Structural Equation Modeling Model Research Articles

Estimating statistical power for structural equation models in developmental cognitive science: A tutorial in R : Power simulation for SEMs.

Determining the compositional structure and dimensionality of psychological constructs lies at the heart of many research questions in developmental science. Structural equation modeling (SEM) provides a versatile framework for formalizing and estimating the relationships among multiple latent constructs. While the flexibility of SEM can accommodate many complex assumptions on the underlying structure of psychological constructs, it makes a priori estimation of statistical power and required sample size challenging. This difficulty is magnified when comparing non-nested SEMs, which prevents the use of traditional likelihood-ratio tests. Sample size estimates for SEM model fit comparisons typically rely on generic rules of thumb. Such heuristics can be misleading because statistical power in SEM depends on a variety of model properties. Here, we demonstrate a Monte Carlo simulation approach for estimating a priori statistical power for model selection when comparing non-nested models in an SEM framework. We provide a step-by-step guide to this approach based on an example from our memory development research in children.

Investigating the Key Factors Affecting Public Transport Ridership in Developing Countries through Structural Equation Modeling

Every country requires efficient public transportation to reduce the reliance on motor vehicles, decrease harmful emissions and noise pollution, and, most importantly, provide accessible transportation for urban populations with limited resources. Yet, several challenges hinder the efficiency of the public transportation system, affecting factors like daily ridership, infrastructure, revenue, and operations. Therefore, this research investigated barriers and their impact on the daily ridership of rail transit services, especially in densely populated areas characterized by high automobile usage, inadequate pedestrian facilities, and limited connectivity to other transportation modes. To accomplish this goal, a self-administered questionnaire was developed, and a survey was conducted to collect responses from commuters at various public transport hubs. Structural equation modeling (SEM) was used to analyze data gathered from a sample of 1000 participants. The findings from the SEM model indicated a positive correlation between Instrumental Attractiveness (IA) and Facility Design and Operation (FDO) with daily travel. Conversely, Service and Information (SI) along with Environmental Comfort (EC) exhibited a negative correlation with daily travel. Private sector employees without personal vehicles and a monthly income below fifty thousand exhibited a strong preference for public transport. Individuals with higher incomes who owned vehicles showed lower preferences towards public transport usage. The main contributors to low ridership in public transport included poor connectivity with metro and feeder buses, lack of availability of discount tickets, insufficient seating at stations, and inadequate route map information. Gasoline prices have a notable impact on daily ridership too, as higher fuel prices tend to attract more travelers to use public transport. The ridership data demonstrate a positive trend in response to increased gasoline prices. The findings from this study are useful for transportation engineers, practitioners, planners, and policymakers.

A new look at genetic variability in pomegranate: landscape genetics and structural equation modeling (SEM)

Pomegranate (Punica granatum L.) is an important horticultural plant in Iran and has been a food source since ancient times. Despite several genetic diversity studies conducted on these taxa, there are no reports on the landscape genetics of these important plants in Iran, nor is there any information on the effects of environmental, spatial, and climatic variables on the genetic structure of pomegranate. Therefore, this study used genetics, structural equation modeling (SEM), and species distribution modeling (SDM) approaches to explore the above-mentioned objectives. We used both cytogenetic and molecular analyses (SSR and SCoT markers) of several pomegranate samples along with environmental, spatial, and climatic variables. The redundancy analysis (RDA) analysis showed a significant positive association between terminal chiasmata with the longitudinal distribution of the pomegranate accession, while total chiasma number and interstitial chiasmata have an association with the altitude. The spatial PCA analysis indicated the role played by both global and local spatial variables in the cytogenetic variability like the univalent and terminal chiasmata formation. The Mantel test indicates that the cytogenetic difference in pomegranate plants is not related to their geographical distance, while the Moran I test result shows that geographical regions with similar spatial conditions affect the cytogenetic structuring of pomegranate plants in a similar way. SEM methods showed a strong negative association between bivalent formation and the environmental variables, while they showed both positive and negative associations between some of the environmental variables and different kinds of chiasmata. The sPCA analysis of molecular data showed a significant global, and local, spatial structuring of the molecular data and the genetic cline formation within the pomegranate accessions studied. Similarly, the spatial PCA analysis of climate variables, precipitation, and temperature revealed a significant structuring of genetic diversity and the formation of genetic clines by these factors. RDA analysis identified some of the loci that are significantly associated with temperature and precipitation. Therefore, both geographical/ or spatial variables and climate factors contribute to the genetic structure of the pomegranate accessions studied. SEM models based on genetic data revealed a positive association between environmental and climate variables with genetic diversity parameters and SSR-SCoT molecular loci studied. Climate change is unlikely to hurt pomegranate growing areas due to the high genetic diversity of pomegranate plants and the diverse genetic lineages present in the country.

