I have to do an analysis of variance on some test scores that were given
to me as percentile scores. My question is, How should I analyze percentile rank data?

The problem, of course, is that percentile rank data are not normally
distributed. Percentile ranks are ordinal and usually form a rectangular (uniform) distribution. The easiest solution is to transform the percentile rank scores into z-scores (standard normal scores) using an inverse normal function. The
z-scores will be normally distributed with mean equal to zero and a standard deviation of one. The range of the
z-scores will be between ±2.33. In Stata, the transformation would look like this:

generate zscore = invnorm(pctrank/100)

Specialists in testing often transform percentile ranks into NCE (normal curve equivalence) scores.
NCEs are a type of standardized score with a mean of 50 and a standard deviation of 21.06. NCEs have a range of
one to 99 and in many ways look a lot like percentile ranks. Here is how the
NCE transformation would look in Stata:

generate nce = invnorm(pctrank/100)*21.06 + 50

Here is a table that gives a rank of percentile rank scores and their equivalent
z-scores and NCE scores:

      pctrank     zscore        nce 
  1.        1  -2.326348   1.007114  
  2.        2  -2.053749   6.748048  
  3.        3  -1.880794   10.39049  
  4.        4  -1.750686   13.13055  
  5.        5  -1.644854   15.35938  
  6.       10  -1.281552   23.01052  
  7.       20  -.8416212   32.27546  
  8.       25  -.6744897   35.79525  
  9.       30  -.5244005   38.95612  
 10.       40  -.2533471   44.66451  
 11.       50          0         50  
 12.       60   .2533471   55.33549  
 13.       70   .5244005   61.04388  
 14.       75   .6744897   64.20476  
 15.       80   .8416212   67.72454  
 16.       90   1.281552   76.98948  
 17.       95   1.644854   84.64062  
 18.       96   1.750686   86.86945  
 19.       97   1.880794   89.60951  
 20.       98   2.053749   93.25195  
 21.       99   2.326348   98.99289