This is a very tricky question. (If you don't mind my saying, it's a
good example of thinking about your analysis before you collect the
data.)
I guess a Friedman's non-parametric repeated measures ANOVA is the way
to go, but I don't know if that will work, I've never seen it done
with more than three or four variables. Then your post hoc tests are
going to be tricky (and hard work, there's a function in R that will
do them automatically, but if you get a significant result you're
going to need 70*69/2 = 2415 (if I've counted right) tests. Which is
going to get boring.
A better way would be to fit a multilevel ordinal logistic model, with
using a reference coding scheme, which will compare each food to the
average. I'm not sure such a thing exists (it can't be done with
SPSS, I'm pretty sure it can't be done with Stata, SAS or R. You
might manage in HLM or MlwiN [Mlwin is free if you're a UK academic]
but it's not easy).
A complex samples approach in SPSS might do the trick (google Huber
White SPSS to find out how to do it), if it will do ordinal logistic
regression with complex samples (I'm not sure if it will, I've never
tried), or you could also use generalized estimating equations in SPSS
(Or SAS, Stata or R). [Again, I'm not positive that this actually
exists. The usual solution if it doesn't is to build up dichotomous
logistic regressions, but that won't give the post hoc tests you want.
Or if it will, I don't know how to do it).
None of these are very easy. It would help if there were someone at
your institution who knew how to do one of them. Where are you?
Jeremy
On 8 July 2011 15:13, Laura Sale <[log in to unmask]> wrote:
> Does anyone know the best way to analyse this data??
>
>
>
> We had 42 participants who were given 70 different food items and they had
> to assign each item to one of three categories:
>
> 1. Low Carbon Footprint
> 2. High Carbon Footprint
> 3. Don't Know
>
> For each participant we have recorded how they categorised each item
> (low/high/don’t know) and so for each food item we will have frequency data
> for how often that particular item was categorised as either low/high/don’t
> know by participants. What would be the best way to analyse this data to
> find out if certain food items are more likely to be classed as having a
> high carbon footprint and some are more likely to be classed as having a low
> carbon footprint?
>
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