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    <title>topic Re: Relationship between categorical variables in Statistical Procedures</title>
    <link>https://communities.sas.com/t5/Statistical-Procedures/Relationship-between-categorical-variables/m-p/318633#M16836</link>
    <description>Oh ok. I said that not because you suggested a different method, but I read somewhere that standard methods of performing FA (using a tetra/polychoric correlations matrix) assume that factors are continuous. I also noticed that having a three-level nominal var in the matrix generate some funky numbers (compared to only having binary vars).&lt;BR /&gt;&lt;BR /&gt;Thanks again for the suggestion!&lt;BR /&gt;</description>
    <pubDate>Tue, 13 Dec 2016 16:44:24 GMT</pubDate>
    <dc:creator>jhs2171</dc:creator>
    <dc:date>2016-12-13T16:44:24Z</dc:date>
    <item>
      <title>Relationship between categorical variables</title>
      <link>https://communities.sas.com/t5/Statistical-Procedures/Relationship-between-categorical-variables/m-p/318597#M16829</link>
      <description>&lt;P&gt;Hello,&amp;nbsp;&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;I am not sure if I should post this here as this technically isn't a SAS question..(but somewhat related). I have about 180 subjects in my dataset, and primarily interested in examining the relationship between three modifiable behavioral factors (two binary and one three-level nominal) that could be potentially associated with the outcome (death; all 180 subjects). Because the data wasn't collected for research per se, I do not have a reference or a control group to compare. My question is: is there any statistical analyses I can do to examine the relationship between these three modifiable risk factor variables? I tried chi-sq and it was helpful, but I am curious to find out if there are other tests I could perform. Can tetrchoric or polychoric (PROC FREQ) corr or performing factor analyses (using the matrix from the correlation procedure) be meaninful in any way? &amp;nbsp;&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;Thank you!&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;</description>
      <pubDate>Tue, 13 Dec 2016 15:43:00 GMT</pubDate>
      <guid>https://communities.sas.com/t5/Statistical-Procedures/Relationship-between-categorical-variables/m-p/318597#M16829</guid>
      <dc:creator>jhs2171</dc:creator>
      <dc:date>2016-12-13T15:43:00Z</dc:date>
    </item>
    <item>
      <title>Re: Relationship between categorical variables</title>
      <link>https://communities.sas.com/t5/Statistical-Procedures/Relationship-between-categorical-variables/m-p/318606#M16830</link>
      <description>&lt;P&gt;In addition to tests for association in PROC FREQ, you might look at correspondence analysis, which is the discrete/categorical analogue of principal component analysis. &amp;nbsp;In SAS, you can carry out correspondence analysis by using &lt;A href="http://support.sas.com/documentation/cdl/en/statug/68162/HTML/default/viewer.htm#statug_corresp_toc.htm" target="_self"&gt;the CORREP procedure.&lt;/A&gt;&amp;nbsp;&amp;nbsp;&lt;/P&gt;</description>
      <pubDate>Tue, 13 Dec 2016 15:59:33 GMT</pubDate>
      <guid>https://communities.sas.com/t5/Statistical-Procedures/Relationship-between-categorical-variables/m-p/318606#M16830</guid>
      <dc:creator>Rick_SAS</dc:creator>
      <dc:date>2016-12-13T15:59:33Z</dc:date>
    </item>
    <item>
      <title>Re: Relationship between categorical variables</title>
      <link>https://communities.sas.com/t5/Statistical-Procedures/Relationship-between-categorical-variables/m-p/318607#M16831</link>
      <description>&lt;P&gt;Logistic regression is a common way of examining data with two outcomes (it looks like death/alive in your case?) with one or more factors that are typically not continuous such as&amp;nbsp; smoker/nonsmoker, low/middle/high value indicator, gender&amp;nbsp;or such.&lt;/P&gt;</description>
      <pubDate>Tue, 13 Dec 2016 16:00:23 GMT</pubDate>
      <guid>https://communities.sas.com/t5/Statistical-Procedures/Relationship-between-categorical-variables/m-p/318607#M16831</guid>
      <dc:creator>ballardw</dc:creator>
      <dc:date>2016-12-13T16:00:23Z</dc:date>
    </item>
    <item>
      <title>Re: Relationship between categorical variables</title>
      <link>https://communities.sas.com/t5/Statistical-Procedures/Relationship-between-categorical-variables/m-p/318613#M16832</link>
      <description>Hello,&lt;BR /&gt;Unfortunately, I only have one outcome (death) otherwise I would have totally tried logistic reg!</description>
      <pubDate>Tue, 13 Dec 2016 16:20:21 GMT</pubDate>
      <guid>https://communities.sas.com/t5/Statistical-Procedures/Relationship-between-categorical-variables/m-p/318613#M16832</guid>
      <dc:creator>jhs2171</dc:creator>
      <dc:date>2016-12-13T16:20:21Z</dc:date>
    </item>
    <item>
      <title>Re: Relationship between categorical variables</title>
      <link>https://communities.sas.com/t5/Statistical-Procedures/Relationship-between-categorical-variables/m-p/318619#M16834</link>
      <description>I see. I didn't know about correspondence analysis, so I appreciate the suggestion! Does this mean the statistics I got from PLCORR such as Tetrachoric values (in PROC FREQ) are not valid?</description>
      <pubDate>Tue, 13 Dec 2016 16:27:07 GMT</pubDate>
      <guid>https://communities.sas.com/t5/Statistical-Procedures/Relationship-between-categorical-variables/m-p/318619#M16834</guid>
      <dc:creator>jhs2171</dc:creator>
      <dc:date>2016-12-13T16:27:07Z</dc:date>
    </item>
    <item>
      <title>Re: Relationship between categorical variables</title>
      <link>https://communities.sas.com/t5/Statistical-Procedures/Relationship-between-categorical-variables/m-p/318625#M16835</link>
      <description>&lt;P&gt;?? I don't see why they wouldn't be valid. &amp;nbsp;Correspondence analysis is a multivariate technique that attempts to reveal relationships in categorical variables. But it doesn't replace or invalidate other statistical methods.&lt;/P&gt;</description>
      <pubDate>Tue, 13 Dec 2016 16:32:17 GMT</pubDate>
      <guid>https://communities.sas.com/t5/Statistical-Procedures/Relationship-between-categorical-variables/m-p/318625#M16835</guid>
      <dc:creator>Rick_SAS</dc:creator>
      <dc:date>2016-12-13T16:32:17Z</dc:date>
    </item>
    <item>
      <title>Re: Relationship between categorical variables</title>
      <link>https://communities.sas.com/t5/Statistical-Procedures/Relationship-between-categorical-variables/m-p/318633#M16836</link>
      <description>Oh ok. I said that not because you suggested a different method, but I read somewhere that standard methods of performing FA (using a tetra/polychoric correlations matrix) assume that factors are continuous. I also noticed that having a three-level nominal var in the matrix generate some funky numbers (compared to only having binary vars).&lt;BR /&gt;&lt;BR /&gt;Thanks again for the suggestion!&lt;BR /&gt;</description>
      <pubDate>Tue, 13 Dec 2016 16:44:24 GMT</pubDate>
      <guid>https://communities.sas.com/t5/Statistical-Procedures/Relationship-between-categorical-variables/m-p/318633#M16836</guid>
      <dc:creator>jhs2171</dc:creator>
      <dc:date>2016-12-13T16:44:24Z</dc:date>
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