Measures Of Model Fit Research Articles

Testing Predictions for Migration of Meandering Rivers: Fit for a Curvature‐Based Model Depends on Streamwise Location and Timescale

AbstractMany meandering rivers migrate, at rates that vary both along‐stream and inversely with the observation interval. Many numerical models have been developed to predict this migration; their success is usually evaluated statistically or by qualitative comparison to observations in map view. We propose a framework to test migration models that unites these statistical, spatial, and temporal perspectives. We measure model fit with a statistic that compares the magnitude and direction of migration between predictions and observations. Model fit is contextualized in space, using a dimensionless coordinate system based in the location along a half‐meander bend; and in time, using a dimensionless observation interval that accounts for channel scale and migration rate. We applied this framework to test predictions for a curvature‐driven model of channel migration, using data from seven rapidly migrating rivers in the Amazon Basin and 103 more slowly migrating rivers across the continental US, as reconstructed from a legacy data set. We find that across both datasets, channel migration rates peak slightly downstream of the bend apex. Migration rate underestimation/overestimation tends to occur when the observed rate is greater/less than its median along the channel. Predicted migration direction opposes observations for slowly migrating locations and upstream of the bend apex. Model forecasts break down if the channel migrates by more than its width. The analysis framework is portable to testing other models of channel migration, and can help improve predictions for the stability of infrastructure along rivers and for landscape change over geologic timescales.

Vascular health is associated with locus coeruleus‐related entorhinal tau deposition

AbstractBackgroundAutopsy studies reported that the locus coeruleus (LC) is one of the first sites of tau accumulation. In addition, LC‐mediated norepinephrine (NE) signaling is thought to play a role in blood‐brain‐barrier maintenance and neurovascular coupling through its projections to the microvasculature, suggesting that the NE‐LC system interacts with cerebrovascular disease and Alzheimer’s disease (AD) pathology. Since vascular risk factors are prevalent in older individuals and contribute to vascular pathology, a common co‐pathology of AD, understanding interrelationships among vascular health, tau and LC integrity as early as possible, can guide prevention.MethodWe included 124 individuals (Table1) from the Harvard Aging Brain Study with MRI and 18F‐Flortaucipir and 11C‐Pittsburgh‐Compound‐B PET‐imaging within one year of each other. LC‐MRI signal intensity was extracted from 5 maximum‐intensity voxels and normalized to pontine tegmentum. White matter signal abnormalities (WMSA intracranial volume adjusted) were obtained from FreeSurfer6. PET measures were partial‐volume corrected. The Framingham‐Risk‐Score (FRS‐CVD, likelihood of a cardiovascular event in 10 years) was calculated. Structural Equation Models were used to test hypothetic models (1: WMSA mediate the relationship between FRS‐CVD and LC, leading to entorhinal (EC) tau; 2: FRS‐CVD is associated with WMSA and LC, and the LC influences WMSA, both leading to EC tau) against the null model (independent paths of LC and WMSA between FRS‐CVD and EC tau;Figure1). Models were bootstrapped (n = 2000). Model fit measures determined which model described the data best (CFI >0.90, RMSEA <0.08, SRMR <0.08). Age and sex were included as covariates.ResultModels null and 2 had poor fit. Model‐1 had an excellent fit, suggesting that a higher likelihood of a cardiovascular event in ten years is positively associated with WMSA, WMSA is associated with LC intensity, which in turn is associated with EC tau (Table2). Posthoc, global Aβ (DVR) was added as covariate in model‐1, this did not change the model‐fit or outcomes.ConclusionPrevious work demonstrated that LC‐intensity correlates strongly with entorhinal tau. Our findings now suggest that presence of increased cardiovascular risk is associated with lower LC‐intensity through its effects on white‐matter damage, emphasizing the importance of mitigating risk factors earlier in life.

Assessing the sustainability capacity of evidence-based programs in community and health settings.

