Adolescence is a critical period during which interactions with parents and peers play key roles in shaping mental well-being, yet there is ongoing debate regarding their relative importance. To address this gap, this research examines the relationships among communication with parents, social interactions with peers, and adolescents' mental well-being using data from 33,824 Chinese adolescents (ages 10-18 years, Mage = 13.55, 47.43% female) across three data collection waves. A random intercepts cross-lagged panel model (RI-CLPM) was combined with local structural equation modeling (LSEM) to disentangle between-person and within-person effects and examine potential age-related moderation. The results revealed significant correlations among the random intercepts of the three variables. The within-person cross-lagged effects of communication with parents on adolescents' mental well-being were significant, but not vice versa. The within-person cross-lagged effects of mental well-being on social interactions with friends were significant but not vice versa. Age moderated between-person level association, with mental well-being and peer interactions showing a U-shaped trend and the association between mental well-being and parent-adolescent communication increasing linearly across age. In conclusion, the findings suggest that parent-adolescent communication is important in predicting adolescents' mental well-being and supporting positive peer interactions across adolescent development.

This study aimed to compare the American and Chinese primary school students' psychosocial problems, utilizing the psychometric network analysis. American (n = 71, Mage = 36.93 years, SD = 10.52 years, all females) and Chinese (n = 74, Mage = 35.70 years, SD = 7.89 years, 85.92% females) primary school teachers were recruited to evaluate children's internalizing, externalizing, and attention problems using the Pediatric Symptom Checklist-17 (PSC-17). Each teacher evaluated 9 children in their classes. A total of 639 American students (Mage = 8.88 years, SD = 2.02 years, 55.56% females) and 666 Chinese students (Mage = 12.88 years, SD = 9.34 years, 57.96% females) were evaluated. Psychometric network analysis revealed distinct structures for American and Chinese children's psychosocial problems. In America, three distinct communities emerged, aligning with the theoretical constructs of the PSC-17. Conversely, while three communities were identified in China, three items assessing attention problems showed strong associations with those assessing internalizing problems, merging into the same community. The remaining two items assessing attention problems and the items assessing externalizing problems formed two other distinct communities. Moreover, in both cultures, the top three nodes with the highest centralities ('Distracted easily,' 'Blame others for his or her troubles,' and 'has trouble concentrating') are similar and all relate to externalizing and attention problems. However, the highest centralized node in the two cultures differed, with "Distracted easily" in America and "Blames others for his or her troubles" in China. In conclusion, the observed patterns in American and Chinese networks highlight the significant impact of culture on children's psychosocial issues. Although PSC-17 is a widely used screening tool in many languages, its application should be carefully considered in different cultural contexts, particularly when making cultural comparisons.

To further investigate the heterogeneity of adolescents' social anxiety, a cross-sectional survey was conducted among 6540 Chinese adolescents aged 11 to 19 years (3294 boys). Latent profile analysis and network analysis were adopted to identify subgroups based on social anxiety symptoms and further reveal core features of each group. Results showed that four subgroups were identified: "Profile 1 (n = 1,768, 26.8%): the low group with diffuse social anxiety"; "Profile 2 (n = 1,202, 18.7%): the moderate group with cognitive disturbance"; "Profile 3 (n = 1995, 30.6%): the moderate group with difficulties in new situations"; and "Profile 4: high group with diffuse social anxiety". Multiple logistic regression analyses showed significant age and gender differences across the four profiles. Network analyses revealed that, with regard to standardized strength indices of SAS-A symptoms, generally, symptoms F4 "worry that others don't like me", and F6 "feel that others make fun of me" emerged as central symptoms across the four networks. Network 4 showed a higher average predictability of all nodes, indicating a greater resistance to intervention. Network comparison tests indicated that there were significant differences regarding network structures in all pairs of networks except for Network 1 versus 2. All pairs of networks differed significantly in network strengths except for Network 2 versus 3, particularly revealing the "essence" and "manifestation" of adolescents' social anxiety. The findings help understand adolescents' social anxiety symptoms and the interactions of symptoms, potentially providing novel perspectives and approaches for the prevention and treatment of adolescents' social anxiety.

