Skip to main content
NCBI home page
NIHPA Author Manuscripts logoLink to NIHPA Author Manuscripts
. Author manuscript; available in PMC: 2024 Oct 1.
Published in final edited form as: J Fam Psychol. 2023 May 29;37(7):955–965. doi: 10.1037/fam0001119

Novel measures of family orientation and childhood self-regulation: A genetically informed twin study

Gianna Rea-Sandin 1,2, Christine Pajunar Li-Grining 3, José M Causadias 4, Leah D Doane 1, Nancy A Gonzales 1, Kathryn Lemery-Chalfant 1
PMCID: PMC10524749  NIHMSID: NIHMS1902085  PMID: 37253205

Abstract

There is a dearth of research examining the relation between culture and childhood self-regulation in family psychology. Family orientation refers to the emphasis on providing support, respect, and obligation to the family system, and it is important for children’s functioning, yet existing literature on related constructs often relies on parent-reported measures. Additionally, twin research has neglected the role of culture in the genetic and environmental contributions to children’s self-regulation. Using observational and self-reported data from children, parents, and teachers, this study: (1) proposed novel coding schemes and factor analytic approaches to capture family orientation, (2) examined associations between family orientation and self-regulation, and (3) tested whether family orientation moderated the heritability of self-regulation in middle childhood. Twin children (N=710; Mage=8.38 years, SD=0.66; 49.1% female; 28.3% Hispanic/Latino/x, 58.5% White) were drawn from the Arizona Twin Project, which recruited children from birth records at 12 months of age. Family orientation values were indexed by parent-reported familism, and family orientation behaviors comprised coded measures of children’s family orientation and experimenter ratings of caregiver and child behavior. Self-regulation was assessed using multiple task-based assessments of executive function and parent- and teacher-reported effortful control. Net of covariates, higher family orientation behaviors positively predicted nearly all measures of children’s self-regulation, and associations were consistent across sex, family socioeconomic status, and race/ethnicity. There was no evidence that family orientation values nor behaviors moderated the heritability of children’s self-regulation. This study highlights the complex nature of cultural variation within the family and its importance for children’s self-regulatory abilities.

Keywords: Behavioral genetics, family orientation, measurement, middle childhood, self-regulation

Self-regulation (i.e., the ability to shift one’s behavior, attention, and emotions both voluntarily and adaptively; Nigg, 2017) is a salient aspect of children’s socioemotional functioning (Liew, 2012). Cultural variation within the family is theorized to shape self-regulation in early childhood (Li-Grining, 2012); however, few studies have empirically tested this link. Also, the examination of culture in middle childhood relies on parent-reported measures, although older children are beginning to form their own value systems (Döring, et al., 2016). Finally, twin designs have demonstrated additive genetic and nonshared environmental influences on self-regulation (Rea-Sandin et al., 2022; Willems et al., 2019), but many twin studies have neglected cultural processes. Using a large, diverse sample of twins in middle childhood, this study developed novel measures of family orientation, examined the link between family orientation and self-regulation, and tested whether family orientation moderated the genetic and environmental contributions to self-regulation. Developing measures of culture within the family can aid intervention efforts seeking to elucidate contexts that promote self-regulation across children from various racial/ethnic and socioeconomic backgrounds.

Self-Regulation in Middle Childhood

Self-regulation is a multifaceted construct that refers to the ability to regulate one’s behavior in service of long-term goals (Nigg, 2017). Various subdisciplines of psychology conceptualize self-regulation in various ways, with some researchers supporting an integrated model of self-regulation that includes executive functioning and effortful control (e.g., Bridgett et al., 2015; Rea-Sandin et al., 2022). Executive functioning refers to top-down cognitive abilities involved in goal-directed behavior, and effortful control is the ability to willfully inhibit, activate, or modulate attention and behavior (Nigg, 2017). This study included task-based measures of executive functioning (Flanker Task, Continuous Performance Task, and Digit Span Backward) and parent- and teacher-reported effortful control that assessed attentional focusing, inhibitory control, activation control, and working memory components of self-regulation.

Middle childhood (ages 5–12) is an important period for the development of self-regulation, as this time is marked by the increasing need for children to regulate their own thoughts and behaviors to successfully navigate home and peer contexts (Votruba-Drzal, 2006). Across development, self-regulation is shaped by biological and social factors (e.g., Bridgett et al., 2015), but less is known about the role of cultural context for children’s self-regulation.

Family Orientation as a Cultural Process in Childhood

Familism is a multidimensional cultural construct that emphasizes support, respect, loyalty, and obligation to the family (Cahill et al., 2021), comprising attitudes regarding the importance of family and behaviors that reflect how family is prioritized (Hernández & Bámaca-Colbert, 2016). Several attitudinal and behavioral measures of familism exist, with most childhood research relying on parent-reports (see Stein et al., 2014). Familism is argued to be specific to Hispanic/Latino individuals (Grau, et al., 2009), but has also been identified in other racial/ethnic groups (Christophe et al., 2022). We use the term familism when describing previous research and family orientation in our study given the racial/ethnic diversity of our sample.

Much of the work on familism focuses on adolescents (Stein et al., 2014), with the limited handful of studies in middle childhood demonstrating the positive effects of parental attitudinal familism on children’s outcomes, including prosocial behaviors and academic motivation (Morcillo et al., 2011; Stein et al., 2020). It is also theorized that family cultural values could support children’s self-regulation (Li-Grining, 2012; Raver, 2004). For example, familism values emphasize the need for children to place the family’s needs before their own (Stein et al., 2014). This could, in turn, bolster children’s ability to inhibit their automatic responses or desires to successfully function within the family system. To our knowledge, only one study has explicitly examined associations between family cultural values and children’s self-regulation. In a mixed-methods study of low-income Mexican American parents and their preschool-aged children, Díaz and McClelland (2017) found that parental beliefs did not significantly predict children’s self-regulation, although it is likely that the study’s small sample size (N=44) resulted in the inability to detect a significant effect. However, through semistructured interviews, parents shared beliefs regarding the expectation for their children to be respectful/obedient to ensure that children would become productive adults. This past study highlights the need for measures of family cultural values that go beyond parent-report as well as the need for larger studies to examine links between culture and self-regulation.

In addition, there is evidence that familism values differentially operate by sex and socioeconomic status (SES). For example, research suggests that familism is more salient for girls compared to boys but may be less promotive for females experiencing particularly high levels of stress (e.g., adolescent mothers, adult caretakers; Cahill et al., 2021). In addition, studies show that Hispanic/Latino families from low SES backgrounds demonstrate higher levels of familism (Bush et al., 2004). In sum, sex and SES may differentially impact how familism is related to children’s self-regulation, highlighting the need to examine the heterogeneity that exists both between and within groups.

Culture Moderating the Heritability of Self-Regulation

The twin design leverages twins’ genetic relatedness to parse the genetic and environmental influences on a trait. Self-regulation and related constructs are highly heritable, with prior studies showing lower heritability estimates for task-based assessments of executive function (e.g., Rea-Sandin et al., 2022; Schachar et al., 2010) and stronger genetic influences on survey measures of effortful control (Willems et al., 2019).

