Search

Developmental researchers commonly utilize longitudinal data to decompose reciprocal and dynamic associations between repeatedly measured constructs to better understand the temporal precedence between constructs. Although the cross-lagged panel model (CLPM) is commonly used in developmental research, it has been criticized for its potential to produce biased estimates due to the fact of ignoring the trait-like, time-invariant nature of stability in constructs across people and aggregating the between and within-person effects together as a single estimate. Recently, a growing set of alternatives have emerged, but the estimates across CLPM and alternatives have rarely been compared in developmental research. The primary purpose of this article is to (a) provide a three-component framework to help developmental researchers to select and specify the most appropriate model, and (b) illustrate how models differ in estimates with an empirical example investigating the reciprocal associations between internalizing and externalizing problems in school-aged children. Methods: We specified CLPM, Random-Intercept Cross-lagged Panel Model (RI-CLPM), Latent Curve Model with Structured Residuals (LCM-SR), Latent Change Score (LCS), and a Random-Mean LCS using four waves of data from ECLS-K: 2011 (N = 8779). Results: The CLPM provided the most evidence of significant cross-lagged paths but the poorest fit to the data compared to other models. Alternative models had excellent fit and found either only negative temporal precedence from internalizing to externalizing problems or simply no evidence of prospective within-person relations between internalizing and externalizing problems.

The purpose of this study was to investigate the associations between elementary students' reading skills and their online reading (i.e., real-time reading) behaviors during silent sentence processing. Thirty-five students participated in this study and their eye movements were recorded during sentence reading tasks. The effects of students' reading skills measured by traditional standardized measures were investigated for widely-used eye tracking measures such as first fixation duration, gaze duration, regression path duration, total duration, word skipping, fixation count, and regression frequency. The eye tracking measures were chosen to represent early/late cognitive processes and temporal/spatial gaze behaviors. Linear mixed-effects regression analyses revealed that children's performances in reading skills predict most of the eye tracking measures.

The current study examined the heterogeneity in the development of school-based prosocial behavior from Grades 1 to 12 and the role of multiple early childhood antecedents in predicting heterogeneous developmental trajectories of prosocial behavior in a sample of 784 children facing early risks and vulnerabilities (predominantly from low-income families and academically at risk; 52.6% male). In alignment with the risk and resilience framework, antecedents consisted of risk and protective factors from both individual (i.e., ego-resilient personality, behavior problems, intelligence, academic performance, gender, and ethnicity) and contextual domains (i.e., maternal support and responsiveness, family socioeconomic adversity, teacher-child warmth and conflict, and peer acceptance and rejection). We identified four distinct prosocial trajectories including a high-stable (52.5%), high-desisting (15%), moderate-increasing (20.6%), and low-stable class (11.9%). Results revealed that the low-stable, high-desisting, and moderate-increasing classes were associated with lower ego resiliency, higher behavior problems, lower teacher-child warmth, higher teacher-child conflict, and peer rejection in early childhood, compared to the high-stable group. Boys and African Americans were more likely to be in the low-stable, high-desisting, and moderate-increasing classes. Individual characteristics such as ego-resilient personality and contextual influences such as teacher-child warmth served as common protective antecedents. Interestingly, teacher-child conflict served as a unique predictor for the high-desisting class, and behavior problems and peer rejection served as unique predictors for the low-stable class.

This investigation examined patterns of codeveloping internalizing and externalizing problems from early childhood through adolescence (i.e., Grades 1 to 12). Subgroups of children with heterogeneous developmental trajectories (i.e., pure and co-occurring internalizing and externalizing problems) were identified and their long-term associations with teacher-child relationship quality and academic (math and reading) performance were assessed. Findings were based on a sample of 784 children (52.6% girls) who were followed from Grade 1 (M[subscript age] = 6.57) to Grade 12 (M[subscript age] = 17.57). Children's internalizing problems, externalizing problems, and teacher-child relationship quality were assessed annually from Grades 1 to 12, and their academic performance was assessed from Grades 1 to 9. Results revealed four distinct trajectories of internalizing and externalizing problems including chronic co-occurring, moderate co-occurring, pure-externalizing, and low-risk groups. Children with chronic co-occurring internalizing and externalizing problems exhibited more sustained teacher-child conflict, lower teacher-child warmth, and lower math and reading performance. Children with pure externalizing and moderate co-occurring problems were also at risk for scholastic difficulties, but to a lesser magnitude than children with chronic co-occurring problems. Compared to children in the low risk group, those in all three risk groups exhibited patterns of scholastic maladjustment that were either sustained or worsened after the transition to middle school.

