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.

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 Pathways to Implementation: Evidence on How Participation in Environmental Governance Impacts on Environmental Outcomes

2019 ◽  
Vol 30 (3) ◽  
pp. 383-399 ◽  
Author(s):  
Nicolas W Jager ◽  
Jens Newig ◽  
Edward Challies ◽  
Elisa Kochskämper
Keyword(s):  
Decision Making ◽  
Latent Variables ◽  
Structural Equation ◽  
Environmental Governance ◽  
Social Outcomes ◽  
Equation Modeling ◽  
Environmental Standard ◽  
Stakeholder Perspectives ◽  
Decision Making Processes ◽  
Modes Of Governance

Abstract There is much enthusiasm among scholars and public administrators for participatory and collaborative modes of governance as a means to tackle contemporary environmental problems. Participatory and collaborative approaches are expected to both enhance the environmental standard of the outputs of decision-making processes and improve the implementation of these outputs. In this article, we draw on a database of 307 coded published cases of public environmental decision-making to identify key pathways via which participation fosters effective environmental governance. We develop a conceptual model of the hypothesized relationship between participation, environmental outputs, and implementation, mediated by intermediate (social) outcomes such as social learning or trust building. Testing these assumptions through structural equation modeling and exploratory factor analysis, we find a generally positive effect of participation on the environmental standard of governance outputs, in particular where communication intensity is high and where participants are delegated decision-making power. Moreover, we identify two latent variables—convergence of stakeholder perspectives and stakeholder capacity building—to mediate this relationship. Our findings point to a need for treating complex and multifaceted phenomena such as participation in a nuanced manner, and to pay attention to how particular mechanisms work to foster a range of social outcomes and to secure more environmentally effective outputs and their implementation.

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.

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 The Effect of Smog-Related Factors on Korean Domestic Tourists’ Decision-Making Process

2020 ◽  
Vol 17 (10) ◽  
pp. 3706
Author(s):  
JunHui Wang ◽  
JooHyang Kim ◽  
JiHyo Moon ◽  
HakJun Song
Keyword(s):  
Decision Making ◽  
Latent Variables ◽  
Behavioral Intentions ◽  
Structural Equation ◽  
Equation Modeling ◽  
Perceived Behavioral Control ◽  
Protection Motivation ◽  
Extended Model ◽  
Related Factors ◽  
Goal Directed Behavior

The present study aims to explore Korean domestic tourists’ decision-making processes by utilizing an extended model of goal-directed behavior (EMGB) as a theoretical framework. Integrating government policy (PLY) and protection motivation for smog (PMS) with the original model of goal-directed behavior (MGB) makes it easier to better understand the formation process of tourists’ behavioral intentions for domestic travel. Structural equation modeling (SEM) is employed to identify the structural relationships among the latent variables. The results of the EMGB indicated that desire had the strongest effect on the behavioral intention of tourists to travel domestically; positive anticipated emotion is the main source of desire, followed by negative anticipated emotion. Government PLY on smog has a significant, positive and indirect effect on behavioral intentions of domestic or potential tourists through the protection motive theory. We found that desires are verified as a determinant of the behavioral intention’s formation, more significant than that of perceived behavioral control, frequency of past behavior and protection motivation. In addition, this study offers theoretical and practical suggestions.

Judgments of acquisition value and transaction value

2019 ◽  
Vol 12 (3) ◽  
pp. 389-410
Author(s):  
Nitin Soni ◽  
Jagrook Dawra
Keyword(s):  
Decision Making ◽  
Factor Analysis ◽  
Structural Equation ◽  
Structural Model ◽  
Equation Modeling ◽  
Behavioral Pricing ◽  
Content Type ◽  
Decision Making Styles ◽  
Transaction Value ◽  
The Impact

Purpose An open question of behavioral pricing literature is: What are the factors which influence consumers’ judgments of acquisition value and transaction value? An important framework to explain consumers’ shopping and purchase decisions is their decision-making styles. This paper aims to examine the influence of consumers’ decision-making styles, that is, perfectionistic high-quality conscious, brand conscious-price equals quality, novelty-fashion conscious, recreational-hedonistic, price conscious-value for money, impulsive-careless, habitual-brand loyal and confused by overchoice on their judgments of acquisition value and transaction value. Design/methodology/approach From the literature, a conceptual framework was formulated. Data was collected from a survey of 304 respondents. The measurement model was tested using exploratory factor analysis and confirmatory factor analysis. The structural model was tested using structural equation modeling. Findings The consumers’ judgments of acquisition value and transaction value vary with their decision-making styles. The measurement and structural models exhibited good fit, and 12 of the 16 proposed hypotheses were found to be significant. Research limitations/implications The respondents for this research study were urban and postgraduate students. Practical implications The results of this study can help managers personalize their promotional offers and market offerings targeted at consumers with different decision-making styles. Originality/value Behavioral pricing literature has not convincingly shown that consumers make the judgments of the two values, acquisition value and transaction value, in a purchase scenario. There is limited literature on the impact of decision-making styles on the marketing variables. The results of this study contribute to the literature by showing that consumers make the judgments of these two values, and these judgments vary with their decision-making styles. Also, this is one of only a few studies to examine the two components of the purchase value in an Indian context.

