The Apple Does Not Fall Far: Stable Predictive Relationships Between Parents' Ratings of Their Own and Their Children’s Self-Regulation Abilities

The Apple Does Not Fall Far: Stable Predictive Relationships Between Parents' Ratings of Their Own and Their Children’s Self-Regulation Abilities | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The Apple Does Not Fall Far: Stable Predictive Relationships Between Parents' Ratings of Their Own and Their Children’s Self-Regulation Abilities Johanna Kneidinger, José C. García Alanis, Ricarda Steinmayr, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4637867/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 03 Oct, 2024 Read the published version in Child and Adolescent Psychiatry and Mental Health → Version 1 posted 12 You are reading this latest preprint version Abstract Self-regulation is a critical skill that influences children's academic, social, and emotional development. This study investigates the stability and predictive relationships between parents' ratings of their own and their children's self-regulation abilities, focusing on executive function and delay aversion due to their strong association with cognitive and emotional control processes. Using data from 1700 families collected during the COVID-19 pandemic, we employed hierarchical structural equation models and cross-lagged panel models to analyze the temporal stability and directional influences of self-regulation assessments. Our analysis revealed a substantial latent correlation (r = 0.48, p < 0.001) between parents' and children's executive function problems, indicating a shared variance of approximately 23%. Significant cross-lagged effects were found, with parental executive function at T1 predicting child executive function at T2 (β = 0.16, p = 0.004). For delay aversion, we found a latent correlation of r = 0.50 (p < 0.001) and significant within-timepoint and temporal stability, but no cross-lagged effects. These findings suggest that higher levels of executive function problems reported by parents at T1 correspond to an increased perception of similar problems in their children at T2. This highlights the importance of parental self-perception in assessing children's abilities, aligning with Murphey's model that parental beliefs influence child outcomes. Our results underscore the significance of considering family dynamics in interventions aimed at promoting self-regulation in children. By understanding the interplay between parental and child self-regulation, researchers and practitioners can design more effective, individualized interventions to promote positive developmental outcomes. Self-regulation Executive function Delay aversion Parental influence Child development Figures Figure 1 Figure 2 Figure 3 Figure 4 Background Self-regulation is an ability that crucially impacts human development during childhood and adolescence ( 1 ). It refers to the process of willingly controlling and adapting one’s actions to achieve short- and long-term goals ( 2 ). Self-regulation is multifaceted and spans multiple domains of human behavior and experience ( 2 , 3 ). For example, in a classroom setting, a child who manages to focus on completing a challenging math problem while resisting the urge to move on to more appealing activities is demonstrating self-regulation. This ability involves various subprocesses, including the intentional control and coordination of thoughts and behaviors, as well as managing physiological responses to maintain calmness and focus under potential stress ( 3 ). Empirical data indicate that children with good self-regulation abilities show fewer conduct problems ( 4 ) and achieve better academic, psychosocial, and mental health outcomes later in life ( 1 , 5 ). However, many traditional approaches for assessing child self-regulation rely on caregiver ratings ( 6 , 7 ), which have been shown to be susceptible to interpersonal dynamics between caregivers and children ( 8 ). In addition, concerns regarding the temporal stability and predictive validity of these measures ( 6 ) remain unaddressed. Understanding the structural covariance and temporal stability of caregiver assessments is therefore essential for developing better interventions and fostering self-regulation in more individualized settings. Caregivers' Influence on Children’s Self-Regulation Self-regulation abilities experience rapid growth during early childhood ( 9 ) and are essential for a successful transition to formal schooling ( 10 ). In a school setting, teachers play a pivotal role in fostering children's self-regulation. They not only introduce children to self-regulated learning, but also provide valuable reinforcers and instructions through their own self-regulation ( 11 ), aiding students in task mastery and goal achievement. In recent years, however, the COVID-19 pandemic showed how disruptions in daily routines and transitioning learning environments to more home-based settings can pose significant challenges for children and adolescents ( 12 , 13 ). Many students encountered difficulties in structuring their day, initiating learning sessions, and maintaining focus on their assignments ( 14 ), partly due to the nature of distance learning characterized by reduced teacher support. This led to an increased risk of students missing out on broader learning opportunities and feeling overwhelmed by academic demands ( 15 ). Children who engaged in distance learning were more reliant on their parents to initiate and maintain self-regulated academic activities ( 16 , 17 ). Additionally, those who found distance learning more challenging were less likely to work independently and often required additional assistance from caregivers to cope with academic requirements ( 13 ). Amidst the shift to distance learning during the pandemic, many parents assumed a crucial role in fostering self-regulation, effectively stepping in as surrogates for teachers ( 18 ). Many families had to manage the added responsibility of helping their children maintain academic focus, structure their routines, and sustain their motivation within the learning process ( 18 ). Such collaboration can be viewed as a co-regulation process ( 11 , 19 ), heavily reliant on the self-regulatory process of the co-regulators, in this case, parents and their children ( 19 ). Murphey ( 20 ) states in his model that parental beliefs can affect how parents perceive their children's characteristics as well as moderate their responses accordingly. The present study Despite emerging research emphasizing the importance of parental self-regulatory capacity during child development ( 21 ) and its potential impact on children's academic, social, motivational, and emotional trajectories ( 22 ), the relationship between parent-child self-regulatory abilities is not well understood ( 23 – 25 ). Although it has been shown that there is a connection between parental and child self-regulation ( 26 – 28 ) and that parental attributions and expectations influence their children’s treatment progress ( 29 ), some questions require further investigation. In particular, the relationship between how parents view their own self-regulation skills and their perceptions of their child's self-regulation requires further elucidation ( 30 ). A better understanding of these relationships can provide insight into the reinforcement and coupling mechanisms that shape the development of cognitive abilities in children and adolescents ( 23 ). Such insights are crucial for helping researchers and practitioners design better and more individualized interventions that promote positive developmental outcomes ( 19 ). To address these questions, we sought to determine the temporal stability of parents’ assessments of their own self-regulatory capacity. We then tested whether these assessments predicted how parents assessed their children’s self-regulation and whether this predictive relationship was strengthened or compromised over time. To accomplish this, we analyzed a large dataset of parental self-report assessments of their own and their children's difficulties in lower-level domains of cognitive-emotional regulation: Executive Function and Delay Aversion . Executive Function emphasizes the dynamic cognitive mechanisms that facilitate humans' capacity to focus attention on relevant characteristics of an ongoing task and inhibit distractions ( 31 , 32 ). Conversely, Delay Aversion refers to individuals’ inclination towards favoring immediate rewards over delayed ones, presumably to avoid the aversive sensation of waiting ( 33 ). Empirical research indicates that Executive Function and Delay Aversion capture complementary facets of self-regulation ( 34 ). Specifically, it has been proposed ( 35 ) that impairments in self-regulation are essentially caused by reduced inhibitory control (pathway of executive dysfunction) and increased Delay Aversion (boring waiting situations are avoided if possible or escaped through impulsive behavior, i.e., motivational pathway). Indeed, empirical data indicate that clinically relevant impairments in self-regulated action, such as those observed in children diagnosed with attention-deficit/hyperactivity disorder (ADHD), are substantiated by a maladaptive interplay of deficient Executive Function and the emergence of Delay Aversion ( 36 – 38 ). The self-report data analyzed in the present study were collected from approximately 1700 families during the COVID-19 pandemic. The data spanned two measurement time points separated by several months. We used hierarchical structural equation models (SEM) to estimate the latent correlation between parents’ and their children’s executive function and delay aversion across multiple measurements. Additionally, we employed a cross-lagged panel model to assess the directional influence of parents’ initial ratings of executive function and delay aversion on their later scores. This analysis aimed to determine whether parental assessments of self-regulatory ability significantly predicted later assessments and whether these influences were specific to the assessment target (either their own or their children’s abilities) or generalized across targets (i.e., establishing cross-lagged relationships). Through these analyses, we aimed to contribute valuable insights into the interplay of parental and child self-regulatory abilities and their potential long-term effects. Method Procedure Data were collected from seven European countries through an anonymous digital survey ( 39 ). The survey aimed to understand parental experiences with distance learning and prompted parents to assess both their own and their children’s self-regulation skills during the pandemic ( 17 , 40 , 41 ). Data collection occurred in two phases. The initial survey phase spanned from April 28th to November 1st, 2020 (assessment timepoint one - T1), followed by a second phase from December 6th, 2020, to February 25th, 2021 (assessment timepoint two - T2). The survey was distributed to parents through various channels. During T1, it was promoted via social media, school blackboards, parent networks, and support groups. For T2, parents received invitations via email. For the current manuscript, we analyzed the subset of data collected in Germany. The data needed to reproduce the analysis and results reported here can be accessed through the supplementary materials repository provided on the open science framework ( https://osf.io/rc934/?view_only=5cb3c5e1d5aa4dc0bf25aa78c752dc3c ). Participants To be eligible for participation, respondents had to be parents of children or adolescents aged between five and 20 years, enrolled in standard schooling, and transitioning to distance learning due to pandemic-induced school closures. Initially, 1,767 parents participated at T1, and 1,082 at T2. After excluding mismatched data and entries with errors, the analyses were based on data from 1,674 participants at T1 and 664 participants at T2. Among these, 543 families participated in both the T1 and T2 phases. Descriptive statistics are provided in Table 1 . The average age of the children was 11.53 years at T1 and increased to 12.08 years by T2. Female children represented 47.85% of the sample at T1 and 46.69% at T2. The mean age of parents at T1 was 43.10 years, with females comprising 85.60% of the sample. By T2, the average parental age was slightly higher at 43.87 years, with females comprising 86.75% of the participants. Table 1 Sample descriptives for both measurement timepoints Measurement timepoint T1 T2 Families N 1674 664 Parents N males 238 87 N females 1433 576 N others* 3 1 Mean age (SD) 43.10 (6.12) 43.87 (6.27) Age range 23–68 28–68 Children N males 871 354 N females 801 310 N others* 2 - Mean age (SD) 11.53 (3.08) 12.08 (3.10) Age range 5–20 5–20 Note. SD = standard deviation, N = Number of cases. *Others contains diverse, intersexual and unassignable Instruments The online survey assessed various facets of parental experiences during distance learning. Additionally, parents were asked to rate their self-regulatory skills and their