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  3. Using Shiny to Teach Econometric Models
  • Cited by 11
Publisher:
Cambridge University Press
Online publication date:
May 2021
Print publication year:
2021
Online ISBN:
9781108883993
DOI:
https://doi.org/10.1017/9781108883993
Subjects:
Research Methods in Politics, Politics and International Relations, Research Methods In Politics
Series:
Elements in Quantitative and Computational Methods for the Social Sciences
Information

Book description

This Element discusses how shiny, an R package, can help instructors teach quantitative methods more effectively by way of interactive web apps. The interactivity increases instructors' effectiveness by making students more active participants in the learning process, allowing them to engage with otherwise complex material in an accessible, dynamic way. The Element offers four detailed apps that cover two fundamental linear regression topics: estimation methods (least squares, maximum likelihood) and the classic linear regression assumptions. It includes a summary of what the apps can be used to demonstrate, detailed descriptions of the apps' full capabilities, vignettes from actual class use, and example activities. Two other apps pertain to a more advanced topic (LASSO), with similar supporting material. For instructors interested in modifying the apps, the Element also documents the main apps' general code structure, highlights some of the more likely modifications, and goes through what functions need to be amended.

References

References

Bahls, P. (2012). Student Writing in the Quantitative Disciplines: A Guide for College Faculty. San Francisco: Jossey-Bass Publishers.
Beauchamp, N. (2017). Predicting and Interpolating State-Level Polls Using Twitter Textual Data. American Journal of Political Science 61(2), 490–503.
Biggs, J., and Tang, C. (2011). Teaching for Quality Learning at University (4th ed.). Maidenhead, UK: Open University Press.
Carsey, T. M., and Harden, J. J. (2013). Monte Carlo Simulation and Resampling Methods for Social Science. Los Angeles: Sage.
Fox, J. (2016). Applied Regression Analysis and Generalized Linear Models (3rd ed.). Los Angeles: Sage.
Gelman, A., and Nolan, D. (2017). Teaching Statistics: A Bag of Tricks (2nd ed.). Oxford: Oxford University Press.
Groth, R. E. (2013). Teaching Mathematics in Grades 6–12: Developing Research-Based Instructional Practices. Los Angeles: Sage.
Hensel, P. R., Mitchell, S. M., Sowers, T. E., and Thyne, C. L. (2008). Bones of Contention: Comparing Territorial, Maritime, and River Issues. Journal of Conflict Resolution 52(1), 117–143.
Kennedy, P. (2003). A Guide to Econometrics (5th ed.). Cambridge, MA: MIT Press.
Menary, R. (2007). Writing as Thinking. Language Sciences 29(5), 621–632.
National Council of Teachers of Mathematics (2000). Principles and Standards for School Mathematics. Reston, VA: NCTM.
Yancey, K. B. (2009). Reflection and Electronic Portfolios: Inventing the Self and Reinventing the University. In Cambridge, D., Cambridge, B. L., and Yancey, K. B. (eds.), Electronic Portfolios 2.0: Emergent Research on Implementation and Impact, pp. 5–16. Sterling, VA: Stylus Publishing.

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