IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2007.06994.html
   My bibliography  Save this paper

Do Online Courses Provide an Equal Educational Value Compared to In-Person Classroom Teaching? Evidence from US Survey Data using Quantile Regression

Author

Listed:
  • Manini Ojha
  • Mohammad Arshad Rahman

Abstract

Education has traditionally been classroom-oriented with a gradual growth of online courses in recent times. However, the outbreak of the COVID-19 pandemic has dramatically accelerated the shift to online classes. Associated with this learning format is the question: what do people think about the educational value of an online course compared to a course taken in-person in a classroom? This paper addresses the question and presents a Bayesian quantile analysis of public opinion using a nationally representative survey data from the United States. Our findings show that previous participation in online courses and full-time employment status favor the educational value of online courses. We also find that the older demographic and females have a greater propensity for online education. In contrast, highly educated individuals have a lower willingness towards online education vis-\`a-vis traditional classes. Besides, covariate effects show heterogeneity across quantiles which cannot be captured using probit or logit models.

Suggested Citation

  • Manini Ojha & Mohammad Arshad Rahman, 2020. "Do Online Courses Provide an Equal Educational Value Compared to In-Person Classroom Teaching? Evidence from US Survey Data using Quantile Regression," Papers 2007.06994, arXiv.org.
    • Handle: RePEc:arx:papers:2007.06994
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2007.06994
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Dries Benoit & Rahim Alhamzawi & Keming Yu, 2013. "Bayesian lasso binary quantile regression," Computational Statistics, Springer, vol. 28(6), pages 2861-2873, December.
    2. Georges Bresson & Guy Lacroix & Mohammad Arshad Rahman, 2021. "Bayesian panel quantile regression for binary outcomes with correlated random effects: an application on crime recidivism in Canada," Empirical Economics, Springer, vol. 60(1), pages 227-259, January.
    3. Chib, Siddhartha & Jeliazkov, Ivan, 2006. "Inference in Semiparametric Dynamic Models for Binary Longitudinal Data," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 685-700, June.
    4. Eric P. Bettinger & Lindsay Fox & Susanna Loeb & Eric S. Taylor, 2017. "Virtual Classrooms: How Online College Courses Affect Student Success," American Economic Review, American Economic Association, vol. 107(9), pages 2855-2875, September.
    5. Rahim Alhamzawi & Haithem Taha Mohammad Ali, 2018. "Bayesian quantile regression for ordinal longitudinal data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 45(5), pages 815-828, April.
    Full references (including those not matched with items on IDEAS)

    Citations

    RePEc Biblio mentions

    As found on the RePEc Biblio, the curated bibliography for Economics:
    1. > Economics of Welfare > Health Economics > Economics of Pandemics > Specific pandemics > Covid-19 > Schools

    Citations

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


    Cited by:

    1. Arjun Gupta & Soudeh Mirghasemi & Mohammad Arshad Rahman, 2021. "Heterogeneity in food expenditure among US families: evidence from longitudinal quantile regression," Indian Economic Review, Springer, vol. 56(1), pages 25-48, June.
    2. Mohit Batham & Soudeh Mirghasemi & Mohammad Arshad Rahman & Manini Ojha, 2021. "Modeling and Analysis of Discrete Response Data: Applications to Public Opinion on Marijuana Legalization in the United States," Papers 2109.10122, arXiv.org, revised May 2023.

