by Ursula Saqui, Ph.D.
This post is the first of a two-part series on the overall process of doing a literature review. Part two covers where to find your resources.
Would you build your house without a foundation? Of course not! However, many people skip the first step of any empirical-based project–conducting a literature review. Like the foundation of your house, the literature review is the foundation of your project.
Having a strong literature review gives structure to your research method and informs your statistical analysis. If your literature review is weak or non-existent, (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.
