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The Association of Punitive Parenting Practices and Adolescent Achievement.

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This article uses a nationally representative dataset to investigate the extent to which academic-related parenting practices and the home environment during middle childhood (ages 11-13) predict achievement in late adolescence (N = 486; age range: 16-18 years). Results from path analyses indicated that parental endorsement of punitive strategies (e.g., lecture, punish, restrict activities) in response to academic underperformance during middle school predict lower literacy and math achievement 5 years later. In contrast, more cognitively stimulating homes predict higher literacy and math achievement 5 years later. Parenting practices and the home environment indicators, however, did not predict changes in achievement. Socioeconomic and race and ethnicity differences in parenting were also found. (PsycINFO Database Record (c) 2015 APA, all rights reserved).
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Running head: PUNITIVE PARENTING AND ACHIEVEMENT
The association of punitive parenting practices and adolescent achievement
Sandra Tang and Pamela E. Davis-Kean
University of Michigan
Citation:
Tang, S., & Davis-Kean, P. E. (2015, August 3). The Association of Punitive Parenting Practices
and Adolescent Achievement. Journal of Family Psychology. Advance online publication.
http://dx.doi.org/10.1037/fam0000137
This article may not exactly replicate the final version published in the APA journal. It is not the
copy of record.
Acknowledgements
This research was supported in part by Award Number T32HD007109 from the Eunice Kennedy
Shriver National Institute of Child Health & Human Development.
Correspondence concerning this article should be addressed to Sandra Tang, Department of
Psychology, University of Michigan, East Hall, 530 Church Street, Ann Arbor, MI 48109-1043,
United States. E-mail address: sandtang@umich.edu
PUNITIVE PARENTING AND ACHIEVEMENT 2
Abstract
This paper uses a nationally representative dataset to investigate the extent to which
academic-related parenting practices and the home environment during middle childhood (ages
11-13) predict achievement in late adolescence (N = 486; ages 16-18). Results from path
analyses indicate that parental endorsement of punitive strategies (e.g., lecture, punish, restrict
activities) in response to academic underperformance during middle school predict lower literacy
and math achievement five years later. In contrast, more cognitively stimulating homes predict
higher literacy and math achievement five years later. Parenting practices and the home
environment indicators, however, did not predict changes in achievement. Socioeconomic and
race and ethnicity differences in parenting were also found.
Keywords: punitive parenting, academic achievement, home environment, adolescence
PUNITIVE PARENTING AND ACHIEVEMENT 3
The Association of Punitive Parenting Practices and Adolescent Achievement
A review of the theoretical literature suggests three primary elements of the home
environment that promote student achievement: parent engagement in children’s learning, a
warm emotional climate, and a cognitively stimulating home environment (Dearing & Tang,
2009). Extant studies indicate that these three elements are positively associated with
achievement during early and middle childhood (Jeynes, 2005; R. H. Bradley, Caldwell, & Rock,
1988; Foster, Lambert, Abbott-Shim, McCarty & Franze, 2005; Son & Morrison, 2010). The
developmental value of these elements for achievement, however, has not been examined
comprehensively among adolescents. In addition, the literature primarily focuses on parents’
participation in school-based activities or assistance with homework while much less attention
has examined the role of parents’ responses to school feedback about their child’s academic
progress. This responsive type of parenting practice is particularly important to investigate
during adolescence because parent educational involvement changes as children enter
adolescence (Hill & Tyson, 2009), and thus, this type of parenting practice may be a more
developmentally appropriate indicator of the way that parents’ are engaged in and monitor their
children’s schooling. Finally, given the large body of literature demonstrating the negative
influence of harsh parenting on children’s behavioral outcomes (for a review, see Gershoff,
2002), this study uses nationally representative data to investigate whether parental endorsement
of punitive parenting strategies in response to poor academic outcomes has a similar negative
relationship with children’s achievement.
Punitive Parenting
A large and growing literature has shown the negative links between punitive and harsh
parenting on children’s behavior. When parents engage in higher levels of harsh parenting
PUNITIVE PARENTING AND ACHIEVEMENT 4
practices, children exhibit higher levels of internalizing and externalizing behaviors (Choe,
Olson, Sameroff, 2013; Gershoff, 2002; Slade & Wissow, 2004). In addition, there is evidence to
suggest that chronic harsh parenting during childhood is associated with changes in brain
development that relate to reduced social and cognitive abilities (Tomoda et al., 2009). Together,
these studies indicate that negative forms of parenting have long-term effects on children’s
development. The majority of the research on negative parenting, however, focuses on the links
between physical punishment and behavioral outcomes of young children while fewer studies
have investigated the repercussions of other types of negative parenting on other important
developmental outcomes such as achievement.
Of the studies that have investigated the link between negative forms of parenting and
academic outcomes, most focus on parenting styles. For example, research on the influence of
parenting styles as defined by Baumrind’s typologies on adolescents’ achievement (e.g.,
Baumrind, 1991; Spera, 2005) gives support to the idea that parenting style has important
implications for adolescents’ academic outcomes. Study results based on adolescent report of
parenting style indicate that authoritative parenting, which is characterized by high regulation,
warmth, and democracy, predicts more favorable academic outcomes for adolescents while
authoritarian parenting, which is characterized by high regulation, low warmth and low
democracy, predicts less favorable academic outcomes (Steinberg, Lamborn, Dornbusch,
Darling, 1992). Research with younger children finds that children have poorer academic
performance when parents use harsh or authoritarian parenting (Dornbusch, Ritter, Liederman,
Roberts, & Fraleigh, 1987). Moreover, there is evidence from work on low income urban
families that parents’ self-reported endorsement of harsh parenting predicts lower academic
achievement for children in elementary school (Shumow, Vandell, & Posner, 1998). Together,
PUNITIVE PARENTING AND ACHIEVEMENT 5
these results suggest that parenting strategies meant to control or punish the child can lead to
lower achievement. These studies, however, examine an overall style of parenting, and few
empirical studies to date have examined the relation between academic-specific punitive
parenting practices and achievement outcomes (for an exception, see Robinson & Harris, 2014),
with an emphasis on the transition period into adolescence.
Based on parent educational involvement theory, there is reason to suspect that punitive
parenting may be problematic for academic-related outcomes. In Hoover-Dempsey and Sandler’s
(1995) model of parent educational involvement, they propose that higher levels of positive and
proactive parent involvement lead to higher achievement because parents transfer skills to their
children and foster positive feelings about school. In contrast, punitive parenting may lead to less
optimal achievement because punitive parenting practices typically do not include a transfer of
skills nor do they foster positive feelings in the child.
Furthermore, punitive parenting may be particularly problematic for the academic
outcomes of preadolescents and adolescents. During this period youth begin to develop more
complex cognitive skills that reorient their understanding of the world and lead them to
renegotiate their relationships with others (Lerner et al., 1996; Paikoff & Brooks-Gunn, 1991).
Parent-child relationships, for example, begin to shift in balance to become more democratic as
young adolescents strive for more autonomy (Steinberg, 1990). Academically, youth of this age
also start to take on more responsibility for their learning as they adapt to a more decentralized
learning environment (Eccles, Midgley et al., 1993).
