And we're going to make sure that the effort column is coded

as an ordered column.

We need an ordinal response here for this call to polr,

P-O-L-R, which is the ordinal logistic regression column, to work properly.

Because we're going to use the capital anova command we'll set the contrast as we

described earlier and then we'll do the ordinal logistic regression.

We've stored that result in N that model and then we'll report the anova on that.

So our results in the end we'll see is that in rating the effort of these three

techniques we have our chi squared result with two degrees of freedom and

we see a non statistically significant result, a p value of .10.

So in this box plot we can say the over all, or

omnibus, test has not concluded that in fact we've seen significant

differences among these levels for this ordinal response.

And that somewhat makes sense I mean we have very similar results for

scroll and search and voice isn't too far away.

It was .1 so we're in the right direction perhaps but not a signficant result so

we're not warranted in following up with post hoc parawise comparisons.

For completeness I've included how we will do them.

And we have seen this pattern before with the multicon library, where we specify

two key comparisons meeting all parawise comparisons in the multiple comparisons

procedures call here adjusted for the home sequential bonfurnic procedure.

So this is how we would carry them out, but were not justified in doing them, so

I'm not actually going to comply with that and

not run the test, now if we had a significant overall test or

omnibus test then we would be justified in looking at peer wise comparisons.