PLS Path Modeling using ADANCO, SmartPLS, and R

Session 1: June 5 – 9, 2017
Albuquerque, NM
Embassy Suites by Hilton

Payment Options
Per Course Qty
Facultyshow details + $1,795.00 (USD)  
Studentshow details + $1,095.00 (USD)  

Course Syllabus

Monday June 5, 2017
9:00-9:30 Welcome and introductions
9:30-10:45 The common factor model: a standard model in behavioral research
10:45-11:00 Snack and refreshment break
11:00-12:30 The composite model: modeling artifacts in business research
12:30-1:30 Lunch break
1:30-3:00 Traditional and consistent partial least squares (PLS & PLSc)
3:00-3:15 Snack and refreshment break
3:15-5:00 Software tutorial: ADANCO and SmartPLS
Tuesday June 6, 2017
9:00-9:30 Q & A
9:30-10:45 Assessing the model fit
10:45-11:00 Snack and refreshment break
11:00-12:30 Assessing the measurement model
12:30-1:30 Lunch break
1:30-3:00 Assessing the structural model; bootstrap
3:00-3:15 Snack and refreshment break
3:15-5:00 Individual consultations
Wednesday June 7, 2017
9:00-9:30 Q & A
9:30-10:45 Analyzing mediating effects
10:45-11:00 Snack and refreshment break
11:00-12:30 Modeling second-order constructs
12:30-1:30 Lunch break
1:30-3:00 Analyzing moderating effects
3:00-3:15 Snack and refreshment break
3:15-5:00 Analyzing non-linear effects
Thursday June 8, 2017
9:00-9:30 Q & A
9:30-10:45 The R package matrixpls
10:45-11:00 Snack and refreshment break
11:00-12:30 Determining the sample size
12:30-1:30 Lunch break
1:30-3:00 Prediction-oriented research
3:00-3:15 Snack and refreshment break
3:15-5:00 Individual consultations
Friday June 9, 2017
9:00-9:30 Q & A
9:30-10:45 Multi-group analysis
10:45-11:00 Snack and Refreshment Break
11:00-12:30 Assessing measurement invariance of composites and wrap-up
12:30-1:30 Lunch break
1:30-5:00 One-on-one consultations with instructor

Why Should You Attend?

  • Get 1 on 1 Consultation With Instructor
  • Professional Networking
  • Peer Socializing
  • Collaboration
  • All Course Resources
  • Breakfast (Embassy guests), Lunches, & Snacks Daily

Course Learning Goals

After engaging in course lectures and discussions as well as completing the hands-on practice activities with real data, participants will be able to:

  • Explain the PLS path modeling algorithm including its consistent variant, PLSc.
  • Specify measurement models as reflective, causal-formative, or composite.
  • Distinguish between behavioral and design research in their disciplines, and bridge probable gaps using variance-based SEM.
  • Specify, estimate, and test different structural equation models using the software ADANCO, SmartPLS, and R (package matrixpls).
  • Conduct confirmatory factor analysis, confirmatory composite analysis, and a mixture of both using variance-based SEM.
  • Assess the fit of structural equation models involving composites.
  • Interpret and present the results of complex structural equation models with partial and full mediation.
  • Model and test second-order constructs.
  • Analyze moderating (interaction) and nonlinear effects.
  • Conduct multi-group analysis.
  • Use PLS path modeling for predictive purposes.

 Course Prerequisites

Required:

  • Proficiency in multiple linear regression.
  • Intermediate proficiency with at least one statistical software package (e.g., SPSS, Stata, SAS, R, etc.).

Not required but advantageous:

  • At least limited experience (e.g., graduate-level course) with multivariate data analysis.
  • At least limited experience (e.g., graduate-level course) with latent variable models, e.g., exploratory and confirmatory factor analysis (EFA; CFA) and structural equation modeling (SEM).

No level of proficiency beyond basic awareness is assumed for skills related to:

  • Advanced mathematical or statistical topics such as matrix algebra or likelihood theory.

PLS Path Modeling using ADANCO, SmartPLS, and R Course Description

Overview: An intermediate 5-day course introducing partial least squares path modeling (PLS) using ADANCO, SmartPLS, and the R package matrixpls. This course covers the newest developments such as consistent PLS and goodness-of-fit tests.

Topics:

  • Construct measurement: reflective, causal-formative, and composite measurement models
  • Traditional and consistent PLS
  • Goodness-of-fit tests and criteria of approximate model fit
  • Assessing measurement models and structural models
  • Second-order constructs
  • Mediating effects
  • Moderating effects
  • Nonlinear effects
  • Multi-group analysis and measurement invariance of composites
  • Prediction-oriented PLS path modeling
  • Software tutorials: ADANCO, SmartPLS, matrixpls

Note: This course will focus primarily on observed cross-sectional data. Examples used in the course are from various fields of business research (such as marketing, supply chain management, information systems research), and social psychology.

