IDEAS home Printed from https://ideas.repec.org/a/spr/psycho/v75y2010i2p228-242.html
   My bibliography  Save this article

Generalized Structured Component Analysis with Latent Interactions

Author

Listed:
  • Heungsun Hwang
  • Moon-Ho Ho
  • Jonathan Lee

Abstract

No abstract is available for this item.

Suggested Citation

  • Heungsun Hwang & Moon-Ho Ho & Jonathan Lee, 2010. "Generalized Structured Component Analysis with Latent Interactions," Psychometrika, Springer;The Psychometric Society, vol. 75(2), pages 228-242, June.
    • Handle: RePEc:spr:psycho:v:75:y:2010:i:2:p:228-242
    DOI: 10.1007/s11336-010-9157-5
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s11336-010-9157-5
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s11336-010-9157-5?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. TENENHAUS, Michel, 2008. "Component-based structural equation modelling," HEC Research Papers Series 887, HEC Paris.
    2. Forrest Young & Jan Leeuw & Yoshio Takane, 1976. "Regression with qualitative and quantitative variables: An alternating least squares method with optimal scaling features," Psychometrika, Springer;The Psychometric Society, vol. 41(4), pages 505-529, December.
    3. Heungsun Hwang & Wayne Desarbo & Yoshio Takane, 2007. "Fuzzy Clusterwise Generalized Structured Component Analysis," Psychometrika, Springer;The Psychometric Society, vol. 72(2), pages 181-198, June.
    4. Pui-Wa Lei, 2009. "Evaluating estimation methods for ordinal data in structural equation modeling," Quality & Quantity: International Journal of Methodology, Springer, vol. 43(3), pages 495-507, May.
    5. Michel Tenenhaus, 2008. "Component-based Structural Equation Modelling," Working Papers hal-00580149, HAL.
    6. Richins, Marsha L & Dawson, Scott, 1992. "A Consumer Values Orientation for Materialism and Its Measurement: Scale Development and Validation," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 19(3), pages 303-316, December.
    7. Heungsun Hwang & Yoshio Takane, 2004. "Generalized structured component analysis," Psychometrika, Springer;The Psychometric Society, vol. 69(1), pages 81-99, March.
    8. Jan Leeuw & Forrest Young & Yoshio Takane, 1976. "Additive structure in qualitative data: An alternating least squares method with optimal scaling features," Psychometrika, Springer;The Psychometric Society, vol. 41(4), pages 471-503, December.
    9. Carolyn P. Egri & David A. Ralston, 2004. "Generation Cohorts and Personal Values: A Comparison of China and the United States," Organization Science, INFORMS, vol. 15(2), pages 210-220, April.
    10. Bagozzi, Richard P & Baumgartner, Hans & Yi, Youjae, 1992. "State versus Action Orientation and the Theory of Reasoned Action: An Application to Coupon Usage," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 18(4), pages 505-518, March.
    Full references (including those not matched with items on IDEAS)

    Citations

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


    Cited by:

    1. Nebojsa S. Davcik, 2013. "The Use And Misuse Of Structural Equation Modeling In Management Research," Working Papers Series 2 13-07, ISCTE-IUL, Business Research Unit (BRU-IUL).
    2. Kwanghee Jung & Yoshio Takane & Heungsun Hwang & Todd Woodward, 2012. "Dynamic GSCA (Generalized Structured Component Analysis) with Applications to the Analysis of Effective Connectivity in Functional Neuroimaging Data," Psychometrika, Springer;The Psychometric Society, vol. 77(4), pages 827-848, October.
    3. Necmi Kemal Avkiran, 2018. "An in-depth discussion and illustration of partial least squares structural equation modeling in health care," Health Care Management Science, Springer, vol. 21(3), pages 401-408, September.
    4. Gyeongcheol Cho & Heungsun Hwang, 2024. "Generalized Structured Component Analysis Accommodating Convex Components: A Knowledge-Based Multivariate Method with Interpretable Composite Indexes," Psychometrika, Springer;The Psychometric Society, vol. 89(1), pages 241-266, March.
    5. Alassane D. Yeo & Aimin Deng & Todine Y. Nadiedjoa, 2020. "The Effect of Infrastructure and Logistics Performance on Economic Performance: The Mediation Role of International Trade," Foreign Trade Review, , vol. 55(4), pages 450-465, November.
    6. Zhou, Lixing & Takane, Yoshio & Hwang, Heungsun, 2016. "Dynamic GSCANO (Generalized Structured Canonical Correlation Analysis) with applications to the analysis of effective connectivity in functional neuroimaging data," Computational Statistics & Data Analysis, Elsevier, vol. 101(C), pages 93-109.

