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Zero-Inflated Generalized Linear Mixed Models: A Better Way to Understand Data Relationships

Author

Listed:
  • Luiz Paulo Fávero

    (Business and Management Department, School of Economics, Business and Accounting, University of São Paulo—USP, São Paulo 05508-010, Brazil
    These authors contributed equally to this work.)

  • Joseph F. Hair

    (Marketing & Quantitative Methods, Mitchell College of Business, University of South Alabama, Mobile, AL 36688, USA
    These authors contributed equally to this work.)

  • Rafael de Freitas Souza

    (Business and Management Department, School of Economics, Business and Accounting, University of São Paulo—USP, São Paulo 05508-010, Brazil
    These authors contributed equally to this work.)

  • Matheus Albergaria

    (Postgraduate Department of Business Management and Accounting, Fundação Escola de Comércio Álvares Penteado—FECAP, São Paulo 01005-010, Brazil
    These authors contributed equally to this work.)

  • Talles V. Brugni

    (Finance and Accounting Department, FUCAPE Business School, Vitória 29075-505, Brazil
    These authors contributed equally to this work.)

Abstract

Our article explores an underused mathematical analytical methodology in the social sciences. In addition to describing the method and its advantages, we extend a previously reported application of mixed models in a well-known database about corruption in 149 countries. The dataset in the mentioned study included a reasonable amount of zeros (13.19%) in the outcome variable, which is typical of this type of research, as well as quite a bit of social sciences research. In our paper, present detailed guidelines regarding the estimation of models where the data for the outcome variable includes an excess number of zeros, and the dataset has a natural nested structure. We believe our research is not likely to reject the hypothesis favoring the adoption of mixed modeling and the inflation of zeros over the original simpler framework. Instead, our results demonstrate the importance of considering random effects at country levels and the zero-inflated nature of the outcome variable.

Suggested Citation

  • Luiz Paulo Fávero & Joseph F. Hair & Rafael de Freitas Souza & Matheus Albergaria & Talles V. Brugni, 2021. "Zero-Inflated Generalized Linear Mixed Models: A Better Way to Understand Data Relationships," Mathematics, MDPI, vol. 9(10), pages 1-28, May.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:10:p:1100-:d:553783
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    References listed on IDEAS

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    Cited by:

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    3. Mostafa Mostafapour & Farzad Movahedi Sobhani & Abbas Saghaei, 2022. "Monitoring Sparse and Attributed Network Streams with MultiLevel and Dynamic Structures," Mathematics, MDPI, vol. 10(23), pages 1-14, November.

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