A popular measure of model fit in structural equation modeling (SEM) is the standardized root mean squared residual (SRMR) fit index. Equivalence testing has been used to evaluate model fit in structural equation modeling (SEM) but has yet to be applied to SRMR. Accordingly, the present study proposed equivalence-testing based fit tests for the SRMR (ESRMR). Several variations of ESRMR were introduced, incorporating different equivalence bounds and methods of computing confidence intervals. A Monte Carlo simulation study compared these novel tests with traditional methods for evaluating model fit. The results demonstrated that certain ESRMR tests based on an analytic computation of the confidence interval correctly reject poor-fitting models and are well-powered for detecting good-fitting models. We also present an illustrative example with real data to demonstrate how ESRMR may be incorporated into model fit evaluation and reporting. Our recommendation is that ESRMR tests be presented in addition to descriptive fit indices for model fit reporting in SEM.

Puberty is a vulnerable period for the onset of major depressive disorder (MDD) due to considerable neurodevelopmental changes. Prior diffusion tensor imaging (DTI) studies in depressed youth have had heterogeneous participants, making assessment of early pathology challenging due to illness chronicity and medication confounds. This study leveraged whole-brain DTI and graph theory approaches to probe white matter (WM) abnormalities and disturbances in structural network topology related to first-episode, treatment-naïve pediatric MDD. Participants included 36 first-episode, unmedicated adolescents with MDD (mean age 15.8 years) and 29 age- and sex-matched healthy controls (mean age 15.2 years). Compared to controls, the MDD group showed reduced fractional anisotropy in the internal and external capsules, unveiling novel regions of WM disruption in early-onset depression. The right thalamus and superior temporal gyrus were identified as network hubs where betweenness centrality changes mediated links between WM anomalies and depression severity. A diagnostic model incorporating demographics, DTI, and network metrics achieved an AUROC of 0.88 and a F1 score of 0.80 using a neural network algorithm. By examining first-episode, treatment-naïve patients, this work identified novel WM abnormalities and a potential causal pathway linking WM damage to symptom severity via regional structural network alterations in brain hubs.

OBJECTIVE: This study aimed to assess the different pathways between predictor factors such as zygosity, atypical swallowing, mouth breathing, breastfeeding and bottle feeding related to anterior open bite (AOB) in twins.
METHODS: The study was conducted in monozygotic (MZ) and dizygotic (DZ) twin children aged 3-15 years. AOB, atypical swallowing, mouth breathing, feeding type, duration of bottle use, and mouth opening status during sleep were recorded during oral examination. Partial least squares structural equation model (PLS-SEM) and sobel tests were performed to assess the total and indirect effects among the variables on AOB.
RESULTS: A total of 404 children (29.2% MZ;70.8% DZ) participated in this study. The effect of zygosity on mouth breathing in the PLS-SEM model was statistically significant. Conversely, it was determined that mouth breathing effected that atypical swallowing (p = 0.001). Atypical swallowing triggered AOB (p = 0.001). The atypical swallowing has a mediation effect between AOB and mouth breathing (p = 0.020). Mouth breathing causes atypical swallowing and therefore indirectly increases the likelihood of AOB. While breastfeeding decreases AOB incidence (p = 0.023), bottle feeding increases AOB incidence (p = 0.046). The sobel tests show that the fully mediator variable feature of mouth breathing is statistically significant in the negative relation between zygosity and atypical swallowing.
CONCLUSIONS: The PLS-SEM model showed that mouth breathing triggers atypical swallowing and atypical swallowing triggers AOB. As a result of this chain of relationships, an indirect effect of zygosity on AOB was observed. According to sobel tests, zygosity has an indirect effect on atypical swallowing through mouth breathing, while mouth breathing has a positive indirect effect on AOB through atypical swallowing.
CONCLUSIONS: This study identified the relationships between different factors and the presence of AOB. The findings of this study demonstrate in detail the relationships between AOB and zygosity, atypical swallowing, mouth breathing, breastfeeding and bottle feeding. Brestfeeding has a reducing effect on the frequency of AOB. Among the nutritional forms, breastfeeding ensures the proper development of the stomatognathic system by working the oro-facial muscles.