Reading and math attainment develop during elementary grades. Questions remain, though, about the co-developmental nature of the relation between reading and math. This study examined dynamic, longitudinal pathways between reading and math in first through fourth grades. Participants of the study were 554 academically at-risk children (M[subscript] age at the first assessment point = 6.57 years; SD = 0.38) from Texas Project Achieve. Children were assessed utilizing the Woodcock-Johnson--III reading and math measures. Results from dynamic bivariate latent change score models indicated unidirectional longitudinal coupling effects from reading to math. Specifically, average and high levels of reading performance were associated with subsequent gains in math growth, in particular for below average performing children in math. In contrast, low levels of reading performance had negligible or no amplifying influences on change in math growth. The nature of the dynamics was replicated even when controlling for nonverbal cognitive abilities. Results demonstrated that good reading skills pave the way for children to develop their math skills. Such findings underscore the importance of considering reading performance in treating math difficulties.

Common ways to test associations between two repeatedly measured constructs have two primary limitations. Studies often report the average effects and ignore the heterogeneity. Independently interpreted autoregression and cross-lagged coefficients (i.e. local effects) may not match the holistic dynamic patterns (i.e. considering all coefficients simultaneously). Our paper aims to address the limitations by introducing vector plots to visualize holistic person-specific dynamic patterns. We utilized a case example of 14-day daily diary data of diabetes self-efficacy and self-care from 200 emerging adults with type 1 diabetes. A dynamic structural equation model was used to generate person-specific coefficients. Vector plots and eigenvalues were generated to visualize person-specific holistic patterns. We found heterogeneity in both local and holistic dynamic patterns. Most participants (N = 178) had mismatching local and holistic patterns. Our study provided important evidence that failing to capture person-specific holistic dynamic patterns might result in incomplete interpretations of dynamic associations. [ABSTRACT FROM AUTHOR]

As internalizing and externalizing problems often co-occur, the current study utilized a longitudinal dataset of 784 at-risk children (predominantly from low-income families and academically at-risk; 52.6% male) followed yearly from Grade 1 to Grade 12 to: (a) explore the heterogeneity in the codevelopment patterns of internalizing and externalizing problems by using a person-centered approach, and (b) investigate early childhood antecedents that might explain differentiated codevelopmental patterns. The antecedents consisted of individual (i.e., ego-resilient personality, intelligence, language ability, gender, and ethnicity) and contextual factors (i.e., maternal support and responsiveness, family socioeconomic adversity, teacher-child relationship conflict, and peer rejection). We identified 4 distinct codevelopment patterns including a chronic co-occurring group (30.1%), a moderate co-occurring group (28.5%), a pure-externalizing group (18.6%), and a low-risk group (22.8%). While children who belonged to any of the 3 higher risk groups exhibited more adverse early childhood antecedents compared with the low-risk group, the chronic co-occurring group displayed the most severe profiles of early childhood antecedents compared with the moderate co-occurring and the pure-externalizing groups. Common antecedents for the 3 higher risk groups were lower ego-resilient personality, higher teacher-child relationship conflict, being male, and being African American. Low language ability and peer rejection were identified as unique antecedents for the chronic co-occurring group.

Self-regulation is increasingly recognized as a key predictor of academic and social competence. A multidisciplinary understanding of this ability is timely and can strengthen theory and practice. The present review aims to inform educators on what cognitive neuroscience can teach us about self-regulation. To do so, we will focus on a decade-long research program examining children with disruptive behavior problems and their peers, and ask whether neural measures of self-regulation can (a) covary with individual differences in behavioral measures of self-regulation, (b) trace developmental patterns, and (c) predict or trace behavioral change with successful treatment of disruptive behavior problems. We show that several studies begin to converge on a set of neural measures derived from the prefrontal cortex that can be consistently linked to processes of self-regulation. Next, we will discuss what these measures mean from a cognitive neuroscience perspective and how this knowledge could influence and/or support psychological models relevant to education.

