Linear Structural Equation Models Research Articles

Pengaruh Team Member Exchange, Komptensi terhadap Produktivitas Kerja dengan Kepuasan Kerja sebagai Variabel Intervening

This research aims to analyses the influence of Team Member Exchange (TMX) and Competency on the Work Productivity of PT Techmicron Batam City with Job Satisfaction as an intervening variable. Data was collected from 100 employees of PT Techmicron Batam City using a questionnaire. The data analysis techniques used are multiple linear regression and Structural Equation Modelling (SEM). The research results show that Team Member Exchange (TMX) has a positive but not significant effect on Employee Work Productivity, Team Member Exchange (TMX) has a positive and significant effect on Job Satisfaction, Competency has a positive and significant effect on Employee Work Productivity, Competency has a positive and significant effect on Employee Job Satisfaction, Job Satisfaction has a positive and significant effect on the Work Productivity of PT Techmicron Batam City Employees. The implications of this research are the importance of compensation in building job satisfaction so that it can increase employee work productivity, as well as the importance of building Team Member Exchange (TMX) relationships in increasing employee job satisfaction.

Spiritual needs of women with breast cancer: A structural equation model

PurposeThe purpose of this study was to develop a structural equation model (SEM) to explore the factors influencing the spiritual needs of breast cancer patients. MethodsA cross-sectional study was conducted in the breast surgery department of a tertiary hospital in China from September 2020 to December 2020; convenience sampling and questionnaires were used to facilitate sampling and data collection. A total of 220 female breast cancer patients were included in the study. The data were analysed using multiple linear regression and structural equation modelling. ResultsCompared with patients with other diseases, patients with breast cancer have greater spiritual needs (76.16 ± 13.19). Multivariate analysis revealed that religious beliefs, education level, social support, and resilience are important factors affecting the mental health of women with cancer (p < 0.05). The structural equation model fit well (RMSEA = 0.056, χ2p = 0.002). Social support directly affected spiritual needs (β = 0.607, p < 0.001) and indirectly affected spiritual needs through resilience (β = 0.353, p < 0.001). Resilience directly affected spiritual needs (β = 0.386, p < 0.05). Education level indirectly affected spiritual needs through social support (β = 0.307, p < 0.001). ConclusionThis study provides a theoretical basis for intervention measures to improve the spiritual needs of female breast cancer patients. Paying more attention to social support and resilience may help solve the problem of meeting the high spiritual needs of breast cancer patients. Further research is needed to develop interventions.

Bias formulas for violations of proximal identification assumptions in a linear structural equation model

Abstract Causal inference from observational data often rests on the unverifiable assumption of no unmeasured confounding. Recently, Tchetgen Tchetgen and colleagues have introduced proximal inference to leverage negative control outcomes and exposures as proxies to adjust for bias from unmeasured confounding. However, some of the key assumptions that proximal inference relies on are themselves empirically untestable. In addition, the impact of violations of proximal inference assumptions on the bias of effect estimates is not well understood. In this article, we derive bias formulas for proximal inference estimators under a linear structural equation model. These results are a first step toward sensitivity analysis and quantitative bias analysis of proximal inference estimators. While limited to a particular family of data generating processes, our results may offer some more general insight into the behavior of proximal inference estimators.

Choice Function-Based Hyper-Heuristics for Causal Discovery under Linear Structural Equation Models.

Causal discovery is central to human cognition, and learning directed acyclic graphs (DAGs) is its foundation. Recently, many nature-inspired meta-heuristic optimization algorithms have been proposed to serve as the basis for DAG learning. However, a single meta-heuristic algorithm requires specific domain knowledge and empirical parameter tuning and cannot guarantee good performance in all cases. Hyper-heuristics provide an alternative methodology to meta-heuristics, enabling multiple heuristic algorithms to be combined and optimized to achieve better generalization ability. In this paper, we propose a multi-population choice function hyper-heuristic to discover the causal relationships encoded in a DAG. This algorithm provides a reasonable solution for combining structural priors or possible expert knowledge with swarm intelligence. Under a linear structural equation model (SEM), we first identify the partial v-structures through partial correlation analysis as the structural priors of the next nature-inspired swarm intelligence approach. Then, through partial correlation analysis, we can limit the search space. Experimental results demonstrate the effectiveness of the proposed methods compared to the earlier state-of-the-art methods on six standard networks.

