Thakkar Structural Equation Modelling

Application for Research and Practice (with AMOS and R)
1. Auflage 2020
ISBN: 978-981-15-3793-6
Verlag: Springer Singapore
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 124 Seiten

Reihe: Studies in Systems, Decision and Control

Structural Equation Modeling provides a conceptual and mathematical understanding of structural equation modelling, helping readers across disciplines understand how to test or validate theoretical models, and build relationships between observed variables. In addition to a providing a background understanding of the concepts, it provides step-by-step illustrative applications with AMOS, SPSS and R software programmes. This volume will serve as a useful reference for academic and industry researchers in the fields of engineering, management, psychology, sociology, human resources, and humanities.

Zielgruppe

Research

Autoren/Hrsg.

Thakkar, Jitesh J.

Inhaltsverzeichnis

Introduction to Structural Equation Modelling.- Technical aspects of SEM.- Procedural Steps in Structural Equation Modelling.- Applications of Structural Equation Modelling with AMOS 21, IBM SPSS.- Applications of Structural Equation Modelling with R.- Applications of SEM.

Über Autor(innen)

Dr. Jitesh J Thakkar  is an Associate Professor in the Department of Industrial and Systems Engineering, Indian Institute of Technology Kharagpur, India. He received his Ph.D. in Supply Chain Management from IIT Delhi. He has over 20 years of teaching, research and industry experience. He has published over 50 research papers in leading national and international journals in the areas of Lean & Sustainable manufacturing, Supply Chain Management, Quality Management, Small and Medium Enterprises, and Performance Measurement. He is the Associate Editor for OPSEARCH – a journal published by Springer. His online course (MOOCs-NPTEL) on “Six Sigma” has benefited university students and industry professionals. He has conducted several training workshops for corporate managers in Six Sigma, Lean Manufacturing, Value Engineering, Project Management, Quality Management, Supply Chain Management and Statistical Decision Making.