IDEAS home Printed from https://ideas.repec.org/a/spr/patien/v16y2023i1d10.1007_s40271-022-00600-z.html
   My bibliography  Save this article

Student-Athlete Preferences for Sexual Violence Reporting: A Discrete Choice Experiment

Author

Listed:
  • Alice M. Ellyson

    (University of Washington
    Seattle Children’s Research Institute
    University of Washington)

  • Avanti Adhia

    (University of Washington
    University of Washington)

  • Emily Kroshus

    (University of Washington
    Seattle Children’s Research Institute
    University of Washington)

  • Davene R. Wright

    (Harvard Medical School and Harvard Pilgrim Health Care Institute)

Abstract

Background Sexual violence (SV) is prevalent among US college athletes, but formal reports are rare. Little is known about adaptations to institution-level reporting policies and procedures that could facilitate reporting. Methods We conducted a discrete choice experiment (DCE) survey with 1004 student-athletes at ten Division I NCAA member institutions to examine how attributes of the reporting system influence the decision to formally report SV to their institution. Changes in utility values were estimated using multinomial logistic regression and mixed multinomial logistic regression. Importance scores were compared to understand student-athlete preferences. Results In order of relative importance, the two attributes most preferred by student-athletes were higher probabilities of students perpetrating SV being found in violation of code of conduct policies (relative importance score = 33), and the availability of substance use amnesty policies (relative importance score = 24). Student-athletes with prior SV experiences were more likely to opt out of formally reporting in the DCE paired choice, had lower estimated utility values for all attributes, and had less between-person heterogeneity. While anonymous reporting and survivor-initiated investigations were preferred by student-athletes on average, there was considerable valuation heterogeneity between student-athletes (sizeable deviations from mean estimated utilities). These two attributes also varied in relative importance; anonymous reporting had higher relative importance after interacting levels with prior SV experiences and competitive status, but lower relative importance after interacting levels with whether a student-athlete played on men’s or women’s sports teams. Conclusions Changes to reporting policies and procedures (e.g., transparency about SV reporting outcomes, implementing substance use amnesty policies) may be promising institution-level interventions to increase formal reporting of SV among student-athletes. More research is needed to understand preference heterogeneity between students and generalize these findings to broader student populations.

Suggested Citation

  • Alice M. Ellyson & Avanti Adhia & Emily Kroshus & Davene R. Wright, 2023. "Student-Athlete Preferences for Sexual Violence Reporting: A Discrete Choice Experiment," The Patient: Patient-Centered Outcomes Research, Springer;International Academy of Health Preference Research, vol. 16(1), pages 77-88, January.
    • Handle: RePEc:spr:patien:v:16:y:2023:i:1:d:10.1007_s40271-022-00600-z
    DOI: 10.1007/s40271-022-00600-z
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s40271-022-00600-z
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s40271-022-00600-z?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. David Hensher & William Greene, 2003. "The Mixed Logit model: The state of practice," Transportation, Springer, vol. 30(2), pages 133-176, May.
    2. Fosgerau, Mogens & Bierlaire, Michel, 2007. "A practical test for the choice of mixing distribution in discrete choice models," Transportation Research Part B: Methodological, Elsevier, vol. 41(7), pages 784-794, August.
    3. David Revelt & Kenneth Train, 1998. "Mixed Logit With Repeated Choices: Households' Choices Of Appliance Efficiency Level," The Review of Economics and Statistics, MIT Press, vol. 80(4), pages 647-657, November.
    4. Hess, Stephane & Train, Kenneth E. & Polak, John W., 2006. "On the use of a Modified Latin Hypercube Sampling (MLHS) method in the estimation of a Mixed Logit Model for vehicle choice," Transportation Research Part B: Methodological, Elsevier, vol. 40(2), pages 147-163, February.
    5. Hess, Stephane & Palma, David, 2019. "Apollo: A flexible, powerful and customisable freeware package for choice model estimation and application," Journal of choice modelling, Elsevier, vol. 32(C), pages 1-1.
    Full references (including those not matched with items on IDEAS)

