OptinMon 30 - Four Critical Steps in Building Linear Regression Models

The Great Likert Data Debate

January 9th, 2009 by

I first encountered the Great Likert Data Debate in 1992 in my first statistics class in my psychology graduate program.Stage 2

My stats professor was a brilliant mathematical psychologist and taught the class unlike any psychology grad class I’ve ever seen since.  Rather than learn ANOVA in SPSS, we derived the Method of Moments using Matlab.  While I didn’t understand half of what was going on, this class roused my curiosity and led me to take more theoretical statistics classes.  The rest is history.

A large section of the class was dedicated to the fact that Likert data was not interval and therefore not appropriate for  statistics that assume normality such as ANOVA and regression.  This was news to me.  Meanwhile, most of the rest of the field either ignored or debated this assertion.

16 years later, the debate continues.  A nice discussion of the debate is found on the Research Methodology blog by Hisham bin Md-Basir.  It’s a nice blog with thoughtful entries that summarize methodological articles in the social and design sciences.

To be fair, though, this blog entry summarizes an article on the “Likert scales are not interval” side of the debate.  For a balanced listing of references, see Can Likert Scale Data Ever Be Continuous?

 


Regression Through the Origin

November 13th, 2008 by

I just wanted to follow up on my last post about Regression without Intercepts.Stage 2

Regression through the Origin means that you purposely drop the intercept from the model.  When X=0, Y must = 0.

The thing to be careful about in choosing any regression model is that it fit the data well.  Pretty much the only time that a regression through the origin will fit better than a model with an intercept is if the point X=0, Y=0 is required by the data.

Yes, leaving out the intercept will increase your df by 1, since you’re not estimating one parameter.  But unless your sample size is really, really small, it won’t matter.  So it really has no advantages.

 


Outliers: To Drop or Not to Drop

September 17th, 2008 by

Should you drop outliers? Outliers are one of those statistical issues that everyone knows about, but most people aren’t sure how to deal with.  Most parametric statistics, like means, standard deviations, and correlations, and every statistic based on these, are highly sensitive to outliers.

And since the assumptions of common statistical procedures, like linear regression and ANOVA, are also based on these statistics, outliers can really mess up your analysis.

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Despite all this, as much as you’d like to, it is NOT acceptable to

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