Lots of item-level heterogeneity in treatment sensitivity across a wide-range of RCTs (consistent with the earlier findings from www.tandfonline.com/doi/abs/10.1... that focused on education-based RCTs).

That leader being @sachaepskamp.bsky.social !

See also additional description in section 4.4 of the updated IRW preprint. osf.io/preprints/ps...

7/ Many of these data are available for reuse on the IRW! Next week we'll share code to help you start replicating these analyses!

6/ Understanding whether an empirical study is one versus the other may help us refine our understanding of what is really happening in the treatment.

5/ This paper shows that many treatments increase the response averages (ie a conventional treatment effect) while others serve to tighten connections between items (while not changing the overall level of item response).

4/ A separate paper (osf.io/preprints/ps...) pushes further to consider treatment effects from these studies from the perspective of network psychometrics.

3/ It goes further and shows that we may misunderstand uncertainty if we do not account for item-level variation and demonstrates identification challenges associated with the estimation of treatment-by-covariate interaction effects (a big deal given interest in heterogeneous treatment effects).

2/ This study, along with previous work (www.tandfonline.com/doi/abs/10.1...), shows that item-level variation in treatment effects is a real problem in empirical data!

See website for more details!
datapages.github.io/irw/

You can access these datasets straight from R or Python using API calls. Paperwork (citation & license) on the provenance of all inclusions available too.