IDEAS home Printed from https://ideas.repec.org/p/osf/socarx/2sx6y.html
   My bibliography  Save this paper

Ordered Beta Regression: A Parsimonious, Well-Fitting Model for Survey Sliders and Visual Analog Scales

Author

Listed:
  • Kubinec, Robert

    (Princeton University)

Abstract

I propose a new model, ordered beta regression, for data collected from human subjects using slider scales/visual analog scales with lower and upper bounds. This model employs the cutpoint technique popularized by ordered logit to simultaneously estimate the probability that the outcome is at the upper bound, lower bound, or any continuous number in between. This model is contrasted with existing approaches, including ordinary least squares (OLS) regression and the zero-one-inflated beta regression (ZOIB) model. Simulation evidence shows that the proposed model, relative to existing approaches, estimates effects with more accuracy while capturing the full uncertainty in the distribution. Furthermore, an analysis of data on U.S. public opinion towards college professors reveals that the proposed model is better able to combine variation across continuous and degenerate responses. The model can be fit with the R package brms.

Suggested Citation

  • Kubinec, Robert, 2020. "Ordered Beta Regression: A Parsimonious, Well-Fitting Model for Survey Sliders and Visual Analog Scales," SocArXiv 2sx6y, Center for Open Science.
    • Handle: RePEc:osf:socarx:2sx6y
    DOI: 10.31219/osf.io/2sx6y
    as

    Download full text from publisher

    File URL: https://osf.io/download/5e5b9b23ef5d8901cc0749d2/
    Download Restriction: no
    File URL: https://libkey.io/10.31219/osf.io/2sx6y?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. John H. Kagel & Alvin E. Roth, 2016. "The Handbook of Experimental Economics, Volume 2," Economics Books, Princeton University Press, edition 1, volume 2, number 10874.
    2. Carpenter, Bob & Gelman, Andrew & Hoffman, Matthew D. & Lee, Daniel & Goodrich, Ben & Betancourt, Michael & Brubaker, Marcus & Guo, Jiqiang & Li, Peter & Riddell, Allen, 2017. "Stan: A Probabilistic Programming Language," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 76(i01).
    3. Silvia Ferrari & Francisco Cribari-Neto, 2004. "Beta Regression for Modelling Rates and Proportions," Journal of Applied Statistics, Taylor & Francis Journals, vol. 31(7), pages 799-815.
    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. Kubinec, Robert & Lee, Haillie Na-Kyung & Tomashevskiy, Andrey, 2021. "Why Corporate Political Connections Can Impede Investment," SocArXiv uks25, Center for Open Science.