Effects of Sheep Grazing and Nitrogen Addition on Dicotyledonous Seedling Abundance and Diversity in Alpine Meadows

Seedling is a crucial stage in the growth and development of plants, and the expansion and persistence of plant populations can be achieved through seed regeneration. Sheep grazing, fertilization, light, soil moisture, vegetation diversity and biomass, and litter all have potential impacts on species regeneration. We measured vegetation diversity, annual net primary productivity (ANPP), litter, ground photosynthetically active radiation (PAR), and soil moisture of alpine meadows under sheep grazing and nitrogen addition treatments, and studied their effects on the dicotyledonous seedling abundance and diversity using linear regression models (LMs) and structural equation models (SEMs). We found that sheep grazing reduced ANPP, increased vegetation diversity and PAR, and decreased soil moisture. Fertilization increased ANPP and litter, decreased vegetation diversity and PAR, but had no effect on soil moisture. Sheep grazing and fertilization both reduced the abundance of dicotyledonous seedlings, and simultaneously fertilization can reduce the diversity of dicotyledonous seedlings, while sheep grazing had no effect on the diversity of dicotyledonous seedlings. LMs showed that vegetation diversity, ANPP, and litter, rather than light and soil moisture, affected dicotyledonous seedling abundance and diversity. SEMs revealed that sheep grazing and fertilization indirectly influenced seedling regeneration through vegetation diversity rather than ANPP and litter. Our research will increase our understanding of the dicotyledonous plant regeneration process in alpine grasslands and facilitate the development of strategies for management and protection of alpine grassland.

The mediating role of ICT learning confidence and technostress between executive functions and digital skills

Having good digital skills is essential today, yet little is known about the cognitive factors that influence the development of these skills. Given the importance of executive functions (EFs) in adapting to environmental changes and acquiring skills, EFs might contribute to acquiring digital skills too. EFs might also influence people’s approach toward ICTs and affect digital skills through emotional variables. Therefore, here, we tested whether cognitive control and flexibility are connected to computer and smartphone skills through emotional factors (learning confidence, stress, and attitude). A total of 269 participants (56 males, M = 30 years) filled out our survey which included questionnaires on demographic variables (age, education, and socioeconomic status), ICT motivation, digital skills, EFs, and technology-related emotional factors. EFs were also investigated through two performance-based measures. We used generalized linear models and structural equation modeling to test the associations between these variables. The results showed that smartphone skills were positively associated with self-reported cognitive flexibility through ICT learning confidence and technology-induced stress. Self-reported cognitive control and age were connected to smartphone skills directly. Self-reported cognitive flexibility was also associated with computer skills through ICT learning confidence. In addition, performance-based cognitive control and the level of education were directly linked to computer skills. These results may provide guidance to support digital skills and create digital skill training.

The cooperative interaction of AOB and comammox clade A drives nitrification and N2O emissions in a long-term organic fertilized paddy soil

