# statistical methods

## 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 […]

## The Wilcoxon Rank Sum Test

The Wilcoxon Rank Sum Test is often described as the non-parametric version of the two-sample t-test. You sometimes see it in analysis flowcharts after a question such as “is your data normal?” A “no” branch off this question will recommend a Wilcoxon test if you’re comparing two groups of continuous measures. So what is this […]

## Pairwise comparisons of proportions

Pairwise comparison means comparing all pairs of something. If I have three items A, B and C, that means comparing A to B, A to C, and B to C. Given n items, I can determine the number of possible pairs using the binomial coefficient: $$\frac{n!}{2!(n – 2)!} = \binom {n}{2}$$ Using the R […]

## Getting Started with Factor Analysis

Take a look at the following correlation matrix for Olympic decathlon data calculated from 280 scores from 1960 through 2004 (Johnson and Wichern, p. 499): 100m LJ SP HJ 400m 100mH DS PV JV 1500m 100m 1.0000 0.6386 0.4752 0.3227 0.5520 0.3262 0.3509 0.4008 0.1821 -0.0352 LJ 0.6386 1.0000 0.4953 0.5668 0.4706 0.3520 0.3998 0.5167 […]

## An Introduction to Loglinear Models

Loglinear models model cell counts in contingency tables. They’re a little different from other modeling methods in that they don’t distinguish between response and explanatory variables. All variables in a loglinear model are essentially “responses”. To learn more about loglinear models, we’ll explore the following data from Agresti (1996, Table 6.3). It summarizes responses from […]