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International Conference on Soft Methods in Probability and Statistics

SMPS 2016: Soft Methods for Data Science pp 431–438Cite as

Handling Uncertainty in Structural Equation Modeling

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 456))

Abstract

This paper attempts to propose an overview of a recent method named partial possibilistic regression path modeling (PPRPM), which is a particular structural equation model that combines the principles of path modeling with those of possibilistic regression to model the net of relations among variables. PPRPM assumes that the randomness can be referred to the measurement error, that is the error in modeling the relations among the observed variables, and the vagueness to the structural error, that is the uncertainty in modeling the relations among the latent variables behind each block. PPRPM gives rise to possibilistic regressions that account for the imprecise nature or vagueness in our understanding phenomena, which is manifested by yielding interval path coefficients of the structural model. However, possibilistic regression is known to be a model sensitive to extreme values. That is way recent developments of PPRPM are focused on robust procedures for the detection of extreme values to omit or lessen their effect on the modeling. A case study on the motivational and emotional aspects of teaching is used to illustrate the procedure.

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Author information

Authors and Affiliations

  1. University of Calabria, Cosenza, Italy

    Rosaria Romano

  2. University of Naples Federico II, Naples, Italy

    Francesco Palumbo

Authors
  1. Rosaria Romano
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  2. Francesco Palumbo
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Corresponding author

Correspondence to Rosaria Romano .

Editor information

Editors and Affiliations

  1. Department of Statistical Sciences, Sapienza University of Rome, Rome, Italy

    Maria Brigida Ferraro

  2. Department of Statistical Sciences, Sapienza University of Rome, Roma, Italy

    Paolo Giordani

  3. Dept of Basic & Applied Sciences Engg, Sapienza University of Rome, Rome, Italy

    Barbara Vantaggi

  4. Dept of Stoc Metds,Polish Academy of Sci, Systems Research Institute, Warsaw, Poland

    Marek Gagolewski

  5. Dept of Statis&OR and MD, Univ de Oviedo, Oviedo, Spain

    María Ángeles Gil

  6. Dept of Stoc Metds,Polish Aca of Science, Systems Res Inst, Warsaw, Poland

    Przemysław Grzegorzewski

  7. Sys Res Inti, Dept of Stoch Methods, Polish Acadmy Sci in Warsaw, Warsaw, Poland

    Olgierd Hryniewicz

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© 2017 Springer International Publishing Switzerland

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Romano, R., Palumbo, F. (2017). Handling Uncertainty in Structural Equation Modeling. In: Ferraro, M., et al. Soft Methods for Data Science. SMPS 2016. Advances in Intelligent Systems and Computing, vol 456. Springer, Cham. https://doi.org/10.1007/978-3-319-42972-4_53

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  • DOI: https://doi.org/10.1007/978-3-319-42972-4_53

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-42971-7

  • Online ISBN: 978-3-319-42972-4

  • eBook Packages: EngineeringEngineering (R0)

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