Exploring potential mechanisms linking urban landscapes to instream stressors and macroinvertebrates in the Southeast Piedmont: Implications for watershed management

Urbanization has greatly degraded stream health, particularly in the rapidly urbanizing southeastern United States. Recent studies identified primary instream stressors affecting biological communities along urban gradients in the Southeast Piedmont. However, a comprehensive understanding of the complex mechanisms is still lacking, which impedes effective management actions. To improve our understanding, we investigated the potential pathways by which urbanization, as represented by the landscape development intensity index (LDII), and important environmental characteristics affected instream stressors and macroinvertebrate communities using structural equation modeling (SEM) and the U.S. Geological Survey Southeast Stream Quality Assessment dataset. We investigated the direct, indirect, and total effects of LDII and environmental characteristics on instream stressors and macroinvertebrate communities as well as the interactions of instream stressors. Moreover, we explored alternative reasonable relationships between variables by using an alternative model approach. We found that LDII had moderate to strong total effects on all instream stressors and macroinvertebrates with complex impact pathways revealed in SEM models, demonstrating the dominant impact of urban landscape development on stream ecosystems in the Southeast Piedmont. Flow disturbance and pesticides were major mediators transmitting most of LDII’s effects on macroinvertebrates. Furthermore, we found potential interactions among instream stressors, which were not widely discussed in previous studies. Compared with agricultural watersheds, urban watersheds represented different land use impact mechanisms. Our findings contributed to an improved understanding of urbanization’s impact on stream ecosystems and demonstrated the usefulness of an alternative model approach that provided insights into the underlying mechanisms. In addition, we demonstrated the utility of LDII in characterizing landscape development and improving SEM models. Finally, we explored the potential of SEM in global watershed management activities by discussing the practical implications of impact pathways and the innovative use of SEM as a decision-making tool.

Are visual analogue scales valid instruments to measure psychological pain in psychiatric patients?

ObjectiveThe Psychological-Physical-Pain Visual Analogue Scale (PPP-VAS) was thought to probably help in identifying patients at risk of suicide. However, no data on its validity to measure psychological pain was available. Our main aim was to investigate the convergent validity of the PPP-VAS using two well-validated scales of psychological pain, the Orbach and Mikulincer Mental Pain scale (OMMP) and the Holden et al. Psychache Scale. MethodsThis multicentre study recruited a total of 1618 adult psychiatric inpatients and outpatients in Italy. Psychological pain was evaluated using the OMMP, Holden et al., and PPP-VAS scales. Psychiatric status, suicidal status, physical pain, depression, and hopelessness were also assessed. ResultsA structural equation model (SEM) using the items of psychological pain from the PPP-VAS showed that items loaded significantly on the psychological pain factor and showed good fit. Similarly, a second SEM model using the three scales of psychological pain showed acceptable fit and converged into a psychological pain construct. Correlations between the PPP-VAS and depression, hopelessness, and physical pain showed moderate correlations (r = 0.43 to r = 0.67). Finally, psychological pain evaluated with the PPP-VAS was significantly related with recent suicidal ideation in all patients (OR [95 % CI] = 1.07 [1.05, 1.09]) and recent suicide attempts in moderately to severely depressed patients, OR [95 % CI] = 1.01 [1.02, 1.03]. ConclusionThe PPP-VAS showed good psychometric properties in evaluating psychological pain. The characteristics of the PPP-VAS makes this scale a great option for its use in clinical practice to detect patients at risk of suicide.

