Discipline: All disciplines (e.g., strategic management, marketing, information systems, HRM, management accounting and controlling)

Transcription

1 Discipline: All disciplines (e.g., strategic management, marketing, information systems, HRM, management accounting and controlling) 1 Lecturers Jörg Henseler (Radboud University Nijmegen) Christian M. Ringle (Hamburg University of Technology - TUHH) Marko Sarstedt (Ludwig-Maximilians-University Munich) 2 Title Latent Variables and Structural Equation Modeling (CB-SEM and PLS-SEM) 3 Outline Key Issues The course teaches the basic concepts of factor analysis as well as covariance- and variancebased structural equation modeling. After this course, participants will be familiar with factor analytic techniques to uncover latent variables. understand the basic concepts of structural equation modeling, the covariancebased path modeling methodology and the PLS algorithm. know the reliability and validity measures that are relevant to evaluate structural equation modeling results. have a basic understanding of advanced analysis issues in structural equation modeling, including moderating effects, the identification and treatment of unobserved heterogeneity and multi-group comparisons. be able to use the software programs IBM SPSS Statistics (in the context of factor analysis), AMOS and SmartPLS to carry out fundamental analyses to successfully conduct their own research projects. Course Format The course will consist of a combination of lectures, exercise sessions, and a final exam. Lecturers will use recent journal articles as well as book chapters to teach the participants the state-of-the-art of research in structural equation modeling. Participants are responsible for reading the assigned materials before class. 1/6

2 4 Faculty Jörg Henseler (Radboud University Nijmegen) Christian M. Ringle (Hamburg University of Technology) Marko Sarstedt (Ludwig-Maximilians-University Munich) personen/professoren/sarstedt/index.html 5 Schedule Schedule Day I: (Tuesday, March 27, 2012) 10:00 10:30 Arrival of participants, reception, check-in and introduction 10:30 12:00 Recap: Elementary statistics and factor analysis 12:00 13:00 Lunch Break 13:00 14:30 Recap factor analysis & exercise session (cont.); Conceptualization and operationalization of constructs in business research 14:30 14:45 Short break 14:45 16:15 Conceptualization and operationalization of constructs in business research 16:15 16:45 Coffee break 16:45 18:15 Confirmatory Factor Analysis 2/6

3 Day II (Wednesday, March 28, 2012) 09:30 11:00 Exercise session I: Introduction to the AMOS software and applications of CFA 11:00 11:15 Short break 11:15 12:45 Essentials of Covariance-based SEM 12:45 14:00 Lunch break 14:00 15:30 Exercise session II: Introduction to the AMOS software and applications 15:30 16:00 Coffee break 16:00 17:30 Advanced topics in SEM, Multigroup analysis Day III (Thursday, March 29, 2012) 09:30 11:00 Fundamentals of PLS path modeling / Assessment of measurement results 11:00 11:15 Short break 11:15 12:45 Assessment of measurement results (cont.) 12:45 14:00 Lunch break 14:00 15:30 Exercise session I: Introduction to the SmartPLS software and applications 15:30 16:00 Coffee break 16:00 17:30 Exercise session II: Example applications in business research Day IV (Friday, March 30, 2012) 09:30 11:00 Advanced topics in PLS path modeling 11:00 11:15 Short break 11:15 12:00 Recap 12:00 13:30 Lunch break 13:30 15:00 In-class exam 15:00 15:30 Wrap-up & Feedback 3/6

4 Location Ludwig-Maximilians-University Munich Computer Lab (IuK-pool, Institut für Information, Organisation und Management, Prof. dr. Dres. h.c. Picot) Ludwigstr. 28 / front building, 2nd floor Munich Max. Number of Participants The number of participants is limited to Cost The course fee amounts to EUR Content Use of the multivariate statistical technique of factor analysis increased during the past decade in all fields of business-related research. As the number of variables to be considered in multivariate techniques increases, so does the need for knowledge of the structure and interrelationships of the variables. Factor analysis can be utilized to examine the underlying patterns or relationships for a large number of variables and to determine whether the information can be condensed or summarized in a smaller set of factors or components. Structural equation modeling depicts an extension of the classical factor analysis which allows explaining relationships among latent variables (constructs). Thus, it allows to empirically validate theoretically established causal models in the various social science disciplines such as marketing (e.g., to perform research on brand equity, consumer behavior, and customer satisfaction) or management (e.g., to evaluate factors that influence of alliance networks on firm performance). Covariance-based structural equation modeling (CBSEM) and partial least squares analysis (PLS) path modeling constitute the two matching statistical techniques for estimating structural equation models. Both apply to the same class of models - structural equations with unobservable variables and measurement error - but they have different structures and objectives. For example, the goal of CBSEM is to account for observed covariances (theory testing), whereas PLS rather aims at explaining variances (prediction-oriented character). Furthermore, CBSEM offers statistical precision in the context of stringent assumptions while PLS trades parameter efficiency for prediction accuracy, simplicity, and fewer assumptions. Lastly, CBSEM requires relatively large samples for accurate estimation and relatively few variables and constructs for convergence. The objective of this course is to define and explain in broad, conceptual terms the fundamental aspects of factor analytic techniques. More precisely, it aims at providing an indepth methodological introduction into the factor analysis, CBSEM and PLS path modeling approach (the nature of causal modeling, analytical objectives, some statistics), including the evaluation of analysis results, and an introduction to complementary analytical techniques. Practical applications and the use of the software applications IBM SPSS Statistics 4/6

