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
Linear Regression
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.
[Read more…] about The Difference Between R-squared and Adjusted R-squared
Member Training: Assumptions of Linear Models
What are the assumptions of linear models? If you compare two lists of assumptions, most of the time they’re not the same.
[Read more…] about Member Training: Assumptions of Linear Models
When Linear Models Don’t Fit Your Data, Now What?
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.
[Read more…] about When Linear Models Don’t Fit Your Data, Now What?
Measures of Model Fit for Linear Regression Models
A well-fitting regression model results in predicted values close to the observed data values.
The mean model, which uses the mean for every predicted value, generally would be used if there were no useful predictor variables. The fit of a proposed regression model should therefore be better than the fit of the mean model. [Read more…] about Measures of Model Fit for Linear Regression Models
Linear Regression Analysis – 3 Common Causes of Multicollinearity and What Do to About Them
Multicollinearity in regression is one of those issues that strikes fear into the hearts of researchers. You’ve heard about its dangers in statistics classes, 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, [Read more…] about Linear Regression Analysis – 3 Common Causes of Multicollinearity and What Do to About Them
classes, 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, [Read more…] about Linear Regression Analysis – 3 Common Causes of Multicollinearity and What Do to About Them