Abstract
The objective of this paper is to provide a basic framework for researchers interested in reporting the results of their PLS analyses. Since the dominant paradigm in reporting Structural Equation Modeling results is covariance based, this paper begins by providing a discussion of key differences and rationale that researchers can use to support their use of PLS. This is followed by two examples from the discipline of Information Systems. The first consists of constructs with reflective indicators (mode A). This is followed up with a model that includes a construct with formative indicators (mode B).
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Blalock, H. M., Jr. (1964). Causal inferences in nonexperimental research. Chapel Hill, NC: University of North Carolina Press.
Blalock, H. M., Jr. (1979). The presidential address: measurement and conceptualization problems: the major obstacle to integrating theory and research. American Sociological Review,44, 881–894.
Blalock, H. M., Jr. (1986). Multiple causation, indirect measurement and generalizability in the social sciences. Synthese, 68, 13–36.
Chin, W. W. (1998a). Commentary: issues and opinion on structural equation modeling. MIS Quarterly,22, vii–xvi.
Chin, W. W. (1998b). The partial least squares approach for structural equation modeling. In G. A. Marcoulides (Ed.), Modern methods for business research (pp. 295–336). Mahwah, NJ: Lawrence Erlbaum Associates.
Chin, W. W., & Gopal, A. (1995). Adoption intention in GSS: Relative importance of beliefs. The Data Base for Advances in Information Systems,26(2&3), 42–64.
Chin, W. W., Marcolin, B. L., & Newsted, P. R. (1996). A partial least squares latent variable modeling approach for measuring interaction effects: Results from a monte carlo simulation study and voice mail emotion/adoption study. In J. I. DeGross, S. Jarvenpaa, & A. Srinivasan (Eds.) Proceedings of the Seventeenth International Conference on Information Systems (pp. 21–41).
Chin, W. W., & Newsted, P. R. (1999). Structural equation modeling analysis with small samples using partial least squares. In R. Hoyle (Ed.), Statistical strategies for small sample research (pp. 307–341). Thousand Oaks, CA: Sage Publications.
Chin, W. W., Peterson, R. A., Brown, S. P. (2008). Structural equation modeling in marketing: some practical reminders. Journal of Marketing Theory and Practice,16(4), 287–298.
Cudeck, R., & Henley, J. J. (2003). A realistic perspective on pattern representation in growth data: Comment on Bauer and Curran (2003). Psychological Methods, 8, 378–383.
George, B., Hinson, S., & Chin, W. W. (2000). Modeling the Technology Adoption Decision: The Impact and Generalizability of the Perceived Characteristics Of Innovating Inventory On Email Adoption. Paper presented at the Diffusion Interest Group in Information Technology (DIGIT), – December 10, 2000. Brisbane, Australia: Queensland University of Technology.
Cohen, J. (1988). Statistical power analysis for the behavioral sciences. Hillside, NJ: L. Erlbaum Associates.
Cohen, J. (1990). Things I have learned (so far). American Psychologist, 45(12), 1304–1312.
Cohen, P., Cohen, J., Teresi, J., Marchi, M., & Velez, C. N. (1990). Problems in the measurement of latent variables in structural equations causal models. Applied Psychological Measurement, 14, 183–196.
Diggins, J. P. (1994). The promise of pragmatism: modernism and the crisis of knowledge and authority. Chicago: The University of Chicago Press.
Efron, B., & Tibshirani, R. J. (1993). An introduction to the bootstrap (monographs on statistics and applied probability, #57). New York: Chapman & Hall.
Fornell, C., & Bookstein, F. L. (1982). Two structural equation models: LISREL and PLS applied to consumer exit-voice theory. Journal of Marketing Research, 19, 440–452.
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobserved variables and measurement error. Journal of Marketing Research, 18, 39–50.
Harris, R. J. (1989). A canonical cautionary. Multivariate Behavioral Research, 24(1), 17–39.
Hoyle, R. H., & Panter, A. T. (1995). Writing about structural equation models. In R. H. Hoyle (Ed.), Structural equation modeling: concepts, issues, and applications (pp. 158–176). Thousand Oaks, CA: Sage Publications.
Geisser, S. (1975). The predictive sample reuse method with applications. Journal of the American Statistical Association, 70, 320–328.
Gray, H. L., & Schucany, W. R. (1972). The generalized jackknife statistic. New York: Marcel Dekker.