    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. Tinessa, Fiore & Marzano, Vittorio & Papola, Andrea, 2020. "Mixing distributions of tastes with a Combination of Nested Logit (CoNL) kernel: Formulation and performance analysis," Transportation Research Part B: Methodological, Elsevier, vol. 141(C), pages 1-23.
    2. Stephane Hess, 2014. "Latent class structures: taste heterogeneity and beyond," Chapters, in: Stephane Hess & Andrew Daly (ed.), Handbook of Choice Modelling, chapter 14, pages 311-330, Edward Elgar Publishing.
    3. Frings, Oliver & Abildtrup, Jens & Montagné-Huck, Claire & Gorel, Salomé & Stenger, Anne, 2023. "Do individual PES buyers care about additionality and free-riding? A choice experiment," Ecological Economics, Elsevier, vol. 213(C).
    4. Tagliafierro, C. & Boeri, M. & Longo, A. & Hutchinson, W.G., 2016. "Stated preference methods and landscape ecology indicators: An example of transdisciplinarity in landscape economic valuation," Ecological Economics, Elsevier, vol. 127(C), pages 11-22.
    5. Fosgerau, Mogens & Bierlaire, Michel, 2007. "A practical test for the choice of mixing distribution in discrete choice models," Transportation Research Part B: Methodological, Elsevier, vol. 41(7), pages 784-794, August.
    6. Marco A. Palma & Dmitry V. Vedenov & David Bessler, 2020. "The order of variables, simulation noise, and accuracy of mixed logit estimates," Empirical Economics, Springer, vol. 58(5), pages 2049-2083, May.
    7. Bliemer, Michiel C.J. & Rose, John M., 2013. "Confidence intervals of willingness-to-pay for random coefficient logit models," Transportation Research Part B: Methodological, Elsevier, vol. 58(C), pages 199-214.
    8. Czajkowski, Mikołaj & Budziński, Wiktor, 2019. "Simulation error in maximum likelihood estimation of discrete choice models," Journal of choice modelling, Elsevier, vol. 31(C), pages 73-85.
    9. Caputo, Vincenzina & Scarpa, Riccardo & Nayga, Rodolfo M. & Ortega, David L., 2018. "Are preferences for food quality attributes really normally distributed? An analysis using flexible mixing distributions," Journal of choice modelling, Elsevier, vol. 28(C), pages 10-27.
    10. Riccardo Scarpa & Mara Thiene & Francesco Marangon, 2008. "Using Flexible Taste Distributions to Value Collective Reputation for Environmentally Friendly Production Methods," Canadian Journal of Agricultural Economics/Revue canadienne d'agroeconomie, Canadian Agricultural Economics Society/Societe canadienne d'agroeconomie, vol. 56(2), pages 145-162, June.
    11. Cherchi, Elisabetta & Guevara, Cristian Angelo, 2012. "A Monte Carlo experiment to analyze the curse of dimensionality in estimating random coefficients models with a full variance–covariance matrix," Transportation Research Part B: Methodological, Elsevier, vol. 46(2), pages 321-332.
    12. Youssef M Aboutaleb & Mazen Danaf & Yifei Xie & Moshe Ben-Akiva, 2020. "Sparse Covariance Estimation in Logit Mixture Models," Papers 2001.05034, arXiv.org.
    13. Laurent Ott & Mehdi Farsi & Sylvain Weber, 2021. "Beyond political divides: analyzing public opinion on carbon taxation in Switzerland," Chapters, in: Axel Franzen & Sebastian Mader (ed.), Research Handbook on Environmental Sociology, chapter 17, pages 313-339, Edward Elgar Publishing.
    14. Basnak, Paul & Giesen, Ricardo & Muñoz, Juan Carlos, 2022. "Estimation of crowding factors for public transport during the COVID-19 pandemic in Santiago, Chile," Transportation Research Part A: Policy and Practice, Elsevier, vol. 159(C), pages 140-156.
    15. Doherty, Edel & Campbell, Danny & Hynes, Stephen, 2012. "Exploring cost heterogeneity in recreational demand," Working Papers 148832, National University of Ireland, Galway, Socio-Economic Marine Research Unit.
    16. Rico Krueger & Akshay Vij & Taha H. Rashidi, 2018. "A Dirichlet Process Mixture Model of Discrete Choice," Papers 1801.06296, arXiv.org.
    17. Riccardo Scarpa & Cristiano Franceschinis & Mara Thiene, 2017. "A Monte Carlo Evaluation of the Logit-Mixed Logit under Asymmetry and Multimodality," Working Papers in Economics 17/23, University of Waikato.
    18. Fosgerau, Mogens & Hess, Stephane, 2008. "Competing methods for representing random taste heterogeneity in discrete choice models," MPRA Paper 10038, University Library of Munich, Germany.
    19. Stefano Mainardi, 2021. "Preference heterogeneity, neighbourhood effects and basic services: logit kernel models for farmers’ climate adaptation in Ethiopia," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(5), pages 6869-6912, May.
    20. Mokas, Ilias & Lizin, Sebastien & Brijs, Tom & Witters, Nele & Malina, Robert, 2021. "Can immersive virtual reality increase respondents’ certainty in discrete choice experiments? A comparison with traditional presentation formats," Journal of Environmental Economics and Management, Elsevier, vol. 109(C).

    More about this item

    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:spr:patien:v:16:y:2023:i:1:d:10.1007_s40271-022-00600-z. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.