (N = 1023).
Parameter Estimate B SE B β
Income → Cognitive Outcomes .722 .259 .131**
Income → Parenting .197 .018 .387***
Depression → Parenting -.018 .005 -.140***
Depression → Attachment -.001 .001 -.045
Parenting → Positive Parenting .361 .040 .459***
Parenting → Lack of Negativity .365 .050 .348***
Parenting → Enrichment 1.000 .741***
Parenting → Self-Regulation .184 .047 .261***
Parenting → Cognitive Outcomes 4.643 1.042 .430***
Parenting → Attachment .058 .012 .294***
Attachment → Self-regulation .238 .125 .066
Self-regulation → Cognitive Outcomes 1.516 .491 .099** Correlation Income and Depression -4.329 .535 -.264***
Figure 1. Theoretical Model of the Formation of Preschoolers’ Self-Regulation
Parenting Income
Maternal Mental Health
Attachment
Figure 2. Structural Equation Model Predicting Cognitive Outcomes.
.13**
.39***
.29*** .07
-.26*** .43***
-.05
-.14*** .26*** .10**
.46*** .35*** .74***
Note:χ² (35, N = 1023) = 81.751, p < .001; RMSEA = .036; SRMR = .024, CFI = .954. To reduce the complexity of the figure, the control variables were not included. All coefficients are standardized.
Lack of Negativity Positive
Parenting
Enrichment Parenting
Depression
Income Attachment
Self-regulation
