Confirmatory composite analysis

In statistics, confirmatory composite analysis (CCA) is a sub-type of structural equation modeling (SEM).[1][2][3] Although, historically, CCA emerged from a re-orientation and re-start of partial least squares path modeling (PLS-PM),[4][5][6][7] it has become an independent approach and the two should not be confused. In many ways it is similar to, but also quite distinct from confirmatory factor analysis (CFA). It shares with CFA the process of model specification, model identification, model estimation, and model assessment. However, in contrast to CFA which always assumes the existence of latent variables, in CCA all variables can be observable, with their interrelationships expressed in terms of composites, i.e., linear compounds of subsets of the variables. The composites are treated as the fundamental objects and path diagrams can be used to illustrate their relationships. This makes CCA particularly useful for disciplines examining theoretical concepts that are designed to attain certain goals, so-called artifacts,[8] and their interplay with theoretical concepts of behavioral sciences.[9]

  1. ^ Henseler, Jörg; Schuberth, Florian (2020). "Using confirmatory composite analysis to assess emergent variables in business research". Journal of Business Research. 120: 147–156. doi:10.1016/j.jbusres.2020.07.026. hdl:10362/103667.
  2. ^ Schuberth, Florian; Henseler, Jörg; Dijkstra, Theo K. (2018). "Confirmatory Composite Analysis". Frontiers in Psychology. 9: 2541. doi:10.3389/fpsyg.2018.02541. PMC 6300521. PMID 30618962.
  3. ^ Henseler, Jörg; Hubona, Geoffrey; Ray, Pauline Ash (2016). "Using PLS path modeling in new technology research: updated guidelines". Industrial Management & Data Systems. 116 (1): 2–20. doi:10.1108/IMDS-09-2015-0382.
  4. ^ Henseler, Jörg; Dijkstra, Theo K.; Sarstedt, Marko; Ringle, Christian M.; Diamantopoulos, Adamantios; Straub, Detmar W.; Ketchen, David J.; Hair, Joseph F.; Hult, G. Tomas M.; Calantone, Roger J. (2014). "Common Beliefs and Reality About PLS". Organizational Research Methods. 17 (2): 182–209. doi:10.1177/1094428114526928. hdl:10362/117915.
  5. ^ Dijkstra, Theo K. (2010). "Latent Variables and Indices: Herman Wold's Basic Design and Partial Least Squares". In Esposito Vinzi, Vincenzo; Chin, Wynne W.; Henseler, Jörg; Wang, Huiwen (eds.). Handbook of Partial Least Squares. Berlin, Heidelberg: Springer Handbooks of Computational Statistics. pp. 23–46. CiteSeerX 10.1.1.579.8461. doi:10.1007/978-3-540-32827-8_2. ISBN 978-3-540-32825-4.
  6. ^ Dijkstra, Theo K.; Henseler, Jörg (2011). "Linear indices in nonlinear structural equation models: best fitting proper indices and other composites". Quality & Quantity. 45 (6): 1505–1518. doi:10.1007/s11135-010-9359-z. S2CID 120868602.
  7. ^ Dijkstra, Theo K. (2017). "A Perfect Match Between a Model and a Mode". In Latan, Hengky; Noonan, Richard (eds.). Partial Least Squares Path Modeling: Basic Concepts, Methodological Issues and Applications. Cham: Springer International Publishing. pp. 55–80. doi:10.1007/978-3-319-64069-3_4. ISBN 978-3-319-64068-6.
  8. ^ Simon, Herbert A. (1969). The sciences of the artificial (3rd ed.). Cambridge, MA: MIT Press.
  9. ^ Henseler, Jörg (2017). "Bridging Design and Behavioral Research With Variance-Based Structural Equation Modeling" (PDF). Journal of Advertising. 46 (1): 178–192. doi:10.1080/00913367.2017.1281780.