Within many public health settings, there remain large challenges to sustaining evidence-based practices. The Program Sustainability Assessment Tool has been developed and validated to measure sustainability capacity of public health, social service, and educational programs. This paper describes how this tool was utilized between January 2014 and January 2019. We describe characteristics of programs that are associated with increased capacity for sustainability and ultimately describe the utility of the PSAT in sustainability research and practice. The PSAT is comprised of 8 subscales, measuring sustainability capacity in eight distinct conceptual domains. Each subscale is made up of five items, all assessed on a 7-point Likert scale. Data were obtained from persons who used the PSAT on the online website (https://sustaintool.org/), from 2014 to 2019. In addition to the PSAT scale, participants were asked about four program-level characteristics. The resulting dataset includes 5,706 individual assessments reporting on 2,892 programs. The mean overall PSAT score was 4.73, with the lowest and highest scoring subscales being funding stability and program adaptation, respectively. Internal consistency for each subscale was excellent (average Cronbach's alpha = 0.90, ranging from 0.85 to 0.94). Confirmatory factor analysis highlighted good to excellent fit of the PSAT measurement model (eight distinct conceptual domains) to the observed data, with a comparative fit index of 0.902, root mean square error of approximation equal to 0.054, and standardized root mean square residual of 0.054. Overall sustainability capacity was significantly related to program size (F = 25.6; p < 0.001). Specifically, smaller programs (with staff sizes of ten or below) consistently reported lower program sustainability capacity. Capacity was not associated with program age and did not vary significantly by program level. The PSAT maintained its excellent reliability when tested with a large and diverse sample over time. Initial criterion validity was explored through the assessment of program characteristics, including program type and program size. The data collected reinforces the ability of the PSAT to assess sustainability capacity for a wide variety of public health and social programs.

Virtual Versus Face-to-face Events: The Effects of Event Type on Attendees' Attitudes and Behavioral Intentions

The recent situation with COVID-19 led to significant changes in the event industry and forced event planners to organize virtual events. However, virtual events are lacking social interactions and are characterized by lower levels of engagement. This study aims at exploring relationships between event types and attendees' behavioral intentions using experimental design. The measurement model fit and constructs' validity were assessed with confirmatory factor analysis, while the study hypotheses were tested with the structural equation modeling. The results demonstrate that event types have significant effects on attendees' risk perceptions that influence attendees' visit intentions. The study also revealed significant moderating effects of attendees' age on the relationship between event type and visit intentions. The study provides important theoretical and managerial implications by introducing new factors affecting attendees' behavioral intentions amid the COVID-19 pandemic, describing the relationship between risk perceptions and attendees' behavioral intentions, and providing practical recommendations for event planners.

Where Did I Go Wrong with My Model? Ten Tips for Getting Results in SEM

Those new to structural equation modeling (SEM) often encounter problems with convergence, software errors, or poor fitting models. Some basic suggestions are offered to help reduce these problems and avoid some common error messages encountered when first testing models. The tips concern initial data management steps, specification of latent variables, avoidance of factor identification problems, special precautions when estimating means, addressing poor measurement model fit, using multistep testing, fitting full structural models, attentiveness to interpretation pitfalls, and troubleshooting software warnings and errors. The advice provided is intended to help save time, effort, and trials and tribulations often encountered by beginner modelers.

Clients' current presentation yields best prediction of criminal recidivism: Jointly modeling repeated assessments of risk and recidivism outcomes in a community sample of paroled New Zealanders.

Clinicians often rely on readily observable intermediate outcomes (e.g., symptoms) to assess the likelihood of events that occur outside of treatment (e.g., relapse). Similarly, those monitoring clients with histories of criminal involvement attempt to prevent adverse outcomes considered likely and intervene when symptoms/risk factors fluctuate. Our aim was to develop a stronger understanding of associations between evolving symptoms/risk factors and case outcomes, yielding clearer practice implications. We used longitudinal, multiple reassessment risk data from 3,421 individuals paroled in New Zealand. We used joint modeling to test the association between individual trajectories of psychosocial risk factor scores, assessed using Dynamic Risk Assessment for Offender Re-entry, and recidivism (official records of parole violations or criminal charges resulting in reconviction). We examined whether recent clinically relevant features of risk presentation (e.g., current levels, recent rate of change) predicted recidivism better than the entirety of the risk assessment trajectory. Although each model demonstrated similar predictive validity, measures of model fit indicated that models using current trajectory features outperformed those using the entire assessment history to predict recidivism. Change in dynamic risk factors is consistently associated with recidivism outcomes. When using changeable factors to monitor clients' current risk for recidivism, practitioners should focus on current presentation rather than the entire assessment history, although differences in predictive discrimination are small. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