In an increasingly diverse world, understanding young adults' intergroup prosocial behavior toward diverse others may inform ways to reduce intergroup conflict and cultivate an equitable and inclusive society. The college years are often the first time that young adults begin to explore their social identities and intergroup relations independently from their parents. Thus, we focused on college students and examined social dominance orientation, social positions, prosocial obligation, and the sense of belongingness in relation to their intergroup prosocial behavior across four domains (i.e., age, gender, race/ethnicity, and department affiliation). Participants were 1163 young adults aged 18–24 years (63.2% females, 34.5% males, and 2.3% gender diverse; 50.7% White, 19.6% Latino, 25% Asian, 2.3% Black) from a large public Southwestern university. Four profiles of intergroup prosocial behavior (self-serving, altruistic, selfish, and reverse ethnic racial bias) were identified and they were differentially related to the social, cognitive, and contextual correlates we examined. Overall, findings highlighted the need to foster intergroup prosocial behavior and the benefits of intergroup prosocial behavior to young adults' sense of belongingness. [ABSTRACT FROM AUTHOR]

The present study examined the event-related potentials (ERPs) and reading-language skills of elementary school children with and without reading difficulties. Typically developing children showed an N400 effect characterized by significantly larger N400 amplitudes elicited by nonwords than real words. Their meaning processing shown by the N400 systematically differed by lexicality. On the other hand, the N400 effect was absent in children with reading difficultiesExploratory analyses were conducted with the N1 and Late Positive Component. Additionally, the relationships between ERPs and reading-language skills were examined; sight word efficiency and phonemic decoding efficiency accounted for significant variance in the N400 effect [ABSTRACT FROM AUTHOR]

Objective: Using preliminary data from the Binge‐Eating Genetics Initiative (BEGIN), we evaluated the feasibility of delivering an eating disorder digital app, Recovery Record, through smartphone and wearable technology for individuals with binge‐type eating disorders. Methods: Participants (n = 170; 96% female) between 18 and 45 years old with lived experience of binge‐eating disorder or bulimia nervosa and current binge‐eating episodes were recruited through the Recovery Record app. They were randomized into a Watch (first‐generation Apple Watch + iPhone) or iPhone group; they engaged with the app over 30 days and completed baseline and endpoint surveys. Retention, engagement, and associations between severity of illness and engagement were evaluated. Results: Significantly more participants in the Watch group completed the study (p =.045); this group had greater engagement than the iPhone group (p's <.05; pseudo‐R2McFadden effect size =.01–.34). Overall, binge‐eating episodes, reported for the previous 28 days, were significantly reduced from baseline (mean = 12.3) to endpoint (mean = 6.4): most participants in the Watch (60%) and iPhone (66%) groups reported reduced binge‐eating episodes from baseline to endpoint. There were no significant group differences across measures of binge eating. In the Watch group, participants with fewer episodes of binge eating at baseline were more engaged (p's <.05; pseudo‐R2McFadden =.01–.02). Engagement did not significantly predict binge eating at endpoint nor change in binge‐eating episodes from baseline to endpoint for both the Watch and iPhone groups. Discussion: Using wearable technology alongside iPhones to deliver an eating disorder app may improve study completion and app engagement compared with using iPhones alone. [ABSTRACT FROM AUTHOR]

The Dysregulation Profile (DP) has emerged as a measure of concurrent affective, behavioral and cognitive dysregulation, associated with severe psychopathology, and poor adjustment. While originally developed with the Child Behavior Checklist, more recently the DP has also been defined on the Strengths and Difficulties Questionnaire (SDQ), mostly with a 5-item, but also a 15-item, SDQ-DP measure. This study evaluated the SDQ-DP by examining its factor structure, measurement invariance, and construct validity. Different SDQ-DP operationalizations were compared. In a United States longitudinal community sample (N = 768), a bifactor model consisting of a general Dysregulation factor and three specific factors of Emotional Symptoms, Conduct Problems, and Hyperactivity-Inattention fitted best, across three different developmental periods (early childhood, middle childhood, and adolescence) and across three different reporters (parents, teachers, and youth). Measurement invariance across reporter, gender, and developmental period was demonstrated. These findings indicate that the SDQ-DP, like the CBCL-DP, reflects a broad syndrome of dysregulation that exists in addition to specific syndromes of emotional symptoms, conduct problems, and hyperactivity-inattention. SDQ-DP bifactor scores were strongly related with scores on the 5- and 15-item SDQ-DP measures and similarly concurrently associated with two markers of self-regulation, ego-resiliency and effortful control, and longitudinally with antisocial behavior and disciplinary measures. As reliability, validity, and stability was weaker for the SDQ-DP 5-item measure, use of all 15 items is recommended. Advantages of using a bifactor approach are discussed as well as the potential of the SDQ-DP as an easy screening measure of children at risk for developing serious psychopathology. (PsycINFO Database Record [ABSTRACT FROM AUTHOR]