children’s self-regulatory skills by indicating their agreement or disagreement (1 = “strongly disagree” to 5 = “strongly agree”) with a series of statements about their own and their children’s daily difficulties with executive function and control over delay aversion. A higher score indicated more pronounced executive function problems and increased delay aversion. The tools utilized for these measurements are elaborated upon in the subsequent sections. Childhood Executive Functioning Inventory (CHEXI) : To assess children’s executive function problems, the survey included an abbreviated version of the Childhood Executive Functioning Inventory - CHEXI ( 42 ). The CHEXI, freely available in many languages ( www.chexi.se ), includes two subscales measuring difficulties in working memory (e.g., “when asked to do several things, he/she only remembers the first or last”) and the inhibition domain (e.g., “has difficulty holding back his/her activity despite being told to do so”). The online survey comprised eight items: four for working memory and four for inhibition. Working memory items showed good internal consistency at T1 (Cronbach’s alpha = 0.86, 95% CI = [0.85–0.87]) and T2 (Cronbach’s alpha = 0.84, 95% CI = [0.83–0.85]). The same was the case for inhibition items at T1 (Cronbach’s alpha = 0.84, 95% CI = [0.83–0.85]) and T2 (Cronbach’s alpha = 0.85, 95% CI = [0.84–0.86]). Adult Executive Functioning Inventory (ADEXI) : To measure parental executive function problems, the survey included an abbreviated version of the Adult Executive Functioning Inventory – ADEXI ( 43 ). Like the CHEXI, the ADEXI is freely available in various languages ( www.chexi.se ) and includes two subscales measuring difficulties in working memory (e.g., “when someone asks me to do several things, I sometimes remember only the first or last”) and the inhibition domain (e.g., “I have a tendency to do things without first thinking about what could happen”). The online survey comprised eight items: four for working memory and four for inhibition. Working memory items showed acceptable internal consistency at T1 (Cronbach’s alpha = 0.79, 95% CI = [0.77–0.81]) and T2 (Cronbach’s alpha = 0.77, 95% CI = [0.76–0.79]). In contrast, the internal consistency of inhibition items was somewhat lower at T1 (Cronbach’s alpha = 0.61, 95% CI = [0.57–0.64]) and T2 (Cronbach’s alpha = 0.63, 95% CI = [0.60–0.66]). Quick Delay Questionnaire (QDQ) Child and parental delay aversion were measured using a brief (two-item) version of the Quick Delay Questionnaire – QDQ ( 44 ). Children’s delay aversion items showed acceptable internal consistency at T1 (Cronbach’s alpha = 0.77, 95% CI = [0.74–0.79]) and T2 (Cronbach’s alpha = 0.77, 95% CI = [0.75–0.79]). Similarly, the parents’ delay aversion items showed acceptable internal consistency at T1 (Cronbach’s alpha = 0.76, 95% CI = [0.74–0.78]) and T2 (Cronbach’s alpha = 0.76, 95% CI = [0.73–0.78]). Statistical Analyses Before analyses, all variables were converted to z-scores, ensuring each variable had a mean of zero and a standard deviation of one. This was done to mitigate potential effects caused by discrepancies in scale between variables and to avoid potential estimation problems resulting from differing variances between the response variables. The standardized data formed the basis for all subsequent analyses. We used structural equation models to estimate the latent correlation and longitudinal associations between parental and child executive function deficits, as well as parental and child delay aversion. All models were estimated in the R programming environment, version 4.3.2, ( 45 ) using the lavaan package ( 46 ). We used the maximum likelihood algorithm with robust Huber-White standard errors and a scaled test statistic (asymptotically) equal to the Yuan-Bentler test statistic to account for possible deviations from multivariate normality. As the variables were standardized, we fixed all estimated indicator means to zero, a fact that informs the degrees of freedom for all reported models. In some specific cases (reported below), the algorithm estimated non-significant residual variances with values below zero. To account for this issue, we refitted the corresponding model with that residual variance fixed to zero. For handling missing data, we used the full information maximum likelihood (FIML) estimator ( 47 , 48 ) as implemented in the lavaan package. We evaluated goodness-of-fit based on the comparative fit index, CFI ( 49 ), and the root mean square error of approximation, RMSEA ( 50 ). We considered CFI values > .95 and RMSEA values .90 and RMSEA values < .08 to indicate acceptable model fit, as recommended by Brown and Cudeck ( 50 ) and Hu and Bentler ( 51 ). Effects were considered statistically significant if the p-value was less than α = 0.05. In a first step, we estimated the measurement and structure models for parents and children at each measurement timepoint separately. These models yielded a good fit. Please refer to the supplemental material on the OSF for further inspection of these models ( https://osf.io/rc934/?view_only=5cb3c5e1d5aa4dc0bf25aa78c752dc3c ). Based on these results, we estimated four different models. In Model 1 and Model 2, we assumed a hierarchical structure for executive function problems: Parents’ and their children’s executive function were modeled as measurement timepoint-specific and parent and child-specific higher-order factors. The working memory and inhibition sub-facets of the CHEXI and ADEXI were estimated to conform to the measurement timepoint-specific and parent and child-specific lower part of the factor hierarchy (i.e., subdomains). The higher-order factors executive function (problems) at timepoint 1 (EF T1 parent and EF T1 child) and executive function (problems) at timepoint 2 (EF T2 parent and EF T2 child) can be interpreted as the common variance shared by the working memory and inhibition subdomains at each measurement timepoint, which are thought to be correlated. In Model 1, we estimated a general trait factor for parental and child executive function problems that integrated the measurement timepoint-specific executive function problems factors. This is equivalent to the assumption that the common variance in executive function problems ratings for parents and their children can each be explained by a single, corresponding trait factor contributing to each of the measurements. Finally, Model 1 estimated the latent correlations between the general trait factor for parental executive function problems and the general trait factor for child executive function problems. In Model 2, we did not include the general trait factors for parental and child executive function. Instead, we estimated the cross-lagged relationships between the higher-order factors EF T1 parent, EF T1 child, EF T2 parent, and EF T2 child to estimate the directionality and longitudinal associations between parental and child executive function across measurements. The primary goal of this model was to extend Model 1 and to provide a more fine-grained understanding of the interrelationships between parental and child executive function both within and between the two measurement timepoints. This model captures correlations within a single measurement timepoint (assessing the initial overlap between parent and child executive function), associations between the same traits measured at different times (allowing us to assess their temporal stability), and relationships between different domains captured at disparate times (allowing us to examine variance in one group of subjects as it may predict changes in the other) (cf. 52). Model 3 and Model 4 were homologous to Model 1 and Model 2, respectively, but concerned parents’ ratings of their own and their children’s delay aversion. One further difference between the executive function models (i.e., Models 1 and 2) and the delay aversion models (i.e., Models 3 and 4) was that the delay aversion models did not include subdomain factors, as delay aversion was assessed using items from only one scale. Finally, in supplemental analyses, we tested whether the cross-lagged relationships in Models 2 and 4 (i.e., the cross-lagged panel models for executive function and delay aversion) were moderated by the amount of time parents and their children spent working together on school assignments from home between measurement timepoints. These additional analyses aimed to explore potential moderating effects of the distance learning context on the longitudinal associations between parental and child executive function and delay aversion. The models showed no substantial moderation effects and can be found in the supplemental analysis section provided on the OSF. Results Relationship between Parental and Child Executive Function Latent correlation. Model 1 estimated the latent correlation between two higher-order factors that captured the trait components of parents’ ratings of their own and their children’s executive function problems across measurements (see Fig. 1 ). Model 1 showed relatively good model fit (χ² [df = 475] = 1333.4, CFI = 0.93, RMSEA = 0.035). The model estimated a substantial latent correlation between the trait factors for parental and child executive function problems of r = 0.48 (95% CI = [0.41, 0.55], p < 0.001), indicating that parents’ assessment of their own and their children’s executive function problems shared approximately 23% of their variance. Results indicated that the hierarchical structure in Model 1 captured large proportions of the variance present in the executive function subdomains of working memory (Children: β T1 = 0.92, β T2 = 0.93; parents: β T1 = 0.80, β T2 = 0.82) and inhibition (Children: β T1 = 0.86, β T2 = 0.89; parents: β T1 = 0.83, β T2 = 0.84), with the proportions of variance explained by the timepoint-specific hierarchical structure being overall somewhat higher for child executive function problems than for parental executive function problems. Cross-lagged relationships. Model 2 estimated the latent relationships between parental and child executive function problems both within and between the two measurement timepoints (see Fig. 2 ). The model estimated the latent correlation coefficient linking the two higher-order factors that captured the common variance shared by the working memory and inhibition subdomains ratings for child and parent executive function problems at T1. Furthermore, the model estimated the latent regression coefficient between the higher-order factors for child executive function at T1 and T2, parent executive function at T1 and T2, as well as the cross-lagged latent regression coefficients linking child executive function at T1 and parent executive function at T2, and parent executive function at T1 and child executive function at T2. Parental executive function problems at T2 (EF parent T2) were fully accounted for by the model, and thus its residual variance was fixed to zero. The model showed relatively good model fit (χ² [df = 472] = 1333.4, CFI = 0.93, RMSEA = 0.035). The model estimated a substantial latent correlation between parent and child executive function problems at T1 of r = 0.45 (95% CI = [0.39, 0.51]), replicating the latent correlation estimated by Model 1. Furthermore, child executive functioning problems at T1 largely predicted child executive functioning problems at T2 (β = 0.77, 95% CI = [0.68, 0.86], p < 0.001), but did not predict parental executive functioning problems at T2 (β = -0.09, 95% CI = [-0.22, 0.05], p = 0.196). In contrast, parent executive function problems at T1 were highly predictive of parental executive function problems at T2 (β = 1.0, 95% CI = [0.98, 1.00], p < 0.001) and were also predictive of child executive function problems at T2 (β = 0.16, 95% CI = [0.05, 0.28], p = 0.004). Relationship between Parental and Child Delay Aversion Latent Correlation. Model 3 estimated the latent correlation between two higher-order factors that captured the trait components of parents’ ratings of their own and their children’s delay aversion across measurements (see Fig. 3 ). Model 3 showed good model fit (χ² [df = 21] = 2197.8, CFI = 0.98, RMSEA = 0.035). The model estimated a substantial latent correlation between the trait factors of parental and child delay aversion of r = 0.50 (95% CI = [0.41, 0.60], p < 0.001), indicating that parents’ assessment of their own and their children’s delay aversion shared approximately 25% of their variance. Cross-Lagged Relationships. Model 4 estimated the latent relationships between parental and child delay aversion both within and between the two measurement timepoints (see Fig. 4 ). The model estimated the latent correlation coefficient linking the two factors that captured the common variance of the ratings for child and parent delay aversion at T1. Furthermore, the model estimated the latent regression coefficients between the factors for child delay aversion at T1 and T2, parent delay aversion at T1 and T2, as well as the cross-lagged latent regression coefficients linking child delay aversion