IDEAS home Printed from https://ideas.repec.org/a/sae/somere/v52y2023i1p231-267.html

How to Interpret the Effect of Covariates on the Extreme Categories in Ordinal Data Models

Author

Listed:
  • Maria Iannario
  • Claudia Tarantola

Abstract

This contribution deals with effect measures for covariates in ordinal data models to address the interpretation of the results on the extreme categories of the scales, evaluate possible response styles, and motivate collapsing of extreme categories. It provides a simpler interpretation of the influence of the covariates on the probability of the response categories both in standard cumulative link models under the proportional odds assumption and in the recent extension of the C ombination of U ncertainty and P reference of the respondents models, the mixture models introduced to account for uncertainty in rating systems. The article shows by means of marginal effect measures that the effects of the covariates are underestimated when the uncertainty component is neglected. Visualization tools for the effect of covariates are proposed, and measures of relative size and partial effect based on rates of change are evaluated by the use of real data sets.

Suggested Citation

  • Maria Iannario & Claudia Tarantola, 2023. "How to Interpret the Effect of Covariates on the Extreme Categories in Ordinal Data Models," Sociological Methods & Research, , vol. 52(1), pages 231-267, February.
    • Handle: RePEc:sae:somere:v:52:y:2023:i:1:p:231-267
    DOI: 10.1177/0049124120986179
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/0049124120986179
    Download Restriction: no
    File URL: https://libkey.io/10.1177/0049124120986179?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Harris, Mark N. & Zhao, Xueyan, 2007. "A zero-inflated ordered probit model, with an application to modelling tobacco consumption," Journal of Econometrics, Elsevier, vol. 141(2), pages 1073-1099, December.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Ioana-Florina Coita & Maria Iannario & Alfonso Iodice D’Enza & Codruţa Mare, 2024. "Modelling the assessment of taxpayer perception on the fiscal system by a hybrid approach for the analysis of challenging data structures," Digital Finance, Springer, vol. 6(1), pages 97-112, March.
    2. Ryan H. L. Ip & K. Y. K. Wu, 2024. "A mixture distribution for modelling bivariate ordinal data," Statistical Papers, Springer, vol. 65(7), pages 4453-4488, September.
    3. Serena Berretta & Sara Garbin & Maria Iannario & Omar Paccagnella, 2024. "A Novel Indicator to Correct for Individual Reported Heterogeneity. An Application to Self-Evaluation of Later-Life Depression," Evaluation Review, , vol. 48(2), pages 221-250, April.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Sarah Brown & Mark N Harris & Jake Prendergast & Preety Srivastava, 2015. "Pharmaceutical Drug Misuse, Industry of Employment and Occupation," Bankwest Curtin Economics Centre Working Paper series WP1501, Bankwest Curtin Economics Centre (BCEC), Curtin Business School.
    2. Borowiecki, Karol J. & Bakhshi, Hasan, 2018. "Did you really take a hit? Understanding how video games playing affects individuals," Research in Economics, Elsevier, vol. 72(2), pages 313-326.
    3. Cheng, Zhiming & Smyth, Russell & Zhang, Le, 2024. "Does childhood adversity affect household portfolio decisions? Evidence from the Chinese Great Famine," China Economic Review, Elsevier, vol. 87(C).
    4. Das, Ujjwal & Das, Kalyan, 2018. "Inference on zero inflated ordinal models with semiparametric link," Computational Statistics & Data Analysis, Elsevier, vol. 128(C), pages 104-115.
    5. Hua Xin & Yuhlong Lio & Hsien-Ching Chen & Tzong-Ru Tsai, 2024. "Zero-Inflated Binary Classification Model with Elastic Net Regularization," Mathematics, MDPI, vol. 12(19), pages 1-17, September.
    6. Karol Borowiecki & Juan Prieto-Rodriguez, 2015. "Video games playing: A substitute for cultural consumptions?," Journal of Cultural Economics, Springer;The Association for Cultural Economics International, vol. 39(3), pages 239-258, August.
    7. Brooks, Robert & Harris, Mark N. & Spencer, Christopher, 2012. "Inflated ordered outcomes," Economics Letters, Elsevier, vol. 117(3), pages 683-686.
    8. Zasimova, Liudmila, 2022. "The association between fast-food consumption and job-related factors among Russian adults," Economics & Human Biology, Elsevier, vol. 46(C).
    9. repec:fgv:epgrbe:v:66:n:1:a:3 is not listed on IDEAS
    10. Attour, Amel & Baudino, Marco & Krafft, Jackie & Lazaric, Nathalie, 2020. "Determinants of energy tracking application use at the city level: Evidence from France," Energy Policy, Elsevier, vol. 147(C).
    11. Mark N. Harris & Ranjodh B. Singh & Preety Srivastava, 2024. "Cannabis and tobacco: substitutes and complements," Journal of Population Economics, Springer;European Society for Population Economics, vol. 37(4), pages 1-29, December.
    12. Sarah Brown & Alan Duncan & Mark N. Harris & Jennifer Roberts & Karl Taylor, 2015. "A Zero-Inflated Regression Model for Grouped Data," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 77(6), pages 822-831, December.
    13. Sara Ellison & Christopher M. Snyder & Hongkai Zhang, 2016. "Costs of Managerial Attention and Activity as a Source of Sticky Prices: Structural Estimates from an Online Market," CESifo Working Paper Series 6285, CESifo.
    14. Gregory N. Price, 2019. "Does Productivity in the Formal Food Sector Drive Human Ebola Virus Infections in Sub‐Saharan Africa?," African Development Review, African Development Bank, vol. 31(2), pages 167-178, June.
    15. Muhammad Asali & Aamer S. Abu‐Qarn & Michael Beenstock, 2017. "The cycle of violence in the Second Intifada: Causality in nonlinear vector autoregressive models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 32(6), pages 1197-1205, September.
    16. Monica Billio & Roberto Casarin & Matteo Iacopini, 2024. "Bayesian Markov-Switching Tensor Regression for Time-Varying Networks," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 119(545), pages 109-121, January.
    17. David Dale & Andrei Sirchenko, 2021. "Estimation of nested and zero-inflated ordered probit models," Stata Journal, StataCorp LLC, vol. 21(1), pages 3-38, March.
    18. William Greene, 2007. "Discrete Choice Modeling," Working Papers 07-6, New York University, Leonard N. Stern School of Business, Department of Economics.
    19. Cristian Roner & Claudia Di Caterina & Davide Ferrari, 2021. "Exponential Tilting for Zero-inflated Interval Regression with Applications to Cyber Security Survey Data," BEMPS - Bozen Economics & Management Paper Series BEMPS85, Faculty of Economics and Management at the Free University of Bozen.
    20. Bilgic, Abdulbaki & Florkowski, Wojciech J. & Yen, Steven T. & Akbay, Cuma, 2013. "Tobacco spending patterns and their health-related implications in Turkey," Journal of Policy Modeling, Elsevier, vol. 35(1), pages 1-15.
    21. Massimo Florio & Francesco Giffoni & Gelsomina Catalano, 2020. "Should governments fund basic science? Evidence from a willingness-to-pay experiment in five universities," Journal of Economic Policy Reform, Taylor & Francis Journals, vol. 23(1), pages 16-33, January.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:sae:somere:v:52:y:2023:i:1:p:231-267. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: SAGE Publications (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.