PSYCHOMETRIC PROPERTIES OF THE STATE-TRAIT ANXIETY INVENTORY-TRAIT (STAI-T) IN ARGENTINIAN ADOLESCENTS

The aim of the present study was to analyze the psychometric properties of the State-Trait Anxiety Inventory- Trait dimension (STAI-T), Form X. A total of 798 Argentinian adolescents aged between 12 and 18 years (M = 14; SD = 1.31) participated in this study. The psychometric properties of the instrument were analyzed from the contributions of the Rasch model, which allowed us to assess the measurement model fit, response threshold order, item and person fit, local dependence, differential item functioning (DIF), reliability, targeting, and unidimensionality. The results showed that the STAI-T, anxiety-absent items, and anxiety-present items presented values close to the expected ones for item and person fit, ordered response categories, appropriate reliability levels, and adequate targeting. Only in anxiety-absent items, it was possible to verify compliance with unidimensionality, whereas in anxiety-present items there were items with local dependence or showing DIF. The present study provides empirical evidence based on a rigorous measurement model that allows corroborating the validity of the scale to assess anxiety in Argentinian adolescents.

Causes and Consequences of Attitude Toward the Government during the Covid-19 Crisis: A Study in the Tunisian Context

ABSTRACT Governments around the world played a crucial role in the attempts of diminishing the negative consequences of the Covid-19 crisis and containing the situation. While Tunisia faced economic, political and social challenges, the health crisis did not improve the situation. The government took several decisions to fight the virus resulting in mitigating citizens’ attitude. This research studied the cognitive, affective and behavioral determinants of citizens’ attitude toward the government during the Covid-19 crisis and its impact on their well-being. We collected 687 online survey responses from Tunisians. We used SPSS to perform exploratory factor analysis. We used R package Lavaan for confirmatory factor analysis, measurement model fit, structural model fit and test of hypotheses. Our findings suggest that feelings of anger, attribution of blame and knowledge of the crisis have a significant impact on attitude (both positive and negative) which consequently influences psychological and social well-being.

Value similarity and trustworthiness predict support for waterfowl management policy

AbstractModern wildlife agencies increasingly emphasize efforts that facilitate the social feasibility of policy via cooperation and support among affected publics. Values and trust are critical components of policy support. Value similarity is an established concept that maps well to indicators of policy support whereas trustworthiness has received limited research attention, particularly in comparison to the variably defined concept of trust. We present a conceptual model that assesses the relationships among salient value similarity (SVS), trustworthiness (ability and benevolence), and support for wildlife policy in Arkansas, USA. We used structural equation modeling (SEM) to test our model among a sample of resident waterfowl hunters ≥18 years old (n = 1,596). Respondents rated the value similarity of Arkansas Game and Fish Commission (AGFC), AGFC's trustworthiness in relation to waterfowl management, and support for waterfowl policy. The measurement model fit observed data well and the structural model indicated good fit between the conceptual model and observed data. Our findings indicate SVS positively influenced perceptions of trustworthiness that, in turn, positively influenced support for waterfowl policy. The significant positive influence SVS and trustworthiness have on policy support suggest important metrics agencies can proactively track and manage among affected publics. Furthermore, knowledge of how value similarity and trustworthiness interact with policy support can help agencies target involvement and engagement processes that improve social feasibility, particularly when a policy does not correspond with current expectations but are intended to benefit the long‐term socioecological sustainability of a resource.

Assessment of Path Model Fit: Evidence of Effectiveness and Recommendations for use of the RMSEA-P

We review the development of path model fit measures for latent variable models and highlight how they are different from global fit measures. Next, we consider findings from two published simulation articles that reach different conclusions about the effectiveness of one path model fit measure (RMSEA-P). We then report the results of a new simulation study aimed at resolving the questions of whether and how the RMSEA-P should be used by organizational researchers. These results show that the RMSEA-P and its confidence interval is very effective with multiple indicator models at identifying misspecifications across large and small sample sizes and is effective at identifying true models at moderate to large sample sizes. We conclude with recommendations for how the RMSEA-P can be incorporated along with other information into model evaluation.

The development of the Chinese version of the Sports Emotional Intelligence Scale.