at T1 and parent delay aversion at T2, and parent delay aversion at T1 and child delay aversion at T2. The model showed good fit (χ² [df = 21] = 2197.8, CFI = 0.99, RMSEA = 0.027). Model 4 estimated a substantial latent correlation between parent and child delay aversion at T1 of r = 0.40 (95% CI = [0.33, 0.47]). This estimate was somewhat lower than the overall latent correlation estimated by Model 3. Furthermore, child delay aversion at T1 largely predicted child delay aversion at T2 (β = 0.70, 95% CI = [0.58, 0.81], p < 0.001), but did not predict parental delay aversion at T2 (β = -0.06, 95% CI = [-0.18, 0.05], p = 0.258). Similarly, parent delay aversion at T1 was predictive of parent delay aversion at T2 (β = 0.71, 95% CI = [0.61, 0.81], p < 0.001), but not for child delay aversion at T2 (β = -0.02, 95% CI = [-0.15, 0.10], p = 0.727). Discussion Self-regulation is a fundamental aspect of human development that significantly impacts various domains of functioning through childhood and adolescence. Our study aimed to investigate the relationship between parental and child self-regulatory abilities, with a specific emphasis on executive function impairments and delay aversion. The overall aim of the present study was to estimate the association between parents' self-perceived levels of self-regulatory skills and their assessments of their children's self-regulatory capacities as well as the longitudinal relations between these abilities. Our findings demonstrate a significant relationship between the trait factors representing parental and child executive functioning deficits, as well as those representing parental and child delay aversion. Specifically, our models showed substantial shared variance between parental and child assessments of executive function problems and delay aversion. Moreover, results revealed predictive relationships between executive functioning deficits and delay aversion at different measurement time points. Whereas deficits in children's self-regulatory abilities at T1 (executive function and delay aversion) only predicted children's deficits at T2, and parental delay aversion at T1 only predicted parental delay aversion at T2, parental deficits in executive functions at T1 predicted both parental as well as child deficits in executive functions at T2. These findings indicate that higher levels of executive function problems reported by parents at T1 correspond to an increased perception of similar problems in their children at T2. This observation is significant as it implies that parents who identify numerous difficulties in their own self-regulation initially are likely to anticipate similar challenges in their children later in development, or alternatively, perceive such difficulties in their children more sensitively. Accordingly, parents' self-perception of their own self-regulation skills appears to influence their assessment of their children's abilities. If parents perceive themselves as well-regulated, they are more likely to rate their children similarly. It seems as if parents draw direct conclusions from themselves to their children. To our knowledge, this is the first study to focus on the extent to which parental perceptions of their own and their children's self-regulation skills influence the outcomes, rather than focusing on the actual relationship between parents' and children's self-regulation skills. Moreover, it is also the first investigation that examines this connection generally and longitudinally. Our results are in line with previous findings ( 20 ) that propose that parental beliefs, in addition to parental behavior, play a role in shaping child outcomes. More specifically, according to Murphey's model ( 20 ), parental beliefs might influence how parents perceive their children's behaviors and corresponding outcomes, potentially moderating parental responses accordingly. In addition, our results align with existing literature highlighting the role of parental self-regulation in child development ( 53 ). We extend the framework of Cuevas and colleagues ( 53 ) by revealing intergenerational ties not only within mother-child executive function associations in early childhood, but across parental genders and child age groups. Parents serve as primary models for children's self-regulatory behaviors, and our findings indicate that parental beliefs about their own self-regulatory skills influence the perceptions of their children's substantially. The present study added new information by showing that this relationship, at least for executive function deficits, remains stable over time. Our findings go beyond previous research by highlighting the stability of these constructs throughout development. The relationship between parental and child executive function is robust, implying that even after reassessment several months later, parents’ rating of their children is dependent on their self-ratings, regardless of the severity of initially observed deficits. As we did not observe such an intergenerational correlation over time for delay aversion, it prompts inquiry into the underlying factors contributing to this discrepancy. One potential explanation for this phenomenon might be that delay aversion was less salient in daily life during the pandemic and therefore less frequently encountered. Furthermore, delay aversion was not recorded as comprehensively within the study as executive function deficits. Since delay aversion is a complex neuropsychological factor that comprises several dimensions ( 34 , 54 ) it is perhaps more difficult to capture (especially within a survey) than executive functions. Strengths and Limitations One of the main strengths of the current study is the large sample size that enhances the generalizability of our findings and provides robust statistical power for detecting relationships between variables. While prior research has predominantly focused on specific age cohorts such as infants ( 28 ), children ( 26 ), or adolescents ( 27 ), our sample encompasses individuals across multiple age groups. This broad inclusion facilitates a more holistic perspective on the topic. Furthermore, assessments were conducted during school closures instead of relying on retrospective reports, providing real-time insights into the impact of distance learning on self-regulation. In contrast to previous research that has predominantly concentrated on maternal abilities, our study extends this focus and includes paternal contributions as well. As noted by Ribner and colleagues ( 28 ) and Jester and colleagues ( 27 ), paternal skills contribute to the association between parental and child self-regulatory skills, too. In the present study, we examine the interrelations across both parental genders rather than isolating analyses to each gender individually, although it is important to note, that significantly more mothers participated in the study. Our study is the first to examine intergenerational connections longitudinally. Also, existing studies have mainly focused on the familial effects on executive function ( 26 , 28 , 53 ) rather than delay aversion. Here, we also consider delay aversion, as it is equally relevant in the context of self-regulation ( 34 , 55 ). Our goal was to measure self-regulation more broadly. At the same time, several limitations within this investigation should be acknowledged. Firstly, the sole reliance on parental self-report measures concerning child self-regulatory abilities may introduce bias ( 8 ), as parents may overestimate or underestimate their children's self-regulatory abilities as well as their impact on those abilities. Future research should incorporate multi-informant assessments as well as neuropsychological testing to provide a more comprehensive understanding of parent-child dynamics. Delay aversion, in particular, is a complex construct that is challenging to accurately quantify ( 34 , 54 ). Consequently, future research should aim to develop more comprehensive methods for its measurement. Secondly, a further limitation is the lack of extensive standardized, validated measurements. Since incorporating numerous scales would have considerably increased the survey length and potentially reduced the response rate, especially among families dealing with mental health issues, only an abbreviated version of all three questionnaires (CHEXI, ADEXI, QDQ) was used. To capture the factors more comprehensively, future investigations should consider incorporating the broader questionnaires and objective tests that capture executive function ( 32 ), attentional control ( 56 ) and delay aversion ( 57 ). Thirdly, the correlational approach limits our ability to establish causal relationships between parental and child self-regulation. Subsequent research should explore the impact of other environmental factors on parent-child interactions and self-regulatory development. Implications Despite these limitations, our study has important implications for both research and practice. By highlighting the significant correlations and longitudinal associations between parental and child self-regulatory abilities, our findings underscore the importance of considering family dynamics in interventions aimed at promoting self-regulation in children. In view of the fact that parental attributions and expectations influence their children’s treatment progress ( 29 ), interventions targeting parental self-regulation may indirectly benefit children's development ( 19 , 53 ), while interventions directly targeting children may have spillover effects on parental self-regulation ( 23 ). Schneider and colleagues ( 58 ), for instance, found that successful treatment of parents’ anxiety disorder is a significant predictor of a better outcome for children’s anxiety sensitivity and agoraphobic cognitions and that even the mere treatment participation (regardless of whether it was successful or not) had a significant positive effect on descendants. As mentioned earlier, to the best of our knowledge, this study is the first to analyze longitudinal familial relationships based on parents' self-assessments and their evaluations of their children, rather than on direct comparison of parent and child self-regulatory skills. This method enables us to analyze the relationship implied by parents between their own abilities and those of their children, rather than the direct correlation between parent and child skills. The results provide important insights into parental expectations and self-perceptions, contributing to a deeper understanding of the implicit beliefs and assumptions parents have regarding the influence of their abilities on their children's development. This can serve as an initial motivation to investigate whether and how parental self-assessment correlates with the actual abilities and performance of their children. Future studies should combine both approaches by collecting neuropsychological data from parents and children, as well as parents' evaluations of their own and their children’s abilities, and vice versa. In light of the study's findings indicating that parents derive perceptions of their children from their own characteristics, it becomes imperative to consider this phenomenon within therapeutic contexts as well. Our study firmly shows that parents' self-perception is a key factor in how they assess their children's condition. Additionally, our results suggest that early identification and support for children with self-regulatory deficits may help mitigate long-term impacts on academic and socio-emotional outcomes. Conclusion In conclusion, our study provides evidence of significant correlations and longitudinal associations between parental and child self-regulatory abilities, emphasizing the role of family dynamics in shaping self-regulatory skills during childhood and adolescence. Despite the constraints of our study, our findings contribute to a deeper understanding of the intergenerational connection of self-regulation and underscore the significance of parental inferences regarding their own abilities in relation to those of their children. By these results, a better understanding of the structural covariance and temporal stability of caregivers' assessments is provided. This has important implications for interventions aimed at promoting positive developmental outcomes in children as well as for therapeutic work. Future research should continue to explore the complex interactions of parental influences, child development, and environmental factors, aiming to develop more effective interventions and support strategies. Specifically, efforts should be made to better target parental involvement in treatment as a way to prevent childhood issues. Therefore, self-regulatory abilities should be examined from different perspectives by conducting neuropsychological tests of the constructs in addition to detailed questionnaire data. In addition to parents' assessments of their own and their children's abilities, children should also assess their own and their parents' abilities to undertake a multifactorial comparison. Combining the approach of previous studies (measuring children's and parents' self-regulation skills) with the approach of this study (measuring parental assessment of parental and child self-regulation skills) and adding children's assessment of child and parental self-regulation skills could help to determine the extent to which parental assessment predicts or influences children's actual performance. Declarations Consent for publication Not applicable. Competing interests The authors have no relevant financial or non-financial interests to disclose. Ethical Approval The study was approved by the local ethics committee at the university of Marburg and all participants provided informed consent prior to participation. Funding None of the authors received any funding for this project. Author Contribution HC, RS, and SS conceptualized the study and supervised the data gathering process in Germany. JK and JCGA analyzed the data, prepared all figures and tables, and wrote the main manuscript. JK, JCGA, RS, and HC interpreted the results. All authors reviewed, edited, revised, and approved the final version of the manuscript. Acknowledgement The authors thank all families who participated in the study. Data Availability The dataset and corde scripts supporting the conclusions of this article is available in the OSF repository (DOI 10.17605/OSF.IO/RC934) under https://osf.io/rc934/?view_only=5cb3c5e1d5aa4dc0bf25aa78c752dc3c. References Robson DA, Allen MS, Howard SJ. Self-regulation in childhood as a predictor of future outcomes: A meta-analytic review. Psychol Bull. 2020;146(4):324–54. Inzlicht M, Werner KM, Briskin JL, Roberts BW. Integrating Models of Self-Regulation. Annu Rev Psychol. 