scholarly journals Infectious Complications Following Radical Cystectomy: A Structural Equation Model

2022 ◽  
Author(s):  
Steven Scott Shipman ◽  
Erin Michelle Buchanan ◽  
Adam Reese ◽  
Kayla Nicole Jordan
Keyword(s):  
Factor Analysis ◽  
Predictive Model ◽  
Latent Variables ◽  
Structural Equation ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Infectious Complications ◽  
Equation Modeling ◽  
Improvement Program ◽  
Acs Nsqip

Objective: To use factor analysis to structure items from the American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP) into latent variables associated with infectious complications, and then to use structural equation modeling (SEM) to organize those latent variables into a predictive model of POIC. Predictive models of post-operative infectious complications (POIC) have traditionally relied upon logistic regression and inconsistent variable groupings. A more standardized approach to a valid construct would allow for more unitary research and improved clinical decision making. Materials and Methods: The study evaluated data from 1580 recipients of radical cystectomies in the ACS NSQIP PUF 2013 database. Pre-operative, operative, and post-operative data were analyzed. Exploratory Factor Analysis (EFA) and theory-based selection were used to create latent variables for a predictive model of POIC which was analyzed with structural equation modeling.Results: After reducing unrelated variables using EFA, two latent variables successfully predicted POIC, a Global Health Variable (Dyspnea, COPD, Diabetes, and Hypertension) and a Proximal Pre-operative Infectious Comorbidity Variable (pre-operative transfusion, pre-operative wound infection, and pre-operative sepsis). The final model produced was well-fit and suggest two unique pathway indicators for understanding which patients are at higher risk for POIC.Conclusion: Discerning the most significant items and their role in the POIC model offer clinical insight into adverse events and new considerations into the prevention of such events. Patients endorsing multiple items in the model may benefit from pre-operative optimization of modifiable conditions and closer post-operative surveillance.

A structural equation model for big data adoption in the healthcare supply chain

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Dindayal Agrawal ◽  
Jitender Madaan
Keyword(s):  
Factor Analysis ◽  
Big Data ◽  
Supply Chain ◽  
Latent Variables ◽  
Structural Equation ◽  
Equation Model ◽  
Equation Modeling ◽  
Data Governance ◽  
Social Perspective ◽  
Content Type

PurposeThe purpose of this study is to examine the barriers to the implementation of big data (BD) in the healthcare supply chain (HSC).Design/methodology/approachFirst, the barriers concerning BD adoption in the HSC were found by conducting a detailed literature survey and with the expert's opinion. Then the exploratory factor analysis (EFA) was employed to categorize the barriers. The obtained results are verified using the confirmatory factor analysis (CFA). Structural equation modeling (SEM) analysis gives the path diagram representing the interrelationship between latent variables and observed variables.FindingsThe segregation of 13 barriers into three categories, namely “data governance perspective,” “technological and expertise perspective,” and “organizational and social perspective,” is performed using EFA. Three hypotheses are tested, and all are accepted. It can be concluded that the “data governance perspective” is positively related to “technological and expertise perspective” and “organizational and social perspective” factors. Also, the “technological and expertise perspective” is positively related to “organizational and social perspective.”Research limitations/implicationsIn literature, very few studies have been performed on finding the barriers to BD adoption in the HSC. The systematic methodology and statistical verification applied in this study empowers the healthcare organizations and policymakers in further decision-making.Originality/valueThis paper is first of its kind to adopt an approach to classify barriers to BD implementation in the HSC into three distinct perspectives.

scholarly journals Good School Governance: An Approach to Principal’s Decision-Making Quality in Indonesian Vocational School

2021 ◽  
Vol 6 (4) ◽  
pp. 796-831
Author(s):  
Didi Supriadi ◽  
◽  
Husaini Usman ◽  
Abdul Jabar ◽  
Ima Widyastuti
Keyword(s):  
Decision Making ◽  
Factor Analysis ◽  
Latent Variables ◽  
Structural Equation ◽  
School Governance ◽  
Vocational School ◽  
Equation Modeling ◽  
Vice Principals ◽  
Delegation Of Authority ◽  
Good School

