On the adoption of partial least squares in psychological research: Caveat emptor

Personality and Individual Differences

Volumn 87, Issue , 2015, Pages 76-84

  Rönkkö, Mikko ^a   McIntosh, Cameron N ^b   Antonakis, John ^c  

^a AALTO UNIVERSITY   (Finland)

^b PUBLIC SAFETY CANADA   (Canada)

^c UNIVERSITY OF LAUSANNE   (Switzerland)

Author keywords

Capitalization on chance; Model fit; Partial least squares; Significance testing; Structural equation modeling

Indexed keywords

EID: 84938250281     PISSN: 01918869     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.paid.2015.07.019     Document Type: Review

References (102)

1. Aguirre-Urreta M.I., Marakas G.M. A rejoinder to Rigdon et al. (2014). Information Systems Research 2014, 25(4):785-788. 10.1287/isre.2014.0545.
2. Aguirre-Urreta M.I., Marakas G.M. Partial least squares and models with formatively specified endogenous constructs: A cautionary note. Information Systems Research 2014, 25(4):761-778. 10.1287/isre.2013.0493.
3. Aguirre-Urreta M.I., Marakas G.M., Ellis M.E. Measurement of composite reliability in research using partial least squares: Some issues and an alternative approach. The, Data Base for Advances in Information Systems 2013, 44(4):11-43. 10.1145/2544415.2544417.
4. Antonakis J., Bastardoz N., Liu Y., Schriesheim C.A. What makes articles highly cited?. The Leadership Quarterly 2014, 25(1):152-179. 10.1016/j.leaqua.2013.10.014.
5. Antonakis J., Bendahan S., Jacquart P., Lalive R. On making causal claims: A review and recommendations. The Leadership Quarterly 2010, 21(6):1086-1120. 10.1016/j.leaqua.2010.10.010.
6. Antonakis J., Bendahan S., Jacquart P., Lalive R. Causality and endogeneity: Problems and solutions. The Oxford handbook of leadership and organizations. Oxford University Press, Oxford 2014, 93-117. Oxford University Press, New York. D. Day (Ed.).
7. Bainter S.A., Bollen K.A. Moving forward in the debate on causal indicators: Rejoinder to comments. Measurement: Interdisciplinary Research and, Perspectives 2015, 13(1):63-74. 10.1080/15366367.2015.1016349.
8. Bentler P.M. SEM with simplicity and accuracy. Journal of Consumer Psychology 2010, 20(2):215-220. 10.1016/j.jcps.2010.03.002.
9. Bobko P., Roth P.L., Buster M.A. The usefulness of unit weights in creating composite scores. A literature review, application to content validity, and meta-analysis. Organizational Research Methods 2007, 10(4):689-709. 10.1177/1094428106294734.
10. Bollen K.A. Structural equations with latent variables 1989, John Wiley & Son Inc., New York, NY.
11. Bollen K.A. Evaluating effect, composite, and causal indicators in structural equation models. MIS Quarterly 2011, 35(2):359-372.
12. Cadogan J.W., Lee N., Chamberlain L. Formative variables are unreal variables: Why the formative MIMIC model is invalid. AMS Review 2013, 3(1):38-49. 10.1007/s13162-013-0038-9.
13. Cashen L.H., Geiger S.W. Statistical power and the testing of null hypotheses: A review of contemporary management research and recommendations for future studies. Organizational Research Methods 2004, 7(2):151-167. 10.1177/1094428104263676.
14. Charles E.P. The correction for attenuation due to measurement error: Clarifying concepts and creating confidence sets. Psychological Methods 2005, 10(2):206-226. 10.1037/1082-989X.10.2.206.
15. Chin W.W. The partial least squares approach to structural equation modeling. Modern methods for business research 1998, 295-336. Lawrence Erlbaum Associates Publishers, Mahwah, NJ. G.A. Marcoulides (Ed.).
16. Chin W.W. PLS-graph user's guide 2001, Soft Modeling Inc., Houston, TX.