 Course Description:

Variance-based structural equation modeling is a family of multivariate techniques that help researchers model causal relationships between constructs if one or more of these constructs are composites. Partial least squares path modeling (PLS) is the most developed family member, and has recently undergone another leap forward with regard to model specification, estimation, and testing. Recent advancements include consistent PLS for the estimation of factor models, bootstrap-based goodness-of-fit tests, the heterotrait-monotrait ratio of correlations for the assessment of discriminant validity, and the MICOM approach to assess the measurement invariance of composites.

The course “PLS Path Modeling using ADANCO, SmartPLS, and R” consists of 16 lectures and three individual consultation sessions. The first three lectures provide the conceptual foundation for confirmatory factor analysis, confirmatory composite analysis, and structural equation modeling in general. The next four lectures cover the basics of variance-based structural equation modeling: Model specification, estimation, and interpretation. This includes tutorials of three software solutions for variance-based structural equation modeling: ADANCO (http://www.composite-modeling.com), SmartPLS (http://www.smartpls.com), and the R package matrixpls (https://cran.r-project.org/package=matrixpls). The last six lectures present and discuss various extensions, such as second-order constructs, moderating effects, non-linear effects, multigroup analysis, mediating effects, and prediction-oriented research.

The course will make ample references to extant literature. The following article provides a solid introduction and can be used as a sort of tutorial:

  • Henseler, Jörg; Hubona, Geoffrey; Ray, Pauline Ash (2016). Using PLS path modeling in new technology research: updated guidelines. Industrial Management & Data Systems, 116 (1), 2-20, http://dx.doi.org/10.1108/IMDS-09-2015-0382 (open access).

Participants from a variety of fields—including marketing, business, information systems research, operations management, accounting, applied psychology, education, human development, public health, prevention science, sociology, medicine, political science, design research, and communication—will benefit from the course.

Participants will receive an electronic copy of all course materials, including lecture slides, practice datasets, software scripts, relevant supporting documentation, and recommended readings. Participants will also have access to a video recording of the course.

Instructor: Jörg Henseler

Stats Camp Instructor Jorg HenselerJörg Henseler, PhD, is a full professor and holds the Chair of Product-Market Relations at the University of Twente, The Netherlands. He is also a visiting professor at Universidade Nova de Lisboa, Portugal. His research interests encompass structural equation modeling and the interface of marketing and design research. Prof. Henseler is a leading expert on partial least squares path modeling (PLS), a variance-based structural equation modeling technique that is particularly useful in – but in no way limited to – studies focused on the success factors for businesses. He has published in scholarly journals including Computational Statistics & Data Analysis, Journal of Supply Chain Management, Journal of the Academy of Marketing Science, MIS Quarterly, Organizational Research Methods, Structural Equation Modeling, and he has edited two handbooks on PLS. He chairs the scientic advisory board of ADANCO, a new software for variance-based structural equation modeling. A popular guest speaker, Prof. Henseler has been invited by universities around the world – including Beijing University of Aeronautics and Astronautics (China), Freie Universität Berlin, University of Cologne, University of Hamburg (all Germany), Corvinus University Budapest (Hungary), Universidad Iberoamericana (Mexico), University of Ljubljana (Slovenia), Universidad de Sevilla (Spain), Université de Fribourg (Switzerland), The University of Manchester, Warwick Business School (both UK), Georgia State University in Atlanta, and Texas Tech University (both USA) – to speak to students, faculty, and professionals about structural equation modeling. On a regular basis, Prof. Henseler provides seminars on PLS at the PLS School (http://www.pls-school.com), through which hundreds of scholars and practitioners have been trained in structural equation modeling.

Software and Computer Support

Participants need to bring a laptop computer with Wi-Fi capabilities.

All statistical software used at Stats Camp will be available, free to participants, on our SMORS (statistical modeling on remote servers) system for the duration of camp.

Instruction will be provided for the methods using the most current version of ADANCO (base program with mixture add-on or base program with combination add-on). ADANCO is available for Windows and Mac environments.  Information for purchasing a personal license can be found at http://www.composite-modeling.com.

Note: We will also make use of the R package matrixpls (https://cran.r-project.org/package=matrixpls) and SmartPLS (http://www.smartpls.com).

 

Recommended Reading

Primary Recommendations:

