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 […]

# visualization

## Look People are Going to Think… (Debate Rhetoric Redux)

I’m still looking at the rhetoric from the presidential debates, this time focusing on the first general election debate between Hillary Clinton and Donald Trump. I turned the data frame from last week into a corpus, did some pre-processing with the tm package (remove capitalization, punctuation, stopwords, stemming the words and then completing stems with […]

## Getting Started with Hurdle Models

Hurdle Models are a class of models for count data that help handle excess zeros and overdispersion. To motivate their use, let’s look at some data in R. The following data come with the AER package. It is a sample of 4,406 individuals, aged 66 and over, who were covered by Medicare in 1988. One […]

## Getting started with Negative Binomial Regression Modeling

When it comes to modeling counts (ie, whole numbers greater than or equal to 0), we often start with Poisson regression. This is a generalized linear model where a response is assumed to have a Poisson distribution conditional on a weighted sum of predictors. For example, we might model the number of documented concussions to […]

## Visualizing the Effects of Logistic Regression

Logistic regression is a popular and effective way of modeling a binary response. For example, we might wonder what influences a person to volunteer, or not volunteer, for psychological research. Some do, some don’t. Are there independent variables that would help explain or distinguish between those who volunteer and those who don’t? Logistic regression gives […]

## Understanding 2-way Interactions

When doing linear modeling or ANOVA it’s useful to examine whether or not the effect of one variable depends on the level of one or more variables. If it does then we have what is called an “interaction”. This means variables combine or interact to affect the response. The simplest type of interaction is the […]

## Understanding Diagnostic Plots for Linear Regression Analysis

You ran a linear regression analysis and the stats software spit out a bunch of numbers. The results were significant (or not). You might think that youâ€™re done with analysis. No, not yet. After running a regression analysis, you should check if the model works well for data. We can check if a model works […]

## Understanding Q-Q Plots

The Q-Q plot, or quantile-quantile plot, is a graphical tool to help us assess if a set of data plausibly came from some theoretical distribution such as a Normal or exponential. For example, if we run a statistical analysis that assumes our dependent variable is Normally distributed, we can use a Normal Q-Q plot to […]

## Stata Tip: Name Your Graphs

An important component of data analysis is graphing. Stata provides excellent graphics facility for quickly exploring and visualizing your data. For example, let’s load the auto data set that comes with Stata (1978 Automobile Data) and make two scatterplots and then two boxplots: sysuse auto twoway scatter price mpg twoway scatter mpg weight graph box […]