Measures of the environment, including parenting and the home environment, are also shown to be genetically influenced, reflecting the notion that individuals play an active role in choosing and constructing their environments that align with their genetic proclivities (Kendler & Baker, 2007). However, less is known about the heritability of broader familial constructs, such as cultural values within the family. Here, acknowledging individual differences and dynamic proximal cultural processes, we developed an indicator of family orientation, which allows us to assess the extent that family orientation is shaped by genetic and environmental factors. Together, integrating culturally- and genetically-informed approaches can provide a richer, more nuanced approach to understand what contributes to individual differences.

Heritability estimates provide useful information regarding main effects of genetics and environment on trait variation at the population level. However, the classical twin model assumes that these estimates are independent (Neale & Maes, 2004), which does not estimate gene-environment interplay, nor considers the role of culture. Rather, several theories and frameworks, such as dual inheritance theory (Boyd & Richerson, 1987) and cultural genomics (Causadias & Korous, 2018) posit that variation in behavior and cognition is a result of complex interactions between genes, environments, and culture that occur across social, developmental, and evolutionary levels (Li, 2003).

The interplay between genes, environment, and culture can be statistically tested through an extension of the classical twin model, which can be referred to as gene-environment interaction or moderated heritability, where the genetic and environmental variance components on a trait are allowed to vary by a moderator (i.e., culture) (Purcell, 2002; van der Sluis et al., 2012). Familism/family orientation involve values and expectations that shape family dynamics and individual adjustment (Cahill et al., 2021), thus potentially interacting with the genetic and environmental influences on children’s cognitive and socioemotional behaviors. For example, emphasizing support, respect, and obligation to the family system could promote genetic influences on self-regulation (i.e., MZ twins being more similar on self-regulation compared to DZ twins). However, it could also be the case that the family cultural context tempers genetic expression (i.e., MZ and DZ twins showing similar levels of self-regulation), resulting in lower heritability and higher environmental influences on children’s self-regulation.

Although prior studies point to the importance of the family context for the heritability of self-regulatory behaviors, this study is the first explicit examination of cultural variability in the family as a moderator of the genetic and environmental influences on self-regulation. In middle childhood, parental involvement increased the additive genetic and decreased the nonshared environmental influences on ADHD (Nikolas et al., 2015), and decreased the heritability of callous-unemotional traits (Tomlinson et al., 2021). However, other studies have not detected moderation when investigating middle childhood executive function (Rea-Sandin, 2018) or effortful control (Lemery-Chalfant et al., 2013). Together, this work reifies the complexity of gene-environment interactions. Examining how family orientation is implicated in the heritability of self-regulation can expand our understanding of culture in children’s development.

Current Study

The present investigation was guided by the following goals. First, we aimed to broaden research on cultural processes in child development by proposing novel coding schemes and factor analytic approaches to capture family orientation among socioeconomically and racially/ethnically diverse children. Proposed measures of family orientation were ascertained from coded videotapes of parent-child interactions, trained experimenter observations, and parent-reported measures. To assess family orientation values, this study used familism scales from the Mexican American Cultural Values Scale (MACVS; Knight et al., 2010): support (desirability to maintain close relationships with family), obligation (e.g., inclusion of family in decision-making), and referents (using the family to define oneself; Knight et al., 2010). The measures were considered in an exploratory factor analysis (EFA) indexing family orientation, and factors were compared to established measures to test validity (Knight et al., 2010).

Second, we examined associations between family orientation and self-regulation (measured using task-based assessments of executive function and parent- and teacher-reported effortful control) in middle childhood. We hypothesized that children higher on family orientation would demonstrate greater self-regulation in middle childhood across racial/ethnic groups, and that associations would be stronger for girls and children from families from lower SES backgrounds (Li-Grining, 2012; Morcillo et al., 2011).

Next, we estimated the additive genetic, shared environmental, and nonshared environmental variance components for the novel measure of family orientation, and for the tasks/scales assessing self-regulation. We hypothesized that family orientation would be influenced by both genetic and environmental influences (Kendler & Baker, 2007), whereas we predicted that differences in self-regulation measures would be attributed to additive genetic and nonshared environmental influences (Schachar et al., 2010; Willems et al., 2019). Finally, we tested whether family orientation moderated the genetic and environmental influences on self-regulation in middle childhood. We hypothesized that differences in individual-level family orientation would be partially heritable but largely environmental (Kendler & Baker, 2007), and that higher family orientation would increase the extent to which environmental influences explained self-regulation (Shanahan & Hofer, 2005). Considering culture in a behavior genetic framework can help researchers better understand the genetic and environmental contributions to the etiology of children’s self-regulation.

Method

Participants

Participants were drawn from the Arizona Twin Project, a demographically representative and diverse sample of twins (N = 710 youth; Lemery-Chalfant et al., 2019) recruited from birth records when the twins were 12 months old. The analytic sample comprised children (MZ pairs=30.2%, same-sex DZ pairs=37.1%, opposite-sex DZ pairs=32.7%; 49.1% female) assessed at age 8.38 years (SD=0.66). The twins were from a range of racial/ethnic backgrounds (58.5% White, 23.7% Hispanic/Latino/x, 3.9% Black, 3.1% Asian, 2.7% Native American, 0.9% Native Hawaiian or Pacific Islander, 4.8% Multiracial, and 2.5% Other/did not disclose). Seven percent of families lived below the poverty line, 23% at or near the poverty line, 16.4% in lower middle class, and 53.7% in middle to upper class at the 8-year wave of data collection. Our sample is representative of the state of Arizona, with families residing in suburban (36%), metropolitan (27%), urban (13%), and rural (24%) contexts. See supplemental materials for details on the current analytic sample.

Procedure

Institutional Review Board approval was obtained at every wave of data collection. Primary caregivers (94.9% mothers) provided written informed consent and twins assented to participation. Primary caregivers completed online or paper surveys about demographics, familial roles and values, and their twins’ effortful control. Teachers (59.7% of dyads had different teachers) completed a survey about the twins’ effortful control. Across two home visits, the twins participated in videotaped interaction tasks and completed computer-based executive functioning tasks and an experimenter assessed the home. After the visit, two experimenters independently completed observational assessments. The study was not preregistered.

Measures

Measures of Family Orientation.

We selected the following measures as indicators of family orientation following theory and the MACVS familism scale (Knight et al., 2010):

Parent-Child Interaction Task.

During the home visit, the primary caregiver recalled three recent times when they had a problem or argument with each twin. While videotaped, the primary caregiver and each twin discussed the topics and identified potential solutions for 7.5 minutes (Eyeberg et al., 2003). Trained coders then evaluated the extent that each of the topics (up to 3 per primary caregiver-child dyad) reflected family orientation based on contents from the videotaped interaction task. Ratings included: 1=definitely not family orientation, 2=maybe not family orientation, 3=maybe family orientation and 4=definitely family orientation. For example, a primary caregiver citing the need for their child to help with chores because that is their role in the family would receive a 4 because it is reflective of cultural values surrounding obligation to the family, whereas the need for the child to help with chores to learn valuable life skills would receive a 2 because it centered around the child as an individual. Each child’s reaction to each topic (child family orientation) was also coded on a 1–4 Likert scale: 1=expresses intense disagreement with caregiver’s statement or does not engage, 2=expresses disagreement with statement but reaction is less intense, 3=child might not agree with caregiver’s statement, but is compliant, and 4=child agrees with caregiver’s statement and actively tries to come up with solutions.