The purpose of this research is to examine the model of good school governance and to establish the correlation between good school governance and the principal’s decision making in Indonesian vocational school contexts. The samples of the present quantitative descriptive study are the vocational school principals, vice-principals, and teachers by considering the representation of all provinces in Indonesia. The data were gathered from a structured questionnaire survey of 838 respondents. The factor analysis was applied to bring out the latent variables representing the attributes, and later, the causality between these variables was established using structural equation modeling (SEM). The result of confirmatory factor analysis shown that good school governance was constructed by six principles, namely; transparency, accountability, responsibility, autonomy, fairness, and participation. Supported by empirical evidence, good school governance has have impacted positively on the quality of the principal’s decision making. The research has affirmed that good school governance facilitates the participation of teachers and educational staff in the decision-making process. Moreover, good school governance improves a decision-making quality through the empowerment of teachers, the delegation of authority, and encouragement of shared decision-making.

Performance evaluation of higher education system amid COVID-19: a threat or an opportunity?

Kybernetes ◽  
2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Bhaskar B. Gardas ◽  
Nima Jafari Navimipour
Keyword(s):  
Higher Education ◽  
Factor Analysis ◽  
Structural Equation Modeling ◽  
Human Resource Development ◽  
Latent Variables ◽  
Structural Equation ◽  
Equation Modeling ◽  
Educational Performance ◽  
Content Type ◽  
Confirmatory Factor

PurposeCOVID-19 is moving the world towards a significant number of structural changes, and this pandemic is influencing each individual, society and industry at large. The present empirical research intends to identify the constructs (latent variables) caused mainly due to the outbreak of COVID-19 and analyze their influence on the education system's performance.Design/methodology/approachA pilot study was carried out with 105 responses to gain deeper insights into the factor structure and validate the scale. Then, the exploratory factor analysis was applied to explore five factors. Later on, the confirmatory factor analysis was employed to check the model's unidimensionality, validity and reliability. Finally, structural equation modeling (SEM) was used to explore the factors influencing educational performance.FindingsFour hypotheses were tested, out of which two were supported, i.e. “compatibility with online mode” and “new opportunities” were found to influence educational performance significantly.Practical implicationsThis investigation aims to provide vital information to the ministry of human resource development and educationists/academicians to understand the influence of the higher education system's factors. Also, it offers some strategies and plans to improve the higher educational systems performance in similar situations.Originality/valueThe previous studies did not identify and analyze the factors that influence the educational system's performance; especially, amid COVID-19 using the exploratory, confirmatory factor analyses and structural equation modeling approach.

scholarly journals Hidden Common Factors in Disaster Loss Statistics: A Case Study Analyzing the Data of Nepal

2018 ◽  
Vol 13 (6) ◽  
pp. 1032-1038
Author(s):  
Daisuke Sasaki ◽  
◽  
Kana Moriyama ◽  
Yuichi Ono
Keyword(s):  
Factor Analysis ◽  
Latent Variables ◽  
Structural Equation ◽  
Common Factors ◽  
Equation Modeling ◽  
Or Education ◽  
Path Diagram ◽  
Disaster Loss ◽  
Starting Point ◽  
Basic Services

This study aims to examine common hidden factors in disaster loss statistics and identify clues for verifying the fitness of the global targets of the Sendai Framework for Disaster Risk Reduction 2015–2030 (SFDRR) to rule countries’ effort in reducing disaster risks. In this study, we first conducted an exploratory factor analysis (EFA), followed by a confirmatory factor analysis (CFA) using structural equation modeling (SEM). As a result of the EFA, we were able to extract three factors, namely Housing, Casualties or Education, and Relocation. In the analysis of SEM, we assumed three latent variables based on the results of the EFA. The relationship between the latent and observed variables was established in a manner that conformed to the implications of the EFA. According to the SEM results, we eventually identified three latent variables, namely Housing, Education and Relocation, as hidden common factors. Based on this identification, our judgment indicates that the latent variables appeared to be related to the following global targets of SFDRR: (b) those concerning the number of affected people and (d) those concerning damages to infrastructure and disruptions to basic services. It was found that relationships between variables could be clearly illustrated by using the path diagram. This study can be considered as a good example of introducing SEM to visualize hidden common factors and their relationships in an intelligible manner. Based on the results, we propose a starting point for discussing the fitness of SFDRR’s global targets by utilizing EFA and CFA (SEM) techniques. The path diagram can indicate the extent to which the indicators contribute to global targets that will be represented as latent variables. In the end, explicit reference should be made to the material data’s limitations in the disaster loss statistics. An effort to elaborate the input data themselves must be made in the near future.