Although, students report an increased desire for more autonomy in their schooling as
they enter adolescence, it is during this time that teachers also begin to exert more control on
students’ learning, which creates a person-environment mismatch that can lead to suboptimal
PUNITIVE PARENTING AND ACHIEVEMENT 6
development (Midgley & Feldlaufer, 1987; Eccles, Buchanan, Flanagan, Fuligni, Midgley &
Yee, 1991). Most of this research, however, has been focused on mismatch between classroom or
school environments and students’ needs (Eccles, Midgley et al., 1993), while fewer studies have
examined the relation between this potential mismatch in parents’ academic socialization and
student achievement. Given the unique circumstances that surround this developmental period, it
is unclear how punitive parenting practices around academics may be related to achievement
outcomes of preadolescent youth.
Warm Parent-Child Relationship
There is a strong empirical base of literature supporting the positive association between
warm parent-child interactions and positive child psychosocial and academic outcomes. When
parents are responsive and warm, their children exhibit more positive academic and behavioral
outcomes (Bouchard, St-Amant, & Deslandes, 1998; R. H. Bradley et al., 1988). Attachment
theorists speculate that the connectedness between parent and child create a stable emotional
base from which the child can explore (Ainsworth, Blehar, Waters, & Wall, 1978) while
motivational researchers posit that these warm interactions lead children to be more receptive to
the values endorsed by parents and motivate them to internalize parental messages (Grolnick,
Ryan, & Deci, 1991). Family educational involvement researchers note that when parents have
positive and warm relationships with their children, they are more likely to be engaged in their
children’s education (Estrada, Arsenio, Hess, & Holloway, 1987). Indeed, study findings based
on Caucasian families indicate that warm affective parent-child relationships in preschool are
associated with higher levels of achievement in elementary and middle school (Estrada et al.,
1987), and among Bangladeshi families predict higher levels of achievement for adolescents
PUNITIVE PARENTING AND ACHIEVEMENT 7
(Uddin, 2011). However, this research is limited by lack of representation of a broader and more
representative sample of adolescents. Thus, it is unclear if parent-child warmth would be
predictive in a more heterogeneous sample.
Cognitive Stimulation
In addition to parenting practices around academics and a warm emotional parent-child
relationship, how parents structure the home environment to facilitate learning also has
implications for children’s achievement. There is little question as to the influential role of the
early home environment on young children’s learning and development. Children who grow up
in more cognitively stimulating homes exhibit higher levels of reading, math, and school
readiness skills than their peers who grow up in less stimulating environments (Davis-Kean,
2005; R. H. Bradley et al., 1988). When parents, for example, converse with their children,
provide books and toys, and take them on trips to the museum, they provide their children with
more resources and opportunities from which to learn. Moreover, extant research indicates that
these early learning experiences occur in a critical window of development that sets up children’s
chances for later academic success (Alexander & Entwistle, 1988).
In contrast, much less is known about the longitudinal influence of the preadolescent’s
home environment on their achievement. Trends in family engagement suggest that parent
involvement in their children’s schooling declines as their children age (Hill & Tyson, 2009), but
it is unclear whether the type of educational involvement activities that parents participate in
during preadolescence, such as providing a cognitively stimulating home environment, matters
for adolescent achievement.
Present Study
PUNITIVE PARENTING AND ACHIEVEMENT 8
In sum, this study uses a nationally representative data set to investigate the extent to
which parental endorsement of punitive or proactive practices in response to children’s low
academic progress in middle school predict reading and math achievement in adolescence after
taking into consideration other aspects of the home environment such as cognitive stimulation
and warmth. A unique contribution of this paper is that we examine an often overlooked area of
parent educational involvement, parents’ responses to poor grades. Specifically, we examine
whether academic-related parenting practices relate to student achievement across time. Based
on findings from the literature on the links between negative parenting and children’s behavioral
outcomes, we hypothesize that punitive parenting strategies will predict lower levels of literacy
and math achievement while higher levels of parental warmth and cognitive stimulation will
predict higher achievement in late adolescence.
Method
Participants
Data for this study were drawn from the Panel Study of Income Dynamics (PSID), an
ongoing longitudinal analysis of the socioeconomic and health outcomes of a nationally
representative sample of families living in the United States. The present analyses are based on
two components of the PSID, the main interview and the Child Development Supplement (CDS).
For the main interview, data were collected from the head of household every year between 1968
and 1997, and biennially thereafter for approximately 8,000 families. In 1997, the PSID collected
additional data on a range of children’s developmental outcomes from families with a child
between the ages of 0 and 12 (n = 3,563 children). Additional waves of CDS data were collected
from families with children under the age of 18 in 2002/2003 (n = 2,907 children) and 2007/2008
(n = 1,506 children).
PUNITIVE PARENTING AND ACHIEVEMENT 9
Given that adolescent achievement is the primary outcome of interest, the analytic sample
was restricted to children age 11 and older by the first wave of the CDS (3,077 cases dropped).
Thus, the analytic sample contained 486 11-13 year olds at wave 1 (M = 12.20, SD = .65).
Approximately 44% of the children in the sample were non-Hispanic White, 44% were non-
Hispanic Black, 7% were Hispanic, 1% were Asian, <1% were Native American, and 3%
identified as another race/ethnicity. In comparison to the original CDS sample at wave 1,
adolescents in the analytic sample had higher letter-word achievement scores at wave 1 (t1 =
3.00, p .01), lower passage comprehension scores in both waves (t1 = -3.24, p ≤ .001; t2 = 4.86,
p ≤ .001), and lower applied problem scores at wave 2 (t2 = -3.89, p ≤ .001). Participants in the
analytic sample exhibited lower levels of warmth (t1 = -8.12, p .001), but higher levels of
cognitive stimulation (t1 = 16.86, p ≤ .001), proactive (t1 = 42.34, p ≤ .001), and punitive
responses (t1 = 13.43, p ≤ .001) than participants excluded from the analysis. Lastly, participants
in the analytic sample were more likely to have higher household incomes (t1 = 2.83, p ≤ .01),
but did not differ on parent educational attainment levels or race/ethnicity in comparison to
children excluded from the analytic sample.
Measures
Achievement. The standardized scores of three subscales of the Woodcock-Johnson
Revised Tests of Achievement, a test with high validity and reliability (Woodcock & Johnson,
1989), were used to measure adolescents’ achievement at waves 1 and 2. The Letter-Word
Identification subscale (M1 = 104.32, SD1 = 19.10, M2 = 108.16, SD2 = 18.86), required children
to orally identify printed letters and words while the Passage Comprehension subscale (M1 =
100.18, SD1 = 14.43, M 2 = 103.62, SD2 = 14.45), asked children to identify a missing keyword
that would make sense in the context of a written passage. The Applied Problems subscale (M1 =
PUNITIVE PARENTING AND ACHIEVEMENT 10
100.91, SD1 = 14.95, M2 = 107.85, SD2 = 14.68) asked children to perform math calculations in
response to problems presented orally and visually. All achievement scores were correlated with
one another at the p < .05 significance level (r range = .43 .72).