A total of 555 videos were coded. The first author and 2 research assistants coded 3 sets of 10 videos. Then, each experimenter coded half of the remaining videos, and the first author coded every 10th video as a reliability check. Discrepancies were resolved as a group. Rater reliability was κ = .86, .81, and .67 for the first, second, and third topics, respectively, and κ = .77, .73, and .68 for the child’s response to the topics. The most common topics included homework, household chores/cleaning up after themselves, screen time/playtime, getting ready for bed, getting ready for school/after school activities, following caregiver instructions, arguing/attitude towards caregiver, and conflict between siblings.

Experimenter observation of twin affect and behavior.

After each of the two home visits, experimenters independently rated each of the twins’ behavior using a revised version of the Bayley Rating Scales (Bayley, 1993; Lemery-Chalfant et al., 2006) that was appropriate for older children. Experimenters evaluated each item on a scale of 1 to 5, with higher scores indicating higher endorsement of the item. Eight items were hypothesized to be related to family orientation: 1) Compliance with child tester, 2) Compliance with primary caregiver, 3) Resistance with primary caregiver (reverse-scored), 4) Primary caregiver reads twins’ cues and responds sensitively and appropriately, 5) Connectedness between parent and twin, 6) Parent intrusiveness (reverse-scored), 7) Reciprocity of parent and twin, and 8) Organization/regulation of parent-twin interaction. Specifically, items 1–3 and 6 could reflect cultural values surrounding respecting caregivers and other adults, whereas items 4, 5, 7, and 8 could reflect values surrounding family interconnectedness and strong emotional relationships between parents and children (Christophe et al., 2022; Knight et al., 2010; Stein et al., 2014). Ratings were averaged across the two raters and across the two home visits. For visit 1, there were medium positive correlations between raters for Twin A, r=.45, p<.001 and Twin B, r=.41, p<.001. For visit 2, there were moderate positive correlations between raters for Twin A, r=.51, p<.001 and Twin B r=.55, p<.001. Ratings across visits were correlated r=.56, p<.001.

Family Decision Making.

Using the Decision Making Questionnaire (Dornbusch et al., 1990), the primary caregiver indicated which family member makes decisions about chores, appearance, homework/schoolwork, social life, bedtime/curfew, health, choosing activities, and money. Each domain was dummy coded, where 1 indicated the decision is made by either the primary or secondary caregiver and 0 if other family members make the decision. The scores were summed to create a family-level variable, with higher scores indicating higher family orientation. Household decisions being made primarily by caregivers could reflect the familism value that parents are the ultimate authority and are responsible for defining familial expectations (Stein et al., 2014).

Family Cultural Values.

The familism (17 items) domain of the MACVS (Knight et al., 2010) is a parent-report measure assessing cultural values for Mexican American individuals, but some items were adjusted to be administered across the entire sample (Christophe et al., 2022). There are 3 subscales within the familism domain (α=.85): support (close family relationships; α=.77), obligation (obligation to provide tangible support to family; α=.67), and referents (the reliance on communal family relationships to define oneself; α=.73). A 5-point Likert scale was used ranging from “not at all” to “completely.” Example items include “Family provides a sense of security because they will always be there for you” (Support), “Older kids should take care of and be role models for their younger brothers and sisters” (Obligation), and “Children should always do things to make their parents happy” (Referents). The Respect (α=.86), Religion (α=.98), Traditional Gender Roles (α=.71), Material Success (α=.77), Competition and Personal Achievement (α=.73), Traditional Values (α=.80), and Mainstream Values (α=.93) scales were also used to validate the proposed measure of family orientation.

Outcome Measures

Executive Functioning Tasks.

The Continuous Performance Task and Flanker Task were administered using the Psychology Experiment Building Language (Mueller, 2013). The Continuous Performance Task (Conners, 2000) assessed response inhibition. The 14-minute-long task required that children press the spacebar for any letter but to not respond when the letter X appeared. The task consisted of 360 letters (in 18 consecutive blocks of 20 trials) that appeared on the screen for 250 milliseconds at a time. Participants were instructed to press the spacebar to proceed to the next block. A “correct rejection” score refers to trials where the child did not press the spacebar when presented with the letter X, and “miss” refers to trials where the child failed to press the spacebar when the target (not X) was presented. A Detectability score was calculated where separate proportion scores were created by dividing the number of correct rejections and misses by the total number of trials (Connors, 2000). The correct rejection and miss proportion scores were z-scored, and then the standardized proportion of misses were subtracted from the standardized proportion of correct rejections (Connors, 2000). This measure has established predictive validity (Connors et al., 2003).

The Flanker Task (Eriksen & Eriksen, 1974) assesses inhibitory control and selective attention. In this 10-minute-long task, children were instructed to respond to a central arrow with the left or right shift key while ignoring congruent (pointing in the same direction) or incongruent (pointing in the opposite direction) flanker arrows. The task comprised 8 practice trials and 160 actual trials. For each trial, researchers recorded reaction time (RT) and whether the child correctly pressed the button corresponding with the direction of the center arrow. An Inverse Efficiency Test score was calculated. For each condition, the mean of correct trials to total trials was calculated and then multiplied by 100 to rescale the variables. Mean RT was divided by the proportion of correct to total trials for each condition (Christie & Klein, 1995), and a mean composite of congruent and incongruent scores was reverse-scored, so that higher scores indicate better performance. The Flanker Task has established convergent validity with other neuropsychological tasks and questionnaire measures of EF (Duckworth & Kern, 2011).

Child Effortful Control.

The Temperament in Middle Childhood Questionnaire (Simonds & Rothbart, 2006) is a parent- and teacher-report measure including the Attentional Focusing (7 items; parent-report α=.90; teacher-report α=.96), Inhibitory Control (8 items; parent-report α=.68; teacher-report α=.81), and Activation Control (15 items; parent-report α=.77; teacher-report α=.57) scales which assess dimensions of Effortful Control. Primary caregivers and teachers rated the twins on a 7-point Likert scale ranging from “extremely untrue of child” to “extremely true of child.” Example items include “When building or putting something together, Twin A/B becomes very involved in what s/he is doing, and works for long periods” (Attentional Focusing), “Twin A/B is good at following instructions” (Inhibitory Control), and “When a child is left out, Twin A/B can ask that child to play” (Activation Control). This measure shows good predictive validity (Kotelnikova et al., 2017). Scales for parent-reported and teacher-reported effortful control were mean-composited and moderately correlated with one another (parent-reported rs ranged .43–.58; teacher-reported rs ranged .56–.69).

Working Memory.

