Presents structural equation models (SEM) development in a historical context for better understanding of commonly used methods
Answers questions on sample size for hypothesis tests, and comparative performance of various methods
Surveys new techniques for path analysis, guidelines for data preparation, and sample size calculation
J. Christopher Westland is Professor in the Department of Information & Decision Sciences at the University of Illinois – Chicago. He has a BA in Statistics and an MBA in Accounting from Indiana University and received his PhD in Computers and Information Systems from the University of Michigan. He has professional experience in the US as a certified public accountant and as a consultant in technology law in the US, Europe, Latin America and Asia. He has authored numerous academic papers and seven books:
Global Electronic Commerce (MIT Press 2000);
Global Innovation Management (Palgrave Macmillan 2nd ed 2017);
Red Wired: China´s Internet Revolution (Marshall Cavendish, 2010);
Structural Equation Modeling (Springer 2015);
Financial Dynamics (Wiley 2003);
Valuing Technology (Wiley 2002) and
Statistical Auditing with R (forthcoming 2018). He is the Editor-in-Chief of Electronic Commerce Research (Springer) and has served on editorial boards of several other information technology journals including Management Science, ISR, ECRA, IJEC, and others. He has served on the faculties at the University of Michigan, University of Southern California, Hong Kong University of Science and Technology, Tsinghua University, University of Science and Technology of China, Harbin Institute of Technology and other academic institutions. In 2012 he received High-Level Foreign Expert status in China under the 1000-Talents Plan and he is currently Overseas Chair Professor at Beihang University. He has advised on patent, valuation and technology strategy for numerous technology firms.
This new edition surveys the full range of available structural equation modeling (SEM) methodologies. The book has been updated throughout to reflect the arrival of new software packages, which have made analysis much easier than in the past. Applications in a broad range of disciplines are discussed, particularly in the social sciences where many key concepts are not directly observable. This book presents SEM´s development in its proper historical context–essential to understanding the application, strengths and weaknesses of each particular method. This book also surveys the emerging path and network approaches that complement and enhance SEM, and that are growing in importance. SEM´s ability to accommodate unobservable theory constructs through latent variables is of significant importance to social scientists. Latent variable theory and application are comprehensively explained and methods are presented for extending their power, including guidelines for data preparation, sample size calculation and the special treatment of Likert scale data. Tables of software, methodologies and fit statistics provide a concise reference for any research program, helping assure that its conclusions are defensible and publishable.
Chapter1. An Introduction to Structural Equation Models.- Chapter2. Partial Least Squares Path Analysis.- Chapter3. Full-information Covariance SEM.- Chapter4. Systems of Regression Equations.- Chapter5. Data Collection, Control and Sample Size.- Chapter6. Survey and Questionnaire Data.- Chapter7. Research Structure and Paradigms.- Chapter8. Frontiers in Latent Variable Analysis.
This new edition surveys the full range of available structural equation modeling (SEM) methodologies. Latent variable theory and application are comprehensively explained and methods are presented for extending their power, including guidelines for data preparation, sample size calculation and the special treatment of Likert scale data.
This new edition surveys the full range of available structural equation modeling (SEM) methodologies. The book has been updated throughout to reflect the arrival of new software packages, which have made analysis much easier than in the past. Applications in a broad range of disciplines are discussed, particularly in the social sciences where many key concepts are not directly observable. This book presents SEM's development in its proper historical context-essential to understanding the application, strengths and weaknesses of each particular method. This book also surveys the emerging path and network approaches that complement and enhance SEM, and that are growing in importance. SEM's ability to accommodate unobservable theory constructs through latent variables is of significant importance to social scientists. Latent variable theory and application are comprehensively explained and methods are presented for extending their power, including guidelines for data preparation, sample size calculation and the special treatment of Likert scale data. Tables of software, methodologies and fit statistics provide a concise reference for any research program, helping assure that its conclusions are defensible and publishable.
