Utilizing Structure Equation Model to Analyze Multi-Crash Contributing Factors for Shanghai Urban Expressway System

Crash risk analysis for Shanghai urban expressways: A Bayesian semi-parametric modeling approach

Effect of meal glycemic load and caffeine consumption on prolonged monotonous driving performance

With its emphasis on practical and conceptual aspects, rather than mathematics or formulas, this accessible book has established itself as the go-to resource on confirmatory factor analysis (CFA). Detailed, worked-through examples drawn from psychology, management, and sociology studies illustrate the procedures, pitfalls, and extensions of CFA methodology. The text shows how to formulate, program, and interpret CFA models using popular latent variable software packages (LISREL, Mplus, EQS, SAS/CALIS); understand the similarities and differences between CFA and exploratory factor analysis (EFA); and report results from a CFA study. It is filled with useful advice and tables that outline the procedures. The companion website offers data and program syntax files for most of the research examples, as well as links to CFA-related resources. New to This Edition *Updated throughout to incorporate important developments in latent variable modeling. *Chapter on Bayesian CFA and multilevel measurement models. *Addresses new topics (with examples): exploratory structural equation modeling, bifactor analysis, measurement invariance evaluation with categorical indicators, and a new method for scaling latent variables. *Utilizes the latest versions of major latent variable software packages--

Objective To explore the application of the Structure Equation Model on validity assessment of the evaluation indicator system,we studied Medical Capital Development Fund projects in 2007 to evaluate their construct validity.Methods Part of the evaluation results (1200 items) were divided into two parts randomly.PART1 was used for Exploratory Factor Analysis (EFA) to explore the Indicator System' structure,and formed model F1.Confirmatory Factor Analysis (CFA) was used to assess the construct validity of model F1.With considerations of Modification Index(MI) and reality,F1 was modified into F3.At last,we take PART2 as the sample to confirm F3.Result The results of model F3 fit indices showed a better fit.All the path coefficients,estimated load factors and other parameters were statistically significant.Conclusion Structural Equation Model is professional,reliable and satisfactory for the evaluation of indicator system's contract validity. Key words: Structural equation model; Indicator system; Exploratory factor analysi; Confirmatory factor analysis

The fuel economy of a vehicle is known to be highly dependent on its average speed during driving, with a maximum in fuel economy being present at around 40-50mph. Comparatively little work has been done on the investigation of how fuel economy varies for the same average speed. In this paper we report a theoretical simulation study of nine drive cycles each having the same average speed. The fuel economy for a typical C-segment vehicle varied from 37.3 to 74.4mpg for the nine drive cycles studied. Of the various metrics considered, standard deviation of speed, appears to be the most promising to explain the range of fuel economy. The results have important implications for transport planners, local authorities and national governments who rely on emissions predictions derived from vehicle average speeds. This research highlights the issues associated with this approach.

Aim - the purpose of this study is to examine the role of employee rewards, recognition, and job-related stress towards employee performance considering the mediating role of perceived organizational support in the call-centers located in Lahore, Pakistan. Design - The data has been gathered through the survey method of the questionnaire. A simple random sampling technique is used for this study. Confirmatory factor analysis (CFA) and structure equation modeling (SEM) techniques have been used for statistical analysis. Findings - Results showed that employee rewards and recognition have a significant and positive effect on employee performance whereas job stress has a significant and negative effect on employee performance. Findings also revealed that perceived organizational support significantly and fully mediates the relationship between employee rewards, recognition, and job stress and employee performance. So this study puts light on crucial factors that lead to better employee performance. Implications - Employee rewards and recognition play an important role in overall employee performance. If the companies will not take it seriously, then it might lead to negative consequences. On the other hand, job stress also plays a vital role in employee performance.