Working memory was assessed using the Digit Span Backward task from the Wechsler Intelligence Scale for Children, Fourth Edition (Wechsler, 2003). The experimenter read a series of numbers aloud, and the child was asked to repeat the series backward, with each trial getting progressively more difficult. There were 7 total sets containing 2 trials each. The task began with 3 digits and the child had to successfully recite both sets before moving onto the next set. The task ended after 9 digits, or if the child had two incorrect trials in a row. Each correct trial received one point and a total sum score was computed, such that higher scores indicated better performance. This task has demonstrated good predictive validity (Conway et al., 2005).

Covariates.

Child age, sex (1=female), race/ethnicity, and SES (a standardized composite of income-to-needs ratio, and primary and secondary caregiver education) were included as covariates and tested as main effects and two-way interactions with the primary predictors. Two dummy code variables (1=Hispanic/Latino/x and 1=European American) indexed race/ethnicity based on parent reports. For twin analyses, unstandardized residual scores were used after regressing out the effects of child sex and age (McGue & Bouchard, 1984).

Analytic Plan

Zero-order correlations, mixed model regression analyses, and moderated heritability models were conducted using MPlus 7.0 (Muthén & Muthén, 2015). For Aim 1 (using a multimethod approach to assess family orientation), proposed measures of family orientation were included in an EFA. 1-, 2-, and 3-factor solutions. An oblique rotation was used because the factors were expected to be correlated. Models were compared using the following fit indices: eigenvalues, chi-square test, RMSEA ~.06, CFI ~.95, and SRMR ~.08 (Fabrigar et al., 1999; Hu & Bentler, 1999). For the final model, loadings above .30 were retained (Costello & Osborne, 2005), and unstandardized factor scores were formed and used in subsequent analyses.

Aim 2 (examining whether family orientation predicts children’s self-regulation) involved conducting mixed model regression analyses. Main effects of all covariates were retained in the final models, regardless of significance. With child sex, family SES, and race/ethnicity (Hispanic/Latino/x and European American) as moderators, interactions were individually tested while estimating the main effects of all family orientation variables. The type=complex command accounted for twin interdependence and full information maximum likelihood with robust standard errors (MLR) was used to handle missing data (Muthén & Kaplan, 1985). Predictors, moderators, and covariates were centered at zero and were used to create interaction terms. Unstandardized beta coefficients and standard errors were reported.

For Aim 3, OpenMX (Neale et al., 2016) was used to fit univariate twin models (Neale & Maes, 2004). Saturated twin models tested the assumptions of the twin design (see Rea-Sandin, 2022). Next, full univariate twin models were fit that decompose the variance of a trait into latent additive genetic (A, linear effect of multiple genes, also known as heritability), shared environmental (C, environmental experiences that increase cotwin similarity), and non-shared environmental (E, environmental experiences that cause twins to become dissimilar and measurement error) factors. Moderated heritability models were tested using full-information maximum-likelihood techniques. The extended univariate gene-environment (GxE) interaction model (Purcell, 2002; van der Sluis et al., 2012; Figure S1) tested whether ACE influences on self-regulation varied as a function of family orientation values and behaviors. See the supplemental material for details. Materials and analysis code for this study are available by emailing the corresponding author.

Results

Descriptive statistics are reported in Table S1. All variables were normally distributed and were within acceptable ranges for skewness and kurtosis (Curran et al., 1996). Zero-order correlations are reported in Table S2. Based on correlations and theoretical connections to familism, these variables were included in the EFA: primary caregiver family orientation, child family orientation, familism–support, familism–obligation, familism–referents, parent decision making, and experimenter post-visit ratings (compliance with child tester, compliance with primary caregiver, lack of resistance with primary caregiver, sensitive parenting, parent-child connectedness, primary caregiver lack of intrusiveness, reciprocity of primary caregiver and child, and regulation of the dyad).

Aim 1: Developing novel measures of family orientation

Table S3 presents standardized factor loadings from the EFA that tested 1-, 2-, and 3-factor solutions of our proposed family orientation variables. The 1-factor solution showed poor model fit, χ2(77)=1725.4, p<.001; SRMR=.128; RMSEA=.179; CFI=.611. The 2-factor solution had better model fit compared to the 1-factor solution, χ2(64)=860.65, p<.001; SRMR=.062; RMSEA=.137; CFI=.812. The 3-factor solution had the best model fit, χ2(52)=138.86, p<.001; SRMR=.022; RMSEA=.050; CFI=.979. The 2-factor solution was selected as the final model, as it supports theory surrounding attitudinal (family orientation values) and behavioral (family orientation behaviors) manifestations of family orientation (Hernández & Bámaca-Colbert, 2016). Primary caregiver family orientation and parent-decision making did not load on either factor, and thus were dropped.

Indicators that were retained from the 2-factor EFA were included in a 2-factor correlated factors model (Figure 1), χ2(53)=925.89, p<.001; SRMR=.089; RMSEA=.157; CFI=.792. Unstandardized factor scores were extracted for use in subsequent analyses. Family orientation values and behaviors were negatively correlated (r=−.15, p<.01). Internal consistency for family orientation values and behaviors was .79 and .88, respectively.

Figure 1.

Figure 1.

Open in a new tab

Correlated factors model of family orientation.

Note. χ2=chi-square; df = degrees of freedom; SRMR = Standardized Root Mean Square Residual; RMSEA = Root Mean Square Error of Approximation; CFI = Comparative Fit Index. PC = primary caregiver; P-C = parent-child. The family orientation values latent variable is at the family-level whereas the family orientation behaviors latent variable is at the twin-level. Unstandardized loadings were used in subsequent analyses, but standardized loadings are presented in the figure.

Independent samples t-tests examined if family orientation values and behaviors differed by sex and race/ethnicity. Boys had fewer family orientation behaviors (M=−.07, SD=.95) compared to girls (M=.10, SD=.80, t(666)=−2.55, p<.05). Hispanic/Latino/x primary caregivers demonstrated higher family orientation values (M=.15, SD=.83) than non-Latinx primary caregivers (M=−.10, SD=.93, t(634)=−3.12, p<.01). Latinx children had comparable levels of family orientation behaviors (M=.10, SD=.86) as non-Latinx children (M=.01, SD=.01, t(634)=−1.17, p=.24). European American children had similar levels of family orientation behaviors (M=.04, SD=.86) as non-European American children (M=.04, SD=.88, t(634)=0.10, p=.92).

The validity of the factors was assessed by examining correlations between family orientation values and behaviors with subscales from the MACVS. Family orientation values, comprised of MACVS familism, were positively correlated with all subscales (rs ranging from .27 to .80, p-values ranging from <.01 to <.001). Family orientation behaviors were negatively correlated with Respect, Competition and Personal Achievement, Traditional Values, and Mainstream Values (rs ranging from −.08 to −.13, p-values ranging from >.05 to <.01) but uncorrelated with Religion, Traditional Gender Roles, and Material Success.

Aim 2: Associations between family orientation and self-regulation in middle childhood

Five models were tested with main effects of predictors and covariates (child sex, family SES, and race/ethnicity) predicting each outcome, then interactions between covariates and predictors were tested separately (Table 1). There was one significant interaction between family orientation behaviors and SES predicting teacher-reported effortful control. It likely occurred by chance and thus was not interpreted but was included in the final model.

Table 1.