The structural equation modeling constructed for runoff change attribution analysis outperforms traditional methods

Recently, with the increasing global warming and intensity of human activities, runoff has significantly decreased. This has had a severe impact on water resources development and utilization, as well as ecological and environmental construction. To understand hydrological cycles and promote sustainable utilization of water resources, it is crucial to conduct attribution analysis of runoff changes. This study employed traditional models, including empirical statistics, elasticity coefficients, hydrological modeling, and introduced a simple and efficient model – structural equation modeling (SEM) – to quantify the impact of various factors on runoff changes. The findings revealed a notable decreasing trend in natural runoff in the Shiyang River from 1960 to 2020, while precipitation, snowmelt, potential evapotranspiration (ET0), and temperature exhibited increasing trends. The integrated the results from multiple models highlighted that human activities emerged as the primary driver influencing runoff changes. From 1972 to 2020, the factors contributing to runoff reduction were ranked in the order: water withdrawal (50 %) > LUCC (20 %) > ET0 (16 %) > temperature (8 %). Factors contributing to runoff increase were ranked: precipitation = water diversion project (20 %) > snowmelt (6 %). The impact of reservoir regulation on runoff initially exhibited a negative correlation (impact rate of 10 %) and later turned positive (impact rate of 6 %). The SEM model, employed for runoff change attribution analysis, demonstrated a good fit and was consistent with results obtained from the SWAT model. Compared to traditional methods, SEM not only allowed for quantitative analysis of the factors’ impact on runoff but also explored the interactions between factors and the direct and indirect effects of factors on runoff. This study introduces innovative methods for the attribution analysis of runoff changes, offering valuable insights crucial for the ecological sustainable development of water resources in arid and semi-arid regions.

Role of environmental and stand factors on forest dieback: An approach using structural equation modelling and machine learning

The Zagros forest region in western Iran is facing dieback processes, impacting both the environment and the local economy. Understanding the cause of forest dieback remains a challenge for forest experts due to the wide set of factors usually involved in the dieback phenomenon. To achieve this goal, we measured woody species dieback intensity (DI) using 124 plots distributed in oak forests across a wide range of environmental conditions. On these plots, we measured stand and vegetation factors (e.g. woody species richness, WSR), various soil properties and topographic indices (in particular the SAGA wetness index) and climatic factors. We then modeled dieback intensity using structural equation modeling (SEM) and machine learning (ML). We found a significant positive correlation between WSR and DI (r=0.48), and very low to low but still significant negative correlations with the SAGA wetness index (r=-0.10) and soil clay content (r=-0.16), respectively. Other soil properties and climate factors showed no significant influence on DI. After six rounds of respecification, the SEM fitting gave satisfactory results based on the validation map of DI indices (Goodness-of-fit Index = 0.948 and Comparative fit index =0.958). The DI map was generated using a random forest (RF) machine learning model to achieve an accuracy of 75% and a kappa index of 0.45, and then categorized into four classes. The map showed that the intensity of dieback was contrasting in our area: 41.1% of plots had no dieback, 58.4% had low DI, and less than 1% had moderate or high DI. We also found that soil clay content and woody species richness were crucial factors, explaining around 50% of the variance in DI. In conclusion, combining the methods from SEM and RF models provides an efficient approach to understand and identify key factors in the complex process of dieback.

Structural equation models of health behaviour, psychological well-being, symptom severity and quality of life in abdominal bloating.

