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…)
One of the most anxiety-laden questions I get from researchers is whether their analysis is “right.”
I’m always slightly uncomfortable with that word. Often there is no one right analysis.
It’s like finding Mr. or Ms. Right. Most of the time, there is not just one Right. But there are many that are clearly Wrong.
What Makes an Analysis Right?
Luckily, what makes an analysis right is easier to define than what makes a person right for you. It pretty much comes down to two things: whether the assumptions of the statistical method are being met and whether the analysis answers the research question.
Assumptions are very important. A test needs to reflect the measurement scale of the variables, the study design, and issues in the data. A repeated measures study design requires a repeated measures analysis. A binary dependent variable requires a categorical analysis method.
But within those general categories, there are often many analyses that meet assumptions. A logistic regression or a chi-square test both handle a binary dependent variable with a single categorical predictor. But a logistic regression can answer more research questions. It can incorporate covariates, directly test interactions, and calculate predicted probabilities. A chi-square test can do none of these.
So you get different information from different tests. They answer different research questions.
An analysis that is correct from an assumptions point of view is useless if it doesn’t answer the research question. A data set can spawn an endless number of statistical tests that don’t answer the research question. And you can spend an endless number of days running them.
When to Think about the Analysis
The real bummer is it’s not always clear that the analyses aren’t relevant until you write up the research paper.
That’s why writing out the research questions in theoretical and operational terms is the first step of any statistical analysis. It’s absolutely fundamental. And I mean writing them in minute detail. Issues of mediation, interaction, subsetting, control variables, et cetera, should all be blatantly obvious in the research questions.
Thinking about how to analyze the data before collecting the data can help you from hitting a dead end. It can be very obvious, once you think through the details, that the analysis available to you based on the data won’t answer the research question.
Whether the answer is what you expected or not is a different issue.
So when you are concerned about getting an analysis “right,” clearly define the design, variables, and data issues, but most importantly, get explicitly clear about what you want to learn from this analysis.
Once you’ve done this, it’s much easier to find the statistical method that answers the research questions and meets assumptions. Even if you don’t know the right method, you can narrow your search with clear guidance.
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.
