Safety in Indian Coal Mines – Where Do We Go from Here

2012 ◽  
Vol 2 (1) ◽  
Author(s):  
Partha Sarathi Paul
Keyword(s):  
Structural Equation Modeling ◽  
Structural Equation ◽  
Oil And Gas ◽  
Mining Industry ◽  
Oil And Gas Industry ◽  
Mine Safety ◽  
Equation Modeling ◽  
Data Set ◽  
Wide Range ◽  
Technical Factors

The 20th century has experienced a considerable amount of success in coal mine safety in India. The mining industry has for many years focused on injury prevention at the workplace through procedures and training, and has achieved considerable success. However, the statistics on major accident events such as fatalities and reportable incidents has not shown the corresponding levels of improvement. In the area of major hazards control, the mining industry approach has emphasized mainly on past experiences and lessons learnt, while other high hazard industries such as the chemical process industry and oil and gas industry have taken system safety techniques to new highs. A literature review on quantitative analysis of mine safely studies revealed that numerous investigators explored a wide- range of techniques, including the investigation of bivariate and multivariate statistical models. It is inferred that the use of these quantitative techniques can provide a new direction of research in mine-safety studies. The important aspect, which was explored in this study through structural equation modeling, is the sequential interrelationships amongst the personal, social, and technical factors leading to accident/injury causation. Interestingly, the accidentinvolved workers are more job stressed, more job dissatisfied and hence, less job involved and often get bored with their jobs. The level of dissatisfaction in mines is quite expected. Further research should be performed using national data set so that the findings can be generalized to all segments of the mining industry.   Keywords - Mine Safety, Quantitative Risk Analysis, Structural Equation Modeling, Personal, Social and Technical Factors

Childhood Intelligence Predicts Adult Trait Openness

2016 ◽  
Vol 37 (2) ◽  
pp. 105-111 ◽  
Author(s):  
Adrian Furnham ◽  
Helen Cheng
Keyword(s):  
Structural Equation Modeling ◽  
Structural Equation ◽  
Social Background ◽  
Study Data ◽  
Equation Modeling ◽  
Direct Effects ◽  
Data Set ◽  
National Child Development Study ◽  
Nationally Representative ◽  
The Uk

Abstract. This study used a longitudinal data set of 5,672 adults followed for 50 years to determine the factors that influence adult trait Openness-to-Experience. In a large, nationally representative sample in the UK (the National Child Development Study), data were collected at birth, in childhood (age 11), adolescence (age 16), and adulthood (ages 33, 42, and 50) to examine the effects of family social background, childhood intelligence, school motivation during adolescence, education, and occupation on the personality trait Openness assessed at age 50 years. Structural equation modeling showed that parental social status, childhood intelligence, school motivation, education, and occupation all had modest, but direct, effects on trait Openness, among which childhood intelligence was the strongest predictor. Gender was not significantly associated with trait Openness. Limitations and implications of the study are discussed.

scholarly journals The reasoned action approach applied to health behavior: Role of past behavior and test of some key moderators using meta-analytic structural equation modeling

2018 ◽  
Author(s):  
Martin S Hagger ◽  
Juho Polet ◽  
Taru Lintunen
Keyword(s):  
Structural Equation Modeling ◽  
Health Behavior ◽  
Indirect Effects ◽  
Structural Equation ◽  
Social Cognitive ◽  
Reasoned Action ◽  
Equation Modeling ◽  
Reasoned Action Approach ◽  
Data Set ◽  
Past Behavior

Rationale: The reasoned action approach (RAA) is a social cognitive model that outlines the determinants of intentional behavior. Primary and meta-analytic studies support RAA predictions in multiple health behaviors. However, including past behavior as a predictor in the RAA may attenuate model effects. Direct effects of past behavior on behavior may reflect non-conscious processes while indirect effects of past behavior through social cognitive variables may represent reasoned processes. Objective: The present study extended a previous meta-analysis of the RAA by including effects of past behavior. The analysis also tested effects of candidate moderators of model predictions: behavioral frequency, behavior type, and measurement lag.Method: We augmented a previous meta-analytic data set with correlations between model constructs and past behavior. We tested RAA models that included and excluded past behavior using meta-analytic structural equation modeling and compared the effects. Separate models were estimated in studies on high and low frequency behaviors, studies on different types of behavior, and studies with longer and shorter measurement lag.Results: Including past behavior attenuated model effects, particularly the direct effect of intentions on behavior, and indirect effects of experiential attitudes, descriptive norms, and capacity on behavior through intentions. Moderator analyses revealed larger intention-behavior and past behavior-behavior effects in high frequency studies, but the differences were not significant. No other notable moderator effects were observed.Conclusion: Findings indicate a prominent role for habitual processes in determining health behavior and inclusion of past behavior in RAA tests is important to yield precise estimates of model effects.

