781. Why Are Eye Exams So Important? (09:56)

Date:
2020-04-24
Main contributors:
James, Jessica
Summary:
Why is it so important to get your eyes examined? What happens during an eye exam? When should someone see their doctor? Most people think the purpose of an eye exam is to update prescriptions and get new, trendy glasses, but the full purpose is much more extensive. Throughout an exam, patients will be tested for early onset and even undiagnosed diseases as well as be treated for vision loss. Experts in the field concur that it is important to see your optometrist every two to three years, depending on your age, to maintain your eye health and prevent vision loss at a later age. Through a review of current literature, the paper will analyze why patients should see their optometrist regularly and clarify the benefits that come with maintaining good eye health habits.

782. Why War (00:13)

Date:
2020
Main contributors:
Ron Osgood
Summary:
North Vietnamese Government Worker Nguyen Xuan Lien wonders what good the war did with so many deaths on both sides.

784. WIM | Statistical models for forecasting American elections (1:01:00)

Date:
2020-10-09
Main contributors:
G. Elliott Morris
Summary:
G. Elliott Morris is a data journalist at The Economist and writes mostly about American politics and elections, usually by engaging in a close study of political science, political polling and demographic data. He is responsible for many of The Economist’s election forecasting models, including their 2020 US presidential election forecast.

785. WIM | Technical Forecasting of Political Conflict (1:19:00)

Date:
2020-11-13
Main contributors:
Dr. Philip Schrodt
Summary:
Over the past couple of decades, technical models, both statistical, machine learning and combinations of these methods, for forecasting various forms of political conflict, including protest, violent substate conflict, and even coups, have become surprisingly common in policy and NGO communities, particularly in Europe, though not, curiously, in US academia. These methods, working with readily available, if noisy, open source data, use a number of familiar predictive analytical approaches such as logit models in the statistical realm and random forests in the machine learning, and consistently outperform human analysts. This talk will first review the current state of the field, with a particular emphasis on why current models work whereas prior to 2005 there was little consistent success with the problems, and then present some challenges that remain unresolved. The talk will assume familiarity with general social science quantitative approaches, but not with the details of specific technical approaches: lots of graphics, a couple tables, no equations.

786. WIM | Text Mining in Python for Social Scientists (1:46:00)

Date:
2020-10-23
Main contributors:
Helge-Johannes Marahrens
Summary:
Textual data are central to the social sciences. However, they often require several pre-processing steps before they can be utilized for statistical analyses. This workshop introduces a range of Python tools to clean, organize, and analyze textual data. It is intended for researchers who are new to working with textual data, but are familiar with Python or have completed the Introduction to Python workshop. Python is best learned hands-on. Python packages: nltk, fuzzywuzzy, re, glob, sklearn, pandas, numpy, matplotlib

787. WIM | Webscraping and APIs Using Python for Social Scientists (1:50:00)

Date:
2020-09-18
Main contributors:
Helge-Johannes Marahrens, Anne Kavalerchik
Summary:
In recent years, social scientists have increased their efforts to access new datasets from the web or from large databases. An easy way to access such data are Application Programming Interfaces (APIs). This workshop introduces techniques for working with APIs in Python to retrieve data from sources such as Wikipedia or The New York Times. It is intended for researchers who are new to working with APIs, but are familiar with Python or have completed the Introduction to Python workshop. Python is best learned hands-on. To side step any issues with installation, we will be coding on Jupyter Notebooks with Binder. This means that participants will be able to follow along on their machines without needing to download any packages or programs in advance. We do recommend requesting a ProPublica Congress API key in advance (https://www.propublica.org/datastore/api/propublica-congress-api). This allows participants to run the API script on their own machines. Helge-Johannes Marahrens is a doctoral student in the department of Sociology at Indiana University. He recently earned an MS in Applied Statistics and is currently working toward a PhD in Sociology. His research interests include cultural consumption, stratification, and computational social science with a particular focus on Natural Language Processing (NLP). Anne Kavalerchik is a doctoral student in the departments of Sociology and Informatics at Indiana University. Her research interests are broadly related to inequality, social change, and technology.