COURSE: Structural Equation Modeling – variance (SmartPLS) and covariance based (lavaan)

PROFESSOR: Prof. Dr. Diógenes de Souza Bido

SEMESTER: 2º/2018

DATE: Sept/21: 1 p.m. to 5 p.m., [break 5 p.m. to 6 p. m.], 6 p.m. to 10 p.m.

           Sept/28: 1 p.m. to 5 p.m., [break 5 p.m. to 6 p. m.], 6 p.m. to 10 p.m.

           Oct/19: 1 p.m. to 5 p.m., [break 5 p.m. to 6 p. m.], 6 p.m. to 10 p.m.

           Oct/26: 1 p.m. to 5 p.m., [break 5 p.m. to 6 p. m.], 6 p.m. to 10 p.m.

 

 

Contents:

Structural Equation Modeling – variance based (SmartPLS):

• Measurement model assessment: convergent and discriminant validity, and reliability.
• Full model (path analysis with latent variables): importance-performance map analysis, multicollinearity, 2nd order latent variable, sample size definition, control variables, common method bias, moderation and Multigroup analysis.

Structural Equation Modeling – covariance based (lavaan – R package):

• Confirmatory factor analysis
• Full model (hypothesis testing)

 

Purpose:

The purpose of the course is to enable participants to: (i) Identify the situations in which PLS-SEM is the most recommended method; (ii) Recognize the differences between variance-based and covariance-based structural equation modeling; (iii) Use SmartPLS and lavaan in their research; (iv) Evaluate the measurement and the structural model; (v) Raise awareness of the need for rigor in their analyses.

 

Assessment:

Final grade = (HW1 + HW2)/2

Approval if:  Final grade equal or greater than 6.0

                                                                                                                         

Delivery by e-mail (diogenesbido@yahoo.com.br):

HW1 = October 15^th

HW2 = November 5^th   

 

Expected students' profile:  MSc and PhD students, professors and researchers.

 

• Students must have completed and passed courses in quantitative methods or data analysis (in their master or Ph.D. course), whose contents include:

          - hypothesis test and statistical significance

          - correlation and linear regression

          - exploratory factor analysis

 

Language:  The course will be taught in Portuguese.

 

• number of students: 40

 

Computer Lab:

• It is recommended that students bring their notebook (with R, R Studio, G*Power 3, and SmartPLS installed. The instructions will be send before the course beginning)

 

 

 

Syllabus:

Day	Content	Bibliographical references
september/21st	Exploratory and confirmatory analysis (Theory and Data) Structural and measurement model How to use SmartPLS: data, project, files Understanding the PLS algorithm: - Linear regression: betas, R² e bootstrap - Path analysis with observed variables: indirect, direct and total effects - Reflective latent variable and principal components.	Software SmartPLS Software SPSS or Minitab or R (Rcmdr package)   Streiner (2005) and   Henseler et al. (2009, p.277-291).  or Mackenzie et al. (2009)
september/21st	Measurement model assessment: reflective - convergent and discriminant validity, and reliability - How to report the results   Emergent variables or Formative latent variables - Convergent validity - Confirmatory Tedrad Analysis - Formative variable in exogenous and endogenous position	Bido et al. (2016)   Gefen & Straub (2005)   Gudergan et al. (2008) or Ross (2014)
september/28th	Full model – path analysis with latent variables in SmartPLS - Importance-performance map analysis - Multicollinearity in the structural model - 2nd order latent variable - sample size and statistical power with G*Power 3 - How to report the results	Video IPMA   Wetzels et al. (2009) or Becker et al. (2012) or Wilson et al. (2007)   Software G*Power 3 & Manual or Faul et al. (2007)
september/28th	Moderation (multiplicative term) Multigroup analysis: observed heterogeneity (a priori) FIMIX: non-observed heterogeneity     HW1: (i) multicollinearity + 2nd order LV; (ii) importance-performance map analysis - Estimate the full model, adjust the measurement model and report and explain the results.	Video MGA Henseler (2012) e Henseler et al. (2016)
october/5th  october/12th	EnANPAD Holiday
october/19th	SmartPLS x lavaan: - Latent variable scores and their uses - Attenuation (consistency at large) - Sample size and statistical power with G*Power 3   Software R, R packages, install packages, load packages   lavaan (Latent Variable Analysis) path analysis from covariance matrix and from raw data (dataset) confirmatory factor analysis and restrictions on parameters	Software R and lavaan package   Rosseel (2015, p.1-8) e materials from lavaan_site or Hair Jr. et al. (2010, p.600-638)   Videos: https://goo.gl/Q7zmyR
october/19th	Reflective: convergent, discriminant validity and reliability   Formative indicators in lavaan Identification and degree of freedom Modification indices	Rosseel (2015, p.9-15) e materials from lavaan_site   Soper (2017)   Videos: https://goo.gl/Q7zmyR
october/26th	Exercices: Kuhnel and Beaujean models Generate the path diagram Goodness of fit indices Defined command  :=     (indirect effect) 2nd order Latent variable Estimation from ordinal data (WLSMV)	Videos: https://goo.gl/Q7zmyR
october/26th	Exercise from published paper.   HW2: Search an article with a structural model and covariance matrix among indicators (caution: do not use the correlations between latent variables). Run the model from these data in lavaan. Report and explain the results.	Videos: https://goo.gl/Q7zmyR