17. 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.
18. Cohen J. Things I have learned (so far). American Psychologist 1990, 45(12):1304-1312.
19. Cohen J., Cohen P., West S.G., Aiken L.S. Applied multiple regression/correlation analysis for the behavioral sciences 2003, Lawrence Erlbaum Associates, London.
20. Corsini R.J. The dictionary of psychology 2002, Brunner/Routledge, New York, N.Y.
21. Davison A.C., Hinkley D.V. Bootstrap methods and their application 1997, Cambridge University Press.
22. Dijkstra T.K. Some comments on maximum likelihood and partial least squares methods. Journal of Econometrics 1983, 22(1-2):67-90.
23. Dijkstra T.K. Latent variables and indices: Herman Wold's basic design and partial least squares. Handbook of partial least squares 2010, 23-46. Spinger, Berlin; New York. E.V. Vinzi, W.W. Chin, J. Henseler, H. Wang (Eds.).
24. Dijkstra T.K. PLS' Janus face - Response to Professor Rigdon's "Rethinking partial least squares modeling: In praise of simple methods". Long Range Planning 2014, 47(3):146-153. 10.1016/j.lrp.2014.02.004.
25. Dijkstra T.K., Henseler J. Consistent partial least squares path modeling. MIS Quarterly 2015, 39(2):297-316.
26. Dijkstra T.K., Schermelleh-Engel K. Consistent partial least squares for nonlinear structural equation models. Psychometrika 2014, 79(4):585-604. 10.1007/s11336-013-9370-0.
27. Dunteman G.H. Principal components analysis 1989, Sage, Newbury Park, CA.
28. Edwards J.R. The fallacy of formative measurement. Organizational Research Methods 2011, 14(2):370-388. 10.1177/1094428110378369.
29. Efron B., Tibshirani R.J. An introduction to the bootstrap 1993, Chapman and Hall/CRC, New York.
30. Evermann J., Tate M. Is my model right? Model quality and model misspecification in PLS - Recommendations for IS research. Social Science Research Network 2013, October 8, (Retrieved from http://papers.ssrn.com/abstract=2337699).
31. Fornell C., Bookstein F.L. Two structural equation models: LISREL and PLS applied to consumer exit-voice theory. Journal of Marketing Research 1982, 19(4):440-452.
32. Fornell C., Cha J. Partial least squares. Advanced methods of marketing research 1994, 52-78. Blackwell, Cambridge, MA. R.P. Bagozzi (Ed.).
33. Fornell C., Larcker D.F. Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research 1981, 18(1):39-50.
34. Fuller W.A. Measurement error models. Wiley series in probability and statistics. Measurement error models. Wiley series in probability and statistics 1987.
35. Gefen D., Rigdon E.E., Straub D.W. An update and extension to SEM guidelines for administrative and social science research. MIS Quarterly 2011, 35(2). (iii-xiv).
36. 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.
37. Goodhue D.L., Lewis W., Thompson R. Does PLS have advantages for small sample size or non-normal data. MIS Quarterly 2012, 36(3):981-1001.
38. Goodhue D.L., Thompson R., Lewis W. Why you shouldn't use PLS: Four reasons to be uneasy about using PLS in analyzing path models. 2013 46th Hawaii International Conference on System Sciences (HICSS) 2013, 4739-4748. 10.1109/HICSS.2013.612.
39. Greene W.H. Econometric analysis 2012, Prentice Hall, Boston.
40. Grice J.W. Computing and evaluating factor scores. Psychological Methods 2001, 6(4):430450.
41. Hair J.F., Hult G.T.M., Ringle C.M., Sarstedt M. A primer on partial least squares structural equations modeling (PLS-SEM) 2014, SAGE, Los Angeles.
42. Hair J.F., Sarstedt M., Pieper T.M., Ringle C.M. The use of partial least squares structural equation modeling in strategic management research: A review of past practices and recommendations for future applications. Long Range Planning 2012, 45(5-6):320-340. 10.1016/j.lrp.2012.09.008.