2021;72:319–45. McClelland M, Geldhof J, Morrison F, Gestsdóttir S, Cameron C, Bowers E, et al. Self-Regulation. In: Halfon N, Forrest CB, Lerner RM, Faustman EM, editors. 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J Stat Softw. 2012;48:1–36. Allison PD. Missing data. The SAGE handbook of quantitative methods in psychology. 2009;72–89. Collins LM, Schafer JL, Kam CM. A comparison of inclusive and restrictive strategies in modern missing data procedures. Psychol Methods. 2001;6(4):330–51. Bentler PM. Comparative fit indexes in structural models. Psychol Bull. 1990;107(2):238–46. Browne MW, Cudeck R. Alternative Ways of Assessing Model Fit. Sociol Methods Res. 1992;21(2):230–58. Hu L, Bentler PM. Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Struct Equ Model. 1999;6(1):1–55. In-Albon T, Meyer AH, Metzke CW, Steinhausen H-CA, Cross-Lag. Panel Analysis of Low Self-Esteem as a Predictor of Adolescent Internalizing Symptoms in a Prospective Longitudinal Study. Child Psychiatry Hum Dev. 2017;48(3):411–22. Cuevas K, Deater-Deckard K, Kim-Spoon J, Wang Z, Morasch KC, Bell MA. A longitudinal intergenerational analysis of executive functions during early childhood. Br J Dev Psychol. 2014;32(1):50–64. Sonuga-Barke EJS, Bitsakou P, Thompson M. Beyond the dual pathway model: evidence for the dissociation of timing, inhibitory, and delay-related impairments in attention-deficit/hyperactivity disorder. J Am Acad Child Adolesc Psychiatry. 2010;49(4):345–55. Shiels K, Hawk LW Jr. Self-regulation in ADHD: the role of error processing. Clin Psychol Rev. 2010;30(8):951–61. Fan J, McCandliss BD, Sommer T, Raz A, Posner MI. Testing the efficiency and independence of attentional networks. J Cogn Neurosci. 2002;14(3):340–7. Müller UC, Sonuga-Barke EJS, Brandeis D, Steinhausen H-C. Online measurement of motivational processes: introducing the Continuous Delay Aversion Test (ConDAT). J Neurosci Methods. 2006;151(1):45–51. Schneider S, In-Albon T, Nuendel B, Margraf J. Parental panic treatment reduces children’s long-term psychopathology: a prospective longitudinal study. Psychother Psychosom. 2013;82(5):346–8. García Alanis JC, The Apple Does Not Fall Far. 2024 Jun 25 [cited 2024 Jun 25]; https://osf.io/rc934/ . Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4637867","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":330334919,"identity":"3d4facb5-e6d7-4b71-a60a-9923e09df799","order_by":0,"name":"Johanna Kneidinger","email":"data:image/png;base64,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","orcid":"","institution":"Philipps-University Marburg","correspondingAuthor":true,"prefix":"","firstName":"Johanna","middleName":"","lastName":"Kneidinger","suffix":""},{"id":330334920,"identity":"fbbd0b12-8c57-4f05-8b18-a2f883ee60ba","order_by":1,"name":"José C. García Alanis","email":"","orcid":"","institution":"Philipps-University Marburg","correspondingAuthor":false,"prefix":"","firstName":"José","middleName":"C. García","lastName":"Alanis","suffix":""},{"id":330334921,"identity":"d0862bd1-7a36-4c75-839b-e33c3fc900cb","order_by":2,"name":"Ricarda Steinmayr","email":"","orcid":"","institution":"TU Dortmund","correspondingAuthor":false,"prefix":"","firstName":"Ricarda","middleName":"","lastName":"Steinmayr","suffix":""},{"id":330334922,"identity":"1bd02556-21d5-447d-aaa1-9a445279c58c","order_by":3,"name":"Silvia Schneider","email":"","orcid":"","institution":"Ruhr University Bochum","correspondingAuthor":false,"prefix":"","firstName":"Silvia","middleName":"","lastName":"Schneider","suffix":""},{"id":330334923,"identity":"0b03fb18-b08c-41bd-a8be-1891a33c5475","order_by":4,"name":"Hanna Christiansen","email":"","orcid":"","institution":"Philipps-University Marburg","correspondingAuthor":false,"prefix":"","firstName":"Hanna","middleName":"","lastName":"Christiansen","suffix":""}],"badges":[],"createdAt":"2024-06-25 16:29:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4637867/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4637867/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s13034-024-00814-z","type":"published","date":"2024-10-03T15:56:55+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":60911684,"identity":"415288a4-3c4e-473f-ad5e-7f4de35dc046","added_by":"auto","created_at":"2024-07-23 12:58:58","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":162586,"visible":true,"origin":"","legend":"\u003cp\u003eHierarchical latent correlation model of executive function problems (Model 1). The item initials in the item labels (WM or I) denote whether the item belonged to the working memory or inhibition sub-facet of the executive function questionnaire. The first number in the item labels denotes the item index (1 to 4) and the second number denotes the measurement timepoint (1 or 2). The figure depicts standardized path coefficients and unstandardized residual variances. Residual covariances between the same item measured at different timepoints are omitted from the plot to avoid clutter.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4637867/v1/cbdbcf1ca5215b469b17ca8b.png"},{"id":60911683,"identity":"07198fcb-32bc-431e-9d1c-3f8c7be26eee","added_by":"auto","created_at":"2024-07-23 12:58:58","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":171981,"visible":true,"origin":"","legend":"\u003cp\u003eCross-lagged panel model of executive function problems (Model 2). The item initials in the item labels (WM or I) denote whether the item belonged to the working memory or inhibition sub-facet of the executive function questionnaire. The first number in the item labels denotes the item index (1 to 4) and the second number denotes the measurement timepoint (1 or 2). The figure depicts standardized path coefficients and unstandardized residual variances. Residual covariances between the same item measured at different timepoints are omitted from the plot to avoid clutter.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4637867/v1/9e186cdbb0e45c77b5dbc785.png"},{"id":60911681,"identity":"8ad88a74-e3c4-4596-9697-3272d1fb1869","added_by":"auto","created_at":"2024-07-23 12:58:58","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":75676,"visible":true,"origin":"","legend":"\u003cp\u003eHierarchical latent correlation model of delay aversion (Model 3). The item initials in the item labels (DA) denote that the items belonged to the delay aversion questionnaire. The first number in the item labels denotes the item index (1 to 2) and the second number denotes the measurement timepoint (1 or 2). The figure depicts standardized path coefficients and unstandardized residual variances. Residual covariances between the same item measured at different timepoints are omitted from the plot to avoid clutter.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4637867/v1/6db259892df6bc4f33c8a104.png"},{"id":60913012,"identity":"e72be146-ef68-4104-85f7-95d0504d8550","added_by":"auto","created_at":"2024-07-23 13:06:58","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":76067,"visible":true,"origin":"","legend":"\u003cp\u003eCross-lagged panel model of delay aversion (Model 4). The item initials in the item labels (DA) denote that the items belonged to the delay aversion questionnaire. The first number in the item labels denotes the item index (1 to 2) and the second number denotes the measurement timepoint (1 or 2). The figure depicts standardized path coefficients and unstandardized residual variances. Residual covariances between the same item measured at different timepoints are omitted from the plot to avoid clutter.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4637867/v1/43a7fb9340da2a5bf25f371d.png"},{"id":66096643,"identity":"c58bc4dc-3377-43aa-b9b5-d56141c4fcf4","added_by":"auto","created_at":"2024-10-07 16:03:40","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":984490,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4637867/v1/01c5720c-a9be-4319-a9a2-5adaa3c48bc7.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Apple Does Not Fall Far: Stable Predictive Relationships Between Parents' Ratings of Their Own and Their Children’s Self-Regulation Abilities","fulltext":[{"header":"Background","content":"\u003cp\u003eSelf-regulation is an ability that crucially impacts human development during childhood and adolescence (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). It refers to the process of willingly controlling and adapting one\u0026rsquo;s actions to achieve short- and long-term goals (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Self-regulation is multifaceted and spans multiple domains of human behavior and experience (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). For example, in a classroom setting, a child who manages to focus on completing a challenging math problem while resisting the urge to move on to more appealing activities is demonstrating self-regulation. This ability involves various subprocesses, including the intentional control and coordination of thoughts and behaviors, as well as managing physiological responses to maintain calmness and focus under potential stress (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Empirical data indicate that children with good self-regulation abilities show fewer conduct problems (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) and achieve better academic, psychosocial, and mental health outcomes later in life (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). However, many traditional approaches for assessing child self-regulation rely on caregiver ratings (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e), which have been shown to be susceptible to interpersonal dynamics between caregivers and children (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). In addition, concerns regarding the temporal stability and predictive validity of these measures (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e) remain unaddressed. Understanding the structural covariance and temporal stability of caregiver assessments is therefore essential for developing better interventions and fostering self-regulation in more individualized settings.\u003c/p\u003e \u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003eCaregivers' Influence on Children\u0026rsquo;s Self-Regulation\u003c/h2\u003e \u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eSelf-regulation abilities experience rapid growth during early childhood (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e) and are essential for a successful transition to formal schooling (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). In a school setting, teachers play a pivotal role in fostering children's self-regulation. They not only introduce children to self-regulated learning, but also provide valuable reinforcers and instructions through their own self-regulation (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e), aiding students in task mastery and goal achievement.