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Better CIs (and p-values?) for std-coefs #705

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mattansb opened this issue Apr 3, 2022 · 1 comment

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enhancement 💥

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mattansb commented Apr 3, 2022

Originally posted on #655

Strictly speaking, both the p values and CIs we currently report for standardized effects aren't strictly accurate because they don't include uncertainty in the means or SDs used to standardize. This is usually ignored by most folks. But, ideally we would include options to compute CIs accounting for those (cf. here and here).

The supplemental (https://static-content.springer.com/esm/art%3A10.1007%2Fs11336-013-9380-y/MediaObjects/11336_2013_9380_MOESM2_ESM.pdf) has code how to do this, if I understand right. Must take a closer look at it.

mattansb added the enhancement 🔥 label

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bwiernik commented Apr 3, 2022

From the fungible package which implements those methods

https://github.com/cran/fungible/blob/master/R/seBeta.R

https://github.com/cran/fungible/blob/master/R/seBetaCor.R

https://github.com/cran/fungible/blob/master/R/seBetaFixed.R

And somewhat cleaner implementation of the normal-theory methods

https://github.com/psychmeta/psychmeta/blob/b9e27791563f4b98b5e1aaeb5bc97d97bb28d055/R/lm_mat.R#L483

mattansb transferred this issue from easystats/effectsize

strengejacke added enhancement 💥 and removed enhancement 🔥 labels

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