In April and May, we’re doing something new: including in membership the workshop Interpreting (Even Tricky) Regression Coefficients with Karen Grace-Martin.
We’ll be releasing the first 3 of 6 modules in April and modules 4-6 in May and holding a special Q&A with Karen at the end of each month.
If you’ve ever wanted to know how to interpret your results or set up your model to get the information you needed, you’ll love this workshop.
Although it’s at Stage 2 and focuses entirely on linear models, everything applies to all sorts of regression models — logistic, multilevel, count models. All of them.
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There are two main types of factor analysis: exploratory and confirmatory. 
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Binary logistic regression is one of the most useful regression models. It allows you to predict, classify, or understand explanatory relationships between a set of predictors and a binary outcome.
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How do you know if the items of a test are hard or easy; fair or biased; accurate at measuring ability or not?
Item Response Theory (IRT).
In this training, you will see, with real life examples, how IRT answers these questions to assess a test.
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How do you know when to use a time series and when to use a linear mixed model for longitudinal data?
What’s the difference between repeated measures data and longitudinal?
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When analyzing longitudinal data, do you use regression or structural equation based approaches? There are many types of longitudinal data and different approaches to analyzing them. Two popular approaches are a regression based approach and a structural equation modeling based approach.
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