Home learning environment. Consistent with the research literature, three aspects of the
home environment were assessed.
Punitive and proactive parenting responses. Primary caregivers responded to 7-items that
asked how likely they would perform a certain action if their child “brought home a report card
with grades or progress lower than their expectations.” Responses were scored on a 5-point
Likert scale ranging from 1 = not at all likely to 5 = very likely. Parent reactions were separated
into two separate categories, punitive and proactive. The punitive response measure was
represented by a summative score of 3 items indicating the number of punitive responses in
which the primary caregiver responded that they were very likely to perform. The three items that
comprised the punitive response measure included punish the child, lecture the child, and limit or
reduce child’s non-school related activities such as play, sports, clubs, etc. (M = .92, SD = 1.01).
The proactive response measure was represented by a summative score of 4 items indicating the
number of proactive actions in which the primary caregiver were very likely to perform. These
four items included contact faculty, talk to the child, keep a closer eye on child’s activities, and
spend more time helping the child with schoolwork (M = 3.12, SD = 1.02). In this sample,
parents generally reported that they would use more proactive responses than punitive responses
if their child brought home a report card with grades or progress that were lower than their
expectations.
Warmth. The affective quality of the parent-child relationship at wave 1 was measured
using a mean composite of 7-items drawn from observer reports of warmth from the Home
PUNITIVE PARENTING AND ACHIEVEMENT 11
Observation for Measurement of the Environment Inventory (Caldwell & Bradley, 1984; α1 =
.87). Sample items “Parent’s voice conveys positive feeling to child” and “How often did
primary caregiver spontaneously praise child for his or her behavior, helpfulness, looks or other
positive qualities?” Responses were scored on a 5-pt Likert scale, ranging from 1 (often) to 5
(never) and reverse-coded so that higher scores indicate more warmth in the parent-child
relationship.
Cognitive stimulation. Cognitive stimulation in the home environment at wave 1 was
assessed using 14 items from the cognitive stimulation subscale of Caldwell and Bradley’s
(1984) Home Observation for Measurement of the Environment-Short Form (HOME-SF).
Individual items were coded in a binary fashion and then summed to create a total score of
cognitive activities in the home (M = 9.58, SD = 1.90). Example items ask about the presence of
books, toys, musical instruments, and trips to the museum. Higher scores indicate more
cognitively stimulating home environments.
Socio-demographic characteristics. Prior literature has indicated that how parents
structure the home environment and promote children’s learning, are likely to be contingent upon
more distal variables, such as parents’ educational attainment, income, and racial and ethnic
background (Brody & Flor, 1998; Cardona, Nicholson, & Fox, 2000; Davis-Kean, 2005;
Dornbusch et al., 1987; Shumow et al., 1998). These characteristics were included in the model
to control for their potentially confounding influence.
Income. Household income is based on the 1997 Census needs standard (M = 17,737, SD
= 3,686). The income measure was obtained at wave 1.
Educational attainment. Parents’ educational attainment was measured at wave 1. This
continuous variable represented the highest number of years of education completed by either the
PUNITIVE PARENTING AND ACHIEVEMENT 12
head of household or spouse. On average, families in the sample had completed a high school
education (M = 12.94, SD = 3.41).
Race and ethnicity. Children’s race and ethnicity were categorized into four dichotomous
indicators to represent non-Hispanic White (reference group), non-Hispanic Black, Hispanic, and
Other. The other group includes children who identified as Asian or Pacific Islander, American
Indian or Alaskan Native, and other.
Analytic Strategy
Path analyses were estimated using M-PLUS Version 6 (Muthén & Muthén, 19982010)
to assess the hypothesized relationships between aspects of the home learning environment in
middle childhood and achievement in adolescence. To handle the missing data across variables
(see Table 1), Full Information Maximum Likelihood (FIML) was used during model estimation,
which is the preferred method of model estimation with missing data (Allison, 2003; Arbuckle,
1996). A set of auxiliary variables, not a part of the analysis was used to help with the FIML
estimation, and meet the Missing at Random (MAR) assumption underlying FIML analyses.
These additional variables also help the precision and efficiency of the model (Acock, 2012;
Graham, Olchowski, & Gilreath, 2007). Auxiliary variables included child age at time of the first
interview, parental endorsement of punitive and proactive responses to academic
underperformance at wave 2, and children’s reading, math, and global self-concepts at waves 2
and 3. Analyses were estimated with child-level probability weights (CH97PRWT) to adjust for
the sampling framework and differential response so that results could be generalized to the
population of interest, middle and high school students in the United States.
In this study, path analyses were estimated in a series of steps. First, the fit of the
hypothesized model in Figure 1 was evaluated. Second, in conjunction with theoretical and
PUNITIVE PARENTING AND ACHIEVEMENT 13
empirical guidelines, additional correlations were added based on modification indices and
theoretical fit with the model. Third, prior levels of achievement were added to the final path
model to evaluate the influence of the home environment on changes in achievement.
The overall fit of the hypothesized path models to the observed data were determined
using multiple indices as suggested by Hoyle and Panter (1995). Model fit was evaluated using
four indices: the chi-square statistic (χ2), comparative fit index (CFI), the root mean square error
of approximation (RMSEA), and the standardized root mean square residual (SRMR). Although
χ2 is sensitive to large sample sizes, it is the most commonly reported fit index because of its
usefulness in assessing how well the proposed model fits the observed data and comparing
nested models (Martens, 2005). CFI, RMSEA, and SRMR, on the other hand, are less dependent
on sample size but take model complexity into account giving more favorable values to more
parsimonious models (Steiger, 1990). Convention states that good model fit occurs when the χ2
statistic is non-significant, the CFI is greater than .90 and when the RMSEA and SRMR values
are below .06 with an upper-bound confidence interval below .10 (Hu & Bentler, 1999; Weston
& Gore, 2006).
Results
Descriptive statistics and weighted inter-correlations are displayed in Table 1. Significant
correlations give initial evidence of the hypothesized relations between the home environment in
middle childhood and early adolescence and achievement in late adolescence.
Home Learning Environment Predicting Adolescent Achievement
Path model results based on the hypothesized model shown in Figure 1 indicate that the
initial model did not fit the observed data very well (χ2 (df) = 92.20(21), p <0.001; CFI = 0.83;
RMSEA = 0.08; SRMR = 0.07). Modification indices suggested adding error covariances
PUNITIVE PARENTING AND ACHIEVEMENT 14
between the home environment variables. In general, modification indices should only be used if
there is a strong theoretical reason for adapting the model based on the information provided by
the modification indices. Given that all of the home environment variables were measured at the
same time point and represent different practices that parents use to promote children’s learning,
and thus, should be correlated, these suggested modifications were added to the model. As a
result, the new models were a better fit to the observed data (see Table 2). Figures 2 and 3
represent the final path models that incorporated the suggested modifications. Although there
were significant correlations among the home environment variables, they are not shown in the
figures for simplicity. Similarly, error covariances among the exogenous variables and
achievement outcomes were estimated but are not shown in the figures.