The Practice of Structural Equation Modeling

2014 ◽  
pp. 2521-2530
Author(s):  
Ho Yin Wong ◽  
Jason Sit ◽  
Jia-Yi Hung
Keyword(s):  
Structural Equation Modeling ◽  
Statistical Modeling ◽  
Structural Equation ◽  
Sem Analysis ◽  
Modeling Technique ◽  
Equation Modeling ◽  
Business Analytics ◽  
Wide Range ◽  
Stage Process ◽  
Point Of Reference

The aim of this chapter is to act as a point of reference for researchers, especially new users, who would like to use structural equation modeling (SEM) to perform business analytics. SEM is a multipurpose statistical modeling technique that can be applied in various business disciplines and is a useful tool to conduct business analytics. This chapter explains some common terminologies in the SEM literature and states general steps of performing SEM analysis along with an integration of the wide range of views and empirical findings on the topic. The chapter discusses the assumptions of SEM analysis, followed by a three-stage process of conducting, which includes assumptions, specification, evaluation, and modifications.

scholarly journals The Influence of Leader Encouragement of Creativity on Innovation Speed: Findings from SEM and fsQCA

2019 ◽  
Vol 11 (9) ◽  
pp. 2693 ◽  
Author(s):  
Cong Cheng ◽  
Liebing Cao ◽  
Huihui Zhong ◽  
Yining He ◽  
Jiahong Qian
Keyword(s):  
Structural Equation Modeling ◽  
Qualitative Analysis ◽  
Fuzzy Set ◽  
Creative Process ◽  
Structural Equation ◽  
Sem Analysis ◽  
Equation Modeling ◽  
Organizational Ambidexterity ◽  
Data Set ◽  
The Impact

Adopting the empowerment perspective of leadership, this study proposes and examines the mediating model that leader encouragement of creativity affects innovation speed through strengthening employees’ engagement in the creative process. Using a sample of 245 participants in China, the results from structural equation modeling (SEM) suggest that the impact of leader encouragement of creativity on innovation speed is significantly mediated by creative process engagement, and positively moderated by organizational ambidexterity at the same time. Additionally, the results from fuzzy-set comparative qualitative analysis (fsQCA) with the same data set reveal that the aforementioned factors have a holistic effect on enhancing innovation speed. The results of fsQCA reinforce and refine the findings of the SEM analysis concerning the limits and conditions for how leader encouragement of creativity affects innovation speed.

Is Self-Compassion Universal? Support for the Measurement Invariance of the Self-Compassion Scale Across Populations

Assessment ◽  
2020 ◽  
Vol 28 (1) ◽  
pp. 169-185 ◽  
Author(s):  
István Tóth-Király ◽  
Kristin D. Neff
Keyword(s):  
Structural Equation Modeling ◽  
Measurement Invariance ◽  
Structural Equation ◽  
Secondary Data ◽  
The Self ◽  
Equation Modeling ◽  
Exploratory Structural Equation Modeling ◽  
Self Compassion ◽  
Wide Range ◽  
Compassion Scale

The Self-Compassion Scale (SCS) is a widely used measure to assess the trait of self-compassion, and, so far, it has been implicitly assumed that it functions the same way across different groups. This assumption needs to be explicitly tested to ascertain that no measurement biases exist. To address this issue, the present study sought to systematically examine the generalizability of the bifactor exploratory structural equation modeling operationalization of the SCS via tests of measurement invariance across a wide range of populations, varying according to features such as student or community status, gender, age, and language. Secondary data were used for this purpose and included a total of 18 samples and 12 different languages ( N = 10,997). Multigroup analyses revealed evidence for the configural, weak, strong, strict, and latent variance–covariance of the bifactor exploratory structural equation modeling operationalization of the SCS across different groups. These findings suggest that the SCS provides an assessment of self-compassion that is psychometrically equivalent across groups. However, findings comparing latent mean invariance found that levels of self-compassion differed across groups.

