If you’ve used much analysis of variance (ANOVA), you’ve probably heard that ANOVA is a special case of linear regression. Unless you’ve seen why, though, that may not make a lot of sense. After all, ANOVA compares means between categories, while regression predicts outcomes with numeric variables.
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Regression models
Can Likert Scale Data ever be Continuous?
A very common question is whether it is legitimate to use Likert scale data in parametric statistical procedures that 
require interval data, such as Linear Regression, ANOVA, and Factor Analysis.
A typical Likert scale item has 5 to 11 points that indicate the degree of something. For example, it could measure agreement with a statement, such as 1=Strongly Disagree to 5=Strongly Agree. It can be a 1 to 5 scale, 0 to 10, etc. [Read more…] about Can Likert Scale Data ever be Continuous?
Member Training: Multinomial Logistic Regression
Multinomial logistic regression is an important type of categorical data analysis. Specifically, it’s used when your response variable is nominal: more than two categories and not ordered.
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Member Training: Centering
Centering variables is common practice in some areas, and rarely seen in others. That being the case, it isn’t always clear what are the reasons for centering variables. 
Is it only a matter of preference, or does centering variables help with analysis and interpretation? [Read more…] about Member Training: Centering
Member Training: Analyzing Likert Scale Data
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. [Read more…] about Member Training: Analyzing Likert Scale Data
The Difference Between R-squared and Adjusted R-squared
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.
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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