As suggested by the correlations, results from Model 1 demonstrate that cognitive
stimulation was the strongest and most consistent predictor of achievement during adolescence.
Higher levels of cognitive stimulation predicted higher achievement scores on the Letter-Word
(β = .14, p .05), Passage Comprehension (β = .22, p ≤ .001), and Applied Problems (β = .18, p
.01) subscales. In contrast, parents’ punitive responses predicted lower achievement scores on
the Passage Comprehension (β = -.21, p ≤ .001) and Applied Problems (β = -.23, p ≤ .001)
subscales. Warmth and parental endorsement of proactive response strategies were not
significant predictors of achievement during adolescence.
Once earlier achievement scores were added in model 2, cognitive stimulation did not
predict changes in achievement. Parental endorsement of punitive responses also did not predict
changes in achievement although the betas continued to be in the expected directions for passage
comprehension (β = -.08, p =.10) and applied problems (β = -.04. p = .41).
Socio-demographic Characteristics Predicting Home Learning Environment
PUNITIVE PARENTING AND ACHIEVEMENT 15
Results also indicate the importance of socio-demographic factors on the quality of the
home learning environment. Higher levels of educational attainment (β = .35, p ≤ .0001)
predicted higher levels of cognitive stimulation. Additionally, race/ethnicity was a significant
and consistent predictor of the home environment. On average, Black families demonstrated
lower levels of warmth (β = -.16, p ≤ .01) and provided less cognitively stimulating home
environments (β = -.19, p ≤ .001) in comparison to White families. Additionally, Black families
were more likely than White families to endorse punitive strategies in response to poor grades (β
= .27, p ≤ .001). Hispanic families, on average, provided less cognitively stimulating home
environments (β = -.15, p ≤ .05) and were more likely to endorse punitive responses to poor
grades (β = .24, p ≤ .001) in comparison to White families.
Discussion
Using a nationally representative dataset, this paper examined the relation of academic-
related parenting practices on adolescents’ reading and math achievement across time. Study
results indicate that parental endorsement of punitive parenting strategies (e.g., lecturing the
child, punishing the child, limiting or reducing child’s non-school related activities) predict
adolescent achievement. On average, students with parents who endorsed higher levels of
punitive parenting strategies in middle school demonstrated lower levels of reading and math
achievement in high school. Cognitive stimulation in the home during middle school also
predicted higher reading and math achievement five years later. Cognitive stimulation and
punitive parenting strategies, however, did not predict changes in achievement. In comparison to
prior studies that examine parents’ academic socialization, this study uniquely adds to the
literature because it investigates how parental endorsement of different types of reactions to
underachievement in middle school is associated with achievement five years later.
Punitive Parenting Strategies Predict Lower Achievement
PUNITIVE PARENTING AND ACHIEVEMENT 16
The most consistent finding of this study indicates that punitive parenting strategies in
response to students’ academic underperformance have long-term, negative relations with later
achievement. When parents endorsed unilateral, punitive strategies such as punishing and
lecturing the child, and limiting non-school related activities in response to poor grades in middle
school, students generally exhibited lower reading and math achievement scores in late
adolescence. These results mirror a robust body of literature that demonstrate negative links
between harsh parenting and children’s behavioral problems (R. H. Bradley, Corwyn, Burchinal,
McAdoo, & Garcia Coll, 2001; Choe et al., 2013; Gershoff, 2002). To parse out the influence of
harsh parenting from academic-related punitive parenting strategies, spanking was added to the
model in analyses not presented here. Despite this addition, the negative associations between
academic-related punitive parenting and lower achievement remained, which suggests that
academic-related punitive parenting is another dimension of harsh parenting that is uniquely
related to sub-optimal child development. Furthermore, this study contrasts the notion recently
touted by popular media that harsh parenting strategies lead to higher academic achievement
(Chua, 2011).
Punitive parenting strategies are likely ineffective in promoting achievement when it does
not directly address the underlying problem that is causing academic underperformance.
Limiting non-school related activities, for example, is helpful if academic underperformance is a
result of the child not spending enough time on school work, but less so if the underperformance
is a result of the child not understanding how to solve a math problem. Similarly, punishing and
lecturing the child does not provide the child with concrete skills or strategies for improving their
academic performance. Rather, punitive disciplinary actions are reflective of controlling and
power-assertive strategies that may be particularly problematic for preadolescents who are
PUNITIVE PARENTING AND ACHIEVEMENT 17
entering a developmental period when they begin to become more autonomous (Paikoff &
Brooks-Gunn, 1991). Moreover, these punitive strategies likely invoke negative feelings about
school and school work, which are related to children’s disengagement from schooling during a
sensitive period when engagement in schooling is important for later educational goals such as
college attendance (Eccles, Wigfield et al., 1993).
Stable Influence of Cognitive Stimulation
In addition to the persistent influence of punitive parenting strategies, cognitive
stimulation provided in the home during middle school predicted higher levels of achievement in
both reading and math achievement in late adolescence. The influence of the home environment
during middle school on student achievement in high school demonstrates that the quality of the
home learning environment has a lasting influence on student achievement. The cognitive
stimulation provided in the home during middle childhood, however, is likely related to the
stimulation provided in the home during early childhood. Although we did not have data at an
earlier time point to test this, there was a moderate correlation (r=.54) between cognitive
stimulation provided in the home at waves 1 and 2, which suggests that while there is some
stability in cognitive stimulation over time there are also unique differences across development
that play a role in predicting student achievement. Thus, given the current emphasis on providing
early learning environments by researchers and policymakers (Shonkoff & Phillips, 2000), this
finding is a reminder that the home environment in middle school is important for students’
success, and consequently, emphasis should be placed on a continuation of providing a
stimulating environment during the child’s middle school years.
Socioeconomic and Race and Ethnicity Group Differences
Finally, in alignment with previous literature (Davis-Kean, 2005), families’ educational
attainment was positively related to the cognitive stimulation provided in the home. On average,
PUNITIVE PARENTING AND ACHIEVEMENT 18
families with higher levels of education provided their children with more cognitively
stimulating homes when the child was in middle school, which in turn, predicted children’s
higher reading and math achievement five years later. Income showed no relation to the
predictors or outcomes in these models. In contrast to existing literature (e.g., Steinberg,
Dornbusch, & Brown, 1992), there was no relation between families’ socioeconomic
characteristics and whether parents endorsed punitive or proactive parenting practices in
response to a poor grade.