\u003c/p\u003e\u003cp\u003eIn recent years, however, the COVID-19 pandemic showed how disruptions in daily routines and transitioning learning environments to more home-based settings can pose significant challenges for children and adolescents (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Many students encountered difficulties in structuring their day, initiating learning sessions, and maintaining focus on their assignments (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e), partly due to the nature of distance learning characterized by reduced teacher support. This led to an increased risk of students missing out on broader learning opportunities and feeling overwhelmed by academic demands (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Children who engaged in distance learning were more reliant on their parents to initiate and maintain self-regulated academic activities (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Additionally, those who found distance learning more challenging were less likely to work independently and often required additional assistance from caregivers to cope with academic requirements (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAmidst the shift to distance learning during the pandemic, many parents assumed a crucial role in fostering self-regulation, effectively stepping in as surrogates for teachers (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). Many families had to manage the added responsibility of helping their children maintain academic focus, structure their routines, and sustain their motivation within the learning process (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). Such collaboration can be viewed as a co-regulation process (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e), heavily reliant on the self-regulatory process of the co-regulators, in this case, parents and their children (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). Murphey (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e) states in his model that parental beliefs can affect how parents perceive their children's characteristics as well as moderate their responses accordingly.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eThe present study\u003c/h2\u003e \u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eDespite emerging research emphasizing the importance of parental self-regulatory capacity during child development (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e) and its potential impact on children's academic, social, motivational, and emotional trajectories (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e), the relationship between parent-child self-regulatory abilities is not well understood (\u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). Although it has been shown that there is a connection between parental and child self-regulation (\u003cspan additionalcitationids=\"CR27\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e) and that parental attributions and expectations influence their children\u0026rsquo;s treatment progress (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e), some questions require further investigation. In particular, the relationship between how parents view their own self-regulation skills and their perceptions of their child's self-regulation requires further elucidation (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). A better understanding of these relationships can provide insight into the reinforcement and coupling mechanisms that shape the development of cognitive abilities in children and adolescents (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). Such insights are crucial for helping researchers and practitioners design better and more individualized interventions that promote positive developmental outcomes (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTo address these questions, we sought to determine the temporal stability of parents\u0026rsquo; assessments of their own self-regulatory capacity. We then tested whether these assessments predicted how parents assessed their children\u0026rsquo;s self-regulation and whether this predictive relationship was strengthened or compromised over time. To accomplish this, we analyzed a large dataset of parental self-report assessments of their own and their children's difficulties in lower-level domains of cognitive-emotional regulation: \u003cem\u003eExecutive Function\u003c/em\u003e and \u003cem\u003eDelay Aversion\u003c/em\u003e.\u003c/p\u003e\u003cp\u003eExecutive Function emphasizes the dynamic cognitive mechanisms that facilitate humans' capacity to focus attention on relevant characteristics of an ongoing task and inhibit distractions (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). Conversely, Delay Aversion refers to individuals\u0026rsquo; inclination towards favoring immediate rewards over delayed ones, presumably to avoid the aversive sensation of waiting (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). Empirical research indicates that Executive Function and Delay Aversion capture complementary facets of self-regulation (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). Specifically, it has been proposed (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e) that impairments in self-regulation are essentially caused by reduced inhibitory control (pathway of executive dysfunction) and increased Delay Aversion (boring waiting situations are avoided if possible or escaped through impulsive behavior, i.e., motivational pathway). Indeed, empirical data indicate that clinically relevant impairments in self-regulated action, such as those observed in children diagnosed with attention-deficit/hyperactivity disorder (ADHD), are substantiated by a maladaptive interplay of deficient Executive Function and the emergence of Delay Aversion (\u003cspan additionalcitationids=\"CR37\" citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe self-report data analyzed in the present study were collected from approximately 1700 families during the COVID-19 pandemic. The data spanned two measurement time points separated by several months. We used hierarchical structural equation models (SEM) to estimate the latent correlation between parents\u0026rsquo; and their children\u0026rsquo;s executive function and delay aversion across multiple measurements. Additionally, we employed a cross-lagged panel model to assess the directional influence of parents\u0026rsquo; initial ratings of executive function and delay aversion on their later scores. This analysis aimed to determine whether parental assessments of self-regulatory ability significantly predicted later assessments and whether these influences were specific to the assessment target (either their own or their children\u0026rsquo;s abilities) or generalized across targets (i.e., establishing cross-lagged relationships). Through these analyses, we aimed to contribute valuable insights into the interplay of parental and child self-regulatory abilities and their potential long-term effects.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003c/div\u003e"},{"header":"Method","content":"\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eProcedure\u003c/h2\u003e \u003cp\u003eData were collected from seven European countries through an anonymous digital survey (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). The survey aimed to understand parental experiences with distance learning and prompted parents to assess both their own and their children\u0026rsquo;s self-regulation skills during the pandemic (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e). Data collection occurred in two phases. The initial survey phase spanned from April 28th to November 1st, 2020 (assessment timepoint one - T1), followed by a second phase from December 6th, 2020, to February 25th, 2021 (assessment timepoint two - T2). The survey was distributed to parents through various channels. During T1, it was promoted via social media, school blackboards, parent networks, and support groups. For T2, parents received invitations via email. For the current manuscript, we analyzed the subset of data collected in Germany. The data needed to reproduce the analysis and results reported here can be accessed through the supplementary materials repository provided on the open science framework (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://osf.io/rc934/?view_only=5cb3c5e1d5aa4dc0bf25aa78c752dc3c\u003c/span\u003e\u003cspan address=\"https://osf.io/rc934/?view_only=5cb3c5e1d5aa4dc0bf25aa78c752dc3c\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eParticipants\u003c/h2\u003e \u003cp\u003eTo be eligible for participation, respondents had to be parents of children or adolescents aged between five and 20 years, enrolled in standard schooling, and transitioning to distance learning due to pandemic-induced school closures. Initially, 1,767 parents participated at T1, and 1,082 at T2. After excluding mismatched data and entries with errors, the analyses were based on data from 1,674 participants at T1 and 664 participants at T2. Among these, 543 families participated in both the T1 and T2 phases.\u003c/p\u003e \u003cp\u003eDescriptive statistics are provided in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The average age of the children was 11.53 years at T1 and increased to 12.08 years by T2. Female children represented 47.85% of the sample at T1 and 46.69% at T2. The mean age of parents at T1 was 43.10 years, with females comprising 85.60% of the sample. By T2, the average parental age was slightly higher at 43.87 years, with females comprising 86.75% of the participants.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSample descriptives for both measurement timepoints\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eMeasurement timepoint\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eT2\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFamilies N\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1674\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e664\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eParents\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN males\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e238\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e87\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN females\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1433\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e576\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN others*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean age (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43.10 (6.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43.87 (6.27)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge range\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23\u0026ndash;68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28\u0026ndash;68\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eChildren\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN males\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e871\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e354\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN females\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e801\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e310\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN others*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean age (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.53 (3.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.08 (3.10)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge range\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u0026ndash;20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u0026ndash;20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eNote.\u003c/em\u003e SD\u0026thinsp;=\u0026thinsp;standard deviation, N\u0026thinsp;=\u0026thinsp;Number of cases.\u003c/p\u003e \u003cp\u003e*Others contains diverse, intersexual and unassignable\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eInstruments\u003c/h2\u003e \u003cp\u003eThe online survey assessed various facets of parental experiences during distance learning. Additionally, parents were asked to rate their self-regulatory skills and their children\u0026rsquo;s self-regulatory skills by indicating their agreement or disagreement (1 = \u0026ldquo;strongly disagree\u0026rdquo; to 5 = \u0026ldquo;strongly agree\u0026rdquo;) with a series of statements about their own and their children\u0026rsquo;s daily difficulties with executive function and control over delay aversion. A higher score indicated more pronounced executive function problems and increased delay aversion. The tools utilized for these measurements are elaborated upon in the subsequent sections.