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Comparing Partial Least Squares and Partial Possibilistic Regression Path Modeling to Likert-Type Scales: A Simulation Study

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Abstract

Partial possibilistic regression path modeling (PPRPM) combines the principles of path modeling with those of possibilistic regression to model the net of relations among latent variables through interval-valued coefficients, in order to take into account the vagueness in the model specification. An interval valued coefficient is defined by a midpoint and a range. Through a simulation study, the paper presents a comparison between PPRPM and partial least squares path modeling (PLSPM), when these are used for analyzing questionnaire data, with responses recorded on Likert scales. The estimates of the two models have similar behaviors, with respect to the simulated scenarios. Focusing on a realistic scenario setup, the results highlight the benefit of PPRPM that allows the model to report the component-wise estimation of vagueness in the inner model.

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Author information

Authors and Affiliations

  1. University of Calabria, Cosenza, Italy

    Rosaria Romano

  2. University of Naples Federico II, Napoli, Italy

    Francesco Palumbo

Authors
  1. Rosaria Romano
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  2. Francesco Palumbo
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Correspondence to Rosaria Romano .

Editor information

Editors and Affiliations

  1. Department of Political Sciences, University of Naples Federico II, Napoli, Italy

    Francesco Palumbo

  2. Department of Statistical Sciences Paolo Fortunati, Alma Mater Studiorum, University of Bologna, Bologna, Italy

    Angela Montanari

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

    Maurizio Vichi

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Romano, R., Palumbo, F. (2017). Comparing Partial Least Squares and Partial Possibilistic Regression Path Modeling to Likert-Type Scales: A Simulation Study. In: Palumbo, F., Montanari, A., Vichi, M. (eds) Data Science . Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Cham. https://doi.org/10.1007/978-3-319-55723-6_24

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