The advantages of using literate programming that combines plain-text and code chunks (e.g., R Markdown and Sweave) are well recognized. This allows the creation of rich, high quality, and reproducible documents. However, collaborative writing and …
Traditional power analysis has a narrow focus on statistical significance, overlooking other related inferential risks concerning estimates uncertainty under hypothetical replications of a study. To evaluate inferential risks related to effect size …
We introduce the PRDA (Prospective and Retrospective Design Analysis) R-package that allows researchers to perform a “Design Analysis” under different experimental scenarios (Altoè et al., 2020). Considering a plausible effect size (or its prior distribution), researchers can evaluate either the inferential risks for a given sample size or the required sample size to obtain a given statistical power. The main aim of PRDA package is to enhance researcher reasoning about inferential risks avoiding automated decisions.
Formalizing research hypotheses as statistical models within attachment theory considering expert judgement and relevant knowledge in the field.
Considering the role of expert knowledge in the formalization of priors within Bayesian analysis. Applied example of elicitation with school teachers considering the difference in average height between boys and girls (Slides in italian).
Expert Elicitation about effect sizes using an example about the difference in average height between boys and girls.