The newly discovered complete ammonia oxidizer (comammox Nitrospira) is able to single-step nitrification capability, and increased our understanding of soil nitrogen cycling. However, the response of comammox and ammonia-oxidizing bacteria (AOB) and archaea (AOA) to long-term fertilization and rhizosphere effects in paddy soils and their relative contribution to the nitrification-derived N2O emissions is still unclear. Here, we collected rhizosphere and bulk soils with thirty years of different fertilization strategies, i.e., non-fertilization (CK), chemical N, P, and K application (NPK), and NPK plus pig manure application (NPKM), respectively, to test changes in nitrification potential rate (PNR), N2O emission fluxes, abundance of ammonia oxidizers and their significant drivers. The result showed that NPKM significantly increased soil C and N levels, the proportion of soil middle-size particles (40.35–148.00 μm class), and soil PNR, but decreased soil N2O emissions, especially in the drying time of paddy (P < 0.05), compared to NPK fertilization. NPKM had the highest values of AOA, AOB, and comammox clade A amoA gene copy numbers (P < 0.05), but clade B was increased by the NPK in the rhizosphere soil. Furthermore, fertilization showed greater effects on ammonia oxidizers (except for clade B) than the rhizosphere effect. Mantel test showed that SOM, TP, pH, NH4+, and NO3− were main abiotic factors causing the niche separation among ammonia oxidizers. Linear regression analysis and structural equation model (SEM) showed that both PNR and N2O emission fluxes were significantly associated with the abundance of AOB and comammox clade A (P < 0.05). Therefore, our results underline the importance of AOB together with comammox clade A in nitrification and N2O production in long-term organic fertilized paddy fields, which could provide new ideas for the mitigation of N2O emission by adopting organic fertilization scenarios in Chinese paddy fields.

Brain structure and connectivity mediate the association of lifestyle and cognition: the Maastricht study

Abstract Life-course exposure to risk and protective factors impacts brain macro- and micro-structure, which in turn affects cognition. The concept of Brain Age Gap (BAG) assesses brain health by comparing an individual’s neuroimaging-based predicted age with their calendar age. Higher BAG implies accelerated brain aging and is expected to be associated with worse cognition. In this study, we comprehensively modelled mutual associations between brain-health and lifestyle factors, brain age, and cognition in a large, middle-aged population. For this study, cognitive test scores, lifestyle, and 3T MRI data for n=4881 participants (mean age (±SD) = 59.2 (±8.6), 50.1% male) were available from The Maastricht Study, a population-based cohort study with extensive phenotyping. Whole brain volumes (grey matter (GM), cerebrospinal fluid (CSF), white matter hyperintensity (WMH)), cerebral microbleeds (CMB), and structural white matter connectivity were calculated. Lifestyle factors were combined into an adapted LIfestyle for BRAin health (LIBRA) weighted sum score, with higher score indicating greater dementia risk. Cognition was calculated by averaging z-scores across three cognitive domains (memory, information processing speed, executive function and attention). BAG was calculated by comparing calendar age to predictions from a neuroimaging-based multivariable regression model. Paths between LIBRA tertiles, BAG and cognitive function were tested using linear regression and structural equation modelling, adjusting for sociodemographic and clinical confounders. Results show that CSF, GM, WMH and CMB best predicted BAG (R2 = 0.455, RMSE = 6.44). In regression analysis, higher LIBRA scores (greater dementia risk) were associated with higher BAG (standardized regression coefficient ß = 0.126, p &amp;lt; 0.001) and worse cognition (ß = -0.046, p = 0.013), while higher BAG was associated with worse cognition (ß=-0.163, p&amp;lt;0.001). In mediation analysis, 24.7% of the total difference in cognition between the highest and lowest LIBRA tertile was mediated by BAG (ßindirect = -0.049, p &amp;lt; 0.001; ßtotal = -0.198, p &amp;lt; 0.001), and an additional 3.8% was mediated via connectivity (ßindirect = -0.006, p &amp;lt; 0.001; ßtotal = -0.150, p &amp;lt; 0.001). Findings suggest that associations between health- and lifestyle-based risk/protective factors (LIBRA) and cognition can be partially explained by structural brain health markers (BAG) and white matter connectivity markers. Lifestyle interventions targeted at high-risk individuals in mid to late life may be effective in promoting and preserving cognitive function in the general public.

