Models for Longitudinal Data II

By the end of this module, you will be able to

- Specify models with alternative error covariance structures
- Describe the difference between analyzing trends vs. analyzing
**fluctuations**with longitudinal data - Run analyses with
**time-varying predictors**(i.e., level-1 predictors) - Interpret and plot results

- Review the resources (lecture videos and slides)
- Complete the assigned readings
- Hoffman (2014) ch 4.1 (USC SSO required)
- Hoffman (2014) ch 8 (USC SSO required)

- Attend the Thursday session and participate in the class exercise
- Complete Homework 8
- (Optional) Read the bonus R code on the generalized estimating
equations (GEE) method
- Additional reference: https://journals.sagepub.com/doi/abs/10.3102/10769986211017480

Note that in some of the videos below the Bayeisan analyses were used; however, for the class this year we will stay with frequentist analyses. The results and interpretations are basically; just note some differences in the terminology.

You can view and download the slides here: PDF

Check your learning: Assume that the temporal correlation decreases with a longer time gap. A researcher collects data at baseline (Time 1), 3-month follow-up (Time 2), and then 5-month follow-up (Time 3). Which correlation should be strongest?

OLS and RI-MLM/RM-ANOVA

Check your learning: The random-intercept model/repeated-measures ANOVA assumes a specific temporal covariance structure. What is that structure called?

Random Slopes

Autoregressive(1) error structure

Check your learning: In an AR(1) covariance structure, what is the implied correlation between Time 2 and Time 4, if \(\rho = .4\)?

Check your learning: When analyzing a conversation between a couple, a researcher is interested in whether a person follow up the partner’s complaints with positive or negative behaviors. Is this an example of studying trends or fluctuations?

Check your learning: In the model above, what is the interpretation
of the contextual effect of `mood1`

?

Note: For the coefficients of `stressor`

and
`stressor_pm`

in the above model, the coefficients are ones
adjusting for the other predictors in the model (e.g.,
`mood1_pm`

, `mood1_pmc`

, `women`

, and
their interactions).