5 ( AMOS ( and SmartPLS ( are an integral part of this course. 7 Prerequisites The course requires basic skills in statistics and multivariate data analysis techniques. Concepts such as mean values, standard deviations and covariance matrices should sound familiar to the participants. In addition, a basic understanding of factor analytic approaches, regression analysis as well as testing procedures is helpful but not an essential requirement for understanding the contents. A recap session on elementary statistics is integrated at the beginning of the course. 8 Course Material Essential Reading Material Factor Analysis & Construct Development Churchill, G. A. (1979): A Paradigm for Developing Better Measures of Marketing Constructs. Journal of Marketing Research, Vol. 16, No. 1, p Herrmann, A./Huber, F./Kressmann, F. (2006): Varianz- und kovarianzbasierte Strukturgleichungsmodelle: Ein Leitfaden zu deren Spezifikation, Schätzung und Beurteilung. Zeitschrift für betriebswirtschaftliche Forschung, Vol. 58, No. 2, p Mooi, E. A./Sarstedt, M. (2011). A Concise Guide to Market Research. The Process, Data, and Methods Using IBM SPSS Statistics, Springer. CBSEM Diamantopoulos, A./Siguaw, J.A. (2000): Introducing LISREL: A Guide for the Uninitiated. Sage Publications. Hair, J.F./Black, W. C./Babin, B. J./Anderson, R. E. (2009): Multivariate Data Analysis, 7th Edition, Prentice Hall (Chapters 12, 13, 14, 15). Lance, C. E./Vandenberg, R. J Confirmatory Factor Analysis. In F. Drasgow & N. Schmitt (Eds.), Measuring and analyzing behavior in organizations: Advances in Measurement and Data Analysis. Jossey-Bass, pp Weiber, R./Mühlhaus, D. (2009): Strukturgleichungsmodellierung: Eine anwendungsorientierte Einführung in die Kausalanalyse mit Hilfe von AMOS, SmartPLS und SPSS. Springer. 5/6 PLS Path Modeling Fornell, C./Bookstein, F. L. (1982): Two Structural Equation Models : LISREL and PLS Applied to Consumer Exit-Voice Theory. Journal of Marketing Research, Vol. 19, No. 11, pp Hair, J. F./Ringle, C. M./Sarstedt, M. (2011): PLS-SEM Indeed a Silver Bullet, Journal of Marketing Theory & Practice, Vol. 19, No. 2, pp

6 Hair, J. F./Sarstedt, M./Ringle, C. M./Mena, J. A. (2012): An Assessment of the Use of Partial Least Squares Structural Equation Modeling in Marketing Research, Journal of the Academy of Marketing Science, forthcoming (online available). Henseler, J./Ringle, C. M./Sinkovics, R. (2009): The Use of Partial Least Squares Path Modeling in International Marketing. Advances in International Marketing (AIM), Vol. 20, 2009, pp Reinartz, W./Haenlein, M./Henseler, J. (2009): An Empirical Comparison of the Efficacy of Covariance-based and Variance-based SEM. International Journal of Research in Marketing, Vol. 26, No. 4, pp Sarstedt, M./Henseler, J./Ringle, C. M. (2011): Multi-Group Analysis in Partial Least Squares (PLS) Path Modeling: Alternative Methods and Empirical Results, Advances in International Marketing, forthcoming. Sarstedt, M./Ringle, C. M. (2008): Heterogenität in varianzbasierter Strukturgleichungsmodellierung: Eine Analyseprozedur zur systematischen Anwendung von FIMIX-PLS. In: Marketing - Zeitschrift für Forschung und Praxis, Vol. 30, No. 4, pp Schloderer, M./Ringle, C. M./Sarstedt, M. (2009): Einführung in varianzbasierte Strukturgleichungsmodellierung: Grundlagen, Modellevaluation und Interaktionseffekte am Beispiel von SmartPLS. In: Meyer, A./Schwaiger, M. (Hrsg.): Theorie und Methoden der Betriebswirtschaft. Vahlen, pp Additional Reading Material Fahrmeir, L./Künstler, R./Pigeot, I./Tutz, G. (2011): Statistik: Der Weg zur Datenanalyse. 7. Auflage, Springer. Gudergan, S. P.; Ringle, C. M.; Wende, S.; Will, A. (2008): Confirmatory Tetrad Analysis in PLS Path Modeling. Journal of Business Research, Vol. 61, No. 12, pp Henseler, J./Fassott, G. (2010): Testing Moderating Effects in PLS Path Models: An Illustration of Available Procedures. In: Esposito Vinzi, V./Chin, W.W./Henseler, J./Wang, H. (Eds.): Handbook of Partial Least Squares. Springer, pp Rigdon, E. E./Ringle, C. M./Sarstedt, M. (2010): Structural Modeling of Heterogeneous Data with Partial Least Squares. In: Malhotra, N. K.: Review of Marketing Research, Vol. 7. Sharpe, /6