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.

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

2012 ◽  
pp. 500-520
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.

Partial least squares structural equation modeling (PLS-SEM)

2014 ◽  
Vol 26 (2) ◽  
pp. 106-121 ◽  
Author(s):  
Joe F. Hair Jr ◽  
Marko Sarstedt ◽  
Lucas Hopkins ◽  
Volker G. Kuppelwieser
Keyword(s):  
Structural Equation Modeling ◽  
Information Systems ◽  
Least Squares ◽  
Partial Least Squares ◽  
Structural Equation ◽  
Management Information Systems ◽  
Marketing Management ◽  
Equation Modeling ◽  
Management Information ◽  
Content Type

Purpose – The authors aim to present partial least squares (PLS) as an evolving approach to structural equation modeling (SEM), highlight its advantages and limitations and provide an overview of recent research on the method across various fields. Design/methodology/approach – In this review article, the authors merge literatures from the marketing, management, and management information systems fields to present the state-of-the art of PLS-SEM research. Furthermore, the authors meta-analyze recent review studies to shed light on popular reasons for PLS-SEM usage. Findings – PLS-SEM has experienced increasing dissemination in a variety of fields in recent years with nonnormal data, small sample sizes and the use of formative indicators being the most prominent reasons for its application. Recent methodological research has extended PLS-SEM's methodological toolbox to accommodate more complex model structures or handle data inadequacies such as heterogeneity. Research limitations/implications – While research on the PLS-SEM method has gained momentum during the last decade, there are ample research opportunities on subjects such as mediation or multigroup analysis, which warrant further attention. Originality/value – This article provides an introduction to PLS-SEM for researchers that have not yet been exposed to the method. The article is the first to meta-analyze reasons for PLS-SEM usage across the marketing, management, and management information systems fields. The cross-disciplinary review of recent research on the PLS-SEM method also makes this article useful for researchers interested in advanced concepts.

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.

Manual de Partial Least Squares Structural Equation Modeling (PLS-SEM) (Segunda Edición)

2019 ◽  
Author(s):  
Joseph F. Hair Jr. ◽  
G. Tomas M. Hult ◽  
Christian M. Ringle ◽  
Marko Sarstedt ◽  
Julen Castillo Apraiz ◽  
...  
Keyword(s):  
Structural Equation Modeling ◽  
Least Squares ◽  
Partial Least Squares ◽  
Structural Equation ◽  
Equation Modeling

How Do Fans Feel? A Case Analysis of Measuring and Modeling Attitudes Using Partial Least Squares Structural Equation Modeling

2016 ◽  
Vol 5 (1) ◽  
pp. 85-88 ◽  
Author(s):  
Lane Wakefield ◽  
Gregg Bennett
Keyword(s):  
Structural Equation Modeling ◽  
Least Squares ◽  
Partial Least Squares ◽  
Structural Equation ◽  
Equation Modeling ◽  
Sport Organizations ◽  
Sports Teams ◽  
Know How ◽  
Fan Communities

Virtual fan communities (VFC) have become very popular among fans of sports teams. A VFC provides an online place for fans to meet and discuss the team, consume media, and develop friendships. Students will learn, in this case study, how to use partial least squares structural equation modeling (PLS-SEM) to assess fan attitudes toward the VFC and sponsors of the firm. Students will also learn how sport organizations can benefit from leadership with statistical know-how. The case is fictional, but it is based on an actual research study conducted in conjunction with a prominent virtual fan community in which ownership had an interest in fans’ attitudes toward their service.

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