    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. D. Fouskakis & G. Petrakos & I. Rotous, 2020. "A Bayesian longitudinal model for quantifying students’ preferences regarding teaching quality indicators," METRON, Springer;Sapienza Università di Roma, vol. 78(2), pages 255-270, August.
    2. Edgar Santos‐Fernandez & Erin E. Peterson & Julie Vercelloni & Em Rushworth & Kerrie Mengersen, 2021. "Correcting misclassification errors in crowdsourced ecological data: A Bayesian perspective," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(1), pages 147-173, January.
    3. Matthias Schmid & Florian Wickler & Kelly O Maloney & Richard Mitchell & Nora Fenske & Andreas Mayr, 2013. "Boosted Beta Regression," PLOS ONE, Public Library of Science, vol. 8(4), pages 1-15, April.
    4. Francis,David C. & Kubinec ,Robert, 2022. "Beyond Political Connections : A Measurement Model Approach to Estimating Firm-levelPolitical Influence in 41 Economies," Policy Research Working Paper Series 10119, The World Bank.
    5. Martinovici, A., 2019. "Revealing attention - how eye movements predict brand choice and moment of choice," Other publications TiSEM 7dca38a5-9f78-4aee-bd81-c, Tilburg University, School of Economics and Management.
    6. Paulus, Anne & Hagemann, Nina & Baaken, Marieke C. & Roilo, Stephanie & Alarcón-Segura, Viviana & Cord, Anna F. & Beckmann, Michael, 2022. "Landscape context and farm characteristics are key to farmers' adoption of agri-environmental schemes," Land Use Policy, Elsevier, vol. 121(C).
    7. Yongping Bao & Ludwig Danwitz & Fabian Dvorak & Sebastian Fehrler & Lars Hornuf & Hsuan Yu Lin & Bettina von Helversen, 2022. "Similarity and Consistency in Algorithm-Guided Exploration," CESifo Working Paper Series 10188, CESifo.
    8. Heinrich, Torsten & Yang, Jangho & Dai, Shuanping, 2020. "Growth, development, and structural change at the firm-level: The example of the PR China," MPRA Paper 105011, University Library of Munich, Germany.
    9. Domenico Piccolo & Rosaria Simone, 2019. "The class of cub models: statistical foundations, inferential issues and empirical evidence," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 28(3), pages 389-435, September.
    10. Jorge I. Figueroa-Zúñiga & Cristian L. Bayes & Víctor Leiva & Shuangzhe Liu, 2022. "Robust beta regression modeling with errors-in-variables: a Bayesian approach and numerical applications," Statistical Papers, Springer, vol. 63(3), pages 919-942, June.
    11. van Kesteren Erik-Jan & Bergkamp Tom, 2023. "Bayesian analysis of Formula One race results: disentangling driver skill and constructor advantage," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 19(4), pages 273-293, December.
    12. Yayan Hernuryadin & Koji Kotani & Tatsuyoshi Saijo, 2020. "Time Preferences of Food Producers: Does “Cultivate and Grow” Matter?," Land Economics, University of Wisconsin Press, vol. 96(1), pages 132-148.
    13. Mhamed Ben Salah & Cédric Chambru & Maleke Fourati, 2022. "The colonial legacy of education: evidence from of Tunisia," ECON - Working Papers 411, Department of Economics - University of Zurich, revised Sep 2024.
    14. Muhammad Suhail Rizwan & Asifa Obaid & Dawood Ashraf, 2017. "The Impact of Corporate Social Responsibility on Default Risk: Empirical evidence from US Firms," Business & Economic Review, Institute of Management Sciences, Peshawar, Pakistan, vol. 9(3), pages 36-70, September.
    15. Xin Xu & Yang Lu & Yupeng Zhou & Zhiguo Fu & Yanjie Fu & Minghao Yin, 2021. "An Information-Explainable Random Walk Based Unsupervised Network Representation Learning Framework on Node Classification Tasks," Mathematics, MDPI, vol. 9(15), pages 1-14, July.
    16. Korkeamäki, Timo & Virk, Nader & Wang, Haizhi & Wang, Peng, 2018. "Learning Chinese? The changing investment behavior of foreign institutions in the Chinese stock market," BOFIT Discussion Papers 19/2018, Bank of Finland Institute for Emerging Economies (BOFIT).
    17. Billur Aksoy & Silvana Krasteva, 2020. "When does less information translate into more giving to public goods?," Experimental Economics, Springer;Economic Science Association, vol. 23(4), pages 1148-1177, December.
    18. Dai, Zhixin & Zheng, Jiwei & Zizzo, Daniel John, 2024. "Theories of reasoning and focal point play with a matched non-student sample," China Economic Review, Elsevier, vol. 83(C).
    19. Hal R. Arkes & John H. Kagel & Dimitry Mezhvinsky, 2017. "Effects of a Management–Labor Context and Team Play on Ultimatum Game Outcomes," Southern Economic Journal, John Wiley & Sons, vol. 83(4), pages 993-1011, April.
    20. Ameztegui, Aitor & Coll, Lluís & Messier, Christian, 2015. "Modelling the effect of climate-induced changes in recruitment and juvenile growth on mixed-forest dynamics: The case of montane–subalpine Pyrenean ecotones," Ecological Modelling, Elsevier, vol. 313(C), pages 84-93.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:osf:socarx:2sx6y. 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: OSF (email available below). General contact details of provider: https://arabixiv.org .

    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.