IDEAS home Printed from https://ideas.repec.org/a/spr/compst/v28y2013i2p565-580.html
   My bibliography  Save this article

Goodness-of-fit indices for partial least squares path modeling

Author

Listed:
  • Jörg Henseler
  • Marko Sarstedt

Abstract

This paper discusses a recent development in partial least squares (PLS) path modeling, namely goodness-of-fit indices. In order to illustrate the behavior of the goodness-of-fit index (GoF) and the relative goodness-of-fit index (GoF rel ), we estimate PLS path models with simulated data, and contrast their values with fit indices commonly used in covariance-based structural equation modeling. The simulation shows that the GoF and the GoF rel are not suitable for model validation. However, the GoF can be useful to assess how well a PLS path model can explain different sets of data. Copyright The Author(s) 2013

Suggested Citation

  • Jörg Henseler & Marko Sarstedt, 2013. "Goodness-of-fit indices for partial least squares path modeling," Computational Statistics, Springer, vol. 28(2), pages 565-580, April.
  • Handle: RePEc:spr:compst:v:28:y:2013:i:2:p:565-580
    DOI: 10.1007/s00180-012-0317-1
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s00180-012-0317-1
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1007/s00180-012-0317-1?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Wim Krijnen & Theo Dijkstra & Richard Gill, 1998. "Conditions for factor (in)determinacy in factor analysis," Psychometrika, Springer;The Psychometric Society, vol. 63(4), pages 359-367, December.
    2. Wold, Herman, 1974. "Causal flows with latent variables : Partings of the ways in the light of NIPALS modelling," European Economic Review, Elsevier, vol. 5(1), pages 67-86, June.
    3. Marko Sarstedt & Christian Ringle, 2010. "Treating unobserved heterogeneity in PLS path modeling: a comparison of FIMIX-PLS with different data analysis strategies," Journal of Applied Statistics, Taylor & Francis Journals, vol. 37(8), pages 1299-1318.
    4. Mohamed Hanafi, 2007. "PLS Path modelling: computation of latent variables with the estimation mode B," Computational Statistics, Springer, vol. 22(2), pages 275-292, July.
    5. Claes Fornell, 1995. "The Quality of Economic Output: Empirical Generalizations About Its Distribution and Relationship to Market Share," Marketing Science, INFORMS, vol. 14(3_supplem), pages 203-211.
    6. Arthur Tenenhaus & Michel Tenenhaus, 2011. "Regularized Generalized Canonical Correlation Analysis," Psychometrika, Springer;The Psychometric Society, vol. 76(2), pages 257-284, April.
    7. Jörg Henseler, 2010. "On the convergence of the partial least squares path modeling algorithm," Computational Statistics, Springer, vol. 25(1), pages 107-120, March.
    8. Monecke, Armin & Leisch, Friedrich, 2012. "semPLS: Structural Equation Modeling Using Partial Least Squares," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 48(i03).
    9. Hirotugu Akaike, 1987. "Factor analysis and AIC," Psychometrika, Springer;The Psychometric Society, vol. 52(3), pages 317-332, September.
    10. Tenenhaus, Michel & Vinzi, Vincenzo Esposito & Chatelin, Yves-Marie & Lauro, Carlo, 2005. "PLS path modeling," Computational Statistics & Data Analysis, Elsevier, vol. 48(1), pages 159-205, January.
    11. John Hulland, 1999. "Use of partial least squares (PLS) in strategic management research: a review of four recent studies," Strategic Management Journal, Wiley Blackwell, vol. 20(2), pages 195-204, February.
    12. Wynne W. Chin & Barbara L. Marcolin & Peter R. Newsted, 2003. "A Partial Least Squares Latent Variable Modeling Approach for Measuring Interaction Effects: Results from a Monte Carlo Simulation Study and an Electronic-Mail Emotion/Adoption Study," Information Systems Research, INFORMS, vol. 14(2), pages 189-217, June.
    13. Reinartz, Werner & Haenlein, Michael & Henseler, Jörg, 2009. "An empirical comparison of the efficacy of covariance-based and variance-based SEM," International Journal of Research in Marketing, Elsevier, vol. 26(4), pages 332-344.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Joseph F. Hair & G. Tomas M. Hult & Christian M. Ringle & Marko Sarstedt & Kai Oliver Thiele, 2017. "Mirror, mirror on the wall: a comparative evaluation of composite-based structural equation modeling methods," Journal of the Academy of Marketing Science, Springer, vol. 45(5), pages 616-632, September.
    2. Sarstedt, Marko & Hair, Joseph F. & Ringle, Christian M. & Thiele, Kai O. & Gudergan, Siegfried P., 2016. "Estimation issues with PLS and CBSEM: Where the bias lies!," Journal of Business Research, Elsevier, vol. 69(10), pages 3998-4010.
    3. Jörg Henseler, 2018. "Partial least squares path modeling: Quo vadis?," Quality & Quantity: International Journal of Methodology, Springer, vol. 52(1), pages 1-8, January.
