OptinMon 10 - 14 Steps

I’ve written about this before–there is just something about statistics that makes people feel…well, not so smart.

This makes people v-e-r-y reluctant to ask questions.

This fact really struck me years and years ago.  Hit me hard.

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It’s easy to think that if you just knew statistics better, data analysis wouldn’t be so hard.

It’s true that more statistical knowledge is always helpful. But I’ve found that statistical knowledge is only part of the story.

Another key part is developing data analysis skills. These skills apply to all analyses. It doesn’t matter which statistical method or software you’re using. So even if you never need any statistical analysis harder than a t-test, developing these skills will make your job easier.

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My poor colleague was pulling her hair out in frustration today.

You know when you’re trying to do something quickly, and it’s supposed to be easy, only it’s not? And you try every solution you can think of and it still doesn’t work?

And even in the great age of the Internet, which is supposed to know all the things you don’t, you still can’t find the answer anywhere?

Cue hair-pulling.

Here’s what happened: She was trying to import an Excel spreadsheet into SAS, and it didn’t work.

Instead she got:

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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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One of the most common—and one of the trickiest—challenges in data analysis is deciding how to include multiple predictors in a model, especially when they’re related to each other.

Let’s say you are interested in studying the relationship between work spillover into personal time as a predictor of job burnout.

You have 5 categorical yes/no variables that indicate whether a particular symptom of work spillover is present (see below).

While you could use each individual variable, you’re not really interested if one in particular is related to the outcome. Perhaps it’s not really each symptom that’s important, but the idea that spillover is happening.

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Like the chicken and the egg, there’s a question about which comes first: run a model or test assumptions? Unlike the chickens’, the model’s question has an easy answer.

There are two types of assumptions in a statistical model.  Some are distributional assumptions about the residuals.  Examples include independence, normality, and constant variance in a linear model.

Others are about the form of the model.  They include linearity and (more…)

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