43. Hancock G.R. A sequential Scheffé-type respecification procedure for controlling type I error in exploratory structural equation model modification. Structural Equation Modeling: A Multidisciplinary Journal 1999, 6(2):158-168. 10.1080/10705519909540126.
44. Hardin A.M., Chang J.C.-J., Fuller M.A., Torkzadeh G. Formative measurement and academic research: In search of measurement theory. Educational and Psychological Measurement 2011, 71(2):281-305. 10.1177/0013164410370208.
45. Hayduk L.A., Cummings G., Boadu K., Pazderka-Robinson H., Boulianne S. Testing! Testing! One, two, three - Testing the theory in structural equation models!. Personality and Individual Differences 2007, 42(5):841-850. 10.1016/j.paid.2006.10.001.
46. Henseler J., Dijkstra T.K., Sarstedt M., Ringle C.M., Diamantopoulos A., Straub D.W., Calantone R.J. Common beliefs and reality about PLS comments on Rönkkö and Evermann (2013). Organizational Research Methods 2014, 17(2):182-209. 10.1177/1094428114526928.
47. Henseler J., Ringle C.M., Sarstedt M. A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science 2015, 43(1):115-135.
48. Henseler J., Ringle C.M., Sinkovics R.R. The use of partial least squares path modeling in international marketing. Advances in International Marketing 2009, 20:277-319.
49. Henseler J., Sarstedt M. Goodness-of-fit indices for partial least squares path modeling. Computational Statistics 2013, 28(2):565-580. 10.1007/s00180-012-0317-1.
50. Herzog W., Boomsma A. Small-sample robust estimators of noncentrality-based and incremental model fit. Structural Equation Modeling: A Multidisciplinary Journal 2009, 16(1):1-27. 10.1080/10705510802561279.
51. Howell R.D. Conceptual clarity in measurement-Constructs, composites, and causes: a commentary on Lee, Cadogan and Chamberlain. AMS Review 2013, 3(1):18-23. 10.1007/s13162-013-0036-y.
52. Howell R.D., Breivik E., Wilcox J.B. Is formative measurement really measurement? Reply to Bollen (2007) and Bagozzi (2007). Psychological Methods 2007, 12(2):238-245.
53. Hu L., Bentler P.M. Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal 1999, 6(1):1-55.
54. Hui B.S., Wold H. Consistency and consistency at large of partial least squares estimates. Systems under indirect observation: Causality, structure, prediction 1982, North-Holland, Amsterdam. K. Jöreskog, S. Wold (Eds.).
55. Jöreskog K.G. Structural analysis of covariance and correlation matrices. Psychometrika 1978, 43(4):443-477.
56. Kline R.B. Principles and practice of structural equation modeling 2005, The Guilford Press, New York, NY. 2nd ed.
57. Kolenikov S. Biases of parameter estimates in misspecified structural equation models. Sociological Methodology 2011, 41(1):119-157.
58. Lee N., Cadogan J.W., Chamberlain L. Material and efficient cause interpretations of the formative model: Resolving misunderstandings and clarifying conceptual language. AMS Review 2014, 4(1-2):32-43. 10.1007/s13162-013-0058-5.
59. Lehmann E., Casella G. Theory of point estimation 1998, Springer, New York.
60. Lohmöller J.-B. The PLS program system: Latent variables path analysis with partial least squares estimation. Multivariate Behavioral Research 1988, 23(1):125-127.
61. Lohmöller J.-B. Latent variable path modeling with partial least squares 1989, Physica-Verlag, Heidelberg.
62. 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. 10.1016/j.ijresmar.2011.03.006.
63. Marcoulides G.A., Chin W.W. You write, but others read: Common methodological misunderstandings in PLS and related methods. New perspectives in partial least squares and related methods 2013, 31-64. Springer, New York, NY. H. Abdi, W.W. Chin, V.E. Vinzi, G. Russolillo, L. Trinchera (Eds.).