Family orientation values and behaviors predicting self-regulation in middle childhood

CPT Flanker Task DSB EC EC-TR
Intercept 0.11(.05)* −10.29(.16)*** 3.99(.06)*** 3.30(.03)*** 3.63(.03)***
Age 0.19(.09)* 1.35(.28)*** 0.37(.10)*** 0.08(.04)* −0.03(.06)
Sex 0.39(.09)*** −1.66(.30)*** −0.12(11) 0.17(.04)*** 0.35(.06)***
SES 0.01(.06) 0.41(.24) 0.27(.09)** 0.11(.03)** 0.08(.05)
Hispanic/Latino/x −0.02(.19) 1.41(.54)** 0.16(21) 0.04(.10) 0.01(.12)
EA 0.15(18) 1.17(.47)* −0.07(.20) 0.05(.09) 0.05(.10)
FO Values 0.07(.06) −0.01(.17) 0.06(.08) 0.06(.03) 0.01(.04)
FO Behaviors 0.32(.05)*** 0.40(.20)* 0.07(.08) 0.16(.03)*** 0.16(.04)***
FO Behaviors x SES - - - - −0.17(.06)**
Open in a new tab

Note. The effects of family orientation and covariates on outcomes were tested simultaneously. Covariates, predictors, and moderators were grand mean centered. Sex (1=female), Hispanic/Latino/x (1= Hispanic/Latino/x), EA=European American (1=non-Hispanic white/European American), FO=family orientation. CPT=Continuous Performance Task. DSB=Digit Span Backward. EC=effortful control; TR=teacher-report; SES=socioeconomic status. Unstandardized partial regression coefficient estimates were reported and robust standard errors are reported in parentheses.

*

p < .05;

**

p < .01;

***

p < .001.

Older children had higher scores on all self-regulation measures, with the exception of teacher-reported effortful control. Girls showed higher scores on the Continuous Performance Task and parent- and teacher-reported effortful control, whereas boys performed better on the Flanker Task. Children from families with higher SES had higher scores on Digit Span Backward and parent-reported effortful control. Both Hispanic/Latino/x and European American children demonstrated better performance on the Flanker Task. Next, family orientation values were not significantly associated with any of the outcome measures. Finally, family orientation behaviors predicted higher scores on all self-regulation measures, with the exception of Digit Span Backward.

Aim 3: Testing whether family orientation moderates the heritability of self-regulation

Univariate Twin Models

Saturated models showed that means, variances, and covariances could be equated across sex, and means and variances could be equated for all variables across zygosity groups. Univariate fit statistics and standardized parameter estimates are reported in Table S4. With the exception of family orientation behaviors (A=32%, C=63%, E=5%), additive genetic and nonshared environmental influences explained the variance for the Continuous Performance Task (A=27%, E=73%), Flanker Task (A=47%, E=53%), and Digit Span Backward (A=32%, 68%). The variance in parent- and teacher-reported effortful control was largely explained by additive genetic influences (80% and 73%, respectively).

Estimates and fit statistics for moderated heritability models can be found in Table S5. Full models were tested first and included ACE variance components and moderated paths. Reduced nested models were fit testing various combinations of variance components and moderated paths. Models estimating A and E variance components and A and E moderated paths were then tested, as univariate models suggested no C influences on self-regulation. Finally, moderated paths were subsequently dropped, with final models only estimating variance components (and no moderated paths). Across all moderators and outcomes, there was no evidence of moderated genetic and environmental variance components, as no moderation models fit significantly better than models that did not include moderation.

Discussion

This study: (1) expanded the measurement of family orientation in middle childhood, (2) examined associations between family orientation and children’s self-regulation, (3) examined the heritability of family orientation and self-regulation, and (4) considered whether the heritability of self-regulation varied by family orientation. The consideration of cultural processes previously identified in Hispanic/Latino/x, Asian, and Black families challenges notions that culture is specific to racial/ethnic minorities and that White performance is the standard (Causadias et al., 2018). Understanding cultural values within the family across all backgrounds can help improve contextual factors relevant to children’s self-regulation and has important implications for clinical work with families and among subgroups.

Expanding the measurement of culture within the family

Using our coded measure of family orientation, trained experimenter observations, and parent-reported measures, a 2-factor EFA solution was selected to represent family orientation values and behaviors. Conceptual and empirical associations with the MACVS (Knight et al., 2010) provide initial support of validity, although validity should be further investigated. Still, this study contributes novel ways to assess cultural variation within the family across cultural groups and complements existing measures.

Interestingly, family orientation values and behaviors were negatively correlated (r=−.15, p<.01), which contrasts with work on positive associations between familism values and parental warmth/support in samples of Hispanic/Latino/x youth (Cahill et al., 2021; Stein et al., 2014). Familism values have been associated with both authoritarian and warm parenting, such that parents are stricter, and show greater rejecting behaviors, but also demonstrate a comparable amount of warmth and support to their children (Mahrer et al., 2019). It is likely that our family orientation factors are tapping into this “no-nonsense” style of parenting, with the values factor being related to strict, authoritarian parenting and the behaviors factor assessing warm/supportive parenting. Familism/family orientation are multifaceted cultural processes, where cultural values and behaviors that center around the family do not translate to just authoritarian parenting or just warm, sensitive parenting (Mahrer et al., 2019). Nevertheless, replication of the unexpected, negative association between family orientation values and behaviors is needed.

Associations between family orientation and children’s self-regulation

Second, there was partial support for hypotheses on the relation between children’s higher family orientation and greater self-regulation in middle childhood. Family orientation behaviors, but not values, predicted higher performance on the Continuous Performance Task and the Flanker Task, as well as elevated parent- and teacher-reported effortful control, net of covariates. Longitudinal associations should be tested, as children’s self-regulation could predict family orientation. However, these results suggest that family orientation behaviors could support the development of children’s self-regulation. For example, families that endorse family orientation values prioritize putting the needs of the family before the individual, potentially shaping how children regulate their emotions to successfully function within the family. Higher levels of parent-child connectedness could also foster self-regulation, as research suggests that children from mother-child dyads with high levels of cooperation on a lab task demonstrated higher executive functioning skills (Hinnant et al., 2013). Other research shows that the Family Check-Up intervention increased parents’ positive behavioral support which promoted children’s effortful control (Hentges et al., 2020).

However, we found that family orientation behaviors were not significantly associated with digit span backward, an executive functioning task that assesses working memory ability. It has been asserted that executive functioning varies as a function of motivational significance, leading to the distinction between “hot,” affective aspects and “cool,” decontextualized aspects of executive functioning processes (Zelazo & Carlson, 2012). Tasks, such as digit span backward, are said to tap into “cool” regulation, with working memory skills helping children complete complex math problems or understand reading material (Diamond, 2013; Zelazo & Carlson, 2012). Thus, given that the family context involves emotional bonds between individuals, it is possible that behavioral manifestations of culture within the family could be more relevant for “hot,” emotion-laden, compared to “cool,” decontextualized aspects of self-regulation (Díaz & McClelland, 2017; Li-Grining, 2012).