ObjectiveTo revise and test the Chinese version of the Sports Emotional Intelligence Scale (SEIS) in sports situations.Materials and methodsAfter pretesting 112 college students, 832 college students were formally tested, and item analysis, validity test, internal consistency reliability analysis, and calibration validity and equivalence test of the Chinese version of the SEIS were performed. The Chinese version of the SEIS had 14 items with four dimensions (evaluation of others’ emotions, self-emotional management, emotion use, and social skills), with a cumulative variance contribution of 57.812 percent; the four-factor measurement model fit well (χ2/df = 3.743, NFI = 0.917, GFI = 0.945, AFGI = 0.913, CFI = 0.926, and RMSEA = 0.062). The internal consistency coefficients (Cronbach’s α) of the Chinese version of the SEIS ranged from 0.824 to 0. 905, and the split-half reliability ranged from 0.807 to 0.891; the correlations between the total SEIS score and its dimensional scores and the calibration variables were significantly correlated (p < 0.05), ranging from 0.255 to 0.603. The gender measure equivalence test was valid, and the mean difference of potential variables across gender groups was not significant in the potential mean analysis.ConclusionThe Chinese version of the SEIS has good reliability and validity, and it is appropriate for assessing emotional intelligence in sports situations.

Improved biomarker discovery through a plot twist in transcriptomic data analysis

BackgroundTranscriptomic analysis is crucial for understanding the functional elements of the genome, with the classic method consisting of screening transcriptomics datasets for differentially expressed genes (DEGs). Additionally, since 2005, weighted gene co-expression network analysis (WGCNA) has emerged as a powerful method to explore relationships between genes. However, an approach combining both methods, i.e., filtering the transcriptome dataset by DEGs or other criteria, followed by WGCNA (DEGs + WGCNA), has become common. This is of concern because such approach can affect the resulting underlying architecture of the network under analysis and lead to wrong conclusions. Here, we explore a plot twist to transcriptome data analysis: applying WGCNA to exploit entire datasets without affecting the topology of the network, followed with the strength and relative simplicity of DEG analysis (WGCNA + DEGs). We tested WGCNA + DEGs against DEGs + WGCNA to publicly available transcriptomics data in one of the most transcriptomically complex tissues and delicate processes: vertebrate gonads undergoing sex differentiation. We further validate the general applicability of our approach through analysis of datasets from three distinct model systems: European sea bass, mouse, and human.ResultsIn all cases, WGCNA + DEGs clearly outperformed DEGs + WGCNA. First, the network model fit and node connectivity measures and other network statistics improved. The gene lists filtered by each method were different, the number of modules associated with the trait of interest and key genes retained increased, and GO terms of biological processes provided a more nuanced representation of the biological question under consideration. Lastly, WGCNA + DEGs facilitated biomarker discovery.ConclusionsWe propose that building a co-expression network from an entire dataset, and only thereafter filtering by DEGs, should be the method to use in transcriptomic studies, regardless of biological system, species, or question being considered.

Empirical Study to Understand the Social Entrepreneurial Intention Towards Technology Management in Social Entrepreneurial Ventures

The purpose of this research is to investigate the social entrepreneurial intentions (SEI) towards the implementation of technology management (TM) in social entrepreneurial ventures (SEV). Drawing on the technology acceptance model (TAM) and resource base view theory, hypotheses were developed. Data were collected from 640 social entrepreneurs (SE) from various industries (IT/software-based market, production, manufacturing, etc.) in Pakistan. SPSS-AMOS was used for a two-step approach of structural equation modeling. Results were determined by using confirmatory factor analysis and the measurement model fit. The significance of the theoretical relationship was assessed using the structural model. The findings of the study reveal the relationship between competitive advantage, and TM perceived usefulness was positively supported. Additionally, the internal resources show a positive relationship between TM perceived usefulness. Further, the TM perceived usefulness has a positive relation towards SEI. The research significantly contributes to the domain of SEI.