Effects of plant diversity and community structure on ecosystem multifunctionality under different grazing potentials in the eastern Eurasian steppe

Grazing potential represents the potential carrying capacity of steppe livestock production. Understanding the impact of changes in plant diversity and community structure on ecosystem multifunctionality (EMF) at different grazing potentials is crucial for the sustainable management of steppe ecosystems. We examined the associations between plant diversity, community structure, above-ground ecosystem multifunctionality (AEMF), and below-ground ecosystem multifunctionality (BEMF) at various grazing potentials. Our assessment employed generalized linear mixed-effects models and structural equation models to determine the impact of these factors on ecosystem multifunctionality. Our study results indicated that ecosystem multifunctionality differed depending on the level of grazing potential and decreased as grazing potential declined. The impact of plant diversity and community structure on above- and below-ground ecosystem multifunctionality varied. Plant diversity and community structure correlated more with AEMF than BEMF. Plant diversity had the most significant effect on EMF under high grazing potential, while community structure had the greatest effect on EMF under moderate and low grazing potential. These improve our understanding of the correlation between steppe plant diversity, community structure, and above- and below-ground ecosystem multifunctionality. This understanding is necessary to develop strategies to increase plant diversity or regulate community structure and the sustainability of steppes.

Climate change affects plant aboveground biomass by regulating the growth periods in alpine grasslands of the Tibetan Plateau, China

Climate change significantly affects plant biomass and phenological occurrence time in alpine grasslands of Tibetan Plateau. The changes in phenological periods are closely related to the length of vegetative and reproductive growth periods, which may further affect aboveground biomass accumulation. In this study, based on fixed-point observations of plant biomass and phenology as well as the corresponding climatic data from 1997 to 2020 in the alpine grasslands of Tibetan Plateau, we used statistical methods such as ordinary linear regression and piecewise structural equation model to explore the characteristics of interannual climate change in the study area, the variation trends of plant biomass and phenological periods, and the correlations between biomass and phenological and climatic factors. The results showed that mean annual temperature and annual precipitation in the study area increased significantly from 1997 to 2020, suggesting a clear "warm-wet" trend. Aboveground biomass and relative biomass of Stipa sareptana var. krylovii (the dominant species) decreased significantly. However, absolute and relative biomass of subdominant species (Kobresia humilis) increased significantly, indicating that the dominance of K. humilis increased. The warm-wet climates enhanced aboveground biomass accumulation of K. humilis by extending the period of reproductive growth. Mean annual temperature and annual precipitation decreased aboveground biomass of S. sareptana by shortening the length of vegetative growth period. In a word, the warmer and wetter climate significantly affected aboveground biomass accumulation by regulating the changes in the phenological period, and the interspecific difference in their response resulted in a larger change in community composition. This study area may show a trend from alpine grassland to alpine meadow, and thus further works are urgently needed.

A Causal Framework for the Comparability of Latent Variables

Measurement invariance (MI) describes the equivalence of measurement models of a construct across groups or time. When comparing latent means, MI is often stated as a prerequisite of meaningful group comparisons. The most common way to investigate MI is multi-group confirmatory factor analysis (MG-CFA). Although numerous guides exist, a recent review showed that MI is rarely investigated in practice. We argue that one reason might be that the results of MG-CFA are uninformative as to why MI does not hold between groups. Consequently, under this framework, it is difficult to regard the study of MI as an interesting and constructive step in the modeling process. We show how directed acyclic graphs (DAGs) from the causal inference literature can guide researchers in reasoning about the causes of non-invariance. For this, we first show how DAGs for measurement models can be translated into path diagrams used in the linear structural equation model (SEM) literature. We then demonstrate how insights gained from this causal perspective can be used to explicitly model encoded causal assumptions with moderated SEMs, allowing for a more enlightening investigation of MI. Ultimately, our goal is to provide a framework in which the investigation of MI is not deemed a “gateway test” that simply licenses further analyses. By enabling researchers to consider MI as an interesting part of the modeling process, we hope to increase the prevalence of investigations of MI altogether.

Non-parametric Regression Among Factor Scores: Motivation and Diagnostics for Nonlinear Structural Equation Models

We provide a framework for motivating and diagnosing the functional form in the structural part of nonlinear or linear structural equation models when the measurement model is a correctly specified linear confirmatory factor model. A mathematical population-based analysis provides asymptotic identification results for conditional expectations of a coordinate of an endogenous latent variable given exogenous and possibly other endogenous latent variables, and theoretically well-founded estimates of this conditional expectation are suggested. Simulation studies show that these estimators behave well compared to presently available alternatives. Practically, we recommend the estimator using Bartlett factor scores as input to classical non-parametric regression methods.

