Q: How high does the count scale have to be before you can consider it continuous? I suspect you're getting at the same issue as in the last question. It's certainly true that when you get into very large numbers, many of the issues with count variables aren't issues anymore.

For ANOVAs, two of the most popular are Eta-squared and partial Eta-squared. In one way ANOVAs, they come out the same, but in more complicated models, their values, and their meanings differ.him

One problem is that the mean age at which infants utter their first word may differ from one sample to another. This means you're not always evaluating that mean that the exact same age. It's not comparable across samples. So another option is to choose a meaningful value of age that is within the values in the data set. One example may be at 12 months.

I recently received this great question: Question: Hi Karen,  ive purchased a lot of your material and read a lot of your pdf documents w.r.t. regression and interaction terms.  Its, now, my general understanding that interaction for two or more categorical variables is best done with effects coding, and interactions  cont v. categorical variables is […]

Here's one example of the flexibility of mixed models, and its resulting potential for confusion and error. In repeated measures and longitudinal studies, the observations are clustered within a subject. That means the observations, and their residuals, are not independent. They're correlated. There are two ways to deal with this correlation.

In this webinar we’re doing something a little different – rather than give you an overivew of a topic, we will interpret together the regression coefficients table from a real data set. This data set is from the dissertation of a client I worked with a few years ago.  She has graciously allowed us to […]

“Everything should be made as simple as possible, but no simpler” – Albert Einstein* For some reason, I’ve heard this quotation 3 times in the past 3 days.  Maybe I hear it everyday, but only noticed because I’ve been working with a few clients on model selection, and deciding how much to simplify a model. […]

A Covariance Matrix, like many matrices used in statistics, is symmetric.  That means that the table has the same headings across the top as it does along the side. The thing to keep in mind when it all gets overwhelming is a matrix is just a table. That's it.

Reason 5: The biggest benefit of doing these calculations is to not waste years and thousands of dollars in grants or tuition pursuing an impossible analysis. If sample size calculations indicate you need a thousand subjects to find significant results but time, money, or ethical constraints limit you to 50, don't do that study.

You're dealing with both a complicated modeling technique (survival analysis, logistic regression, multilevel modeling) and tricky effects in the model (dummy coding, interactions, and quadratic terms). The only way to figure it all out in a situation like that is to break it down into parts. Trying to understand all those complicated parts together is a recipe for disaster. But if you can do linear regression, each part is just one step up in complexity. Take one step at a time.