We present the R package galamm, whose goal is to provide common ground between structural equation modeling and mixed effect models. It supports estimation of models with an arbitrary number of crossed or nested random effects, smoothing splines, mixed response types, factor structures, heteroscedastic residuals, and data missing at random. Implementation using sparse matrix methods and automatic differentiation ensures computational efficiency. We here briefly present the implemented methodology, give an overview of the package and an example demonstrating its use.

Researchers may have at their disposal the raw data of the studies they wish to meta-analyze. The goal of this study is to identify, illustrate, and compare a range of possible analysis options for researchers to whom raw data are available, wanting to fit a structural equation model (SEM) to these data. This study illustrates techniques that directly analyze the raw data, such as multilevel and multigroup SEM, and techniques based on summary statistics, such as correlation-based meta-analytical structural equation modeling (MASEM), discussing differences in procedures, capabilities, and outcomes. This is done by analyzing a previously published collection of datasets using open source software. A path model reflecting the theory of planned behavior is fitted to these datasets using different techniques involving SEM. Apart from differences in handling of missing data, the ability to include study-level moderators, and conceptualization of heterogeneity, results show differences in parameter estimates and standard errors across methods. Further research is needed to properly formulate guidelines for applied researchers looking to conduct individual participant data MASEM.

BACKGROUND: Small airway dysfunction not only affects asthma control, but also has adverse effects on the psychological and/or social activities of asthma patients. However, few long-term observational studies have explored the complex relationship between small airway dysfunction and asthma control and health-related quality of life in patients with asthma exacerbations.
METHODS: The study recruited 223 patients with exacerbations of asthma (i.e. those with at least one asthma attack over the past year) and 228 patients without exacerbations of asthma (i.e. those without asthma attacks over the past year). We evaluated SAD in patients with asthma exacerbations using impulse oscillometry method. At each evaluation time point within one year of follow-up, the attending physician conducts a case investigation of the patients. We analyzed the correlation between SAD and general characteristics (age, obesity, smoking history), type 2 inflammation (blood eosinophils, exhaled nitric oxide), FEV1, as well as asthma control (ACT) and health-related quality of life (mini-AQLQ) in patients with asthma exacerbations, and constructed a structural equation model to evaluate the causality of these clinical variables.
RESULTS: The SAD prevalence in patients with asthma exacerbation is as high as 75%. SAD is connected with poor asthma control and poor health-related quality of life. The structural equation model indicates that age, obesity, FeNO, and FEV1 are independent predictive factors of SAD. SAD is the main determinant factor of asthma control, which in turn affected health-related quality of life. FEV1 and age directly affect asthma control and affect health-related quality of life through asthma control. In addition, there is a bidirectional relationship between FEV1 and small airway dysfunction and between asthma control and health-related quality of life.
CONCLUSIONS: Small airways are involved from an early stage in asthma. Abnormal function of the small airways can significantly increase airway resistance in asthma patients, while worsening their clinical symptoms. In addition, aging is also a key risk factor for asthma control. Especially, small airway dysfunction links asthma control with health-related quality of life.

Methane fluxes (FCH4) vary significantly across wetland ecosystems due to complex mechanisms, challenging accurate estimations. The interactions among environmental drivers, while crucial in regulating FCH4, have not been well understood. Here, the interactive effects of six environmental drivers on FCH4 were first analyzed using 396,322 half-hourly measurements from 22 sites across various wetland types and climate zones. Results reveal that soil temperature, latent heat turbulent flux, and ecosystem respiration primarily exerted direct effects on FCH4, while air temperature and gross primary productivity mainly exerted indirect effects by interacting with other drivers. Significant spatial variability in FCH4 regulatory mechanisms was highlighted, with different drivers demonstrated varying direct, indirect, and total effects among sites. This spatial variability was then linked to site-specific annual-average air temperature (17.7%) and water table (9.0%) conditions, allowing the categorization of CH4 sources into four groups with identified critical drivers. An improved estimation approach using a random forest model with three critical drivers was consequently proposed, offering accurate FCH4 predictions with fewer input requirements. By explicitly accounting for environmental interactions and interpreting spatial variability, this study enhances our understanding of the mechanisms regulating CH4 emissions, contributing to more efficient modeling and estimation of wetland FCH4.