Influence of moderate-to-high intensity physical activity on depression levels: a study based on a health survey of Chinese university students

ObjectiveThe study aims to examine how moderate-to-vigorous physical activity (MVPA) affects the severity of depression symptoms among Chinese college students. Additionally, it seeks to analyze the mediating mechanisms involving self-rated health and general self-efficacy.MethodsThe study utilized data from the 2023 Chinese College Health Tracking Survey and employed multiple linear regression and structural equation modeling techniques to investigate the impacts of MVPA on depression levels and its underlying mediating mechanisms among college students. The primary cohort comprised 49,717 enrolled college students from 106 universities in China.ResultsA total of 41,620 valid questionnaires were collected (response rate: 83.7%), with females accounting for 58.6%. In the past month, approximately 30.2% of college students engaged in MVPA. Self-rated health (B = − 0.282, P < 0.001) and general self-efficacy (B = − 0.133, P < 0.001) significantly influenced college students’ depression scores. Even after controlling for other variables, participating in MVPA remained significantly associated with reduced depression scores (B = − 0.062, P = 0.002). The results of the structural equation model showed that MVPA not only directly decreased college students’ depression scores but also indirectly reduced the likelihood of depression occurrence by improving their physical health status and general self-efficacy.ConclusionThe lack of physical activity among Chinese college students is evident. Engaging in MVPA can reduce the likelihood of depression among college students. MVPA achieves this reduction by enhancing college students’ general self-efficacy and improving their physical health. The factors influencing depression levels among college students are multifaceted. For future interventions targeting college students’ mental health, comprehensive approaches that incorporate behavioral and psychological factors should be emphasized.

Trees diversity explains variations in biodiversity-ecosystem function relationships across environmental gradients and conservation status in riparian corridors

Riparian ecosystems are recognized as large reservoirs of biodiversity providing important ecosystem services. However, the relationship between tree species diversity and ecosystem functions and the underlying ecological mechanisms have less been studied in riparian corridors. This study assessed the effect of tree diversity (taxonomic, functional, and phylogenetic) on riparian ecosystem functions (habitat quality–HbQ and aboveground carbon–AGC) across environmental gradients (distances to stream bed) and conservation status. Data were collected from 96 inventory plots installed in a 1 km buffer zone on either side of permanent streams in the Upper Ouémé watershed in northern Benin. We employed linear mixed effects models and structural equation modeling to analyze the data. We found that ecosystem functions (HbQ, AGC) and diversity attributes including species richness (SR), Faith’s phylogenetic diversity index (PD) and community-weighted mean of maximum height (CWMHmax) were significantly lower away from the streambed than nearby. The correlation between SR and ecosystem functions was significant and positive across and within the distance gradient and conservation status. We also found that the CWMHmax was the best predictor of both ecosystem functions, and that PD mediated the effect of SR on AGC and HbQ in areas close to the streambed and in unprotected areas, respectively. Our study reveals the influence of human activities on biodiversity and ecosystem functioning relationships in riparian corridors and provides new insights into the importance of tall stature trees and species with distant lineages for the functioning of these ecosystems. Based on the variables measured and tested in this study, we argue that riparian corridor management policies should consider plant traits and phylogeny and promote wider riparian buffers for the co-benefits of biodiversity conservation and climate change mitigation.

Does inclusive leadership influence employee innovation? A multilevel investigation

PurposeThe purpose of this research is to investigate the cross-level effects and mechanisms of inclusive leadership on employee innovation in team contexts, and further explore the boundary conditions of inclusive leadership.Design/methodology/approachThis study collected data from 237 leader-member dyads in 60 teams of Chinese firms. The research utilized multilevel linear models and multilevel structural equation models in the R language to test the hypothesized model.FindingsThe findings suggest that inclusive leadership has a positive impact on both employee incremental and radical innovation. Team psychological safety and employee role breadth self-efficacy mediate the effects. Employee risk avoidance propensity negatively moderates the mediating role of role breadth self-efficacy in the relationship between inclusive leadership and incremental innovation.Practical implicationsLeaders should pay attention to team psychological safety, employee role breadth self-efficacy and employee individual risk avoidance propensity that influence employee innovation to maximize the effectiveness of inclusive leadership.Originality/valueThis research expanded the level of analysis from individual to team, exploring cross-level effects and mechanisms of inclusive leadership on employee innovation in team contexts, and clarified the effectiveness conditions of inclusive leadership.