\u003c/p\u003e \u003cp\u003e \u003cb\u003eChildhood Executive Functioning Inventory (CHEXI)\u003c/b\u003e: To assess children\u0026rsquo;s executive function problems, the survey included an abbreviated version of the Childhood Executive Functioning Inventory - CHEXI (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e). The CHEXI, freely available in many languages (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u003ca href=\"https://osf.io/rc934/?view_only=5cb3c5e1d5aa4dc0bf25aa78c752dc3c\" target=\"_blank\"\u003ewww.chexi.se\u003c/a\u003e\u003c/span\u003e\u003cspan address=\"http://www.chexi.se\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), includes two subscales measuring difficulties in working memory (e.g., \u0026ldquo;when asked to do several things, he/she only remembers the first or last\u0026rdquo;) and the inhibition domain (e.g., \u0026ldquo;has difficulty holding back his/her activity despite being told to do so\u0026rdquo;). The online survey comprised eight items: four for working memory and four for inhibition. Working memory items showed good internal consistency at T1 (Cronbach\u0026rsquo;s alpha\u0026thinsp;=\u0026thinsp;0.86, 95% CI = [0.85\u0026ndash;0.87]) and T2 (Cronbach\u0026rsquo;s alpha\u0026thinsp;=\u0026thinsp;0.84, 95% CI = [0.83\u0026ndash;0.85]). The same was the case for inhibition items at T1 (Cronbach\u0026rsquo;s alpha\u0026thinsp;=\u0026thinsp;0.84, 95% CI = [0.83\u0026ndash;0.85]) and T2 (Cronbach\u0026rsquo;s alpha\u0026thinsp;=\u0026thinsp;0.85, 95% CI = [0.84\u0026ndash;0.86]).\u003c/p\u003e \u003cp\u003e \u003cb\u003eAdult Executive Functioning Inventory (ADEXI)\u003c/b\u003e: To measure parental executive function problems, the survey included an abbreviated version of the Adult Executive Functioning Inventory \u0026ndash; ADEXI (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e). Like the CHEXI, the ADEXI is freely available in various languages (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u003ca href=\"https://osf.io/rc934/?view_only=5cb3c5e1d5aa4dc0bf25aa78c752dc3c\" target=\"_blank\"\u003ewww.chexi.se\u003c/a\u003e\u003c/span\u003e\u003cspan address=\"http://www.chexi.se\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and includes two subscales measuring difficulties in working memory (e.g., \u0026ldquo;when someone asks me to do several things, I sometimes remember only the first or last\u0026rdquo;) and the inhibition domain (e.g., \u0026ldquo;I have a tendency to do things without first thinking about what could happen\u0026rdquo;). The online survey comprised eight items: four for working memory and four for inhibition. Working memory items showed acceptable internal consistency at T1 (Cronbach\u0026rsquo;s alpha\u0026thinsp;=\u0026thinsp;0.79, 95% CI = [0.77\u0026ndash;0.81]) and T2 (Cronbach\u0026rsquo;s alpha\u0026thinsp;=\u0026thinsp;0.77, 95% CI = [0.76\u0026ndash;0.79]). In contrast, the internal consistency of inhibition items was somewhat lower at T1 (Cronbach\u0026rsquo;s alpha\u0026thinsp;=\u0026thinsp;0.61, 95% CI = [0.57\u0026ndash;0.64]) and T2 (Cronbach\u0026rsquo;s alpha\u0026thinsp;=\u0026thinsp;0.63, 95% CI = [0.60\u0026ndash;0.66]).\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eQuick Delay Questionnaire (QDQ)\u003c/strong\u003e \u003cp\u003eChild and parental delay aversion were measured using a brief (two-item) version of the Quick Delay Questionnaire \u0026ndash; QDQ (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e). Children\u0026rsquo;s delay aversion items showed acceptable internal consistency at T1 (Cronbach\u0026rsquo;s alpha\u0026thinsp;=\u0026thinsp;0.77, 95% CI = [0.74\u0026ndash;0.79]) and T2 (Cronbach\u0026rsquo;s alpha\u0026thinsp;=\u0026thinsp;0.77, 95% CI = [0.75\u0026ndash;0.79]). Similarly, the parents\u0026rsquo; delay aversion items showed acceptable internal consistency at T1 (Cronbach\u0026rsquo;s alpha\u0026thinsp;=\u0026thinsp;0.76, 95% CI = [0.74\u0026ndash;0.78]) and T2 (Cronbach\u0026rsquo;s alpha\u0026thinsp;=\u0026thinsp;0.76, 95% CI = [0.73\u0026ndash;0.78]).\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analyses\u003c/h2\u003e \u003cp\u003eBefore analyses, all variables were converted to z-scores, ensuring each variable had a mean of zero and a standard deviation of one. This was done to mitigate potential effects caused by discrepancies in scale between variables and to avoid potential estimation problems resulting from differing variances between the response variables. The standardized data formed the basis for all subsequent analyses.\u003c/p\u003e \u003cp\u003eWe used structural equation models to estimate the latent correlation and longitudinal associations between parental and child executive function deficits, as well as parental and child delay aversion. All models were estimated in the R programming environment, version 4.3.2, (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e) using the \u003cem\u003elavaan\u003c/em\u003e package (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e). We used the maximum likelihood algorithm with robust Huber-White standard errors and a scaled test statistic (asymptotically) equal to the Yuan-Bentler test statistic to account for possible deviations from multivariate normality. As the variables were standardized, we fixed all estimated indicator means to zero, a fact that informs the degrees of freedom for all reported models. In some specific cases (reported below), the algorithm estimated non-significant residual variances with values below zero. To account for this issue, we refitted the corresponding model with that residual variance fixed to zero. For handling missing data, we used the full information maximum likelihood (FIML) estimator (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e) as implemented in the \u003cem\u003elavaan\u003c/em\u003e package.\u003c/p\u003e \u003cp\u003eWe evaluated goodness-of-fit based on the comparative fit index, CFI (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e), and the root mean square error of approximation, RMSEA (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e). We considered CFI values\u0026thinsp;\u0026gt;\u0026thinsp;.95 and RMSEA values\u0026thinsp;\u0026lt;\u0026thinsp;.06 to indicate good model fit, and CFI values\u0026thinsp;\u0026gt;\u0026thinsp;.90 and RMSEA values\u0026thinsp;\u0026lt;\u0026thinsp;.08 to indicate acceptable model fit, as recommended by Brown and Cudeck (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e) and Hu and Bentler (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e). Effects were considered statistically significant if the p-value was less than α\u0026thinsp;=\u0026thinsp;0.05.\u003c/p\u003e \u003cp\u003eIn a first step, we estimated the measurement and structure models for parents and children at each measurement timepoint separately. These models yielded a good fit. Please refer to the supplemental material on the OSF for further inspection of these models (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://osf.io/rc934/?view_only=5cb3c5e1d5aa4dc0bf25aa78c752dc3c\u003c/span\u003e\u003cspan address=\"https://osf.io/rc934/?view_only=5cb3c5e1d5aa4dc0bf25aa78c752dc3c\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBased on these results, we estimated four different models. In Model 1 and Model 2, we assumed a hierarchical structure for executive function problems: Parents\u0026rsquo; and their children\u0026rsquo;s executive function were modeled as measurement timepoint-specific and parent and child-specific higher-order factors. The working memory and inhibition sub-facets of the CHEXI and ADEXI were estimated to conform to the measurement timepoint-specific and parent and child-specific lower part of the factor hierarchy (i.e., subdomains). The higher-order factors executive function (problems) at timepoint 1 (EF T1 parent and EF T1 child) and executive function (problems) at timepoint 2 (EF T2 parent and EF T2 child) can be interpreted as the common variance shared by the working memory and inhibition subdomains at each measurement timepoint, which are thought to be correlated. In Model 1, we estimated a general trait factor for parental and child executive function problems that integrated the measurement timepoint-specific executive function problems factors. This is equivalent to the assumption that the common variance in executive function problems ratings for parents and their children can each be explained by a single, corresponding trait factor contributing to each of the measurements. Finally, Model 1 estimated the latent correlations between the general trait factor for parental executive function problems and the general trait factor for child executive function problems.\u003c/p\u003e \u003cp\u003eIn Model 2, we did not include the general trait factors for parental and child executive function. Instead, we estimated the cross-lagged relationships between the higher-order factors EF T1 parent, EF T1 child, EF T2 parent, and EF T2 child to estimate the directionality and longitudinal associations between parental and child executive function across measurements. The primary goal of this model was to extend Model 1 and to provide a more fine-grained understanding of the interrelationships between parental and child executive function both within and between the two measurement timepoints. This model captures correlations within a single measurement timepoint (assessing the initial overlap between parent and child executive function), associations between the same traits measured at different times (allowing us to assess their temporal stability), and relationships between different domains captured at disparate times (allowing us to examine variance in one group of subjects as it may predict changes in the other) (cf. 52).\u003c/p\u003e \u003cp\u003eModel 3 and Model 4 were homologous to Model 1 and Model 2, respectively, but concerned parents\u0026rsquo; ratings of their own and their children\u0026rsquo;s delay aversion. One further difference between the executive function models (i.e., Models 1 and 2) and the delay aversion models (i.e., Models 3 and 4) was that the delay aversion models did not include subdomain factors, as delay aversion was assessed using items from only one scale.\u003c/p\u003e \u003cp\u003eFinally, in supplemental analyses, we tested whether the cross-lagged relationships in Models 2 and 4 (i.e., the cross-lagged panel models for executive function and delay aversion) were moderated by the amount of time parents and their children spent working together on school assignments from home between measurement timepoints. These additional analyses aimed to explore potential moderating effects of the distance learning context on the longitudinal associations between parental and child executive function and delay aversion. The models showed no substantial moderation effects and can be found in the supplemental analysis section provided on the OSF.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eRelationship between Parental and Child Executive Function\u003c/h2\u003e \u003cp\u003e \u003cb\u003eLatent correlation.\u003c/b\u003e Model 1 estimated the latent correlation between two higher-order factors that captured the trait components of parents\u0026rsquo; ratings of their own and their children\u0026rsquo;s executive function problems across measurements (see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Model 1 showed relatively good model fit (χ\u0026sup2; [df\u0026thinsp;=\u0026thinsp;475]\u0026thinsp;=\u0026thinsp;1333.4, CFI\u0026thinsp;=\u0026thinsp;0.93, RMSEA\u0026thinsp;=\u0026thinsp;0.035). The model estimated a substantial latent correlation between the trait factors for parental and child executive function problems of r\u0026thinsp;=\u0026thinsp;0.48 (95% CI = [0.41, 0.55], p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating that parents\u0026rsquo; assessment of their own and their children\u0026rsquo;s executive function problems shared approximately 23% of their variance.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eResults indicated that the hierarchical structure in Model 1 captured large proportions of the variance present in the executive function subdomains of working memory (Children: β\u003csub\u003eT1\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;0.92, β\u003csub\u003eT2\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;0.93; parents: β\u003csub\u003eT1\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;0.80, β\u003csub\u003eT2\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;0.82) and inhibition (Children: β\u003csub\u003eT1\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;0.86, β\u003csub\u003eT2\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;0.89; parents: β\u003csub\u003eT1\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;0.83, β\u003csub\u003eT2\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;0.84), with the proportions of variance explained by the timepoint-specific hierarchical structure being overall somewhat higher for child executive function problems than for parental executive function problems.\u003c/p\u003e \u003cp\u003e \u003cb\u003eCross-lagged relationships.