Timeline for Empirical logit transformation on percentage data
Current License: CC BY-SA 3.0
8 events
| when toggle format | what | by | license | comment | |
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| Dec 11, 2017 at 18:27 | comment | added | Chris | As per @whuber... Are you sure this isn't a binomial process? I would pick a trials count for each record and model it as success and failures. glm binomial supports this. If it isn't binomial in nature, perhaps try inverse hyperbolic sine transformations worthwhile.typepad.com/worthwhile_canadian_initi/2011/07/… | |
| Jul 23, 2015 at 18:48 | answer | added | HaberdashPI | timeline score: 2 | |
| Jul 31, 2014 at 21:27 | comment | added | user3237820 | Thanks for these. However, although I do have the raw counts, the particular research I am carrying out means that I would expect that the raw counts would increase with my predictor variables. Therefore, percentages are giving me a more reliable measure in my analyses. | |
| Jul 31, 2014 at 21:25 | vote | accept | user3237820 | ||
| Jul 30, 2014 at 22:36 | comment | added | conjugateprior | If your outcome variables are 'displayed in percentages' this suggests that they aren't originally percentages. Presumably they are counts. @whuber is suggesting starting instead with a logistic (or multinomial logistic) regression model, for which even conditional normality is not a requirement. | |
| Jul 30, 2014 at 19:13 | comment | added | whuber♦ | A worthy option for your consideration is a generalized linear model. Please search our site for threads on GLMs. If you still want to transform the response, then search for threads about transformations, logarithms, and regression: many of them explicitly discuss whether and how to add a "start value" to the data before re-expressing them. | |
| Jul 30, 2014 at 19:09 | answer | added | emudrak | timeline score: 17 | |
| Jul 28, 2014 at 15:16 | history | asked | user3237820 | CC BY-SA 3.0 |