 

[21/09/2018]

Software SmartPLS available at:

• v.3 (free 30-day trial): <https://www.smartpls.com/#downloads>.
• v.2 (free): <https://www.smartpls.com/smartpls2>.

 

Software R available at: <https://www.r-project.org/>.

 

Software G*Power 3 at: <http://www.gpower.hhu.de/>.

 

A-priori Sample Size Calculator for Structural Equation Models (online) at: <https://www.danielsoper.com/statcalc/calculator.aspx?id=89>

<http://www.quantpsy.org/rmsea/rmsea.htm>.

<http://timo.gnambs.at/en/scripts/powerforsem>.

 

STREINER, D.L. Finding our way: an introduction to path analysis. Canadian Journal of Psychiatry. v.50, n.2, p.115-122, 2005. [EBSCO]

HENSELER, J.; RINGLE, C. M.; SINKOVICS, R. R. The use of partial least squares path modeling in international marketing. Advances in International Marketing, Advances in International Marketing., v. 20, p. 277–319, 2009. available at: <http://is.gd/4EGtMc>.

MACKENZIE, S. B.; PODSAKOFF, P. M.; PODSAKOFF, N. P. Construct measurement and validation procedures in MIS and behavioral research: integrating new and existing techniques. MIS Quarterly, v. 35, n. 2, p. 293–334, 2011. available at: <http://is.gd/aeK8cl>.

 

BIDO, D.S. et al. Escalas como Ferramentas de Diagnóstico e Gestão: críticas ao uso da análise fatorial exploratória para a validação. [Relatório de pesquisa – CNPq], 2016.

GEFEN, D.; STRAUB, D. W. 2005. “A Practical Guide to Factorial Validity Using PLS-Graph: Tutorial and Annotated Example,” Communications of the Association for Information Systems, v.16, p. 91-109, 2005.

 

[28/09/2018]

GUDERGAN, S.; RINGLE, C.; WENDE, S.; WILL, A. Confirmatory tetrad analysis in PLS path modeling. Journal of Business Research, v. 61, n. 12, p. 1238–1249, 2008.

ROOS, J. M. The Vanishing Tetrad Test: Another test of model misspecification. Measurement: Interdisciplinary Research and Perspectives, v. 12, n. 3, p. 109–114, 2014.

 

Video “Importance-Performance Map Analysis (IPMA)”, disponível em: <https://www.smartpls.com/documentation/importance-performance-matrix>.

BECKER, J. M.; KLEIN, K.; WETZELS, M. Hierarchical Latent Variable Models in PLS-SEM: Guidelines for Using Reflective-Formative Type Models. Long Range Planning, v.45, n.5-6, p.359–394, 2012.

