This Thursday, Sept 13, Ted Bell and Mandy Hampton Wray will be presenting a data analysis problem involving a longitudinal intervention study. Ted wrote out some notes summarizing the problem — this should give you a gist of what they’re looking for. See you all Thursday!
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Longitudinal data sets from an intervention.
Time series:
Pre-test Post-Test Longit 1 Longit 2 Longit3
Between subject controlled factors: intervention vs. control groups
Considerations:
Testing dates are not on fixed schedule, but show considerable variability between Post-test, L1, and other dates
Some data points are missing.
Many potential dependent variables.
Many potential predictors and individual subject characteristics.
Important covariates: age, gender, pretest score?
We have some dyadic interaction data for families and children.
Questions of interest:
Do our treatment groups differ from controls longitudinally on any of a variety of continuous and categorical variables?
Does the above interact with any within subject factor, such as pre-test score, or intervention gains, or intervention fidelity, or age, or gender?
Stats questions:
What are the most straightforward statistical techniques to apply in these situations?
Program/platform recommendations
Hi Ted and Mandy —
sorry I’ll miss this! My travel schedule is in perfect anti-harmony with meth-lab this summer.
But I wanted to say that I am dealing, I think, with a very similar kind of dataset, looking at change across therapy sessions with measurements sometimes administered at certain sessions, for session that sometimes occurred weekly. Lots of variation in schedules and missing data.
At this point I am using SPSS mixed. Also considered Mplus and R (package lavaan) all of which I think could handle it similarily.
I’m following recommendations from Singer & Willett “Applied longitudinal data analysis” (with syntax posted on UCLA site) to explore the data.
I’d enjoy putting our heads together and sharing notes when I’m back (first week of term), if you want!
Amber