    4. Nitzl, Christian, 2016. "The use of partial least squares structural equation modelling (PLS-SEM) in management accounting research: Directions for future theory development," Journal of Accounting Literature, Elsevier, vol. 37(C), pages 19-35.
    5. Linder, Christian, 2019. "Customer orientation and operations: The role of manufacturing capabilities in small- and medium-sized enterprises," International Journal of Production Economics, Elsevier, vol. 216(C), pages 105-117.
    6. Yeo, Vincent Cheow Sern & Goh, See-Kwong & Rezaei, Sajad, 2017. "Consumer experiences, attitude and behavioral intention toward online food delivery (OFD) services," Journal of Retailing and Consumer Services, Elsevier, vol. 35(C), pages 150-162.
    7. Gupta, Prashant & Seetharaman, A. & Raj, John Rudolph, 2013. "The usage and adoption of cloud computing by small and medium businesses," International Journal of Information Management, Elsevier, vol. 33(5), pages 861-874.
    8. Asif Khan & Chih-Cheng Chen & Kwanrat Suanpong & Athapol Ruangkanjanases & Santhaya Kittikowit & Shih-Chih Chen, 2021. "The Impact of CSR on Sustainable Innovation Ambidexterity: The Mediating Role of Sustainable Supply Chain Management and Second-Order Social Capital," Sustainability, MDPI, vol. 13(21), pages 1-25, November.
    9. José Roberto Frega & Alex Antonio Ferraresi & Carlos Olavo Quandt & Claudimar Pereira da Veiga, 2018. "Relationships Among Knowledge Management, Organisational Innovativeness and Performance: Covariance-Based Versus Partial Least-Squares Structural Equation Modelling," Journal of Information & Knowledge Management (JIKM), World Scientific Publishing Co. Pte. Ltd., vol. 17(01), pages 1-19, March.
    10. Petschnig, Martin & Heidenreich, Sven & Spieth, Patrick, 2014. "Innovative alternatives take action – Investigating determinants of alternative fuel vehicle adoption," Transportation Research Part A: Policy and Practice, Elsevier, vol. 61(C), pages 68-83.
    11. Ringle, Christian M. & Sarstedt, Marko & Schlittgen, Rainer & Taylor, Charles R., 2013. "PLS path modeling and evolutionary segmentation," Journal of Business Research, Elsevier, vol. 66(9), pages 1318-1324.
    12. A. Q. Adeleke & A. Y. Bahaudin & A. M. Kamaruddeen, 2018. "Organizational Internal Factors and Construction Risk Management among Nigerian Construction Companies," Global Business Review, International Management Institute, vol. 19(4), pages 921-938, August.
    13. Sarstedt, Marko & Ringle, Christian M. & Smith, Donna & Reams, Russell & Hair, Joseph F., 2014. "Partial least squares structural equation modeling (PLS-SEM): A useful tool for family business researchers," Journal of Family Business Strategy, Elsevier, vol. 5(1), pages 105-115.
    14. Christian Nitzl & Wynne W. Chin, 2017. "The case of partial least squares (PLS) path modeling in managerial accounting research," Journal of Management Control: Zeitschrift für Planung und Unternehmenssteuerung, Springer, vol. 28(2), pages 137-156, May.
    15. Sebastian Goebel & Barbara E. Weißenberger, 2017. "The Relationship Between Informal Controls, Ethical Work Climates, and Organizational Performance," Journal of Business Ethics, Springer, vol. 141(3), pages 505-528, March.
    16. Tenenhaus, Arthur & Philippe, Cathy & Frouin, Vincent, 2015. "Kernel Generalized Canonical Correlation Analysis," Computational Statistics & Data Analysis, Elsevier, vol. 90(C), pages 114-131.
    17. Suning Zhu & Ashish Gupta & David Paradice & Casey Cegielski, 2019. "Understanding the Impact of Immersion and Authenticity on Satisfaction Behavior in Learning Analytics Tasks," Information Systems Frontiers, Springer, vol. 21(4), pages 791-814, August.
    18. Ringle, Christian M. & Götz, Oliver & Wetzels, Martin & Wilson, Bradley, 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," MPRA Paper 15390, University Library of Munich, Germany.
    19. Chung-Ho Su, 2018. "Exploring Sustainability Environment Educational Design and Learning Effect Evaluation through Migration Theory: An Example of Environment Educational Serious Games," Sustainability, MDPI, vol. 10(10), pages 1-26, September.
    20. Loureiro, Sandra Maria Correia & Araújo, Cristiano Mineiro Branco de, 2014. "Luxury values and experience as drivers for consumers to recommend and pay more," Journal of Retailing and Consumer Services, Elsevier, vol. 21(3), pages 394-400.

    More about this item

    Keywords

    Partial least squares path modeling (PLS); Goodness-of-fit index (GoF); C39;
    All these keywords.

    JEL classification:

    • C39 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Other

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:compst:v:28:y:2013:i:2:p:565-580. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.