J. Christopher Westland is Professor in the Department of Information & Decision Sciences at the University of Illinois - Chicago. He has a BA in Statistics and an MBA in Accounting from Indiana University and received his PhD in Computers and Information Systems from the University of Michigan. He has professional experience in the US as a certified public accountant and as a consultant in technology law in the US, Europe, Latin America and Asia. He has authored numerous academic papers and seven books:
Global Electronic Commerce (MIT Press 2000);
Global Innovation Management (Palgrave Macmillan 2nd ed 2017);
Red Wired: China's Internet Revolution (Marshall Cavendish, 2010);
Structural Equation Modeling (Springer 2015);
Financial Dynamics (Wiley 2003);
Valuing Technology (Wiley 2002) and
Statistical Auditing with R (forthcoming 2018). He is the Editor-in-Chief of Electronic Commerce Research (Springer) and has served on editorialboards of several other information technology journals including Management Science, ISR, ECRA, IJEC, and others. He has served on the faculties at the University of Michigan, University of Southern California, Hong Kong University of Science and Technology, Tsinghua University, University of Science and Technology of China, Harbin Institute of Technology and other academic institutions. In 2012 he received High-Level Foreign Expert status in China under the 1000-Talents Plan and he is currently Overseas Chair Professor at Beihang University. He has advised on patent, valuation and technology strategy for numerous technology firms.
Über den Autor
J. Christopher Westland is Professor in the Department of Information & Decision Sciences at the University of Illinois - Chicago. He has a BA in Statistics and an MBA in Accounting from Indiana University and received his PhD in Computers and Information Systems from the University of Michigan. He has professional experience in the US as a certified public accountant and as a consultant in technology law in the US, Europe, Latin America and Asia. He has authored numerous academic papers and seven books: Global Electronic Commerce (MIT Press 2000); Global Innovation Management (Palgrave Macmillan 2nd ed 2017); Red Wired: China's Internet Revolution (Marshall Cavendish, 2010); Structural Equation Modeling (Springer 2015); Financial Dynamics (Wiley 2003); Valuing Technology (Wiley 2002) and Statistical Auditing with R (forthcoming 2018). He is the Editor-in-Chief of Electronic Commerce Research (Springer) and has served on editorialboards of several other information technology journals including Management Science, ISR, ECRA, IJEC, and others. He has served on the faculties at the University of Michigan, University of Southern California, Hong Kong University of Science and Technology, Tsinghua University, University of Science and Technology of China, Harbin Institute of Technology and other academic institutions. In 2012 he received High-Level Foreign Expert status in China under the 1000-Talents Plan and he is currently Overseas Chair Professor at Beihang University. He has advised on patent, valuation and technology strategy for numerous technology firms.
Inhaltsverzeichnis
Chapter1. An Introduction to Structural Equation Models.- Chapter2. Partial Least Squares Path Analysis.- Chapter3. Full-information Covariance SEM.- Chapter4. Systems of Regression Equations.- Chapter5. Data Collection, Control and Sample Size.- Chapter6. Survey and Questionnaire Data.- Chapter7. Research Structure and Paradigms.- Chapter8. Frontiers in Latent Variable Analysis.
Klappentext
This new edition surveys the full range of available structural equation modeling (SEM) methodologies. The book has been updated throughout to reflect the arrival of new software packages, which have made analysis much easier than in the past. Applications in a broad range of disciplines are discussed, particularly in the social sciences where many key concepts are not directly observable. This book presents SEM's development in its proper historical context-essential to understanding the application, strengths and weaknesses of each particular method. This book also surveys the emerging path and network approaches that complement and enhance SEM, and that are growing in importance. SEM's ability to accommodate unobservable theory constructs through latent variables is of significant importance to social scientists. Latent variable theory and application are comprehensively explained and methods are presented for extending their power, including guidelines for data preparation, sample size calculation and the special treatment of Likert scale data. Tables of software, methodologies and fit statistics provide a concise reference for any research program, helping assure that its conclusions are defensible and publishable.
Presents structural equation models (SEM) development in a historical context for better understanding of commonly used methods
Answers questions on sample size for hypothesis tests, and comparative performance of various methods
Surveys new techniques for path analysis, guidelines for data preparation, and sample size calculation