The objective of this study was to investigate the inter-relationship between psychosocial variables and their impact on symptom severity and quality of life (QoL) concerning abdominal bloating. The study adopted a cross-sectional design with purposive sampling. Participants who consented and met the criteria for bloating based on the Rome IV classification completed designated questionnaires. Independent variables comprised health beliefs, intentions, health-promoting behaviors, social support, depression, and anxiety, while dependent variables included bloating severity (general and within 24 h) and QoL. Structural Equation Modeling (SEM) was conducted utilizing Mplus 8.0 to analyze the relationships between these factors. A total of 323 participants, with a mean age of 27.69 years (SD = 11.50), predominantly females (64.7%), volunteered to participate in the study. The final SEM model exhibited good fit based on various indices (CFI = 0.922, SRMR = 0.064, RMSEA (95% CI) = 0.048 (0.041-0.054), p-value = 0.714), with 15 significant path relationships identified. The model explained 12.0% of the variance in severity within 24 h, 6% in general severity, and 53.8% in QoL. The findings underscore the significant influence of health beliefs, intentions, behaviors, social support, depression, and anxiety on symptom severity and QoL in individuals experiencing abdominal bloating.

Effect of metformin and metformin/linagliptin on gut microbiota in patients with prediabetes

Lifestyle modifications, metformin, and linagliptin reduce the incidence of type 2 diabetes (T2D) in people with prediabetes. The gut microbiota (GM) may enhance such interventions' efficacy. We determined the effect of linagliptin/metformin (LM) vs metformin (M) on GM composition and its relationship to insulin sensitivity (IS) and pancreatic β-cell function (Pβf) in patients with prediabetes. A cross-sectional study was conducted at different times: basal, six, and twelve months in 167 Mexican adults with prediabetes. These treatments increased the abundance of GM SCFA-producing bacteria M (Fusicatenibacter and Blautia) and LM (Roseburia, Bifidobacterium, and [Eubacterium] hallii group). We performed a mediation analysis with structural equation models (SEM). In conclusion, M and LM therapies improve insulin sensitivity and Pβf in prediabetics. GM is partially associated with these improvements since the SEM models suggest a weak association between specific bacterial genera and improvements in IS and Pβf.

Machine Learning to Enhance the Detection of Terrorist Financing and Suspicious Transactions in Migrant Remittances

Migrant remittances have become significant in poverty alleviation and microeconomic development in low-income countries. However, the ease of conducting global migrant remittance transfers has also introduced the risk of misuse by terrorist organizations to quickly move and conceal operational funds, facilitating terrorism financing. This study aims to develop an unsupervised machine learning algorithm capable of detecting suspicious financial transactions associated with terrorist financing in migrant remittances. To achieve this goal, a structural equation model (SEM) and an outlier detection algorithm were developed to analyze and identify suspicious transactions among the financial activities of migrants residing in Belgium. The results show that the SEM model classifies a significantly high number of transactions as suspicious, making it prone to detecting false positives. Finally, the study developed an ensemble outlier detection algorithm that comprises an isolation forest (IF) and a local outlier factor (LOF) to detect suspicious transactions in the same dataset. The model performed exceptionally well, being able to detect over 90% of suspicious transactions.

Populism Versus Nativism: Socio-Economic, Socio-Cultural, and Emotional Predictors

Previous research on predictors of populism and nativism has predominantly focused on socio-economic (e.g., education, employment, social status), and socio-cultural explanations (e.g., social identity and social status). In recent years, however, the role of negative emotions has become increasingly important in the study of populism and related concepts. In the current study, we examined two distinct negative emotions: anxiety and anger. We collected survey data from participants in 15 European countries ( N = 7,726) and tested three sets of measures reflecting different explanations, economic (e.g., education), socio-cultural (e.g., social identity), and emotional in predicting populist attitudes (compromising items on anti-elitism and people-centrism) and nativism. We tested these different predictors using structural equation modeling (SEM). The results of our basic SEM models show that negative emotions (anxiety and anger) predict both populist attitudes (people-centrism and anti-elitism) and nativism. In particular, anxiety predicts anger, which in turn predicts both populist attitudes and nativism. In our advanced SEM models, people-centrism was predicted by anger and social identity, whereas nativism was predicted by anger, anxiety, social identity, and education. Our study shows that negative emotions (anger, anxiety) are much better predictors of populist attitudes and nativism than mere socio-economic and socio-cultural factors.

