scholarly journals The Relationship between Socio-Economic Development, Corruption and Health Indicators: Application of Partial Least Squares Structural Equation Modeling

2017 ◽  
Vol 5 (2) ◽  
pp. 191-206 ◽  
Author(s):  
Özlem Yorulmaz
Keyword(s):  
Economic Development ◽  
Structural Equation Modeling ◽  
Least Squares ◽  
Partial Least Squares ◽  
Structural Equation ◽  
Health Indicators ◽  
Equation Modeling ◽  
Socio Economic Development ◽  
The Relationship

scholarly journals Using Partial Least Squares Structural Equation Modeling to Measure the Moderating Effect of Gender: An Empirical Study

Mathematics ◽  
2021 ◽  
Vol 9 (24) ◽  
pp. 3150
Author(s):  
Vera Gelashvili ◽  
Juan-Gabriel Martínez-Navalón ◽  
José Ramón Saura
Keyword(s):  
Structural Equation Modeling ◽  
Least Squares ◽  
Partial Least Squares ◽  
Environmental Sustainability ◽  
Structural Equation ◽  
Three Dimensions ◽  
Equation Modeling ◽  
The Relationship ◽  
Gender Influences ◽  
Practical Implications

Over recent decades, much has been written about sustainable development and its main dimensions (environmental, social, and economic). However, to date, there is a lack of academic literature on the relationship between these dimensions of sustainability, as well as whether sustainability is moderated by an individual’s gender. To fill this gap in the literature, the aim of this study is two-fold: first, we want to empirically analyze whether there is a relationship between these dimensions and, if so, whether this relationship is positive; secondly, we aim to evaluate whether gender influences the link between these dimensions of sustainability. To this end, a sample of 411 responses was analyzed using the Using Partial Least Squares Structural Equation Modeling (PLS-SEM) methodology. The results revealed that there is a positive relationship between the three dimensions of sustainability; however, the effect of gender was observed only in the case of the relationship between social and environmental sustainability. The paper concludes with a discussion of the results and an outline of their theoretical and practical implications for industry.

Partial least squares structural equation modeling of chemistry attitude in introductory college chemistry

2018 ◽  
Vol 19 (4) ◽  
pp. 1270-1286 ◽  
Author(s):  
James Ross ◽  
Leslie Nuñez ◽  
Chinh Chu Lai
Keyword(s):  
Structural Equation Modeling ◽  
Least Squares ◽  
Partial Least Squares ◽  
Structural Equation ◽  
At Risk Students ◽  
Affective Domain ◽  
Equation Modeling ◽  
College Chemistry ◽  
The Subject ◽  
Introductory College

Students’ decisions to enter or persist in STEM courses is linked with their affective domain. The influence of factors impacting students’ affective domain in introductory college chemistry classes, such as attitude, is often overlooked by instructors, who instead focus on students’ mathematical abilities as sole predictors of academic achievement. The current academic barrier to enrollment in introductory college chemistry classes is typically a passing grade in a mathematics prerequisite class. However, mathematical ability is only a piece of the puzzle in predicting preparedness for college chemistry. Herein, students’ attitude toward the subject of chemistry was measured using the original Attitudes toward the Subject of Chemistry Inventory (ASCI). Partial least squares structural equation modeling (PLS-SEM) was used to chart and monitor the development of students’ attitude toward the subject of chemistry during an introductory college chemistry course. Results from PLS-SEM support a 3-factor (intellectual accessibility,emotional satisfaction, andinterestandutility) structure, which could signal the distinct cognitive, affective, and behavioral components of attitude, according to its theoretical tripartite framework. Evidence of a low-involvement hierarchy of attitude effect is also presented herein. This study provides a pathway for instructors to identify at-risk students, exhibiting low affective characteristics, early in a course so that academic interventions are feasible. The results presented here have implications for the design and implementation of teaching strategies geared toward optimizing student achievement in introductory college chemistry.

An Introduction to Structural Equation Modeling (SEM) and the Partial Least Squares (PLS) Methodology

2011 ◽  
pp. 110-129 ◽  
Author(s):  
Nicholas J. Ashill
Keyword(s):  
Structural Equation Modeling ◽  
Information Systems ◽  
Organizational Behavior ◽  
Least Squares ◽  
Partial Least Squares ◽  
Structural Equation ◽  
Academic Research ◽  
Equation Modeling ◽  
Formative Measurement ◽  
Statistical Assumptions

Over the past 15 years, the use of Partial Least Squares (PLS) in academic research has enjoyed increasing popularity in many social sciences including Information Systems, marketing, and organizational behavior. PLS can be considered an alternative to covariance-based SEM and has greater flexibility in handling various modeling problems in situations where it is difficult to meet the hard assumptions of more traditional multivariate statistics. This chapter focuses on PLS for beginners. Several topics are covered and include foundational concepts in SEM, the statistical assumptions of PLS, a LISREL-PLS comparison and reflective and formative measurement.

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