This study examines the performance of fitting bivariate latent change score (BLCS) models under small sample sizes (i.e., N <= 100) and limited numbers of repeated measures (i.e., t <= 5). Under these realistic conditions, we compared the performance of two estimation methods: maximum likelihood (ML) and Bayesian methods with various priors (i.e., non-informative priors and data-dependent priors). Results indicate that, in general, the Bayesian estimation method, especially with non-informative priors, achieved higher convergence rates and smaller empirical standard deviations for parameter estimates, whereas the ML method exhibited lower relative biases for parameter estimates. When the proportional and coupling coefficients are positive, the Bayesian method with non-informative priors provides accurate and stable parameter estimates for a sample size of >= 50 and >= 5 waves. When negative coefficients are included, either the ML or Bayesian method can be recommended, provided there are >= 4 waves, substantial coefficient effects, and minimal measurement error.

Emerging research highlights the significant role of happiness orientations (i.e., preferred and prioritized ways of pursuing happiness) in well-being and psychosocial functioning. Despite the increasing use of the Hedonic, Eudaimonic, and Extrinsic Motives for Activities (HEEMA) scale to measure happiness orientations across diverse populations, evidence is lacking regarding its measurement invariance, which leaves the validity of group comparisons in happiness orientation uncertain. Thus, we tested the measurement invariance of the HEEMA scale using data from 1,182 U.S. participants (ages 18-86) and 1,531 Chinese participants (ages 18-75). Results showed that the configural and metric invariance of the scale with four factors (i.e., pleasure, comfort, eudaimonic, and extrinsic orientations) was maintained across countries, age groups, and sex groups. Partial scalar invariance was established across countries, and full scalar invariance was established across age and sex groups. Among the invariant models, we found group differences in the levels of pleasure, comfort, eudaimonic, and extrinsic orientations. Moreover, these happiness orientations had differential associations with two criterion indicators: mental illness and proenvironmental behaviors. Our findings support the use of the HEEMA scale in U.S. and Chinese contexts and in different sex and age groups, and also demonstrate the distinction among four orientations.

This paper explores the utilisation of Bayesian structural equation modelling (BSEM) in psychology, highlighting its advantages over frequentist methods for handling complex models and small sample sizes. Basic concepts and fundamental issues relevant to BSEM are introduced, such as prior setting, model convergence, and model fit evaluation and so on. The paper also provides illustrative examples of commonly employed BSEMs, including confirmatory factor analysis (CFA) models, mediation models and multigroup CFA models, accompanied by empirical data and computer codes to facilitate implementation. Our goal is to provide researchers with novel ideas for empirical research and equip them to overcome challenges inherent to traditional methods. As BSEM continues to gain traction in various fields, we anticipate its development will feature improved methods, techniques and reporting standards.

Objective: A diagnosis of chronic kidney disease (CKD) may increase the risk for depression. The network perspective focuses on dynamic relationships among individual symptoms, which could advance our understanding of the development of depression during the transition to a diagnosis of CKD. The aim of this study was to use network analysis to examine the longitudinal associations of depressive symptoms from before to after a diagnosis of CKD. Method: The analytic sample included 1,386 participants from the Chinese Health and Retirement Longitudinal Study. Participants were aged 45 years or older and reported a doctor's diagnosis of CKD in any wave of interviews between 2011 and 2018. Depressive symptoms were measured by the 10-item version of the Center for Epidemiological Studies Depression. Cross-lagged panel network analysis was conducted to examine relationships between symptoms at three time points: prediagnosis; onset of diagnosis, and postdiagnosis). Results: After controlling for other symptoms and covariates, feeling unable to get going and less happiness at prediagnosis were the most predictive of other symptoms at the diagnosis of CKD. Feeling effortful to do everything and depressed mood at the diagnosis of CKD were the most predictive of other symptoms at postdiagnosis. Conclusions: Fatigue (i.e., feeling unable to get going, feeling effortful to do everything), less happiness, and depressed mood were central symptoms during the transition to a diagnosis of CKD. These findings highlight the benefits of identifying and managing these central symptoms to reduce the risk of activating other depressive symptoms.

