I had never come across these until I was reminding myself about stanines. Stanine scoring (q.v.) splits a wider score range into nine levels, a sten score splits it into ten and loses the advantage the stanine transformation has of only needing one digit.
Details #
The transformation involves steps of 0.5 of the SD of the distribution which might be that of the dataset that is being recoded (a sten transformation) or might be the SD or perhaps the percentiles of the .5 SD steps of a standard Gaussian distribution from a referential dataset (arguably “sten SD scores” or “sten referential distribution scores”). I have never seen sten scores used though the excellent wikipedia page (https://en.wikipedia.org/wiki/Sten_scores) points out that they are used for Cattell’s 16PF personality questionnaire. That shows how much I’ve used or even seen much about the 16PF (though I have a soft spot for Cattell!)
Try also #
- Dataset
- Distribution
- Gaussian (“Normal”) distribution
- Percentiles/centiles/quantiles
- Stanine scores/transformation
- Transformations
Chapters #
Not covered in the OMbook.
Online resources #
My rblog post about dichotomisation explains the issue noted above about dichotomisation.
My shiny apps have one (ECDF plot with quantiles and CIs for quantiles) that allows you to upload data and shows the imprecision of estimating quantiles. If you put in:
0.023, 0.067, 0.159, 0.309, 0.5, 0.691, 0.841, 0.933, 0.977
for the quantiles you want you are asking for a stanine mapping of your data. You could pull these CSV files which contain datasets from a standard Gaussian distribution to your machine and upload them to that app to see the effects of dataset size:
I might create some things that might touch on this though I think they’d be explanatory rather than of real use for routine data … for the reasons above including that I’ve never seen stanines used in our field!
Dates #
First created 1.v.25.
