StatLab Articles

Analysis of Ours to Shape Comments, Part 4

Introduction We’re still analyzing the comments submitted to President Ryan’s Ours to Shape website. In the fourth installment of this series (we’re almost done, I promise), we’ll look at the sentiment – aka positive-negative tone, polarity, affect – of the comments to President Ryan’s Ours to Shape website. We don’t have a pre-labeled set of […]

Analysis of Ours to Shape Comments, Part 3

Introduction To recap, we’re exploring the comments submitted to President Ryan’s Ours to Shape website (as of December 7, 2018). In the first post we looked at the distribution of comments across Ryan’s three categories – community, discovery, and service – and across the contributors’ primary connection to the university. We extracted features like length […]

Analysis of Ours to Shape Comments, Part 2

Introduction In the last post, we began exploring the comments submitted to the Ours to Shape website. We looked at the distribution across categories and contributors, the length and readability of the comments, and a few key words in context. While I did more exploration of the data than reported, the first post gives a […]

Analysis of Ours to Shape Comments, Part 1

Introduction As part of a series of workshops on quantitative analysis of text this fall, I started examining the comments submitted to President Ryan’s Ours to Shape website. The site invites people to share their ideas and insights for UVA going forward, particularly in the domains of service, discovery, and community. The website was only […]

How to use the field calculator in Python for QGIS 3

Recently, I have taken the dive into python scripting in QGIS. QGIS is a really nice open source (and free!) alternative to ESRI’s ArcGIS. While QGIS is a little quirky and generally not quite as user friendly as ArcGIS, it still provides nearly the same functionality. Personally, I’ve become a fan of it an now […]

Assessing Type S and Type M Errors

The paper Beyond Power Calculations: Assessing Type S (Sign) and Type M (Magnitude) Errors by Andrew Gelman and John Carlin introduces the idea of performing design calculations to help prevent researchers from being misled by statistically significant results in studies with small samples and/or noisy measurements. The main idea is that researchers often overestimate effect […]

Interpreting Log Transformations in a Linear Model

Log transformations are often recommended for skewed data, such as monetary measures or certain biological and demographic measures. Log transforming data usually has the effect of spreading out clumps of data and bringing together spread-out data. For example, below is a histogram of the areas of all 50 US states. It is skewed to the […]

Getting Started with Matching Methods

A frequent research question is whether or not some “treatment” causes an effect. For example, does taking aspirin daily reduce the chance of a heart attack? Does more sleep lead to better academic performance for teenagers? Does smoking increase the risk of chronic obstructive pulmonary disease (COPD)? To truly answer such questions, we need a […]