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

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

Nicholas J. Ashill
Copyright: © 2011 |Pages: 20
ISBN13: 9781609606152|ISBN10: 1609606159|EISBN13: 9781609606169
DOI: 10.4018/978-1-60960-615-2.ch006
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MLA

Ashill, Nicholas J. "An Introduction to Structural Equation Modeling (SEM) and the Partial Least Squares (PLS) Methodology." Student Satisfaction and Learning Outcomes in E-Learning: An Introduction to Empirical Research, edited by Sean B. Eom and J. B. Arbaugh, IGI Global, 2011, pp. 110-129. https://doi.org/10.4018/978-1-60960-615-2.ch006

APA

Ashill, N. J. (2011). An Introduction to Structural Equation Modeling (SEM) and the Partial Least Squares (PLS) Methodology. In S. Eom & J. Arbaugh (Eds.), Student Satisfaction and Learning Outcomes in E-Learning: An Introduction to Empirical Research (pp. 110-129). IGI Global. https://doi.org/10.4018/978-1-60960-615-2.ch006

Chicago

Ashill, Nicholas J. "An Introduction to Structural Equation Modeling (SEM) and the Partial Least Squares (PLS) Methodology." In Student Satisfaction and Learning Outcomes in E-Learning: An Introduction to Empirical Research, edited by Sean B. Eom and J. B. Arbaugh, 110-129. Hershey, PA: IGI Global, 2011. https://doi.org/10.4018/978-1-60960-615-2.ch006

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Abstract

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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