Contents
Which is an example of longitudinal data analysis?
The seminar will feature examples from Applied Longitudinal Data Analysis: Modeling Change and Event Occurrence by Judith D. Singer and John B. Willett The seminar will address the following issues.
How does multilevel modeling handle correlations among time points?
Handles correlations among time points, assuming CS or UN. It is OK if some kids have more waves of data than others. The repeated statement assumes kids all measured at the same time points (for computing covariance structures). Handles correlations among time points, using mixed can even handle many different kinds of covariance structures.
What is the repeated statement in multilevel modeling?
The repeated statement assumes kids all measured at the same time points (for computing covariance structures). Handles correlations among time points, using mixed can even handle many different kinds of covariance structures. Assumes no correlations among time points for a given person.
How are treatments measured in a longitudinal analysis?
The treatments are: The treatments were administered to one ear of the test animal. The y variable = difference in temperature between the treated ear and the untreated ear. This is measured at times 0, 30 minutes, 60 minutes, and 90 minutes after the treatment is administered.
How is the time factor used in longitudinal analysis?
The Time factor measures whether the mean response differs over time when we average over all animals and all treatments. The Time*Treatment interaction which is sensitive to whether the pattern across time depends upon the specific treatment used. The errors are assumed to be independently normally distributed with mean 0 and constant variance.
How are multilevel models used to analyze slopes?
Models for evaluating changes in “elevation” and “slope” over time. Using multilevel models to analyze “treatment effects” over time. The seminar will focus on the construction and interpretation of these models with the aims of appealing to users of all multilevel modeling packages (e.g., HLM, SAS PROC MIXED, MLwiN, SPSS mixed, etc.).