On the effect of measurementmodel misspecification in PLS Path Modeling: the reflective case

The specification of a measurement model as reflective or formative is the object of a lively debate. Part of the existing literature focuses on measurement model misspecification. This means that a true model is assumed and the impact on the path coefficients of using a wrong model is investigated. The majority of these studies is restricted to Structural Equation Modeling (SEM). Regarding PLS-Path Modeling (PLS-PM), a few authors have carried out simulation studies to investigate the robustness of the estimates, but their focus is the comparison with SEM. The present paper discusses the misspecification problem in the PLS-PM context from a novel perspective. First, a real application on Alumni Satisfaction will be used to verify whether different assumptions for the measurements models influence the results. Second, the results of a Monte- Carlo simulation study, in the reflective case, will help to bring some clarity on a complex problem that has not been sufficiently studied yet.

[1]  George M. Marakas,et al.  Revisiting Bias Due to Construct Misspecification: Different Results from Considering Coefficients in Standardized Form , 2012, MIS Q..

[2]  Adamantios Diamantopoulos,et al.  Advancing formative measurement models , 2008 .

[3]  Mark A. Fuller,et al.  Formative Measurement and Academic Research: In Search of Measurement Theory , 2011 .

[4]  R. Bagozzi,et al.  On the nature and direction of relationships between constructs and measures. , 2000, Psychological methods.

[5]  Cheryl Burke Jarvis,et al.  A Critical Review of Construct Indicators and Measurement Model Misspecification in Marketing and Consumer Research , 2003 .

[6]  Varun Grover,et al.  Investigating Two Contradictory Views of Formative Measurement in Information Systems Research , 2010, MIS Q..

[7]  Hisashi Q. Higuchi On the nature of , 1999 .

[8]  P. Bentler,et al.  Formative Constructs Implemented via Common Factors , 2011 .

[9]  C. Fornell A National Customer Satisfaction Barometer: The Swedish Experience: , 1992 .

[10]  Roy D. Howell,et al.  Formative measurement: a critical perspective , 2013, DATB.

[11]  Leon J. Gross,et al.  A Commentary , 1988 .

[12]  Jeffrey R. Edwards,et al.  The Fallacy of Formative Measurement , 2011 .

[13]  J. Wilcox,et al.  Reconsidering formative measurement. , 2007, Psychological methods.

[14]  Judy A. Siguaw,et al.  Formative versus Reflective Indicators in Organizational Measure Development: A Comparison and Empirical Illustration , 2006 .

[15]  H. Winklhofer,et al.  Index Construction with Formative Indicators: An Alternative to Scale Development , 2001 .

[16]  Cheryl Burke Jarvis,et al.  The problem of measurement model misspecification in behavioral and organizational research and some recommended solutions. , 2005, The Journal of applied psychology.

[17]  J. Wilcox,et al.  Questions about formative measurement , 2008 .

[18]  P. Hackl,et al.  On measurement of intangible assets: A study of robustness of partial least squares , 2000 .

[19]  Michel Tenenhaus,et al.  PLS path modeling , 2005, Comput. Stat. Data Anal..

[20]  Scott B. MacKenzie,et al.  Construct Measurement and Validation Procedures in MIS and Behavioral Research: Integrating New and Existing Techniques , 2011, MIS Q..

[21]  R. Lennox,et al.  Conventional wisdom on measurement: A structural equation perspective. , 1991 .

[22]  Andrew M. Hardin,et al.  A Commentary on the Use of Formative Measurement , 2011 .

[23]  Pedro Simões Coelho,et al.  Comparison of Likelihood and PLS Estimators for Structural Equation Modeling: A Simulation with Customer Satisfaction Data , 2010 .

[24]  Natale Carlo Lauro,et al.  Comparing maximum likelihood and PLS estimates for structural equation modeling with formative blocks , 2013 .