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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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Regression is one of the most common analyses in statistics. Most of us learn it in grad school, and we learned it in a specific software. Maybe SPSS, maybe another software package. The thing is, depending on your training and when you did it, there is SO MUCH to know about doing a regression analysis in SPSS.

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The objective for quasi-experimental designs is to establish cause and effect relationships between the dependent and independent variables. However, they have one big challenge in achieving this objective: lack of an established control group.

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