Familial values provide a cultural lens that impacts how caregivers parent their children, as well as shape how children view the world and themselves (Hernández & Bámaca-Colbert, 2016). However, we found that family orientation values, defined by primary caregiver reports of familism support, obligation, and referents, did not predict children’s self-regulation. Middle childhood is a period marked by increased agency, as youth are beginning to form their own value systems that may, or may not, align with their caregivers (Döring, et al., 2016; Stein et al., 2014). This suggests that children’s endorsement of family values may be more salient for their own self-regulatory behaviors. We show some evidence of this, as family orientation behaviors, which included children’s endorsement of family cultural values, was associated with increased self-regulation. Although more research is needed to evaluate children’s endorsement of family orientation, our study suggests that cultural components of family-level policy and interventions should not merely encourage discussion of family orientation values, but should specifically target and support the improvement of family orientation behaviors.

We also showed that associations between family orientation and self-regulation were consistent across SES. Providing support within the family can be adaptive when resources are scarce (Li-Grining, 2012), and families from high SES households can draw more upon stability and resources that support parent-child relationships (Kraus et al., 2012). This suggests that family orientation could be an effective target for clinical interventions seeking to promote children’s self-regulation. Furthermore, consistent with our hypotheses and previous research (Christophe et al., 2022), associations with children’s self-regulation were similar across racial/ethnic groups. Although researchers posit that valuing and prioritizing the family is a universal, rather than specific, cultural practice (Hardway & Fuligni, 2006), these findings do not minimize the importance of familism for racial/ethnic minority groups. Indeed, they suggest the importance of supporting and promoting family orientation values across all racial/ethnic groups.

Genetically-informed models

Aligned with our hypotheses and broader research examining the heritability of the environment (Kendler & Baker, 2007), univariate twin models revealed that family orientation behaviors were largely influenced by the shared environment, with the remaining variance explained by additive genetic and nonshared environmental factors. Yet, there was no evidence of moderation, which adds to mixed findings in existing research (Rea-Sandin, 2018; Nikolas et al., 2015; Tomlinson et al., 2021). Still, it may be that gene-environment effects were not detected because only considering the role of one variable on the heritability of our outcome likely underestimates the effect of broader contextual factors (Shanahan & Hofer, 2005).

Future Directions

In the future, new research should address limitations of this study. The discussion prompt likely led caregivers to list topics that were related to the family, potentially limiting the amount of variability in caregivers’ true family orientation. Also, caregivers provided varying levels of context for why particular topics were chosen, making some videos more difficult to code. For instance, many caregivers brought up the desire for their children to limit screen time but did not explain why (e.g., focus on homework, help with younger siblings). Given the openended nature of the discussion task, capturing the same level of detail across every family was unlikely. Next, the child’s endorsement of family orientation was in response to the caregiver. This captured the dyadic nature of family orientation, but future work should also consider independent assessments of children’s values and behaviors surrounding the family. Still, multiple trained experimenters being involved in coding and completing ratings reduced our risk of rater bias. Finally, measures of executive functioning and effortful control were considered as separate indicators of self-regulation, increasing the risk for Type I error. However, forming a composite measure would not have been representative of general self-regulation, as we did not assess every facet of executive functioning (e.g., set-shifting).

Psychological research often portrays Western samples as the ideal manifestation of human behavior and focuses on general attention to culture without delving into specific cultural attitudes and behaviors (Causadias et al., 2018). The present study challenged this bias by considering how family orientation values and behaviors promoted self-regulation using a diverse, representative sample that captured cultural variability between families (Coll et al., 2000) while employing multiple measures that tapped the multidimensional nature of self-regulation (Nigg, 2017). Importantly, this study provides preliminary evidence that coding parent-child discussions based on videotaped interactions is a novel way to assess cultural constructs. Future work can build on the present investigation by examining whether associations with self-regulation could vary by other aspects of culture (e.g., acculturation; Li-Grining, 2012).

Overall, this study suggests that family orientation should be considered in behavior genetic studies to improve understanding of the ways in which culture and biology influence human development. Notably, family orientation behaviors seemed to be important for children’s mean self-regulation. This finding should inform efforts to incorporate families’ lived cultural experiences in both policies and clinical practice. Understanding how cultural factors influence the genetic and environmental contributions to children’s development can guide initiatives devoted to increasing inclusivity and representation across the field of psychology, in the arenas of behavioral genetics, clinical practice, and child and family policy.

Supplementary Material

Supplemental Material

Acknowledgements

This research was supported by grants from the National Institute of Child Health and Human Development (R01HD079520, R01HD086085, and F31HD103374) and the National Institute on Drug Abuse (T32DA050560). Special thanks to the staff and students for their dedication to the Arizona Twin Project, and the participating families who generously shared their experiences.

