You Write, but Others Read: Common Methodological Misunderstandings in PLS and Related Methods

Conference paper
Part of the Springer Proceedings in Mathematics & Statistics book series (PROMS, volume 56)

Abstract

PLS and related methods are currently enjoying widespread popularity in part due to the availability of easy to use computer programs that require very little technical knowledge. Most of these methods focus on examining a fit function with respect to a set of free or constrained parameters for a given collection of data under certain assumptions. Although much has been written about the assumptions underpinning these methods, many misconceptions are prevalent among users and sometimes even appear in premier scholarly journals. In this chapter, we discuss a variety of methodological misunderstandings that warrant careful consideration before indiscriminately applying these methods.

Key words

Structural equation models Path models Confirmatory factor analysis Multiple regression Path analysis Covariance structure analysis Latent class Mixture analysis Equivalent models Power Model identification Formative indicators Reflective indicators Mode A Mode B Scale invariance 

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Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  1. 1.Research Methods and Statistics, Graduate School of Education and Interdepartmental Graduate Program in Management, A. Gary Anderson Graduate School of ManagementUniversity of CaliforniaRiversideUSA
  2. 2.Department of Decision and Information Systems, C. T. Bauer College of BusinessUniversity of HoustonHoustonUSA

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