    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. Gyeongcheol Cho & Heungsun Hwang & Marko Sarstedt & Christian M. Ringle, 2020. "Cutoff criteria for overall model fit indexes in generalized structured component analysis," Journal of Marketing Analytics, Palgrave Macmillan, vol. 8(4), pages 189-202, December.
    2. Heungsun Hwang & Gyeongcheol Cho, 2020. "Global Least Squares Path Modeling: A Full-Information Alternative to Partial Least Squares Path Modeling," Psychometrika, Springer;The Psychometric Society, vol. 85(4), pages 947-972, December.
    3. Hye Won Suk & Heungsun Hwang, 2016. "Functional Generalized Structured Component Analysis," Psychometrika, Springer;The Psychometric Society, vol. 81(4), pages 940-968, December.
    4. Zhou, Lixing & Takane, Yoshio & Hwang, Heungsun, 2016. "Dynamic GSCANO (Generalized Structured Canonical Correlation Analysis) with applications to the analysis of effective connectivity in functional neuroimaging data," Computational Statistics & Data Analysis, Elsevier, vol. 101(C), pages 93-109.
    5. Seohee Park & Seongeun Kim & Ji Hoon Ryoo, 2020. "Latent Class Regression Utilizing Fuzzy Clusterwise Generalized Structured Component Analysis," Mathematics, MDPI, vol. 8(11), pages 1-16, November.
    6. Kwanghee Jung & Yoshio Takane & Heungsun Hwang & Todd Woodward, 2012. "Dynamic GSCA (Generalized Structured Component Analysis) with Applications to the Analysis of Effective Connectivity in Functional Neuroimaging Data," Psychometrika, Springer;The Psychometric Society, vol. 77(4), pages 827-848, October.
    7. Rainer Schlittgen, 2018. "Estimation of generalized structured component analysis models with alternating least squares," Computational Statistics, Springer, vol. 33(1), pages 527-548, March.
    8. Gyeongcheol Cho & Christopher Schlaegel & Heungsun Hwang & Younyoung Choi & Marko Sarstedt & Christian M. Ringle, 2022. "Integrated Generalized Structured Component Analysis: On the Use of Model Fit Criteria in International Management Research," Management International Review, Springer, vol. 62(4), pages 569-609, August.
    9. A. R. Abdul-Aziz & Albert Luguterah & Bashiru I. I. Saeed, 2021. "Comparative Performance of Estimation Maximization Among Residual Estimators: A Structural Equation Modelling Perspective," International Journal of Statistics and Probability, Canadian Center of Science and Education, vol. 10(2), pages 138-138, March.
    10. Kadziński, MiŁosz & Greco, Salvatore & SŁowiński, Roman, 2012. "Extreme ranking analysis in robust ordinal regression," Omega, Elsevier, vol. 40(4), pages 488-501.
    11. Alam, Mohammad Zahedul & Hu, Wang & Kaium, Md Abdul & Hoque, Md Rakibul & Alam, Mirza Mohammad Didarul, 2020. "Understanding the determinants of mHealth apps adoption in Bangladesh: A SEM-Neural network approach," Technology in Society, Elsevier, vol. 61(C).
    12. van Rosmalen, J.M. & Koning, A.J. & Groenen, P.J.F., 2007. "Optimal Scaling of Interaction Effects in Generalized Linear Models," Econometric Institute Research Papers EI 2007-44, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    13. Arif ÇETİN & Salih GÜNEY, 2019. "İBB Spor İstanbul’da Çalışanların Örgütsel Adalet ve Örgütsel Güven Algılarının Örgütsel Bağlılık Davranışı Üzerindeki Etkisi ve Buna Yönelik Bir Uygulama," Istanbul Management Journal, Istanbul University Business School, vol. 0(87), pages 93-109, June.
    14. Florian Schuberth & Jörg Henseler & Theo K. Dijkstra, 2018. "Partial least squares path modeling using ordinal categorical indicators," Quality & Quantity: International Journal of Methodology, Springer, vol. 52(1), pages 9-35, January.
    15. 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.
    16. Arthur Tenenhaus & Michel Tenenhaus, 2011. "Regularized Generalized Canonical Correlation Analysis," Psychometrika, Springer;The Psychometric Society, vol. 76(2), pages 257-284, April.
    17. Michio Yamamoto, 2012. "Clustering of functional data in a low-dimensional subspace," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 6(3), pages 219-247, October.
    18. Pasquale Dolce & Cristina Davino & Domenico Vistocco, 2022. "Quantile composite-based path modeling: algorithms, properties and applications," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 16(4), pages 909-949, December.
    19. Stéphanie Bougeard & Hervé Abdi & Gilbert Saporta & Ndèye Niang, 2018. "Clusterwise analysis for multiblock component methods," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 12(2), pages 285-313, June.
    20. Pearce, Antony & Pons, Dirk, 2019. "Advancing lean management: The missing quantitative approach," Operations Research Perspectives, Elsevier, vol. 6(C).

    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:psycho:v:75:y:2010:i:2:p:228-242. 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.