Post

@easystats.github.io

What does a sound data analysis workflow look like? In a first step: - Define your estimand (*quantity and unit of interest*) & research question - Map reality to your model (choose variables wisely) How the {modelbased}📦fits into this post-estimation workflow (second step): 👇 #rstats #easystats

9/10/26, 6:22 am3 reposts14 likes

easystats

@easystats.github.io

Regression coefficients rarely give direct answers - "holding variables constant" is tricky. {modelbased} delivers your actual *quantity* of interest directly: • Expected outcomes (predictions) → estimate_means() • Group comparisons → estimate_contrasts() • Effect sizes & trends → estimate_slopes()

easystats

@easystats.github.io

An estimand needs two things: your *quantity* and your *unit* (target population) of interest. In {modelbased}, you control this via the `estimate` argument to say who is your target population: • A specific individual (e.g. conditional) • An average across individuals (marginal/population)

easystats

@easystats.github.io

Once your estimand is set, {modelbased} maps it directly: • Quantity → means, contrasts, or slopes • Unit → estimate = "average", "counterfactual", etc. Check out the updated vignette on the modelisation-approach: easystats.github.io/modelbased/a...

easystats.github.ioThe Modelisation Approach: An Analytical Workflow with Integrated Post-Estimation Framework