There was, however, evidence of race and ethnicity differences in parental endorsement
of punitive parenting practices around academics, which echoes prior studies that find race and
ethnicity differences in punitive and harsh parenting more generally (C. R. Bradley, 1998;
Pinderhughes, Dodge, Zelli, Bates, & Pettit, 2000; Portes, Dunham, & Williams, 1986). Black
and Hispanic families, for example, were more likely to endorse punitive parenting practices in
response to a poor grade in comparison to White parents. In turn, endorsement of punitive
parenting practices predicted lower levels of reading and math achievement five years later. As
other researchers have noted, these race and ethnicity differences in parenting suggest that
parents’ responses to academic underperformance are related to cultural variations in their
childrearing beliefs and values, thus leading parents to gravitate towards certain parenting
strategies (e.g., Brody & Flor, 1998; Deater-Deckard, & Dodge, 1997; Domenech Rodríguez,
Donovick, & Crowley, 2009). Future within-group studies should test this model to investigate
whether the pathways between punitive and proactive parenting and child achievement replicate
for different cultural groups.
Limitations
PUNITIVE PARENTING AND ACHIEVEMENT 19
There are some important limitations of this study to note. First, even though this is a
nationally representative sample, the race and ethnicity subgroups are limited in size and so
multi-group analyses were not possible. Thus, important processes outlined in these models are
not available for examination by group. Given findings from a recent meta-analysis examining
family involvement among middle school children that identified academic socialization
practices as the most effective form of involvement that translates to student achievement (Hill &
Tyson, 2009), it is important to examine these processes among ethnic-minority families who
may be at-risk for poor outcomes.
Second, this study is correlational in nature and even though we accounted for important
covariates related to achievement, it is still possible that we did not identify an important
construct accounting for the outcomes. For example, we do not have schooling variables
available to account for how teachers or schools report achievement information to parents. This
may be important in explaining some of parents’ endorsement of punitive parenting strategies.
Importantly, we do not directly measure parents’ use of punitive parenting strategies.
Instead we assess parents’ endorsement of these strategies. Regardless, prior research
demonstrates that parents generally underreport the level of their punitive parenting (Lee,
Lansford, Pettit, Bates, & Dodge, 2012), thus suggesting that parent reports of punitive parenting
would attenuate the relations. We also do not know why parents endorse using these punitive
behaviors related to schooling. It is possible that they are endorsing these punitive parenting
strategies in response to children not doing homework or paying attention in class whereas they
may endorse alternative types of parenting strategies if the reason for the poor grade was a result
of the child not understanding the content. Regardless, these behaviors are predicting negatively
to achievement and thus suggest long term consequences of this type of parenting.
PUNITIVE PARENTING AND ACHIEVEMENT 20
Research and Intervention Implications
The study results indicated the stable influence that the home environment, namely,
cognitive stimulation, has on achievement for adolescents. Additionally, parental endorsement of
punitive strategies in response to bad grades has negative effects on adolescents’ reading and
math achievement and this is generalizable to the broader population given the use of a
nationally representative study. Thus, the implications for future research and intervention are
important. First, additional research on the culture of punitive punishment related to race and
ethnicity will be important to conduct. What is considered success or failure for students varies
by culture and being sensitive to these cultural differences will be important for any intervention
that may be designed. However, educating parents that use of punitive behaviors in response to
children receiving poor grades in school may lead to lower grades in the future may provide an
excellent point for intervening with middle school children. Specifically, it is important to
educate parents that their child’s academic underperformance may be a result of a learning issue
rather than a behavioral issue. As such, the strategies that parents use to mitigate poor school
performance should reflect the cause of the low grades. Second, how teachers communicate
students’ underperformance to parents may help to alleviate the punitive punishment in the home
environment and increase achievement at an important transition point in preadolescence.
Teachers, for example, could provide comments with grades so that parents can understand the
reasons behind their child’s academic underperformance (e.g., lack of comprehension of the
concepts versus not submitting homework on time). Finally, this age is a time of instability in
parent-child relationships with parents struggling to find effective solutions for struggling
learners. This research provides an avenue for parents to consider in creating a better learning
environment in the home and hopefully reduce some of the disparities seen in achievement
PUNITIVE PARENTING AND ACHIEVEMENT 21
across socio-demographic groups. Future research on ways to provide better information on
achievement for vulnerable populations may provide important resources for developing positive
outcomes for preadolescent youth.
PUNITIVE PARENTING AND ACHIEVEMENT 22
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batteryrevised. Allen, TX7 DLM.
PUNITIVE PARENTING AND ACHIEVEMENT 30
Figure 1. Hypothesized Model of the Influences of Socio-demographic Background and Home Environment on Adolescent Achievement
PUNITIVE PARENTING AND ACHIEVEMENT 31
Table 1.
Weighted Zero-Order Correlations and Descriptives
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
Achievement
1 Letter Word (w2) 1
2 Passage Comp (w2) 0.65 1
3 Applied Prob (w2) 0.59 0.63 1
Home Environment
4 Cognitive Stimulation 0.14 0.23 0.20 1
5 Warmth 0.17 0.15 0.14 0.38 1
6 Proactive Response -0.04 -0.03 -0.09 0.10 0.14 1
7 Punitive Response -0.10 -0.23 -0.25 -0.08 0.03 0.34 1
Controls
8 Income 0.05 -0.06 -0.07 -0.04 -0.02 0.06 0.09 1
9 Educational Attainment 0.27 0.43 0.36 0.42 0.25 0.02 -0.14 -0.28 1
10 White 0.20 0.31 0.25 0.31 0.18 0.00 -0.27 -0.19 0.44 1
11 Black -0.24 -0.22 -0.17 -0.23 -0.20 0.00 0.23 -0.06 -0.18 -0.58 1
12 Hispanic 0.00 -0.14 -0.16 -0.21 0.00 0.08 0.20 0.34 -0.40 -0.49 -0.18 1
13 Other Race -0.02 -0.08 -0.02 0.03 -0.04 -0.10 -0.09 0.01 -0.04 -0.37 -0.13 -0.11 1
Autocorrelation
14 Letter Word (w1) 0.61 0.57 0.46 0.22 0.20 0.04 -0.05 -0.06 0.21 0.24 -0.27 0.04 -0.11 1
15 Passage Comp (w1) 0.58 0.60 0.48 0.26 0.23 0.01 -0.12 -0.12 0.33 0.33 -0.23 -0.12 -0.13 0.65 1
16 Applied Prob (w1) 0.45 0.51 0.73 0.20 0.21 0.05 -0.20 -0.07 0.33 0.39 -0.28 -0.19 -0.07 0.59 0.61 1
M / % 104.32 100.17 100.92 9.65 3.56 3.12 0.92 177737 12.94 62% 17% 13% 8% 108.34 102.61 107.33
SD 19.14 14.45 14.98 2.10 0.85 1.02 1.01 3686.17 3.41 19.04 14.30 15.35
N343 341 340 486 455 482 482 471 480 483 483 483 483 486 484 484
Min 45 48 49 1 1 0 0 8350 0 0 0 0 0 53 42 60
Max 168 171 168 14 5 4 3 34845 17 1 1 1 1 200 166 183
Note. Bolded correlations are significant at p ≤ .05
PUNITIVE PARENTING AND ACHIEVEMENT 32
Table 2.
