Premiere: Friday 23 June, starting at 3pm BST//10am EDT
Presenters:
Bridging the gap between raw dataset and applied econometrics: a mini research process approach
Andy Chung, University of Reading
Abstract
This paper presents my experience in developing a mini research process using real-world raw dataset by integrating computer lab sessions with a formative mini project assessment in my econometrics module for an MSc Economics programme.
While the learning outcomes of the econometrics module often include applying the econometrics techniques with computer packages and real world data, it does not mean that students would be able to handle raw dataset for applied econometrics methods covered in class. This problem is particularly prominent for UK MSc Economics programmes because students normally have to write their dissertation as capstone. To bridge the gap between handling raw real-world data and the application of textbook econometrics methods, I integrated a series of computer lab sessions with a formative mini project assessment to constitute a mini research process in which my students could take their first step to be an applied econometrician.
The dataset ‘Annual Population Survey 2013’ was provided to students. It was downloaded from UK Data service website without any processing. In the computer lab sessions, students were asked to work together in answering a research question, ‘Does place of birth affect income? If so, by how much?’ (RQ1), with the dataset provided. Meanwhile, students had to submit a mini project on answering a similar research question, ‘Do females earn less than males? If so, by how much?’ (RQ2) with the same dataset after the term. In computer lab sessions, students worked in groups for answering RQ1 from scratch and I facilitated this group research process by providing them step-by-step guidance and discussing with them with their work at different stages. Then, they conducted their individual research for RQ2 in completing their mini project. Students’ performance in their projects was very good and their feedback on this arrangement was very positive.
Leveraging Tableau for Data Viz and Analysis in Large Lecture Statistics
Martin Gray Hunter, University of Arizona
Abstract
We have built 3 Tableau case assignments that we use in our large lecture statistics course to reinforce the statistical concepts from lecture and past assignments, build technical/data skills, and to help them build their professional portfolio. Students either visualize their results from previous statistical testing (simple t-tests) or explore the data to identify potential relationships between variables to motivate statistical testing (multivariate and non-linear regression). The online program is free for students and is both compatible and consistently laid out on both PCs and Macs.
Students are required to create and then publish their work to their online Tableau Public profile so they have tangible proof they can include on their resumes that employers can easily see. Additional trainings are provided outside of the normal scope of the course that more interested students can use to play around with their visuals and make them more complex and/or ascetically pleasing to separate themselves from the other 500+ students in the course. There is also a final group project with a Tableau Data Visualization role focused on visualizing data and extracting key insights that might be relevant to their chosen client.
Introducing R to non-programmers with Jupyter notebooks and Google Colab
Theodore Svoronos, Harvard University Kennedy School of Government
Abstract
We have built 3 Tableau case assignments that we use in our large lecture statistics course to reinforce the statistical concepts from lecture and past assignments, build technical/data skills, and to help them build their professional portfolio. Students either visualize their results from previous statistical testing (simple t-tests) or explore the data to identify potential relationships between variables to motivate statistical testing (multivariate and non-linear regression). The online program is free for students and is both compatible and consistently laid out on both PCs and Macs.
Students are required to create and then publish their work to their online Tableau Public profile so they have tangible proof they can include on their resumes that employers can easily see. Additional trainings are provided outside of the normal scope of the course that more interested students can use to play around with their visuals and make them more complex and/or ascetically pleasing to separate themselves from the other 500+ students in the course. There is also a final group project with a Tableau Data Visualization role focused on visualizing data and extracting key insights that might be relevant to their chosen client.
Watch via our YouTube channel on Friday 23 June, starting at 3pm BST//10am EDT.
Centre for Teaching and Learning Economics 