Regression models

Member Training: Analyzing Likert Scale Data

August 31st, 2022 by

Is it really ok to treat Likert items as continuous? And can you just decide to combine Likert items to make a scale? Likert-type data is extremely common—and so are questions like these about how to analyze it appropriately. (more…)


The Difference Between R-squared and Adjusted R-squared

August 22nd, 2022 by

When is it important to use adjusted R-squared instead of R-squared?

R², the the Coefficient of Determination, is one of the most useful and intuitive statistics we have in linear regression.Stage 2

It tells you how well the model predicts the outcome and has some nice properties. But it also has one big drawback.

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Member Training: Assumptions of Linear Models

June 30th, 2022 by

Stage 2What are the assumptions of linear models? If you compare two lists of assumptions, most of the time they’re not the same.
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When Linear Models Don’t Fit Your Data, Now What?

June 20th, 2022 by

When your dependent variable is not continuous, unbounded, and measured on an interval or ratio scale, linear models don’t fit. The data just will not meet the assumptions of linear models. But there’s good news, other models exist for many types of dependent variables.

Today I’m going to go into more detail about 6 common types of dependent variables that are either discrete, bounded, or measured on a nominal or ordinal scale and the tests that work for them instead. Some are all of these.

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What Is Specification Error in Statistical Models?

June 8th, 2022 by

When we think about model assumptions, we tend to focus on assumptions like independence, normality, and constant variance. The other big assumption, which is harder to see or test, is that there is no specification error. The assumption of linearity is part of this, but it’s actually a bigger assumption.

What is this assumption of no specification error? (more…)


Linear Regression Analysis – 3 Common Causes of Multicollinearity and What Do to About Them

February 11th, 2022 by
Multicollinearity in regression is one of those issues that strikes fear into the hearts of researchers. You’ve heard about its dangers in statistics Stage 2classes, and colleagues and journal reviews question your results because of it. But there are really only a few causes of multicollinearity. Let’s explore them.Multicollinearity is simply redundancy in the information contained in predictor variables. If the redundancy is moderate, (more…)