The idea here is that the ϕ statistic is supposed to range between 0 (no at all association) and 1 (perfect association), but it doesn’t always do this when your contingency table is bigger than 2×2, ...The idea here is that the ϕ statistic is supposed to range between 0 (no at all association) and 1 (perfect association), but it doesn’t always do this when your contingency table is bigger than 2×2, which is a total pain. However, if you’re using the associationTest() function to do your analysis, then you won’t actually need to use this at all, because it reports the Cram'er’s V statistic as part of the output.
The idea here is that the \(\phi\) statistic is supposed to range between 0 (no at all association) and 1 (perfect association), but it doesn’t always do this when your contingency table is bigger tha...The idea here is that the \(\phi\) statistic is supposed to range between 0 (no at all association) and 1 (perfect association), but it doesn’t always do this when your contingency table is bigger than 2×2, which is a total pain. This seems to be a fairly popular measure, presumably because it’s easy to calculate, and it gives answers that aren’t completely silly: you know that V really does range from 0 (no at all association) to 1 (perfect association).