References

  1. Bayley N (1993). Bayley scales of infant development: Manual. Psychological Corporation. [Google Scholar]
  2. Boyd R, & Richerson PJ (1987). The evolution of ethnic markers. Cultural Anthropology, 2, 65–79. [Google Scholar]
  3. Bridgett D, Burt N, Edwards E, & Deater-Deckard K (2015). Intergenerational transmission of self-regulation: A multidisciplinary review and integrative conceptual framework. Psychological Bulletin, 141, 602–654. 10.1037/a0038662 [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Bush KR, Supple AJ, & Lash SB (2004). Mexican adolescents’ perceptions of parental behaviors and authority as predictors of their self-esteem and sense of familism. Marriage & Family Review, 36, 35–65. 10.1300/J002v36n01_03 [DOI] [Google Scholar]
  5. Cahill KM, Updegraff KA, Causadias JM, & Korous KM (2021). Familism values and adjustment among Hispanic/Latino individuals: A systematic review and meta-analysis. Psychological Bulletin, 147, 947–985. 10.1037/bul0000336 [DOI] [Google Scholar]
  6. Christophe NK, & Stein GL (2022). Facilitating the study of familism across racial/ethnic groups: Creation of the Short Attitudinal Familism Scale. Journal of Family Psychology, 36, 534–544. 10.1037/fam0000954 [DOI] [PubMed] [Google Scholar]
  7. Causadias JM, & Korous KM (2017). How are genes related to culture? An introduction to the field of cultural genomics. The handbook of culture and biology, 151–177. [Google Scholar]
  8. Causadias JM, Vitriol JA, & Atkin AL (2018). Do we overemphasize the role of culture in the behavior of racial/ethnic minorities? Evidence of a cultural (mis)attribution bias in American psychology. American Psychologist, 73, 243–255. 10.1037/amp0000099 [DOI] [PubMed] [Google Scholar]
  9. Christie J, & Klein R (1995). Familiarity and attention: Does what we know affect what we notice?. Memory & Cognition, 23, 547–550. 10.3758/BF03197256 [DOI] [PubMed] [Google Scholar]
  10. Coll CG, Akerman A, & Cicchetti D (2000). Cultural influences on developmental processes and outcomes: Implications for the study of development and psychopathology. Development and Psychopathology, 12, 333–356. [DOI] [PubMed] [Google Scholar]
  11. Conners CK (2000). Conners’ continuous performance test II: Technical guide. Toronto, Canada: Multi-Health Systems. [Google Scholar]
  12. Conners CK, Epstein JN, Angold A, & Klaric J (2003). Continuous Performance Test performance in a normative epidemiological sample. Journal of Abnormal Child Psychology, 31, 555–562. 10.1023/A:1025457300409 [DOI] [PubMed] [Google Scholar]
  13. Conway AR, Kane MJ, Bunting MF, Hambrick DZ, Wilhelm O, & Engle RW (2005). Working memory span tasks: A methodological review and user’s guide. Psychonomic Bulletin & Review, 12, 769–786. 10.3758/BF03196772 [DOI] [PubMed] [Google Scholar]
  14. Costello AB, & Osborne J (2005). Best practices in exploratory factor analysis: Four recommendations for getting the most from your analysis. Practical Assessment, Research, and Evaluation, 10, 1–9. 10.7275/jyj1-4868 [DOI] [Google Scholar]
  15. Curran PJ, West SG, & Finch JF (1996). The robustness of test statistics to nonnormality and specification error in confirmatory factor analysis. Psychological Methods, 1, 16–29. 10.1037/1082-989X.1.1.16 [DOI] [Google Scholar]
  16. Diamond A (2013). Executive functions. Annual Review of Psychology, 64, 135–168. 10.1146/annurev-psych-113011-143750 [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Díaz G, & McClelland MM (2017). The influence of parenting on Mexican American children’s self‐regulation. PsyCh journal, 6, 43–56. 10.1002/pchj.158 [DOI] [PubMed] [Google Scholar]
  18. Döring A, Daniel E, & Knafo-Noam A (2016). Introduction to the special section value development from middle childhood to early adulthood-new insights from longitudinal and genetically informed research: Value development from middle childhood to early adulthood. Social Development, 25, 471–481. 10.1111/sode.12177 [DOI] [Google Scholar]
  19. Dornbusch SM, Ritter PL, Mont-Reynaud R, & Chen ZY (1990). Family decision making and academic performance in a diverse high school population. Journal of Adolescent Research, 5, 143–160. 10.1177/074355489052003 [DOI] [Google Scholar]
  20. Duckworth AL, & Kern ML (2011). A meta-analysis of the convergent validity of self-control measures. Journal of Research in Personality, 45, 259–268. 10.1016/j.jrp.2011.02.004 [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Eriksen BA, & Eriksen CW (1974). Effects of noise letters upon the identification of a target letter in a nonsearch task. Attention, Perception, & Psychophysics, 16, 143–149. 10.3758/BF03203267 [DOI] [Google Scholar]
  22. Eyberg SM, Nelson MM, Ginn NC, Bhuiyan N, & Boggs SR (2003). Dyadic Parent-Child Interaction Coding System (DPICS) comprehensive manual for research and training. 4th PCIT International; Gainsville, FL. [Google Scholar]
  23. Fabrigar LR, Wegener DT, MacCallum RC, & Strahan EJ (1999). Evaluating the use of exploratory factor analysis in psychological research. Psychological Methods, 4, 272–299. 10.1037/1082-989X.4.3.272 [DOI] [Google Scholar]
  24. Galindo C, & Fuller B (2010). The social competence of Latino kindergartners and growth in mathematical understanding. Developmental Psychology, 46, 579–592. 10.1037/a0017821 [DOI] [PubMed] [Google Scholar]
  25. Grau JM, Azmitia M, & Quattlebaum J (2009). Latino families: Parenting, relational, and developmental processes. In Villaruel FA, Carlo G, Grau JM, Azmitia M, Cabrera NJ, & Chahin TJ (Eds.), Handbook of U.S. Latino psychology (pp. 153–169). Thousand Oaks, CA: Sage. [Google Scholar]
  26. Hardway C, & Fuligni A (2006). Dimensions of family connectedness among adolescents with Mexican, Chinese, and European backgrounds. Developmental Psychology, 42, 1246–1258. 10.1037/0012-1649.42.6.1246 [DOI] [PubMed] [Google Scholar]
  27. Hentges R, Weaver CM, Shaw DS, Dishion TJ, Wilson MN, & Lemery-Chalfant K (2020). The long-term indirect effect of the early Family Check-Up intervention on adolescent internalizing and externalizing symptoms via inhibitory control. Development and Psychopathology, 32, 1544–1554. 10.1017/S0954579419001482 [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Hernandez MM & Bamaca-Colbert MY (2016). A behavioral process model of familism. Journal of Family Theory & Review, 8, 463–483. 10.1111/jftr.12166 [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Hinnant JB, Nelson JA, O’Brien M, Keane SP, & Calkins SD (2013). The interactive roles of parenting, emotion regulation and executive functioning in moral reasoning during middle childhood. Cognition & Emotion, 27, 1460–1468. 10.1080/02699931.2013.789792 [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Hu LT, & Bentler PM (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6, 1–55. [Google Scholar]
  31. Kendler KS, & Baker JH (2007). Genetic influences on measures of the environment: a systematic review. Psychological Medicine, 37, 615–626. 10.1017/S0033291706009524 [DOI] [PubMed] [Google Scholar]
  32. Knight GP, Gonzales NA, Saenz DS, Bonds DD, Germán M, Deardorff J, Roosav MW, & Updegraff KA (2010). The Mexican American cultural values scale for adolescents and adults. The Journal of Early Adolescence, 30, 444–481. 10.1177/0272431609338178 [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Kotelnikova Y, Olino TM, Klein DN, Mackrell SV, & Hayden EP (2017). Higher and lower order factor analyses of the Temperament in Middle Childhood Questionnaire. Assessment, 24, 1050–1061. 10.1177/1073191116639376 [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Kraus MW, Piff PK, Mendoza-Denton R, Rheinschmidt ML, & Keltner D (2012). Social class, solipsism, and contextualism: How the rich are different from the poor. Psychological Review, 119, 546–572. 10.1037/a0028756 [DOI] [PubMed] [Google Scholar]
