Ok, so I'm working on a project just now and I want to find out if the means of my dependent variable X for ONE person at times A, B and C are significantly from one another. I'm not especially interested in finding out if this speaker is significantly different from other speakers, since that's part of my regression analysis that I'm also doing.
To give you an idea of what I'm looking at, let's say that we interview a speaker and find out that they say the word 'bag' in a particular way and we take measurements of that. In year 1, I take 200 observations of this word, in year 2, I take 240 observations, and in year 3, I take 175 observations. Each observation has its own line in my excel sheet, who says it, what kind of word it is (noun, verb etc), what came before it, and so on. Now what I want to know is if the way that the person says 'bag' is significantly different in Year 1 compared to Andrew Luck Jersey Year 2 compared to year 3.
The data is not normal, so ANOVA seems to be out, plus the data are unbalanced (so I have different numbers of observations at times A, B and C). I did think that KruskalWallis would work, but that seems to only apply Authentic Andrew Luck Jersey to group means while I'm just interested in comparing the value of X for one person at three different times.
In terms of stats packages, I have SPSS and R, although if the solution is with R, I'll need stepbystep instructions for it cause I'm a noob with it.
Any statsaces out there able to T.Y. Hilton Colts Jersey help a brother out?
posted by Scottie_Bob to science nature (13 answers total) 1 user marked this as a favorite
you said you are interested in the value of ONE person at three different times. Does this mean you're only interested in comparing the data from one particular individual (and that you don't care at all about the other data)?
Or does it mean that you want to employ a repeated measures (within subjects) analysis?
If it's the former, and your data is not normal for that particular subject, a kruskalwallis test sounds like the Andrew Luck Colts Jersey way to go.
If it's the latter, a friedman test is the way to go.
(this T.Y. Hilton Jersey is based on this info).
posted by spacediver at 9:35 AM on December 6, 2012
Yeah, ONE person at THREE different times, with the dependent variable of 'how do they pronounce BAG'. I do care about the other data, but since it's kind of all over the place, I want to split it up so I can do the following:
Speaker A at Year 1, Year 2 and Year 3
Speaker B at Year 1, Year 2 and Year 3
Speaker C at Year 1 and Year 3 (no data for year 2)
I don't really want to compare Speaker A, Speaker B and Speaker C, since I'm also doing a mixedeffects linear regression model, which will show me which variables are most important in the modelling the variation.
I think.
posted by Scottie_Bob at 10:12 AM on December 6, 2012
It's unclear how you set up your design.
When you say you had 200 observations in year 1, you're talking about 200 observations per speaker? Or are you talking about 200 speakers?
And was the way in which they pronounced "bag" just one of these observations?
posted by spacediver at 10:19 AM on December 6, 2012
Yeah, the first one; 200 observations per speaker per year (well, it's unbalanced, so it's not exactly the same amount per speaker per year), and yes, the way in which they pronounced "bag" was one of those observations. If the speaker had said something like "I grabbed the bag and ran away from the man" then that'd be four tokens of "a" (grabbed, bag, ran, man).
posted by Scottie_Bob at 10:25 AM on December 6, 2012
Ok so you have one measurement for this speaker at three different times. I fail to see how you can do Authentic T.Y. Hilton Jersey any sort of statistical analysis if you only have three numbers to compare. And how can the data be nonnormal if the three conditions only comprise one data point?