IDEAS home Printed from https://ideas.repec.org/a/spr/psycho/v54y1989i2p249-259.html
   My bibliography  Save this article

Incorporating prior theory in covariance structure analysis: A bayesian approach

Author

Listed:
  • Claes Fornell
  • Roland Rust

Abstract

No abstract is available for this item.

Suggested Citation

  • Claes Fornell & Roland Rust, 1989. "Incorporating prior theory in covariance structure analysis: A bayesian approach," Psychometrika, Springer;The Psychometric Society, vol. 54(2), pages 249-259, June.
  • Handle: RePEc:spr:psycho:v:54:y:1989:i:2:p:249-259
    DOI: 10.1007/BF02294519
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/BF02294519
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1007/BF02294519?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. Sik-Yum Lee, 1981. "A bayesian approach to confirmatory factor analysis," Psychometrika, Springer;The Psychometric Society, vol. 46(2), pages 153-160, June.
    2. Yoshio Takane, 1981. "Multidimensional successive categories scaling: A maximum likelihood method," Psychometrika, Springer;The Psychometric Society, vol. 46(1), pages 9-28, 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. Namwoon Kim & Jin K. Han & Rajendra K. Srivastava, 2002. "A Dynamic IT Adoption Model for the SOHO Market: PC Generational Decisions with Technological Expectations," Management Science, INFORMS, vol. 48(2), pages 222-240, February.
    2. Wagner Kamakura, 1991. "Estimating flexible distributions of ideal-points with external analysis of preferences," Psychometrika, Springer;The Psychometric Society, vol. 56(3), pages 419-431, September.
    3. Lehmann, Donald R., 2020. "The evolving world of research in marketing and the blending of theory and data," International Journal of Research in Marketing, Elsevier, vol. 37(1), pages 27-42.
    4. Astrea Camstra & Anne Boomsma, 1992. "Cross-Validation in Regression and Covariance Structure Analysis," Sociological Methods & Research, , vol. 21(1), pages 89-115, August.

    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. Jiang, Xiaomo & Mahadevan, Sankaran, 2009. "Bayesian structural equation modeling method for hierarchical model validation," Reliability Engineering and System Safety, Elsevier, vol. 94(4), pages 796-809.
    2. Tammo H.A. Bijmolt & Michel Wedel & Wayne S. DeSarbo, 2021. "Adaptive Multidimensional Scaling: Brand Positioning Based on Decision Sets and Dissimilarity Judgments," Customer Needs and Solutions, Springer;Institute for Sustainable Innovation and Growth (iSIG), vol. 8(1), pages 1-15, June.
    3. David Kaplan & Chansoon Lee, 2018. "Optimizing Prediction Using Bayesian Model Averaging: Examples Using Large-Scale Educational Assessments," Evaluation Review, , vol. 42(4), pages 423-457, August.
    4. Yoshio Takane & Justine Sergent, 1983. "Multidimensional scaling models for reaction times and same-different judgments," Psychometrika, Springer;The Psychometric Society, vol. 48(3), pages 393-423, September.
    5. Lai-Fa Hung & Wen-Chung Wang, 2012. "The Generalized Multilevel Facets Model for Longitudinal Data," Journal of Educational and Behavioral Statistics, , vol. 37(2), pages 231-255, April.
    6. Asim Ansari & Kamel Jedidi & Sharan Jagpal, 2000. "A Hierarchical Bayesian Methodology for Treating Heterogeneity in Structural Equation Models," Marketing Science, INFORMS, vol. 19(4), pages 328-347, August.
    7. P. Bentler, 1986. "Structural modeling and psychometrika: An historical perspective on growth and achievements," Psychometrika, Springer;The Psychometric Society, vol. 51(1), pages 35-51, March.
    8. Wayne DeSarbo & Donald Lehmann & Gregory Carpenter & Indrajit Sinha, 1996. "A stochastic multidimensional unfolding approach for representing phased decision outcomes," Psychometrika, Springer;The Psychometric Society, vol. 61(3), pages 485-508, September.
    9. Wayne DeSarbo & Joonwook Park & Vithala Rao, 2011. "Deriving joint space positioning maps from consumer preference ratings," Marketing Letters, Springer, vol. 22(1), pages 1-14, March.
    10. J. Ramsey, 1986. "A PROC MATRIX program for preference-dissimilarity multidimensional scaling," Psychometrika, Springer;The Psychometric Society, vol. 51(1), pages 163-170, March.
    11. Byron Gajewski & Larry Price & Valorie Coffland & Diane Boyle & Marjorie Bott, 2013. "Integrated analysis of content and construct validity of psychometric instruments," Quality & Quantity: International Journal of Methodology, Springer, vol. 47(1), pages 57-78, January.
    12. Bijmolt, T.H.A. & Wedel, M. & DeSarbo, W.S., 2002. "Adaptive Multidimensional Scaling : The Spatial Representation of Brand Consideration and Dissimilarity Judgments," Other publications TiSEM 26b65f04-0d5f-42d6-8a85-8, Tilburg University, School of Economics and Management.
    13. Hong-Tu Zhu & Sik-Yum Lee, 2001. "A Bayesian analysis of finite mixtures in the LISREL model," Psychometrika, Springer;The Psychometric Society, vol. 66(1), pages 133-152, March.
    14. Janice Kirner Providelo & Suely Penha Sanches, 2011. "Roadway and traffic characteristics for bicycling," Transportation, Springer, vol. 38(5), pages 765-777, September.
    15. Yoshio Takane & J. Carroll, 1981. "Nonmetric maximum likelihood multidimensional scaling from directional rankings of similarities," Psychometrika, Springer;The Psychometric Society, vol. 46(4), pages 389-405, December.
    16. Gert Storms, 1995. "On the robustness of maximum likelihood scaling for violations of the error model," Psychometrika, Springer;The Psychometric Society, vol. 60(2), pages 247-258, June.
    17. Diana Mindrila, 2023. "Bayesian Latent Class Analysis: Sample Size, Model Size, and Classification Precision," Mathematics, MDPI, vol. 11(12), pages 1-18, June.
    18. J. Carroll, 1985. "Review," Psychometrika, Springer;The Psychometric Society, vol. 50(1), pages 133-140, March.
    19. Elena A. Erosheva & S. McKay Curtis, 2017. "Dealing with Reflection Invariance in Bayesian Factor Analysis," Psychometrika, Springer;The Psychometric Society, vol. 82(2), pages 295-307, June.
    20. Asim Ansari & Kamel Jedidi, 2000. "Bayesian factor analysis for multilevel binary observations," Psychometrika, Springer;The Psychometric Society, vol. 65(4), pages 475-496, December.

    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:54:y:1989:i:2:p:249-259. 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.