Goodness-of-fit indices for partial least squares path modeling

This paper discusses a recent development in partial least squares (PLS) path modeling, namely goodness-of-fit indices. In order to illustrate the behavior of the goodness-of-fit index (GoF) and the relative goodness-of-fit index (GoF rel ), we estimate PLS path models with simulated data, and contr...

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Bibliographic Details
Published inComputational statistics Vol. 28; no. 2; pp. 565 - 580
Main Authors Henseler, Jörg, Sarstedt, Marko
Format Journal Article
LanguageEnglish
Published Berlin/Heidelberg Springer-Verlag 01.04.2013
Springer Nature B.V
Subjects
Algorithms
Computation
Computational mathematics
Computer simulation
Correlation analysis
Customer satisfaction
Economic Theory/Quantitative Economics/Mathematical Methods
Estimates
Least squares method
Mathematical analysis
Mathematical models
Mathematics and Statistics
Original Paper
Probability and Statistics in Computer Science
Probability Theory and Stochastic Processes
Software
Statistics
Structural equation modeling
Studies
Variables
C39
Goodness-of-fit index (GoF)
Partial least squares path modeling (PLS)
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Summary:This paper discusses a recent development in partial least squares (PLS) path modeling, namely goodness-of-fit indices. In order to illustrate the behavior of the goodness-of-fit index (GoF) and the relative goodness-of-fit index (GoF rel ), we estimate PLS path models with simulated data, and contrast their values with fit indices commonly used in covariance-based structural equation modeling. The simulation shows that the GoF and the GoF rel are not suitable for model validation. However, the GoF can be useful to assess how well a PLS path model can explain different sets of data.
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ISSN:0943-4062
1613-9658
DOI:10.1007/s00180-012-0317-1