    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. Georges Bresson & Guy Lacroix & Mohammad Arshad Rahman, 2021. "Bayesian panel quantile regression for binary outcomes with correlated random effects: an application on crime recidivism in Canada," Empirical Economics, Springer, vol. 60(1), pages 227-259, January.
    2. Mohit Batham & Soudeh Mirghasemi & Mohammad Arshad Rahman & Manini Ojha, 2021. "Modeling and Analysis of Discrete Response Data: Applications to Public Opinion on Marijuana Legalization in the United States," Papers 2109.10122, arXiv.org, revised May 2023.
    3. Yu-Zhu Tian & Man-Lai Tang & Wai-Sum Chan & Mao-Zai Tian, 2021. "Bayesian bridge-randomized penalized quantile regression for ordinal longitudinal data, with application to firm’s bond ratings," Computational Statistics, Springer, vol. 36(2), pages 1289-1319, June.
    4. Mohammad Arshad Rahman & Angela Vossmeyer, 2019. "Estimation and Applications of Quantile Regression for Binary Longitudinal Data," Advances in Econometrics, in: Topics in Identification, Limited Dependent Variables, Partial Observability, Experimentation, and Flexible Modeling: Part B, volume 40, pages 157-191, Emerald Group Publishing Limited.
    5. Ivan Jeliazkov & Shubham Karnawat & Mohammad Arshad Rahman & Angela Vossmeyer, 2023. "Flexible Bayesian Quantile Analysis of Residential Rental Rates," Papers 2305.13687, arXiv.org, revised Sep 2023.
    6. Laura Liu & Hyungsik Roger Moon & Frank Schorfheide, 2023. "Forecasting with a panel Tobit model," Quantitative Economics, Econometric Society, vol. 14(1), pages 117-159, January.
    7. Benjamin T. Skinner, 2019. "Making the Connection: Broadband Access and Online Course Enrollment at Public Open Admissions Institutions," Research in Higher Education, Springer;Association for Institutional Research, vol. 60(7), pages 960-999, November.
    8. Elisa Failache, 2023. "Taking advantage of COVID-19? Online learning, descentralization and tertiary education," Documentos de Trabajo (working papers) 23-09, Instituto de Economía - IECON.
    9. M Paula Cacault & Christian Hildebrand & Jérémy Laurent-Lucchetti & Michele Pellizzari, 2021. "Distance Learning in Higher Education: Evidence from a Randomized Experiment [A Randomized Assessment of Online Learning]," Journal of the European Economic Association, European Economic Association, vol. 19(4), pages 2322-2372.
    10. Sloczynski, Tymon, 2018. "A General Weighted Average Representation of the Ordinary and Two-Stage Least Squares Estimands," IZA Discussion Papers 11866, Institute of Labor Economics (IZA).
    11. Hamilton, Laura T. & Daniels, Heather & Smith, Christian Michael & Eaton, Charlie, 2022. "The Private Side of Public Universities: Third-party providers and platform capitalism," University of California at Berkeley, Center for Studies in Higher Education qt7p0114s8, Center for Studies in Higher Education, UC Berkeley.
    12. Qi Li & Vasilis Sarafidis & Joakim Westerlund, 2021. "Essays in honor of Professor Badi H Baltagi," Empirical Economics, Springer, vol. 60(1), pages 1-11, January.
    13. Todd E. Clark & Gergely Ganics & Elmar Mertens, 2022. "Constructing Fan Charts from the Ragged Edge of SPF Forecasts," Working Papers 22-36, Federal Reserve Bank of Cleveland.
    14. Bosshardt, William & Chiang, Eric P., 2018. "Evaluating the effect of online principles courses on long-term outcomes," International Review of Economics Education, Elsevier, vol. 28(C), pages 1-10.
    15. Kaganovich, Michael & Sarpca, Sinan & Su, Xuejuan, 2020. "Competition in Higher Education," Working Papers 2020-3, University of Alberta, Department of Economics.
    16. Peter Hinrichs, 2021. "COVID-19 and Education: A Survey of the Research," Economic Commentary, Federal Reserve Bank of Cleveland, vol. 2021(04), pages 1-6, March.
    17. Laia Navarro-Sola, 2021. "Secondary Schools with Televised Lessons: The Labor Market Returns of the Mexican Telesecundaria," Working Papers 2021-053, Human Capital and Economic Opportunity Working Group.
    18. Eric P. Chiang & Jose J. Vazquez, 2018. "Using Technology to Complete the Natural Learning Path in a Principles of Economics Course," Journal of Economics Teaching, Journal of Economics Teaching, vol. 2(2), pages 104-114, January.
    19. Marigee Bacolod & Stephen Mehay & Elda Pema, 2018. "Who succeeds in distance learning? Evidence from quantile panel data estimation," Southern Economic Journal, John Wiley & Sons, vol. 84(4), pages 1129-1145, April.
    20. Kofoed, Michael S. & Gebhart, Lucas & Gilmore, Dallas & Moschitto, Ryan, 2021. "Zooming to Class?: Experimental Evidence on College Students' Online Learning during COVID-19," IZA Discussion Papers 14356, Institute of Labor Economics (IZA).

    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:arx:papers:2007.06994. 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: arXiv administrators (email available below). General contact details of provider: http://arxiv.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.