64. Marcoulides G.A., Ing M. Automated structural equation modeling strategies. Handbook of structural equation modeling 2012, 690-704. Guilford Press, New York, NY. R. Hoyle (Ed.).
65. Markus K.A., Borsboom D. Frontiers in test validity theory: Measurement, causation and meaning 2013, Psychology Press, New York, N.Y.
66. McDonald R.P. Path analysis with composite variables. Multivariate Behavioral Research 1996, 31(2):239-270.
67. McIntosh C.N. Rethinking fit assessment in structural equation modelling: A commentary and elaboration on Barrett (2007). Personality and Individual Differences 2007, 42(5):859-867. 10.1016/j.paid.2006.09.020.
68. McIntosh C.N., Edwards J.R., Antonakis J. Reflections on partial least squares path modeling. Organizational Research Methods 2014, 17(2):210-251. 10.1177/1094428114529165.
69. Moosbrugger H., Schermelleh-Engel K., Klein A. Methodological problems of estimating latent interaction effects. Methods of Psychological Research Online 1997, 2(2):95-111.
70. Muchinsky P.M. The correction for attenuation. Educational and Psychological Measurement 1996, 56(1):63-75. 10.1177/0013164496056001004.
71. Nickerson R.S. Confirmation bias: A ubiquitous phenomenon in many guises. Review of General Psychology 1998, 2(2):175-220.
72. Penev S., Raykov T. On the relationship between maximal reliability and maximal validity of linear composites. Multivariate Behavioral Research 2006, 41(2):105-126. 10.1207/s15327906mbr4102_1.
73. Raju N.S., Bilgic R., Edwards J.E., Fleer P.F. Accuracy of population validity and cross-validity estimation: An empirical comparison of formula-based, traditional empirical, and equal weights procedures. Applied Psychological Measurement 1999, 23(2):99-115.
74. Raykov T. Evaluation of convergent and discriminant validity with multitrait-multimethod correlations. British Journal of Mathematical and Statistical Psychology 2011, 64(1):38-52.
75. Raykov T., Marcoulides G.A. Equivalent structural equation models: A challenge and responsibility. Structural Equation Modeling: A Multidisciplinary Journal 2007, 14(4):695-700. 10.1080/10705510701303798.
76. Rhemtulla M., Bork R., van, Borsboom D. Calling models with causal indicators "Measurement Models" implies more than they can deliver. Measurement: Interdisciplinary Research and, Perspectives 2015, 13(1):59-62. 10.1080/15366367.2015.1016343.
77. Rigdon E.E. Rethinking partial least squares path modeling. Long Range Planning 2012, 45(5-6):341-358. 10.1016/j.lrp.2012.09.010.
78. Rigdon E.E., Becker J.-M., Rai A., Ringle C.M., Diamantopoulos A., Karahanna E., Dijkstra T.K. Conflating antecedents and formative indicators: A comment on Aguirre-Urreta and Marakas. Information Systems Research 2014, 25(4):780-784. 10.1287/isre.2014.0543.
79. 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). (iiv-8).
80. Rönkkö M. The effects of chance correlations on partial least squares path modeling. Organizational Research Methods 2014, 17(2):164-181. 10.1177/1094428114525667.
81. Rönkkö M. matrixpls: Matrix-based partial least squares estimation (Version 0.6.0) Retrieved from. http://cran.r-project.org/web/packages/matrixpls.
82. Rönkkö M., Evermann J. A critical examination of common beliefs about partial least squares path modeling. Organizational Research Methods 2013, 16(3):425-448. 10.1177/1094428112474693.
83. Rönkkö M., Evermann J., Aguirre-Urreta M.I. Estimating formative measurement models in IS research - Analysis of the past and recommendations for the future. MIS Quarterly 2015, (submitted for publication).