Precisely estimating factor scores is challenging, especially when models are mis-specified. Stemming from network analysis, centrality measures offer an alternative approach to estimating the scores. Using a two-fold simulation design with varying availability of a priori theoretical knowledge, this study implemented hybrid centrality to estimate factor scores and compared the performance with traditional methods, where both proper and improper specifications were considered. In supervised scenarios, network scores using hybrid centrality performed similarly to CFA scores for correctly specified models. The network scores were more accurate when sample sizes were small or test reliability was low, and also demonstrated higher robustness under misspecification. In the second fold, network scores performed better than EFA scores when structure knowledge was unavailable, as the LoGo algorithm of network analysis demonstrated the best performance in retaining the correct factor structure. The results suggest that network analysis can be a better choice for factor score estimation under various conditions.

Bayesian structural equation model (BSEM) integrates the advantages of the Bayesian methods into the framework of structural equation modeling and ensures the identification by assigning priors with small variances. Previous studies have shown that prior specifications in BSEM influence model parameter estimation, but the impact on model fit indices is yet unknown and requires more research. As a result, two simulation studies were carried out. Normal distribution priors were specified for factor loadings, while inverse Wishart distribution priors and separation strategy priors were applied for the variance–covariance matrix of latent factors. Conditions included five sample sizes and 24 prior distribution settings. Simulation Study 1 examined the model-fitting performance of BCFI, BTLI, and BRMSEA proposed by Garnier-Villarreal and Jorgensen (Psychol Method 25(1):46–70, 2020) and the PPp value. Simulation Study 2 compared the performance of BCFI, BTLI, BRMSEA, and DIC in model selection between three data generation models and three fitting models. The findings demonstrated that prior settings would affect Bayesian model fit indices in evaluating model fitting and selecting models, especially in small sample sizes. Even under a large sample size, the highly improper factor loading priors resulted in poor performance of the Bayesian model fit indices. BCFI and BTLI were less likely to reject the correct model than BRMSEA and PPp value under different prior specifications. For model selection, different prior settings would affect DIC on selecting the wrong model, and BRMSEA preferred the parsimonious model. Our results indicate that the Bayesian approximate fit indices perform better when evaluating model fitting and choosing models under the BSEM framework.

Problem No studies have been conducted to examine the relationships between perceived stress, positive/negative dyadic coping, and prenatal depression symptoms in Chinese couples with gestational diabetes mellitus (GDM). Background GDM is a stressful event for pregnant women and their partners, which may result in clinically significant prenatal depression symptoms in couples. Aim This study aims to examine the relationships and differences in perceived stress, positive/negative dyadic coping, and prenatal depression symptoms between Chinese pregnant women with GDM and their partners and to explore the mediating role of positive/negative dyadic coping. Methods A cross-sectional study was conducted in Guangzhou, China, from January to October 2021. 402 pairs of GDM couples completed the questionnaires, including the Edinburgh Postnatal Depression Scale, the Chinese version of the Dyadic Coping Inventory, and the Perceived Stress Scale. Dyadic data was analyzed using the actor-partner interdependence mediation model. Findings 37.6% of pregnant women with GDM and 24.6% of their partners experienced clinically significant prenatal depression symptoms. Depression symptoms in couples mutually influence each other. Perceived stress was directly or indirectly related to their and partners’ prenatal depression symptoms in GDM couples, with negative dyadic coping acting as a mediator. Maternal negative dyadic coping was also a partner-mediator. Discussion The findings of the present study may provide healthcare professionals with a better understanding of the effect of the interpersonal interaction between the couples as a dyad on prenatal depression symptoms in Chinese context. Conclusion There were intrapersonal and interpersonal associations among perceived stress, negative dyadic coping, and prenatal depression symptoms in pregnant women with GDM and their partners. It suggests a need for screening clinically significant prenatal depression symptoms and decreasing perceived stress and negative dyadic coping among couples with GDM with a focus on pregnant women with GDM.

