Handbook of Partial Least Squares pp 655-690 | Cite as

# How to Write Up and Report PLS Analyses

## 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).

## Keywords

Order Factor Formative Indicator Structural Path Average Variance Extract Database Package## Preview

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.Google Scholar - 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.CrossRefGoogle Scholar - Blalock, H. M., Jr. (1986). Multiple causation, indirect measurement and generalizability in the social sciences.
*Synthese, 68*, 13–36.Google Scholar - Chin, W. W. (1998a). Commentary: issues and opinion on structural equation modeling.
*MIS Quarterly,22*, vii–xvi.Google Scholar - 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.Google Scholar - 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.Google Scholar - 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).Google Scholar - 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.Google Scholar - 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.CrossRefGoogle Scholar - 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.CrossRefGoogle Scholar - 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.Google Scholar - Cohen, J. (1988).
*Statistical power analysis for the behavioral sciences*. Hillside, NJ: L. Erlbaum Associates.zbMATHGoogle Scholar - Cohen, J. (1990). Things I have learned (so far).
*American Psychologist, 45*(12), 1304–1312.CrossRefGoogle Scholar - 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.CrossRefGoogle Scholar - Diggins, J. P. (1994).
*The promise of pragmatism: modernism and the crisis of knowledge and authority*. Chicago: The University of Chicago Press.Google Scholar - Efron, B., & Tibshirani, R. J. (1993).
*An introduction to the bootstrap (monographs on statistics and applied probability, #57)*. New York: Chapman & Hall.Google Scholar - 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.CrossRefGoogle Scholar - Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobserved variables and measurement error.
*Journal of Marketing Research, 18*, 39–50.CrossRefGoogle Scholar - Harris, R. J. (1989). A canonical cautionary.
*Multivariate Behavioral Research, 24*(1), 17–39.CrossRefGoogle Scholar - 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.Google Scholar - Geisser, S. (1975). The predictive sample reuse method with applications.
*Journal of the American Statistical Association, 70*, 320–328.zbMATHCrossRefGoogle Scholar - Gray, H. L., & Schucany, W. R. (1972).
*The generalized jackknife statistic*. New York: Marcel Dekker.zbMATHGoogle Scholar - 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.CrossRefGoogle Scholar - Lohmöller, J.-B. (1989).
*Latent variable path modeling with partial least squares*. Heidelberg: Physica-Verlag.zbMATHGoogle Scholar - MacCallum, R. C. (2003). Working with imperfect models.
*Multivariate Behavioral Research, 38*, 113–139.CrossRefGoogle Scholar - 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.CrossRefGoogle Scholar - 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.Google Scholar - 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.Google Scholar - McDonald, R. P., & Moon-Ho R. H. (2002). Principles and practice in reporting structural equation analyses.
*Psychological Methods, 7*, 64–82.CrossRefGoogle Scholar - 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.CrossRefGoogle Scholar - Menand, L. (2002).
*The metaphysical club: A story of ideas in America*. New York: Farrar, Straus and Giroux.Google Scholar - Rozeboom, W. W. (2005). Meehl on metatheory.
*Journal of Clinical Psychology, 61*, 1317–1354.CrossRefGoogle Scholar - Schneeweiss, H. (1990). Models with latent variables: LISREL versus PLS, Contemporary Mathematics, 112, 33–40.MathSciNetGoogle Scholar
- 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.CrossRefGoogle Scholar - Steiger, J. H. (1979). Factor indeterminacy in the 1930’s and the 1970’s – some interesting parallels.
*Psychometrika, 44*, 157–167.CrossRefMathSciNetGoogle Scholar - Steiger, J. H. (1988). Aspects of person-machine communication in structural modeling of correlations and covariances.
*Multivariate Behavioral Research, 23*, 281–290.CrossRefGoogle Scholar - 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.CrossRefGoogle Scholar - Stone, M. (1974). Cross-validatory choice and assessment of statistical predictions.
*Journal of the Royal Statistical Society, Series B, 36*(2), 111–133.zbMATHGoogle Scholar - 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.Google Scholar - Tukey, J. W. (1958). Bias and confidence in not-quite large samples.
*Annals of Mathematical Statistics, 29*, 614.CrossRefGoogle Scholar - Werts, C. E., Linn, R. L., & Jöreskog, K. G. (1974). Intraclass reliability estimates: Testing structural assumptions.
*Educational and Psychological Measurement, 34*, 25–33.CrossRefGoogle Scholar - 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.MathSciNetGoogle Scholar - 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.Google Scholar - 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.Google Scholar - Wold, H. (1988). Specification, predictor. In S. Kotz & N. L. Johnson (Eds.), Encyclopedia of statistical sciences (Vol. 8), New York: Wiley, 587–599.Google Scholar