Post-fire Pinus radiata invasion in a threatened biodiversity hotspot forest: A multi-scale remote sensing assessment

Biological invasions are one of the most relevant factors of biodiversity loss, especially after fire disturbances. Wildfires can accelerate invasions of fire-prone species, like Pinus radiata, and dramatically alter ecosystems. However, how to assess the main impacts of this invasion process on the composition, structure, and functionality of ecosystems, including the post-fire revegetation processes, has not been fully resolved. This study aimed to evaluate the impacts of P. radiata invasion on fire-damaged forest ecosystems using combined remote sensing and in situ data, focusing particularly on changes in biodiversity, ecosystem structure, and functionality. The recovery of forest leaf area index (LAI) and the fraction of photosynthetically active radiation (FPAR) were monitored using Sentinel-2 time series products. Then the pre- and post-fire native community composition and the relationships of invasion and biodiversity with biotic and abiotic components were characterized using structural equation modeling (SEM). The postfire P. radiata density was mapped to quantify invasion intensity in three burned native forest fragments using generalized additive modeling (GAM) regressions based on UAS multispectral data. Biophysical metrics indicate that all forest fragments impacted by high, medium-high, and medium-low severity fires achieved a partial recovery of their canopy. The SEM model showed that microtopographic features and vegetation height explain native species diversity under pre-fire conditions due to their close relationship with favorable microclimatic conditions for species establishment. Vegetation height determined the abundance of P. radiata in post-fire conditions, and it negatively impacted diversity by promoting the homogenization of vegetation cover and altering diversity patterns. The general composition and abundance metrics also showed a substantial modification associated with the heavy (significant) invasion of P. radiata species. Predictive mapping of P. radiata density showed high accuracies (R2 =0.73 and explained deviation of 80%). The maps depicted an intense concentration of the invasive tree with a mean density of 76,217 individuals per ha−1 and high invasion spots with more than 176,000 individuals per ha−1. The quantification of invasion and mapping is a fundamental input for prioritizing areas and resources for a large-scale restoration program, and is a priority to avoid the loss of these highly threatened forest ecosystems.

Analogical Visuospatial Matrix Reasoning, Verbal Relational Reasoning, Verbal IQ and Memory Relationships With Maths and Science: A Behavioural and fMRI Study

There is no research that combines behavioural and functional magnetic resonance imaging (fMRI) research approaches to investigate the relationships between analogical reasoning and the mediating and moderating effects of working memory, verbal IQ, visuospatial and verbal reasoning on students’ performance of maths and science. This paper investigates all these multiple relationships between working memory (WM), verbal IQ, visuospatial and verbal reasoning with performance in maths and science accuracy. The multimethod research consists of a behavioural survey and a fMRI neuroscience investigating the interactive and multiple relationships of all cognitive independent variables (IVs) with the dependent variables (DVs) of maths and science performance. Using structural Equation Modelling (SEM) and Warp Partial Least Squares (Warp PLS) analytical approach, the research discusses the findings of both, the behavioural and fMRI data. The adolescent sample (N=34) for the behavioural and fMRI study, was recruited from consenting schools in London, UK, and consisted of equal numbers of girls and boys between 11-15 years old. The hypothesised behavioural SEM model predicted positive relationships between all the independent variables (IV) of visuospatial matrix reasoning (VSMatAR), verbal analogical reasoning (VerbAR), verbal working memory (VWM), visuospatial working memory (VSWM) and verbal IQ, with performance in maths and science accuracy. The findings support all hypotheses except two and identifies verbal IQ, Verbal WM and VerbAR as the three strongest predictors of maths and science accuracy. The hypothesised fMRI SEM, produced mixed results, which supported some of the hypothesised relationships between the IVs of verbal AR and VSMatrix AR associations and four brain regions, namely, the bilateral dorso lateral prefrontal cortex (DLPFC), Parietal, Temporal and pre-supplementary motor area (PSMA). The paper makes two original contributions, firstly, by testing the hypothesis that Verbal WM mediates the independent variables’ effects on maths and science outcomes. And secondly, by using Warp PLS to test the SEM (behavioural and fMRI) models’ multiple non-linear interactions simultaneously between the IVs with the DVs of maths and science accuracy. The research produced some interesting new insights relevant for educational practitioners regarding the strong relationships between Verbal analogical reasoning and maths and science performance for adolescent students.

