The DOI matches its Crossref record; 54 of the 54 references it cites were found in a registry or an index.
Statistical Inference with PLSC Using Bootstrap Confidence Intervals1
Abstract
Partial least squares (PLS) is one of the most popular statistical techniques in use in the Information Systems field. When applied to data originating from a common factor model, as is often the case in the discipline, PLS will produce biased estimates. A recent development, consistent PLS (PLSc), has been introduced to correct for this bias. In addition, the common practice in PLS of comparing the ratio of an estimate to its standard error to a t distribution for the purposes of statistical inference has also been challenged. We contribute to the practice of research in the IS discipline by providing evidence of the value of employing bootstrap confidence intervals in conjunction with PLSc, which is a more appropriate alternative than PLS for many of the research scenarios that are of interest to the field. Such evidence is direly needed before a complete approach to the estimation of SEM that relies on both PLSc and bootstrap CIs can be widely adopted. We also provide recommendations for researchers on the use of confidence intervals with PLSc.
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Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019) · European Business Review · doi:10.1108/ebr-11-2018-0203 · Cited by 27,541
Partial Least Squares Structural Equation Modeling (PLS-SEM) Using R: A Workbook
Hair, J. F., Hult, G. T. M., Ringle, C. M., Sarstedt, M., Danks, N. P., & Ray, S. (2021) · Classroom Companion: Business · doi:10.1007/978-3-030-80519-7 · Cited by 10,343
Partial least squares structural equation modeling
Sarstedt, M., Ringle, C. M., & Hair, J. F. (2017) · Handbook of Market Research · doi:10.1007/978-3-319-05542-8_15-1 · Cited by 3,285
How to Specify, Estimate, and Validate Higher-Order Constructs in PLS-SEM
Sarstedt, M., Hair, J. F., Cheah, J.-H., Becker, J.-M., & Ringle, C. M. (2019) · Australasian Marketing Journal · doi:10.1016/j.ausmj.2019.05.003 · Cited by 3,210
Partial least squares structural equation modeling in HRM research
Ringle, C. M., Sarstedt, M., Mitchell, R., & Gudergan, S. P. (2020) · The International Journal of Human Resource Management · doi:10.1080/09585192.2017.1416655 · Cited by 1,943
Metadata
- Authors
- Aguirre-Urreta, M. I., & Rönkkö, M.
- Venue
- MIS Quarterly
- Publisher
- MIS Quarterly
- Published
- 2018-09-01
- Volume
- 42
- Issue
- 3
- Pages
- 1001-1020
- ISSN
- 0276-7783 (ISSN-L)
- 2162-9730
- OpenAlex
- W2889039935
- Cited by
- 293
- References
- 54
- FWCI
- 16.19
- Type
- journal article
- Language
- en
- Open access
- closed
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