I have the following
myData, where the response variable
high-rated denotes if an application is high-rated or not, and the independent variable
isWindows denotes if the application supports Windows:
high-rated isWindows Yes 1 Yes 0 No 1 No 1 Yes 0 ....
I want to statistically compare the distribution of
isWindows in high-rated app and low-rated app. Is it correct to use the Wilcoxon rank sum test (or Mann-Whitney U test) like below?
high_rated_app <- subset(myData, high-rated == "Yes") low_rated_app <- subset(myData, high-rated == "No") wilcox.test(as.numeric(high_rated_app$isWindows), as.numeric(low_rated_app$isWindows))
Secondly, if the above test returns a significant p-value (< 0.05), I proceed to calculate the magnitude of difference using Cliff's delta:
library(effsize) d <- cliff.delta(as.numeric(high_rated_app$isWindows), as.numeric(low_rated_app$isWindows))
Can I conclude with this statement: "high-rated app is statistically significantly different from low-rated app in whether an app is Windows, with an effect size of