WETZELS, M.; ODEKERKEN-SCHRÖDER, G.; OPPEN, C. VAN. Using PLS path modeling for assessing hierarchical construct models: guidelines and empirical illustration. MIS Quarterly, v. 33, n. 1, p. 177–195, 2009.

WILSON, B.; HENSELER, J. Modeling reflective higher-order constructs using three approaches with PLS path modeling: a Monte Carlo comparison. Australian and New Zealand Marketing Academy (ANZMAC) Conference. Anais... p.791–800, 2007. Disponível em: <http://doc.utwente.nl/91758/1/BWilson_2.pdf>.

 

Software G*Power (free)  e  G*Power 3.1 manual. January 31, 2014. Disponíveis em: <http://www.gpower.hhu.de/>.

FAUL, F.; ERDFELDER, E.; LANG, A.-G.; BUCHNER, A. G*Power 3: a flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behavior Research Methods, v. 39, n. 2, p. 175–91, 2007.

 

CHIN, W. W.; THATCHER, J. B.; WRIGHT, R. T.; STEEL, D. Controling for common method variance in PLS analysis: the measured latent marker variable approach. In: ABDI, H.; CHIN, W. W.; VINZI, V. E.; RUSSOLILLO, G.; TRINCHERA, L. (Ed.). New Perspectives in Partial Least Squares and Related Methods. New York: Springer, 2013. p.231-239. 

HENSELER, J.; HUBONA, G.; RAY, P. A. Using PLS Path Modeling in New Technology Research : Updated Guidelines Using PLS Path Modeling in New Technology Research : Updated Guidelines. Industrial Management & Data Systems, v. 116, p. 2–20, 2016.

ONÇA. S.S.; BIDO, D.S.; GODOY, A.S. Impacto da potência de equipes de trabalho e dos conflitos intragrupais na aprendizagem grupal. In: Encontro da ANPAD, XXXVIII, 2014, Rio de Janeiro. Anais... Rio de Janeiro: ANPAD, 2014, p.1-16.

 

Video: “PLS Multigroup Analysis” available at: <https://www.smartpls.com/documentation/pls-multigroup-analysis>.

HENSELER, J. PLS-MGA: A Non-Parametric Approach to Partial Least Squares-based Multi-Group Analysis. In: W. Gaul et al. (eds.), Challenges at the Interface of Data Analysis, Computer Science, and Optimization, Studies in Classification, Data Analysis, and Knowledge Organization, DOI 10.1007/978-3-642-24466-7 50, © Springer-Verlag Berlin Heidelberg, 2012. available at: <http://is.gd/AQRBth>.

HENSELER, J.; RINGLE, C. M.; SARSTEDT, M. Testing Measurement Invariance of Composites Using Partial Least Squares. International Marketing Review, forthcoming. 2016. available at: <http://info.smartpls.com/data/uploads/micom.pdf>.

 

 

[19/10/2018 e 26/10/2018]

ROSSEEL, Y. The lavaan tutorial. Belgium: Ghent University, Department of Data Analysis, 2015. available at: <http://lavaan.ugent.be/tutorial/tutorial.pdf>. 

lavaan website:  <http://lavaan.ugent.be/resources/teaching.html>

HAIR Jr., J.F.; BLACK, W.C.; BABIN, B.J.; ANDERSON, R.E. Multivariate Data Analysis, 7/e. Harlow: Pearson Prentice Hall, 2010. [Chapter 13 (p.600-638): Confirmatory Factor Analysis].

 

SOPER, D. S. A-priori Sample Size Calculator for Structural Equation Models [Software]. , 2017. Disponível em: <http://www.danielsoper.com/statcalc>. .

 

What to report?

GEFEN, D.; RIGDON, E. E.; STRAUB, D. An update and extension to SEM guidelines for administrative and social science research. MIS Quarterly, v.35, n.2, p.iii-xiv, 2011