The Effects of Chance Correlations on Partial Least Squares Path Modeling

Organizational Research Methods

Volumn 17, Issue 2, 2014, Pages 164-181

  Rönkkö, Mikko ^a  

^a AALTO UNIVERSITY   (Finland)

Author keywords

chance correlations; Monte Carlo simulation; partial least squares; structural equation modeling

Indexed keywords

EID: 84899627625     PISSN: 10944281     EISSN: 15527425     Source Type: Journal    
DOI: 10.1177/1094428114525667     Document Type: Article

References (60)

1. 45 Journals used in FT Research Rank. (2012, February 22). Financial Times. Retrieved fromhttp://www.ft.com/intl/cms/s/2/3405a512-5cbb-11e1-8f1f-00144feabdc0.html.
2. Aguirre-UrretaM. I.MarakasG.EllisM.SunW. (2008). A Monte Carlo investigation of partial least squares, with implications for both structural and measurement models. AMCIS 2008 Proceedings. Retrieved fromhttp://aisel.aisnet.org/amcis2008/246/.
3. Bentler P. M.,Huang W.On components, latent variables, PLS and simple methods: Reactions to Ridgon's rethinking of PLS.Long Range Planning. 2014;:.
4. Bollen K. A.Structural equations with latent variables. New York, NY: John Wiley & Son Inc; 1989:.
5. Cassel C. M.,Hackl P.,Westlund A. H.On measurement of intangible assets: A study of robustness of partial least squares.Total Quality Management. 2000;11 (7): 897-907.
6. Charles E. P.The correction for attenuation due to measurement error: Clarifying concepts and creating confidence sets.Psychological Methods. 2005;10 (2): 206-226.
7. Chin W. W.Modern methods for business research. Marcoulides G. A., ed. Mahwah, NJ: Lawrence Erlbaum Associates Publishers; 1998:295-336.
8. Chin W. W.Handbook of partial least squares. Vinzi E. V.Chin W. W.Henseler J.Wang H., ed. New York: Springer; 2010:83-97.
9. Chin W. W.,Marcolin B. L.,Newsted P. R.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.Information Systems Research. 2003;14 (2): 189-217.
10. Chin W. W.,Newsted P. R.Statistical strategies for small sample research. Hoyle R. H., ed. Thousand Oaks, CA: Sage Publications; 1999:307-342.
11. Ciavolino E.,Al-Nasser A. D.Comparing generalised maximum entropy and partial least squares methods for structural equation models.Journal of Nonparametric Statistics. 2009;21 (8): 1017-1036.
12. Cohen J.,Cohen P.,West S. G.,Aiken L. S.Applied multiple regression/correlation analysis for the behavioral sciences. London: Lawrence Erlbaum Associates; 2003:.
13. Dijkstra T. K.Reflection on "maximum likelihood" and "partial least squares" methods (Doctoral dissertation). Rijksuniversiteit te Groningen; 1981:.
14. Dijkstra T. K.Handbook of partial least squares. Vinzi E. V.Chin W. W.Henseler J.Wang H., ed. New York: Spinger; 2010:23-46.
15. Dijkstra T. K.PLS' Janus face.Long Range Planning. 2014;:.
16. DijkstraT. K.HenselerJ. (2012). Consistent and asymptotically normal PLS estimators for linear structural equations. Working paper. http://www.rug.nl/staff/t.k.dijkstra/research.
17. Dijkstra T. K.,Schermelleh-Engel K.Consistent partial least squares for nonlinear structural equation models. 2013;:.
18. Gefen D.,Rigdon E. E.,Straub D.An update and extension to SEM guidelines for administrative and social science research.MIS Quarterly. 2011;35 (2): iii-A7.
19. Glass G. V.,Maguire T. O.Abuses of factor scores.American Educational Research Journal. 1966;3 (4): 297-304.
20. Goodhue D. L.,Lewis W.,Thompson R.Statistical power in analyzing interaction effects: Questioning the advantage of PLS with product indicators.Information Systems Research. 2007;18 (2): 211-227.
21. Goodhue D. L.,Lewis W.,Thompson R.Comparing Pls to regression and Lisrel: A response to Marcoulides, Chin, and Saunders.MIS Quarterly. 2012a;36 (3): 703-A10.
22. Goodhue D. L.,Lewis W.,Thompson R.Does PLS have advantages for small sample size or non-normal data.MIS Quarterly. 2012b;36 (3): 981-1001.
23. Hair J. F.Multivariate data analysis: A global perspective. 2010). Multivariate data analysis: A global perspective (7th ed.). Upper Saddle River, NJ: Pearson Education.Upper Saddle River, NJ: Pearson Education; 2010:.
24. Hair J. F.,Ringle C. M.,Sarstedt M.Partial least squares: The better approach to structural equation modeling?.Long Range Planning. 2012;45 (5-6): 312-319.
25. Hair J. F.,Sarstedt M.,Ringle C. M.,Mena J. A.An assessment of the use of partial least squares structural equation modeling in marketing research.Journal of the Academy of Marketing Science. 2012;40 (3): 414-433.
26. Hardin A.,Marcoulides G. A.A commentary on the use of formative measurement.Educational and Psychological Measurement. 2011;71 (5): 753-764.
27. Henseler J.,Chin W. W.A comparison of approaches for the analysis of interaction effects between latent variables using partial least squares path modeling.Structural Equation Modeling: A Multidisciplinary Journal. 2010;17 (1): 82-109.
28. Henseler J.,Dijkstra T.,Sarstedt M.,Ringle C.,Diamantopoulos A.,Straub D.Common beliefs and reality about PLS: Comments on Rönkkö & Evermann (2013).Organizational Research Methods. 2014;17 (2): 182-209.
