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.
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.
Moderated mediation, also known as Conditional Process Modeling, is great tool for understanding one type of complex relationship among variables.
Multinomial logistic regression is an important type of categorical data analysis. Specifically, it’s used when your response variable is nominal: more than two categories and not ordered.
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In most regression models, there is one response variable and one or more predictors. From the model’s point of view, it doesn’t matter if those predictors are there to predict, to moderate, to explain, or to control. All that matters is that they’re all Xs, on the right side of the equation.
Interrupted time series analysis is a useful and specialized tool for understanding the impact of a change in circumstances on a long-term trend. The data for interrupted time series is a specific type of longitudinal data and must meet two criteria.