Breaking the budget chains: Empowering Iraqi small- and medium-sized enterprises through Beyond Budgeting

Background: Traditional budgeting has faced criticism, leading researchers to explore alternative management control methods such as Beyond Budgeting. Despite its potential benefits, Beyond Budgeting adoption remains limited.Aim: This research investigates Beyond Budgeting’s effect on Iraqi small- and medium-sized enterprise (SME) performance, focusing on competitive advantage.Setting: The study targets Iraqi SMEs, considering their diverse business landscape. A random sample of 353 accountants was chosen from a research community of 22 974 individuals. SPSS and AMOS 26 software and a custom questionnaire were used.Method: The authors conducted multiple linear regression analysis and Structural Equation Modeling to address our study’s objectives. Additionally, we utilised maximum likelihood procedures to assess factor loadings, construct validity and reliability.Results: Beyond Budgeting implementation significantly affected both financial and nonfinancial performance. It demonstrated positive effects on cost leadership and competitive advantage differentiation. However, an indirect negative impact on nonfinancial performance was observed through the influence on cost leadership, suggesting a trade-off between cost reduction and other nonfinancial factors.Conclusion: The study acknowledges limitations in sample size and the need for considering additional factors. Further research is required to understand Beyond Budgeting’s limited adoption in Iraq. The study contributes valuable insights on Beyond Budgeting as a substitute for traditional approaches, benefiting managers and policymakers.Contribution: This study emphasises Beyond Budgeting’s significant impact on Iraqi SME performance, highlighting the relationship between Beyond Budgeting, competitive advantage strategies, financial and nonfinancial performance. The findings provide insights for enhancing budgeting practices in Iraqi SMEs.

Early-life cumulative exposure to excess bodyweight and midlife cognitive function: longitudinal analysis in three British birth cohorts

Excess bodyweight (BMI >25 kg/m2) in midlife (age 40-65 years) has been linked to future cognitive decline and an increased risk of dementia. Whether chronic exposure to excess bodyweight in the early decades of life (<40 years) is associated with compromised cognitive function by midlife, however, remains unclear. This study therefore aimed to test potential bidirectional direct and indirect pathways linking cumulative exposure to excess bodyweight and cognitive function in the early decades of life. In this longitudinal analysis, harmonised measures of BMI and cognitive function were available in 19 742 participants aged 47-53 years recruited to the 1946 National Survey of Health and Development (n=2131), the 1958 National Child Development Study (n=9385), and the 1970 British Cohort Study (n=8226). Individual BMI trajectories spanning three decades from age 10-40 years were created for each participant and excess bodyweight duration, BMI change between ages, and cumulative excess bodyweight exposure were calculated. Harmonised measures of verbal and non-verbal ability, mathematical ability, and reading ability were used to create a latent factor for childhood cognitive function, and immediate and delayed recall, animal naming, and letter-search speed tests were used for midlife cognitive function. Multivariable linear regression and structural equation models (SEM) were used to test for potential bidirectional relationships between cognition and excess bodyweight in both individual cohorts and pooled datasets while accounting for other potential early-life confounders. Increases in BMI during adolescence and greater cumulative exposure to excess bodyweight across early life were associated with lower midlife cognitive function in all cohorts (eg, pooled difference in cognitive function per 10 years excess bodyweight duration -0·10; 95% CI -0·12 to -0·08; p<0·001). Further adjustment for childhood cognitive function attenuated many of these associations towards the null (eg, pooled difference in cognitive function per 10 years excess bodyweight duration -0·04; 95% CI -0·06 to -0·02; p=0·001), however, with any remaining associations then fully attenuating once further adjusted for other early-life factors (eg, pooled difference in cognitive function per 10 years excess bodyweight duration 0, -0·03 to 0·01; p=0·38). In the reverse direction, low childhood cognition was associated with greater cumulative exposure to excess bodyweight over the next four decades, although much of this relationship was found to probably be explained via other potentially modifiable upstream early-life factors such as childhood disadvantage. SEM in all cohorts suggested the presence of modest direct and indirect pathways connecting earlier cognitive function to later excess bodyweight, but scarce evidence for an effect of early-life excess bodyweight on cognitive function by midlife. The association between cumulative exposure to excess bodyweight in early life and lower cognitive function in midlife is probably confounded by a persistently lower cognitive function from childhood. Initiatives to improve early-life factors such as childhood disadvantage and education, however, might exert dual but independent benefits on both of these factors before old age. Alzheimer's Research UK, Diabetes Research and Wellness Foundation, Diabetes UK, British Heart Foundation, and Medical Research Council.