This paper explores the intersection of gender and sports participation in adolescence, focusing on traditionally male-dominated sports like football. We aim to develop a structural model based on the Theory of Planned Behavior (TPB) to examine gender-differentiated patterns and factors influencing adolescent participation in football. We analyzes data from 1,147 adolescents using Structural Equation Modeling (SEM). The results indicate that a blend of attitudes, subjective norms, perceived behavioral control, and past behavior effectively predicts adolescents\' willingness to engage in football. Moreover, the study investigates the roles of perceived behavioral control, past behavior, and behavioral intentions in actual football participation, uncovering significant gender disparities in the progression from past behavior to intentions and subsequent behavior. This research highlights the complex role of gender in football participation and provides strategic insights for increasing girls\' involvement in the sport. Our study sets the stage for future research on enhancing girls\' participation in football.

The social relations model (SRM) is the standard approach for analyzing dyadic data stemming from round-robin designs. The model can be used to estimate correlation-coefficients that reflect the overall reciprocity or accuracy of judgements for individual and dyads on the sample- or population level. Within the social relations structural equation modeling framework and on the statistical grounding of stochastic measurement and classical test theory, we show how the multiple indicator SRM can be modified to capture inter-individual and inter-dyadic differences in reciprocal engagement or inter-individual differences in reciprocal accuracy. All models are illustrated on an open-access round-robin data set containing measures of mimicry, liking, and meta-liking (the belief to be liked). Results suggest that people who engage more strongly in reciprocal mimicry are liked more after an interaction with someone and that overestimating one\'s own popularity is strongly associated with being liked less. Further applications, advantages and limitations of the models are discussed.

BACKGROUND: Despite COVID-19 being highly contagious and spreading to several countries, the university community has overlooked prevention measures. For more than five decades, the Health Belief Model (HBM) has been a widely used conceptual framework in health behavior. structural equation modeling(SEM) analysis is an advanced statistical method capable of rectifying failures of the basic models and showing complex relations Thus this study aimed to determine the magnitude of COVID-19 prevention behavior and identify its associated factors using HBM and SEM analysis.
METHODS: An institutional-based cross-sectional study was conducted among academic staff of the University of Gondar in Ethiopia from April 10 to May 10/2021. Daniel Soper\'s sample size calculator was used to determine the sample size. Proportional allocation to each campus followed by a simple random sampling technique was employed to select study subjects. A pre-tested, structured questionnaire was used to collect the data. Structural equation modeling analysis was employed to show the relationship between health belief model constructs and their effect on preventive behavior.
RESULTS: A total of 602 academic staff participated. The magnitude of good COVID-19 preventive behavior was 24.8%. The HBM explained 55% of the variance in preventive behavior. Perceived barriers (β = -0.37, p < 0.05), self-efficacy (β = 0.32, p < 0.05), perceived susceptibility (β = 0.23, p < 0.05), and perceived benefit (β = 0.16, p < 0.05) were the direct significant predictors of COVID 19 prevention behavior.
CONCLUSIONS: only a quarter of the academic staff have good COVID-19 preventive behavior. The HBM explained a great amount of variance in preventive behavior and Perceived barriers, benefits, susceptibility, and self-efficacy significantly associated with prevention behavior. Carefully planned intervention that considers those significant perceptions should be designed and implemented to raise COVID-19 prevention behavior.