Econ 5/Poli 5D: Final Project Description Due before 11:59 PM on Friday, March 13th Purpose This project is designed to improve your data analytics skills by putting into practice the tools you’ve acquired during our class. You will explore data – either from suggested datasets or ones of your choosing (and approved by your instructor) – and present interesting findings both descriptively and visually. This project is intended to present challenges such as aggregating data, handling missing values, discovering relationships between variables, etc. that you might encounter working as a data analyst or research assistant. Outstanding projects can be included in your portfolio when you interview for jobs or apply to graduate school. Requirements While it’s not expected that your project comprise a publishable peer-reviewed journal article, the best projects will present evidence that supports answers to specific questions about phenomena observed in society whose answers are not obvious. Here are some examples of good project ideas: 1. How is a candidate’s ideological polarization related to her campaign funding sources and amounts? 2. How have the number of gun-related crimes changed over time by state and following passage of gun-related legislation? 3. What happens to the number of traffic fatalities following major sporting events, e.g. the Super Bowl? 4. What is the relationship between Trump’s tweets about the Middle East and oil prices? About China and aluminum/steel prices? About X and Y? 5. How does weather (temperature, rain, wind) impact finishing times at the World Marathon Majors (or results at other sporting events)? 6. Is there any association between hospital density (hospitals per capita) and life expectancy? Medical cost? 7. How sensitive is 2018 congressional campaign spending in 2018 to how close the vote was in the 2016 presidential election? 8. What is the relationship between cost of tuition and earnings at one’s first job after graduation? 10 years later? How does this vary by major, region, type of university, gender, race, etc.? 9. How has state-wide legalization of marijuana (for medicinal and/or recreational use) affected fatalities from marijuana? From alcohol? 10. How are the wage and employment gaps (between female and male workers) in a state/country related to educational and health outcomes of children in that state/country? Here are some examples of not-so-good project ideas: 1. What are the impacts of terrorism? (Too vague – needs to be more specific.) 2. How is education related to wages? (It’s well established that education increases earnings.) 3. What is the optimal prices at which to buy and sell Bitcoin? (Likely too ambitious.) You must pick a dataset or multiple datasets from which your analysis can provide answers to your question of interest. Several example datasets have been provided, or you may choose your own and have them approved by your instructor. After composing your question and choosing relevant datasets, you will analyze the data to thoroughly answer your question. Your analysis will consist of both descriptive and visual analysis: • Descriptive analysis includes anything that describes the central tendency (i.e. mean, median, etc.) or spread (i.e. standard deviation, range, etc.) of the data. • Visual analysis includes anything that illustrates data, generally in the form of plots. Both forms of analysis help readers understand how the data are distributed, relationships between variables, differences across groups, and in general support the answer that you present. Your analysis should be written like a research paper or technical report for a scholarly or professional audience. Grading Your final project makes up 30% of your final grade in this course. The homeworks will include some questions related to your projects, but homework grades are not included in the 30%. Your final project should include a thorough investigation of your question of interest. Evidence in support of your answer should include both descriptive evidence (e.g. differences in statistics across groups) and visual evidence (e.g. various plots). There is no minimum or maximum length – the project should be long enough to sufficiently defend your arguments. All code used to produce your complete project should be submitted along with your writeup. Your coding should be completed in R unless preapproved by your instructor. It is expected that you will use some commands not yet covered in class; learning how to use new commands (with R help menus and Stack Overflow, for example) is a learning objective for this course. Projects will be graded on the basis of completeness and degree to which it answers the question of interest. Outstanding, high quality work will receive As, good but not exceptional projects Bs, acceptable but insufficiently defended work Cs, and incomplete or insubstantial work Ds or Fs. Late assignments will lose one letter grade per day late. Below is a guideline for what is considered outstanding, high quality work. • Question: the writer’s question is specific, not obvious, and answerable within the timeframe of one quarter. She explains why her question is interesting and relevant to modern audiences. • Argument: the writer’s argument clearly and specifically answers the question of interest. She presents thorough evidence in favor of her argument. • Evidence: the writer presents both descriptive and visual evidence that defends her argument. All of the evidence presented is concise and relevant to the question of interest. None of the presented evidence could have conveyed the same information in less space (that is, one plot instead of multiple plots, one column instead of multiple columns in a table, etc.). The writer presents a variety of evidence and does not draw upon the same subset of data or the same kind of plot or descriptive statistics to produce all of her evidence. • Figures: All figures have titles, labels, captions, and legends when appropriate. When multiple data series are included in the same figure, different colors or patterns are used to distinguish each series from one another. Formatting and axis scaling are chosen to make the figure easily understandable. • Discussion: the writer explains how the presented evidence supports her argument. None of the plots or tables exist without references in the text of the paper that explain what the figures depict and why the reader should care. Limitations of the evidence, including potential sources of bias, are discussed. • Conclusion: the writer leaves the reader with a clear takeaway given the findings presented. The writer explains how her argument should affect the decisions made by policymakers, business leaders, or other persons of influence. Deliverables You will submit two items: 1. Report: This contains the writeup of your results. Please submit your report in PDF format. 2. Code: All code used to generate the results in your report must be submitted at the same time your submit your report. Your code should include comments so that it is easy for the grader to follow your methodology, and it should run without errors. All plots and statistics should be replicable by running the code you submit. a. It is okay to use multiple scripts. For example, you may want to use a script to clean your raw data and another to generate summary statistics and plots. This is often a good strategy if one part of your workflow takes a long time to run and the b. Please add comments at the top of the scripts that describe the code (i.e. the author, last date updated, what the code does). If you use multiple scripts, you should label the order that the scripts should be run. You do not need to upload the data you use. However, if you use a dataset outside of the provided datasets, the instructor may ask you to share your data. Additionally, please refrain from cleaning the raw data in a way that is not replicable (i.e. do not open the data in Excel and
make changes).
