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?
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:
Any suggestions? I can't find any consensus on the best next step and am new to stats.Code:<style type="text/css"> table.tableizer-table { border: 1px solid #CCC; font-family: Arial, Helvetica, sans-serif; font-size: 10px; } .tableizer-table td { padding: 4px; margin: 3px; border: 1px solid #ccc; } .tableizer-table th { background-color: #104E8B; color: #FFF; font-weight: bold; } </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> </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> </td></tr> <tr><td>Race/Ethnicity</td><td> </td><td> </td><td> </td><td> </td><td> </td><td> </td><td> </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> </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> </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> </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> </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> </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>
Thanks,
Jenny
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?
Fischer's LSD test appears to require parametric data, you are doing a series of t test. So I don't think it makes much sense to use that.
https://en.wikipedia.org/wiki/Post_hoc_analysis
This tends to support the view that there are no formal ad hoc test for non-parametric data but offers some solutions [if you can read it]
http://www.researchgate.net/post/Can...riedmans_ANOVA
"Very few theories have been abandoned because they were found to be invalid on the basis of empirical evidence...." Spanos, 1995
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