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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. 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.
    2. 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.
    3. Golan, Amos & Judge, George G. & Miller, Douglas, 1996. "Maximum Entropy Econometrics," Staff General Research Papers Archive 1488, Iowa State University, Department of Economics.
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    2. Jun-Hwa Cheah & Hiram Ting & T. Ramayah & Mumtaz Ali Memon & Tat-Huei Cham & Enrico Ciavolino, 2019. "A comparison of five reflective–formative estimation approaches: reconsideration and recommendations for tourism research," Quality & Quantity: International Journal of Methodology, Springer, vol. 53(3), pages 1421-1458, May.
    3. Antonello D’Ambra & Pietro Amenta & Antonio Lucadamo, 2019. "Analyzing Customer Requirements to Select a Suitable Service Configuration Both for Users and for Company Provider," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 146(1), pages 383-394, November.
    4. Renato Civitillo & Paolo Ricci & Biagio Simonetti, 2019. "Management and performance of Non-Profit Institutions: finding new development trajectories—evidence from Italy," Quality & Quantity: International Journal of Methodology, Springer, vol. 53(5), pages 2275-2290, September.
    5. Enrico Ciavolino & Sergio Salvatore & Piergiorgio Mossi & Gloria Lagetto, 2019. "High-order PLS path model for multi-group analysis: the prosumership service quality model," Quality & Quantity: International Journal of Methodology, Springer, vol. 53(5), pages 2371-2384, September.
    6. Anna Crisci & Enrica Sepe & Pasqualina Malafronte, 2019. "What influences teachers’ job satisfaction and how to improve, develop and reorganize the school activities associated with them," Quality & Quantity: International Journal of Methodology, Springer, vol. 53(5), pages 2403-2419, September.
    7. Sharon Tan & Evan Lau & Hiram Ting & Jun-Hwa Cheah & Biagio Simonetti & Tan Hiok Lip, 2019. "How Do Students Evaluate Instructors’ Performance? Implication of Teaching Abilities, Physical Attractiveness and Psychological Factors," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 146(1), pages 61-76, November.

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