29. Hwang H.,Malhotra N. K.,Kim Y.,Tomiuk M. A.,Hong S.A comparative study on parameter recovery of three approaches to structural equation modeling.Journal of Marketing Research (JMR). 2010;47 (4): 699-712.
30. Kline R. B.Principles and practice of structural equation modeling. 2010). Principles and practice of structural equation modeling (3rd ed.). New York, NY: Guilford Press.New York, NY: Guilford Press; 2010:.
31. Le H.,Schmidt F. L.,Putka D. J.The multifaceted nature of measurement artifacts and its implications for estimating construct-level relationships.Organizational Research Methods. 2009;12 (1): 165-200.
32. Lehmann E.,Casella G.Theory of point estimation. New York, NY: Springer; 1998:.
33. Liang H.,Saraf N.,Hu Q.,Xue Y.Assimilation of enterprise systems: The effect of institutional pressures and the mediating role of top management.MIS Quarterly. 2007;31 (1): 59-87.
34. Lohmöller J. B.Latent Variable Path Modeling With Partial Least Squares. New York, NY: Physica-Verlag; 1989:.
35. Lu I. R. R.,Kwan E.,Thomas D. R.,Cedzynski M.Two new methods for estimating structural equation models: An illustration and a comparison with two established methods.International Journal of Research in Marketing. 2011;28 (3): 258-268.
36. Lu I. R. R.,Thomas D. R.Avoiding and correcting bias in score-based latent variable regression with discrete manifest items.Structural Equation Modeling: A Multidisciplinary Journal. 2008;15 (3): 462-490.
37. Marcoulides G. A.,Chin W. W.,Saunders C.When imprecise statistical statements become problematic: A response to Goodhue, Lewis, and Thompson.MIS Quarterly. 2012;36 (3): 717-728.
38. McIntosh C. N.,Edwards J. R.,Antonakis J.Reflections on partial least squares path modeling.Organizational Research Methods. 17 (2): 210-251.
39. Mosier C. I.On the reliability of a weighted composite.Psychometrika. 1943;8 (3): 161-168.
40. Muchinsky P. M.The correction for attenuation.Educational and Psychological Measurement. 1996;56 (1): 63-75.
41. Noonan R.,Wold H.Systems under indirect observation: Causality, structure, prediction. Joüreskog K.Wold S., ed. Amsterdam: North-Holland; 1982:.
42. Nunnally J.Psychometric theory. New York, NY: McGraw-Hill; 1978:.
43. Peng D. X.,Lai F.Using partial least squares in operations management research: A practical guideline and summary of past research.Journal of Operations Management. 2012;30 (6): 467-480.
44. Qureshi I.,Compeau D.Assessing between-group differences in information systems research: A comparison of covariance- and component-based SEM.MIS Quarterly. 2009;33 (1): 197-214.
45. R Core Team. (2013). R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing. Retrieved fromhttp://www.R-project.org/.
46. RaiA.GoodhueD. L.HenselerJ.ThompsonR. (2013). To PLS or not to PLS: That is the question. AMCIS 2013 Proceedings. Retrieved fromhttp://aisel.aisnet.org/amcis2013/Panels/PanelSubmissions/2.
47. Reinartz W. J.,Haenlein M.,Henseler J.An empirical comparison of the efficacy of covariance-based and variance-based SEM.International Journal of Research in Marketing. 2009;26 (4): 332-344.
48. Rigdon E. E.Structural equation modeling: A second course. Hancock G. R.Mueller R. O., ed. Charlotte, NC: Information Age; 2013:.
49. Ringle C. M.,Sarstedt M.,Straub D. W.A critical look at the use of PLS-SEM in MIS Quarterly.MIS Quarterly. 2012;36 (1): iii-xiv.
50. RönkköM. (2013). matrixpls: Matrix-based partial least squares estimation. Retrieved fromhttp://cran.r-project.org/web/packages/matrixpls.
51. Rönkkö M.,Evermann J.A critical examination of common beliefs about partial least squares path modeling.Organizational Research Methods. 2013;16 (3): 425-448.
52. Rosseel Y.lavaan: An R package for structural equation modeling.Journal of Statistical Software. 2012;48 (2): 1-36.
53. SanchezG.TrincheraL. (2013). plspm: Tools for partial least squares path modeling. Retrieved fromhttp://cran.r-project.org/web/packages/plspm/index.html.
54. Savalei V.,Bentler P. M.The handbook of marketing research: Uses, misuses, and future advances. Grover R.Vriens M., ed. Thousand Oaks, CA: Sage Publications; 2007:330-364.
55. Schermelleh-Engel K.,Werner C. S.,Klein A. G.,Moosbrugger H.Nonlinear structural equation modeling: Is partial least squares an alternative?.Asta-Advances in Statistical Analysis. 2010;94 (2): 167-184.
56. Tenenhaus M.Component-based structural equation modelling.Total Quality Management. 2008;19 (7-8): 871-886.
57. Treiblmaier H.,Bentler P. M.,Mair P.Formative constructs implemented via common factors.Structural Equation Modeling: A Multidisciplinary Journal. 2011;18 (1): 1-17.
58. Wold H.Systems under indirect observation: Causality, structure, prediction. Jöreskog K. G.Wold S., ed. Amsterdam, NY: North-Holland; 1982:1-54.
59. Zimmerman D. W.Correction for attenuation with biased reliability estimates and correlated errors in populations and samples.Educational and Psychological Measurement. 2007;67 (6): 920-939.
60. Zimmerman D. W.,Williams R. H.Properties of the Spearman correction for attenuation for normal and realistic non-normal distributions.Applied Psychological Measurement. 1997;21 (3): 253-270.