Cited by 456

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Evaluation of Reflective Measurement Models

Hair, Joseph F., Hult, G. Tomas M., Ringle, Christian M., Sarstedt, Marko, Danks, Nicholas P., Ray, Soumya·2021·Classroom Companion: Business

Abstract

The goal of reflective measurement model assessment is to ensure the reliability and validity of the construct measures and therefore provides support for the suitability of their inclusion in the path model. This chapter introduces the key criteria that are relevant in reflective measurement model assessment: indicator reliability, internal consistency reliability (Cronbach’s alpha, reliability coefficient rho A , and composite reliability rho C ), convergent validity, and discriminant validity. We illustrate their use by means of the SEMinR package and a well-known model on corporate reputation.

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Crossref · 10.1007/978-3-030-80519-7_4

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Image 1
CC-BY · From the PDF
. . Fig. 4.2 Discriminant validity assessment using the HTMT. (Source: authors' own figure)
. . Fig. 4.2 Discriminant validity assessment using the HTMT. (Source: authors' own figure)CC-BY · From the PDF
. . Fig. 4.4 Indicator loadings and indicator reliability. (Source: authors' screenshot from RStudio)
. . Fig. 4.4 Indicator loadings and indicator reliability. (Source: authors' screenshot from RStudio)CC-BY · From the PDF
. . Fig. 4.5 Construct reliability and convergent validity table. (Source: authors' screenshot from RStudio)
. . Fig. 4.5 Construct reliability and convergent validity table. (Source: authors' screenshot from RStudio)CC-BY · From the PDF
. . Fig. 4.6 Reliability charts. (Source: authors' screenshot from R)
. . Fig. 4.6 Reliability charts. (Source: authors' screenshot from R)CC-BY · From the PDF
. . Fig. 4.7 Fornell-Larcker criterion table. (Source: authors' screenshot from RStudio)
. . Fig. 4.7 Fornell-Larcker criterion table. (Source: authors' screenshot from RStudio)CC-BY · From the PDF
. . Fig. 4.8 HTMT result table. (Source: authors' screenshot from RStudio)
. . Fig. 4.8 HTMT result table. (Source: authors' screenshot from RStudio)CC-BY · From the PDF
. . Fig. 4.9 Bootstrapping processing. (Source: authors' screenshot from RStudio)
. . Fig. 4.9 Bootstrapping processing. (Source: authors' screenshot from RStudio)CC-BY · From the PDF
. . Fig. 4.10 Bootstrapped results and confidence intervals for HTMT. (Source: authors' screenshot from RStudio)
. . Fig. 4.10 Bootstrapped results and confidence intervals for HTMT. (Source: authors' screenshot from RStudio)CC-BY · From the PDF

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Citations

Cites

Research works printed but not linked: 11

  1. Aguirre-Urreta, M. I., & Rönkkö, M. (2018). Statistical inference with PLSc using bootstrap confidence intervals. MIS Quarterly, 42(3), 1001–1020. · DOI
  2. Diamantopoulos, A., Sarstedt, M., Fuchs, C., Wilczynski, P., & Kaiser, S. (2012). Guidelines for choosing between multi-item and single-item scales for construct measurement: A predictive validity perspective. Journal of the Academy of Marketing Science, 40(3), 434–449. · DOI
  3. Dijkstra, T. K. (2010). Latent variables and indices: Herman Wold’s basic design and partial least squares. In V. Esposito Vinzi, W. W. Chin, J. Henseler, & H. Wang (Eds.), Handbook of partial least squares: Concepts, methods and applications Springer Handbooks of Computational Statistics Series (Vol. II, pp. 23–46). Berlin: Springer. · DOI
  4. Dijkstra, T. K. (2014). PLS’ Janus face–response to professor Rigdon’s ‘rethinking partial least squares modeling: In praise of simple methods. Long Range Planning, 47(3), 146–153. · DOI
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  6. Franke, G., & Sarstedt, M. (2019). Heuristics versus statistics in discriminant validity testing: A comparison of four procedures. Internet Research, 29(3), 430–447. · DOI
  7. Hair, J. F., Hult, T., Ringle, C. M., & Sarstedt, M. (2022). A primer on partial least squares structural equation modeling (PLS-SEM) (3rd ed.). Thousand Oaks: Sage.
  8. Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2–24. · DOI · Paper page
  9. Hulland, J. (1999). Use of partial least squares (PLS) in strategic management research: A review of four recent studies. Strategic Management Journal, 20(2), 195–204. · DOI · Paper page
  10. Radomir, L., & Moisescu, O. I. (2019). Discriminant validity of the customer-based corporate reputation scale: Some causes for concern. Journal of Product & Brand Management, 29(4), 457–469. · DOI
  11. Trizano-Hermosilla, I., & Alvarado, J. M. (2016). Best alternatives to Cronbach’s alpha reliability in realistic conditions: Congeneric and asymmetrical measurements. Frontiers in Psychology, 7, 769. · DOI

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Metadata

Authors
Hair, J. F., Hult, G. T. M., Ringle, C. M., Sarstedt, M., Danks, N. P., & Ray, S.
Venue
Classroom Companion: Business
Published
2021
Pages
75-90
ISSN
  • 2662-2866
  • 2662-2874
Cited by
456
References
5
Language
en

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