No matter what statistical model you’re running, you need to go through the same steps. The order and the specifics of how you do each step will differ depending on the data and the type of model you use.
These steps are in 4 phases. Most people think of only the third as modeling. But the phases before this one are fundamental to making the modeling go well. It will be much, much easier, more accurate, and more efficient if you don’t skip them.
And there is no point in running the model if you skip phase 4.
If you think of them all as part of the analysis, the modeling process will be faster, easier, and make more sense.
Phase 1: Define and Design
In the first 5 steps, the object is clarity. You want to make everything as clear as possible to yourself. The more clear things are at this point, the smoother everything will be. (more…)
My 8 year-old son got a Rubik’s cube in his Christmas stocking this year.
I had gotten one as a birthday present when I was about 10. It was at the height of the craze and I was so excited.
I distinctly remember bursting into tears when I discovered that my little sister sneaked playing with it, and messed it up the day I got it. I knew I would mess it up to an unsolvable point soon myself, but I was still relishing the fun of creating patterns in the 9 squares, then getting it back to 6 sides of single-colored perfection. (I loved patterns even then). (more…)
Spending the summer writing a research grant proposal? Stuck on how to write up the statistics section?
An excellent handbook that outlines how to prepare the statistical content for grant proposals is “Statistics Guide for Research Grant Applicants.” Sections include “Describing the Study Design”, “Sample Size Calculations”, and “Describing the Statistical Methods,” among others.
The navigation for the guide is not obvious–it is in the left margin menu, among other menus, toward the bottom. You have to scroll down from the top of the page to see it.
The authors, JM Bland, BK Butland, JL Peacock, J Poloniecki, F Reid, P Sedgwick, are statisticians at St. George’s Hospital Medical School, London.
The steps you take to analyze data are just as important as the statistics you use. Mistakes and frustration in statistical analysis come as much, if not more, from poor process than from using the wrong statistical method.
Benjamin Earnhart of the University of Iowa has written a short (and humorous) article entitled “Respect Your Data” (requires LinkedIn account) that describes 23 practical steps that data analysts must take. This article was published in the newsletter of the American Statistical Association and has since been expanded and annotated
I recently had this question in consulting:
I’ve got 12 out of 645 cases with Mahalanobis’s Distances above the critical value, so I removed them and reran the analysis, only to find that another 10 cases were now outside the value. I removed these, and another 10 appeared, and so on until I have removed over 100 cases from my analysis! Surely this can’t be right!?! Do you know any way around this? It is really slowing down my analysis and I have no idea how to sort this out!!
And this was my response:
I wrote an article about dropping outliers. As you’ll see, you can’t just drop outliers without a REALLY good reason. Being influential is not in itself a good enough reason to drop data.
