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The Impact of Missing Values on PLS Model Fitting

  • Moritz Parwoll
  • Ralf Wagner
Conference paper
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)

Abstract

The analysis of interactive marketing campaigns frequently requires the investigation of latent constructs. Consequently, structural equation modeling is well established in this domain. Noticeably, the Partial-Least-Squares (PLS) algorithm is gaining popularity in the analysis of interactive marketing applications which may be attributed to its accuracy and robustness when data are not normally distributed. Moreover, the PLS algorithm also appraises incomplete data. This study reports from a simulation experiment in which a set of complete observations is blended with different patterns of missing values. We consider the impacts on the overall model fit, the outer model fit, and the assessment of significance by bootstrapping. Our results cast serious doubts on PLS algorithms’ ability to cope with missing values in a data set.

Keywords

Partial Little Square Path Coefficient Composite Reliability Average Variance Extract Partial Little Square Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.SVI Endowed Chair for International Direct Marketing, DMCC - Dialog Marketing Competence CenterUniversity of KasselKasselGermany

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