Parent–adolescent emotion dynamics have attracted increasing attention in recent years because adolescence is a challenging period for both adolescents and parents. However, how emotions are coconstructed between parents and adolescents is less clear. This study examined whether mothers' and adolescents' emotion regulation strategy was linked with their own and each other's depression using the actor–partner interdependence model (APIM). The participants were 173 mother–adolescent pairs (Mother: Mage = 43.05 years old, SD = 3.78; Adolescent: Mage = 13.00 years old, SD = 0.90). The results showed that the more mothers used cognitive reappraisal, the lower their depression levels were; and the more mothers and adolescents used expressive suppression, the higher their levels of depression were. Additionally, maternal expressive suppression was associated with adolescent depressive symptoms. Moreover, the results revealed that for mothers with higher levels of expressive suppression, their adolescents' usage of expressive suppression was significantly positively related to adolescents' depression, while for those mothers with lower levels of expressive suppression, there was no significant correlation between adolescents' usage of expressive suppression and depression. The findings underscore the significance of recognising the interdependence and interconnected nature of emotions within parent–adolescent relationships for a comprehensive understanding of their emotional well‐being.

Objective Although previous research has indicated that human errors represent the primary cause of incidents in nuclear power safety, few studies have investigated the potential impact of psychological factors on the performance of operators. This study makes a pioneering contribution to the field by integrating the effect of personal states and personality traits on work evaluation.Method A total of 101 commissioning workers in nuclear power plants were recruited and monitored for ten consecutive workdays. The research collected their personality traits and personal states which were indexed by cognitive tasks before daily work. The participants rated their work performance after work as the dependent variable.Results The results of the LASSO regression analysis indicated that the perceptual speed, visual selective attention, and executive control before work were significantly correlated with the self-rated work performance. Furthermore, the interaction between personal states and personality traits exhibited moderating effects, with the effect of mindfulness being the most prominent.Conclusion The present study enhanced understanding of how personality traits may moderate the effect of pre-work personal states on daily work performance. It also provided practical insights for the selection and support of commissioning workers, with a particular focus on the role of appropriate personality traits and robust personal states.

Children's interpretations of parenting behaviors offer valuable insights into cultural meanings of parenting. This study examined how Chinese urban and rural children interpreted six different types of parental responses to children's negative emotions (PRCNE), which have traditionally been identified as supportive (e.g., emotion-focused responses, problem-focused responses, and expressive encouragement) versus nonsupportive ones (e.g., minimization, parental distress, punitive responses) in Western cultures. Based on surveyed samples of 976 children, demographically matched samples of 102 urban (M-age = 14.23 years) and 100 rural (M-age = 14.38 years) children were generated for analysis, using propensity score matching (PSM). Results revealed that compared with rural children, urban children rated problem-focused responses as more normative, whereas parental distress and punitive responses as less normative. Additionally, urban children evaluated emotion-focused responses, problem-focused responses, expressive encouragement, and minimization as less negative, and parental distress as less positive than rural children. In urban communities, emotion-focused responses, problem-focused responses, and encouragement were evaluated most positively and least negatively, followed by minimization, and lastly parental distress and punitive responses. In rural communities, emotion-focused responses and problem-focused responses were evaluated most positively and least negatively, followed by encouragement and minimization, and lastly parental distress and punitive responses. The findings highlight the diverse interpretations children have towards PRCNE across different cultural contexts.

In routine Generalizability Theory (G-theory) research, the task of establishing or assessing cut-off scores for performance evaluation is consistently sought after yet simultaneously poses significant challenges. While many studies have presented diverse indices to evaluate potential cut-off scores, these indices are frequently limited by their design-specific nature, limiting adaptability across different assessment contexts. This paper reframes G-theory within the context of a Linear Mixed-Effects Model (LMM) and employs LMM-based bootstrapping techniques to generate Intervals of Predicted Total Scores (IPTS) for each individual to evaluate the suitability of potential cut-off score candidates in the assessment. We propose PredC, to quantify the proportion of individuals whose 95

Evidence for the protective role of dispositional mindfulness for athletic performance, stress, and mood among elite athletes has been demonstrated through correlational and interventional studies. The effects of state mindfulness on athletic functioning in day-to-day training contexts remains unclear. We examined the effects of state mindfulness on mood, biological markers of stress, and self-rated athletic performance in elite athletes during daily training. We used a diary study design to collect data on state mindfulness, mood, self-rated athletic performance, and salivary cortisol directly following training sessions of 78 elite athletes. For each athlete, a total of 27 data points were obtained across 9 weeks with data collected on a separate day, 3 days per week. Data were analyzed with multilevel structural equation modeling. At both the between-person and within-person levels, state mindfulness was significantly and negatively related to total mood disturbance and maladaptive dimensions of mood, including anger, confusion, depression, fatigue, and tension. Conversely, state mindfulness was positively related to vigor and self-rated athletic performance. Relations between state mindfulness and biological markers of stress were nonsignificant. Overall, findings of the present study provide preliminary empirical evidence supporting the utility of mindfulness interventions for improving state mindfulness of elite athletes. Such interventions may increase the positive mood of athletes and their performance during training.

