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.
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.
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.
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.
Most survival analysis models for time-to-event data, like Cox regression, assume independence. The survival time for one individual cannot influence the survival time for another.
This assumption doesn’t hold in many study designs. You may have animals clustered into litters, matched pairs, or patients in a multi-center trial with correlated survival times within a center.