#growthmodels — Public Fediverse posts

Recent #PeerReview work on #MixedModels and #GrowthModels had me going back to these classical texts on the genre:

1) https://journals.sagepub.com/doi/10.3102/01623737018004265

2) https://www.cambridge.org/core/journals/development-and-psychopathology/article/design-and-analysis-of-longitudinal-studies-of-development-and-psychopathology-in-context-statistical-models-and-methodological-recommendations/3F89776EE1F27BA4E4F49435AFBB7662

They are excellent tutorials on the most basic aspects of incorporating trajectories / functional change into such models. And the assumptions these require.

#HLM #History #SurvivalAnalysis

Another #PeerReview finished (review of revision).

Paper ~ 5400 words
Review ~ 1000 words
Duration ~ 2 hours

Main question:
If there is clear evidence from visual inspection and models misfit that the data do not follow a trajectory that can be described by a functional form, should one still fit a model that assumes such a growth trajectory?

Very nice paper in this regard:
https://link.springer.com/article/10.1007/s10654-020-00615-6

#Psychometrics #GrowthModels