Essentially, that's what needs to be done, but it's repeated measures,
not independent groups. In a sense this is a good thing, because it
increases power. But it's a bad thing too, because it makes it harder
to analyse.
On 10 July 2011 01:46, Clare Jonas <[log in to unmask]> wrote:
> I fear I'm going to sound a bit dim, here - is there a problem with using a chi-square and then looking at standardised residuals to work out significance for individual cells? This is what first occurred to me as an analysis.
> Clare
>
> On 10 Jul 2011, at 08:27, Jeremy Miles <[log in to unmask]> wrote:
>
>> Aha! I think Brian has got it.
>>
>> Sometimes we (and I mean me) obsess about doing the "right"
>> significance test. When maybe we just need to show what happened. A
>> useful thing to remember is that Piaget, Milgram and Skinner never did
>> a significance test.
>>
>> Jeremy
>>
>> On 9 July 2011 22:09, Brian K. Saxby <[log in to unmask]> wrote:
>>> 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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