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The GME estimator for the regression model with a composite indicator as explanatory variable

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  • Enrico Ciavolino
  • Maurizio Carpita

Abstract

We use the generalized maximum entropy (GME) estimator to take into account the measurement error in the regression model with a composite indicator, Likert-type scales based, as explanatory variable. We show that, the reliability measure of the observed composite indicator can be used to define an estimator of the error variance and the supports required by the GME approach. As well as to obtain an estimate of the slope parameter of the model, that has statistical properties similar to the classical ordinary least squares adjusted for attenuation estimator, GME approach allows to estimate the measurement error that can be used to adjust the composite indicator of the latent explanatory variable. An extensive simulation and two case studies show the usefulness of this approach. Copyright Springer Science+Business Media Dordrecht 2015

Suggested Citation

  • Enrico Ciavolino & Maurizio Carpita, 2015. "The GME estimator for the regression model with a composite indicator as explanatory variable," Quality & Quantity: International Journal of Methodology, Springer, vol. 49(3), pages 955-965, May.
    • Handle: RePEc:spr:qualqt:v:49:y:2015:i:3:p:955-965
    DOI: 10.1007/s11135-014-0061-4
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    References listed on IDEAS

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    1. Golan, Amos & Judge, George G. & Miller, Douglas, 1996. "Maximum Entropy Econometrics," Staff General Research Papers Archive 1488, Iowa State University, Department of Economics.
    2. Maurizio Carpita & Silvia Golia, 2012. "Measuring the quality of work: the case of the Italian social cooperatives," Quality & Quantity: International Journal of Methodology, Springer, vol. 46(6), pages 1659-1685, October.
    3. Enrico Ciavolino & Amjad Al-Nasser, 2009. "Comparing generalised maximum entropy and partial least squares methods for structural equation models," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 21(8), pages 1017-1036.
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