  35. Lemery-Chalfant K, Goldsmith HH, Schmidt NL, Arneson CL, & Van Hulle CA (2006). Wisconsin Twin Panel: Current directions and findings. Twin Research and Human Genetics, 9, 1030–1037. 10.1375/twin.9.6.1030 [DOI] [PubMed] [Google Scholar]
  36. Lemery-Chalfant K, Kao K, Swann G, & Goldsmith HH (2013). Childhood temperament: Passive gene–environment correlation, gene–environment interaction, and the hidden importance of the family environment. Development and Psychopathology, 25, 51–63. 10.1017/S0954579412000892 [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Lemery-Chalfant K, Oro V, Rea-Sandin G, Miadich S, Lecarie E, Clifford S, Doane LD, & Davis M (2019). Arizona Twin Project: Specificity in risk and resilience for developmental psychopathology and health. Twin Research and Human Genetics 22, 681–685. 10.1017/thg.2019.113 [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Li SC (2003). Biocultural orchestration of developmental plasticity across levels: the interplay of biology and culture in shaping the mind and behavior across the life span. Psychological Bulletin, 129, 171–194. 10.1037/0033-2909.129.2.171 [DOI] [PubMed] [Google Scholar]
  39. Li-Grining CP (2012). The role of cultural factors in the development of Latino preschoolers’ self-regulation. Child Development Perspectives, 6, 210–217. 10.1111/j.1750-8606.2012.00255.x [DOI] [Google Scholar]
  40. Liew J (2012). Effortful control, executive functions, and education: Bringing self-regulatory and social-emotional competencies to the table. Child Development Perspectives, 6, 105–111. 10.1111/j.1750-8606.2011.00196.x [DOI] [Google Scholar]
  41. Mahrer NE, Holly LE, Luecken LJ, Wolchik SA, & Fabricius W (2019). Parenting style, familism, and youth adjustment in Mexican American and European American families. Journal of Cross-Cultural Psychology, 50, 659–675. 10.1177/0022022119839153 [DOI] [Google Scholar]
  42. McGue M, & Bouchard TJ (1984). Adjustment of twin data for the effects of age and sex. Behavior Genetics, 14, 325–343. 10.1007/BF01080045 [DOI] [PubMed] [Google Scholar]
  43. Morcillo C, Duarte CS, Shen S, Blanco C, Canino G, & Bird HR (2011). Parental familism and antisocial behaviors: Development, gender, and potential mechanisms. Journal of the American Academy of Child & Adolescent Psychiatry, 50, 471–479. 10.1016/j.jaac.2011.01.014 [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Mueller ST (2013). The Psychology Experiment Building Language (Version 0.13) [Software]. Available from http://pebl.sourceforge.net
  45. Muthén B, & Kaplan D (1985). A comparison of some methodologies for the factor analysis of non‐normal Likert variables. British Journal of Mathematical and Statistical Psychology, 38, 171–189. [Google Scholar]
  46. Muthén LK, & Muthén B (2015). Mplus. The comprehensive modelling program for applied researchers: user’s guide, 5. [Google Scholar]
  47. Neale MC, Hunter MD, Pritikin JN, Zahery M, Brick TR, Kirkpatrick RM, Estabrook R, Bates TC, Maes HH, & Boker SM (2016). OpenMx 2.0: Extended structural equation and statistical modeling. Psychometrika, 81, 535–549. 10.1007/s11336-014-9435-8 [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. Neale MC, Maes HM Methodology for Genetic Studies of Twins and Families. 2004. Retrieved March 2, 2004, http://www.vipbg.vcu.edu/~vipbg/mx/book2004a.pdf.
  49. Nigg JT (2017). Annual Research Review: On the relations among self‐regulation, self‐ control, executive functioning, effortful control, cognitive control, impulsivity, risk‐ taking, and inhibition for developmental psychopathology. Journal of Child Psychology and Psychiatry, 58, 361–383. 10.1111/jcpp.12675 [DOI] [PMC free article] [PubMed] [Google Scholar]
  50. Nikolas MA, Klump KL, & Burt SA (2015). Parental involvement moderates etiological influences on attention deficit hyperactivity disorder behaviors in child twins. Child Development, 86, 224–240. 10.1111/cdev.12296 [DOI] [PMC free article] [PubMed] [Google Scholar]
  51. Purcell S (2002). Variance components models for gene–environment interaction in twin analysis. Twin Research and Human Genetics, 5, 554–571. [DOI] [PubMed] [Google Scholar]
  52. Raver CC (2004). Placing emotional self-regulation in sociocultural and socioeconomic contexts. Child Development, 75, 346–353. 10.1111/j.1467-8624.2004.00676.x [DOI] [PubMed] [Google Scholar]
  53. Rea-Sandin G (2018). Genetic and environmental influences on executive functioning in middle childhood: the role of early adversity (Doctoral dissertation, Arizona State University; ). [Google Scholar]
  54. Rea-Sandin G (2022). Novel Measures of Culture and Their Relation to Self-regulation in Middle Childhood: A Genetically Informed Twin Study (Doctoral dissertation, Arizona State University; ). [Google Scholar]
  55. Rea-Sandin G, Clifford S, Doane LD, Davis MC, Grimm KJ, Russell M, & Lemery-Chalfant K (2022). Genetic and Environmental Links Between Executive Functioning and Effortful Control in Middle Childhood. Journal of Experimental Psychology – General. 10.1037/xge0001298 [DOI] [PMC free article] [PubMed] [Google Scholar]
  56. Schachar RJ, Forget-Dubois N, Dionne G, Boivin M, & Robaey P (2010). Heritability of response inhibition in children. Journal of the International Neuropsychological Society, 17, 238–247. 10.1017/S1355617710001463 [DOI] [PubMed] [Google Scholar]
  57. Shanahan MJ, & Hofer SM (2005). Social context in gene–environment interactions: Retrospect and prospect. The Journals of Gerontology Series B: Psychological Sciences and Social Sciences, 60, 65–76. 10.1093/geronb/60.Special_Issue_1.65 [DOI] [PubMed] [Google Scholar]
  58. Simonds J, & Rothbart MK (2006). Temperament in middle childhood questionnaire. Downloaded in http://www.bowdoin.edu/~sputnam/rothbart-temperament-questionnaires.
  59. Stein GL, Cupito AM, Mendez JL, Prandoni J, Huq N, & Westerberg D (2014). Familism through a developmental lens. Journal of Latina/o Psychology, 2, 224–250. 10.1037/lat0000025 [DOI] [Google Scholar]
  60. Stein GL, Mejia Y, Gonzalez LM, Kiang L, & Supple AJ (2020). Familism in action in an emerging immigrant community: an examination of indirect effects in early adolescence. Developmental Psychology, 56, 1475–1483. 10.1037/dev0000791 [DOI] [PubMed] [Google Scholar]
  61. Tomlinson RC, Hyde LW, Dotterer HL, Klump KL, & Burt SA (2021). Parenting moderates the etiology of callous‐unemotional traits in middle childhood. Journal of Child Psychology and Psychiatry. 10.1111/jcpp.13542 [DOI] [PMC free article] [PubMed] [Google Scholar]
  62. van der Sluis S, Posthuma D, & Dolan CV (2012). A Note on False Positives and Power in G × E Modelling of Twin Data. Behavior Genetics, 42, 170–186. 10.1007/s10519-011-9480-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
  63. Votruba-Drzal E (2006). Economic disparities in middle childhood development: Does income matter?. Developmental Psychology, 42, 1154–1167. 10.1037/0012-1649.42.6.1154 [DOI] [PubMed] [Google Scholar]
  64. Wechsler D (2003). Wechsler Intelligence Scale for Children, Fourth Edition (WISC-IV). San Antonio, TX: The Psychological Corporation. [Google Scholar]
  65. Willems YE, Dolan CV, van Beijsterveldt CE, de Zeeuw EL, Boomsma DI, Bartels M, & Finkenauer C (2018). Genetic and environmental influences on self-control: Assessing self-control with the ASEBA self-control scale. Behavior Genetics, 48, 135–146. 10.1007/s10519-018-9887-1 [DOI] [PMC free article] [PubMed] [Google Scholar]
  66. Zelazo PD, & Carlson SM (2012). Hot and cool executive function in childhood and adolescence: Development and plasticity. Child Development Perspectives, 6, 354–360. 10.1111/j.1750-8606.2012.00246.x [DOI] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplemental Material
NIHMS1902085-supplement-Supplemental_Material.pdf (453.1KB, pdf)

RESOURCES