Interplay of diversity, inclusion, and politics: Impact on employee well-being

Workforce diversity literature highlights adverse effects of surface diversity on employee affect. We examine the applicability of surface diversity effects in the Indian social milieu. Since diversity effects are context-specific, in a first, we test the moderating impact of employee's perceived organisational politics on the inclusion mediated diversity-well-being link among 617 employees from Indian organisations. Post ascertaining the measurement model fit using AMOS 22, we used PROCESS to test for moderated-mediation effects. Perception of organisational politics moderated the surface diversity–inclusion–well-being link such that inclusion was found to be weaker for employees who perceived high politics. We discuss the implications for managers.

Evaluating the Relationship between HSE Management and Job Satisfaction and Performance Quality among the Production Center (Employees of Hacoupian Clothing Industrial ‎Company: A Case Study)

Introduction: The productivity of an organization is closely related to the improvement of the safety and health of employees and the development of knowledge and skills of human resources in advancing the organization goals. Based on a combination of questionnaire and survey, this study examines the impact of implementing safety management practices on the performance and job satisfaction of employees in Iran's production units.&#x0D; Research Method: This descriptive-cross-sectional study was conducted in 1400 on 200 employees and managers of the Hacoupian clothing industrial company located in Tehran province. 130 of them were selected based on random sampling using the Morgan table. Data collection tools included a researcher-made questionnaire, which was further confirmed by experts by applying their opinions regarding its validity and reliability. It was approved by experts. The analysis method in this study is mainly descriptive and the type of research is relational. Data and research hypotheses were respectively tested using Structural equation modeling (SEM) and Partial least squares (PLS) approach through SPSS26 software.&#x0D; Results: Findings showed the appropriate fit of measurement and structural models. In addition, results demonstrated the positive effect of improving safety on the job satisfaction of employees in production units, as well as the negative effects of not paying attention to the health of the working environment on the quality of employees' performance.&#x0D; Conclusion: The more companies and governments pay attention to the issue of operational health and safety, the organizational and environmental situation will be formed in such a way that it will optimally motivate employees and increase the quality of production and operations in the organization.

No probifactor model fit index bias, but a propensity toward selecting the best model.

Based on an extensive Monte Carlo simulation study, Greene et al. (2019) investigated the behavior of various measures of model fit for competing types of confirmatory factor analysis models of psychopathology, the correlated factors model and the bifactor model. Greene et al. (2019) found that fit indices mostly favored a bifactor model over a correlated factors model, which led to the conclusion of a "probifactor fit index bias." Here we show that this conclusion is misleading as far as conditions without complexities in the data-generating model are concerned and in fact incorrect in conditions with complexities (cross-loadings or correlated residuals) in the data-generating model. Specifically, we demonstrate that the very same data Greene et al. (2019) generated from a correlated three-factor model can be likewise obtained from a higher-order or a bifactor model, so that there is no basis for maintaining that the "true" to-be recovered model conformed to a correlated factors structure. Moreover, we show that a standard bifactor model was factually closer aligned with the data generated in conditions with added complexities. As such, fit indices necessarily and correctly favored the bifactor model in most conditions. We explain the observed behavior of several fit indices, thereby showing that the results were not characterized by bias, but were in fact in line with the expected and desired behavior. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

The impact of environmental management accounting on environmental and financial performance: empirical evidence from Bangladesh

PurposeThis study aims to investigate the impact of environmental management accounting (EMA) on manufacturing companies’ environmental and financial performance in Bangladesh. Thus, this research recognizes essential factors such as EMA, environmental performance (EP), financial performance (FP), environmental information systems (EIS), knowledge management (KM), green innovation and energy efficiency (EE).Design/methodology/approachThis research uses a quantitative approach and uses 323 responses from the manufacturing firms. This research tests the study model through the “Partial Least Square-Structural Equation Modeling” (PLS-SEM) technique using Smart PLS v3.3 software. This research uses AMOS v24 and 40% sample consideration to check the robustness. The study passes various model fit measures, i.e. reliability, validity, factor analysis and goodness of fit.FindingsThe research finds that EMA is positively and significantly associated with EP and FP. The study also finds a substantial relationship between recognized factors with EMA and EP. This research connects the stakeholder theory and institutional theory to the EMA model and shows the pressures from stakeholders and institutions reassuring the manufacturing firms to implement EMA. This research evidences that EMA enhances EP and FP.Originality/valueThe policymakers, regulators and government can consider these findings to formulate policy regarding companies’ EP and FP. Particularly, company executives can focus on KM, EIS, green innovation and EE factors for EP and FP.

