On 26 Jun 2002 20:46:50 -0700, [EMAIL PROTECTED] (David Emery) wrote: > Hello, > > I've implemented my first statistical research study for college which > attempts to determined subjects' levels of interest (ordinal) compared > to levels of attributes (continuous) during an educational > presentation. Due to nonlinear relationships, I used ANOVA instead of > multiple regression.
'Ordinal' works out as pretty close to 'interval' for a lot of purposes, especially whenever you start with a scale with only a few points. You could consider using your scores of (integers, 1-to-5) for correlation. On the other hand, there is the really odd result that you write, where var#4 show the (relatively) huge difference, and the groups are badly out of order. (Unless that is a typographical error where 3.09 was 2.09.) > Sometimes the simplest answers are the hardest > ones for me to find, so I was wondering if anyone could give me a hint > about how I would further present the precision of my findings, > including sampling error... in this case I had 54 subjects with about > 48 repeated measures each. > > The data I obtained is below, and to be honest, I am not entirely sure > what the F-ratio is. For X2 and X4, p=0.0, which I believe indicates > more significance, but I'm not entirely sure why that is the case > either. What p=0.0 denotes was merely shorthand for "P < .0005" ... or whatever the limit of precision that was used elsewhere. And that signifies that you don't expect results that extreme to happen by chance unless you do a WHOLE LOT of trials. [ ... ] > Interest Level > Attribute -2 -1 0 1 2 F-Ratio [ ... ] > X4/10 3.20 2.62 2.69 2.24 3.09 27.17 The 48 repetitions are hardly supposed to be independent trials and independent tests of separate, important hypotheses. Before you start correcting for 'multiple tests', you want to reduce the multiplicity to something less than a handful. What to do? Select out the important ones (a-priori: meaning, before considering 'tests.') Select out the 'reliable' ones based on correlation with other measures. Create one or a few 'total-scores' or averages where the logical dimension seems united. Create one or a few 'composite scores' where the logical dimensions are several, but you can argue for an underlying similarity based on intercorrelations. -- Rich Ulrich, [EMAIL PROTECTED] http://www.pitt.edu/~wpilib/index.html . . ================================================================= Instructions for joining and leaving this list, remarks about the problem of INAPPROPRIATE MESSAGES, and archives are available at: . http://jse.stat.ncsu.edu/ . =================================================================
