Karen Grace-Martin

In this video I will answer a question from a recent webinar, Random Intercept and Random Slope Models.

We are answering questions here because we had over 500 people live on the webinar so we didn’t have time to get through all the questions.

If you missed the webinar live, this and the other questions in this series may make more sense if you watch that first. It was part of our free webinar series, The Craft of Statistical Analysis, and you can sign up to get the free recording, handout, and data set at this link:

http://TheCraftofStatisticalAnalysis.com/random-intercept-random-slope-models

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Pretty much all of the common statistical models we use, with the exception of OLS Linear Models, use Maximum Likelihood estimation.

This includes favorites like:

• All Generalized Linear Models, including logistic, probit, beta, Poisson, negative binomial regression
• Linear Mixed Models
• Generalized Linear Mixed Models
• Parametric Survival Analysis models, like Weibull models
• Structural Equation Models

That’s a lot of models.

If you’ve ever learned any of these, you’ve heard that some of the statistics that compare model fit in competing models require (more…)

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We’ve talked a lot around here about the reasons to use syntax — not only menus — in your statistical analyses.

Regardless of which software you use, the syntax file is pretty much always a text file. This is true for R, SPSS, SAS, Stata — just about all of them.

This is important because it means you can use an unlikely tool to help you code: Microsoft Word.

I know what you’re thinking. Word? Really?

Yep, it’s true. Essentially it’s because Word has much better Search-and-Replace options than your stat software’s editor.

Here are a couple features of Word’s search-and-replace that I use to help me code faster:

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The normal distribution is so ubiquitous in statistics that those of us who use a lot of statistics tend to forget it’s not always so common in actual data.

And since the normal distribution is continuous, many people describe all numerical variables as continuous. I get it: I’m guilty of using those terms interchangeably, too, but they’re not exactly the same.

Numerical variables can be either continuous or discrete.

The difference? Continuous variables can take any number within a range. Discrete variables can only take on specific values. For numeric discrete data, these are often, but don’t have to be, whole numbers*.

Count variables, as the name implies, are frequencies of some event or state. Number of arrests, fish (more…)

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Outliers are one of those realities of data analysis that no one can avoid.

Those pesky extreme values cause biased parameter estimates, non-normality in otherwise beautifully normal variables, and inflated variances.

Everyone agrees that outliers cause trouble with parametric analyses. But not everyone agrees that they’re always a problem, or what to do about them even if they are.

Sometimes a nonparametric or robust alternative is available — and sometimes not.

There are a number of approaches in statistical analysis for dealing with outliers and the problems they create. It’s common for committee members or Reviewer #2 to have very strong opinions that there is one and only one good approach.

Two approaches that I’ve commonly seen are: 1) delete outliers from the sample, or 2) winsorize them (i.e., replace the outlier value with one that is less extreme).

The problem with both of these “solutions” is that they also cause problems — biased parameter estimates and underweighted or eliminated valid values. (more…)

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