We had an interesting discussion the other day regarding a regression discontinuity disaster. In my post I shone a light on this fitted model: Most of the commenters seemed to understand the concern with these graphs, that the upward slopes in the curves directly contribute to the estimated negative value at the discontinuity leading to […]

# Category: Statistical graphics

## “Data is Personal” and the maturing of the literature on statistical graphics

Traditionally there have been five ways to write about statistical graphics: 1. Exhortations to look at your data, make graphs, do visualizations and not just blindly follow statistical procedures. 2. Criticisms and suggested improvements for graphs, both general (pie-charts! double y-axes! colors! labels!) and specific. 3. Instruction and examples of how to make effective graphs […]

## What pieces do chess grandmasters move, and when?

Dan Goldstein posted a version of the above image (with R code!) which came from Ashton Anderson. My graph above is slightly modified from the original, which looks like this: The original was just fine, but I had a few changes to make. I thought the color scheme could be improved, also I wanted change […]

## Going beyond the rainbow color scheme for statistical graphics

Yesterday in our discussion of easy ways to improve your graphs, a commenter wrote: I recently read and enjoyed several articles about alternatives to the rainbow color palette. I particularly like the sections where they show how each color scheme looks under different forms of color-blindness and/or in black and white. Here’s a couple of […]

## What are some common but easily avoidable graphical mistakes?

John Kastellec writes: I was thinking about writing a short paper aimed at getting political scientists to not make some common but easily avoidable graphical mistakes. I’ve come up with the following list of such mistakes. I was just wondering if any others immediately came to mind? – Label lines directly – Make labels big […]

## Florence Nightingale´s 199th anniversary

## Do regression structures affect research capital? The case of pronoun drop

A linguist pointed me with incredulity to this article by Horst Feldmann, “Do Linguistic Structures Affect Human Capital? The Case of Pronoun Drop,” which begins: This paper empirically studies the human capital effects of grammatical rules that permit speakers to drop a personal pronoun when used as a subject of a sentence. By de‐emphasizing the […]

## What’s the upshot?

Yair points us to this page, The Upshot, Five Years In, by the New York Times data journalism team, listing their “favorite, most-read or most distinct work since 2014.” And some of these are based on our research: There Are More White Voters Than People Think. That’s Good News for Trump. (Story by Nate Cohn. […]

## Ballot order update

Darren Grant writes: Thanks for bringing my work on ballot order effects to the attention of a wider audience via your recent blog post. The final paper, slightly modified from the version you posted, was published last year in Public Choice. Like you, I am not wedded to traditional hypothesis testing, but think it is […]

## “The Long-Run Effects of America’s First Paid Maternity Leave Policy”: I need that trail of breadcrumbs.

Tyler Cowen links to a research article by Brenden Timpe, “The Long-Run Effects of America’s First Paid Maternity Leave Policy,” that begins as follows: This paper provides the first evidence of the effect of a U.S. paid maternity leave policy on the long-run outcomes of children. I exploit variation in access to paid leave that […]

## David Weakliem on the U.S. electoral college

The sociologist and public opinion researcher has a series of excellent posts here, here, and here on the electoral college. Here’s the start: The Electoral College has been in the news recently. I [Weakliem] am going to write a post about public opinion on the Electoral College vs. popular vote, but I was diverted into […]

## R fixed its default histogram bin width!

I remember hist() in R as having horrible defaults, with the histogram bars way too wide. (See this discussion: A key benefit of a histogram is that, as a plot of raw data, it contains the seeds of its own error assessment. Or, to put it another way, the jaggedness of a slightly undersmoothed histogram […]

## “Principles of posterior visualization”

What better way to start the new year than with a discussion of statistical graphics. Mikhail Shubin has this great post from a few years ago on Bayesian visualization. He lists the following principles: Principle 1: Uncertainty should be visualized Principle 2: Visualization of variability ≠ Visualization of uncertainty Principle 3: Equal probability = Equal […]

The post “Principles of posterior visualization” appeared first on Statistical Modeling, Causal Inference, and Social Science.

## “Principles of posterior visualization”

What better way to start the new year than with a discussion of statistical graphics. Mikhail Shubin has this great post from a few years ago on Bayesian visualization. He lists the following principles: Principle 1: Uncertainty should be visualized Principle 2: Visualization of variability ≠ Visualization of uncertainty Principle 3: Equal probability = Equal […]

The post “Principles of posterior visualization” appeared first on Statistical Modeling, Causal Inference, and Social Science.

## “Check yourself before you wreck yourself: Assessing discrete choice models through predictive simulations”

Timothy Brathwaite sends along this wonderfully-titled article (also here, and here’s the replication code), which begins: Typically, discrete choice modelers develop ever-more advanced models and estimation methods. Compared to the impressive progress in model development and estimation, model-checking techniques have lagged behind. Often, choice modelers use only crude methods to assess how well an estimated […]

The post “Check yourself before you wreck yourself: Assessing discrete choice models through predictive simulations” appeared first on Statistical Modeling, Causal Inference, and Social Science.

## “Check yourself before you wreck yourself: Assessing discrete choice models through predictive simulations”

Timothy Brathwaite sends along this wonderfully-titled article (also here, and here’s the replication code), which begins: Typically, discrete choice modelers develop ever-more advanced models and estimation methods. Compared to the impressive progress in model development and estimation, model-checking techniques have lagged behind. Often, choice modelers use only crude methods to assess how well an estimated […]

The post “Check yourself before you wreck yourself: Assessing discrete choice models through predictive simulations” appeared first on Statistical Modeling, Causal Inference, and Social Science.

## Exploring model fit by looking at a histogram of a posterior simulation draw of a set of parameters in a hierarchical model

Opher Donchin writes in with a question: We’ve been finding it useful in the lab recently to look at the histogram of samples from the parameter combined across all subjects. We think, but we’re not sure, that this reflects the distribution of that parameter when marginalized across subjects and can be a useful visualization. It […]

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## Exploring model fit by looking at a histogram of a posterior simulation draw of a set of parameters in a hierarchical model

Opher Donchin writes in with a question: We’ve been finding it useful in the lab recently to look at the histogram of samples from the parameter combined across all subjects. We think, but we’re not sure, that this reflects the distribution of that parameter when marginalized across subjects and can be a useful visualization. It […]

The post Exploring model fit by looking at a histogram of a posterior simulation draw of a set of parameters in a hierarchical model appeared first on Statistical Modeling, Causal Inference, and Social Science.

## Graphs and tables, tables and graphs

Jesse Wolfhagen writes: I was surprised to see a reference to you in a Quartz opinion piece entitled “Stop making charts when a table is better”. While the piece itself makes that case that there are many kinds of charts that are simply restatements of tabular data, I was surprised that you came up as […]

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## Perhaps you could try a big scatterplot with one dot per dataset?

Joe Nadeau writes: We are studying variation in both means and variances in metabolic conditions. We have access to nearly 200 datasets that involve a range of metabolic traits and vary in sample size, mean effects, and variance. Some traits differ in mean but not variance, others in variance but not mean, still others in […]

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