\u003c/b\u003e Model 2 estimated the latent relationships between parental and child executive function problems both within and between the two measurement timepoints (see Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The model estimated the latent correlation coefficient linking the two higher-order factors that captured the common variance shared by the working memory and inhibition subdomains ratings for child and parent executive function problems at T1. Furthermore, the model estimated the latent regression coefficient between the higher-order factors for child executive function at T1 and T2, parent executive function at T1 and T2, as well as the cross-lagged latent regression coefficients linking child executive function at T1 and parent executive function at T2, and parent executive function at T1 and child executive function at T2. Parental executive function problems at T2 (EF parent T2) were fully accounted for by the model, and thus its residual variance was fixed to zero. The model showed relatively good model fit (χ\u0026sup2; [df\u0026thinsp;=\u0026thinsp;472]\u0026thinsp;=\u0026thinsp;1333.4, CFI\u0026thinsp;=\u0026thinsp;0.93, RMSEA\u0026thinsp;=\u0026thinsp;0.035).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe model estimated a substantial latent correlation between parent and child executive function problems at T1 of r\u0026thinsp;=\u0026thinsp;0.45 (95% CI = [0.39, 0.51]), replicating the latent correlation estimated by Model 1. Furthermore, child executive functioning problems at T1 largely predicted child executive functioning problems at T2 (β\u0026thinsp;=\u0026thinsp;0.77, 95% CI = [0.68, 0.86], p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), but did not predict parental executive functioning problems at T2 (β = -0.09, 95% CI = [-0.22, 0.05], p\u0026thinsp;=\u0026thinsp;0.196). In contrast, parent executive function problems at T1 were highly predictive of parental executive function problems at T2 (β\u0026thinsp;=\u0026thinsp;1.0, 95% CI = [0.98, 1.00], p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and were also predictive of child executive function problems at T2 (β\u0026thinsp;=\u0026thinsp;0.16, 95% CI = [0.05, 0.28], p\u0026thinsp;=\u0026thinsp;0.004).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eRelationship between Parental and Child Delay Aversion\u003c/h2\u003e \u003cp\u003e \u003cb\u003eLatent Correlation.\u003c/b\u003e Model 3 estimated the latent correlation between two higher-order factors that captured the trait components of parents\u0026rsquo; ratings of their own and their children\u0026rsquo;s delay aversion across measurements (see Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Model 3 showed good model fit (χ\u0026sup2; [df\u0026thinsp;=\u0026thinsp;21]\u0026thinsp;=\u0026thinsp;2197.8, CFI\u0026thinsp;=\u0026thinsp;0.98, RMSEA\u0026thinsp;=\u0026thinsp;0.035). The model estimated a substantial latent correlation between the trait factors of parental and child delay aversion of r\u0026thinsp;=\u0026thinsp;0.50 (95% CI = [0.41, 0.60], p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating that parents\u0026rsquo; assessment of their own and their children\u0026rsquo;s delay aversion shared approximately 25% of their variance.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eCross-Lagged Relationships.\u003c/b\u003e Model 4 estimated the latent relationships between parental and child delay aversion both within and between the two measurement timepoints (see Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The model estimated the latent correlation coefficient linking the two factors that captured the common variance of the ratings for child and parent delay aversion at T1. Furthermore, the model estimated the latent regression coefficients between the factors for child delay aversion at T1 and T2, parent delay aversion at T1 and T2, as well as the cross-lagged latent regression coefficients linking child delay aversion at T1 and parent delay aversion at T2, and parent delay aversion at T1 and child delay aversion at T2. The model showed good fit (χ\u0026sup2; [df\u0026thinsp;=\u0026thinsp;21]\u0026thinsp;=\u0026thinsp;2197.8, CFI\u0026thinsp;=\u0026thinsp;0.99, RMSEA\u0026thinsp;=\u0026thinsp;0.027).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eModel 4 estimated a substantial latent correlation between parent and child delay aversion at T1 of r\u0026thinsp;=\u0026thinsp;0.40 (95% CI = [0.33, 0.47]). This estimate was somewhat lower than the overall latent correlation estimated by Model 3. Furthermore, child delay aversion at T1 largely predicted child delay aversion at T2 (β\u0026thinsp;=\u0026thinsp;0.70, 95% CI = [0.58, 0.81], p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), but did not predict parental delay aversion at T2 (β = -0.06, 95% CI = [-0.18, 0.05], p\u0026thinsp;=\u0026thinsp;0.258). Similarly, parent delay aversion at T1 was predictive of parent delay aversion at T2 (β\u0026thinsp;=\u0026thinsp;0.71, 95% CI = [0.61, 0.81], p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), but not for child delay aversion at T2 (β = -0.02, 95% CI = [-0.15, 0.10], p\u0026thinsp;=\u0026thinsp;0.727).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eSelf-regulation is a fundamental aspect of human development that significantly impacts various domains of functioning through childhood and adolescence. Our study aimed to investigate the relationship between parental and child self-regulatory abilities, with a specific emphasis on executive function impairments and delay aversion. The overall aim of the present study was to estimate the association between parents' self-perceived levels of self-regulatory skills and their assessments of their children's self-regulatory capacities as well as the longitudinal relations between these abilities.\u003c/p\u003e \u003cp\u003eOur findings demonstrate a significant relationship between the trait factors representing parental and child executive functioning deficits, as well as those representing parental and child delay aversion. Specifically, our models showed substantial shared variance between parental and child assessments of executive function problems and delay aversion. Moreover, results revealed predictive relationships between executive functioning deficits and delay aversion at different measurement time points. Whereas deficits in children's self-regulatory abilities at T1 (executive function and delay aversion) only predicted children's deficits at T2, and parental delay aversion at T1 only predicted parental delay aversion at T2, parental deficits in executive functions at T1 predicted both parental as well as child deficits in executive functions at T2.\u003c/p\u003e \u003cp\u003eThese findings indicate that higher levels of executive function problems reported by parents at T1 correspond to an increased perception of similar problems in their children at T2. This observation is significant as it implies that parents who identify numerous difficulties in their own self-regulation initially are likely to anticipate similar challenges in their children later in development, or alternatively, perceive such difficulties in their children more sensitively. Accordingly, parents' self-perception of their own self-regulation skills appears to influence their assessment of their children's abilities. If parents perceive themselves as well-regulated, they are more likely to rate their children similarly. It seems as if parents draw direct conclusions from themselves to their children.\u003c/p\u003e \u003cp\u003e To our knowledge, this is the first study to focus on the extent to which parental perceptions of their own and their children's self-regulation skills influence the outcomes, rather than focusing on the actual relationship between parents' and children's self-regulation skills. Moreover, it is also the first investigation that examines this connection generally and longitudinally. Our results are in line with previous findings (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e) that propose that parental beliefs, in addition to parental behavior, play a role in shaping child outcomes. More specifically, according to Murphey's model (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e), parental beliefs might influence how parents perceive their children's behaviors and corresponding outcomes, potentially moderating parental responses accordingly. In addition, our results align with existing literature highlighting the role of parental self-regulation in child development (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e). We extend the framework of Cuevas and colleagues (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e) by revealing intergenerational ties not only within mother-child executive function associations in early childhood, but across parental genders and child age groups. Parents serve as primary models for children's self-regulatory behaviors, and our findings indicate that parental beliefs about their own self-regulatory skills influence the perceptions of their children's substantially. The present study added new information by showing that this relationship, at least for executive function deficits, remains stable over time. Our findings go beyond previous research by highlighting the stability of these constructs throughout development. The relationship between parental and child executive function is robust, implying that even after reassessment several months later, parents\u0026rsquo; rating of their children is dependent on their self-ratings, regardless of the severity of initially observed deficits.\u003c/p\u003e \u003cp\u003eAs we did not observe such an intergenerational correlation over time for delay aversion, it prompts inquiry into the underlying factors contributing to this discrepancy. One potential explanation for this phenomenon might be that delay aversion was less salient in daily life during the pandemic and therefore less frequently encountered. Furthermore, delay aversion was not recorded as comprehensively within the study as executive function deficits. Since delay aversion is a complex neuropsychological factor that comprises several dimensions (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e) it is perhaps more difficult to capture (especially within a survey) than executive functions.\u003c/p\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eStrengths and Limitations\u003c/h2\u003e \u003cp\u003eOne of the main strengths of the current study is the large sample size that enhances the generalizability of our findings and provides robust statistical power for detecting relationships between variables. While prior research has predominantly focused on specific age cohorts such as infants (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e), children (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e), or adolescents (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e), our sample encompasses individuals across multiple age groups. This broad inclusion facilitates a more holistic perspective on the topic. Furthermore, assessments were conducted during school closures instead of relying on retrospective reports, providing real-time insights into the impact of distance learning on self-regulation. In contrast to previous research that has predominantly concentrated on maternal abilities, our study extends this focus and includes paternal contributions as well. As noted by Ribner and colleagues (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e) and Jester and colleagues (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e), paternal skills contribute to the association between parental and child self-regulatory skills, too. In the present study, we examine the interrelations across both parental genders rather than isolating analyses to each gender individually, although it is important to note, that significantly more mothers participated in the study.\u003c/p\u003e \u003cp\u003eOur study is the first to examine intergenerational connections longitudinally. Also, existing studies have mainly focused on the familial effects on executive function (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e) rather than delay aversion. Here, we also consider delay aversion, as it is equally relevant in the context of self-regulation (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e). Our goal was to measure self-regulation more broadly.\u003c/p\u003e \u003cp\u003eAt the same time, several limitations within this investigation should be acknowledged. Firstly, the sole reliance on parental self-report measures concerning child self-regulatory abilities may introduce bias (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e), as parents may overestimate or underestimate their children's self-regulatory abilities as well as their impact on those abilities. Future research should incorporate multi-informant assessments as well as neuropsychological testing to provide a more comprehensive understanding of parent-child dynamics. Delay aversion, in particular, is a complex construct that is challenging to accurately quantify (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e). Consequently, future research should aim to develop more comprehensive methods for its measurement.