Determining causality in travel mode choice

This article presents one of the pioneering studies on causal modeling in travel mode choice decision-making using causal discovery algorithms. These models are a major advancement from conventional correlation-based techniques. We propose a novel methodology that combines causal discovery with structural equation modeling (SEM). This modeling approach overcomes some of the limitations of SEM by combining the strengths of both causal discovery and SEM. Causal discovery algorithms determine causal graphs from observational data and domain knowledge, and SEMs estimate direct causal effects and test the performance of causal discovery algorithms. In this study, we test four causal discovery algorithms: Peter-Clark (PC), Fast Causal Inference (FCI), Fast Greedy Equivalence Search (FGES), and Direct Linear Non-Gaussian Acyclic Models (DirectLiNGAM). The results show that DirectLiNGAM based SEM model best captures causality in mode choice behavior. It passes several goodness-of-fit tests, including Root Mean Square Error of Approximation (RMSEA) and Goodness-of-Fit Index (GFI), and it achieves the lowest Bayesian Information Criterion (BIC) value. The analyses are conducted on data collected from the 2017 National Household Travel Survey in the New York Metropolitan area.

Validity arguments for creating a short motivation scale through adaptation of the Japanese motivated strategies for learning questionnaire

Background The coronavirus disease pandemic has substantially altered the mode of medical education, transitioning it from in-person to online. Online learning requires a strong motivational foundation to support self-directed studying. In our previous study, an adapted 31-item Japanese motivation measurement scale using the motivational section from the Japanese version of the Motivated Strategies for Learning Questionnaire (J-MSLQ) was developed. The present study aimed to collect evidence on the validity of the motivation section of the J-MSLQ and clarify whether further revision was necessary. Methods The motivation section of the J-MSLQ comprises a seven-point Likert scale survey containing 31 items. Of 127 fourth-year Japanese medical students invited to participate, 112 responded. Confirmatory factor analysis with structural equation modeling (SEM) was performed using R and R studio for all data analysis. Results The initial SEM model that incorporated all 31 items exhibited inadequate goodness of fit metrics: χ2 = 823.674 (p < 0.001), the comparative fit index (CFI) = 0.786, the root mean square error of approximation (RMSEA) = 0.093, the standardized root mean square residual (SRMR) = 0.112, the goodness of fit index (GFI) = 0.664, and the adjusted goodness of fit index (AGFI) = 0.602. We optimized the scale by eliminating 12 items with a Z test statistic < 10. Improvements in the goodness of fit metrics of the optimized SEM model were observed, demonstrating moderate model fit: χ2 = 205.602 (p < 0.001), CFI = 0.929, RMSEA = 0.080, SRMR = 0.063, GFI = 0.837, AGFI = 0.768. Conclusions A compact 18-item version of the motivation scale was developed and validated, which alleviated the respondent burden and enabled the researchers to collect data in a shorter time.

