A Useful Graph for Interpreting Interactions between Continuous Variables

by Jeff Meyer

What’s a good method for interpreting the results of a model with two continuous predictors and their interaction?

Let’s start by looking at a model without an interaction.  In the model below, we regress a subject’s hip size on their weight and height. Height and weight are centered at their means.

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February 2019 Member Webinar: What’s the Best Statistical Package for You?

Choosing statistical software is part of The Fundamentals of Statistical Skill and is necessary to learning a second software (something we recommend to anyone progressing from Stage 2 to Stage 3 and beyond). You have many choices for software to analyze your data: R, SAS, SPSS, and Stata, among others. They are all quite good, but […]

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Descriptives Before Model Building

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When a model has a binary outcome, one common effect size is a risk ratio. As a reminder, a risk ratio is simply a ratio of two probabilities. (The risk ratio is also called relative risk.)

Recently I have had a few questions about risk ratios less than one.

A predictor variable with a risk ratio of less than one is often labeled a “protective factor” (at least in Epidemiology). This can be confusing because in our typical understanding of those terms, it makes no sense that a risk be protective.

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Rescaling Sets of Variables to Be on the Same Scale

by Christos Giannoulis, PhD Attributes are often measured using multiple variables with different upper and lower limits. For example, we may have five measures of political orientation, each with a different range of values. Each variable is measured in a different way. The measures have a different number of categories and the low and high […]

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