IDEAS home Printed from https://ideas.repec.org/h/spr/prbchp/978-3-031-34589-0_1.html

Empirical Validation of the 10-Times Rule for SEM

In: State of the Art in Partial Least Squares Structural Equation Modeling (PLS-SEM)

Author

Listed:
  • Ralf Wagner

    (University of Kassel, Chair for Sustainable Marketing)

  • Malek Simon Grimm

    (University of Kassel, Chair for Sustainable Marketing)

Abstract

Structural equation modeling has become indispensable in empirical research due to its ability to measure latent constructs. However, researchers always face the central dilemma of determining a sufficient sample size to ensure a converting model, true estimates, and sufficient statistical power. The prominent 10-times rule suggests that the minimum sample size should be 10 times the maximum number of arrowheads pointing at a latent variable anywhere in the partial least squares path model. Despite its prominence in research practice, this rule of thumb lacks systematic validation. This study contributes a simulation assessing the validity of 10-times traits in which an original trait was tested against a 60-times trait, a 50-times trait, a 40-times trait, a 30-times trait, a 20-times trait, and a 10-times trait. The results show that the 10-times trait is substantially biased and that researchers are well-advised to use at least a 30-times trait.

Suggested Citation

  • Ralf Wagner & Malek Simon Grimm, 2023. "Empirical Validation of the 10-Times Rule for SEM," Springer Proceedings in Business and Economics, in: Lăcrămioara Radomir & Raluca Ciornea & Huiwen Wang & Yide Liu & Christian M. Ringle & Marko Sarstedt (ed.), State of the Art in Partial Least Squares Structural Equation Modeling (PLS-SEM), pages 3-7, Springer.
    • Handle: RePEc:spr:prbchp:978-3-031-34589-0_1
    DOI: 10.1007/978-3-031-34589-0_1
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a
    for a similarly titled item that would be available.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Thomas Appiah & Blessed Assefuah & Veronica Venyo Agblewornu & Nelly Mac-Arhin Forson & Grace Donkoh, 2025. "Adoption of electronic payment platforms by fast moving consumer goods (FMCG) businesses in Ghana: PLS-SEM and FSQCA approaches," SN Business & Economics, Springer, vol. 5(5), pages 1-28, May.
    2. Meike Rombach & David Dean & Frank Vriesekoop & Bin Jiang & Zeyuan Zhou & Wendy Hao & Wim Koning, 2025. "Understanding factors determining Chinese consumer’s willingness to eat cultured meat, insect, and plant-based proteins," International Review on Public and Nonprofit Marketing, Springer;International Association of Public and Non-Profit Marketing, vol. 22(1), pages 77-109, March.
    3. Stefanos Balaskas & Ioannis Stamatiou & Kyriakos Komis & Theofanis Nikolopoulos, 2025. "Perceptions of Greenwashing and Purchase Intentions: A Model of Gen Z Responses to ESG-Labeled Digital Advertising," Risks, MDPI, vol. 13(8), pages 1-33, August.
    4. Stefanos Balaskas & Ioanna Yfantidou & Antiopi Panteli & Kyriakos Komis & Theofanis Nikolopoulos, 2025. "From Intention to Action: Modeling Post-Visit Responsible Behavior in Ecotourism," Tourism and Hospitality, MDPI, vol. 6(4), pages 1-35, September.
    5. Yantong Guo & Hayyan Nassar Waked, 2026. "Trust-Mediated adoption of AI Robo-Advisors in inland china: an extended UTAUT perspective," Journal of Financial Services Marketing, Palgrave Macmillan, vol. 31(1), pages 1-13, March.
    6. Stefanos Balaskas & Maria Konstantakopoulou & Ioanna Yfantidou & Kyriakos Komis, 2025. "Algorithmic Burnout and Digital Well-Being: Modelling Young Adults’ Resistance to Personalized Digital Persuasion," Societies, MDPI, vol. 15(8), pages 1-33, August.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    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:prbchp:978-3-031-34589-0_1. 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.

    We have no bibliographic references for this item. You can help adding them by using 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.