Lehrende: Prof. Dr. Christian M. Ringle
Veranstaltungsart: Seminar / Übung
Orga-Einheit: Graduate School | ZUGS
Anzeige im Stundenplan: SEM
Credits: 4,0
Standort: Campus der Zeppelin Universität
Unterrichtssprache: Englisch
Min. | Max. Teilnehmerzahl: 5 | 15
Inhalte: This seminar is designed for doctoral candidates, but welcomes later-stage Master students and "Humboldt"-students applying structural equation modeling in their research projects. Structural equation modeling (SEM) and path modeling with latent variables (LVP) are used to empirically validate theoretically developed causal models in the social sciences disciplines such as marketing to perform, for example, research on brand equity, consumer behavior, and customer satisfaction). Covariance-based structural equation modeling (CB-SEM) and partial least squares structural equation modeling (PLS-SEM) constitute the two corresponding statistical techniques to perform an SEM analysis. CB-SEM is usually used in social sciences to empirically estimate relationships in causal models. Apparently, there has been little concern about the frequent inability of empirical data to meet methodological requirements or about the common occurrence of improper solutions. In comparison with CB-SEM, Wold’s basic PLS-SEM design or basic method of soft modeling rather represents a different statistical method. Soft modeling refers to the ability of PLS-SEM to be more flexible in handling various statistical modeling problems in situations where it is difficult or impossible to meet the hard assumptions of more traditional multivariate statistics. Within this context, "soft" is only attributed to distributional assumptions and not to the concepts, the models or the estimation techniques. Representing a well-substantiated alternative to CB-SEM, PLS-SEM is relatively unknown and rarely used in business research, which fails to appreciate its importance for estimating LVP in a variety of contexts, ranging from theoretical and applied research in marketing, management and other social sciences disciplines. The goal of PLS-SEM is the explanation of variances (prediction-oriented character of the methodology) rather than explaining covariances (theory testing via CB-SEM). Thus, the application of the PLS-SEM approach is of special interest if the premises of CB-SEM are violated and the assumed relations of cause-and-effect are not sufficiently explored. An additional key advantage of the PLS-SEM method is the relatively unre-stricted incorporation of latent variables in the path model that either draw on reflective or formative measurements models.
Lernziele: The objectives of this course are (1) to provide an in-depth introduction into the PLS-SEM method (the nature of causal modeling, analytical objectives, some statistics), (2) the evaluation of measurement results, (3) advanced and complementary PLS-SEM analysis techniques, and, (4) getting to know the CB-SEM method as an alternative to PLS-SEM. Practical applications and the use of the SmartPLS and AMOS software are an integral part of this course. Content 1. Fundamentals of PLS path modeling 2. Assessment of measurement results 3. Introduction to the software SmartPLS 4. Examples and special problems of PLS-SEM in business research 5. Some advanced topics on PLS-SEM 6. An Introduction to CB-SEM 7. Introduction to the software AMOS 8. Examples and special problems of CB-SEM in business research Who should attend? This course has been designed for full-time faculty and Ph.D. students who are interested to learn how to use structural equation modeling (SEM) using PLS path modeling (PLS-SEM) in their own research applications. A basic knowledge of multivariate statistics and SEM techniques is helpful, but not required. Teaching methods | Presentations (with intensive interaction) | Computer exercises (SmartPLS software application; www.smartpls.de) | Practical examples