Researchers in psychology, education, and organizational behavior often encounter multilevel data with hierarchical structures. Bayesian approach is usually more advantageous than traditional frequentist-based approach in small sample sizes, but it is also more susceptible to the subjective specification of priors. To investigate the potentially detrimental effects of inaccurate prior information on Bayesian approach and compare its performance with that of traditional method, a series of simulations was conducted under a multilevel model framework with different settings. The results reveal the devastating impacts of inaccurate prior information on Bayesian estimation, especially in the cases of larger intraclass correlation coefficient, smaller level 2 sample size, and smaller prior variance. When the dependent variable is non-normal or binary, these negative effects are more noticeable. The present study investigated the impacts of inaccurate prior information and provides advice on the specification of priors.

This study proposes a Bayesian approach to testing informative hypotheses in confirmatory factor analysis (CFA) models. The informative hypothesis, which is formulated by the constrained loadings, can directly represent researchers' theories or expectations about the tau equivalence in reliability analysis, item-level discriminant validity, and relative importance of indicators. Support for the informative hypothesis is quantified by the Bayes factor. We present the adjusted fractional Bayes factor of which the prior distribution is specified using a part of the data and adjusted according to the hypotheses under evaluation. This Bayes factor is derived and computed using the Markov chain Monte Carlo posterior samples of model parameters. Simulation studies investigate the performance of the proposed Bayes factor. A classic example of CFA models is used to illustrate the construction of the informative hypothesis, the specification of the prior distribution, and the computation and interpretation of the Bayes factor. (PsycInfo Database Record (c) 2026 APA, all rights reserved).

Existing literature has documented that parenting links to children's hostile attribution biases (HAB). However, little is known about the role played by parental emotion socialization in children's HAB. To address this research gap, the present study investigated the role of parental responses to children's negative emotions (PRCNE) in predicting adolescents' HAB using a longitudinal study. Adolescents (N = 203; Mage  = 13.61 years old at Time 1), who were recruited from a city in mainland China, reported on their mothers' PRCNE and their own HAB at two waves over a year. The results showed that mothers' supportive responses (composed of emotion-focused responses and problem-focused responses) significantly predicted adolescents' reduced HAB over time; however, PRCNE including expressive encouragement, minimization, and nonsupportive responses (composed of punitive responses and parental distress) had no significant relation with adolescents' HAB. These findings add to the existing literature investigating antecedents to adolescents' social information processing deficits and biases.

We explored the effect of cognitive states and personalities on work performance. We recruited 101 commissioning workers in nuclear power plants (NPPs) and tracked their cognitive states before daily work and self-evaluated work performance after work over ten consecutive workdays. Corsi blocks-tapping task, go/no-go task, visual search task, and multitasking paradigm were used to measure the pre-work states of working memory, perception, attention, and executive control respectively. Conscientiousness, mindfulness, body awareness, and anxiety were measured as personality traits. The machine-learning regression results showed that the states of perception, attention, and executive control before work were strongly correlated to the subjective evaluation of work performance. Further, the interaction between cognitive states and personalities exhibited moderating effects, among which the effect with mindfulness was the most prominent. The outcomes of this study support the importance of pre-work cognitive states and personalities of commissioning workers to their daily work performance.

The Lee‐Jones model posits that antecedent individual and interpersonal factors predicate the development of fear of cancer recurrence (FCR) through cognitive and emotional processing, which further to behavioral, emotional, and/or physiological responses. We analyzed data from FoRtitude, a FCR intervention grounded in the Lee‐Jones FCR model, to evaluate associations between FCR antecedents, resources (e.g., breast cancer self‐efficacy, BCSE) and psychological and behavioral consequences.