Factors affecting the implementation of STEAM education among primary school teachers in various countries and Vietnamese educators: comparative analysis

ABSTRACT STEAM education, recognised as an innovative model with wide-ranging benefits for students, can be challenging for teachers to implement, impacting their readiness. This study delves into the international experiences related to the preparedness of educators to implement STEAM education at the elementary level, drawing insights from 477 primary school teachers across 30 Vietnamese provinces. Utilising direct and online questionnaires, data was analyzed using linear structural equation modelling. The findings offer valuable insights into the effectiveness of STEAM education, particularly in elementary schools. Additionally, the research explores factors influencing STEAM education implementation in Vietnam, contributing to the global academic community and identifying elements impacting the global trend toward STEM/STEAM education. This study's comprehensive approach sheds light on the challenges faced by teachers and aims to enhance the knowledge base, making a significant contribution to the discourse on STEAM education.

Changes in Relationship between Forest Biomass Productivity and Biodiversity of Different Type Subtropical Forests in Southern China

Forest productivity is influenced by various factors, including biodiversity, environmental factors, functional traits, and forest types. However, the relative importance of these factors in determining the productivity of subtropical forests in southern China remains controversial. In this study, we analyzed a dataset of 24 forest plots from four subtropical forest types in the Nanling Mountains with the main goal of identifying and quantifying the relative contribution of the main driving factors of forest productivity in these forests. Generalized linear regression and structural equation modeling were used to examine the relationship between forest biomass productivity (aboveground, belowground and total), biodiversity (taxonomic diversity, phylogenetic diversity and functional diversity), and environmental variables (i.e., physiography and climate). The results indicated that both environmental factors and biodiversity played pivotal roles in explaining the biomass productivity of the Nanling subtropical forests. Environmental factors had the greatest influence on total productivity, while the impacts of different types of biodiversity on various productivity components (aboveground and belowground) varied notably. Taxonomic diversity showed the strongest positive effect on the aboveground and belowground biomass productivity. However, phylogenetic and functional diversity had negative effects on productivity. Furthermore, these relationships also exhibited variations when considering different altitude gradients, with low altitudes generally leading to negative biodiversity–productivity correlations. We contextualized our results regarding the three state-of-the-art theories about biodiversity–productivity relationships (selection probability, niche complementarity, and biomass ratio) and concluded that both selection probability and niche complementarity are the driving mechanisms of productivity in the subtropical forests of the Nanling Mountains. This study offers valuable insights into the functioning and biodiversity mechanisms of subtropical forest ecosystems in southern China.

Promises of parallel outcomes

Summary A key challenge in causal inference from observational studies is the identification and estimation of causal effects in the presence of unmeasured confounding. In this paper, we introduce a novel approach for causal inference that leverages information in multiple outcomes to deal with unmeasured confounding. An important assumption in our approach is conditional independence among multiple outcomes. In contrast to existing proposals in the literature, the roles of multiple outcomes in the conditional independence assumption are symmetric; hence, the name parallel outcomes. We show nonparametric identifiability with at least three parallel outcomes and provide parametric estimation tools under a set of linear structural equation models. Our proposal is evaluated through a set of synthetic and real data analyses.