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Tutorials for Sarbanes-Oxley Paper Data

Dan Neely (from University of Milwaukee-Wisconsin) and I just had the following article published at the Journal of Business Ethics:

Saxton, G. D., & Neely, D. G. (2018). The Relationship Between Sarbanes–Oxley Policies and Donor Advisories in Nonprofit Organizations. Journal of Business Ethics.

This page contains tutorials on how to download the IRS 990 e-file data that was used for the control variables in our study.

Tutorials

I hope you have found this helpful. If so, please spread the word, and happy coding!




Using Your Twitter API Key

Below is an embedded version of an iPython notebook I have made publicly available on nbviewer. To download a copy of the code, click on the icon with three horizontal lines at the top right of the notebook (just below this paragraph) and select “Download Notebook.” I hope you find it helpful. If so, please share, and happy coding!




Analyzing Big Data with Python PANDAS

This is a series of iPython notebooks for analyzing Big Data — specifically Twitter data — using Python’s powerful PANDAS (Python Data Analysis) library. Through these tutorials I’ll walk you through how to analyze your raw social media data using a typical social science approach.

The target audience is those who are interested in covering key steps involved in taking a social media dataset and moving it through the stages needed to deliver a valuable research product. I’ll show you how to import your data, aggregate tweets by organization and by time, how to analyze hashtags, how to create new variables, how to produce a summary statistics table for publication, how to analyze audience reaction (e.g., # of retweets) and, finally, how to run a logistic regression to test your hypotheses. Collectively, these tutorials cover essential steps needed to move from the data collection to the research product stage.

Prerequisites

I’ve put these tutorials in a GitHub repository called PANDAS. For these tutorials I am assuming you have already downloaded some data and are now ready to begin examining it. In the first notebook I will show you how to set up your ipython working environment and import the Twitter data we have downloaded. If you are new to Python, you may wish to go through a series of tutorials I have created in order.

If you want to skip the data download and just use the sample data, but don’t yet have Python set up on your computer, you may wish to go through the tutorial “Setting up Your Computer to Use My Python Code”.

Also note that we are using the iPython notebook interactive computing framework for running the code in this tutorial. If you’re unfamiliar with this see this tutorial “Four Ways to Run your Code”.

For a more general set of PANDAS notebook tutorials, I’d recommend this cookbook by Julia Evans. I also have a growing list of “recipes” that contains frequently used PANDAS commands.

As you may know from my other tutorials, I am a big fan of the free Anaconda version of Python 2.7. It contains all of the prerequisites you need and will save you a lot of headaches getting your system set up.

Chapters:

At the GitHub site you’ll find the following chapters in the tutorial set:

Chapter 1 – Import Data, Select Cases and Variables, Save DataFrame.ipynb
Chapter 2 – Aggregating and Analyzing Data by Twitter Account.ipynb
Chapter 3 – Analyzing Twitter Data by Time Period.ipynb
Chapter 4 – Analyzing Hashtags.ipynb
Chapter 5 – Generating New Variables.ipynb
Chapter 6 – Producing a Summary Statistics Table for Publication.ipynb
Chapter 7 – Analyzing Audience Reaction on Twitter.ipynb
Chapter 8 – Running, Interpreting, and Outputting Logistic Regression.ipynb

I hope you find these tutorials helpful; please acknowledge the source in your own research papers if you’ve found them useful:

    Saxton, Gregory D. (2015). Analyzing Big Data with Python. Buffalo, NY: https://social-metrics.org

Also, please share and spread the word to help build a vibrant community of PANDAS users.

Happy coding!




Producing a Summary Statistics Table in iPython using PANDAS

Below is an embedded version of an iPython notebook I have made publicly available on nbviewer. To download a copy of the code, click on the icon with three horizontal lines at the top right of the notebook (just below this paragraph) and select “Download Notebook.” I hope you find it helpful. If so, please share, and happy coding!