Jarvis, C. B., MacKenzie, S. B., & Podsakoff, P. M. (2003). A critical review of construct indicators and measurement model misspecification in marketing and consumer research. Journal of Consumer Research, 30, 199–218.
Lohmöller, J.-B. (1989). Latent variable path modeling with partial least squares. Heidelberg: Physica-Verlag.
MacCallum, R. C. (2003). Working with imperfect models. Multivariate Behavioral Research, 38, 113–139.
MacCallum, R. C., & Browne, M. W. (1993). The use of causal indicators in covariance structure models: Some practical issues. Psychological Bulletin, 114(3), 533–554.
Majchrak, A., Beath, C., Lim, R., & Chin, W. W. (2005). Managing client dialogues during information systems design to facilitate client learning. MIS Quarterly, 29(4), 653–672.
Mathieson, K., Peacock, E., & Chin, W. W. (2001). Extending the technology acceptance model: The influence of perceived user resources. The Data Base for Advances in Information Systems, 32(3), 86–112.
McDonald, R. P., & Moon-Ho R. H. (2002). Principles and practice in reporting structural equation analyses. Psychological Methods, 7, 64–82.
Meehl, P. E. (1990). Appraising and amending theories: The strategy of lakatosian defense and two principles that warrant it. Psychological Inquiry, 1(2), 108–141.
Menand, L. (2002). The metaphysical club: A story of ideas in America. New York: Farrar, Straus and Giroux.
Rozeboom, W. W. (2005). Meehl on metatheory. Journal of Clinical Psychology, 61, 1317–1354.
Schneeweiss, H. (1990). Models with latent variables: LISREL versus PLS, Contemporary Mathematics, 112, 33–40.
Shah, R., & Goldstein, S. M. (2006). Use of structural equation modeling in operations management research: looking back and forward. Journal of Operations Management, 24, 148–169.
Steiger, J. H. (1979). Factor indeterminacy in the 1930’s and the 1970’s – some interesting parallels. Psychometrika, 44, 157–167.
Steiger, J. H. (1988). Aspects of person-machine communication in structural modeling of correlations and covariances. Multivariate Behavioral Research, 23, 281–290.
Steiger, J. H. (2001). Driving fast in reverse, the relationship between software development, theory, and education in structural equation modeling. Journal of the American Statistical Association, 96, 331–338.
Stone, M. (1974). Cross-validatory choice and assessment of statistical predictions. Journal of the Royal Statistical Society, Series B, 36(2), 111–133.
Tenenhaus, M., Amato, S., & Esposito Vinzi, V. (2004). A global goodness-of-fit index for PLS structural equation modelling, In Proceedings of the XLII SIS Scientific Meeting (pp. 739–742). Vol. Contributed Papers, CLEUP, Padova.
Tukey, J. W. (1958). Bias and confidence in not-quite large samples. Annals of Mathematical Statistics, 29, 614.
Werts, C. E., Linn, R. L., & Jöreskog, K. G. (1974). Intraclass reliability estimates: Testing structural assumptions. Educational and Psychological Measurement, 34, 25–33.
Williams, J., & MacKinnon, D. P. (2008). Resampling and distribution of the product methods for testing indirect effects in complex models. Structural Equation Modeling, 15, 23–51.
Wold, H. (1980). Model construction and evaluation when theoretical knowledge is scarce: Theory and application of partial least squares. In J. Kmenta & J. B. Ramsey (Eds.), Evaluation of econometric models. New York: Academic Press.
Wold, H. O. A. (1982). Soft modeling: The basic design and some extensions. In K. G. Jöreskog & H. O. A. Wold (Eds.), Systems under indirect observations: Causality, structure, prediction, Part 2 (pp. 1–54). Amsterdam, The Netherlands: North-Holland.
Wold, H. (1988). Specification, predictor. In S. Kotz & N. L. Johnson (Eds.), Encyclopedia of statistical sciences (Vol. 8), New York: Wiley, 587–599.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Chin, W.W. (2010). How to Write Up and Report PLS Analyses. In: Esposito Vinzi, V., Chin, W., Henseler, J., Wang, H. (eds) Handbook of Partial Least Squares. Springer Handbooks of Computational Statistics. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-32827-8_29
Download citation
DOI: https://doi.org/10.1007/978-3-540-32827-8_29
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-32825-4
Online ISBN: 978-3-540-32827-8
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)