Syllabus

Title
2103 Advanced Quantitative Methods
Instructors
Prof. Marko Sarstedt
Contact details
  • Type
    PI SE
  • Weekly hours
    2
  • Language of instruction
    Englisch
Registration
09/19/16 to 10/03/16
Registration via LPIS
Notes to the course
Subject(s) Doctoral/PhD Programs
Dates
Day Date Time Room
Tuesday 01/17/17 01:00 PM - 06:00 PM D1.1.078
Wednesday 01/18/17 09:00 AM - 01:30 PM TC.4.15
Wednesday 01/18/17 02:30 PM - 07:00 PM TC.5.12
Thursday 01/19/17 09:00 AM - 01:30 PM TC.3.11
Thursday 01/19/17 02:30 PM - 06:30 PM TC.4.15

Contents

Structural equation modeling depicts an extension of the classical factor analysis, which allows explaining relationships amonglatent variables (constructs). 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 (CB-SEM) and partial least squares SEM (PLS-SEM) constitute the two matching statistical techniques for estimating structural equation models. Both apply to the same class of models—structural equations with unobservable variablesand measurement error—but they have different structures and objectives.

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, HermannWold’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 insituations 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. 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). The application of the PLS-SEM method is of special interest if the premises of CB-SEM are violatedand the assumed relations of cause-and-effect are not sufficiently explored. An additional advantage of the PLS-SEM method is the unrestricted incorporation of latent variables in the path model that either draws on reflective or formative measurements models.

Learning outcomes

The objective of this course is to define and explain in broad,conceptual terms the fundamental aspects of the PLS-SEM method. More precisely,it aims at providing an introduction into PLS-SEM (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 application SmartPLS 3 (http://www.smartpls.com)  are an integral part of this course. After taking this class, participants will have an in-depth understanding of the PLS-SEM method and complementary analysis techniques. They will be able to set up, estimate, and interpret path modelsand run more complex analyses such as moderation, mediation, invariance assessment, and multi group analysis.

Teaching/learning method(s)

Presentations and SmartPLS exercises. Most of the workshop will involve “hands-on” analysis of the dataset using the SmartPLSsoftware. SmartPLS software output diagnostics and proper interpretation of the results will be covered. Potential obstacles and “rules-of-thumb” to ensure appropriate application of the technique will be covered. The dataset from the book on PLS-SEM (Sage, 2017) by Hair, Hult, Ringle, and Sarstedt will be used for demonstration purposes.

Assessment

Grading is based on in-class participation (50%) and a take-home exam (50%), which will be administered after the course.

Prerequisites for participation and waiting lists

Participants should have a basic understanding of statistical concepts(e.g., correlation, covariance, standard deviations). A basic knowledge of multivariate statistics and SEM techniques is helpful, but not required

Readings

1 Author: J. F. Hair, G. T. M. Hult, C. M. Ringle and M. Sarstedt
Title: A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM), 

Publisher: Thousand Oaks: Sage
Edition: 2
Year: 2017
Recommendation: Strongly recommended (but no absolute necessity for purchase)
Type: Book
2 Author: Joe F. Hair, Marko Sarstedt, Lucy Matthews and Christian M. Ringle
Title: "Identifying and Treating Unobserved Heterogenity with FIMIX-PLS: Part 1 - Method" European Business Review", forthcoming

Year: 2016
Recommendation: Strongly recommended (but no absolute necessity for purchase)
Type: Script
3 Author: J. F. Hair, M. Sarstedt, C. M. Ringle and J. A. Mena
Title: "An Assessment of the Use of Partial Least Squares Structural Equation Modeling in Marketing Research" Journal of the Academy of Marketing Science 40 (3): 414 - 433

Year: 2012
Recommendation: Strongly recommended (but no absolute necessity for purchase)
Type: Journal
4 Author: J. F. Hair, C. M. Ringle and M. Sarstedt
Title: "PLS-SEM: Indeed a Silver Bullet."  Journal of Marketing Theory and Practice 19(2): 139 - 151

Year: 2011
Recommendation: Strongly recommended (but no absolute necessity for purchase)
Type: Journal
5 Author: Jörg Henseler, Christian M. Ringle and Marko Sarstedt
Title: "Testing Measurement Invariance of Composites Using Partial Least Squares"  International Marketing Review, forthcoming

Year: 2016
Recommendation: Strongly recommended (but no absolute necessity for purchase)
Type: Script
6 Author: Jörg Henseler, Christian M. Ringle and Marko Sarstedt
Title: "A New Criterion for Assessing Discriminant Validity in Variance-based Structural Equation Modeling"  Journal of the Academy of Marketing Science, 43 (1), 115 - 135

Year: 2015
Recommendation: Strongly recommended (but no absolute necessity for purchase)
Type: Journal
7
8 Author: Jörg Henseler, Theo K. Dijkstra, Marko Sarstedt, Christian M. Ringle, Adamantios Diamantopoulos, Detmar W. Straub
Title: "Common Beliefs and Reality about Partial Least Squares: Comments on Rönkkö & Evermann (2013) Organizational Research Methods 17 (2): 182 - 209

Year: 2014
Recommendation: Strongly recommended (but no absolute necessity for purchase)
9 Author: Lucy Matthews, Marko Sarstedt, Joe F. Hair and Christian M. Ringel
Title: "Identifying and Treating Unobserved Heterogeneity with FIMIX-PLS: Part II - A case Study", European Business Review, forthcoming

Year: 2016
Type: Script
10 Author: C. M. Ringle, M. Sarstedt and D. W. Straub
Title: "A Critical Look at the Use of PLS-SEM in MIS Quarterly." 36 (1): iii - xiv

Year: 2012
Recommendation: Strongly recommended (but no absolute necessity for purchase)
Type: Script

Availability of lecturer(s)

The lecturer will be available after class and in the time following the workshop.

Unit details

Unit Date Contents
1 Day 1

09:00 – 10:30

Introduction and fundamentals of measurement

10:30 – 10:45

Break

10:45 – 12:15

Fundamentals of PLS-SEM

12:15 – 13:15

Lunch

13:15 – 15:00

Assessment of PLS-SEM results and exercises

15:00 – 15:15

Break

15:15 – 18:00

Assessment of PLS-SEM results and exercises

2 Day 2

09:00 – 10:30

Assessment of PLS-SEM results and exercises

10:30 – 10:45

Break

10:45 – 12:15

Assessment of PLS-SEM results and exercises

12:15 – 13:15

Lunch

13:15 – 15:00

Moderation

15:00 – 15:15

Break

15:15 – 18:00

Mediation

3 Day 3

09:00 – 10:30

Higher-order Modeling

10:30 – 10:45

Break

10:45 – 12:15

Measurement invariance

12:15 – 13:15

Lunch

13:15 – 15:00

Multigroup analysis

15:00 – 15:15

Break

15:15 – 18:00

Treating unobserved heterogeneity

Last edited: 2016-06-16



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