I recently opened a very large data set titled “1998 California Work and Health Survey” compiled by the Institute for Health Policy Studies at the University of California, San Francisco. There are 1,771 observations and 345 variables...

Latent Class Analysis is a method for finding and measuring unobserved latent subgroups in a population based on responses to a set of observed categorical variables.

This free, one-hour webinar is part of our regular Craft of Statistical Analysis series. In it, we will introduce and demonstrate two of the core concepts of mixed modeling—the random intercept and the random slope. Most scientific fields now recognize the extraordinary usefulness of mixed models, but they’re a tough nut to crack for someone who […]

Have you ever worked with a data set that had so many observations and/or variables that you couldn’t see the forest for the trees? You would like to extract some simple information but you can’t quite figure out how to do it. Get to know Stata’s collapse command...

Stata allows you to describe, graph, manipulate and analyze your data in countless ways. But at times (many times) it can be very frustrating trying to create even the simplest results. Join us and learn how to reduce your future frustrations. This one hour demonstration is for new and intermediate users of Stata. If you’re […]

One of Stata’s incredibly useful abilities is to temporarily store calculations from commands. Why is this so useful?

In the webinar we will travel beyond “statistical significance” to “practical significance”, “how big of a difference” rather than “is there a difference”.

A perfectly accurate test would put every transaction into boxes a and d. Thieves are stopped but customers are not. A test that is so bad it's worthless would have a lot of b's (angry customers without groceries) and c's (happy thieves with groceries) and possibly both.

In this webinar, we will review the interpretation of p-values and see an alternative approach based on Bayesian data analysis.

Many of the common effect size statistics, like eta-squared and Cohen's d, can't be calculated in a logistic regression model. So now what do you use?