Doktorandenprogramme Verband der Hochschullehrer für Betriebswirtschaft e. V. Syllabus
Discipline: Marketing
1 Lecturers
Univ. Doc. Dr. Jörg Henseler (University of Cologne, Radboud University Nijmegen) PD Dr. Christian M. Ringle (University of Hamburg)
Dr. Marko Sarstedt (Ludwig-Maximilians-University Munich)
2 Title
Latent Variables and Structural Equation Modeling
3 Outline Issues
The course teaches the basic concepts of factor analysis as well as covariance- and variance-based 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
covariance-based 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 PASW (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.
Doktorandenprogramme Verband der Hochschullehrer für Betriebswirtschaft e. V. Syllabus
4 Faculty
Univ. Doc. Dr. Jörg Henseler (University of Cologne, Radboud University Nijmegen) http://www.henseler.com/
PD Dr. Christian M. Ringle (University of Hamburg) http://www.ibl-unihh.de/tea_rin.htm
Dr. Marko Sarstedt (Ludwig-Maximilians-University Munich)
http://www.imm.bwl.uni-muenchen.de/personen/mitarbeiter/sarstedt/index.html [email protected]
5 Administration Schedule
Day I: (Tuesday, July 27, 2010)
10:00 – 10:30 Arrival of participants, reception, check-in and introduction 10:30 – 12:00 Recap: Elementary statistics and introduction to factor analysis 12:00 – 13:00 Lunch Break
13:00 – 14:30 Introduction to factor analysis (cont.) / Exercise session I: Exploratory factor analysis using PASW Statistics
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
Day II (Wednesday, July 28, 2010) 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
Doktorandenprogramme Verband der Hochschullehrer für Betriebswirtschaft e. V. Syllabus
Day III (Thursday, July 29, 2010)
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, July 30, 2010)
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
Location
Ludwig-Maximilians-University Munich Computer Lab (CIP III)
Ludwigstr. 28 / rear building, ground floor 80539 Munich
Max. Number of Participants
The number of participants is limited to 15-20.
Cost tba
Doktorandenprogramme Verband der Hochschullehrer für Betriebswirtschaft e. V. Syllabus
6 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 in-depth 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 PASW Statistics (http://www.pasw.com), AMOS (http://www.spss.com/de/Amos/) and SmartPLS (http://www.smartpls.de) are an integral part of this course.
Doktorandenprogramme Verband der Hochschullehrer für Betriebswirtschaft e. V. Syllabus
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
8.1.1 Factor Analysis & Construct Development
Backhaus, K./Erichson, B./Plinke, W./Weiber, R. (2008): Multivariate Analyse-methoden: Eine anwendungsorientierte Einführung, 12. Auflage. Springer (Chapter 7).
Churchill, G. A. (1979): A Paradigm for Developing Better Measures of Marketing Constructs. Journal of Marketing Research, Vol. 16, No. 1, p. 64-73.
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. 34-66.
Malhotra, N. K. (2010): Marketing Research: An Applied Orientation. 6th edition, Prentice Hall (Chapter 19).
8.1.2 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. 2001. Confirmatory Factor Analysis. In F. Drasgow & N. Schmitt (Eds.), Measuring and analyzing behavior in organizations: Advances in Measurement and Data Analysis. Jossey-Bass, pp. 221–256.
Weiber, R./Mühlhaus, D. (2009): Strukturgleichungsmodellierung: Eine anwendungsorientierte Einführung in die Kausalanalyse mit Hilfe von AMOS, SmartPLS und SPSS. Springer.
Doktorandenprogramme Verband der Hochschullehrer für Betriebswirtschaft e. V. Syllabus
8.1.3 PLS path modeling
Chin, W. W. (1998): The Partial Least Squares Approach to Structural Equation Modeling. In: Marcoulides, G.A. (ed.): Modern Methods for Business Research. Lawrence Erlbaum, pp. 295-358.
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. 440-452.
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. 277-320.
Reinartz, W./Haenlein, M./Henseler, J. (2009): An Empirical Comparison fot he Efficacy of Covariance-based and Variance-based SEM. International Journal of Research in Marketing, Vol. 26, No. 4, pp. 332-344.
Ringle, C.M./Spreen, F. (2007): Beurteilung von Ergebnissen einer PLS-Pfad-analyse. In: Das Wirtschaftsstudium (WISU), Vol. 36, No. 2, pp. 211-216.
Ringle, C. M./Boysen, N./Wende, S./Will, A. (2006): Messung von Kausalmodellen mit dem Partial-Least-Squares-Verfahren. In: Das Wirtschaftsstudium (WISU), Vol. 35, No. 1, pp. 81-87.
Ringle, C. M./Götz, O./Wetzels, M./Wilson, B. (2009): On the Use of Formative Measurement Specifications in Structural Equation Modeling: A Monte Carlo Simulation Study to Compare Covariance-based and Partial Least Squares Model Estimation Methodologies. METEOR Research Memoranda, RM/09/014, Maastricht University.
Sarstedt, M./Ringle, C. M. (2008): Heterogenität in varianzbasierter Strukturgleichungsmodellierung: Eine Analyseprozedur zur systematischen Anwendung von FIMIX-PLS. In: Marketing ZFP (Zeitschrift für Forschung und Praxis), Vol. 30, No. 4, pp. 241-257.
Schloderer, M./Ringle, C. M./Sarstedt, M. (2009): Einführung in varianzbasierte Strukturgleichungsmodellierung: Grundlagen, Modellevaluation und Interaktions-effekte am Beispiel von SmartPLS. In: Meyer, A./Schwaiger, M. (Hrsg.): Theorie und Methoden der Betriebswirtschaft. Vahlen, pp. 573-601.
Additional Reading Material
Albers, S. (2010): PLS and Success Factors in Marketing. In: Esposito Vinzi, V./Chin, W. W./Henseler, J./Wang, H. (Eds.): Handbook of Partial Least Squares. Springer, in press.
Doktorandenprogramme Verband der Hochschullehrer für Betriebswirtschaft e. V. Syllabus
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, in press.
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. Sharpe, in press.
Ringle, C. M./Sarstedt, M./Mooi, E. A (2010): Response-based Segmentation Using Finite Mixture Partial Least Squares: Theoretical Foundations and an Application to American Customer Satisfaction Index Data. Annals of Information Systems (AIS), Vol. 8. Springer, pp. 19-49.
Ringle, C. M./Wende. S./Will, A. (2010): The Finite Mixture Partial Least Squares Approach: Methodology and Application. In: Esposito Vinzi, V./Chin, W.W./Henseler, J./Wang, H. (Eds.): Handbook of Partial Least Squares. Springer, in press.
Sarstedt, M. (2008): A Review of Recent Approaches for Capturing heterogeneity in Partial Least Squares Path Modelling. In: Journal of Modelling in Management, Vol. 3, No. 2, pp.140-161.
Schlittgen, R. (2009): Multivariate Statistik, München-Wien.
9 To Prepare
All participants are required to read the essential reading material prior to the course. If participants wish to catch up on elementary statistics and data analysis they should revert to Fahrmeir et al. (2009; Statistik) and Backhaus et al. (2008; Multivariate Analysemethoden). We also strongly recommend Schlittgen (2009; Multivariate Statistik).
10 Assessment
A 90-minute in-class exam will be offered at day IV.
11 Credits
