Date:         Thu, 4 Feb 2010 13:02:01 -0800
Reply-To:     J P <jp7837@yahoo.com>
Sender:       "SPSSX(r) Discussion" <SPSSX-L@LISTSERV.UGA.EDU>
From:         J P <jp7837@yahoo.com>
Subject:      Re: basic stat question
In-Reply-To:  <27452194.post@talk.nabble.com>
Content-Type: multipart/alternative;

Thanks to everyone for your assistance. The NPARS does the trick.




________________________________
From: rblack <ryan.andrew.black@GMAIL.COM>
To: SPSSX-L@LISTSERV.UGA.EDU
Sent: Thu, February 4, 2010 8:50:39 AM
Subject: Re: basic stat question

Hi Bruce,

Although I've never tried, it should also be possible to use a generalized
linear model (intercept only), assuming the expected proportions are .50.

GENLIN {Variable Name}
  /MODEL INTERCEPT=YES
DISTRIBUTION=BINOMIAL LINK=LOGIT
  /PRINT SOLUTION.

Best,

Ryan


Bruce Weaver wrote:
>
>
> J P-6 wrote:
>>
>> Hello,
>>
>> Apologies for a non-SPSS question.
>>
>> I would like to compare two proportions but I only have data for one
>> group. In other words I need to compare the proportion in my data to a
>> fixed value.  What is the best way? I would like to use the one sample
>> means t-test, but am not sure if it is valid. A colleague recommends a
>> 2x2 chi-sq test which is equal to a z-test of proportions, I believe. I
>> cannot find a link between the z-test and one sample t-test.
>>
>> Is it valid to use the one sample t-test to compare a proportion to a
>> standard?Â
>>
>> Thank you,
>> John
>>
>>
>
> No, don't use a t-test.  The usual test would be a z-test for a single
> proportion.  You can see an example here (mind the line-wrap):
>
> http://www.cliffsnotes.com/study_guide/Test-for-a-Single-Population-Proportion.topicArticleId-25951,articleId-25941.html
>
> But SPSS does not compute this test directly, AFAIK.  However, a
> chi-square goodness of fit test is equivalent to the z-test.  Remember
> than z-squared = chi-square with df = 1.  Here's some code to analyze the
> example given on that web-page.
>
> new file.
> dataset close all.
>
> data list list / category count (2f5.0).
> begin data
> 1 32
> 2 38
> end data.
>
> weight by count.
>
> NPAR TESTS
>  /CHISQUARE=category
>  /EXPECTED=.4 .6
>  /MISSING ANALYSIS.
>
> * Or, if you have the "exact" statistics module, you can do it this way.
>
> NPAR TESTS
>  /CHISQUARE=category
>  /EXPECTED=.4 .6
>  /MISSING ANALYSIS
>  /METHOD=EXACT TIMER(5).
>
> IIRC, the "exact" p-value from this second analysis is computed using the
> binomial distribution.
>
> HTH.
>
>
>

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