Weighted Unstandardized and Standardized FIML Estimates and Selected Fit Indices for Models (N = 486)
Model 1
Model 2
95% CI
95% CI
Pathways
b
(SE)
UL
B
b
(SE)
LL
UL
B
Home to Achievement
Home to Letter-Word (LW)
Proactive Response -> LW2
-0.00
0.01
-0.03
-0.01
0.00
0.01
-0.02
0.02
0.00
Punitive Response -> LW2
-0.02
0.01
-0.04
-0.08
0.01
0.01
-0.01
0.03
0.04
Warmth -> LW2
-0.01
0.02
-0.05
-0.05
-0.02
0.01
-0.05
0.01
-0.09
Cognitive Stim-> LW2
0.01*
0.01
0.00
0.14
-0.00
0.00
-0.01
0.01
-0.04
LW1 -> LW2
0.45***
0.08
0.29
0.61
0.43
PC1 -> LW2
0.39***
0.10
0.20
0.59
0.29
AP1 -> LW2
0.12
0.09
-0.05
0.29
0.09
Home to Passage Comprehension (PC)
Proactive Response -> PC2
0.01
0.01
0.03
0.04
0.01
0.01
-0.01
0.02
0.04
Punitive Response -> PC2
-
0.03***
0.01
-0.01
-0.21
-0.01
0.01
-0.03
0.00
-0.08
Warmth -> PC2
-0.01
0.01
0.01
-0.07
-0.02
0.01
-0.04
0.00
-0.11
Cognitive Stim -> PC2
0.02***
0.00
0.02
0.22
0.00
0.00
0.00
0.01
0.06
PC1 -> PC2
0.22***
0.06
0.10
0.34
0.28
LW1 -> PC2
0.32***
0.07
0.19
0.45
0.32
AP1 -> PC2
0.17**
0.06
0.04
0.29
0.17
Home to Applied Problems (AP)
Proactive Response -> AP2
-0.00
0.01
0.02
-0.03
-0.01
0.01
-0.02
0.01
-0.04
Punitive Response -> AP2
-
0.03***
0.01
-0.02
-0.23
-0.01
0.01
-0.02
0.01
-0.04
Warmth -> AP2
0.00
0.01
0.03
0.00
-0.01
0.01
-0.03
0.00
-0.09
Cognitive Stim -> AP2
0.01**
0.01
0.02
0.18
0.00
0.00
0.00
0.01
0.04
AP1 -> AP2
0.04
0.07
-0.09
0.17
0.05
LW1 -> AP2
0.08
0.08
-0.07
0.23
0.07
PUNITIVE PARENTING AND ACHIEVEMENT 33
PC1 -> AP2
0.68***
0.07
0.54
0.81
0.67
Controls to Home
Controls to Proactive Response
Income -> Proactive Response
0.14
0.15
0.43
0.05
0.14
0.15
-0.16
0.43
0.05
Edu Attainment -> Proactive Response
0.02
0.02
0.06
0.07
0.02
0.02
-0.02
0.06
0.07
Black -> Proactive Response
0.02
0.16
0.35
0.01
0.03
0.16
-0.29
0.35
0.01
Hispanic -> Proactive Responsec
0.23
0.17
0.57
0.08
0.23
0.17
-0.10
0.57
0.08
Other -> Proactive Responsec
-0.34
0.25
0.16
-0.09
-0.33
0.25
-0.83
0.16
-0.09
Controls to Punitive Response
Income -> Punitive Response
0.07
0.15
0.36
0.03
0.07
0.15
-0.23
0.36
0.02
Edu Attainment -> Punitive Response
0.01
0.02
0.04
0.02
0.00
0.02
-0.03
0.04
0.01
Black -> Punitive Responsed
0.72***
0.16
1.02
0.27
0.72***
0.16
0.41
1.02
0.27
Hispanic -> Punitive Responsee
0.72***
0.21
1.13
0.24
0.73***
0.21
0.32
1.13
0.24
Other -> Punitive Responsede
-0.11
0.19
0.26
-0.03
-0.11
0.19
-0.47
0.26
-0.03
Controls to Warmth
Income -> Warmth
0.23
0.14
0.50
0.09
0.23
0.14
-0.04
0.51
0.09
Edu Attainment -> Warmth
0.02
0.02
0.06
0.06
0.02
0.02
-0.03
0.06
0.05
Black -> Warmthb
-0.39**
0.15
-0.10
-0.16
-0.38**
0.15
-0.67
-0.10
-0.16
Hispanic -> Warmth
-0.22
0.25
0.26
-0.08
-0.24
0.24
-0.72
0.24
-0.09
Other -> Warmthb
0.23
0.20
0.63
0.07
0.23
0.20
-0.17
0.63
0.07
Controls to Cognitive Stimulation
Income -> Cognitive Stimulation
0.57
0.34
1.22
0.10
0.55
0.34
-0.11
1.21
0.10
Edu Attainment -> Cognitive Stimulation
0.22***
0.04
0.30
0.35
0.22***
0.04
0.14
0.30
0.35
Black -> Cognitive Stimulationa
-
1.07***
0.33
-0.43
-0.19
-
1.05***
0.32
-1.68
-0.41
-0.19
Hispanic -> Cognitive Stimulation
-0.91*
0.46
-0.02
-0.15
-0.90*
0.46
-1.80
-0.01
-0.15
Other -> Cognitive Stimulationa
0.00
0.47
0.92
0.00
0.02
0.47
-0.90
0.95
0.00
Correlations
Cognitive Stimulation & Warmth
0.24*
0.11
0.45
0.14
0.25*
0.11
0.03
0.46
0.15
PUNITIVE PARENTING AND ACHIEVEMENT 34
Cognitive Stimulation & Proactive Response
0.22*
0.11
0.45
0.12
0.22*
0.11
0.00
0.44
0.12
Cognitive Stimuation & Punitive Response
0.06
0.10
0.26
0.04
0.06
0.10
-0.14
0.26
0.03
Warmth & Proactive Response
0.18**
0.07
0.31
0.20
0.18**
0.07
0.05
0.31
0.20
Warmth & Punitive Response
0.02
0.05
0.13
0.03
0.02
0.05
-0.08
0.13
0.03
Proactive Response & Punitive Response
0.33***
0.05
0.43
0.34
0.33***
0.05
0.22
0.43
0.34
Letter-Word (w2) & Passage Comprehension (w2)
0.02***
0.00
0.02
0.64
0.01***
0.00
0.00
0.01
0.39
Select Model Fit Indices
χ2 (df), p-value
42.08 (15), p ≤ 0.001
52.64 (27), p ≤ 0.01
CFI
0.93
0.96
RMSEA; 90% CI
0.06; (0.04-0.08)
0.04; (0.03-0.06)
SRMR
0.06
0.03
Note. * p ≤ .05, ** p ≤ .01, *** p≤ .001; b = unstandardized coefficients, SE = standard error, CI = confidence interval, LL = lower bound, UL = upper bound, B =
standardized coefficients; superscripts indicate significant differences between groups within each column.