An Evaluation of the Reflective Constructs Influencing Community Pharmacists’ Decision-to-Procure from Pharmaceutical Suppliers: A Structural Equation Modeling Study

In low-and middle-income countries, patients’ access to affordable medicines is a perennial challenge at all levels of healthcare delivery. Community pharmacists are expected to bridge the supply and demand gap. Unlike the plethora of studies done at the hospital and government institutions level, there is a paucity of empirical evidence on the factors influencing medicine procurement decisions from supply channels at the community pharmacy level. The study aimed to investigate the influence of community pharmacists’ professional and entrepreneurial considerations on their decision-to-procure from medicine suppliers. A cross-sectional survey which adopted a self-administered questionnaire approach to obtain primary data from randomly sampled respondents (N=398) from three purposively selected states in southwestern, Nigeria. Structural equation modeling (SEM) methodology was used to test the hypothesized model at p&lt;0.05. The study revealed acceptable model fit and validity measures for measurement and structural models. Cost and profitability factors were significant predictors of decision-to-procure compared to service and product quality factors among respondents [β=0.343, p=0.001 vs. β=0.044, p=0.606]. Product selection had a positive moderating effect on cost and profitability factors [t=1.980, p=0.048] and a negative directional effect on service and product quality factors in the hypothesized model [t=-2.960, p=0.003]. Community pharmacists should balance financial considerations with a professional focus to ensure patients’ access to quality and affordable medicines. Study outcomes suggest the development and implementation of universally acceptable guidelines on medicine procurement in community pharmacy settings in low-and middle-income countries. The study validated the SEM model for evaluating priorities influencing community pharmacists’ buying decisions. To the best of the authors’ knowledge, this is the first study to use the SEM framework to explore factors informing drug procurement decisions from the community pharmacists’ perspective in low-and middle-income countries.

The Impact of Brand Experience on Brand Engagement with Meditating Role of Brand Love and Brand Trust

The present study aims to investigate the impact of brand experience on brand engagement with mediating role of brand love and brand trust. Within the literature, there have been a select few studies that have focused on these factors in case of Bank Melli Iran. To this end, a case study approach was utilized in which 384 customers of Bank Melli Iran in Sari (both male and female) were selected based on available non-random sampling method and the formula of Cochran to participate in this study. Data collection method was library and field type. In the field method, we used the standard questionnaire. To evaluate the validity while using content validity, structural validity (convergent, divergent validity and composite reliability) was also used. The reliability of the questionnaire was assessed by Cronbach's alpha test. The results of convergent, divergent and composite reliability studies showed a good fit of measurement models, and also the results of Cronbach's alpha study showed a good reliability of each variable. After ensuring the validity and reliability of the research questionnaire, the final data collection was performed and the results were analyzed using structural equation modeling under partial least squares (PLS). Finally, the results showed that brand experience, both directly and indirectly, mediated by brand love and brand trust, has a positive effect on brand engagement.

Integrating macro and micro level crash frequency models considering spatial heterogeneity and random effects

Safety literature has traditionally developed independent model systems for macroscopic and microscopic level analysis. The current research effort contributes to the literature on crash frequency by building a bridge between these two divergent streams of crash frequency research. The study proposes an integrated micro–macro level model for crash frequency estimation. Specifically, the study develops an integrated model system that allows for the influence of independent variables at the microscopic level to be incorporated within the macroscopic propensity estimation. The empirical analysis is based on the data drawn from 300 traffic analysis zones, 1818 roadway segments, and 4184 intersections from the City of Orlando, Florida for the years 2018 and 2019. The study considers a host of exogenous variables including roadway and traffic factors, land-use, built environment, and sociodemographic characteristics for the model estimation. The proposed model system can also accommodate for hierarchical correlations such as correlation between all segments or intersections in a zone. The study findings highlight the presence of common spatial unobserved factors influencing crash frequency across segment level and intersection level as well as presence of significant parameter variability across both micro and macro level in the crash frequency. The empirical analysis is further augmented by employing several goodness of fit and predictive measures. The results clearly demonstrate the improved performance offered by the proposed integrated micro–macro model relative to the non-integrated macro model. The overall model fit measures and interpretations encourage the application of the proposed model for crash frequency analysis.