How to do a post hoc analysis on non-parametric data in SAS

#1
Good morning!

I know there is a statistically significant difference between groups for my non-parametric data-set but I'm not sure how to find where those differences lie between groups..

I'm comparing race/ethnicity (Asian Pacific Islander, Hispanic, etc) by academic discipline (dental, nursing, medicine, etc), and am able to tell that there is a difference, but not between which groups. I'm running this in SAS, and the code I used to find the difference is:

PROC GLM data = local.analysisfile;
CLASS school;
MODEL race = school;
MEANS school /LSD;
run;

Here's an example of the data:

Code:
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</style><table class="tableizer-table">
<tr class="tableizer-firstrow"><th></th><th>Dental</th><th>Law</th><th>Medicine</th><th>Nursing</th><th>Pharmacy</th><th>Social Work</th><th>Total</th></tr>
 <tr><td>&nbsp;</td><td>n = 50</td><td>n = 78</td><td>n = 117</td><td>n = 72</td><td>n = 67</td><td>n = 131</td><td>&nbsp;</td></tr>
 <tr><td>Race/Ethnicity</td><td>&nbsp;</td><td>&nbsp;</td><td>&nbsp;</td><td>&nbsp;</td><td>&nbsp;</td><td>&nbsp;</td><td>&nbsp;</td></tr>
 <tr><td>Asian/Pacific Islander</td><td>18</td><td>13</td><td>25</td><td>3</td><td>29</td><td>5</td><td>93</td></tr>
 <tr><td>&nbsp;</td><td>(36%)</td><td>(16.7%)</td><td>(21.4%)</td><td>(4.2%)</td><td>(43.3%)</td><td>(3.8%)</td><td>(18.1%)</td></tr>
 <tr><td>Black/African American</td><td>3</td><td>14</td><td>3</td><td>11</td><td>6</td><td>23</td><td>60</td></tr>
 <tr><td>&nbsp;</td><td>(6.0%)</td><td>(18%)</td><td>(2.6%)</td><td>(15.3%)</td><td>(9.0%)</td><td>(17.6%)</td><td>(11.7%)</td></tr>
 <tr><td>Hispanic</td><td>4</td><td>5</td><td>3</td><td>6</td><td>1</td><td>5</td><td>24</td></tr>
 <tr><td>&nbsp;</td><td>(8.0%)</td><td>(6.4%)</td><td>(2.6%)</td><td>(8.3%)</td><td>(1.5%)</td><td>(3.8%)</td><td>(4.7%)</td></tr>
 <tr><td>White/Caucasian</td><td>24</td><td>46</td><td>82</td><td>52</td><td>29</td><td>95</td><td>328</td></tr>
 <tr><td>&nbsp;</td><td>(48.0%)</td><td>(59.0%)</td><td>(70.1%)</td><td>(72.2%)</td><td>(43.3%)</td><td>(72.5%)</td><td>(63.7%)</td></tr>
 <tr><td>Other</td><td>1</td><td>0</td><td>4</td><td>0</td><td>2</td><td>3</td><td>10</td></tr>
 <tr><td>&nbsp;</td><td>(2.0%)</td><td>(0.0%)</td><td>(3.4%)</td><td>(0.0%)</td><td>(3.0%)</td><td>(2.3%)</td><td>(1.9%)</td></tr>
</table>
Any suggestions? I can't find any consensus on the best next step and am new to stats.

Thanks,
Jenny
 

hlsmith

Less is more. Stay pure. Stay poor.
#2
You code box is unbelievably jumbled up.


Race is a class variable, how can your treat it as a dependent variable in this model? Do you have it coded as 1, 2, 3,...,etc. If so, that makes no sense. What is your sample size?