scholarly journals A systematic review of the main factors that determine agility in sport using structural equation modeling

2016 ◽  
Vol 52 (1) ◽  
pp. 115-123 ◽  
Author(s):  
Vladimir Hojka ◽  
Petr Stastny ◽  
Tomas Rehak ◽  
Artur Gołas ◽  
Aleksandra Mostowik ◽  
...  
Keyword(s):  
Factor Analysis ◽  
Structural Equation Modeling ◽  
Latent Variables ◽  
Structural Equation ◽  
Elite Athletes ◽  
Principal Component ◽  
Maximum Strength ◽  
Equation Modeling ◽  
Factorial Model ◽  
Main Factors

Abstract While tests of basic motor abilities such as speed, maximum strength or endurance are well recognized, testing of complex motor functions such as agility remains unresolved in current literature. Therefore, the aim of this review was to evaluate which main factor or factor structures quantitatively determine agility. In methodological detail, this review focused on research that explained or described the relationships between latent variables in a factorial model of agility using approaches such as principal component analysis, factor analysis and structural equation modeling. Four research studies met the defined inclusion criteria. No quantitative empirical research was found that tried to verify the quality of the whole suggested model of the main factors determining agility through the use of a structural equation modeling (SEM) approach or a confirmatory factor analysis. From the whole structure of agility, only change of direction speed (CODS) and some of its subtests were appropriately analyzed. The combination of common CODS tests is reliable and useful to estimate performance in sub-elite athletes; however, for elite athletes, CODS tests must be specific to the needs of a particular sport discipline. Sprinting and jumping tests are stronger factors for CODS than explosive strength and maximum strength tests. The authors suggest the need to verify the agility factorial model by a second generation data analysis technique such as SEM.

Simulating the Effects of Common and Specific Abilities on Test Performance: An Evaluation of Factor Analysis

2014 ◽  
Vol 57 (5) ◽  
pp. 1919-1928 ◽  
Author(s):  
Dennis J. McFarland
Keyword(s):  
Factor Analysis ◽  
Test Performance ◽  
Test Scores ◽  
Latent Variables ◽  
Structural Equation ◽  
Linear Models ◽  
Principal Component ◽  
Equation Modeling ◽  
Multiple Sources ◽  
Simulated Test

Purpose Factor analysis is a useful technique to aid in organizing multivariate data characterizing speech, language, and auditory abilities. However, knowledge of the limitations of factor analysis is essential for proper interpretation of results. The present study used simulated test scores to illustrate some characteristics of factor analysis. Method Linear models were used to simulate test scores that were determined by multiple latent variables. These simulated test scores were evaluated with principal components analysis and, in certain cases, structural equation modeling. In addition, a subset of simulated individuals characterized by poor test performance was examined. Results The number of factors recovered and their identity do not necessarily correspond to the structure of the latent variables that generated the test scores. The first principal component may represent variance from multiple uncorrelated sources. Practices such as correction or control for general cognitive ability may produce misleading results. Conclusions Inferences from the results of factor analysis should be primarily about the structure of test batteries rather than the structure of human mental abilities. Researchers and clinicians should consider multiple sources of evidence to evaluate hypotheses about the processes generating test results.