INTRODUCTION: Influencer marketing has evolved from the Pre-internet era to the era of advanced technology and artificial intelligence (Dwivedi et. al 2021) from the perspective of the larger evolution of marketing. PURPOSE: This paper aims to investigate the impact of influencer credibility, i.e., trustworthiness, attractiveness, expertise, and Similarity, on consumer purchase intention. RESEARCH METHODOLOGY: The data from 179 respondents were collected using a structured questionnaire through a convenient sampling method and gathered data were analysed through (CFA) Confirmatory factor analysis and Structure Equation Modelling using SPSS Amos version 23. FINDINGS: The Result demonstrated that attractiveness, expertise, and similarity have a significant positive correlation with intent to purchase. H1 (Attractiveness), H3(expertise), and H4(similarity) were accepted. On the other hand, trustworthiness did not have a significant positive correlation with the intent to purchase. Therefore, H2(trustworthiness) was not supported. The trustworthiness did not impact the purchase intention, and H2 was rejected

The aim of this research is to assess the validity of the concept and the determination of the reliability of the training environment scale, to test the contribution of each element and indicator in reflecting training environment variables, and to evaluate the extent to which the theoretical model of the training environment corresponds with empirical data. The sample for this research was 224 people with a sampling technique using probability sampling. The sample consisted of 132 male athletes and 92 female athletes, sample characteristics of 128 samples aged 12 - 15 years and 96 samples aged 16 - 18 years. The data collection method uses a training environment scale and data analysis uses Structure Equation Modeling (SEM) with Confirmatory Factor Analysis (CFA) through the Lisrel 8.71 program. Analysis of research results shows that student athletes in Indonesia are included in the "good" category regarding the training environment. Training environment variables show valid and reliable data, formed from various aspects and indicators. Aspects related to facilities have a large role in representing the training environment, while family aspects are less prominent in describing the training environment. The measurement model of training environment variables is in line with empirical data. This research can test the harmony between the theoretical model of training environment variables and the empirical data contained in the training environment. Key words: training environment, student athlete, validity and reliability, confirmatory factor analysis

We develop a latent variable forest (LV Forest) algorithm for the estimation of latent variable scores with one or more latent variables. LV Forest estimates unbiased latent variable scores based on confirmatory factor analysis (CFA) models with ordinal and/or numerical response variables. Through parametric model restrictions paired with a nonparametric tree-based machine learning approach, LV Forest estimates latent variable scores using models that are unbiased with respect to relevant subgroups in the population. This way, estimated latent variable scores are interpretable with respect to systematic influences of covariates without being biased by these variables. By building a tree ensemble, LV Forest takes parameter heterogeneity in latent variable modeling into account to capture subgroups with both good model fit and stable parameter estimates. We apply LV Forest to simulated data with heterogeneous model parameters as well as to real large-scale survey data. We show that LV Forest improves the accuracy of score estimation if parameter heterogeneity is present.

In this paper idea is to use the Structure Equation Model approach to the model organizational structure of local governments. The hypothesis is that a complex hierarchical model can be used for improving efficiency in the public sector. The final model with satisfactory parameters showed that this is possible. Process assumed identification of an optimal set of questions as directly measured variables. Latent variables are indirectly measured by a certain set of directly measured variables. The iterative approach filters the optimal set of the directly measured variable and the optimal setting of relations between latent variables. Latent variables are organized in two levels. Certain hierarchical seating is assumed according to the relevant literature. This is confirmed with the applied structural equation model that is further used for the confirmatory factor analysis. Measured variables that are determined according to the questioner are used to extract those factors, where relations between those factors have been analyzed. Fact that organizational cohesiveness has the highest loading of 0.881, gives a conditional conclusion that for group management as whole, this is the key factor.

Previous research on the construct validity of assessment center (AC) ratings has usually struggled to find support for dimension factors as an underlying source of variance of these ratings. Confirmatory factor analysis (CFA) remains the most widely used method to specify and validate the internal structure of AC ratings. However, the research support for dimension effects in AC ratings remains mixed. In addition, competing CFA models (e.g., correlated dimensions-correlated exercises models) are often plagued by non-convergence and estimation problems. Recently, it has been proposed that increasing the number of indicators per dimension and exercise combination might help to find support for dimension factors, in addition to exercise factors, in CFAs of AC ratings. Furthermore, it was also suggested that the increased ratio of indicators to dimensions may also solve some of the methodological problems associated with CFA models used to model AC ratings. However, in this research it remained unclear whether the support for dimension factors was solely due to the use of a larger indicator-dimension ratio or due to parceling that combines several behavioral indicators per dimension and exercise combination into more reliable measures of the targeted dimension. These are important empirical questions that have been left unanswered in the literature but can be potentially meaningful in seeking more balanced support for dimension effects in AC research. Using data from N = 213 participants from a 1-day AC, we aimed to investigate the impact of using different indicator-dimension ratios when specifying CFA models of AC ratings. Therefore, we investigated the impact of using different indicator-dimension ratios in the form of item parcels with data from an operational AC. On average, using three parcels eventually led to support for dimension factors in CFAs. However, exercise-based CFA models still performed better than dimension-based models. Thus, the present results point out potential limits concerning the generalizability of recent results that provided support for dimension factors in ACs.