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.

Factors Related to Data-Based Decision-Making: Examining Experience, Professional Development, and the Mediating Effect of Confidence on Teacher Graph Literacy

2020 ◽  
pp. 002221942097218
Author(s):  
Eric L. Oslund ◽  
Amy M. Elleman ◽  
Kelli Wallace
Keyword(s):  
Professional Development ◽  
Decision Making ◽  
Latent Variables ◽  
Structural Equation ◽  
Mediating Effect ◽  
Student Response ◽  
Equation Modeling ◽  
Training Experience ◽  
The Impact ◽  
Data Based Decision Making

In tiered instructional systems (Response to Intervention [RTI]/Multitier System of Supports [MTSS]) that rely on ongoing assessment of students at risk of experiencing academic difficulties, the ability to make informed decisions using student data is critical for student learning. Prior research has demonstrated that, on average, teachers have difficulty analyzing and interpreting student progress-monitoring (PM) data presented graphically (i.e., graph literacy). This study examines the impact that teacher training, experience, and confidence have on teacher graph literacy, using structural equation modeling. Data were gathered from a nationally representative sample of 309 teachers and included latent variables related to their experience (e.g., years teaching, years working with RTI), training (e.g., hours of data-based decision-making [DBDM] professional development), and confidence (e.g., confidence in interpreting data, confidence in determining student response) as well as data-based decision-making skills on a graph literacy assessment. Findings indicate that latent experience and confidence factors predicted graph literacy but training did not. Furthermore, training increased teacher confidence but experience did not. Finally, confidence did not mediate the effect of experience or training on graph literacy.

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.

scholarly journals DEVELOPING SOCIAL INTELLIGENCE SCALE INSTRUMENTS FOR VOCATIONAL SCHOOL (SMK) STUDENTS

2019 ◽  
Vol 5 (2) ◽  
pp. 145-154
Author(s):  
Awaluddin Tjalla
Keyword(s):  
Factor Analysis ◽  
Structural Equation Modeling ◽  
Confirmatory Factor Analysis ◽  
Structural Equation ◽  
Social Intelligence ◽  
Vocational School ◽  
Equation Modeling ◽  
Intelligence Scale ◽  
Confirmatory Factor ◽  
Total Gain

AbstractThis study aims to develop scale instruments that can be used to identify potentials in relation to the readiness of students to enter the workforce with very high commissioning characteristics. The study was conducted at SMK Negeri 57 South Jakarta, using a simple random technique for 135 students. Data were analyzed using a Confirmatory Factor Analysis (CFA) with the SEM (Structural Equation Modeling) method. The findings of the study show that: (1) Steps to develop a valid and reliable social intelligence scale instrument are; synthesis, construct, type of instrument, instrument lattice, instrument items, theoretical validation, trial, calculate validity and reliability, instrument assembly. (2) The dimensions and indicators underlying the concept of social intelligence scale are; component of social awareness (dimensions of basic empathy, dimensions of alignment, empathic accuracy, social understanding), components of social facilities (dimensions of synchronization, self-appearance, influence, concern). (3) Calculation of instrument validity using total gain correlation formula, Pearson product moment with a significance level of 0.05; r table = 0.176. (4) Instrument reliability is α = 0.858.AbstrakPenelitian ini bertujuan untuk mengembangkan instrumen skala yang dapat digunakan untuk mengidentifikasi potensi dalam relasinya dengan kesiapan siswa untuk memasuki dunia kerja dengan karakteristik commissioning yang sangat tinggi. Penelitian dilakukan di SMK Negeri 57 Jakarta Selatan, menggunakan teknik acak sederhana untuk 135 siswa. Data dianalisis menggunakan Confirmatory Factor Analysis (CFA) dengan metode SEM (Structural Equation Modeling). Temuan penelitian menunjukkan bahwa: (1) Langkah-langkah untuk mengembangkan instrumen skala kecerdasan sosial yang valid dan dapat diandalkan adalah; sintesis, konstruk, jenis instrumen, instrumen kisi, item instrumen, validasi teoretis, percobaan, menghitung validitas dan reliabilitas, perakitan instrumen. (2) Dimensi dan indikator yang mendasari konsep skala kecerdasan sosial adalah; komponen kesadaran sosial (dimensi empati dasar, dimensi keselarasan, akurasi empatik, pemahaman sosial), komponen fasilitas sosial (dimensi sinkronisasi, penampilan diri, pengaruh, kepedulian). (3) Perhitungan validitas instrumen menggunakan rumus korelasi total gain, Pearson product moment dengan tingkat signifikansi 0,05; r tabel = 0,176. (4) Keandalan instrumen adalah α = 0,858.How to Cite : Tjalla, A. (2018).  Developing Social Intelligence Scale Instruments for Vocational School (SMK) Students. TARBIYA: Journal of Education in Muslim Society, 5(2), 145-154. doi:10.15408/tjems.v5i2.10622.

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