Research Note—Statistical Power in Analyzing Interaction Effects: Questioning the Advantage of PLS with Product Indicators

Published Online:https://doi.org/10.1287/isre.1070.0123

References

  • Aiken L. S., West S. G.Multiple Regression: Testing and Interpreting Interactions (1991) (Sage Publications, Beverly Hills, CA) Google Scholar
  • Baroudi J., Orlikowski W. The problem of statistical power in MIS research. MIS Quart. (1989) 13(1):87–106CrossrefGoogle Scholar
  • Carte T., Russell C. In pursuit of moderation: Nine common problems and their solutions. MIS Quart. (2003) 27(3):479–501CrossrefGoogle Scholar
  • Cassel C., Hackl P., Westlund A. Robustness of partial least-squares method of estimating latent variable quality structures. J. Appl. Statist. (1999) 26(4):435–446CrossrefGoogle Scholar
  • Chin W. W., Marcoulides G. A. The partial least squares approach to structural equation modeling. Modern Methods for Business Research (1998) (London, UK)295–336Google Scholar
  • Chin W. W.PLS Graph User’s Guide Version 3.0 (2001) (Soft Modeling, Inc., Houston, TX) Google Scholar
  • Chin W. W., Marcolin B., Newsted P. A partial least squares latent variable modeling approach for measuring interaction effects: Results from a Monte Carlo simulation study and an electronic-mail emotion/adoption study. Proc. 17th Internat. Conf. Inform. Systems (1996) Cleveland, OH:21–41Google Scholar
  • Chin W. W., Marcolin B., Newsted P. A partial least squares latent variable modeling approach for measuring interaction effects: Results from a Monte Carlo simulation study and an electronic-mail emotion/adoption study. Inform. Systems Res. (2003) 14(2):189–217LinkGoogle Scholar
  • Cohen J.Statistical Power Analysis for the Behavioral Sciences (1988) (L. Erlbaum Associates, Hillside, NJ) Google Scholar
  • Fornell C., Larcker D. Evaluating structural equation models with unobservable variables and measurement error. J. Marketing Res. (1981) 18:39–50CrossrefGoogle Scholar
  • Goodhue D., Lewis W., Thompson R., Sprague R. Small sample size and statistical power in MIS research. Proc. 39th Hawaii Internat. Conf. Systems Sci., (CD) (2006) (IEEE Computer Society Press, Los Alamitos, CA) 1–10CrossrefGoogle Scholar
  • Joreskog K. G., Yang F., Marcoulides G. A., Schumacker R. E. Nonlinear structural models: The Kenny-Judd model with interaction effect. Advanced Structural Equation Modeling, Issues and Techniques (1996) (Lawrence Erlbaum Assoc., Mahway, NJ) 57–88Google Scholar
  • Kenny D. A., Judd C. M. Estimating the nonlinear and interactive effects of latent variables. Psych. Bull. (1984) 96:201–210CrossrefGoogle Scholar
  • Larsen R. J., Marx M. L.An Introduction to Mathematical Statistics and Its Applications (1981) (Prentice-Hall, Inc., Englewood Cliffs, NJ) Google Scholar
  • Marcoulides G. A., Saunders C. PLS: A silver bullet? MIS Quart. (2006) 30(2):iii–ixCrossrefGoogle Scholar
  • Mazen A., Magid M., Hemmasi M., Lewis M. F. Statistical power in contemporary management research. Acad. Management J. (1987) 30(2):369–380CrossrefGoogle Scholar
  • Neter J., Wasserman W.Applied Linear Statistical Models: Regression, Analysis of Variance, and Experimental Designs (1974) (Richard D. Irwin, Inc., Homewood, IL) Google Scholar
  • Sawyer A. G., Ball A. D. Statistical power and effect size in marketing research. J. Marketing Res. (1981) 18(3):275–290CrossrefGoogle Scholar
  • Venkatraman. The concept of fit in strategy research: Toward verbal and statistical correspondence. Acad. Management Rev. (1989) 14(3):423–444CrossrefGoogle Scholar
  • Weill P., Olson M. H. Managing investment in information technology: Mini case examples and implications. MIS Quart. (1989) 13(1):3–17CrossrefGoogle Scholar
INFORMS site uses cookies to store information on your computer. Some are essential to make our site work; Others help us improve the user experience. By using this site, you consent to the placement of these cookies. Please read our Privacy Statement to learn more.