Hi Laura,
It looked to me like it should be simple, although I didn't know the
answer, so I guess we can all take some reassurance in that it's
definitely not simple if Jeremy says it's tricky!
How crucial is it that you have some statistical analysis behind what
you want to say? Could you get away with simple descriptions of the
data? One way to present it visually is a 100% stacked bar graph (could
even use Excel for this) - each bar represents a food, and the graph
will show the proportion it was rated as hi vs low vs don't know, and
you can compare across foods. If you stack the data so they are hi,
don't know, low, and sort your data in order of descending hi, then
ascending low values, you should be able to see clearly the hi carbon
footprint foods at one end and the low carbon at the other. The middle
will be something of a risotto though.
On 09/Jul/2011 13:13, Jeremy Miles wrote:
> 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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