Celebrity Worship Among University Students in Malaysia

2011 ◽  
Vol 16 (4) ◽  
pp. 334-342 ◽  
Author(s):  
Viren Swami ◽  
Tomas Chamorro-Premuzic ◽  
Khairul Mastor ◽  
Fatin Hazwani Siran ◽  
Mohammad Mohsein Mohammad Said ◽  
...  
Keyword(s):  
Factor Analysis ◽  
Structural Equation Modeling ◽  
Structural Equation ◽  
Attitude Scale ◽  
Equation Modeling ◽  
Future Research ◽  
The West ◽  
Factor Solution ◽  
Movie Stars ◽  
Three Factor

The present study examined conceptual issues surrounding celebrity worship in a Malay-speaking population. In total, 512 Malay and 269 Chinese participants from Malaysia indicated who their favorite celebrity was and completed the Celebrity Attitude Scale (CAS) as well as a range of demographic items. Results showed that the majority of Malay and Chinese participants selected pop stars and movie stars as their favourite celebrities, mirroring findings in Western settings. In addition, exploratory factor analysis revealed a three-factor solution of the CAS that was consistent with previous studies conducted in the West. Structural equation modeling further revealed that participant’s age was negatively associated with celebrity worship and that self-rated attractiveness was positively associated with celebrity worship. Overall, the present results suggest that celebrity worship in Malaysia may be driven by market and media forces, and future research may well be guided by use of the CAS.

Meta-Accuracy and Perceived Reciprocity From the Perception-Meta-Perception Social Relations Model

2019 ◽  
Vol 50 (1) ◽  
pp. 24-37
Author(s):  
Ben Porter ◽  
Camilla S. Øverup ◽  
Julie A. Brunson ◽  
Paras D. Mehta
Keyword(s):  
Structural Equation Modeling ◽  
Social Relations ◽  
Latent Variables ◽  
Structural Equation ◽  
Multilevel Structural Equation Modeling ◽  
Equation Modeling ◽  
Complete Understanding ◽  
Social Relations Model ◽  
Flexible Framework ◽  
Multilevel Structural Equation

Abstract. Meta-accuracy and perceptions of reciprocity can be measured by covariances between latent variables in two social relations models examining perception and meta-perception. We propose a single unified model called the Perception-Meta-Perception Social Relations Model (PM-SRM). This model simultaneously estimates all possible parameters to provide a more complete understanding of the relationships between perception and meta-perception. We describe the components of the PM-SRM and present two pedagogical examples with code, openly available on https://osf.io/4ag5m . Using a new package in R (xxM), we estimated the model using multilevel structural equation modeling which provides an approachable and flexible framework for evaluating the PM-SRM. Further, we discuss possible expansions to the PM-SRM which can explore novel and exciting hypotheses.

scholarly journals Connecting Climate and Communicable Disease to Penta Helix Using Hierarchical Likelihood Structural Equation Modelling

Symmetry ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 657
Author(s):  
Rezzy Eko Caraka ◽  
Maengseok Noh ◽  
Rung-Ching Chen ◽  
Youngjo Lee ◽  
Prana Ugiana Gio ◽  
...  
Keyword(s):  
Latent Variables ◽  
Structural Equation ◽  
Healthy Lifestyle ◽  
Communicable Disease ◽  
Well Being ◽  
Equation Modeling ◽  
Human Beings ◽  
The Sustainable Development ◽  
Healthcare Facilities ◽  
Hierarchical Likelihood

Design: Health issues throughout the sustainable development goals have also been integrated into one ultimate goal, which helps to ensure a healthy lifestyle as well as enhances well-being for any and all human beings of all social level. Meanwhile, regarding the clime change, we may take urgent action to its impacts. Purpose: Nowadays, climate change makes it much more difficult to control the pattern of diseases transmitted and sometimes hard to prevent. In line with this, Centres for Disease Control (CDC) Taiwan grouped the spread of disease through its source in the first six main groups. Those are food or waterborne, airborne or droplet, vector-borne, sexually transmitted or blood-borne, contact transmission, and miscellaneous. According to this, academics, government, and the private sector should work together and collaborate to maintain the health issue. This article examines and connects the climate and communicable aspects towards Penta-Helix in Taiwan. Finding: In summary, we have been addressing the knowledge center on the number of private companies throughout the health care sector, the number of healthcare facilities, and the education institutions widely recognized as Penta Helix. In addition, we used hierarchical likelihood structural equation modeling (HSEMs). All the relationship variables among climate, communicable disease, and Penta Helix can be interpreted through the latent variables with GoF 79.24%.

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