A Comparison of Accuracy Based on Data Imputation between Unconstrained Structural Equation Modeling and Weighted Least Squares Regression

2011 ◽  
Vol 130-134 ◽  
pp. 730-733
Author(s):  
Narong Phothi ◽  
Somchai Prakancharoen
Keyword(s):  
Structural Equation Modeling ◽  
Least Squares ◽  
Structural Equation ◽  
Weighted Least Squares ◽  
Equation Modeling ◽  
Data Imputation ◽  
Least Squares Regression ◽  
Waveform Generator ◽  
Data Set ◽  
Testing Group

This research proposed a comparison of accuracy based on data imputation between unconstrained structural equation modeling (Uncon-SEM) and weighted least squares (WLS) regression. This model is developed by University of California, Irvine (UCI) and measured using the mean magnitude of relative error (MMRE). Experimental data set is created using the waveform generator that contained 21 indicators (1,200 samples) and divided into two groups (1,000 for training and 200 for testing groups). In fact, training group was analyzed by three main factors (F1, F2, and F3) for creating the models. The result of the experiment show MMRE of Uncon-SEM method based on the testing group is 34.29% (accuracy is 65.71%). In contrast, WLS method produces MMRE for testing group is 55.54% (accuracy is 44.46%). So, Uncon-SEM is high accuracy and MMRE than WLS method that is 21.25%.

The impact of leader-member exchange on knowledge sharing and performance

2016 ◽  
Vol 7 (3) ◽  
pp. 289-305 ◽  
Author(s):  
Maryam Sharifkhani ◽  
Javad Khazaei Pool ◽  
Sobhan Asian
Keyword(s):  
Knowledge Sharing ◽  
Structural Equation ◽  
Oil And Gas ◽  
Oil And Gas Industry ◽  
Equation Modeling ◽  
Leader Member Exchange ◽  
Content Type ◽  
Member Exchange ◽  
And Performance ◽  
The Impact

Purpose The purpose of this study is to investigate the relationship between leader-member exchange (LMX), knowledge sharing and performance. Design/methodology/approach To reach the objective, a sample was used which consisted of some oil and gas companies in Singapore with experience in balanced scorecard (BSC) perspectives. The partial least-squares structural equation modeling approach was used to test the model. Findings The results showed that LMX affects knowledge sharing and performance positively and meaningfully. Moreover, knowledge sharing affects performance. Originality/value An integrated model of LMX, knowledge sharing and performance was tested in the oil and gas industry. The combination of a developed country context and the significance of LMX enhances the contextual contribution of the paper.

A New Dimension of Organizational Justice: Procedural Voice

2009 ◽  
Vol 105 (2) ◽  
pp. 411-426 ◽  
Author(s):  
Denise Jepsen ◽  
John Rodwell
Keyword(s):  
Structural Equation Modeling ◽  
Organizational Justice ◽  
Latent Variable ◽  
Structural Equation ◽  
Factor Model ◽  
Model Fit ◽  
Equation Modeling ◽  
Wide Range

Dimensionality of the Colquitt justice measures was investigated across a wide range of service occupations. Structural equation modeling of data from 410 survey respondents found support for the 4-factor model of justice (procedural, distributive, interpersonal, and informational), although significant improvement of model fit was obtained by including a new latent variable, “procedural voice,” which taps employees' desire to express their views and feelings and influence results. The model was confirmed in a second sample ( N = 505) in the same organization six months later.

scholarly journals The Effect of Weighting Data on the Goodness of Fit Indicators of the Six Sigma Structural Equation Modeling

2021 ◽  
Vol 2 (2) ◽  
pp. 36-49
Author(s):  
Mohammed Al-Ghmadi ◽  
Ezz Abdelfattah ◽  
Ahmed Ezz
Keyword(s):  
Structural Equation Modeling ◽  
Covariance Matrix ◽  
Six Sigma ◽  
Structural Equation ◽  
Goodness Of Fit ◽  
Estimation Method ◽  
Equation Modeling ◽  
Rank Test ◽  
Data Set ◽  
Variance Covariance Matrix

The main core of Structural Equation Modeling (SEM) is the parameter estimation process. This process implies a variance-covariance matrix Σ that is close as possible to the sample variance-covariance matrix of data input (S). The six Sigma survey uses ordinal (rank) values from 1 to 5. There are several weighted correlation coefficients that overcome the problems of assigning equal weights to each rank and provide a locally most powerful rank test. This paper extends the SEM estimation method by adding the ordinal weighted techniques to enhance the goodness of fit indicators.  A two data sets of the Six Sigma model with different statistics properties are used to investigate this idea.   The weight 1.3 enhances the goodness of fit indicators with data set that has a negative skewness, and the weight 0.7 enhances the goodness of fit indicators with data set that has a positive skewness through treating the top-rankings.

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