# regression models

### Likert Scale Items as Predictor Variables in Regression

May 22nd, 2009 by

I was recently asked about whether it’s okay to treat a likert scale as continuous as a predictor in a regression model.  Here’s my reply.  In the question, the researcher asked about logistic regression, but the same answer applies to all regression models.

1. There is a difference between a likert scale item (a single 1-7 scale, eg.) and a full likert scale , which is composed of multiple items.  If it is a full likert scale, with a combination of multiple items, go ahead and treat it as numerical. (more…)

### The Distribution of Independent Variables in Regression Models

April 9th, 2009 by

I often hear concern about the non-normal distributions of independent variables in regression models, and I am here to ease your mind.

There are NO assumptions in any linear model about the distribution of the independent variables.  Yes, you only get meaningful parameter estimates from nominal (unordered categories) or numerical (continuous or discrete) independent variables.  But no, the model makes no assumptions about them.  They do not need to be normally distributed or continuous.

It is useful, however, to understand the distribution of predictor variables to find influential outliers or concentrated values.  A highly skewed independent variable may be made more symmetric with a transformation.

### Is Multicollinearity the Bogeyman?

April 8th, 2009 by

Multicollinearity occurs when two or more predictor variables in a regression model are redundant.  It is a real problem, and it can do terrible things to your results.  However, the dangers of multicollinearity seem to have been so drummed into students’ minds that it created a panic.

True multicolllinearity (the kind that messes things up) is pretty uncommon.  High correlations among predictor variables may indicate multicollinearity, but it is NOT a reliable indicator that it exists.  It does not necessarily indicate a problem.  How high is too high depends on (more…)