From d391b5155f567addf7c3e624cef2c43cbc1df03b Mon Sep 17 00:00:00 2001 From: Jim Keener Date: Mon, 25 Nov 2013 09:31:23 -0500 Subject: [PATCH] Fixed links ending in ) --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 8a569668..6f391e3c 100644 --- a/README.md +++ b/README.md @@ -119,12 +119,12 @@ Continuous variables are anything measured on a quantitative scale that could be would be something like weight measured in kg. [Ordinal data](http://en.wikipedia.org/wiki/Ordinal_data) are data that have a fixed, small (< 100) number of levels but are ordered. This could be for example survey responses where the choices are: poor, fair, good. [Categorical data](http://en.wikipedia.org/wiki/Categorical_variable) are data where there are multiple categories, but they aren't ordered. One example would be sex: male or female. [Missing data](http://en.wikipedia.org/wiki/Missing_data) are data -that are missing and you don't know the mechanism. You should code missing values as `NA`. [Censored data](http://en.wikipedia.org/wiki/Censoring_(statistics)) are data +that are missing and you don't know the mechanism. You should code missing values as `NA`. [Censored data](http://en.wikipedia.org/wiki/Censoring_(statistics\)) are data where you know the missingness mechanism on some level. Common examples are a measurement being below a detection limit or a patient being lost to follow-up. They should also be coded as `NA` when you don't have the data. But you should also add a new column to your tidy data called, "VariableNameCensored" which should have values of `TRUE` if censored and `FALSE` if not. In the code book you should explain why those values are missing. It is absolutely critical to report -to the analyst if there is a reason you know about that some of the data are missing. You should also not [impute](http://en.wikipedia.org/wiki/Imputation_(statistics))/make up/ +to the analyst if there is a reason you know about that some of the data are missing. You should also not [impute](http://en.wikipedia.org/wiki/Imputation_(statistics\))/make up/ throw away missing observations. In general, try to avoid coding categorical or ordinal variables as numbers. When you enter the value for sex in the tidy