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.
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.
Interactions in statistical models are never especially easy to interpret. Throw in non-normal outcome variables and non-linear prediction functions and they become even more difficult to understand. (more…)
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.
How do you know which method to use when you want to compare groups?
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