At The Analysis Factor, we are on a mission to help researchers improve their statistical skills so they can do amazing research.
We all tend to think of “Statistical Analysis” as one big skill, but it’s not.
Over the years of training, coaching, and mentoring data analysts at all stages, I’ve realized there are four fundamental stages of statistical skill:
Stage 1: The Fundamentals
Stage 2: Linear Models
Stage 3: Extensions of Linear Models
Stage 4: Advanced Models
There is also a stage beyond these where the mathematical statisticians dwell. But that stage is required for such a tiny fraction of data analysis projects, we’re going to ignore that one for now.
If you try to master the skill of “statistical analysis” as a whole, it’s going to be overwhelming.
And honestly, you’ll never finish. It’s too big of a field.
But if you can work through these stages, you’ll find you can learn and do just about any statistical analysis you need to. (more…)
Survival analysis isn’t just a single model.
It’s a whole set of tests, graphs, and models that are all used in slightly different data and study design situations. Choosing the most appropriate model can be challenging.
In this article I will describe the most common types of tests and models in survival analysis, how they differ, and some challenges to learning them.
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Every statistical model and hypothesis test has assumptions.
And yes, if you’re going to use a statistical test, you need to check whether those assumptions are reasonable to whatever extent you can.
Some assumptions are easier to check than others. Some are so obviously reasonable that you don’t need to do much to check them most of the time. And some have no good way of being checked directly, so you have to use situational clues.
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Most of us know that binary logistic regression is appropriate when the outcome variable has two possible outcomes: success and failure.
There are two more situations that are also appropriate for binary logistic regression, but they don’t always look like they should be.
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Sample size estimates are one of those data analysis tasks that look straightforward, but once you try to do one, make you want to bang your head against the computer in frustration. Or, maybe 
that’s just me.
Regardless of how they make you feel, they are super important to do for your study before you collect the data.
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I recently held a free webinar in our The Craft of Statistical Analysis program about Binary, Ordinal, and Nominal Logistic Regression.
It was a record crowd and we didn’t get through everyone’s questions, so I’m answering some here on the site. They’re grouped by topic, and you will probably get more out of it if you watch the webinar recording. It’s free.
The following questions refer to this logistic regression model: (more…)