Colorectal cancer information avoidance is associated with screening adherence.

Colorectal cancer (CRC) is the fourth most common cancer among U.S. men and women and the second deadliest. Effective screening modalities can either prevent CRC or find it earlier, but fewer than two thirds of U.S. adults are adherent to CRC screening guidelines. We tested whether people who defensively avoid CRC information have lower adherence to CRC screening recommendations and weaker intentions for being screened and whether CRC information avoidance adds predictive ability beyond known determinants of screening. Participants, aged 45-75 years, completed a survey about known structural determinants of CRC screening (healthcare coverage, healthcare use, provider recommendation), CRC information avoidance tendencies, and screening behavior (n = 887) and intentions (n = 425). Models were tested with multivariable regression and structural equation modeling (SEM). To the extent that participants avoided CRC information, they had lower odds of being adherent to CRC screening guidelines (OR = 0.55) and if non-adherent, less likely to intend to be screened (b=-0.50). In the SEM model, avoidance was negatively associated with each known structural determinant of screening and with lower screening adherence (ps < 0.01). Fit was significantly worse for nested SEM models when avoidance was not included, (i.e., the paths to avoidance were fixed to zero). Information avoidance was associated with screening behavior and other known structural determinants of screening adherence, potentially compounding its influence. Novel strategies are needed to reach avoiders, including health communication messaging that disrupts avoidance and interventions external to the healthcare system, with which avoiders are less engaged.

Diet Pattern Analysis in Alzheimer's Disease Implicates Gender Differences in Folate-B12-Homocysteine Axis on Cognitive Outcomes.

Low plasma B12 and folate levels or hyperhomocysteinemia are related to cognitive impairment. This study explores the relationships among diet pattern, blood folate-B12-homocysteine levels, and cognition measurement in Alzheimer's disease (AD) while exploring whether a gender effect may exist. This cross-sectional study enrolled 592 AD patients (246 males, 346 females) and the demographic data, blood biochemical profiles, Mini-Mental State Examination (MMSE), and a Food Frequency Questionnaire (FFQ) for quantitative assessment of dietary frequency were collected. Structural Equation Modeling (SEM) was employed to explore the associations among dietary patterns, blood profiles, and cognition. A least absolute shrinkage and selection operator regression model, stratified by gender, was constructed to analyze the weighting of possible confounders. Higher MMSE scores were related to higher frequencies of coffee/tea and higher educational levels, body mass index, and younger age. The SEM model revealed a direct influence of dietary frequencies (skimmed milk, thin pork, coffee/tea) and blood profiles (homocysteine, B12, and folate) on cognitive outcomes. At the same time, the influence of dietary pattern on cognition was not mediated by folate-B12-homocysteine levels. In males, a direct influence on the MMSE is attributed to B12, while in females, homocysteine is considered a more critical factor. Dietary patterns and blood profiles are both associated with cognitive domains in AD, and there are gender differences in the associations of dietary patterns and the levels of B12 and homocysteine. To enhance the quality of dietary care and nutritional status for individuals with dementia, our study results still require future validations with multi-center and longitudinal studies.

STRATEGIES TO IMPROVE THE PERFORMANCE OF MSME’s IN KUNINGAN REGENCY

This study examines the variables of Affiliate Marketing, Viral Marketing, Sales Volume, and Sales Performance. Conducted in Kuningan Regency, West Java, with a population of Micro, Small and Medium Enterprises (MSMEs) in the region, the number of research samples was 160. The type of research used is Quantitative Descriptive with Structural Equation Modelling (SEM) as the analysis model. The results showed a significant influence between Affiliate Marketing and Viral Marketing on Sales Volume. However, there is no significant influence between Affiliate Marketing and Sales Performance. In contrast, Viral Marketing is proven to have a significant influence on Sales Performance. In addition, Sales Volume is shown to have a significant influence on Sales Performance. The findings contribute to practical understanding for MSMEs and related parties in optimising marketing strategies, especially through SEM model analysis. This study also identifies the existence of indirect effects, showing that both Affiliate Marketing and Viral Marketing have an influence on Sales Performance through the intermediary variable of Sales Volume

Untangling pathways of risk factors associated with hypertension among dysglycemia adults in eastern China: a structural equation model approach.