Infant sleep problems have been associated with a myriad of adverse child and parent outcomes, yet whether these problems may pose a risk for parents on the road has received little research attention. This study sought to test whether mothers of infants with insomnia are at an elevated risk for vehicular crashes, by comparing their objectively measured driving performance with that of mothers of well-sleeping infants and with that of women without children. Fifty-four women from these three groups completed a simulated driving task. Outcome measures included standard deviation of lateral position, number of lane crossings, standard deviation of speed, average speed and maximum speed. Women additionally reported on their driving behaviour using the Driving Behaviour Questionnaire, and on sleep, sleepiness and insomnia symptoms using 7-day sleep diaries and questionnaires. Mothers of infants with insomnia demonstrated greater lane deviation (Wald = 9.53, p = 0.009), higher maximum speed (Wald = 6.10, p = 0.04) and poorer self-rated driving behaviour (Wald = 7.44, p = 0.02) compared with control groups. Analyses also indicated that driving performance in mothers of infants with insomnia tended to be poorer relative to control groups with the progression of time on task. While further research is needed to assess the scope of these effects, our findings suggest that parents, healthcare providers and policymakers should be aware of the potential consequences of infant sleep problems on road safety, and collaborate to establish strategies to mitigate these risks.

This article focuses on dimensions of national ethos among educated youths in Malaysia by developing a confirmatory factor analysis (CFA) model. The main objective is to evaluate national ethos dimensions and develop a CFA model of the national ethos formation. These objectives are achieved by applying four main dimensions to confirm the formation of national ethos: values, feelings and spirit, beliefs and identity dimensions. The study was conducted using a quantitative approach through survey techniques involving 431 students of Universiti Putra Malaysia, comprising 241 Malay and 190 Chinese youth groups. The data analysis used the SEM-AMOS approach to develop a CFA model of the national ethos. The results confirm national ethos of educated youth is formed through the four main dimensions measured. The CFA model has achieved a good level of compatibility based on the set indicators (CMIN = 600.947, DF = 115, CMIN / DF = 5.226, p = 0.000, SRMR = 0.0937, RMSEA = 0.099, CFI = 0.927 and PNFI = 0.771). This study implies a CFA model as a new approach in national ethos formation among educated youths in Malaysia. A future evaluation of a non-educated youth group should also be implemented to assure that this model becomes a holistic model.

There is no information in Peru on the prevalence of mental health problems associated with COVID-19 in older adults. In this sense, the aim of the study was to gather evidence on the factor structure, criterion-related validity, and reliability of the Spanish version of the Fear of COVID-19 Scale (FCV-19S) in this population. The participants were 400 older adults (mean age = 68.04, SD = 6.41), who were administered the Fear of COVID-19 Scale, Revised Mental Health Inventory-5, Patient Health Questionnaire-2 items, and Generalized Anxiety Disorder Scale 2 items. Structural equation models were estimated, specifically confirmatory factor analysis (CFA), bifactor CFA, and structural models with latent variables (SEM). Internal consistency was estimated with composite reliability indexes (CRI) and omega coefficients. A bifactor model with both a general factor underlying all items plus a specific factor underlying items 1, 2, 4, and 5 representing the emotional response to COVID better represents the factor structure of the scale. This structure had adequate fit and good reliability, and additionally fear of COVID had a large effect on mental health. In general, women had more fear than men, having more information on COVID was associated to more fear, while having family or friends affected by COVID did not related to fear of the virus. The Spanish version of the Fear of COVID-19 Scale presents evidence of validity and reliability to assess fear of COVID-19 in the Peruvian older adult population.

Identifying strategies to improve PANSS based dimensional models in schizophrenia: Accounting for multilevel structure, Bayesian model and clinical staging