PUNITIVE PARENTING AND ACHIEVEMENT 35
Figure 2. Path Model of Home Environment Influences on Adolescent Achievement Controlling for Race and Socioeconomic Background
(Model 1). Note. Only standardized paths significant at least at p <.05 are shown. Estimated error covariances are not shown in the figure. W1
= Wave 1, W2 = Wave 2
PUNITIVE PARENTING AND ACHIEVEMENT 36
Figure 3. Path Model of Home Environment Influences on Adolescent Achievement Controlling for Race, Socioeconomic Background, and
Prior Achievement (Model 2). Note. Only standardized paths significant at least at p <.05 are shown. Estimated error covariances are not
shown in the figure. W1= Wave 1, W2 = W2, LW = Letter-Word, PC = Passage Comprehension, AP = Applied Problems.
... Hill and Tyson's meta-analysis (2009) found that, when parents created educationally stimulating and supportive home environments, children had better academic outcomes, whereas parents' home-based involvement via homework help, in general, was associated with more negative child achievement. Research on specific academic-focused parenting strategies used by parents in the home found that certain types of strategies were associated with lower achievement whereas some strategies had no relation to children's achievement (Tang and Davis-Kean, 2015). ...
... Parents' academic-focused parenting strategies are another type of home-based involvement that parents engage in on a consistent basis. In a study using nationally representative data, Tang and Davis-Kean (2015) examined parents' endorsement of using different parenting strategies in response to their child bringing home a report card with grades or progress lower than expected. When parents endorsed punitive parenting strategies in response to their child's poor grades (e.g., lecture or punish the child, limit/reduce the child's nonschool-related activities), students exhibited lower levels of literacy and math achievement 5 years later (Tang and Davis-Kean, 2015). ...
... In a study using nationally representative data, Tang and Davis-Kean (2015) examined parents' endorsement of using different parenting strategies in response to their child bringing home a report card with grades or progress lower than expected. When parents endorsed punitive parenting strategies in response to their child's poor grades (e.g., lecture or punish the child, limit/reduce the child's nonschool-related activities), students exhibited lower levels of literacy and math achievement 5 years later (Tang and Davis-Kean, 2015). The authors speculated that punitive parenting strategies are likely ineffective in promoting achievement when the parenting response does not directly address the underlying problem causing academic underperformance, and thus, it is important for parents to understand the cause of the poor grade prior to using a punitive response. ...
Chapter
In much parenting research, a parent’s cognitive ability has been measured by proxy variable using the demographic information on the parent’s education attainment (e.g., years of formal schooling). In this chapter, we review why this one variable is important for understanding both parenting beliefs and behaviors and subsequent influence on child development. This chapter begins with an overview of the important role parent education provides as a demographic variable that relays information on the social resources that may be available for parenting children. This is followed by a discussion of the important relation that parent educational attainment has with constructing the home environment in which children develop. In the next section, we outline the literature on parent and school interactions and how parent educational attainment relates to parental educational involvement in school and home. This chapter concludes with suggestions for the future direction of research in understanding the complexity of the family environment and child development.
... Parental responses to grades, particularly grades that are poor or lower-than-expected, are an important aspect of parental involvement to consider, as how parents respond to grades may support or undermine later academic achievement (Robinson and Harris 2013;Tang and Davis-Kean 2015). This type of parental involvement in education may be qualitatively different and may have different consequences compared to involvement that is not triggered by academic difficulties (Robinson and Harris 2013). ...
... Robinson and Harris (2013) found that parents' endorsement of punitive responses during primary school (e.g., punish the child, limit child's nonschool activities) were negatively related to achievement during secondary school, and parents' endorsement of nonpunitive responses during primary school (e.g., contact child's teacher or principal, spend more time helping child with homework, keep a close eye on child's activities, tell child to spend more time on schoolwork) were positively related to achievement during secondary school, controlling for prior achievement. Similarly, endorsement of punitive responses but not nonpunitive responses at ages 11-13 were negatively related to adolescent academic achievement five years later in another study (Tang and Davis-Kean 2015). However, the link between punitive responses and academic achievement did not remain once prior academic achievement was controlled. ...
... Although only a few studies have directly examined punitive responses to grades (Robinson and Harris 2013;Tang and Davis-Kean 2015), the literature on punitive parenting, more broadly, provides evidence that this form of punitive parenting may be negatively related to grades. Previous literature suggests that punitive parenting is associated with socioemotional adjustment problems (e.g., defiance and aggression; Roche et al. 2007Roche et al. , 2011 and socioemotional adjustment problems are associated with poor academic achievement (Okano et al. 2020). ...
Article
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Parental involvement in education has generally been shown to foster adolescent academic achievement, yet little is known about whether two important forms of parental involvement—how parents respond to academic underachievement and how parents provide cognitive stimulation in the home—are related to academic achievement for African American adolescents. This study uses two waves of data to evaluate whether these forms of parental involvement are related to future academic achievement for low-income African American adolescents and whether there are gender differences in these associations. African American mothers and adolescents (N = 226; 48% girls) were interviewed when adolescents were ages 14 and 16. Mothers of girls reported higher mean levels of punitive responses to grades than mothers of boys, but child gender did not moderate associations between parental involvement and academic achievement. Cognitive stimulation in the home was related to changes in academic achievement from 14 to 16 years of age, controlling for age 14 academic achievement. This study provides evidence that nonpunitive responses to inadequate grades and cognitive stimulation at home are linked to academic achievement among African American adolescents.
... Naz et al. (2011) also demonstrated that CP causes lower class participation, create distraction leading to inhibition of learning and creativity, reluctance and academic decline in students (Arif and Rafi 2007). Further, it was observed that chronic use of CP can also affect the child's brain development leading to low cognitive skills (Tang and Davis-Kean 2015). A major 2002 meta-analysis of 88 studies found associations between parental CP and negative outcomes , Zaman et al. 2013. ...
... The R 2 change indicated that 0.8% and 0.1% of variance in academic attainment was accounted for by corporal punishment. This finding is also consistent with the findings of previous researchers which indicate that chronic use of corporal punishment can affect the child's brain development which leading to low cognitive skills (Arif and Rafi 2007;Tang and Davis-Kean 2015). ...
Article
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The study explored the relationship of corporal punishment (CP), emotional intelligence (EI) and psycho-social health (PSH) with academic attainments of school children. Two hundred school children from classes V to X were selected purposively and conveniently from government and non-government Bangla medium schools in Dhaka City. A survey tool package comprising: 1) CP scale-father version, 2) CP scale-mother version, 3) EI scale and 4) SF-12 health survey questionnaire was administered on the respondents. Grade points averages (GPAs) recorded by the respondents in their previous class final examinations were considered as academic attainments of the children. Data were analyzed using Karl Pearson's product-moment correlation and step-wise multiple regression methods. Results indicated that there are significant negative relationships between CP, EI, and PSH and academic attainments of the children. Moreover, multiple regression analyses indicated that PSH and CP (father) were important predictors of academic attainments which jointly explained 2.6% of variance in academic attainments. R 2 change furthermore indicated that PSH was the best predictor which alone explained 1.7% of variation in academic attainments.