Introduction: Diabetes-specific family conflict is a risk factor for diabetes indicators (e.g., higher hemoglobin A1c (HbA1c), lower adherence), but little longitudinal data are available to understand associations across time. To better inform targets and timing of interventions, we examined (a) whether fluctuations in conflict covary with diabetes indicators within adolescents across time; (b) whether reciprocal associations exist; and (c) whether aspects of the parent-adolescent relationship (e.g., parental acceptance) buffer associations across time., Method: Adolescents ( N = 235, ages 11.5-15.5 at baseline, 53.6% female) completed measures of diabetes-related conflict with mothers and with fathers (separately), parental acceptance, and adherence every 6 months across 1 year (three time points). HbA1c was obtained from medical records. Data were collected in 2009., Results: Bivariate between-person correlations indicated that at each time point, adolescents who reported more conflict with mothers and fathers also had higher HbA1c and lower adherence. Within-person correlations (fluctuations across three time points) indicated that fluctuations in conflict with mothers were associated with fluctuations in HbA1c but not adherence. Actor-partner multilevel models indicated that fluctuations in family conflict at each time point were not associated with future diabetes indicators. Parental acceptance did not moderate associations of family conflict and diabetes indicators., Discussion: While findings corroborate extant literature noting that adolescents with high average diabetes-specific family conflict may benefit from interventions designed to reduce conflict, conflict at one time point may not be predictive of future diabetes indicators. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Background: Data that can be easily, efficiently, and safely collected via cell phones and other digital devices have great potential for clinical application. Here, we focus on how these data could be used to refine and augment intervention strategies for binge eating disorder (BED) and bulimia nervosa (BN), conditions that lack highly efficacious, enduring, and accessible treatments. These data are easy to collect digitally but are highly complex and present unique methodological challenges that invite innovative solutions., Objective: We describe the digital phenotyping component of the Binge Eating Genetics Initiative, which uses personal digital device data to capture dynamic patterns of risk for binge and purge episodes. Characteristic data signatures will ultimately be used to develop personalized models of eating disorder pathologies and just-in-time interventions to reduce risk for related behaviors. Here, we focus on the methods used to prepare the data for analysis and discuss how these approaches can be generalized beyond the current application., Methods: The University of North Carolina Biomedical Institutional Review Board approved all study procedures. Participants who met diagnostic criteria for BED or BN provided real time assessments of eating behaviors and feelings through the Recovery Record app delivered on iPhones and the Apple Watches. Continuous passive measures of physiological activation (heart rate) and physical activity (step count) were collected from Apple Watches over 30 days. Data were cleaned to account for user and device recording errors, including duplicate entries and unreliable heart rate and step values. Across participants, the proportion of data points removed during cleaning ranged from <0.1% to 2.4%, depending on the data source. To prepare the data for multivariate time series analysis, we used a novel data handling approach to address variable measurement frequency across data sources and devices. This involved mapping heart rate, step count, feeling ratings, and eating disorder behaviors onto simultaneous minute-level time series that will enable the characterization of individual- and group-level regulatory dynamics preceding and following binge and purge episodes., Results: Data collection and cleaning are complete. Between August 2017 and May 2021, 1019 participants provided an average of 25 days of data yielding 3,419,937 heart rate values, 1,635,993 step counts, 8274 binge or purge events, and 85,200 feeling observations. Analysis will begin in spring 2022., Conclusions: We provide a detailed description of the methods used to collect, clean, and prepare personal digital device data from one component of a large, longitudinal eating disorder study. The results will identify digital signatures of increased risk for binge and purge events, which may ultimately be used to create digital interventions for BED and BN. Our goal is to contribute to increased transparency in the handling and analysis of personal digital device data., Trial Registration: ClinicalTrials.gov NCT04162574; https://clinicaltrials.gov/ct2/show/NCT04162574., International Registered Report Identifier (irrid): DERR1-10.2196/38294., (©Robyn E Kilshaw, Colin Adamo, Jonathan E Butner, Pascal R Deboeck, Qinxin Shi, Cynthia M Bulik, Rachael E Flatt, Laura M Thornton, Stuart Argue, Jenna Tregarthen, Brian R W Baucom. Originally published in JMIR Research Protocols (https://www.researchprotocols.org), 02.06.2022.)