http://spssx-discussion.165.s1.nabble.com/Re-example-output-from-crosstabs-column-proportions-tp3422615p3422651.html
Here is another example; this is from my actual dataset, and it's a larger
than 2x2 table. Variables are Party (4 levels) and Tea Party (2 levels,
yes or no). I used compare column percentages, with Bonferroni correction.
And this is what I got. Hope it lines up right.
Republican Democrat Libertarian Socialist
Tea Party No Count 23a 76b 41c
% within Party 41.8% 100.0% 69.5% 100.0%
% within Party 58.2% .0% 30.5% .0%
> Below is an output from crosstabs using column proportion testing. But
> what does it show? That 50/50 is significantly different from 20/80?
> Martin Sherman
>
> x * y Crosstabulation
> y
> 1.00 2.00 Total
> x 1.00 Count 40 a 10 b 50
> % within x 80.0% 20.0% 100.0%
> % within y 50.0% 20.0% 38.5%
> 2.00 Count 40 a 40 b 80
> % within x 50.0% 50.0% 100.0%
> % within y 50.0% 80.0% 61.5%
> Total Count 80 50 130
> % within x 61.5% 38.5% 100.0%
> % within y 100.0% 100.0% 100.0%
> Each subscript letter denotes a subset of y categories whose column
> proportions do not differ significantly from each other at the .05 level.
>
>
> -----Original Message-----
> From: SPSSX(r) Discussion [mailto:
[hidden email]] On Behalf Of
> Bruce Weaver
> Sent: Wednesday, March 09, 2011 6:10 PM
> To:
[hidden email]
> Subject: Re: chi-square post-hoc tests
>
> This is the second or third time I've seen someone mention z-tests under
> CROSSTABS. I'm not familiar with that--is it new in v19?
>
> Thanks,
> Bruce
>
>
>
> Bridgette Portman wrote:
>>
>> That seems like so much extra work. What about the "compare column
>> proportions" option under "z-tests" in Crosstabs --> Cells? Is anyone
>> familiar with using this? If I am interpreting it right, it allows for
>> the kind of pairwise comparisons I'm trying to do, with the option for
>> a Bonferroni adjustment to the alpha level.
>>
>> Bridgette
>>
>>
>>> If one of the elements remains as 2 levels (e.g., 2 X 3), use
>>> logistic regression, with the 2-level variable as the outcome. Then
>>> use appropriate a priori contrasts to disentangle the df (2 df in the
>>> case of the 3 level variable).
>>>
>>> If none of the elements are 2 levels, then you need to consider a
>>> multinomial logistic regression.
>>>
>>> Joe Burleson
>>>
>>> -----Original Message-----
>>> From: SPSSX(r) Discussion [mailto:
[hidden email]] On Behalf
>>> Of Bridgette Portman
>>> Sent: Wednesday, March 09, 2011 1:00 AM
>>> To:
[hidden email]
>>> Subject: chi-square post-hoc tests
>>>
>>> I have another question.
>>>
>>> I'm confused about how to perform post-hoc tests for chi-square
>>> contingency tables larger than 2 x 2. I've been reading up on it in
>>> books and on the internet, and there seem to be two different methods
>>> advised.
>>> Some say to do multiple pairwise comparisons (2x2 tables) with a
>>> Bonferroni correction. Others say to look at the standardized
>>> residuals.
>>> I'm not sure which is the better way. Is there any easy way to
>>> perform posthoc tests on contingency tables in SPSS?
>>>
>>> Thanks,
>>> Bridgette
>>>
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>
>
> -----
> --
> Bruce Weaver
>
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>
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> "When all else fails, RTFM."
>
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