Introduction As part of a series of workshops on quantitative analysis of text this fall, I started examining the comments submitted to President Ryanâ€™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 apply a graduated color symbology to a layer using Python for QGIS 3

I was recently working on a project in QGIS 3 with a member of UVA Health’s Oncology department. This person wanted to take a set of patient data (after identifying info had been removed) and after doing some other stuff, apply a graduated color scheme to the results, shading them from light to dark based […]

## 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 […]

## A Beginner’s Guide to Text Analysis with quanteda

A lot of introductory tutorials to quanteda assume that the reader has some base of knowledge about the program’s functionality or how it might be used. Other tutorials assume that the user is an expert in R and on what goes on under the hood when you’re coding. This introductory guide will assume none of […]

## 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 […]

## Getting Started with Moderated Mediation

In a previous post we demonstrated how to perform a basic mediation analysis. In this post we look at performing a moderated mediation analysis. The basic idea is that a mediator may depend on another variable called a “moderator”. For example, in our mediation analysis post we hypothesized that self-esteem was a mediator of student […]

## Getting started with Multivariate Multiple Regression

Multivariate Multiple Regression is the method of modeling multiple responses, or dependent variables, with a single set of predictor variables. For example, we might want to model both math and reading SAT scores as a function of gender, race, parent income, and so forth. This allows us to evaluate the relationship of, say, gender with […]

## Visualizing the Effects of Proportional-Odds Logistic Regression

Proportional-odds logistic regression is often used to model an ordered categorical response. By “ordered”, we mean categories that have a natural ordering, such as “Disagree”, “Neutral”, “Agree”, or “Everyday”, “Some days”, “Rarely”, “Never”. For a primer on proportional-odds logistic regression, see our post, Fitting and Interpreting a Proportional Odds Model. In this post we demonstrate […]