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 webinar will present an overview and an example of how latent class analysis works to find subgroups, how to interpret the output, the steps involved in running it. We will discuss extensions and uses of the latent classes in other analyses and similarities and differences with related techniques.
Note: This training is an exclusive benefit to members of the Statistically Speaking Membership Program and part of the Stat’s Amore Trainings Series. Each Stat’s Amore Training is approximately 90 minutes long.
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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 didn’t receive training in their methodology.
But it turns out that mixed models are actually an extension of linear models. If you have a good foundation in linear models, the extension to mixed models is more of a step than a leap. (Okay, a large step, but still).
You’ll learn what random intercepts and slopes mean, what they do, and how to decide if one or both are needed. It’s the first step in understanding mixed modeling.
Date: Friday, August 21, 2015
Time: 12pm EDT (New York time)
Cost: Free
***Note: This webinar has already taken place. Sign up below to get access to the video recording of the webinar.
Whenever we run an analysis of variance or run a regression one of the first things we do is look at the p-value of our predictor variables to determine whether
they are statistically significant. When the variable is statistically significant, did you ever stop and ask yourself how significant it is? (more…)
Why is it we can model non-linear effects in linear regression?
What the heck does it mean for a model to be “linear in the parameters?” (more…)
Lately, I’ve gotten a lot of questions about learning how to run models for repeated measures data that isn’t continuous.
Mostly categorical. But once in a while discrete counts.
A typical study is in linguistics or psychology where (more…)
A Science News article from July 2014 was titled “Scientists’ grasp of confidence intervals doesn’t inspire confidence.” Perhaps that is why only 11% of the articles in the 10 leading psychology journals in 2006 reported confidence intervals in their statistical analysis.
How important is it to be able to create and interpret confidence intervals?
The American Psychological Association Publication Manual, which sets the editorial standards for over 1,000 journals in the behavioral, life, and social sciences, has begun emphasizing parameter estimation and de-emphasizing Null Hypothesis Significance Testing (NHST).
Its most recent edition, the sixth, published in 2009, states “estimates of appropriate effect sizes and confidence intervals are the minimum expectations” for published research.
In this webinar, we’ll clear up the ambiguity as to what exactly is a confidence interval and how to interpret them in a table and graph format. We will also explore how they are calculated for continuous and dichotomous outcome variables in various types of samples and understand the impact sample size has on the width of the band. We’ll discuss related concepts like equivalence testing.
By the end of the webinar, we anticipate your grasp of confidence intervals will inspire confidence.
Note: This training is an exclusive benefit to members of the Statistically Speaking Membership Program and part of the Stat’s Amore Trainings Series. Each Stat’s Amore Training is approximately 90 minutes long.
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