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

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  • 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
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    File URL: http://arxiv.org/pdf/2007.06994
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    References listed on IDEAS

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    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, 2020. "Bayesian Panel Quantile Regression for Binary Outcomes with Correlated Random Effects: An Application on Crime Recidivism in Canada," Papers 2001.09295, arXiv.org.
    3. 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.
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    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

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    Cited by:

    1. Arjun Gupta & Soudeh Mirghasemi & Mohammad Arshad Rahman, 2020. "Heterogeneity in Food Expenditure amongst US families: Evidence from Longitudinal Quantile Regression," Papers 2010.02614, arXiv.org.

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