\u003c/p\u003e \u003cp\u003eSecondly, a further limitation is the lack of extensive standardized, validated measurements. Since incorporating numerous scales would have considerably increased the survey length and potentially reduced the response rate, especially among families dealing with mental health issues, only an abbreviated version of all three questionnaires (CHEXI, ADEXI, QDQ) was used. To capture the factors more comprehensively, future investigations should consider incorporating the broader questionnaires and objective tests that capture executive function (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e), attentional control (\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e) and delay aversion (\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThirdly, the correlational approach limits our ability to establish causal relationships between parental and child self-regulation. Subsequent research should explore the impact of other environmental factors on parent-child interactions and self-regulatory development.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eImplications\u003c/h2\u003e \u003cp\u003eDespite these limitations, our study has important implications for both research and practice. By highlighting the significant correlations and longitudinal associations between parental and child self-regulatory abilities, our findings underscore the importance of considering family dynamics in interventions aimed at promoting self-regulation in children. In view of the fact that parental attributions and expectations influence their children\u0026rsquo;s treatment progress (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e), interventions targeting parental self-regulation may indirectly benefit children's development (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e), while interventions directly targeting children may have spillover effects on parental self-regulation (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). Schneider and colleagues (\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e), for instance, found that successful treatment of parents\u0026rsquo; anxiety disorder is a significant predictor of a better outcome for children\u0026rsquo;s anxiety sensitivity and agoraphobic cognitions and that even the mere treatment participation (regardless of whether it was successful or not) had a significant positive effect on descendants.\u003c/p\u003e \u003cp\u003eAs mentioned earlier, to the best of our knowledge, this study is the first to analyze longitudinal familial relationships based on parents' self-assessments and their evaluations of their children, rather than on direct comparison of parent and child self-regulatory skills. This method enables us to analyze the relationship implied by parents between their own abilities and those of their children, rather than the direct correlation between parent and child skills. The results provide important insights into parental expectations and self-perceptions, contributing to a deeper understanding of the implicit beliefs and assumptions parents have regarding the influence of their abilities on their children's development. This can serve as an initial motivation to investigate whether and how parental self-assessment correlates with the actual abilities and performance of their children. Future studies should combine both approaches by collecting neuropsychological data from parents and children, as well as parents' evaluations of their own and their children\u0026rsquo;s abilities, and vice versa. In light of the study's findings indicating that parents derive perceptions of their children from their own characteristics, it becomes imperative to consider this phenomenon within therapeutic contexts as well. Our study firmly shows that parents' self-perception is a key factor in how they assess their children's condition. Additionally, our results suggest that early identification and support for children with self-regulatory deficits may help mitigate long-term impacts on academic and socio-emotional outcomes.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, our study provides evidence of significant correlations and longitudinal associations between parental and child self-regulatory abilities, emphasizing the role of family dynamics in shaping self-regulatory skills during childhood and adolescence. Despite the constraints of our study, our findings contribute to a deeper understanding of the intergenerational connection of self-regulation and underscore the significance of parental inferences regarding their own abilities in relation to those of their children. By these results, a better understanding of the structural covariance and temporal stability of caregivers' assessments is provided. This has important implications for interventions aimed at promoting positive developmental outcomes in children as well as for therapeutic work. Future research should continue to explore the complex interactions of parental influences, child development, and environmental factors, aiming to develop more effective interventions and support strategies. Specifically, efforts should be made to better target parental involvement in treatment as a way to prevent childhood issues. Therefore, self-regulatory abilities should be examined from different perspectives by conducting neuropsychological tests of the constructs in addition to detailed questionnaire data. In addition to parents' assessments of their own and their children's abilities, children should also assess their own and their parents' abilities to undertake a multifactorial comparison. Combining the approach of previous studies (measuring children's and parents' self-regulation skills) with the approach of this study (measuring parental assessment of parental and child self-regulation skills) and adding children's assessment of child and parental self-regulation skills could help to determine the extent to which parental assessment predicts or influences children's actual performance.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003cstrong\u003eConsent for publication\u003c/strong\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eCompeting interests\u003c/strong\u003e \u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eEthical Approval\u003c/strong\u003e \u003cp\u003eThe study was approved by the local ethics committee at the university of Marburg and all participants provided informed consent prior to participation.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eNone of the authors received any funding for this project.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eHC, RS, and SS conceptualized the study and supervised the data gathering process in Germany. JK and JCGA analyzed the data, prepared all figures and tables, and wrote the main manuscript. JK, JCGA, RS, and HC interpreted the results. All authors reviewed, edited, revised, and approved the final version of the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors thank all families who participated in the study.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe dataset and corde scripts supporting the conclusions of this article is available in the OSF repository (DOI 10.17605/OSF.IO/RC934) under https://osf.io/rc934/?view_only=5cb3c5e1d5aa4dc0bf25aa78c752dc3c.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eRobson DA, Allen MS, Howard SJ. Self-regulation in childhood as a predictor of future outcomes: A meta-analytic review. 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Psychother Psychosom. 2013;82(5):346\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGarc\u0026iacute;a Alanis JC, The Apple Does Not Fall Far. 2024 Jun 25 [cited 2024 Jun 25]; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://osf.io/rc934/\u003c/span\u003e\u003cspan address=\"https://osf.io/rc934/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"child-and-adolescent-psychiatry-and-mental-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"caph","sideBox":"Learn more about [Child and Adolescent Psychiatry and Mental Health](http://capmh.biomedcentral.com)","snPcode":"13034","submissionUrl":"https://submission.nature.com/new-submission/13034/3","title":"Child and Adolescent Psychiatry and Mental Health","twitterHandle":"@IACAPAP","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Self-regulation, Executive function, Delay aversion, Parental influence, Child development","lastPublishedDoi":"10.21203/rs.3.rs-4637867/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4637867/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSelf-regulation is a critical skill that influences children's academic, social, and emotional development. This study investigates the stability and predictive relationships between parents' ratings of their own and their children's self-regulation abilities, focusing on executive function and delay aversion due to their strong association with cognitive and emotional control processes. Using data from 1700 families collected during the COVID-19 pandemic, we employed hierarchical structural equation models and cross-lagged panel models to analyze the temporal stability and directional influences of self-regulation assessments.\u003c/p\u003e \u003cp\u003eOur analysis revealed a substantial latent correlation (r\u0026thinsp;=\u0026thinsp;0.48, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) between parents' and children's executive function problems, indicating a shared variance of approximately 23%. Significant cross-lagged effects were found, with parental executive function at T1 predicting child executive function at T2 (β\u0026thinsp;=\u0026thinsp;0.16, p\u0026thinsp;=\u0026thinsp;0.004). For delay aversion, we found a latent correlation of r\u0026thinsp;=\u0026thinsp;0.50 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and significant within-timepoint and temporal stability, but no cross-lagged effects.\u003c/p\u003e \u003cp\u003eThese findings suggest that higher levels of executive function problems reported by parents at T1 correspond to an increased perception of similar problems in their children at T2. This highlights the importance of parental self-perception in assessing children's abilities, aligning with Murphey's model that parental beliefs influence child outcomes. Our results underscore the significance of considering family dynamics in interventions aimed at promoting self-regulation in children. By understanding the interplay between parental and child self-regulation, researchers and practitioners can design more effective, individualized interventions to promote positive developmental outcomes.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e","manuscriptTitle":"The Apple Does Not Fall Far: Stable Predictive Relationships Between Parents' Ratings of Their Own and Their Children’s Self-Regulation Abilities","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-23 12:58:53","doi":"10.21203/rs.3.rs-4637867/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-07-23T13:12:03+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-07-22T17:32:26+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-07-19T20:43:39+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-07-16T14:11:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"46713589159036854768644069746547755417","date":"2024-07-16T13:04:52+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"269988492809182784342653736885089249270","date":"2024-07-12T18:23:33+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"130964668056656198403910156255299572581","date":"2024-07-12T10:18:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"163718116445748284723047172350263320391","date":"2024-07-11T16:21:04+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-07-10T09:51:26+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-07-10T09:13:17+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-06-27T11:54:56+00:00","index":"","fulltext":""},{"type":"submitted","content":"Child and Adolescent Psychiatry and Mental Health","date":"2024-06-25T16:26:47+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"child-and-adolescent-psychiatry-and-mental-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"caph","sideBox":"Learn more about [Child and Adolescent Psychiatry and Mental Health](http://capmh.biomedcentral.com)","snPcode":"13034","submissionUrl":"https://submission.nature.com/new-submission/13034/3","title":"Child and Adolescent Psychiatry and Mental Health","twitterHandle":"@IACAPAP","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"87dc0a9c-9f85-420d-8098-b63daf073812","owner":[],"postedDate":"July 23rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-10-07T15:58:47+00:00","versionOfRecord":{"articleIdentity":"rs-4637867","link":"https://doi.org/10.1186/s13034-024-00814-z","journal":{"identity":"child-and-adolescent-psychiatry-and-mental-health","isVorOnly":false,"title":"Child and Adolescent Psychiatry and Mental Health"},"publishedOn":"2024-10-03 15:56:55","publishedOnDateReadable":"October 3rd, 2024"},"versionCreatedAt":"2024-07-23 12:58:53","video":"","vorDoi":"10.1186/s13034-024-00814-z","vorDoiUrl":"https://doi.org/10.1186/s13034-024-00814-z","workflowStages":[]},"version":"v1","identity":"rs-4637867","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4637867","identity":"rs-4637867","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}