  • Aguirre-Urreta, Miguel, and Rönkkö, Mikko (2015). Sample size determination and statistical power analysis in PLS using R: an annotated tutorial. Communications of the Association for Information Systems, 36 (1), 33-51, (article 3), http://aisel.aisnet.org/cais/vol36/iss1/3.
  • Dijkstra, Theo K.; Henseler, Jörg (2015). Consistent partial least squares path modeling. MIS Quarterly, 39 (2), 297-316, http://www.henseler.com/Dijkstra-Henseler-MISQ-2015.pdf (open access).
  • Henseler, Jörg (2017). Bridging design and behavioral research with variance-based structural equation modeling. Journal of Advertising, electronic pre-print available, http://dx.doi.org/10.1080/00913367.2017.1281780 (open access).
  • Henseler, Jörg; Chin, Wynne W. (2010). A comparison of approaches for the analysis of interaction effects between latent variables using partial least squares path modeling. Structural Equation Modeling, 17 (1), 82-109, http://dx.doi.org/10.1080/10705510903439003.
  • Henseler, Jörg; Hubona, Geoffrey; Ray, Pauline Ash (2016). Using PLS path modeling in new technology research: updated guidelines. Industrial Management & Data Systems, 116 (1), 2-20, http://dx.doi.org/10.1108/IMDS-09-2015-0382 (open access).
  • Henseler, Jörg; Ringle, Christian M.; Sarstedt, Marko (2016). Testing measurement invariance of composites using partial least squares. International Marketing Review, 33 (3), 1-27, http://dx.doi.org/10.1108/IMR-09-2014-0304.
  • Henseler, Jörg; Ringle, Christian M.; Sarstedt, Marko (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43 (1), 115-135, http://dx.doi.org/10.1007/s11747-014-0403-8 (open access).
  • Nitzl, Christian; Roldán, José Luis; Cepeda, Gabriel (2016). Mediation analyses in partial least squares structural equation modeling: Helping researchers discuss more sophisticated models. Industrial Management & Data Systems, 116 (9), 1849-1864, http://dx.doi.org/10.1108/IMDS-07-2015-0302.
  • Riel, Allard van; Henseler, Jörg; Kémény, Ildikó; Sasovova, Zuzana (2017). Estimating hierarchical constructs using partial least squares: The case of second-order composites of factors. Industrial Management & Data Systems, 117 (3), in print (open access).

 

Secondary Recommendations:

  • Cepeda Carrión, Gabriel; Henseler, Jörg; Ringle, Christian M.; Roldán, José Luis (2016). Prediction-oriented modeling in business research by means of PLS path modeling. Journal of Business Research, 69 (10), 4545-4551.
  • URL: http://dx.doi.org/10.1016/j.jbusres.2016.03.048 Dijkstra, Theo K.; Henseler, Jörg (2015). Consistent and asymptotically normal PLS estimators for linear structural equations. Computational Statistics & Data Analysis, 81 (1), 10-23, http://dx.doi.org/10.1016/j.csda.2014.07.008 (open access).
  • Dijkstra, Theo K.; Henseler, Jörg (2011). Linear indices in nonlinear structural equation models: best fitting proper indices and other composites. Quality & Quantity, 45 (6), 1505-1518, http://dx.doi.org/10.1007/s11135-010-9359-z.
  • Henseler, Jörg (2010). On the convergence of the partial least squares path modeling algorithm. Computational Statistics, 25 (1), 107-120, http://dx.doi.org/10.1007/s00180-009-0164-x (open access).
  • Henseler, Jörg; Dijkstra, Theo K.; Sarstedt, Marko; Ringle, Christian M.; Diamantopoulos, Adamantios; Straub, Detmar W.; Ketchen, David J., Jr.; Hair, Joseph F.; Hult, G. Tomas M.; Calantone, Roger J. (2014). Common beliefs and reality about PLS: Comments on Rönkkö & Evermann (2013). Organizational Research Methods, 17 (2), 182-209, http://dx.doi.org/10.1177/1094428114526928 (open access).
  • Henseler, Jörg; Fassott, Georg (2010). Testing moderating effects in PLS path models: an illustration of available procedures. In: Esposito Vinzi, Vincenzo; Chin, Wynne W.; Henseler, Jörg; Wang, Huiwen (Eds.). Handbook of Partial Least Squares: Concepts, Methods and Applications. Heidelberg, Dordrecht, London, New York: Springer, pp. 713-735. http://dx.doi.org/10.1007/978-3-540-32827-8_31.
  • Henseler, Jörg; Fassott, Georg; Dijkstra, Theo K.; Wilson, Bradley (2012). Analysing quadratic effects of formative constructs by means of variance-based structural equation modelling. European Journal of Information Systems, 21 (1), 99-112, http://dx.doi.org/10.1057/ejis.2011.36.
  • Henseler, Jörg; Sarstedt, Marko (2013). Goodness-of-fit indices for partial least squares path modeling. Computational Statistics, 28 (2), 565-580, http://dx.doi.org/10.1007/s00180-012-0317-1 (open access).
  • Rigdon, Edward E. (2012). Rethinking partial least squares path modeling: in praise of simple methods, Long Range Planning, 45 (5-6), 341-358, http://dx.doi.org/10.1016/j.lrp.2012.09.010.
  • Sarstedt, Marko; Henseler, Jörg; Ringle, Christian M. (2011). Multi-group analysis in partial least squares (PLS) path modeling: Alternative methods and empirical results. Advances in International Marketing, 22, 195-218, http://dx.doi.org/10.1108/S1474-7979(2011)0000022012.
Statistical Programming and Data Analysis with R Seminar Kyle Lang TTU