84. Rönkkö M., McIntosh C., Antonakis J. On the small-sample properties of "Consistent partial least squares for nonlinear structural equation models.". Psychometrika 2015, (submitted for publication).
85. Rönkkö M., Ylitalo J. Construct validity in partial least squares path modeling. ICIS 2010 Proceedings 2010, (Retrieved from http://aisel.aisnet.org/icis2010_submissions/155).
86. Rosseel Y. Lavaan: An R package for structural equation modeling. Journal of Statistical Software 2012, 48(2):1-36.
87. Sarstedt M., Ringle C.M., Hair J.F. PLS-SEM: Looking back and moving forward. Long Range Planning 2014, 47(3):132-137. 10.1016/j.lrp.2014.02.008.
88. Satorra A., Bentler P.M. Corrections to test statistics and standard errors in covariance structure analysis. Latent variables analysis: Applications for developmental research 1994, 399-419. Sage Publications, Inc., Thousand Oaks, CA, US. A. von Eye, C.C. Clogg (Eds.).
89. Sosik J.J., Kahai S.S., Piovoso M.J. Silver bullet or voodoo statistics?: A primer for using the partial least squares data analytic technique in group and organization research. Group Organization Management 2009, 34(1):5-36. 10.1177/1059601108329198.
90. Stevens J.P. Applied multivariate statistics for the social sciences, fifth edition 2012, Routledge, New York. 5 ed.
91. Ten Berge J.M.F., Krijnen W.P., Wansbeek T., Shapiro A. Some new results on correlation-preserving factor scores prediction methods. Linear Algebra and Its Applications 1999, 289(1-3):311-318. 10.1016/S0024-3795(97)10007-6.
92. Tenenhaus M., Esposito Vinzi V., Chatelin Y.-M., Lauro C. PLS path modeling. Computational Statistics & Data Analysis 2005, 48(1):159-205. 10.1016/j.csda.2004.03.005.
93. Tong X., Zhang Z., Yuan K.-H. Evaluation of test statistics for robust structural equation modeling with nonnormal missing data. Structural Equation Modeling: A Multidisciplinary Journal 2014, 21(4):553-565. 10.1080/10705511.2014.919820.
94. Westland J.C. Partial least squares path analysis. Structural equation models 2015, 23-46. Springer International Publishing, (Retrieved from http://link.springer.com/chapter/10.1007/978-3-319-16507-3_3).
95. Willaby H.W., Costa D.S.J., Burns B.D., MacCann C., Roberts R.D. Testing complex models with small sample sizes: A historical overview and empirical demonstration of what partial least squares (PLS) can offer differential psychology. Personality and Individual Differences 2015, 84:73-78. 10.1016/j.paid.2014.09.008.
96. Wold H. Soft modeling - The basic design and some extensions. Systems under indirect observation: Causality, structure, prediction 1982, 1-54. (Amsterdam: North-Holland). K.G. Jöreskog, S. Wold (Eds.).
97. Wold H. Partial least squares. Encyclopedia of Statistical Sciences 1985, Vol. 6:581-591. Wiley, New York.
98. Wooldridge J.M. Introductory econometrics: A modern approach 2009, South Western, Cengage Learning, Mason, OH. 4th ed.
99. Yuan K.-H., Chan W., Bentler P.M. Robust transformation with applications to structural equation modelling. British Journal of Mathematical and Statistical Psychology 2000, 53(1):31-50.
100. Yuan K.-H., Marshall L.L., Bentler P.M. Assessing the effect of model misspecifications on parameter estimates in structural equation models. Sociological Methodology 2003, 33(1):241-265.
101. Yuan K.-H., Tian Y., Yanagihara H. Empirical correction to the likelihood ratio statistic for structural equation modeling with many variables. Psychometrika 2013, 1-27. 10.1007/s11336-013-9386-5.
102. 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. 10.1177/0013164406299132.