Using structural equation model (SEM) to test a conceptual model of pathways of developing hypertension among dysglycemia (IFG and T2DM) adults in Eastern China, emphasizing the unique mediation effect of insulin resistance and obesity on the relationship between modified/unmodified factors and hypertension. Participants with dysglycemia (n = 10,401) were extracted from the survey of Chronic Disease and Risk Factor Surveillance in Nanjing, the capital of Jiangsu Province in China. Dietary patterns were identified by using principal component analysis (PCA). SEM was employed to evaluate multiple pathways of hypertension among participants with IFG and T2DM. Three dietary patterns were derived using PCA. The tuber animal food pattern (OR = 0.825, 95% CI 0.723-0.940) and the balanced food pattern (OR = 0.812, 95% CI 0.715-0.922) were negatively associated with hypertension, while the Chinese rural food pattern (OR = 1.163, 95% CI 1.019-1.328) was positively associated with hypertension. The best SEM model showed that BMI (0.140), smoking (0.048) and Chinese rural food pattern (0.022) positively associated with hypertension; while tuber animal food pattern (- 0.025) had a negative direct effect on hypertension. Notably, insulin resistance could mediate the link between lifestyles (smoking and dietary patterns) and hypertension. Accordingly, we emphasized the importance of lifestyle intervention, mainly including obesity management, choosing healthy diets and decreasing smoking control, which may profoundly benefit this high-risk group among Chinese population.

Pengaruh Financial Literacy, Lifestyle, dan Self-Control Terhadap Saving Behavior Generasi Z Jakarta Selatan

Background – Z Generation is a generation with digital native and multitasking characters who are surrounded by lifestyle trends and technological developments that are constantly changing rapidly, this makes the level of financial literacy and self-control low where saving behavior is necessary as an important aspect of financial life in order to achieve stability of finances and future preparation. Aim – The purpose of this study was to examine and analyze whether financial literacy, lifestyle, and self-control have an influence on the saving behavior of Z Generation South Jakarta. Design/ methodology/ approach – This research approach uses a quantitative approach by obtaining data from distributing questionnaires to 349 respondents which are then processed using data analysis techniques using the SEM (Structural Equation Modeling) model with SmartPLS software version 3.2.9 (Partial Least Square). Findings – Based on the results of the study proving that financial literacy has a significant positive effect on saving behavior, these results mean that the higher the financial literacy of Z Generation, the higher their saving behavior will be. Meanwhile, lifestyle and self-control do not have an influence on saving behavior, this can be interpreted that everyone has a different lifestyle and self-control, so whatever the lifestyle and self-control of Z Generation, it does not have an influence on the saving behavior of Generation Z, South Jakarta it can be caused by other factors. Conclusion - Gen Z's lifestyle does not have a significant influence on the savings decisions of individuals in Gen Z. Similar to Gen Z's self control which does not support saving behavior. Research implication – This research adds insight into the factors related to saving behavior, especially financial literacy, lifestyle, and self-control of the younger generation. It is necessary to highlight these factors for generation Z in South Jakarta to be able to care more about their finances which will grow and improve behavior in saving for a controlled and peaceful future. Limitations – In this research, the limitation is that the area only took four sub-districts in South Jakarta, including Cilandak sub-district, Jagakarsa sub-district, Kebayoran Lama sub-district, and Pasar Minggu sub-district. In addition, the population and sample focus on Z Generation with the age chosen as a sample of 16-26 years, where future researchers are expected to use a wider number of sub-districts or research in one area and choose a variety of ages to provide even better results and able to provide more extensive information.