Ten Ways Learning a Statistical Software Package is Like Learning a New Language

January 31st, 2014 by

Someone recently asked me if they need to learn R.  In responding, it struck me that this is another way that learning a stat package is like learning a new language.

The metaphor is extremely helpful for deciding when and how to learn a new stat package, and to keep you going when the going gets rough. (more…)

Opposite Results in Ordinal Logistic Regression, Part 2

July 22nd, 2013 by

I received the following email from a reader after sending out the last article: Opposite Results in Ordinal Logistic Regression—Solving a Statistical Mystery.

And I agreed I’d answer it here in case anyone else was confused.

Karen’s explanations always make the bulb light up in my brain, but not this time.

With either output,
The odds of 1 vs > 1 is exp[-2.635] = 0.07 ie unlikely to be  1, much more likely (14.3x) to be >1
The odds of £2 vs > 2 exp[-0.812] =0.44 ie somewhat unlikely to be £2, more likely (2.3x) to be >2

SAS – using the usual regression equation
If NAES increases by 1 these odds become (more…)

Opposite Results in Ordinal Logistic Regression—Solving a Statistical Mystery

July 5th, 2013 by

A number of years ago when I was still working in the consulting office at Cornell, someone came in asking for help interpreting their ordinal logistic regression results.

The client was surprised because all the coefficients were backwards from what they expected, and they wanted to make sure they were interpreting them correctly.

It looked like the researcher had done everything correctly, but the results were definitely bizarre. They were using SPSS and the manual wasn’t clarifying anything for me, so I did the logical thing: I ran it in another software program. I wanted to make sure the problem was with interpretation, and not in some strange default or (more…)

EM Imputation and Missing Data: Is Mean Imputation Really so Terrible?

April 15th, 2009 by

I’m sure I don’t need to explain to you all the problems that occur as a result of missing data.  Anyone who has dealt with missing data—that means everyone who has ever worked with real data—knows about the loss of power and sample size, and the potential bias in your data that comes with listwise deletion.

Listwise deletion is the default method for dealing with missing data in most statistical software packages.  It simply means excluding from the analysis any cases with data missing on any variables involved in the analysis.

A very simple, and in many ways appealing, method devised to (more…)

SPSS, SAS, R, Stata, JMP? Choosing a Statistical Software Package or Two

March 16th, 2009 by

In addition to the five listed in this title, there are quite a few other options, so how do you choose which statistical software to use?

The default is to use whatever software they used in your statistics class–at least you know the basics.

And this might turn out pretty well, but chances are it will fail you at some point. Many times the stat package used in a class is chosen for its shallow learning curve, (more…)

The Exposure Variable in Poisson Regression Models

January 23rd, 2009 by

Poisson Regression Models and its extensions (Zero-Inflated Poisson, Negative Binomial Regression, etc.) are used to model counts and rates. A few examples of count variables include:

– Number of words an eighteen month old can say

– Number of aggressive incidents performed by patients in an impatient rehab center

Most count variables follow one of these distributions in the Poisson family. Poisson regression models allow researchers to examine the relationship between predictors and count outcome variables.

Using these regression models gives much more accurate parameter (more…)