... Both dimensions are punitive in nature, but harsh punishment implies a physical component [26,27]. Reactive behavioural control has been shown to predict academic underperformance [28] and externalising problems [29]. In contrast, proactive control is a non-invasive form of parental control by which parents establish rules and limitations in order to encourage desired child behaviour [27]. ...
... six items, α W1 = 0.78, α W2 = 0.81, α W3 = 0.82) Parental monitoring of behaviour (e.g., "My parents remind me of the rules they made.", six items, α W1 = 0.66, α W2 = 0.65, α W3 = 0.69) Perceptions of parents scale (PPS) [72] and research assessment package for schools [73] Autonomy support (e.g., "My parents take into account my opinion on affairs that concern me.", eight items α W1 = 0.69, α W2 = 0.71, α W3 = 0.73) Psychological control scale (PCS) [74] Psychological control (e.g., "My parents do not talk to me when I disappointed them until I please them again", 8 items, α W1 = 0.70, α W2 = 0.75, α W3 = 0.75) Verbal hostility scale (VHS) [28] Hostility (e.g., "My parents yell or shout when I misbehave.", 6 items, α W1 = 0.85, α W1 = 0.87, α W3 = 0.87) ...
Article
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Research has indicated that a strictly dimensional or parental style approach does not capture the full complexity of parenting. To better understand this complexity, the current study combined these two approaches using a novel statistical technique, i.e., subspace K-means clustering. Four objectives were addressed. First, the study tried to identify meaningful groups of parents in longitudinal adolescent reports on parenting behaviour. Second, the dimensional structure of every cluster was inspected to uncover differences in parenting between and within clusters. Third, the parenting styles were compared on several adolescent characteristics. Fourth, to examine the impact of change in parenting style over time, we looked at the cluster membership over time. Longitudinal questionnaire data were collected at three annual waves, with 1,116 adolescents (mean age = 13.79 years) at wave 1. Based on five parenting dimensions (support and proactive, punitive, psychological and harsh control), subspace K-means clustering, analysed per wave separately, identified two clusters (authoritative and authoritarian parenting) in which parenting dimensions were interrelated differently. Authoritative parenting seemed to be beneficial for adolescent development (less externalising problem behaviour and higher self-concept). Longitudinal data revealed several parenting group trajectories which showed differential relations with adolescent outcomes. Change in membership from the authoritative cluster to the authoritarian cluster was associated with a decrease in self-concept and an increase in externalising problem behaviour, whereas changes from the authoritarian cluster to the authoritative cluster were associated with an increase in self-concept and a decrease in externalising problem behaviour.
... Another example: according to many authors, the best parental strategy is to grant autonomy to children; the worst strategy is to intervene too much or act too punitively (McNeal, 2012;Robinson and Harris, 2014: 191;Ruiz de Miguel, 2001;Tang and Davis-Kean, 2015). Once again, causal relationships are reversed: greater autonomy is offered with good grades while increased supervision and punishment with failing grades (Martín-Criado and Gómez-Bueno, 2017b). ...
... In general, it shows negative associations with child/adolescent externalizing problem behavior (Hanisch et al., 2014;Laible, Carlo, & Raffaelli, 2000). Results from previous studies also suggested a positive association between parental support, on the one hand, and social development (e.g., Barber, Stolz, & Olsen, 2005), academic performance (Tang & Davis-Kean, 2015) and morality (Bronstein, Fox, Kamon, & Knolls, 2007), on the other. Proactive control involves the use of a structured environment to anticipate and prevent possible undesirable child or adolescent behavior. ...
Article
Previous studies often assumed that parenting practices are similar across families. This assumption is difficult to hold, especially throughout adolescence, a period of major change for both adolescents and their parents. By combining a person-centered and a variable-centered approach, the present study adds to the literature by identifying trajectory classes in parenting behaviors and assessing their associations with externalizing problem behavior. The study aimed (a) to examine the existence of subgroups with different trajectories for five parenting dimensions (i.e., Support, Proactive Control, Punitive Control, Harsh Punitive Control, Psychological Control) in mothers and fathers separately, and (b) to assess whether membership of a subgroup is associated with the development of rule-breaking and aggressive behavior, respectively. The current study used four waves of data, with adolescents' age ranging from 12 to 18 years. Mothers (N= 747) and fathers (N= 645) reported on their own parenting behavior, whereas adolescent (N= 1,116) reported on externalizing problem behavior. Latent Class Growth Analyses per parenting dimension showed that trajectory classes could be distinguished for support, proactive, punitive, and psychological control, but not harsh punitive control, and this for both mother and father. Conditional growth models per parenting dimension and per parent did not show different trajectories for aggressive and rule-breaking behavior across adolescence for the distinct parenting trajectories. However, analyses indicated that depending on the parenting trajectory, there was a difference in initial (age 12) levels of problem behavior. Suggestions for additional research on longitudinal heterogeneity of parenting among mothers and fathers of adolescents are outlined.
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While taking advantage of the educational benefits of smartphones, students also apply this device in inappropriate ways that cause certain disciplinary and educational problems. This study examines the effect of self-management training on smartphone dependence among male high school students. Methods: In this quasi-experimental study, data were collected using the Cell Phone Addiction Scale (Koo, 2009), which was completed by the trial and control groups before and after the educational intervention. After assessing their normal distribution, the data were analysed using the Chi-square test, the independent and paired t -tests, Mann–Whitney's U -test, and the Wilcoxon test at a significance level of p < .05. Results: The results showed significant post-intervention reductions in the mean score of smartphone dependence (35.10) and its three domains, including withdrawal/tolerance (14.80), life dysfunction (8.70), and compulsion/persistence (11.60), in the trial group compared to the controls (44.80, 16.2, 12.10, and 16.50) and also in the mean score of certain applications of smartphones ( p < .05). Discussion and conclusions: Despite the existing limitations, the results confirmed the efficacy of self-management training in reducing smartphone dependence in the students. The implementation of this programme is recommended for reducing dependence and promoting the proper use of this device.
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School performance in childhood and adolescence is an important indicator for social inequality and various life outcomes in adulthood. Previous research confirmed genetic as well as environmental influences on individual differences in school grades, yet little is known on what lies behind the environmental influences. The aim of this study is to identify external covariates that account for variance in school grades and to disentangle genetic and (non-)shared environmental components in the association between these often assumed “environmental” variables and school grades. The sample consists of 2101 pairs of monozygotic and dizygotic same-sex twins (aged 11 and 17) from the German TwinLife study. Multiple regression analysis showed that our measured external variables (e.g., parental behavior, home environment, peer characteristics) explain about 7 – 9% of variance in the grade point average (GPA) in both age groups. In order to determine genetic and environmental sources of this variance component, we applied a bivariate Cholesky decomposition. Results indicate that after correcting for parental socio-economic status the relation between external covariates and the GPA is entirely due to shared environmental effects at age 11, while the association between the same set of covariates and GPA at age 17 is due to common genetic sources. This pattern largely remains when considering the covariates individually: Effects are strongest for home environment and negative parental involvement in both age groups and additionally for delinquent peer affiliations at age 17. We discuss possible underlying effects of gene × environment interactions and provide implications for further research.
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